Volume-5 Issue-5

 Download Abstract Book

S. No

Volume-5 Issue-5, June 2016, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



J. Samatha, K. Bhagya Laxmi

Paper Title:

A Survey on Big Data Analysis and Challenges

Abstract:     One of contemporary big challenges in information systems is the issues associated with coping with and utilization of vast amounts of data. In this paper we present applications of big data , analysis of big data. The analysis of big data involves phases such as acquisition / recording, extraction / cleaning / annotation, integration / aggregation / representation, analysis / modeling, interpretation. We also discuss the challenges introduced in these phases.

   Bigdata, volume, velocity, variety, extraction, integration, analysis.


1.    E.Dumbill, “what is big data? An introduction to the big data    landscape”, Strata O’Reilly, 11 January 2012.
2.    David Loshin, Addressing five emerging challenges of big data,   whitepaper.

3.    Marko Grobelnik, “Big data tutorial”,Stavanger,8 May 2012.

4.    Oracle enterprise architecture white paper “An enterprise architect’s guide to big data” May 2015.

5.    Amir H. Payberah “Introduction to big data”, Swedish institute of computer science, 8 April 2014.

6.    www.intel.com/bigdata

7.    Kostas Glinos, ”E-infrastructures for bigdata” ERCIM news, number 89, April 2012.

8.   Silva Robak, Bogdan Franczyk, Marcin Robak “Research problems associated with big data utilization in logistics and supply chains  design and management” ACSIS, Vol 3,2014





Aarti Pandey, Prabhat Pandey

Paper Title:

A Survey on Semantically Data Classification Analysis Algorithm for Social Media

Abstract: Now in these days a number of users are participating in the social media and they are actively participating in conversation with their friends and community. Due to this sometimes the youth and teen agers are participating in non-social communities. Thus a new kind of data model is required to design by which the user communication and their patterns are accurately classified according to their semantics meaning. Thus a text content analysis technique is designed using the available automatic text classification technique. Using this technique the correlation between different words and their utilization in different semantics sentences are analyzed and based on the effects of these words a rule based classification technique is developed.

    sentiment, opinion, semantic, Data Processing


1.              Xia Hu, Lei Tang, Jiliang Tang, Huan Liu, “Exploiting Social Relations for Sentiment Analysisin Microblogging”, permission and/or a fee.WSDM ’13, February 4–8, 2013, Rome, Italy.Copyright 2013 ACM 978-1-4503-1869-3/13/02
2.              Fei Jiang, Anqi Cui, Yiqun Liu, Min Zhang, and Shaoping Ma, “Every Term Has Sentiment:Learning from Emoticon Evidencesfor Chinese Microblog Sentiment Analysis”,c Springer-Verlag Berlin Heidelberg 2013

3.              Eric Baucom,AzadeSanjari, Xiaozhong Liu,Miao Chen, “Mirroring the Real World in Social Media: Twitter,Geolocation, and Sentiment Analysis”,Copyright 2013ACM,78-1-4503-2415-1/13/10http://dx.doi.org/10.1145/2513549.2513559 Min Wang, Donglin Cao, Lingxiao Li, Shaozi Li, RongrongJi, “Microblog Sentiment Analysis Based on Cross-mediaBag-of-words Model”,ICIMCS’14, July 10–12, 2014, Xiamen, Fujian, China.Copyright 2014 ACM 978-1-4503-2810-4/14/07

4.              Felipe Bravo-Marquez, Marcelo Mendoza,Barbara Poblete, “Combining Strengths, Emotions and Polarities forBoosting Twitter Sentiment Analysis”,WISDOM’13, August 11 2013, Chicago, IL, USACopyright 2013 ACM 978-1-4503-2332-1/13/08.

5.              Pedro Calais Guerra, Wagner Meira Jr.,Claire Cardie, “Sentiment Analysis on Evolving Social Streams:How Self-Report Imbalances Can Help”,WSDM’14, February 24–28, 2014, New York, New York, USA.Copyright 2014 ACM 978-1-4503-2351-2/14/02





Zhivko Kiss’ovski, Vasil Vachkov

Paper Title:

Radiation of Monopole Microwave Plasma Antenna

Abstract:  The radiation of cylindrical plasma monopole at low gas pressure is theoretically investigated by applying the theory for dielectric resonator antenna (DRA). The plasma column is placed in a thin dielectric tube with a longitudinal length equal to half wavelength of the surface wave which sustains the discharge. The resonance wavelength of the TM011 mode at frequency 2.45 GHz is obtained by dielectric waveguide model (DWM) in which dielectric is replaced by plasma medium. The expression for electric field in far-field zone of this plasma monopole is derived and the result shows that its radiation pattern is similar to that of metal dipole antenna. The radiated field strength of plasma monopole is greater than that of metal antenna with the same electrical conductivity and dimensions.

plasma antenna, dielectric resonator antenna, plasma, surface waves 


1.             T. Anderson ”Plasma Antennas”, Artech House; 2011.
2.             E.N.  Istomin,  D.M.  Karfidov, I.M.  Minaev, A.A.  Rukhadze,  V.P. Tarakanov, K.F. Sergeichev,  A.Yu. Trefilov, Plasma Physics Reports,; 32: 388-400 (2006).

3.             Vachkov, Zh. Kiss’ovski, European  Phys. J: Appl. Phys, 72/3, 30801 (2015)

4.             Zh. Kiss’ovski, V. Vachkov, S. Iordanova, I. Koleva, “Microwave discharges in a finite length vessel”, Journal of Physics: Conference Series; 356: 012009 (2012).

5.             N. N. Bogachev, L. L. Bogdankevich, N. G. Gusein-zade, V. P. Tarakanov, Acta Polytechnica 53(2):1-3,( 2013).

6.             N N Bogachev, I L Bogdankevich, N G. Gusein-Zade, K F. Sergeychev  Acta Polytechnica 55, p.34 (2015).

7.             Vachkov, A. Ivanov, Zh. Kiss’ovski, ANNUAL JOURNAL OF ELECTRONICS, v. 2, p.72, ISSN 1313-1842  (2010)

8.             Zh. Kiss’ovski, V. Vachkov, IJEAT, v. 45, p.234, (2015)

9.             K-M Luk and K-W Leung, Dielectric Resonator Antennas, Institute of Physics PUBLISHING, Dirac House Bristol, 2003

10.          Y M Pan, S Y Zheng, and B J Hu, IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 13, p.710, (2014)

11.          Zh. Kiss’ovski, M. Kolev, A. Ivanov, St. Lishev, I. Koleva, 2009 ”Small surface wave discharge at atmospheric pressure”; J Phys. D: Appl. Phys.; 42, 182004 (2009).

12.          S. Nonaka, Jpn. J App. Phys., vol. 31, 1890 (1992)

13.          Yu. M. Aliev, H. Schlüter and A. Shivarova, Guided-wave-produced plasmas, Springer, Berlin, 2000

14.          Balanis C, Antenna theory,  John Wiley & Sons, New Jersey, 2005





V. S. Lavanya, V. K. Vaidyan

Paper Title:

Extending ANN for Optical Elements – EDFA Characteristics

Abstract:   Artificial Neural Network has proved to be one of the best and widely used soft computation techniques in diversified fields such as Biology, Medicine, Energy, Bioinformatics etc. Modelling in Communication has come far way forward when the industry realized its benefits over conventional method of research and development. It mainly helps in two ways. The first advantage is such that the fabrication cost or wastage is highly reduced, second being the time to final solution implementation. There are various computational methods available in market, which were effectively used in the modelling of different application in diversified fields. In this work, we will discuss how effectively we can use ANN for optical elements and extend it to address the rapid explosion of information traffic and emerging applications in communication. We consider here a basic set up of forward pumped EDFA in a WDM long haul communication system and analyze the characteristics of it through proper signaling. The characterization of the gain, and amplifier noise is again modelled with the help of ANN by appropriately using the experimental data for both modelling and testing. The simulated output from the model agrees well with the experimental data and this approach can be extended to serve as a prediction tool for designing the complex systems in optical communication. The computational time(~ms) taken to model the system and mean-square error(10-5 ) limited is very promising to adapt the model for future activities as desired in further modelling or fabrication of the amplifier with preferred throughput. The results of modeling envisage how favorable ANN is on building the prediction formula in optical communication networks.

ANN, EDFA, Modelling, Optical Amplifier


1.             E. Desurvire and J.R. Simpson, “Amplification of Spontaneous Emission in Erbium-Doped Single-Mode Fibers”, J. Lightwave Tech., Vol.7, No.5, 835,1989.
2.             P.C. Becker, N.A. Olsson, and J.R. Simpson, “Erbium-Doped Fiber Amplifiers”, Academic Press, 1999.

3.             G.P Agrwwal, “Fiber-Optic Communication Systems”, Wiley Interscience, 3rd ed., 2002.

4.             M. Melo, O. Frazao, A.L.J. Teixeira, L.A. Gomes, J.R. Ferreira D. Rocha, H.M. Salgado, “Tunable L-band erbium-doped fiber ring laser by means of induced cavity
loss using a fiber taper”, Applied Physics B, Vol.77, 139, 2003.

5.             R. J. Mears, S. R. Baker, “Erbium Fiber Amplifiers and Lasers”, Optical and Quantum Electronics, Vol.24, 517, 1992.

6.             E. Desurvire, J. Simpson, and P.C. Becker, High-gain erbium-doped traveling-wave fiber amplifier,” Optics Letters, vol. 12, No. 11, 1987, pp. 888–890

7.             S. Lavanya, v. K. Vaidyan, “optimized flattened gain spectrum in c –band wdm using automatic gain control in bi-directionally pumped EDFA”, INTERNATIONAL Journal of Engineering Research & Technology, Vol-4, No-10, Pages: 430 – 434,  October 2015

8.             J. Hertz, A. Krogh, and R.G. Palmer, Introduction to the “Theory Of Neural Computation, Addison-Wesley, Reading, Mass., 1991.

9.             S. Haykin, Neural Networks: A Comprehensive Foundation, MacMillan College Publishing Co., New York, 1994.

10.          W.S. McCulloch and W. Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bull. Mathematical Bio-

11.          R. Rosenblatt, Principles of Neurodynamics, Spartan Books, New York, 1962.

12.          M. Minsky and S. Papert, Perceptrons: An lntroduction to Computational Geometry, MIT Press, Cambridge, Mass., 1969.

13.          J.J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” in Roc. Nat‘l Academy of Sciences, USA 79,1982, pp.

14.          P. Werbos, “Beyond Regression: New Tools for Prediction and  Analysis in the Behavioral Sciences,” PhD thesis, Dept. of  Applied Mathematics, Harvard University, Cambridge, Mass.,1974.

15.          D.E. Rumelhart and J.L. McClelland, Parallel Distributed Processing: Exploration in the Microstructure of Cognition, MIT Press, Cambridge, Mass., 1986.

16.          J.A. Anderson and E. Rosenfeld, Neurocomputing: Foundations   of Research, MIT Press, Cambridge, Mass., 1988.

17.          S. Brunak and B. Lautrup, Neural Networks, Computers with Intuition, World Scientific, Singapore, 1990.

18.          J. Eeldman, M.A. Fanty, and N.H. Goddard, “Computing with Structured Neural Networks,” Computer, Vol. 21, No. 3, Mar. 1988, pp. 91-103.

19.          D.O. Hebb, The OrganizationofBehavior, JohnWiley&Sons, New York, 1949.

20.          R.P. Lippmann, “An Introduction to Computing with Neural Nets,”lEEEASSP Magazine, Vol. 4, No. 2, Apr. 1987, pp. 4-22.

21.          A.K. Jain and J. Mao, “Neural Networks and Pattern Recognition,” in Computational Intelligence: Imitating Life, J.M. Zurada, R. J. Marks 11, and C.J. Robinson, eds., IEEE  Press, Piscataway, N.J., 1994, pp. 194-212.

22.          T. Kohonen, Self Organization and Associative Memory, Third Edition, Springer-Verlag, New York, 1989.

23.          G.A. Carpenter and S. Grossberg, Pattern Recognition by Self Organizing Neural Networks, MIT Press, Cambridge, Mass., 1991.

24.          “The First Census Optical Character Recognition System Conference,” R.A. Wilkinson et al., eds., . Tech. Report, NISTIR 4912, US Dept. Commerce, NIST, Gaithersburg, Md., 1992.

25.          V. S. Lavanya, V. K. Vaidyan, “ANN based model of automatically gain controlled EDFA in WDM systems”, J Optoelectronics and Advanced Materials, Vol-17, No-11-12, Pages: 1772 – 1777, December 2015

26.          Steffen Nissen, Implementation of a fast artificial neural network library (FANN), Department of Computer Science,  University of Copenhagen (DIKU) October 31, 2003

27.          Martin Davis and Hillary Putnam. A computing procedure for quantification theory. ACM, 7:201–215, 1960.

28.          John W. Dawson. G¨odel and the origins of computer science. In A. Beckmann, U. Berger, B. L¨owe, and J.V. Tucker, editors, Logical Approaches to Computational Barriers, CiE’06, volume 3988 of LNCS, pages 133–137, 2006.

29.          F. Debart, P. Enjabert, and M. Lescot. Multimodal logic programming using equational and order-sorted logic. Theoretical computer science, 105(1):141–166, 1992.

30.          Angluin, Frazier and Pitt (1990): D. Angluin, M. Frazier and L. Pitt, Learning conjunctions of Horn clauses. In Proceedings of the Thirtieth-First IEEE Symposium on Foundations of Computer Science, IEEE Computer Society Press, Washington DC.

31.          Anthony and Biggs (1992): M. Anthony and N. Biggs, Computational Learning Theory: an Introduction, Cambridge University Press.

32.          Bartlett (1992): P.L. Bartlett, Lower bounds on the Vapnik-Chervonenkis Dimension of multi-layer threshold networks. Technical report IML92/3, Intelligent Machines Laboratory, Department of Electrical Engineering and Computer Engineering, University of Queensland, Qld 4072, Australia, September 1992.

33.          Baum (1990): E.B. Baum, Polynomial time algorithms for learning neural nets. In Proceedings of the Third Workshop on Computational Learning Theory. Morgan Kaufmann, San Mateo, CA.

34.          Arthur d’Avila Garcez, Gerson Zaverucha, and Luis A.V. de Carvalho. Logical inference and inductive learning in artificial neural networks. In C. Hermann, F. Reine, and A. Strohmaier, editors, Knowledge Representation in Neural Networks, pages 33–46. Logos Verlag, Berlin, 1997.

35.          Saleh, A. A. M., R. M. Jopson, J. D. Evankow, and J. Aspell, Modeling of gain in erbium-doped fiber amplifiers, IEEE Photon. Technol. Lett., Vol. 2, No. 10, 714 – 717, 1990.

36.          Giles, C. R. and E. Desurvire, Modeling erbium-doped fiber amplifiers, J. Lightwave Technol., Vol. 9, No. 2, 271 – 283, 1991.

37.          Lu, Y. B. and P. L. Chu, Gain flattening by using dual-core fiber in erbium-doped fiber amplifier, IEEE Photon. Technol. Lett., Vol. 12, No. 12, 1616 – 1617, 2000.

38.          Martin, J. C., Erbium transversal distribution influence on the effectiveness of a doped fiber: Optimization of its performance, Opt. Commun., Vol. 194, 331 – 339, 2001.

39.          R. Beale and T. Jackson ,Neural Computing – an introduction, Physics Publishing 1990

40.          J. David Bolter, Turing’s Man – Western culture in the computer age, Duckworth 1984

41.          Alison Cawsey, Artificial Intelligence – The essence of, Prentice Hall 1998

42.          Cheng, C. and M. Xiao, Optimization of an erbium-doped fiber amplifier with radial effects, Opt. Commun., Vol. 254, 215 – 222, 2005.

43.          Cheng, C. and M. Xiao, Optimization of a dual pumped L-band erbium-doped fiber amplifier by genetic algorithm, J. Lightwave Technol., Vol. 24, No. 10, 3824 – 3829, 2006.

44.          Chang, C. L., L. Wang, and Y. J. Chiang, A dual pumped double- pass L-band EDFA with high gain and low noise, Opt. Commun., Vol. 267, 108 – 112, 2006.

45.          Choi, B. H., H. H. Park, and M. J. Chu, New pumped wavelength of 1540-nm band for long-wavelength-band erbium-doped fiber amplifier (L-band EDFA), J. Quantum Electron., Vol. 39, No. 10, 1272 – 1280, 2003.

46.          Yeh, C. H., C. C. Lee, and S. Chi, S- plus C-band erbium-doped fiber amplifier in parallel structure, Opt. Commun., Vol. 241, 443 – 447, 2004.

47.          Singh, R., Sunanda, and E. K. Sharma, Gain flattening by long period gratings in erbium doped fibers, Opt. Commun., Vol. 240, 123 – 132, 2004.





Tsvetana Kostadinova Antipesheva

Paper Title:

Training Mechanics In The Preparation of Teachers of Engineering, Technology and Entrepreneurship

Abstract:    In this paper are considered some basic pedagogical issues related to technical training of educators. The suggestion is how much they will study mechanics and how to teach the knowledge. It is displayed a formula and a scheme which illustrates the material.

training, mechanics


1.              Andreev, M., Integrativni tendentsii v obuchenieto, Narodna prosveta, S., 1986





Zlatko Vlajcic, Srecko Budi, Cedna Tomasovic Loncaric, Mislav Malic, Mladen Petrovecki

Paper Title:

Histological Evaluation of Human” in Vivo” Cutaneus Surgical Incisions Created by the Standard Scalpel, Conventional and Colorado Needle Electrosurgery, Radiofrequency, PEAK Plasma blade and Ultracision Harmonic Scalpel

Abstract:     We hypothesize that thermal damage to the subcutaneous microvasculature of skin incision may have contributed to the incision site complication rate. The purpose of this study was to histologically compare the zone of thermal necrosis for human cutaneus surgical incision made by different surgical cutting devices on vital tissue. Furthermore, for each specimen, the presence and character of micro bleeding was noted. Material And Methods: Human skin incisions were made “in vivo” on the lower abdomen prior to abdominoplasty by the standard scalpel, conventional and Colorado needle eletrosurgery, radiogrequency Ellman, PEAK PlasmaBlade and Ultracision Harmonic Scalpel. After formaldehyde fixation, the specimen was transported to pathology for histological evaluation and measurement of the thermal necrosis zone and micro bleeding zone.  Results:  As statistically significant (P < 0.05) we have three groups considering thermal necrosis zone:  first group is only Standard Scalpel, second group PlasmaBlade and Conventional Electrosurgery and third group Colorado Needle Electrosurgery, Radiofrequency and Ultracision Harmonic Scalpel. With microbleeding zone, results are more dispersed, but also with statistically significances (P < 0.05) in between two groups of instruments: first group is Standard Scalpel, Conventional Electrosurgery, PlasmaBlade and Ultracision; and the second group consists of Colorado Needle Electrosurgery and Radiofregquency.

 cutting devices, histology, incisions


1.     Massarweh NN, Cosgriff N, Slakey PD, Electrosurgery: History, Principles, and Current and Future Uses. Journal of the America College of Surgeons. March 2006Volume 202, Issue 3, Pages 520–530
2.     Fine RE, Vose JG., Traditional electrosurgery and a low thermal injury dissection device yield different outcomes following bilateral skin-sparing mastectomy: a case report. J Med Case Rep. 2011 May 28;5:212. doi: 10.1186/1752-1947-5-212

3.     Ruidiaz ME, Messmer D, Atmodjo DY, et all. Comparative healing of human cutaneous surgical incisions created by the PEAK PlasmaBlade, conventional electrosurgery, and a standard scalpel. Plast Reconstr Surg. 2011 Jul;128(1):104-11.

4.     Charoenkwan K1, Chotirosniramit N, Rerkasem K. Scalpel versus electrosurgery for abdominal incisions. Cochrane Database Syst Rev. 2012 Jun 13;6:CD005987.

5.     Arashiro DS1, Rapley JW, Cobb CM, Killoy WJ. Histologic evaluation of porcine skin incisions produced by CO2 laser, electrosurgery, and scalpel. Int J Periodontics Restorative Dent. 1996 Oct;16(5):479-91.

6.     Molgat YM1, Pollack SV, Hurwitz JJ, et all. Comparative study of wound healing in porcine skin with CO2 laser and other surgical modalities: preliminary findings. Int J Dermatol. 1995 Jan;34(1):42-7.

7.     Chang EI, Carlson GA, Vose JG, et all. Comparative healing of rat fascia following incision with three surgical instruments. J Surg Res. 2011 May 1;167(1): Epub 2011 Jan 22.

8.     Loh SA, Carlson GA, Chang EI, et all. Comparative healing of surgical incisions created by the PEAK PlasmaBlade, conventional electrosurgery, and a scalpel. Plast Reconstr Surg. 2009 Dec;124(6):1849-59.





Aswathy Mariam Jacob, S Viswanatha Rao, Sakuntala S Pillai

Paper Title:

Cross Layer Optimization Techniques in Sensor-MAC

Abstract:   Wireless Sensor Networks (WSN) is a field which has gained much importance in the past decade. WSN contain sensor nodes which are battery powered and hence reducing energy consumption is the most challenging issue in such systems. One important method to reduce energy consumption in WSN is to do cross layer optimization. Cross layer design can be between different layers of the OSI model. This paper is a survey on cross layer optimization involving Sensor-MAC (S-MAC).

Cross layer optimization, Energy conservation, Sensor-MAC(S-MAC),Wireless Sensor Networks (WSN).


1.           Mihail L. Sichitiu, “Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks”, Twenty-third Annual Joint Conference of the IEEE Computer and Communications societies, Volume 3, INFOCOM, 2004.
2.           Piyush Charan, Rajeev Paulus, Mukesh Kumar, Arvind Kumar Jaiswal,”A survey on the Performance Optimization in Wireless Sensor Network Using Cross layer Design”, International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012.

3.           Goran Martinovic, Josip Balen, Drago Zagar, “A Cross-Layer Approach and Performance Benchmarking in Wireless Sensor Networks”, Sensors, Signals, Visualization, Imaging, Simulation And Materials, 2009.

4.           Kazem Sohraby, Daniel Minoli, Taieb Znati, “Medium Access Control Protocols for Wireless Sensor Networks”, Wireless Sensor Networks, Technology, Protocols, and Applications, Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

5.           Bhaskaran Raman, Pravin Bhagwat, Srinivasan Seshan, “Arguments for Cross-Layer Optimizations in Bluetooth Scatternets”, Proceedings of 2001 Symposium on Applications and the Internet, 2001.

6.           Zhiwei Zhao, Xinming Zhang, Peng Sun and Pengxi Liu, “A Transmission Power Control MAC Protocol for Wireless Sensor Networks”, Proceedings of the Sixth International Conference on Networking, 2007.

7.           Qian Hu, Zhenzhou Tang, “An Improved Adaptive MAC Protocol for Wireless Sensor Networks based on Cross-layer Architecture”, International Conference on Wireless Communications and Signal Processing (WCSP), 2009.

8.           Yaw-Wen Kuo and Kwuang-Jyz Liu, “Enhanced Sensor Medium   Access Control Protocol for Wireless Sensor Networks in the ns-2 Simulator”, IEEE Systems Journal, 2014.

9.           Tuirkmen Canhl, Farid Nait-Abdesselam and Ashfaq Khokhar, “A   Cross-Layer Optimization Approach for Efficient Data Gathering in Wireless Sensor Networks”, IEEE International Networking and Communications Conference (INCC), 2008.

10.        Yuexia Hou, Honggang Wang, Jianxing Liang and Changxing Pei, “A Cross-Layer Protocol for Event-Driven Wireless Sensor Networks ‘,The 1st International Conference on Information Science and Engineering (ICISE), 2009

11.        Felipe D. Cunha, Raquel A. F. Mini and Antonio A.F. Loureiro, “Sensor-MAC with Dynamic Duty Cycle in Wireless Sensor Networks”, Globecom-Ad Hoc and Sensor Networking Symposium, 2012.

12.        Qingxu Xiong, Xiang LI, “Cross-layer Design of MAC and Application Semantics in Wireless Sensor Networks”, IEEE Computer Society, 2014.

13.        Taejoon Kim, David J. Love, Mikael Skoglund and Zhong-Yi Jin, “An Approach to Sensor Network Throughput Enhancement by PHY-Aided MAC”, IEEE Transactions On Wireless Communications, Vol. 14, No. 3, February 2015.

14.        Halil Yetgin, Kent Tsz Kan Cheung, Mohammed El-Hajjar and Lajos Hanzo, “Cross-layer network lifetime optimisation considering transmit and signal processing power in wireless sensor networks”, IET Wireless Sensor Systems, 2014.

15.        Volkan Dedeoglu, Sylvie Perreau and Alex Grant, “Cross-layer Energy Minimization in Correlated Data Gathering Wireless Sensor Networks”, IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012.

16.        Jingxian Wu and Geoffrey Ye Li, “Cross-Layer Design of Random On-Off Accumulative Transmission with Iterative Detections”, IEEE Globecom, 2011.

17.        Chih-Kuang Lin, Titos Kokkinos and Francis Mullany, “Extended-range Wireless Sensor Networks with Enhanced IEEE 802.15.4 CSMA/CA”, IEEE Sensors, 2011.

18.        D.Dessales, A-M.Poussard, R.Vauzelle, N.Richard, F.Gaudaire and C.Martinsons, “Physical Layer Study In A Goal Of Robustness And Energy Efficiency For Wireless Sensor Networks”, Conference on Design and Archtectures for Signal and Image Processing (DASIP), 2010.

19.        Jingxian Wu and Ye (Geoffrey) Li, “Low Power Collision-Tolerant Media Access Control with On-Off Accumulative Transmission”, ICC, 2010

20.        Thomas Beluch, Daniela Dragomirescu, Florian Perget and Robert Plana, “Cross-layered Synchronization Protocol for Wireless Sensor Networks”, Ninth International Conference on Networks, 2010.

21.        Kusumamba S, S M Dilip Kumar, “A Reliable Cross Layer Routing Scheme (CL-RS) for Wireless Sensor Networks to Prolong Network Lifetime”, IEEE International Advance Computing Conference (IACC), 2015.

22.        Marwan Al-Jemeli, and Fawnizu A. Hussin, “An Energy Efficient Cross-Layer Network Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks”, IEEE Sensors Journal, Vol. 15, NO. 2, February 2015.

23.        Hongfeng Wang,  Dingding Zhou and Shi Dong, “Cross Layer Optimization Routing Algorithm for Wireless AD HOC”,   International Journal of Smart Home Vol. 9, No. 7, 2015.

24.        Munish Gupta, Paramjeet Singh and Shveta Rani, “Optimizing Physical Layer Energy Consumption for Reliable Communication in Multi-hop Wireless Sensor Networks”,  Indian Journal of Science and Technology, Vol 8(13), 54605, July 2015.

25.        M.Amsanandhini, A. Jayamathi, “I-MAC with Minimum Delay and Cross Layer Optimization for Wireless Sensor Networks”, International Journal of Innovative Research in Computer and Communication Engineering, Vol 2, Issue 4, April 2014.

26.        Arwa Hamid, Samina Ehsan and Bechir Hamdaoui, “Rate-Constrained Data Aggregation in Power-Limited Multi-Sink Wireless Sensor Networks”, International Wireless Communications and Mobile Computing Conference (IWCMC), 2014.

27.        Alaa Awad and Amr Mohamed, “Distributed Cross-Layer Optimization for Healthcare Monitoring Applications”, International Workshop on Resource Allocation, Corporation and Competition in Wireless Networks, 2014.

28.        Jekishan K. Parmar and Mrudang Mehta, “A Cross Layered Approach to Improve Energy Efficiency of Underwater Wireless Sensor Network”, IEEE International Conference on Computing Research (ICCIC), 2014.

29.        Eleni Stai and Symeon Papavassiliou, “User Optimal Throughput-Delay Trade-off in Multihop Networks Under NUM Framework”, IEEE Communications Letters, Vol. 18, No. 11, November 2014.

30.        Santhosha Rao and Kumara Shama, “Cross Layer Protocols for Multimedia Transmission in Wireless Networks”, International Journal of Computer Science and Engineering Survey(IJCSES), Volume 3, No.3, June 2012.

31.        Mr.M.D.Nikose, “A Review Of Cross Layer Design”, International Journal of Emerging Trends in Engineering & Technology (IJETET) Vol. 02, No. 01, 2013





Pranoti P. Mahakalkar, Aarti J. Vyavahare

Paper Title:

Performance Analysis of Efficient Framework of Image Segmentation using Energy Minimization Function

Abstract:    Image segmentation plays very vital role in many image processing applications and domains. Efficient image segmentation leads to accurate results to end users. There are number of image segmentation techniques presented so far with different objectives. The existing segmentation techniques are based on various features of image. Target objects segmentation from the input image which may from different application areas such as medical, security systems etc.  The segmentation of images those are having many complex areas, mixed pixel intensities or noise corrupted data. The existing level set based image segmentation methods needs the prior information about the total number of image segments which is practically impossible for each image. Therefore to overcome such limitations and research challenges of image segmentation, in this paper we proposed the new image segmentation energy function with two distribution descriptors in order to distinguish automatically background and target region from input image. This paper presents the extensive analysis of this proposal method against the existing method in terms of execution time and JD error rates. In this propose scheme, first single background descriptor models the heterogeneous background with multiple regions. Then, the target descriptor takes into account the intensity distribution and incorporates local spatial constraint. The proposed descriptors, which have more complete distribution information, construct the unique energy function to differentiate the target from the background and are more tolerant of image noise. The simulation and evaluation of this proposed method is done by using well known image processing tool MATLAB.

 Image Segmentation, Image processing, Energy Minimization, Level Set Methods, Region based, Edge based, Minimizer


1.       Tranos Zuva,Oludayo O,Olugbara,Sunday O,Ojo and Seleman Ngwira,Image Segmentation Available Techniques,Developmaents and Open Issues, Canadian Journal on Image processing and Computer Vision Vol 2 No:3 March 2011 .
2.       StanleyOsher and J. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comp.Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988

3.       Kan Cheng, Lixu Gu, and Jianrong Xu,A Novel Shape Prior Based Level Set Method for Liver Segmentation From MR Images, Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, in conjunction with The 2nd International Symposium & Summer School on Biomedical and Health Engineering Shenzhen, China, May 30-31, 2008.

4.       Samir BARA,Mounir Ait Kerroum,Ahmed Hammouch and Driss Aboutajdine,Variational Image Segmentation Models:Application to medical images MRI, -978-1-61284-732-0/11/$26.00 ©2010 IEEE.

5.       Kaihua Zhang a, Lei Zhang a, 1 and Su Zhang,A Variational Multi Phase Level Set Approach To Simultaneous Segmentation And Bias Correction

6.       Pan Lin, Chong-Xun Zheng, Yong Yang,Model-Based Medical Image Segmentation: A Level Set Approach,Proceedings of the 5th World Congress on Intelligent
Control and Automation. June 15-19, 2004, Hangzhou. P.R.China

7.       Zongjie Cao, Yiming Pi, Xiaobo Yang, Jintao Xiong, a Variational Level Set SAR Image Segmentation Approach Based on Statistical Model

8.       Chunming Li, RuiHuang, Zhaohua Ding, Chris Gatenby, Dimitris Metaxas,and John Gore, A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity,D. Metaxas et al. (Eds.): MICCAI 2008, Part II, LNCS 5242, pp. 1083–1091, 2008. Springer-Verlag Berlin Heidelberg 2008

9.       El Hadji S. Diop, Silèye O. Ba, Taha Jerbi and Valérie Burdin,Variational and Shape Prior -based Level Set Model for Image Segmentation

10.    Luminita A. Vese & Tony F. Chan, A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, International Journal of Computer Vision 50(3), 271–293, 2002© 2002 Kluwer Academic Publishers. Manufactured in the Netherlands.





Fatah Bouteldjaoui, Mohamed Bessenasse, Ahmed Kettab

Paper Title:

Assessment of Climatic Variability in Zahrez Basin (Algeria)

Abstract:     The knowledge of the climatic behavior especially that one of semi-arid regions is required to optimize the management of water resources. Numerous studies have been carried out to analyze the precipitation variability throughout the world in general and more especially in Mediterranean basin and in African region [1]. The water resources which are available in Algeria are limited[2-3]. They are also subjected to cyclical extremes variations i.e. succession of cycles of severe drought. The drought observed during these last years in Algeria has also affected those located more to the south, characterized by semi-arid to arid climate. The decrease in rainfall and consequently   that in runoff might penalize development projects linked with water supply. The Zahrez basin (Fig.1) is one of the endorheic basins of the vast steppes region in the central northern part of Algeria. The Zahrez hydrological basin covers approximately 8,989 km2. The catchment lies between longitudes 2° 15’ to 4° 08’E and latitudes 34° 35’  to  35° 30’N. The area is characterized by a semi-arid climate, typically Mediterranean, with an irregular annual rainfall. The mean annual rainfall and potential evapotranspiration are 250 and 1380 mm, respectively, exceeding rainfall for most of the year [4].  The objective of this work is the identification and the consequences of climate variability, based on statistical analysis evolution of the annual rainfall series, over a period of 34 years (1973/1974 -2006/2007 ), a set of stations (09) covering the study area. This analysis consists of the study of the interannual evolution of Nicholson rainfall indices, and the implementation of statistical tests of homogeneity of the time series. These tests are Pettitt test, the Buishand test, the Hubert segmentation procedure and  Bois control ellipse. The results of the interannual evolution of rainfall indices show that 67% of retained stations are characterized by the alternating of wet period (1974-1982) and dry (1983-2007). Moreover, the homogeneity statistical tests indicate a break in stationarity of the rainfall series in Charef, Benhafaf and Aïn Maabed stations.

   Climate variability, water resources, semi-arid, statistic tests, Zahrez  basin, Algeria


1.           . Meddi, “Impact des Changements Climatiques sur les Eaux Souterraines (Cas du Bassin Hydrographique Cheliff-Zahrez) ”, conference Groundwater& Climate in Africa, Kampala, Uganda, 2008.
2.           Kettab, “Water resources in Algeria : strategies, investments, and vision” , Desalination, vol.136, no.1-3, pp.25-33, 2001. 

3.           Kettab, “Water for all with quality and quantity: it is the concern of all”, Desalination and Water Treatment, vol.52, pp.1965–1966, 2014.

4.           F. Bouteldjaoui, M. Bessenasse, and A. Gendouz, “Etude comparative des différentes méthodes d’estimation de l’évapotranspiration en zone semi-aride (cas de la région de Djelfa) ”, Revue Nature & Technologie, no.07, Juin, pp.109-116, 2012.

5.           M. F. Sidi Moussa, and M.Deramchi, “Synthèse des études et exploitation des données existantes sur le Synclinal de Djelfa”, Agence Nationale des Ressources Hydrauliques (ANRH), Rapport Technique, 40P, 1993. 

6.           M. F. Sidi Moussa , “Ressources Hydrauliques de la zone du projet GTZ-HCDS. Coopération Algero-Allmande”,  Agence Nationale des Ressources Hydrauliques (ANRH). Rapport Technique, 176 P, 2000.

7.           N. H. Lubès, J.M. Masson, J.E. Paturel, E. Servat, and B.Kouamé, “De différents aspects de la variabilité de la pluviométrie en Afrique de l’Ouest et Centrale non sahélienne”, Rev. Sci. Eau, 12(2), pp 363-387, 1999.

8.           A.N. Pettitt, “A non-parametric approach to the change-point problem”, Appl. Statist, vol. 28, no. 2, pp.126-135,1979.

9.           T. A. Buishand, “Some methods for testing the homogeneity of rainfall records”, Journal of Hydrology,vol. 58, pp.11–27. 1982.

10.        T. A. Buishand, “Tests for detecting a shift in the mean of hydrological time series”, Journal of Hydrology, vol. 73, pp.51-69, 1984.

11.        N. H. Lubès, J.M. Masson, J.E. Paturel, and E. Servat,  “ Variabilité climatique et statistiques. Etude par simulation de la puissance et de la robustesse de quelques tests utilisés pour vérifier l’homogénéité de chroniques”, Revue des Scienes de l’Eau, no. 3, pp. 383-408, 1998.

12.        D. Sighonou, “ analyse et redéfinition des régimes climatiques et hydrologiques du Cameroun : Perspective d’évolution des ressources en eau”, Thèse de Doctorat, Université de Yaoundé, Faculté des sciences, 173 P, 2004

13.        P. Hubert., J.P. Carbonnel, and A. Chaouche, “Segmentation des séries hydrométéorologiques – Application à des séries de précipitations et de débits de l’Afrique de l’Ouest”, J. Hydrol, vol.110, pp. 349-367, 1989.

14.        Chrystelle A, “ Impact du changement climatique sur la ressource en eau en région Langueduc  Roussillon. Thèse DEA, Université Pierre et Marie Curie, Université Paris Sud, Ecole des Mines de Paris, 49 P, 2002.

15.        Kouakou, A. Goula Bi Tié, S. Issiaka, “Impacts de la variabilité climatique sur les ressources en eau de Surface en Zone Tropicale Humide : Cas du Bassin
Versant Transfrontalier de la Comoé (Côte d’ivoire –Burkina Faso) ”, European Journal of Scientific Research, vol. 16, n°.1, pp. 31-43, 2007.

16.        JF. Boyer, “Khronostat statistical time series analyses software”, Montpellier, UMR 5569 Hydrosciences, IRD-Maison des sciences de l’eau, 1998.





Anju T S, Nelwin Raj N R

Paper Title:

Satellite Image Denoising Based on Entropy Thresholding using Shearlet Transform

Abstract:  Satellite images have become universal standard in almost all applications of image processing. However, satellite images are susceptible to noise arising due to unresolved flaws in acquisition and transmission system. Development of a denoising algorithm in satellite images is still a challenging task for many researchers. Most of the state of the art denoising schemes employ wavelet transform but the main limitation of wavelet transform is that it can preserve only point singularity. Shearlet transformation is a sparse, multiscale and multidimensional alternative to wavelet transform. Shearlet transform is optimal in representing image containing edges. In this paper, a novel image denoising algorithm utilizing shearlet transform and entropy thresholding is presented which was found to exhibit superior performance among other state of the art image denoising algorithms in terms of peak signal to noise ratio (PSNR).

    Denoising, Discrete Shearlet Transform, Entropy Thresholding


1.        S.Mallat, and W.L.Hwang, “Singularity Detection and Processing with Wavelets,”IEEE Trans. Information Theory, vol.38, no.2, March   1992, pp.617-643.
2.        J.L.Starck, E.J.Candes, and D.L.Donoho, “The curvelet transform for image denoising,”IEEE Trans. on image processing, vol.11, 2002, pp.670-684.

3.        M. Do and M. Vetterli, “The contourlet transform: An efficient directional multiresolution image representation,” IEEE Trans. on image processing, vol.14, no. 12,
Dec. 2005, pp.2091-2106.

4.        G. R. Easley, D. Labate, and W.Q. Lim, “Sparse directional image representations using the discrete shearlet transform,’’ apple. Comput. Harmon. Analysis, vol.25, Jan. 2008, pp.25-46.

5.        L.Moisan, “Periodic plus smooth image decomposition,” Journal of Mathematical Imaging and Vision, vol.39, no.2, 2011, pp.161-179.

6.        P.J. Burt, and E.H. Adelson, “The Laplacian pyramid as a compact image code,”IEEE Trans. Commun, vol.31, no.4, 1983, pp.532-540.

7.        L.Ramiro and A. K. C. Wong, “A study into entropy-based thresholding for image edge detection, ’Vision Interface, 1995, pp. 38-44.

8.        S.Mallat, A wavelet tour on Signal Processing, 1999, Academic Press.

9.        B.Qi, “Image denoising based on non-subsampled shearlet trans- form,”IEEE Trans. on image processing, vol.10, no.1, 2013,pp.238-242.





Athira S Vijay, Nelwin Raj N. R

Paper Title:

Adaptive Deblurring by Estimation of Motion Blur Kernels

Abstract:   One of the challenges in the field of photography is the motion blur. Motion blur is the smudging of images caused by the relative motion between the camera and the pictured object during the exposure time. Blur kernel is the fundamental cause for blurring. Thus, in order to restore the original image through deconvolution, we need to estimate the blur kernel. In this paper, the blur kernels are estimated by using a piecewise linear model. Then, estimated kernel is regularized by adjusting the spacing and curvature of the control points. In addition to this, the control parameters of the energy function is also optimized in order to achieve better edge enhancement. The estimated kernel is then optimized by using Gauss- Newton method. In order to improve the PSNR of the deblurred image, wavelet multiframe denoising is used. In addition to this, the quality of image is enhanced by using a colour image enhancement technique. The experimental result shows that, kernel estimation along with wavelet multiframe denoising and Colour image enhancement technique can improve the PSNR values as well as the quality of the resultant deblurred image. In addition to this, the proposed algorithm can accurately estimate the unknown kernel masked in the blurred image, without any prior knowledge.

  Motion blur, Piecewise-linear curve, Kernel estimation, Deblurring, Wavelet multiframe denoising, PSF, Blind deconvolution, Image enhancement.


1.       S. Cho and S. Lee, “Fast motion deblurring”, ACM Trans. Graph., vol. 28, no. 5, 2009, pp. 1-8.
2.       J.-F. Cai, R. Chan, and M. Nikolova, “Fast two-phase image deblurring under impulse noise,” J. Math. Imag. Vis., vol. 36, no. 1, pp. 46–53, 2010.

3.       J.. Hui and L. Chaoqiang, “Motion blur identification from image gradients,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1–8.

4.       B. Kang, J. W. Shin, and P. Park, “Piecewise linear motion blur identification using morphological filtering in frequency domain,” in Proc. ICCAS-SICE, 2009, pp. 1928-1930.

5.       K. Patanukhom and A. Nishihara, “Identification of piecewise linear uniform motion blur,” in Proc. IEEE Region 10 Conf.,Nov. 2007, pp. 1-4.

6.       Sungchan Oh, and Gyeonghwan Kim,“Robust Estimation of Motion Blur Kernel Using A Piecewise-Linear Model,” IEEE Transactions on Image Processing, Vol. 23, no. 3, March 2014.

7.       KatrinaEllisonn(2014). Simulated Annealing Algorithm[Online}.Available: http://katrinaeg.com/simulated-annealing.html

8.       Markus A. Mayer, Anja Borsdorf, Martin Wagner, Joachim Hornegger, Christian Y. Mardin, and Ralf P. Tornow, “Wavelet Denoising Of Multiframe Optical Coherence Tomography Data”, Optical Society of America,2012

9.       Anish Kumar, Vishwakarma, et al, “Color Image Enhancement Techniques: A Critical Review”, Indian Journal of Computer Science and Engineering (IJCSE)

10.    L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” in Proc. Eur. Conf. Comput. Vis., 2010, pp. 150–170.





Archana Sahu, Amit Mishra, Shiv Kumar Sahu

Paper Title:

Performance Evaluation of Spam Filtering Using Bayesian Approach

Abstract:    Spam filtering is the technique to find out spams. This field is important aspect of text classification. Spam filtering technique is used with email servers, and population of spam is usually more than genuine emails, this is why spam filtering has become important technique. Most of existing spams filtering techniques are unable to detect spam because spammers know how to make spam to reach the destined email account without being filtered. In such situation, naïve bayes spam filter is proved to be a great technique, because several aspects are there to improve the performance of spam filter. Hence, it is an important research field in detecting spams. In this dissertation, technique for spam detection and filtering has been proposed based on Naïve Bayes classification technique, which is the existing spam filtering technique. Some enhancements are made in making it adaptive to new kind of spams. In existing spam filtering techniques, static filtering technique has been used, but we proposed dynamic and enhanced filtering technique, which helps in fast and accurate spam detection. Regular training of classifier should be done, database of spam should be updated all the time, and also a particular word should not be always behaved as spam word or a genuine word. Experimental results show that proposed enhancements improves accuracy of spam filtering.

   Spam filtering, detecting, field, accuracy proposed enhancements, classifier Regular, proposed, spam


1.             Meena, M.J.; Chandran, K.R.; , “Naïve Bayes text classification with positive features selected by statistical method,”, 2009. ICAC 2009. First International Conference on Advanced Computing, vol., no., pp.28-33, 13-15 Dec. 2009
2.             Yan Zhou; Mulekar, M.S.; Nerellapalli, P.; “Adaptive spam filtering using dynamic feature space,” , 2005. ICTAI 05. 17th IEEE International Conference on Tools with Artificial Intelligence, vol., no., pp.8 pp.-309, 16-16 Nov. 2005

3.             Haiyi Zhang; Di Li; , “Naïve Bayes Text Classifier”, 2007. GRC 2007. IEEE International Conference on Granular Computing, vol., no., pp.708, 2-4 Nov. 2007

4.             Pelletier, L.; Almhana, J.; Choulakian, V.;, “Adaptive filtering of spam,” , 2004. Proceedings. Second Annual Conference on Communication Networks and Services Research, vol., no., pp. 218- 224, 19-21 May 2004

5.             Saha, D.; , “Web Text Classification Using a Neural Network,” , 2011 Second International Conference on Emerging Applications of Information Technology (EAIT), vol., no., pp.57-60, 19-20 Feb. 2011

6.             Lijuan Zhou; Linshuang Wang; XuebinGe; Qian Shi; , “A clustering-Based KNN improved algorithm CLKNN for text classification,”  2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR), 2010, vol.3, no., pp.212-215, 6-7 March 2010

7.             Amayri, O.; Bouguila, N.; , “Online spam filtering using support vector machines,”  IEEE Symposium on Computers and Communications, 2009. ISCC 2009., vol., no., pp.337-340, 5-8 July 2009

8.             Yin; Zhang Chaoyang; , “An Improved Bayesian Algorithm for Filtering Spam E-Mail,”  2nd International Symposium

9.             on Intelligence Information Processing and Trusted Computing (IPTC), 2011, vol., no., pp.87-90, 22-23 Oct. 2011

10.          Sang-Bum Kim; Kyoung-Soo Han; Hae-Chang Rim; Sung HyonMyaeng; , “Some Effective Techniques for Naive Bayes Text Classification,” IEEE Transactions on Knowledge and Data Engineering, vol.18, no.11, pp.1457-1466, Nov. 2006

11.          Zhang Yang; Zhang Lijun; Yan Jianfeng; Li Zhanhuai; , “Using association features to enhance the performance of Naive Bayes text classifier,” Fifth International Conference on Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003.Proceedings, vol., no., pp. 336- 341, 27-30 Sept. 2003

12.          M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. “A bayesian approach to filtering junk e-mail”. In Learning for Text Categorization: Papers from the 1998 Workshop, Madison, Wisconsin, 1998.

13.          Tarek M Mahmoud, alaa Ismail EI Nashar, Tarek Abd – EI – Hafeez ans Marwa Khairy “En Efficient Three Phase Email spam Filtering Technique” British Journal of Managnent & Computer Science 4(9), 1184-1201, 2014





Divya Velayudhan, Salim Paul

Paper Title:

A Review on Compressive Sensed Image Reconstruction using Group-based Sparse Representation

Abstract:     Compressive Sensing (CS) – a novel sensing paradigm asserts that signals can be reconstructed from fewer samples than that recommended by Nyquist sampling theorem, when it can be expressed in a sparse basis. Conventional approaches for compressive sensed image recovery utilized fixed basis (DCT, wavelets) that do not yield higher level of sparsity for the entire signal resulting in poor performance. This paper reviews the performance of Group-based sparse representation (GSR) model for CS recovery which yields high degree of sparsity for natural images in the domain of group. GSR stacks together non-local similar patches in an image to form a group and the sparse representation of each group is achieved using self-adaptive dictionary learning technique. Thus GSR takes advantage of the intrinsic local sparsity and non-local self-similarity of images simultaneously in a unified framework. The GSR driven optimization problem is solved using split-bregman iteration. Experimental results obtained on images for CS recovery reveals the performance achieved by GSR over many current state-of-the-art schemes.

    Compressive sensing, Sparse representation, self-similarity, split-Bregman.


1.           D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol.52, no. 4, pp. 1289–1306, 2006.
2.           E. J. Candès and T. Tao, “Near-optimal signal recovery from random projections: Universal encoding strategies,” IEEE Trans. Inf. Theory, vol. 52, pp. 5406–5425, 2006.

3.           E. Candes and M. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. vol. 25, no. 2, pp.21–30, Mar. 2008

4.           M. N. Do and M. Vetterli, “The contourlet transform: An efficient directional multiresolution image representation,” IEEE Trans. on Image Processing, vol. 14, no. 12, pp. 2091–2106, Dec. 2005

5.           C. Li, W. Yin, and Y. Zhang, “TVAL3: TV Minimization by Augmented Lagrangian and Alternating Direction Algorithm,” 2009

6.           L. He, H. Chen and L. Carin, “Tree-structured compressive sensing with variational Bayesian analysis,” IEEE Signal Processing Letter, vol. 17, no. 3, pp. 233–236, 2010

7.           L. He and L. Carin, “Exploiting structure in wavelet-based Bayesian compressive sensing,” IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3488–3497, 2009

8.           C. Chen, E. W. Tramel, and J. E. Fowler, “Compressed-Sensing Recovery of Images and Video Using Multihypo-thesis Predictions,” Proc. of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 1193–1198, Nov. 2011

9.           J. Zhang, R. Xiong, S. Ma, and D. Zhao, “High-Quality Image Restoration from Partial Random Samples in Spatial Domain”, Proc. of IEEE Visual Communications
and Image Processing, pp. 1–4, Tainan, Taiwan, Nov. 2011.

10.        J. Zhang, R. Xiong, C. Zhao, S. Ma, D. Zhao. “Exploiting Image Local and Nonlocal Consistency for Mixed Gaussian-Impulse Noise Removal”, Prof. of IEEE Int. Conf. on Multimedia & Expo, pp. 592–597, Melbourne, Australia, Jul. 2012.

11.        J. Zhang, D. Zhao, C. Zhao, R. Xiong, S. Ma, and W. Gao, “Compressed Sensing Recovery via Collaborative Sparsity”, Proc. of IEEE Data Compression Conference, pp. 287–296, Snowbird, Utah, USA, Apr. 2012

12.        M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. on Signal Process., vol. 54, no. 11, pp. 4311–4322, 2006

13.        M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. vol. 15, no. 12, 2006, pp. 3736–3745.

14.        W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process., vol. 20, no. 7, Jul. 2011, pp. 1838–1857.

15.        Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proc. Int. Conf. CVPR, Jun. 2005, pp. 60–65
16.        J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in Proc. IEEE 12th Int. Conf. Comput. Vis., Tokyo, Japan, Sep. 2009, pp. 2272–2279
17.        J. Zhang, D. Zhao, F. Jiang, and W. Gao, “Structural group sparse representation for image compressive sensing recovery,” in Proc. IEEE DCC, Snowbird, UT, USA, Mar. 2013, pp. 331–340.

18.        J. Zhang, C. Zhao, D. Zhao, and W. Gao, “Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization,” Signal Process., vol. 103, pp. 114–126, Oct. 2014

19.        T. Goldstein and S. Osher, “The split Bregman algorithm for L1 regularized problems,” SIAM  J. Imaging Sci, vol. 2, Apr.2009, pp. 323-343

20.        C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented Lagrangian method with applications to total variation minimization,” Computational Optimization
and Applications, Vol. 56, no. 3, pp. 507–530, Dec. 2013

21.        C. Chen, E. W. Tramel, and J. E. Fowler, “Compressed-sensing re
covery of images and video using multi-hypothesis predictions,” in Proc. 45th Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 2011, pp. 1193–1198





Sulthana Shafi, George M Josep

Paper Title:

Data Modeling, Estimation and Recovery of Dynamic and Static Sparse Signals-A Review

Abstract:   For sparse signal, compressed sensing is the present dogma, using only fewer measurements for sampling, compression and reconstruction of signals satisfying the Nyquist theorm. Here the outgrowth of compressive sensing using different algorithms for time invariant till time varying sparse signals and its recovery are surveyed. Thus these algorithms are effective in recovering dynamic and static sparse signal vectors. Algorithms exhibiting correlation and optimization approaches are reviewed. Also different mathematical models are reviewed which improves the quality of estimated solutions to best optimal solution.

  Compressed sensing, Multiple measurement vector, OFDM, Lasso, Homotopy, kalman filter, Expectation Maximization.


1.          Haifeng, Li, Li Rui and Li Bei, “Block MMV for the reconstruction of multiband signals”,34th Chinese Control Conference (CCC), 2015.
2.          Emmanuel j candees, Eclolepolytech,Paris Micheal B Wakin,“An Introduction To Compressive Sampling”,IEEE trans. on signal processing,Vol.25,No.2,march 2008.

3.          Chepuri, Sundeep Prabhakar, and Geert Leus,”Compression schemes for time-varying sparse signals”, 48th Asilomar Conference on Signals Systems and Computers, 2014.

4.          Shamaiah, Manohar, and Haris Vikalo,”Estimating Time-Varying Sparse Signals Under Communication Constraints”, IEEE Transactions on Signal Processing,2011.

5.          Dsp.rice.edu Internet source.

6.          Muhammed Salman Asif,”Dynamic Compressive Sensing: Sparse Recovery Algorithm For Streaming Signals And Video”,Georgia Institute Of Technology,2013.

7.          users.ece.gatech.edu ,Internet Source.

8.          M.S Asif and J Romberg,”Sparse Recovery Of Streaming Signals Using L1 Homotopy”,IEEE trans. on signal processing ,Vol. 62,No.16, pp. 4209-4223,2014.

9.          ” L1-Homotopy:-A Matlab Toolbox for Homotopy Algorithm in L1 Norm Minimization problem.”[Online]

10.       “EM-GM GAMP:An algorithm for sparse Representation.”[Online]

11.       Zhang,Zhilin, “Sparse Signal Recovery Exploiting Spatiotemporal Correlation”, Series:UC San Diego Electronic Theses and Dissertations,signal and image processing, 2012.

12.       Zhilin Zhang, “Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning.”, IEEE Journal of Selected Topics in Signal Processing ,vol. 5, no. 5, pp. 912-926, 2011.

13.       Zhilin Zhang, Bhaskar D. Rao,” Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity”, ICML 2011 Workshop on Structured Sparsity: Learning and Inference, July, 2011 .

14.       Zhilin Zhang ,Bhaskar D. Rao,”Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors”, ICASSP, 2011.

15.       Zhilin Zhang, Bhaskar D. Rao,” Sparse Signal Recovery in the Presence of Correlated Multiple Measurement Vectors”, ICASSP,2010.

16.       Zhilin Zhang, Bhaskar D. Rao,” Clarify Some Issues on the Sparse Bayesian Learning for Sparse Signal Recovery”, Technical Report, 2011.

17.       Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Zhouyue Pi, Bhaskar D. Rao, “Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals”, IEEE Trans. On Neural Systems and Rehabilitation Engineering, vol. 22, no. 6, pp. 1186-1197, 2014.

18.       Zhilin Zhang, Bhaskar D. Rao, Tzyy-Ping Jung,” Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities”, Asilomar Conference on Signals, Systems, and Computers (Asilomar 2013), California,2013 .

19.       Submitted to University of Hong Kong.

20.       Zhang, Z., and B. Rao. “Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra- Block Correlation”, IEEE Transactions on Signal Processing, 2013.

21.       Zhang, Zhilin, and Bhaskar D. Rao. “Recovery of block sparse signals using the framework of block sparse Bayesian learning”,IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2012.

22.       Soussen, Charles, Jerome Idier, David Brie,and Junbo Duan, “From Bernoulli Gaussian Deconvolution to Sparse Signal Restoration”,IEEE Transactions on Signal Processing, 2011.

23.       F. Gorodnitsky and B. D. Rao, “Sparse signal reconstruction from limited data using FOCUSS: a reweighted minimum norm algorithm,” IEEE Trans.on Signal Processing,vol.45, no. 3, pp. 600-616, 1997.

24.       D. Donoho, “Compressed sensing,” Information Theory, IEEE Transactions on,, vol. 52, no. 4, pp. 1289-1306,2006.

25.       Applied and Numerical Harmonic Analysis,2013.

26.       E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,”Communications on pure and applied mathematics, vol.59, no. 8, pp. 1207-1223, 2006.

27.       www.dsp.ece.rice.edu ,Internet Source.

28.       E. Candes and T. Tao, “Decoding by linear programming,”Information Theory, IEEE Transactions on, vol.51, no. 12, pp. 4203-4215, 2005.

29.       Wei, Wang, Jia Min, and Guo Qing. “A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal”,International Symposium on Wireless Personal Multimedia Communications (WPMC), 2014.

30.       B. Natarajan, “Sparse approximate solutions to linear systems,” SIAM journal on computing, vol. 24, no. 2, pp. 227-234, 1995.

31.       M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” J. R. Statist. Soc. B, vol. 68, pp. 49-67, 2006.

32.       R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Transactions on Information Theory, vol. 56, no. 4, pp. 1982-2001,2010.

33.       Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Blocksparse signals: uncertainty relations and efficient recovery,” IEEE Transactions on Signal Processing, vol. 58, no. 6, pp. 3042-3054, 2010.

34.       M. Stojnic, F. Parvaresh, and B. Hassibi, “On the reconstruction of blocksparse signals with an optimal number of measurements,” IEEE Transactions on Signal Processing, vol. 57, no. 8, pp. 3075-3085, 2009.

35.       E. Elhamifar and R. Vidal, “Block-sparse recovery via convex optimization,” Signal Processing, IEEE Transactions on, vol. 60, no. 8, pp. 4094-4107, 2012.

36.       Zhang, Z., Tzyy-Ping Jung, S. Makeig, and B.D. Rao. “Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning”,IEEE Transactions on Biomedical Engineering,2012.

37.       dsp.ucsd.edu,Internet Source.

38.       B. D. Rao and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” in Proc. IEEE Digital Signal Processing Workshop, Bryce Canyon, UT, 1998.

39.       S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz- Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Trans. On Signal Processing, vol. 53, no. 7, pp. 2477-2488, 2005.

40.       Y. C. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Trans. On Information Theory, vol. 55, no. 11, pp. 5302-5316, 2009.

41.       Y. C. Eldar and H. Rauhut, “Average case analysis of multichannel sparse recovery using convex relaxation,” IEEE Trans. on Information Theory, vol. 56, no. 1, pp. 505-519, 2010.

42.       Y. Jin and B. Rao, “Support recovery of sparse signals in the presence of multiple measurement vectors,” arXiv preprint arXiv:1109.1895, 2011.

43.       Rao, Bhaskar D., Zhilin Zhang, and Yuzhe Jin,”Sparse signal recovery in the presence of intra-vector and intervector correlation”, International Conference on Signal Processing and Communications (SPCOM), 2012.

44.       Choi, Jun, and Byonghyo Shim. “Statistical Recovery of Simultaneously Sparse Time-Varying Signals from Multiple Measurement Vectors”, IEEE Transactions on Signal Processing, 2015.

45.       N. Vaswani, “Kalman filtered compressed sensing,” in Proc. of the15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, USA, 2008,
pp. 893-896.

46.       D. Zachariah, S. Chatterjee, and M. Jansson, “Dynamic iterative pursuit,” Signal Processing, IEEE Transactions on, vol. 60, no. 9, pp. 4967-4972, 2012.

47.       D. Sejdinovic, C. Andrieu, and R. Piechocki, “Bayesian sequential compressed sensing in sparse dynamical systems,” in Communication, Control, and Computing
(Allerton), 2010 48th Annual Allerton Conference on, 2010, pp. 1730-1736.

48.       N. Vaswani and W. Lu, “ModiïnˇA˛ed-CS: Modifying compressive sensing for problems with partially known support,” Signal Processing, IEEE Transactions on, vol. 58, no. 9, pp. 4595-4607, 2010.

49.       J. Ziniel and P. Schniter, “Dynamic compressive sensing of time-varying signals via approximate message passing,” arXiv preprint arXiv:1205.4080, 2012.

50.       M. Salman Asif and J. Romberg, “Dynamic updating for ‘1 minimization,” Selected Topics in Signal Processing, IEEE Journal of, vol. 4, no. 2, pp. 421-434, 2010.

51.       Zhang, Zhilin, Bhaskar D. Rao, and Tzyy-Ping Jung, “Compressed sensing for energy-efficient wireless telemonitoring: Challenges and opportunities”,Asilomar Conference on Signals Systems and Computers, 2013.

52.       Wan, Jing, Zhilin Zhang, Bhaskar D. Rao,Shiaofen Fang, Jingwen Yan, Andrew J.Saykin, and Li Shen. “Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer’s Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning”,IEEE Transactions on Medical Imaging, 2014.

53.       boufounos.com,Internet Source.

54.       3b Jun Won Choi and Byonghyo Shim, “Statistical Recovery Of Simultaneously Sparse Time Varying Signals From MMV”, IEEE trans. on signal processing ,Vol. 63,
No.22,pp. 6136 – 6148, 2015.

55.       4b M.Shamaiah and H.Vikalo, “Estimation Of Time Varying Sparse Signal In Sensor Networks.”, IEEE trans. on signal processing ,Vol. 59,No.6, pp. 2961 – 2964, 2011.

56.       Shamaiah, Manohar, and Haris Vikalo,”Estimation of Time-Varying Sparse Signals in Sensor Networks”, Signals and Communication Technology, 2014.

57.       D. L. Donoho and M. Elad, “Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization,” PNAS, vol. 100, no. 5, pp. 2197 – 2202, 2003.

58.       M. Elad, “Sparse representations are most likely to be the sparsest possible,” EUROSIP Journal on Applied Signal Processing, vol. 2006, pp. 1-12, 2006.

59.       arxiv.org,Internet Source.

60.       R. Tibshirani, “Regression shrinkage and selection via  the lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267-288, 1996.

61.       Y. Cho and L. K. Saul, “Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models,” in Proc. of the 34th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), Taipei, pp. 1705-1708.

62.       P. Hansen, “Analysis of discrete ill-posed problems by means of the l – curve,” SIAM review, vol. 34, no. 4, pp. 561-580, 1992.

63.       P. Hansen and D. O Leary, “The use of the l – curve in the regularization of discrete ill-posed problems,” SIAM Journal on Scientific Computing, vol. 14, no. 6, pp. 1487  ¸S 1503, 1993.

64.       C. Stein, “Estimation of the mean of a multivariate normal distribution,” The annals of Statistics, pp. 1135- 1151, 1981.

65.       V. Solo, “A sure – fired way to choose smoothing parameters in ill conditioned inverse problems,” in Image Processing, 1996. Proceedings., International Conference on, vol. 3, 1996, pp. 89-92.

66.       R. Tibshirani, J. Bien, J. Friedman, T. Hastie, N. Simon, J. Taylor, and R. Tibshirani, “Strong rules for discarding predictors in lasso-type problems,”Journal of the Royal

67.       Statistical Society: Series B (Statistical Methodology), vol. 74, no. 2, pp. 245-266, 2012.

68.       T. Sun and C.-H. Zhang, “Scaled sparse linear regression,” Biometrika, vol. 99, no. 4, pp. 879-898, 2012.
69.       Jiawei Zhou ,Laming Chen and Yuantao Gu,”Dynamic Zero Point Attracting Projection For Time-Varying Sparse Signal Recovery”, National Natural Science Foundation Of China,IEEE,2015.
70.       B. D. Rao, K. Engan, S. F. Cotter, J. Palmer, and K. Kreutz-Delgado, “Subset selection in noise based on diversity measure minimization,” IEEE Trans. on Signal Processing, vol. 51, no. 3, pp. 760-770, 2003.

71.       Gorodnitsky, J. George, and B. Rao, “Neuromagnetic source imaging with focuss: a recursive weighted minimum norm algorithm,” Electroencephalography and clinical Neurophysiology, vol. 95, no. 4, pp. 231-251,1995.

72.       H. Mohimani, M. Babaie-Zadeh, and C. Jutten, “A fast approach for overcomplete sparse decomposition based on smoothed l0 norm,” IEEE Trans. on Signal Processing, vol. 57, no. 1, pp. 289-301, 2009.

73.       Seneviratne and V. Solo, “On vector l0 penalized multivariate regression,” in Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, 2012, pp. 3613-3616.

74.       S. Mallat and Z. Zhang, “Matching pursuits with time frequency dictionaries,” Signal Processing, IEEE Transactions on, vol. 41, no. 12, pp. 3397-3415, 1993.

75.       D. Needell and J. A. Tropp, “CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis, vol. 26, no. 3, pp. 301-321, 2009

76.       J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” Information Theory, IEEE Transactions on, vol. 53, no. 12, pp. 4655-4666, 2007.

77.       W. Dai and O. Milenkovic, “Subspace pursuit for compressive sensing signal reconstruction,” Information Theory, IEEE Transactions on, vol. 55, no. 5, pp. 2230-2249, 2009.

78.       W. Dai and O. Milenkovic, “Subspace pursuit for compressive sensing signal reconstruction,” Information Theory, IEEE Transactions on, vol. 55, no. 5, pp. 2230-2249, 2009.

79.       J. Vila and P. Schniter,”Expectation-maximization Gaussian-mixture approximate message passing,” arXiv:1207.3107,2012.

80.       .M. Bayati and A. Montanari, “The dynamics of message passing on dense graphs, with applications to compressed sensing,” Information Theory, IEEE Transactions on, vol. 57, no. 2, pp. 764-785, 2011.

81.       Asif, M. Salman, and Justin Romberg. “Sparse Recovery of Streaming Signals Using ll Homotopy”, IEEE Transactions on Signal Processing, 2014.

82.       Zhou, Jiawei, Laming Chen, and Yuantao Gu,”Dynamic zero-point attracting projection for time-varying sparse signal recovery”,IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2015.

83.       Hao, Jinping, Filippo Tosato, and Robert J.Piechocki, “Sequential Compressive Sensing in Wireless Sensor Networks”, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012.

84.       P. Schniter, L. Potter, and J. Ziniel, “Fast Bayesian matching pursuit,” in Information Theory and Applications Workshop, 2008, 2008, pp. 326-333.

85.       H. Zayyani, M. Babaie-Zadeh, and C. Jutten, “Bayesian pursuit algorithm for sparse representation,” in Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, 2009, pp. 1549-1552.

86.       C. Herzet and A. Dr emeau, “Bayesian Pursuit Algorithms” [Online]. Available: http:// hal.inria.fr / hal- 00673801.

87.       M. Tipping, “Sparse Bayesian learning and the relevance vector machine,” The Journal of Machine Learning Research, vol. 1, pp. 211-244, 2001.

88.       D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2153-2164, 2004.

89.       X. Tan and J. Li, “Computationally efficient sparse Bayesian learning via belief propagation”, IEEE Transactions on Signal Processing, vol. 58, no. 4, 2010.

90.       .K. Qiu and A. Dogandzic, “Variance-component based sparse signal reconstruction and model selection,” IEEE Trans. on Signal Processing, vol. 58, no. 6, pp. 2935- 2952, 2010.

91.       . S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Transactions on Signal Processing, vol. 56, no. 6, pp. 2346-2356, 2008.

92.       M.Figueiredo,”Adaptive sparseness for supervised learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.25,no.9,pp.1150-1159,2003.

93.       S. Babacan, R. Molina, and A. Katsaggelos, “Bayesian compressive sensing using laplace priors,” Image Processing, IEEE Transactions on, vol. 19, no. 1, pp. 53-63,  2010.

94.       Bajwa, Waheed U., Marco F. Duarte, and Robert Calderbank. “Conditioning of Random Block Subdictionaries With Applications to Block-Sparse Recovery and Regression”,IEEE Transactions on Information Theory,2015.

95.       nuit-blanche.blogspot.ca,Internet Source.

96.       Guangwu Xu and Zhiqiang Xu,”Compressed Sensing Matrices From Fourier Matrices”,IEEE Transactions on Information Theory,vol. 61, no. 1, jan 2015.

97.       Z. Zhang, T.-P. Jung, S. Makeig, and B. D. Rao,”Compressed sensing for energy-efficient wireless telemonitoring of non-invasive fetal ECG via block sparse
Bayesian learning,” IEEE Trans. on Biomedical Engineering, accepted.

98.       “Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware,” IEEE Trans. on Biomedical Engineering, accepted.

99.       “Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel ECG for wireless telemonitoring,” submitted to IEEE Trans. on Biomedical Engineering,2012.

100.    Ranjitha prasad,Chandra R.Murthy and Bhaskar D.Rao,”Joint Channel Estimation and Data Detection in MIMO-ofdm Systems: A Sparse Bayesian Learning Approach” ,Signal Processing,IEEE Transactions on, vol.63,no.20,2015.

101.    Jing Wang. “Low-complexity Subspace Tracking Based Channel Estimation Method for OFDM Systems In Time-Varying Channels”, IEEE International Conference on Communications, 06/2006.

102.    Huang, M., X. Chen, L. Xiao, S. Zhou, and J. Wang, “Kalman-filter-based channel estimation for orthogonal frequency-division multiplexing systems in time-varying channels”,IET Communications, 2007.

103.    Jing Wang, “Low-complexity Subspace Tracking Based Channel Estimation Method for OFDM Systems In Time-Varying Channels”,2006 IEEE International Conference on Communications, 06/2006





Pallvi Dehariya, Shiv K Sahu, Amit Mishra

Paper Title:

A Result Evolution of An Artificial Immune System for Intrusion Detection System to Improve the Detection Rate

Abstract:    This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system.  The proposed intrusion detection system will use the concepts of the artificial immune systems (AIS) which is a promising biologically inspired computing model. AIS concepts that can be applied to improve the effectiveness of IDS.

   Intrusion detection system, Artificial Immune system, clustering


1.             Cho, Sung-Bae. 2003. .Artificial Life Technology for Adaptive Information Processing. Chapter 2 in Future Directions for Intelligent Systems and Information Sciences: The Future of Speech and Image Technologies, Brain Computers, WWW, and Bioinformatics, edited by Nikola Kasabov, Volume 45 of Studies in Fuzziness
and Soft Computing, 13.33. Heidelberg, Germany: Physica-Verlag. ISBN 3-7908-1276-5.

2.             Dasgupta, Dipankar. 1999, October. .Immunity-Based Intrusion Detection System: A General Framework.. Proceedings of the 22nd National Information Systems Security Conference (NISSC). National Institute of Standards and Technology and National Computer Security Center, Hyatt Regency.Crystal City, Virginia, United States.

3.             Dasgupta, Dipankar, Yuehua Cao, and Congjun Yang. 2003, July 13.17. .An Immunogenetic Approach to Spectra Recognition.. Edited by Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, Proceedings of the Genetic and Evolutionary Computation (GECCO) Conference, Volume 1. Orlando, Florida, United States: Morgan Kaufmann, 149.155. ISBN 1-55860-611-4.

4.             Dasgupta, Dipankar, and Stephanie Forrest. 1996, June 19.21. .Novelty Detection in TimeSeries Data using Ideas from Immunology.. Proceedings of the 5th International Conference on Intelligent Systems. Reno, Nevada, United States.

5.             Nong Ye and Xiangyang Li. A scalable clustering technique for intrusion signature recognition. In Proc. 2nd IEEE SMC Information Assurance Workshop, pages 1-4, 2001.

6.             Yu Guan, Ali A. Ghorbani, and Nabil Belacel. Y-means: a clustering method for intrusion detection. In Canadian Conference on Electrical and Computer Engineering, pages 1-4, Montral, Qubec, Canada, May 2003.

7.             Teuvo Kohonen. Self-Organizing Map. Springer-Verlag, New York, 1997

8.             J. D. Banfield and A. E. Raftery. Model-based Gaussian and non-Gaussian clustering.

9.             FAQ: Network Intrusion Detection Systems, Version 0.8.3,    March 21, 2000 [Intrusion Detection

10.          I.T. Jolliffe. Principal Component Analysis. Springer-Verlag, New York, 1989.

11.          Kohonen, T. 1995. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Berlin, Heidelberg: Springer. (Second Extended Edition 1997).

12.          Leonid Portnoy, “Intrusion Detection with Unlabeled Data using Clustering”, Undergraduate Thesis, Columbia University, New York, NY, Dec. 2000.

13.          Lane, T., and Brodley, C. E. 1999. Temporal sequence learning and data reduction for anomaly detection. ACM Transactions on Information and System Security 2(3): 295—331.

14.          Michael Sobirey’s Intrusion Detection Systems http://www.rnks.informatik.tucot.

15.          “NIST Special Publication on Intrusion Detection Systems“, SP 800-31 Computer Security Resource Center (CSRC), National Institute of   Standards and
Technology (NIST), Nov. 2001, p.15.

16.          P.Lichodzijewski, A. n. Zincir-Heywood and M. I. Heywood, “Host-based intrusion detection using Neural Gas,” Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002 (in press).

17.          Salvatore J. Stolfo, Wei Fan, Wenke Lee, “Cost-based Modeling for Fraud and Intrusion Detection: Results from the JAM Project”, Proceedings of the 2000 DARPA Information Survivability Conference   and Exposition, 2000.

18.          Vesanto J., Alhoniemi E., “Clustering of the Neural Gas Map,” IEEE Transactions on Neural Networks, 11(3), pp 586-600, 2000

19.          Wenke Lee, Sal Stolfo, and Kui Mok. Mining in a data environment: Experience in network intrusion detection. In Proc. 5thACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pages 114{124, San Diego, CA, August 1999.

20.          Wenke Lee and Sal Stolfo, “Data Mining Approaches for Intrusion Detection”, Proceedings of the Seventh USENIX Security Symposium (SECURITY ’98), San Antonio, TX, January 1998.

21.          Wei Fan, Wenke Lee, Sal Stolfo, and Matt Miller (2000) “A Multiple Model CostSensitive Approach for Intrusion Detection”, Eleventh European Conference on Machine Learning (ECML ’00) 2000.

22.          Wei Fan, Matt Miller, Sal Stolfo, Wenke Lee, and Phil Chan, “Using Artificial Anomalies to Detect Unknown and Known Network Intrusions”, CA, November 2001





Vidhya.V.S.Nair, Subha V

Paper Title:

Person Recognition from Activity using Bag of Words

Abstract:     In this paper the discriminant pattern hidden in the way of doing an activity for every person is explored. This pattern can be utilized for person recognition purpose in uncontrolled scenarios unlike finger print, iris, retina etc. (based on physical biometrics). This method is based on single video camera based data. From the video of various activities, background subtraction is done to remove insignificant data. From the binary video obtained after background subtraction structural tensor based features are detected and extracted. The extracted features defines the variation from the mean position are then clustered by means of k-means clustering. Histogram of cluster centroids is calculated using Bag Of Words (BOW) and classified by category classifier. Histogram of input video action sequence is compared with each of dataset and predicts the category, which corresponds to the label of person.

    Activity based identification, Background subtraction, Silhouette, Structural Tensor, Bag Of Words, Category classifier, Structured Support Vector Machine.


1.              Jain, A. Ross, S. Prabhakar,”An introduction to biometric recognition”, IEEE Trans. Circuits Syst. Video Tech., vol. 14,pp. 420,2004.
2.              R.V. Yampolskiy, V. Govindaraju,”Behavioural biometrics: a survey and classification”, Int. J. Biom.,pp. 81113, 2008

3.              R. Tanawongsuwan, A. Bobick,”Gait recognition from time-normalized jointangle trajectories in the walking plane,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR,pp. II- 726II-731.,2001.

4.              Iosifidis, Anastasios Tefas and Ioannis Pitas, “ Person Identification From Actions Based On Dynemes And Discriminant Learning”,IEEE vol.978, No.1, pp. 4673- 4989,2013

5.              Eftychia Fotiadou and Nikos Nikolaidis , “Activity-based methods for person recognition in motion capture sequences,” Pattern Recognition Letters , vol.49 ,pp.4854, 2014

6.              Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, “Review and evaluation of commonly implemented background subtraction algorithms”, IEEE International Conference on Pattern Recognition, pp. 14,2008.

7.              H. Bay, A. Ess, T. Tuytelaars and L.Van Gool, “SURF: Speeded Up Robust Features.”, Computer Vision and Image Understanding,vol.110, no.3, pp.346-359, 2008.

8.              J. Wang, M. She, S. Nahavandi, and A. Kouzani,”A review of vision-based gait recognition methods for human identification,” International Conference on Digital Image Computing: Techniques and Applications, pp.320327, 2010.

9.              D.A.R. Vigo, F.S. Khan, J. van de Weijer, T. Gevers,”The Impact of Color on Bag-of-Words Based Object Recognition,”, International Conference on Pattern Recognition (ICPR), pp. 1549 – 1553 ,2010.

10.           CJC. Burges,”A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discovery, vol. 2, no. 2, pp. 121167, 1998.

11.           S. Das, R. Wilson, M. Lazarewicz, L. Finkel,”Gait recognition by two-stage principal component analysis,” 7th International Conference on Automatic Face and Gesture Recognition, pp. 579584, 2006.

12.           D. Gokalp and S. Aksoy,” Scene classification using bag-of-regions representa
tions”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’07), pp. 18, June 2007.





Gayathri S A, Renjith R J

Paper Title:

Super Resolution of Hyper Spectral Image Based On NABO Spectral Unmixing

Abstract:      Hyperspectral imaging has become an important image analysis technique in remote sensing. Processing and enhancing hyperspectral images are a difficult task. The spectral information contained in the hyperspectral images are extracted by spectral unmixing techniques. This paper proposes a novel method for enhancing spatial resolution of hyperspectral images based on spectral unmixing. Many applications needs images containing both high spectral resolution and high spatial resolution. In this paper a NABO (Negative Abundance Oriented)spectral unmixing based hyperspectral-multispectral image fusion algorithm is proposed for the purpose of enhancing the spatial resolution of hyperspectral image(HSI). As a result, a high-spatial-resolution HSI is reconstructed based on the high spectral characters of the HSI represented by endmember spectra and the high spatial characters of the multispectral image(MSI) represented by abundance fractions. Experiments were done on Airborne Visible/Infrared Imaging Spectrometer data. NABO unmixing based fusion gives better results than existing Endmember Extraction (EE).

 Hyperspectral Imaging, Linear Mixing Model, Spectral Unmixing, Multispectral Images, Endmember Extraction Algorithms, Resolution Enhancement


1.              J.Bioucas-Dias,A.Plaza,N.Dobigeon,M.Parente,Q.Du,P.Gaderand J.Chanussot, “Hyperspectral unmixing overview: Geometrical, Statistical, and Sparse regression – based approaches”, IEEE J.Select.Topics Appl. Earth Observ. Remote Sensing, vol.5,no.2,pp. 354-379, 2012.
2.              R. Gomez, A. Jaziri, and M. Kafatos, “Wavelet-based hyperspectral and multispectral image fusion,” in Proc. SPIE., vol. 4383, 2001, pp. 3642.

3.              R.C.Hardie,M.T.Eismann and G.L.Wilson, “MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor”, IEEE Trans. Image Process., vol.13,no.9,Sep.2004.

4.              N. Yokoya, T. Yairi, and A. Iwasaki, “Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion”, IEEE Trans. Geosci.Remote Sens., vol. 50, no. 2, Feb. 2012, pp. 528-537.

5.              Mohamed Amine Bendoumi,Mingyi He, and Shaohui Mei, “Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing”, IEEE Trans. Geosci.Remote Sens., vol.52, no.10, Oct. 2014.

6.              X. Liu, W. Xia, B. Wang and L. Zhang “An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data”, IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, Feb. 2011, pp. 757772.

7.              J.Nascimento and J.Bioucas-Dias, “Vertex component analysis: A fast algorithm to unmix hyperspectral data”, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, Apr. 2005, pp. 898-910.

8.              J. Li and J. Bioucas-Dias, “Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data”, IEEE Trans. Geosci. Remote Sens., vol. 3, 2008, pp. 250-253.

9.              J. Plaza, E. M. T. Hendrix, I. Garca, G. Martin and A. Plaza, “On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms”, J. Math. Imaging Vis, Vol.42, no.2/3, Feb.2012, pp. 163-175.

10.           Ruben Marrero, Sebastian Lopez, Gustavo M Callic, Miguel Angel Veganzones, Antonio Plaza, Jocelyn Channusot and Roberto Sarmiento, “A novel negative abundance-oriented hyperspectral unmixing algorithm”, IEEE Trans. Geosci. Remote Sens., vol. 53, no.7,July 2015.

11.           N. Ohgi, A. Iwasaki, T. Kawashima and H.Inada, “Japanese hyper-multispectral mission”, IGARSS, Honolulu, HI, USA, Jul.2010, pp.3756-3759.

12.           G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen and W. M. Porter, “The airborne visible/infrared imaging spectrometer(AVIRIS)”, Remote Sens. Environ, vol. 44, no. 2/3, May/Jun 1993, pp. 127-143.





Agus Wibawa, Admaji, Ide Bagus Hapsara, Totok Ruki Biyanto

Paper Title:

Failure Analysis of High Pressure Heater in PT. PJB UP Paiton

Abstract:    The aim of this paper is to analyze the cause of harm in high pressure heater in PT. PJB UP Paiton and to prevent it from happening again. In PT. PJB UP Paiton, several problem related to high pressure heater had occurred before. When the high pressure heater harmed, tube plugging usually applied to fix the trouble. Through this process, the high pressure heater was not fully recovered. The efficiency and failure rate of high pressure heater is decreased and increased respectively. Hence, a root cause failure analysis is conducted to accurately determine the cause of the problem. The result shows that the cause of failure in high pressure heater are the increase of feedwater velocity, the increase of extraction steam velocity, change of flow patter and heat transfer inside high pressure heater and radial displacement tube that over limit. Based on this result, redesign of high pressure heater is performed by increasing the capacity of feedwater flow in high pressure heater and decreasing the feedwater velocity.

 High Pressure Heater, redesign, root cause failure analysis.


1.             Kim, K.H. and Kim, H.J., Design modification of a feedwater heater impingement baffle to mitigate shell wall thinning by flow acceleration corrosion. Nuclear Engineering and Design 262, 2013, pp.409-417.
2.             Heo, G. and Lee, S.K., Internal leakage detection for feedwater heaters in power plants using neural networks. Expert Systems with Applications 39(5), 2012, pp.5078-5086.
3.             Álvarez-Fernández, M., del Portillo-Valdés, L. and Alonso-Tristán, C., Thermal analysis of closed feedwater heaters in nuclear power plants.Applied Thermal Engineering 68(1), 2014, pp.45-58.
4.             Huang, C.C., Hsieh, J.S., Chen, P.C. and Lee, C.H., Flow analysis and flow-induced vibration evaluation for low-pressure feedwater heater of a nuclear power plant. International Journal of Pressure Vessels and Piping 85(9), 2008, pp.616-619.

5.             Hwang, K.M., Woo, L., Jin, T.E. and Kim, K.H., A study on the shell wall thinning causes identified through experiment, numerical analysis and ultrasonic test of high-pressure feedwater heater. Nuclear Engineering and Design 238(1), 2008, pp.25-32.





Simran Khokha, Ritu Gupta, K. Rahul Reddy

Paper Title:

Bluetooth Home Automation System Based on AVR Microcontroller

Abstract:     A smart home covers a variety of theoretical and practical approaches that deals with methodology of living today and in the future [1]. Technology has influenced and changed the life of humans in many ways. To design a device that will be serviceable to others is a huge contribution to the society [2]. Today mobile phones (smart phones, android etc.) can preforms almost all the tasks that once only PCs used to handle. With these advanced features and thought of elderly in mind, a device is designed. This device provides a much more advanced and a safer home to us. Bluetooth Home Automation System is a complex technology that uses information technology to control the electrical appliances and monitors the environment. The design and implementation presented in this paper is of a device which will use bluetooth technology for basic home automation and a wireless home network is desirable which does not incur any additional cost of wiring. The advantages and disadvantages are also discussed, along with the future scope and application areas. 

 Bluetooth, Microcontroller, Home Appliance, Android, AVR, Atmega 8


1.             Dengler, Sebastian; Awad, Abdalkarim; Dressler, Falko, “Sensor/Actuator Networks in Smart Homes for Supporting Elderly and Handicapped People.” Advanced Information Networking and Applications Workshops, 2007, AINAW ’07. 21st International Conference on, Volume 2,  21-23 May 2007 Page(s):863 – 868.
2.             Piyare, R and Tazil, M, “Bluetooth Based Home Automation System Using Cell Phone.” IEEE 15th International Symposium on Consumer Electronics (2011).

3.             Shepherd, R, “BIuetooth Wireless Technology in the Home.” Electronics & Communication Engineering Journal 13 (2001): 195-203. IEEE/IEE Electronic Library. 15 Oct. 2007.

4.             T. Tamura, T. Togawa, M. Ogawa, and M. Yoda, “Fully automated health monitoring system in the home,” Med. Eng. Physics, 20, pp. 573–579, 1998.

5.             Jiang, Li, Da-You Liu, and Bo Yang, “Smart Home Research.” Machine Learning and Cybernetics (2004). 15 Oct. 2007.

6.             S. K. Das, D. J. Cook, A. Bhattacharya, E. O. Heierman, III, and T.-Y. Lin, “The Role of Prediction Algorithms on the MavHome Smart Home Architectures,” IEEE Wireless Communications (Special Issue on Smart Homes), Vol. 9, No.  6, pp. 77–84, Dec. 2002.

7.             Yamazaki, T, “Beyond the Smart Home.” Hybrid Information Technology, 2006. ICHIT’06. Vol 2. International Conference on, Volume 2, Nov. 2006 Page(s):350 – 355.





Asha Jayachandran, Preetha V.H

Paper Title:

Median Filter Based Adaptive Compensation Method for Depth Map Pre-Processing

Abstract:      Depth Image Based Rendering (DIBR) is 2D to 3D conversion technology using color image and its corresponding depth image that is widely employed in applications like 3D TV, free view television etc. 3-D viewing is the next most happening technology. Since transmission of 3D video demands a lot of bandwidth, a new technology that renders virtual views using a color image and its corresponding depth image was proposed. If the depth map is incomplete, the virtual views generated will contain holes or disocclusions which affect the quality of 3D viewing. Since holes occur when the intensity in depth map changes significantly, smoothening methods were proposed reduce the number of holes. Since smoothening methods affect the edges and destroys the original information in the depth map, Adaptive Compensation method (ADC) which processes the image in different modes was proposed. Improved Adaptive Compensation method does not produce satisfactory results for images with large number of holes. Though an improvement in PSNR and SSIM improvement is observed, the number of holes in the warped image is increased. A median filtering is incorporated in Adaptive Compensation method to reduce the number of holes. The experimental results indicate an improvement in PSNR and SSIM as well as a reduction in number of holes.

   Depth Image Based Rendering, 3D TV, Inpainting, Adaptive Compensation Method, Disocclusion, Median Filter, Holes, Virtual views.


1.          Redert et al., “Advanced three-dimensional television system technologies“,Proc. IEEE Int. Symp. 3D Data Process. Vis. Transmiss.,Jun. 2002, pp 313-319.
2.          Chih-Hsien Hsia, “Improved Depth Image-Based Rendering Using an Adaptive Compensation Method on an Autostereoscopic 3-D Display for a Kinect Sensor“,IEEE SENSORS JOURNAL., vol. 15,No.2,Feb. 2015

3.          Ming-Fu Hung, Shaou-Gang Miaou, and Chih-Yuan Chiang, “Dual Edge-Confined Inpainting of 3D Depth Map Using Color Images Edges and Depth Images Edges,” Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific, Nov. 2015, pp. 1-9.

4.          L. Zhang and W. J. Tam, “Stereoscopic image generation based on depth images for 3D TV,” IEEE Trans. Broadcast., vol. 51, no. 2, pp. 191-199, Jun. 2005.

5.          W. J. Tam, G. Alain, L. Zhang, T. Martin, and R. Renaud, “Smoothing depth maps for improved steroscopic image quality,” Proc. SPIE, vol. 5599, pp. 162-172,
Oct. 2004.

6.          P.-J. Lee and Effendi, “Nongeometric distortion smoothing approach for depth map preprocessing,” IEEE Trans. Multimedia, vol. 13, no. 2, pp. 246-254, Apr.

7.          Fehn, K. Hopf, and Q. Quanta, “Key technologies for an advanced 3D TV system,”Proc. SPIE, vol. 5599, pp. 66-80, Oct. 2004.

8.          Middlebury Stereo Vision Database. [Online]. Available: http://vision.middlebury.edu/stereo/data/





Archana Suryavanshi, A. A. Shinde

Paper Title:

Implementing Home Automation System Using ZIGBEE IEEE 802.15.4 Standard

Abstract: Wireless technology evolution has greatly enhanced automation systems.. The major requirement of this field has been low data rate, extended battery life and secure system.Voice controlled home automation system designed using. Zigbee IEEE 802.15.4 protocol. Home automation system recognizes user commands with help of HM 2007 voice recognitio chip. This system assists disable persons and persons with limitations. System facilitates controlling of all household equipment’s like light and fan with single or multiple user commands.

  Home Automation System, Zigbee, Voice recognition, IEEE 802.15.4, 


1.       Amrutha S, Aravind S, Ansu Mathew, Swathy Sugathan, Rajasree R, and Priyalakshmi S,  “Speech Recognition Based Wireless Automation of Home Loads- E Home” International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 4, Issue 1, January 2015.
2.       Amrutha S, Aravind S, Ansu Mathew, Swathy Sugathan, Rajasree R, Priyalakshmi, “Voice Controlled Smart Home , International Journal of Emerging Technology and Advanced Engineering www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 1, January 2015.

3.       T. Poongothai , S. Navaneethan , G. Divya Priya , K. Madan Mohan , “Home Appliance Based Device Monitoring and Control Inputting Through Capacitive Touch” International Journal of Engineering Trends and Applications (IJETA) – Volume 2 Issue 2, Mar-Apr 2015.

4.       M.R.manikandan1, A.Raghuram2, D.Saravanan3, S.Vignesh4, R.Thenmozhi Selvan , “Device Control Using Voice Recognition in Wireless Smart Home System” International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Special Issue 2, March 2015.

5.       Thoraya obaid, haliemah rashed, ali abu el nour, muhammad rehan, “zigbee based voice controlled wireless smart home system” International Journal of Wireless & Mobile Networks (IJWMN) Vol. 6, No. 1, February 2014.

6.       Jaypal J. Baviskar_,Afshan Y. Mullay, Amol J. Baviskarz and Niraj ‘Implementation of 802.15.4 for

7.       designing of home automation and power monitoring system,’ 2014 IEEE Students Conference on Electrical, Electronics and Computer Science.

8.       Dhawan S. Thakur and Aditi Sharma, “Voice Recognition Wireless Home Automation System Based On Zigbee” IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 6, Issue 1 (May. – Jun. 2013).

9.       Faisal Baig, Saira Beg and Muhammad Fahad Khan “Zigbee Based Home Appliances Controlling Through Spoken Commands Using Handheld Devices” International Journal of Smart Home Vol. 7, No. 1, January, 2013.

10.    JinsungByun, Insung Hong, Byoungjoo Lee, and Sehyun Park” Intelligent Household LED Lighting System Considering Energy Efficiency and User Satisfaction” , IEEE network,volume59,No.1,Feb 2013.

11.    Chee-Hoe Pang, Jer-Vui Lee, Yea-DatChuah, Yong-Chai Tan and N. Debnach” Design of a Microcontroller based Fan Motor Controller for Smart Home Environment” International Journal of Smart Home Vol. 7, No. 4, July, 2013 .

12.    Faisal Baig, Saira Beg, Muhammad Fahad Khan, Science and Technology Islamabad, Pakistan, ‘Controlling Home Appliances Remotely through Voice Command’, International Journal of Computer Applications (0975 – 888) Volume 48– No.17, 2012.





Lekshmi Shyam, Kumar G.S

Paper Title:

Detection of Glaucoma and Diabetic Retinopathy from Fundus Images by Bloodvessel Segmentation

Abstract:  Blood vessel segmentation of fundus images has obtained considerable importance during the past few years since it facilitates the early detection of eye diseases. A method based on high pass filtering and morphological operation is introduced in the proposed method for vessel segmentation. This method can be utilized to detect diseases effecting eyes like glaucoma and diabetic retinopathy. Glaucoma is detected by feature extraction and classification. The local binary pattern of the optic disc is extracted to classify the images on the basis of texture. Sparse representation classifier is utilized to classify the glaucomatous eye.  Diabetic retinopathy is a disease caused by the complexity of diabetes. It damages the small blood vessels in the retina resulting in loss of vision.  The blood vessel segmentation is an important task in Diabetic Retinopathy detection. Optic disc in the fundus image is detected by Hough transform. After the segmentation the vessels and optic disc are removed from the original image. Diabetic Retinopathy is characterized by the presence of exudates. The exudates are detected by means of imtool operator in the matlab. The simulations are performed on matlab 2011 and the data are collected from DIARETDB1 and HRF databases.

  Blood vessel segmentation, Diabetic retinopathy, Fundus images, Glaucoma, Hough transform, Sparse representation classifier  


1.    D.Jeyashree, G. Sharmilaand K. Ramasamy, “Combined Approach on Analysis of Retinal Blood Vessel Segmentation for Diabetic Retinopathy and Glaucoma Diagnosis”, International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014.
2.    MadhusudhananMishra,“Glaucomadetection based on phase information of fundus images”, International Journal of recent trends in engineering and technology, Vol 6,Issue 4,March 2016.

3.    R.Priya and P.Aruna, “Diagnosis of diabetic retinopathy using machine learning techniques”, ICTACT Journal on Soft Computing, Volume 3, Issue 4, July 2013.

4.    AshaGowdaKaregowda, AsfiyaNasiha, M.A.Jayaram and A.S .Manjunath “Exudate detection in retinal images using back propagation neural network”, International Journal of Computer Applications, Volume 25– No.3, July 2011.

5.    J.Ramya,S.Soundarya,A.Nagoormeeral, Rahmathnish  and E.Revathi“Detection of exudates in color fundus images”, International Journal of Innovative Research in Science,Engineering and Technology, Vol. 3, Issue 3, March 2014.

6.    ShraddhaTripathi, Krishna Kant Singh , B.K.Singh and AkanshaMehrotra, “Detection Automatic Detection of Exudates in Retinal Fundus Images using Differential Morphological Profile”, International Journal of Engineering and Technology, Vol 5 No 3 Jun-Jul 2013.

7.    Kullayamma and P. MadhaveeLatha, “Retinal Image Analysis for Exudates Detection”, International Journal of Engineering Research and Applications, Vol. 3, Issue 1, January -February 2013, pp.1871-187.

8.    Nirmala K, Venkateswaran N and Vinoth Kumar C, “Fractal Feature Based Svm Classification OfGlaucomatous Image Using Pca And Gabor Filter”, International Journal of Advanced Engineering Technology, Vol. VII, Issue 1, March 2016, pp.156-160.





Rekha Raj, Salim Paul

Paper Title:

A Novel Approach of Image Encryption and Decryption using Coupled Chaotic System

Abstract: Security is an important problem while transmitting information through an open network. Secure transmission can be done by encrypting the information. There are several methods of encryption. A novel encryption and decryption technique is discussed in this paper. Here the cryptosystem used is a coupled chaotic system in which two one dimensional chaotic maps are combined and used for encryption. A new algorithm is developed for  the implementation of the coupled chaotic system. Security analysis and Statistical analysis show that this system can encrypt images effectively and can withstand several attacks like brute force attack, chosen plain-text attack etc

Cipher, Coupled chaotic system, Encryption, Decryption, Security key, Symmetric


1.    Yicong Zhou, Long Bao and C.L.Philip Chen”A New 1D Chaotic System for Image Encryption”, Signal Process. 97(2014) pp.172-182.
2.    Kanso and M. Ghebleh “A Novel Image Encryption Algorithm Based on a 3D Chaotic Map,”Commun Nonlinear SciNumerSimulat17 (2012) pp.2943–2959

3.    G.A.Sathishkumar ,Dr.K.Bhoopathy bagan and Dr.N.Sriraam “Image Encryption Based on Diffusion and Multiple Chaotic Maps” ,International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.2, March2011,pp.181-194

4.    Shoaib Ansari, Neelesh Gupta and Sudhir Agrawal, “An Image Encryption Approach Using Chaotic Map in Frequency Domain” ,International journal of Emerging Technology and Advanced Engineering-Volume 2, Issue 8,

5.    Gururaj Hanchinamani and Linganagouda Kulakarni, “Image Encryption Based on 2-DZaslavskii Chaotic Map and Pseudo Hadmard Transform”, International Journal of Hybrid Information Technology Vol.7, No.4 (2014),pp.185-200.

6.    Xiaoling Huang,Guodong Ye, and Kwok-Wo Wong, “Chaotic Image Encryption Algorithm Based on Circulant Operation”, Abstract and Applied Analysis,Volume2013

7.    Xianhan Zhang and Yang Cao, “A Novel Chaotic Map and an Improved Chaos-Based Image Encryption Scheme”, The Scientific World Journal Volume 2014(2014).A. E. Rohlem S, Elagooz, and  H. Dahshan, “A novel approach for

8.    designing the s-box of advanced encryption standard algorithm (AES) using chaotic map”, IEEE  Conference Publications 2005,pp.455-464

9.    Fu C1, Chen JJ, Zou H, Meng WH, Zhan YF and Yu YW, “A chaos based digital image encryption scheme with an improved diffusion strategy”, Opt  Express 2012,

10. Dr. Prerna Mahajan and  Abhishek Sachdeva, “A Study of Encryption Algorithms Aes, Des and Rsa for Security”, Global Journal of  Computer  Science and Technology Network, Web and Security 2013,Volume.13, Issue15, pp.15-22





Lekshmi T, Smitha P S

Paper Title:

Decorrelation By Principal Component Analysis For Multi Channel Acoustic Echo Cancellation System

Abstract: In multi-channel acoustic echo cancellation (MAEC) system, thenon-uniqueness problem and misalignment problem occurs due to the correlation between the reference signals. It could affect convergence performance of the adaptive filtering. So many methods are proposed to get minimum error rate. In this paper, fuzzy logic is used to get minimum error function. The decorrelation is applied through the PCA method. The adaptive fuzzy fusion algorithm improvises, update and check operators obtain optimal solution for defined objective function. To obtain better solution the control parameters are adjusted. It achieves a superior performance in the echo reduction gain and offers the possibility of frequency selective decorrelation to further preserve the sound quality of the system. Simulationresult for the proposed algorithm has shown a significant improvement in convergence rate compared with existing system.

Multi channel AEC, non-uniqueness problem, Misalignment problem, Principal component analysis.


1.    J. Herre, H. Buchner, W. Kellermann,“Acoustic echo cancellation for surround sound using perceptually motivated convergence enhancement,”IEEE ICASSP.,2007,pp.17-20. .
2.    J. Benesty, D. R. Morgan, and M. M. Sondhi, ‘A Better Understanding and an Improved Solution to the Specific Problems of Stereophonic Acoustic Echo Cancellation ,” inIEEETranc.speech audio process,vol.6,no.2,pp 156-165,Mar.1998.

3.    W. H. Khong, J.Benesty,andP.A. Naylor,“Stereophonic Acoustic Echo Cancellation: Analysis of the Misalignment in the Frequency Domain,” IEEE Signal process.Lett., vol. 13,no. 1, pp 33-36. Jan.2006.

4.    T. S.Wada and B.-H. Juang, “Multi-channel acoustic echo cancellation based on residual echo enhancement with effective channel decorrelation via resampling ,”inProc.IWAENC,2010.

5.    D. R.Morgan, J. L. Hall, and J. Benesty, “Investigation of several types of nonlinearities for use in stereo acoustic echo cancellation, IEEE Trans. Speech Audio Process ,”Proc.IEEETranc.speech audio process., vol. 9, no. 6, pp. 686696, Sep. 2001.

6.    J.Wung,T.S.Wada,and B.H.Juang,“Inter-Channel Decorrelation By SubBand Resampling In Frequency Domain,” inProc.IEEE ICASSP , 2012, pp.29-32.

7.    J.Wung,T.S.Wada, and B.H.Juang,“Inter channel decorrelation by sub band resampling for multi channel acoustic echo cancellation,” IEEE Tranc. on signal processing ,vol. 62,no. 8,April 15,2014.

8.    A.M.Geethu,K.S.Smitha, and D.Chengzhi,“A Fuzzy Logic Based Acoustic Echo Cancellation System,”International Journal of Engineering and Advanced Technology ,vol. 4, no. 6,August 2015, ISSN.2249-8958.

9.    M.M.Sondhi, D.R. Morgan,andJ.L.Hall,“Stereo phonic acoustic echo cancellation -An overview of the fundamental problem, ”IEEE Signal processing. Lett,vol. 2,no. 8,pp.148-151,Aug.1995.





S Viswanatha Rao, Sakuntala S Pillai

Paper Title:

Increasing Throughput by Duty Cycle Adaptation in Wireless Sensor Networks with Energy Harvesting

Abstract:  Limited lifetime of batteries is a major constraint in Wireless Sensor Networks (WSNs). Reduction in duty cycle to conserve energy resulted in reduced throughput. With the advances in energy harvesting technologies there is considerable research interest in enhancing the performance of WSNs by incorporating the energy harvesting scenario in wireless nodes.  To ensure proper operation of the sensor nodes in WSNs with energy harvesting, the design of MAC protocols need special consideration. This paper evaluates the performance of an energy harvesting WSN node, based on IEEE 802.15.4 MAC. The study establishes the fact that by suitably adapting the duty cycle, throughput of the node can be increased in addition to extending its lifetime considerably.

 duty cycle adaptation, energy harvesting, MAC, IEEE 802.15.4, Wireless Sensor Network.


1.       David Culler, Deborah Estrin, Mani Srivastava, “Overview of Sensor Networks”, Computer, August 2004
2.       F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communications Magazine, Vol. 40, No. 8, pp. 102-114, August 2002

3.       Demirkol, C. Ersoy, and F. Alagoz, “Energy efficient medium access control protocols for wireless sensor networks and its state-of-art,” in IEEE International Symposium on Industrial Electronics, Vol. 1, pp. 669-674, May 2004.

4.       W. Seah, Z. Eu, and H. Tan, “Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) – survey and challenges,” in Wireless VITAE 2009, pp. 1–5, May 2009.

5.       IEEE Std. 802.15.4-2006: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Std., September 2006.

6.       Mirza, M. Owrang, and C. Schurgers, “Energy-efficient wakeup scheduling for maximizing lifetime of IEEE 802.15.4 networks,” in Proceedings of First IEEE International Conference on Wireless Internet (WICON), pp. 130-137, 2005.

7.       H. Yoo, M. Shim, and D. Kim, “Dynamic duty-cycle scheduling schemes for energy-harvesting wireless sensor networks,” in IEEE Communications Letters, Vol. 16, No. 2, pp. 202-204, February 2012.

8.       T. N. Le, A. Pegatoquet, O. Sentieys, O. Berder, and C. Belleudy, “Duty-cycle power manager for thermal-powered wireless sensor networks,” in IEEE 24th International Symposium on Personal Indoor and Mobile Communications (PIMRC), pp. 1645-1649, 2013.

9.       Castagnetti, A. Pegatoquet, T. N. Le, and M. Auguin, “A joint duty-cycle and transmission power management for energy harvesting WSN,” in IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 928-936, May 2014.

10.    V. Raghunathan, A. Kansal, J. Hsu, J. Friedman, and M. B. Srivastava, “Design considerations for solar energy harvesting wireless embedded systems,” in IEEE
Information Processing in Sensor Networks (IPSN), pp. 457-462, 2005.

11.    “The Network Simulator – NS-2,”  http://www.isi.edu/nsnam/ns/





Girish L, Gowreesh S S, Kousik S

Paper Title:

Computational Analysis of a Multi-Cylinder Four Stroke SI Engine Exhaust Manifold System

Abstract:   In an internal combustion engines exhaust system plays a vital role in the enhancement of the combustion efficiency. A well designed exhaust manifold increases the performance of an IC engines. The designing of exhaust manifold is a complex procedure and is dependent on many parameters. The present work is fundamentally based. on the investigation of modelling of exhaust manifold of a multi-cylinder four stroke SI engine using computational analysis. The work is majorly focused on reducing .the backpressure at the outlet and also by increasing the velocity of the exhaust gases at the outlet of exhaust manifold system, which is leading to increase the performance of the engine.  Commercially available CFD software tool is used for carrying out the present analysis. Flow through the exhaust manifold is analyzed using pressure and mass flow boundary conditions.

internal combustion, IC engines, CFD software, fundamentally


1.    Mohd Sajid Ahmed, Kailash B A, Gowreesh, “ Design and analysis of a multi-cylinder four stroke SI engine exhaust manifold using CFD technique”, volume:02 Issue:09 Dec-2015
2.    Vivekanand Navadagi, Siddaveer Sangamad. “CFD analysis of exhaust manifold of multi-cylinder petrol engine for optimal geometry to reduce back pressure”, volume: 3 Issue :3 March-2014

3.    Rajesh Bisane, Dhanajay katpatal, “Experimental investigation and CFD analysis of single cylinder four stroke CI engine exhaust system”, volume:03 Issue:06 Jun

4.    KS Umesh, VK Pravin, K Rajagopal, “ CFD analysis and experimental verification of effect of manifold geometry on volumetric efficiency and back pressure for multi-cylinder SI engine”, volume:3, Issue:7 July-2013

5.    KS Umesh, VK Pravin, K Rajagopal, “CFD analysis of exhaust manifold of multi-cylinder SI engine to determine optimal geometry for reducing emission”, volume:3 Issue:4 Oct- 2013

6.    PL.S. Muthaiah, Dr.M. Senthil kumar, Dr. S. Sendilvelan, “CFD analysis of catalytic converter to reduce particulate matter and achieve limited back pressure in Diesel engine”,  volume:10 Issue:5 Oct-2010

7.    P. Seenikannan, V. M. Periasamy and P. Nagaraj, “ A design strategy for volumetric efficiency improvement in a multi-cylinder stationary diesel engine its validty under transient operation”,volume:5 issue:3, 2008

8.    Yasar Deger, Bukhard simperl, Luis P. Jimenez, “Coupled CFD-FE-Analysis for the exhaust manifold of a diesel engine 2004





Sunil S, Gowreesh S S, Veeresh B R

Paper Title:

Heat Transfer Enhancement and Thermal Performance of Extended Fins

Abstract: A fin is an extended surface1which is used to increase the rate of heat transfer by connecting to the heating surface. The heat transfer rate can be increased by convection process and also by increasing surface area by means of extended surfaces. In the present analysis effect of increase in total surface area to improve the rate of heat transfer is studied. Thermal Analysis is performed for various perforated fin extensions with varied diameter. The analysis is carried out using commercially available finite element analysis software. Analysis called steady state thermal has been used to find out the temperature variations and heat flux of the fins.

 extended surface, increase, process variations, temperature, be increased


1.    Nitish Kumar Jha, Kailash B A, ‘Heat Transfer Enhancement and Thermal Performance of Extended Surfaces with Cavity’. International journal of innovative research in science,engineering and technology, volume 4, issue 10, October 2015.
2.    V. Karthikeyan, R. Suresh Babu, G. Vignesh Kumar. ‘Design and Analysis of Natural Convective Heat Transfer Coefficient Comparison between Rectangular Fin Arrays with Perforated and Fin Arrays with Extension’. International journal of science, engineering and technology research (IJSETR), Volume 4, Issue 2, February 2015.

3.    Shital B. Salunkhe, Dr. Rachayya R.Arakerimath. ‘CFD and Experimental Analysis of Various Extended Surfaces for Heat transfer Enhancement’. International journal of engineering technology, management and applied sciences, volume 3, issue 1, January 2015.

4.    Pardeep singh, Harvinder lal, Baljith singh ubhi, ‘Design and Analysis for Heat Transfer through Fin with Extensions’. International journal of innovative research in science, volume 3, issue 5, may 2015.

5.    Mukesh Didwania, Gopal Krishnan, Ravikant, ‘Study and Analysis of Heat Transfer through Two Different Shape Fins using CFD Tool’. International journal of IT, engineering and applied sciences research, volume 2, issue 4, April 2013.





Alpyspayeva Gal’ya Aitpaevna, Sayakhimova Sholpan Nazarbekovna

Paper Title:

Ecological Culture of the City Environment of Astana

Abstract:  The article «Ecological Culture of the City Environment Of Astana» deals with the urban environment of ecology in the historical retrospection of Astana – the capital of the modern Republic of Kazakhstan. The solution of urban environment problems, the authors analyze in the context of social and cultural development of the city. On the basis of archival materials the natural character of the environmental problems of pre-revolutionary city Akmola is justified. Using archival sources shows the inadequacy and utopian ideas of purposeful formation of ecologically safe urban districts in the Soviet city of Tselinograd. The features of the solution of environmental problems of the city through the use of new technologies in the project for the construction of Astana are shown.

city, urban environment, the ecological environment of the city, urbanization, Akmola – the city of the XIX century, Tselinograd – Soviet city, Astana – the capital of Kazakhstan.


1.    The first General population census of Russian empire. 1897. Publishing center of the statistics committee M.I.A.Under edition by. N.A.Trojnitsky.
2.    LXXXI. Akmolinsk area. SP., 1904.-136p.

3.    The state archive of Astana. F.286. I.1. D.38. P.53.

4.    The state archive of Astana. F.32. I.10. D.1165. P.91.

5.    The state archive of Astana. F.32. I.3.D.8. P.51.

6.    The state archive of Astana. F.32. I.5. D.245. P.3.

7.    The state archive of Astana. F.32. I.10. D.8. P.1.

8.    The state archive of Astana. F.32. I.5. D. 245. P.15.





Nikita Runijha, Abhishek Shrivastava

Paper Title:

A Novel Algorithm for Finger Knuckle Print Recognition

Abstract:   Biometrics is the technique of authentication of a person on the basis of biometrics traits. Due to its reliability and accuracy it has been explored extensively. Fingerprint, iris, hand geometry, palm, face etc are some of the common biometrics traits that can be used successfully for authentication of a person. The accuracy and reliability of the biometrics based authentication system depends on the various important features and feature extraction techniques. Extracted features from the biometrics must be having uniqueness for making biometrics system reliable. This paper present a finger knuckle print based biometric system for person authentication. Radon transform is used for extracting the features of the inner knuckle print image. Simulation results reveals that the proposed system perform very well in recognizing the person with good accuracy.

knuckle print, Biometrics, finger features, recognition system

1.       A.K. Jain, P. Flynn, A. Ross, Handbook of Biometrics, Springer, 2007.
2.       D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003.

3.       N. Ratha, R. Bolle, Automatic Fingerprint Recognition Systems, Springer, 2004.

4.       K. Delac, M. Grgic, Face Recognition, I-Tech Education and Publishing, 2007.

5.       H. Wechsler, Reliable Face Recognition Methods – System Design, Implementation and Evaluation, Springer, 2006.

6.       J. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Analysis and Machine Intelligence 15 (11) (1993) 1148-1161.

7.       J. Daugman, How iris recognition works, IEEE Trans. Circuits and Systems for Video Technology 14 (1) (2004) 21-30.

8.       R. B. Hill, Retinal identification, in Biometrics: Personal Identification in Networked Society, A. Jain, R. Bolle, and S. Pankati, Eds., Kluwer Academic, 1999.

9.       H. Borgen, P. Bours, S.D. Wolthusen, Visible-Spectrum Biometric Retina Recognition, in: Proceedings of the International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008, pp.1056-1062.

10.    Z.H. Guo, D. Zhang, L. Zhang, W.M. Zuo, Palmprint verification using binary orientation co-occurrence vector, Pattern Recognition Letters 30 (13) (2009) 1219-1227.

11.    D. Zhang, W. K. Kong, J. You, M. Wong, Online palmprint identification, IEEE Trans. Pattern Analysis and Machine Intelligence 25 (9) (2003) 1041-1050.

12.    W. K. Kong, D. Zhang, Competitive coding scheme for palmprint verification, in: Proceedings of the ICPR’04, 2004, pp. 520-523.

13.    Kong, D. Zhang, M. Kamel, Palmprint identification using feature-level fusion, Pattern Recognition 39 (3) (2006) 478-487.

14.    Z.N. Sun, T.N. Tan, Y.H. Wang, S.Z. Li, Ordinal palmprint representation for personal identification, in: Proceedings of CVPR’05, 2005, pp. 279-284.

15.    D.S. Huang, W. Jia, D. Zhang, Palmprint verification based on principal lines, Pattern Recognition 41 (4) (2008) 1316-1328.

16.    W. Jia, D.S. Huang, D. Zhang, Palmprint verification based on robust line orientation code, Pattern Recognition 41 (5) (2008) 1504-1513.

17.    A.K. Jain, A. Ross, S. Pankanti, A prototype hand geometry-based verification system, in: Proceedings of the 2nd International Conference on Audio- and Video-based Biometric Person Authentication, 1999, pp. 166–171.

18.    R. Sanchez-Reillo, C. Sanchez-Avila, A. Gonzalez-Marcos, Biometric identification through hand geometry measurements, IEEE Trans. Pattern Analysis and Machine Intelligence 22 (10) (2000) 1168-1171.

19.    A.K. Jain, N. Duta, Deformable matching of hand shapes for verification, in: Proceedings of ICIP’99, 1999, pp. 857–861.

20.    J.G. Wang, W.Y. Yau, A. Suwandy, E. Sung, Personal recognition by fusing palmprint and palm vein images based on “Lapacianpalm” representation, Pattern Recognition 41 (5) (2008) 1531-1544.

21.    Kumar, K.V. Prathyusha, Personal authentication using hand vein triangulation, in: Proceedings of SPIE Biometric Technology for Human Identification, vol. 6944, 2008, pp. 69440E-69440E-13.

22.    D.L. Woodard, P.J. Flynn, Finger surface as a biometric identifier, Computer Vision and Image Understanding 100 (3) (2005) 357–384.

23.    D.L. Woodard, P.J. Flynn, Personal identification utilizing finger surface features, in: Proceedings of CVPR’05, vol. 2, 2005, pp. 1030-1036.

24.    Ravikanth, A. Kumar, Biometric authentication using finger-back surface, in: Proceedings of CVPR’07, 2007, pp. 1-6.

25.    Kumar, C. Ravikanth, Personal authentication using finger knuckle surface, IEEE Trans. Information Forensics and Security 4 (1) (2009) 98-109.

26.    Kumar, Y. Zhou, Human identification using knucklecodes, in: Proceedings of BTAS’09, 2009.

27.    Kumar, Y. Zhou, Personal identification using finger knuckle orientation features, Electronic Letters 45 (20) (2009) 1023-1025.

28.    H. Hollien, Forensic voice identification, Academic Press, 2002.

29.    M. Burge, W. Burger, Ear biometrics, in: Biometrics:Personal Identification in Networked Society, A.K. Jain, R.Bolle, S. Pankanti, Eds., pp. 273-286, Kluwer Academic, 1999.

30.    M.S. Nixon, T.N. Tan, R. Chellappa, Human Identification Based on Gait, Springer, 2006.

31.    R. Plamondona and G. Loretteb, Automatic signature verification and writer identification — the state of the art, Pattern Recognition 22 (2) (1989) 107-131.

32.    M.S. Nixon, T.N. Tan, R. Chellappa, Human Identification Based on Gait, Springer, 2006.

33.    R. Plamondona and G. Loretteb, Automatic signature verification and writer identification — the state of the art, Pattern Recognition 22 (2) (1989) 107-131.





Arpit Varshnry, Smrati Singh, Deepti Gupta

Paper Title:

Simulation of Standalone Wind Energy Conversion System using PMSG

Abstract: In this paper a wind energy conversion system (WECS) is designed to supply power to a standalone system consisting of permanent magnet synchronous generator (PMSG), a rectifier system, and inverter system to get the desired constant ac voltage respectable of variable wind speed to extract power from the fluctuating wind, controlling of the wind turbine is done by controlling the pitch angle of turbine. This power is transferred to dc link capacitor through controlled rectifier. This constant dc link voltage is converted into ac of desired amplitude and frequency. Based on extensive simulation results using MATLAB/SIMULINK, it has been established that the performance of the controllers both in transient as well as in steady state is quite satisfactory and it can also maintain maximum power point tracking

 PMSG, WECS, Inverter, Rectifier, Pitch controller, Variable speed wind turbine


1.       S. Müller, M. Deicke, and W. De DonckerRik, “Doubly fed induction generator system for wind turbines,” IEEE Ind. Appl. Mag., vol. 8, no.3, pp. 26–33, May/Jun. 2002.
2.       H. Polinder, F. F. A. van der Pijl, G. J. de Vilder, and P. J. Tavner, “Comparison of direct-drive and geared generator concepts for wind turbines,” IEEE Trans. Energy Convers., vol. 21, no. 3, pp. 725–733, Sep. 2006  H. Poor, An Introduction to Signal Detection and Estimation.   New York: Springer-Verlag, 1985, ch. 4.

3.       T. F. Chan and L. L. Lai, “Permanent-magnet machines for distributed generation: A review,” in Proc. 2007 IEEE Power Engineering Annual Meeting, pp. 1–6.

4.       Chinchilla, M.; Arnaltes, S.; Burgos, J.C. Control of permanent-magnet generator applied to variable-speed wind-energy system connected to the grid. IEEE Trans. Energy Convers. 2006, 21, 130–135.

5.       Thongam, J.S.; Bouchard, P.; Ezzaidi, H.; Ouhrouche, M. Wind Speed Sensorless Maximum Power Point Tracking Control of Variable Speed Wind Energy Conversion Systems. In Proceeding of the IEEE International Conference on Electric Machines and Drives, Miami, FL, USA, 3–6 May 2009; pp. 1832–1837.

6.       Tan, K.; Islam, S. Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. IEEE Trans. Energy Convers. 2004, 19, 392–399.

7.       Rolan, A.; Luna, A.; Vazquez, G.; Aquilar, D.; Azevedo, G. Modeling of a Variable Speed Wind Turbine with Permanent Magnet Synchronous Generator. In Proceeding of the IEEE International Symposium on Industrial Electronics, Seoul, Korea, 5–8 July 2009; pp. 734–739.

8.       Janardan gupta, Ashwani kumar “Fixed pitch wind turbine based permanent magnet synchronous machine model for wind energy conversion” www.onlinejet,net

9.       Alejandro Rolan’, Alvaro Luna, Gerardo Vazquez,Daniel Aguilar, Gustavo Azevedo “Modeling of a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator” IEEE International Symposium on Industrial Electronics (ISlE 2009) Seoul Olypic Parktel, Seoul, Korea July 5-8, 2009

10.    Jianzhong Zhang, Ming Cheng, Zhe Chen, Xiaofan Fu” Pitch Angle Control for Variable Speed Wind Turbines” DRPT2008 6-9 April 2008 Nanjing China

11.    C. N. Bhende, S. Mishra, Senior Member, IEEE, and Siva Ganesh Malla “Permanent Magnet Synchronous Generator-Based Standalone Wind Energy Supply System” IEEE Transactions on Sustainable Energy, VOL. 2, NO. 4, October 2011 361





Aayesha Ali, Ritesh Bohra

Paper Title:

Design and Development of Mine Monitoring System using Embedded System

Abstract:  Coal mine is the area which is very sensitive and prone to accident. Toppling of the roof in coal mine tunnel, hazardous gases, flooding are the main reason of accidents in the coal mines. The life of the mine workers are always in danger due to theses threats. It is very important to assess the situation inside the coal mine in term of safety and security of the mine workers. This paper present the monitoring system design for the coal miner which can detect the hazardous gas, humidity and temperature and with the built in wireless module can send these information to the receiver section.

  Robot, coal-mines, SAR, sensors, Wireless.


1.        Bharathi, B. Suchitha Samuel, “Design and Construction of Rescue Robot and Pipeline Inspection Using Zigbee”,International Journal of Scientific Engineering and Research ISSN (Online): 2347-3878 Volume 1 Issue 1, September 2013.
2.        Bruno Siciliano, Oussama Khatib, Springer handbooks of robotics: Part 50. Search and Rescue Robotics, 2008.

3.        Dip N. Ray, R. Dalui, A. Maity, S. Majumder, “Sub-terranean Robot: A Challenge for the Indian Coal Mines”, The Online Journal on Electronics and Electrical Engineering (OJEEE), Vol. (2) – No. (2), pp. 217-222.

4.        Jeremy Green, “Mine Rescue Robots Requirements – Outcomes from an industry workshop”, Proceedings of 6th Robotics and Mechatronics Conference (RobMech) Durban, South Africa, October 30-31, 2013, pp. 111-116.

5.        Robert H. King , “Preliminary Specifications For Robotic Applications in Mines”, A Presentation for the Second Conference on Robotics in Construction June 24- 26, 1985 at Carnegie-Mellon University, pp. 104-110.

6.        Erkmen, Ismet, et al. “Snake robots to the rescue!.” Robotics & Automation Magazine, IEEE 9.3 (2002): 17-25.

7.        Casper, Jennifer, and Robin Roberson Murphy. “Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 33.3 (2003): 367-385.

8.        Matsuno, Fumitoshi, and Satoshi Tadokoro. “Rescue robots and systems in Japan.” Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on. IEEE, 2004.

9.        Wang Tingjun, Sun Jin and Chen Yankang, “Design of a mobile machinism for missing miner search robots in underground mines”, Journal of China University of Mining and Technology (Egnish edition), vol.16 no.2 Jun. 2006

10.     Zeng Weixin, “Exploration for Human Factors in the Design of Coal- mine Safety and Rescue Devices”, IEEE International Conference on  Robotics , July 5, 2006.

11.     Gabriely, Y.; Rimon, E.; ” CBUG: A Quadratically Competitive Mobile Robot Navigation Algorithm”. Robotics, IEEE Transactions on Volume 24, Issue 6, Dec. 2008 Page(s):1451 – 1457.

12.     GAO junyao, GAO xueshan, ZHU jianguo, ZHU wei, WEI boyu,  WANG shilin ,”Coal Mine Detect and Rescue Robot Technique  Research” , IEEE International Conference on Information and Automation,June 22 – 25, 2009.

13.     Murphy, Robin R., et al. “Mobile robots in mine rescue and recovery.” Robotics & Automation Magazine, IEEE 16.2 (2009): 91-103.

14.     J. Baca, M. Ferre, R. Aracil and A. Campos. 2010. “A Modular Robot Systems Design and Control Motion Modes for Locomotion and Manipulation Tasks”, International Conference on Intelligent Robots and Systems.

15.     Zhigang, Niu, and Wang Lu. “Hazardous Gas Detecting Method Applied in Coal Mine Detection Robot.” Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on. Vol. 2. IEEE, 2011.

16.     Heng, Iem, Andy S. Zhang, and Ali Harb. “Using solar robotic technology to detect lethal and toxic chemicals.” Global Humanitarian Technology Conference (GHTC), 2011 IEEE. IEEE, 2011.

17.     Kuntze, H., et al. “SENEKA-sensor network with mobile robots for disaster management.” Homeland Security (HST), 2012 IEEE Conference on Technologies for. IEEE, 2012.

18.     P.K. Mishra et al., “RFID Technology for Tracking and Tracing the Explosives and Detonators in Mining Services Applications,”J. Applied Geophysics, vol. 76, Jan. 2012, pp. 33–43.

19.     R.Aswini, Jyothi.K.G and Neethu Johny , International Journal of Emerging Trends in Electrical and Electronics (IJETEE) Vol. 1, Issue. 3, March-2013.

20.     B. Bharathi, B. Suchitha Samuel, “Design and Construction of Rescue Robot and Pipeline Inspection Using Zigbee”, International Journal of Scientific Engineering and Research ISSN (Online): 2347-3878 Volume 1 Issue 1, September 2013.





M. Amr Mokhtar

Paper Title:

Physical Layer Comparison Between LTE, OFDM and WIMAX

Abstract:   this paper presents simulation results along with underlying assumptions. In the first part, LTE uplink and performed link level simulations of Single Carrier Frequency Domain Equalization (SC-FDE) and SC-FDMA in comparison with OFDM, has been investigated. Two types of multipath channels, i.e. ITU Pedestrian A and ITU Vehicular A channels, have been used. In addition an Additive White Gaussian Noise (AWGN) channel is also used. Furthermore, the simulation of PAPR is performed for SC-FDMA and OFDMA systems. In the second part of this paper, the capacity of the MIMO system and performed a comparison with SISO, has been analyzed, and two significant 4G evolved technologies like LTE and WIMAX. They played an important role in the high speed communication systems with higher data rates, higher system capacity and robustness against bad channel conditions, thanks also to the two advanced technologies like MIMO (multi input multi output) and multicarrier aggregation for updating the LTE and WIMAX with higher bandwidth, higher data rates and better coverage.



1.    Sassan Ahmadi, Mobile WIMAX. A systems approach to understanding IEEE 802.16m radio access technology, Academic press Elsevier, and 2011.
2.    Eric Dahlman, Stefan Parkvall and Johan Skold, 4G LTE-Advanced for Mobile Broadband, Academic press Elsevier, 2011.

3.    Zakhia Abichar and J.Morris Chang, WIMAX VS.  LTE: who will lead the broadband mobile internet?, IEEE computer society, 2010.

4.    Jeffrey G. Andrews, Arunabha Ghosh and Rias Muhamed, Fundamentals of WIMAX Understanding Broadband Wireless Networking, prentice hall series, February 2007.

5.    Eric Dahlam, Stefan Parkvall, Johan Skold, 3G Evolution – HSPA and LTE for Mobile Broadband. Elsevier Ltd.2008.2ndEd.

6.    Berge Ayvazian, WIMAX advanced to harmonized with TD-LTE, white paper, Heavy reading website, November 2013





Garima Govil, Amardeep Dixit

Paper Title:

Effect of Compression Level on the Performance of Image Transmission & Compression System under AWGN Channel

Abstract:    Data compression is “process of reducing the amount of data required to represent a given quantity of information”. Therefore, data and information are not having the same meaning as is often mentioned. Instead, Data is to convey information in their vehicle. Because the same information can be carried across the channel by varying the amount of data, This unnecessary data, which do not have actual information, is commonly referred to as redundant. Data redundancy is the core concept of image compression. Image compression encodes the actual data in few bits. Here we are analyzing the effect of compression level on different performance assets like PSNR (Peak Signal to noise ratio), MSE (Mean Squared Error), BER (Bit Error Rate) in the image transmission and compression system under AWGN Channel. We are using DCT (Discrete Cosine Transform) coding for the image compression. DCT is similar to DFT (Discrete Fourier Transform) rather deals only with the real values, So the computation complexity of the system decreases.

 Keywords:   AWGN, BER, DCT, PSNR, MSE, Transform Coding, QPSK.

1.        Nikita Bansal, Sanjay Kumar Dubey , “Image Compression using Hybrid Transform Technique” , Journal of Global Research in Computer Science, Vol. 4 No.1 Jan 2013.
2.        A.K. Katharotiya, S. Patel and M. Goyani, “Comparative Analysis between DCT & DWT Techniques of Image Compression”, Journal of Information Engineering and Applications, Vol  1, No.2, 2011.
3.        Oussama Ghorbel , Walid Ayedi , Mohamed Wasim Jmal  and Mohamed  Abid , “DCT & DWT Images Compression Algorithms in Wireless Sensors Networks: Comparative Study and Performance analysis”  International Journal of Wireless & Mobile Networks (IJWMN) Vol. 4, No. 6, December 2012 4.        A.M.Raid, W.M.Khedr, M. A. El-dosuky and Wesam Ahmed, “Jpeg Image Compression Using Discrete Cosine Transform – A Survey” , International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.2, April 2014, DOI : 10.5121/ijcses.2014.5204
5.        Priyanka Dixit, Mayanka Dixit, “Study of JPEG Image Compression Technique Using Discrete Cosine Transformation”, International Journal of Interdisciplinary Research and Innovations (IJIRI), Vol. 1, Issue 1, pp: (32-35), Month: October-December 2013.
6.        S. Anitha, “Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transform”, International Journal of Scientific & Engineering Research, Vol. 2, No. 8, 2011.





Ammu Archa.P, Lekshmy.D.Kumar

Paper Title:

Entity Resolution Methods–A Survey

Abstract:     In the real world, entities have two or more references in databases. Such multiple representations do not share anything in common and thus make duplicate detection a difficult task. Entity resolution or record linkage or deduplication is the process of identifying the records that refer to the same entity. Entity resolution is a challenging task particularly for entities that are highly heterogeneous and of low data quality. Due to the high importance and difficulty of the entity resolution problem, there are numerous approaches that have been proposed to solve ER problems. As there are different entity resolution approaches there is a strong need for comparative evaluations of different schemes. In this paper, different frameworks for entity resolution are studied. 

Keywords:    ER Diagram .

1.          Peter Christen, “A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication”, ieee transactions on knowledge and data engineering, vol. 24, no. 9, september 2012 1537
2.          Lingli Li, Jianzhong Li, and Hong Gao, “Rule-Based Method for Entity Resolution”, IEEE trans on knowledge and data engineering, vol. 27, no. 1, January 2015.
3.          Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios, “Duplicate Record Detection”, IEEE January 2007.
4.          Chatterjee and A. Segev, “Data Manipulation in Heterogeneous Databases”, ACM SIGMOD Record, vol. 20, no. 4, pp. 64-68, Dec. 1991.
5.          IEEE Data Eng. Bull., S. Sarawagi, ed., “special issue ondata cleaning”, vol. 23, no. 4, Dec. 2000.
6.          T. Churches, P. Christen, K. Lim, and J. X. Zhu, “Preparation of name and address data for record linkage using hidden Markov models”, Biomed Central Medical Informatics and Decision Making, 2(9), 2002.
7.          L. Breiman, J.H. Friedman, R.A. Olshen, , and C.J Stone. “Classification and Regression Trees”. Wadsworth, Belmont”, Ca, 1983.
8.          H.B. Newcombe, J.M. Kennedy, S. Axford, and A. James, “Automatic Linkage of Vital Records”,  vol 130, Science, no. 3381, pp. 954-959, Oct. 1959.
9.          Breiman, L., Friedman, J.H., Olshen, R., and Stone, C.J., 1984,”Classification and Regression Tree Wadsworth & Brooks/Cole Advanced Books & Software”, Pacific California.
10.       S. Sarawagi and A. Bhamidipaty, “Interactive Deduplication Using Active Learning,” Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’02), pp. 269-278, 2002.
11.       Mikhail Bilenko and Raymond J. Mooney, “Adaptive Duplicate Detection Using Learnable String Similarity Measures”, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2003), Washington DC, pp.39-48, August, 2003
12.       A. K. McCallum, K. Nigam, and L. Ungar, “Efficient clustering of high-dimensional data sets with application to reference matching”., Boston, MA, Aug. 2000.





Sabin S Sabu, Sandhya L, Subha Varier G

Paper Title:

Robust Video Compression System for Onboard Space Application

Abstract:  To efficiently transmit the huge volume of data captured during the stage separation of a spacecraft system, it is very necessary and important to find out efficient and advanced video compression techniques. In space missions, the available bandwidth for video transmission and power are critical parameters under consideration. Commercially available video compression techniques generally fail to meet the constrained power and bandwidth requirement of the space missions. This anticipates the need for better compression tools which suits the demands of onboard systems in terms of higher compression efficiency and lesser computational time. In this paper, we propose to develop an entropy based video compression approach based on H.264 standard which tends to exploit the pertinent temporal and spatial redundancy in video frames. The most time consuming part of  H.264 encoder is the inter prediction stage. Here we compared four types of search algorithm for inter prediction in terms of PSNR time and chooses the best search algorithm for our proposed system.

Keywords:     H.264, compression efficiency, inter prediction, PSNR, temporal redundancy

1.        F. O. Devaux, J. Meessen, C. Parisot, J. F. Delaigle, B. Macq and C. De Vleeschouwer, “Remote Interactive Browsing of Video Surveillance Content Based on JPEG 2000,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 8, pp. 1143-1157, Aug. 2009.
2.        Neelamani, R. de Queiroz, Zhigang Fan, S. Dash and R. G. Baraniuk, “JPEG compression history estimation for color images,” in IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1365-1378, June 2006.
3.        Choi, J. Lee and B. Jeon, “Fast Coding Mode Selection With Rate-Distortion Optimization for MPEG-4 Part-10 AVC/H.264,” in  IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 12, pp. 1557-1561, Dec. 2006.
4.        j. Chen, Z. x. Zhang and X. Luo, “Efficient Block-Matching Motion Estimation Algorithm Based on Temporal and Spatial Correlation for H.264,” Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP ’08 International Conference on, Harbin, pp. 446-449, 2008.
5.        C. Shenolikar and S. P. Narote, “Different approaches for motion estimation,” Control, Automation, Communication and Energy Conservation, 2009. INCACEC 2009. 2009 International Conference on, Perundurai, Tamilnadu, pp. 1-4, 2009.
6.        Nisar and T. S. Choi, “An advanced center biased three step search algorithm for motion estimation,” Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on, New York, NY, pp. vol.1, 95-98, 2000.
7.        Lai-Man Po and Wing-Chung Ma, “A novel four-step search algorithm for fast block motion estimation,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 313-317, Jun 1996.
8.        Jo Yew Tham, S. Ranganath, M. Ranganath and A. A. Kassim, “A novel unrestricted center-biased diamond search algorithm for block motion estimation,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no. 4, pp. 369-377, Aug 1998.





Kamlesh Patel, Abhishek Thoke

Paper Title:

An Improved Detection and Prevention Method for Defending Packet Drop and DOS Attacks in Mobile Adhoc Networks

Abstract:   In recent year with the widespread use of mobile device, Mobile Ad hoc networks (MANETs) technology has been attracted attention day by day. Specially, MANETs suit for military operations and the emergent disasters rescue that need to overcome terrain and special purpose in urgent. The fact that mobile ad-hoc networks lack fixed infrastructure and use wireless link for communication makes them very susceptible to an adversary’s malicious attacks. Black hole attack is one of the severe security threats in ad-hoc networks which can be easily employed by exploiting vulnerability of on-demand routing protocols such as AOMDV. Furthermore, DOS attack is a fairly new type of attack to cripple the availability of Internet services and resources. A DOS attack can originate from anywhere in the network and typically overwhelms the victim server by sending a huge number of packets. In this paper, we have proposed a solution based on malicious detection and prevention method to defend black hole and DOS attacks imposed by both single and multiple nodes. Result of a simulation study proves the particular solution maximizes network performance by minimizing generation of control (routing) packets. The effectiveness of our mechanism is illustrated by simulations conducted using network simulator ns-2.

Keywords:      AOMDV, Routing Protocol, Black-hole, DOS, Communication, Network Simulator

1.          Pradip M. Jawandhiya and Mangesh M. Ghonge, “A Survey of Mobile Ad Hoc Network Attacks”, / International Journal of Engineering Science and Technology, Vol. 2(9), PP. 4063-4071, 2010.
2.          G.S. Mamatha and S.C. Sharma, “A Robust Approach to Detect and Prevent Network Layer Attacks in MANETS”, International Journal of Computer Science and Security, vol. 4, issue 3, Aug 2010, pp. 275-284.
3.          Mohammad Al-Shurman, and Seungjin Park, “Black Hole Attack in Mobile Ad Hoc Networks”, ACMSE, April 2004, pp.96-97.
4.          Anu Bala, Munish Bansal and Jagpreet Singh, “Performance Analysis of MANET under Black-hole Attack”, First International Conference on Networks & Communications, 2009, pp. 141-145.
5.          Gao Xiaopeng and Chen Wei,”A Novel Gray Hole Attack Detection Scheme for Mobile Ad-Hoc Networks”, 2007 IFIP International Conference on Network and Parallel Computing – Workshops, 2007, pp. 209-214
6.          Piyush Agrawal, R and Sajal K. Das, “Cooperative Black and Gray Hole Attacks in Mobile Ad Hoc Networks”, 2nd international conference on Ubiquitous information management and communication, 2008, pp.310-314.
7.          Chen Wei, and Gao Xiaopeng,“A New Solution for Resisting Gray Hole Attack in Mobile Ad-Hoc Networks”, Second International Conference on Communications and Networking in China, August 2007, pp. 366-370.
8.          Sukla Banerjee, “Detection/Removal of Cooperative Black and Gray Hole Attack in Mobile Ad-Hoc Networks”, World Congress on Engineering and Computer Science, October 2008, pp. 337-342.
9.          Adnan Nadeem and Michael Howarth, “Adaptive Intrusion Detection & Prevention of Denial of Service attacks in MANETs”, Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly Pages 926-93
10.       Priyadharshini and Dr. K. Kuppusamy, “Prevention of DDOS Attacks using New Cracking Algorithm”, International Journal of Engineering Research and Applications, Vol. 2, Issue 3, May-Jun 2012, pp.2263-2267
11.       Analysis on Impact of Black Hole Attack on AODV and AOMDV”, CHAPTER 2, available online: http://shodhganga.inflibnet.ac.in/bitstream/10603/24748/7/07_chapter2.pdf.
12.       Juan-Carlos Ruiz, JesúsFriginal, David de-Andrés, Pedro Gil, “Black Hole Attack Injection in Ad hoc Networks”.
13.       Fan-Hsun Tseng1, and Han-Chieh Chao, “A survey of black hole attacks in wireless mobile ad hoc networks”, Tseng et al. Human-centric Computing and Information Sciences 2011
14.       Neetika Bhardwaj, Rajdeep Singh, “Detection and Avoidance of Black-hole Attack in AOMDV Protocol in MANETs”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), PP. 376 – 383, Volume 3, Issue 5, May 2014.
15.       Bounpadith Kannhavong, Hidehisa Nakayama, Yoshiaki Nemoto, and Nei Kato, Abbas Jamalipour, “A survey of routing attacks in mobile ad hoc networks”
16.       The Network Simulator. NS-2 [Online] http://www.isi.edu/nsnam/ns/





Amina K, Lekshmy P L

Paper Title:

A Survey on Data Mining Classifiers for Face Verification

Abstract:    Nowadays the human face plays an important role inour social interaction, conveying peoples identity. Face recognition is a rapidly growing field today for many uses in the fields of biometric authentication, security and many other areas. An automatic face recognition system will find many applications such as human computer interface, model based video coding and security control systems. Face Recognition System is a computer application for automatically identifying or verifying a person from a digital image or a single frame from a video source. This can be done by comparing selected facial characteristics of the likeness and a facial database. The difficulties of face recognition arising from face characteristics, geometry, image quality and image content. In this paper there are different data mining classifiers are used for face verification. Also we shall see their advantages, disadvantages and solutions to overcome the problems.

Keywords: Face recognition system, support vector machine (SVM), Discriminative Multi-Projection Vectors (DMPV), Gaussian mixture model (GMM).

1.       Xiaoguang lu, Image analysis for face recognition, department of computer science and engineering. Michigan state university, east lansing, MI, 48824.
2.       Neva cherniavsky, ivan laptev, Josef sivic, Andrew zisserman, Semi supervised learning of facial attributes in video, laboratoire d’informatique de l’ecole normale superieuer, ENS/INRIA/CNRS UMR 8548, dept. Of engineering science, university of oxford.
3.       David maship gata Lapedriza, and Jordi Vitri Boosted Online Learning for Face Recognition , IEEE transactions on systems, Vol 39, no.2, april 2009.
4.       Marcos del Pozo-Baos, Carlos M. Travieso, Jess B. Alonso, Miguel A. Ferrer Discriminative Multi-Projection Vectors: Modifying the Discriminative Common Vectors Approach for Face Verification,Departament of Sealesy Comunicaciones University of Las Palmas de Gran Canaria.
5.       Haoxiang Li,Zhe Lin,Jonathan Brandt, Probabilistic Elastic Matching for Pose Variant Face Verification,2013 IEEE Conference on Computer Vision and Pattern Recognition.
6.       Meina Kan, Dong Xu,Shiguang Shan,Wen Li,Xilin Chen, Learning Prototype Hyperplanes for Face Verification in the Wild,  IEEE transactions on image processing vol.22, no.8, august 2013.
7.       Sina Mohseni1, Niloofar Zarei, Saba Ramazani, Facial Expression Recognition using Anatomy Based Facial Graph, 2014 IEEE International Conference on Systems, Man, and Cybernetics October 5-8, 2014, San Diego, CA, USA.





Arya Krishnan G, Nishy Reshmi S

Paper Title:

A Survey On The Techniques For Traffic Sign Detection And Workzone Identification

Abstract:     Road Sign Recognition is a field of computer vision.Fast real-time and robust automatic traffic sign detection can significantly increase driving safety and comfort.Automatic detection and recognition of traffic sign is also important for an automated intelligent driving vehicle or for driver assistance systems.This paper provides a comprehensive survey on traffic sign detection and recognition techniques based on image and video data on automated driving vehicles and a comparative study between different methods used by various researchers.This also contains a new challenge faced by an autonomous vehicle that how they  respond to  an unexpected road conditions,such as highway workzones,because such unusual events can alter previously known traffic rules and road geometry.

Keywords:  Computer Vision, Highway worzone recognition,Traffic sign recognition  

1.        S. Ali, Ameer Ali  and Colin Cole ,“Smart   Driving: A  New          Approach to Meeting Driver Needs”,International  Conference on Industrial  Engineering,2010
2.        Tang Jin; L Xiong;Xie Bin; C Fangyan; Liu  Bo,” A method for traffic signs detection and   recognition”, (ICCSE),2010.
3.        Jesmin F.K,Sharif Bhuiyan,and Reza R A,” Image Segmentation and  Analysis for Road-Sign  Detection”,IEEE Transactions on Intelligent Transportation Systems,March   2011.
4.        Y.Aoyagi,T.Asakura. “A study on traffic sign  recognition in scene image using genetic algorithms  and neural networks” International conference  on Industrial Electronics Control and  Instrumentation 1996.
5.        Robert E.U ,”Introduction to Artificial Neural Networks”, Proceedings of the 1995 IEEE IECON International Conference,1995.
6.        M Seetha, Muralikrishna, B.L. B.L.Malleswari,  Nagaratna, P.Hegde, “Artificial Neural Networks and  methods for  Image Classification”,Journal of Theoretical and Applied Information  Technology,2008.
7.        Grigorescu C.and Petkov N., “Distance sets for  shape  filters and shape recognition”, IEEE Transactions  on image Processing.
8.        Mrs. C. Mythili and Dr. V. Kavitha, “Efficient Technique for Color Image Noise Reduction”, The   Research Bulletin  ACM,Vol.II (III), 2011.
9.        Y W Seo and Jongho, “Recognition of  Highway Workzones for Reliable Autonomous Driving”, IEEE Transactions on Transportation  Systems,April 2015.





Leila Farahzadi, Rosa Urbano Gutierrezi, Alireza Riyahi Bakhtiari, Hamid Reza Azemati, Seyed Bagher Hosseini

Paper Title:

Assessment of Alternative Building Materials in the Exterior Walls for Reduction of Operational Energy and CO2 Emissions

Abstract:     The increase in energy demand which leads to global warming is one of the main environmental issues that drive to detrimental ecological, social and economic impacts. Recently, these impacts are being exposed faster than expected. Since buildings and their materials are one of the major sources of energy consumption and carbon dioxide emissions, environmental assessment of building materials and replacing them with the more environmentally friendly alternatives are increasingly needed to address environmental performance issues. In this study, the operational energy consumption (thermal energy) and carbon dioxide production in a typical building in Tehran is calculated by applying computer simulation –Design Builder software – in two cases of using conventional building materials and alternative ones. The results show a considerable reduction in the operational energy consumption and carbon dioxide emissions in case of applying the alternative- environmentally friendly- building materials

Keywords:   Alternative Building Materials, Assessment, CO2, Energy.

1.          M. Foroughi, “Recyclable Building Materials in Architecture”, 1st Conference on Sustainable architecture, Sama Technical & Vocational Institute, Hamedan; 21  Feb. 2010, Available: http://www.civilica.com/Paper-NCSUSTAINARCH01-NCSUSTAINARCH01_027.html [In Persian]
2.          IEA: International Energy Agency. “World Energy Outlook 2007”, 2008: 73, ISBN 978-92-64-06130-9; Available: http://www.worldenergyoutlook.org/publications/weo-2007/ 
3.          D. Behboudi, E. Barghi Gol’ozari, “Environmental Impact of Energy Consumption and Economic growth in Iran”, Quarterly Journal of Quantitative Economics; 2009, 5(4): pp.35-53 [In Persian]
4.          IEA: International Energy Agency. “World Energy Outlook 2009”, 2010: 73, ISBN 978-92-64-06130-9; Available: http://www.worldenergyoutlook.org/publications/weo-2009/ 
5.          R. Ghasemieh, Sh. Rostami, R. Mohammadirad, H. Boor, “Examining ways to reduce Burning Waste Gases in Iran”, 8th National Conference on Energy, Tehrjuan; 24-25 May, 2011; Available:  http://www.civilica.com/Paper-NEC08-NEC08_042.html [In Persian]
6.          Energy balance in 1391, Electricity and Energy Affairs Deputy, Office of Electricity and Energy macro planning, Tehran, Ministry of Energy; 2014, pp. 84 & 251
7.          M. Buyle, J. Braet, A. Audenaert, “Life cycle assessment in the construction sector: A review”, Journal of Renewable and Sustainable Energy Reviews, 2013, Vol. 26: 379-388 
8.          G. Treloar, R. Fay, B. Ilzor, P. Love, “Building Materials Selection: Greenhouse Strategies for Built Facilities”, Journal of Facilities. 2004, 19 (3/4): 139-149
9.          C. T. Griffin, B. Reed, S. Hsu, “Comparing the embodied energy of structural systems in buildings”, Journal of Structures and Architecture, CRC Press, Print ISBN: 978-0-415-49249-2, Proceedings of the 1st International Conference on Structures & Architecture, 2010, pp.1333-1339
10.       F. Pacheco-Torgal, J. Faria, S. Jalali, “Embodied Energy versus Operational Energy: Showing the Shortcomings Of The Energy Performance Building Directive (EPBD)”, Journal of Materials Science Forum, 2013,730-732: 587-591
11.       Saynajoki, J. Heinonen, S. Junnila, “Carbon Footprint Assessment of a Residential Development Project”, International Journal of Environmental Science and Development, 2011, 2(2): 116-123
12.       Ogunkah, J. Yang, “Investigating Factors Affecting Material Selection: The Impacts on Green Vernacular Building Materials in the Design-Decision Making Process”, Journal of Buildings, 2012; 2: 1-32
13.       Yang, I. C. B. Ogunkah, “A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components”, Journal of Building construction as Planning Research, 2013,1: 89-130
14.       Henriksson, “Environmental assessment of residential buildings: What does it take to build Green?”, Chalmers University of Technology (M.SC Thesis), Department of Energy and Environment, Göteborg: Sweden, 2010, pp. I
15.       Radivojević, M. Nedić, “Environmental Evaluation of Building Materials-Example of Two Residential Building in Belgrade”, Journal of Architecture and Civil Engineering, 2008, 6(1): 97 – 111
16.       Y. U. G. Abeysundra, S. Babel, Sh. Gheewala, “Integration of Environmental Economic and Social Assessments for Selecting Sustainable Materials for Buildings in Sri Lanka: A Life Cycle Perspective”, International Conference on Green and Sustainable Innovation (ICGSI), Chiangmai Thailand, Nov 29th-Dec 1st, 2006  
17.       S. Seo, S. Tucker, M. Ambrose, “Selection of Sustainable Building Material using LCADesign Tool”, Sustainable Ecosystems (CSIRO), Victoria, Australia, Proceedings of the International Conference on Sustainable Building Asia, Korea, Seoul 27-29 June 2007, pp. 87-94  
18.       M. Asif, A. Davidson, T. Muneer, “Life Cycle of Window Materials – A Comparative Assessment”, Napier University, Edinburgh: UK, 2002, pp.1
19.       H. Ghorbani, V. Rahimi, S. A. Nosrati, “Concrete and the Environment”, 3rd Conference on Environmental Engineering, University of Tehran, Tehran, 7-8 October, 2009 Available: http://www.civilica.com/Paper-CEE03-CEE03_281.html [In Persian]
20.       F. Ahmadi, “Evaluating the Performance of Concrete Structures for Environmental Sustainability”, Journal of Cement Technology, 2013,60: 21-26 [In Persian]
21.       G. A. Weisenberger, “Framing system’s environmental impact depends on more than just the choice of materials”, Journal of Modern Steel Construction, 2010
22.       O. F. Kofoworola, H. G. Shabbit, “Life Cycle Energy Assessment of a Typical Office Building in Thailand”, Journal of Energy and Buildings, 2010, 41(10):1076-1083
23.       Farahzadi, “Designing a Condominium in Tehran with Ecological Perspective by Using Eco-Friendly Materials”, M.A. Thesis, Engineering Faculty, Science & Research Branch of Islamic Azad University, Iran, 2014 [In Persian]
24.       J. Denison, C. Halligan, “Building Materials and the Environment”, Stephen George & Partners LLP, 2010; Version 1.1  
25.       “National building Regulations-Section 5: Building Materials and Products”, Office of the National Building Regulations, Department of Housing and Construction, Ministry of Roads and Urban Development, Tehran: Iran, 2010
26.       D. Danesian, “Building Materials”, Faculty of Engineering, Architecture and Urbanism, Technical University of Payam Noor, Tehran, Iran, 2010
27.       Nasrollahzade, “Building Materials”, Ketabhaye Darsi Publication: Tehran, Iran, 2008
28.       Z. Lei, S. Jingying “Computer Simulation of Building Energy Consumption and Building Energy Efficiency”, 2nd International Conference on Computer Application and System Modeling (ICCASM), Taiyuan, Shanxi, China, 27-29 July, 2012   
29.       H.Z. Cui, F. C. Sham, T.Y. Lo, H. T. Lum, “Appraisal of Alternative Building Materials for Reduction of CO2 Emissions by Case Modeling”, Int. J. Environ. Res., 2011,5(1):93-100
30.       J. Gonza´lez, J.G. Navarro, “Assessment of the decrease of CO2 emissions in the construction field through the selection of materials: Practical case study of three houses of low environmental impact”, Journal of Building and Environment, 2006, 41:902–909
31.       B. V. V. Reddy, K. S. Jagadish, “Embodied energy of common and alternative building materials and technologies”, Journal of Energy and Buildings, 2005,35(2):129-127





Omer Hamid

Paper Title:

Intraocular Pressure Model Predictive Control: A Simulation of Circadian and Mean Intraocular Pressure Control

Abstract:      Pharmacokinetics/Pharmacodynamics (PK/PD) models of four ophthalmic drugs taken from the literature, employed in building model predictive control (MPC) systems. The drugs are: Latanoprost, Bunazosin, Timolol, and PF-04475270. MPC successfully controlled the mean intraocular pressure (MIOP) to a set point without overshoot or noticeable steady state error. The drug model representation order is vital in the suppression of circadian intraocular pressure variation, while the mean intraocular pressure is controllable irrespective of the model order.

Keywords: glaucoma, intraocular pressure, Circadian pattern, model predictive control, pharmacokinetics/ pharmacodynamics.

1.              Gramar E. Tausch M: The risk profile of the glaucomatous patient. Curr Opin Ophthalmol 1995, 6:78-88.
2.              Anita Kumari, Pramod K. Sharma, Vipin K. Grag, and Garima Grag. Ocular inserts-Advancement in therapy of eye diseases. J Adv Pharm Technol Res. 2010 Jul-Sep 1(3): 291-296.
3.              Deepika Jain, Richa Raturi, Vikas Jain, Praveen Pansal, and Ranjit Singh. Recent Technologies in pulsatile drug delivery systems. Biomatter 2011. Jul1:1(1), 57-65.
4.              S. S. Chrai and J. R. Robinson. Corneal permeation of topical pilocarpine nitrate in the rabbit. Am. J. Ophthalmol. 77:735-739 (1974).
5.              C. Molteno. (1969).  New implant for drainage in glaucoma. Br. J. Ophthalmol. 53. 606–615.
6.              Anne L Coleman, MD., Richard Hill, MD., M. Roy Wilson, MD., Neil Choplin, MD., Ronit Kota S-Neumann, MD., Mae Tam, MD., Jason Bacharach, MD., AND William C. PANEK, MD. (1995)  Initial Clinical Experience With the Ahmed Glaucoma Valve Implant. Am. J Ophthalmol. 120(1). 23-31
7.              Gedde SJ, Schhiffman JC Feur WJ, Hemdon LW, Brandt JD, Budenz DL. Tube versus Trabeculectomy Study group. Am. J. Ophthalmol 2012 May. 153(5:789-803) e2Epup 2012 Jan15.
8.              Patent US20140194834-Auto-Regulation System for Intraocular Pressure – Google Patents. http://wwwhttp://www.google.com/patents/US20140194834. 5/1/2016.
9.              Omer Hamid “Intraocular Pressure Model Predictive Control” American Journal of Biomedical Engineering 2016, 6 (1), pp 1-11
10.           Daniel Piso, Patricia Veiga-Crespo and Elena Vecino. (2012) Modern monitoring intraocular pressure sensing devices on application specific integrated circuits. Journal of Biomaterial and Nanobiotechnology, 3,301-309.
11.           Kaweh Mansouri, M.D., M.P.H., Felipe A. Medeiros, M.D., Ph.D., Ali Tafreshi, B.S., and Robert N. Weinreb, M.D. (2012). Continuous 24-hour Intraocular Pressure Monitoring With a Contact Lens Sensor: Safety, Tolerability, and Reproducibility in Glaucoma Patients. Arch Ophthalmol. 130(12):doi:10.1001/archophthalmol.2012.2280.
12.           Raeesa M. Moosa, Yahya E. Choonara, Lisa C. du Toit, Pradeep Kumar, Trevor Carmichael, Lomas Kumar Tomar, Charu Tyagi  and Viness Pillay. (2013). A review of topically administered mini-tablets for drug delivery to the       anterior segment of the eye. Royal Pharmaceutical Society. Journal of Pharmacy and Pharmacology. 66. 490–506.
13.           Zimmer A, Mutschler E, Lambrecht G, Mayer D, and Kreuter J. Pharmacokinetic and pharmacodynamic aspects of an ophthalmic pilocarpine nanoparticle-delivery-system. Pharmaceutical Research, Vol. 11.  No. 1994.
14.           Mohammadi S, Jones L, Gorbet M (2014) Extended Latanoprost release from commercial contact lenses: In Vitro studies using corneal models. PloS ONE 9(9) e106653. Doi10.1371/journal.pone.0106653.
15.           Gause S, et al, Mechanistic modeling of ophthalmic drug delivery to anterior chamber by eye drops and contact lenses. Adv Colloid Interface (2015), http://dx..doi.org/10.1016/j.cis.201508.002
16.           Murdan S. Electro-responsive drug delivery from hydrogels. Journal of controlled release 92 (2003) 1-17.
17.           Singh G. 2014. Hydrogel as a novel drug delivery system: a review. J. Fundam. Pharm. Res., 2(1):35-48
18.           Luo R, Cao Y, Shi P, Chen Ch. Near-Infrared light responsive multi-compartmental hydrogel particles synthesized through droplets assembly induced by superhydrophopic surface. Small. 2014 Dec 10; 10(23):4886-94.
19.           Sakanaka K, Kawazu K, Tomonari M, Kitahara T, Nakashima M, Kawakami S, Nishida K, Nakamura J, and Sasaki H (2004). Ocular pharmacokinetic/pharmacodynamic modeling for bunazosin after instillation into rabbits. Pharm Res. 21. No 5. 770–776.
20.           Sakanaka K, Kawazu K, Tomonari M, Kitahara T, Nakashima M, Kawakami S, Nishida K, Nakamura J, Sasaki H and HIGUCHI S (2008). Ocular Pharmacokinetic/Pharmacodynamic Modeling for Timolol in Rabbits Using Telemetry System. Biol. Pharm Bull 31(5) 970-975.
21.           Kenneth T. Luu, Eric Y. Zhang, Ganesh Prasanna, Cathie Xiang, Scott Anderson, Jay Fortner, and Paolo Vicini. (2009). Pharmacokinetic-Pharmacodynamic and Response Sensiti-zation Modeling of the Intraocular Pressure-Lowering Effect of the EP4 Agonist 5-{3-[(2S)-2-{(3R)-3-hydroxy-4-[3-(trifluoromethyl)phenyl]butyl}-5-oxopyrrolidin-1-yl]propyl}thiophene-2-carboxylate (PF-04475270). The J Pharmacol Exp Ther  331(2). 627–635.
22.           Luu, K. T., Raber, S. R., Nickens, D. J. and Vicini, P. (2010), A Model-Based Meta-Analysis of the Effect of Latanoprost Chronotherapy on the Circadian Intraocular Pressure of Patients With Glaucoma or Ocular Hypertension. Clinical Pharmacology & Therapeutics, 87: 421–425. doi: 10.1038/clpt.2009.306
23.           Durairaj C1, Shen J, Cherukury M. Mechanism – based translational pharmacokinetic – pharmacodynamic model to predict intraocular pressure lowering effect of drugs in patients with glaucoma or ocular hypertension. Pharm Res. 2014 Aug;31(8):2095-106. doi: 10.1007/s11095-014-1311-9. Epub 2014 Feb 19.
24.           Fogagnolo P., Orzalesi N., Ferreras A., and Rossetti L. The circadian curve of intraocular pressure: Can we estimate its characteristics during office hours. Investigative Ophthalmology & Visual Science, May 2009, Vol 50, No. 5. 2209-2215.
25.           Agnifili L, Mastropasqua R, Frezzotti P, Frezzotti P, Fasanella V,  Motolese H, Pedrotti E, Di Iorio A, Mattei P, Motolese E, and Mastropasqua L. (2015): Circadian intraocular pressure patterns in healthy subjects, primary open angle and normal tension glaucoma patients with a contact lens sensor. Acta Ophthalmol  93: e14–e21.





Muhammad Muneeb Khan, Muhammad Aamir Shafi, Nasrullah Khan

Paper Title:

Development of Prototype of Grid Tie Inverter (Grid Synchronization and Load Sharing)

Abstract:  Design the prototype model of grid tie inverter which includes synchronization, load sharing and reverse metering technique. Main part of the system that control everything is the SPWM based inverter which take the information from grid and independent source of energy and then synchronize the both signals. According to the demand of the load Microcontroller (MCU) makes decision that either the grid feed the load or independent source of energy or both share the load. By sharing the common load with the grid, design an algorithm by which the sharing power with respect to the main grid using droop control technique. This technique minimizes the contribution of the main grid towards the load. Sine Pulse Width Modulation (SPWM) Grid Tie inverter is the most commonly used technique because it is less complicated, more efficient the power loss is minimum and the output sine wave is very close to true sine wave. While in multi-level inverter there is more power loss due to number of components and due to the limitations the output wave is not much like true sine wave. Load sharing by designing buck-boost converter and an adaptive algorithm load sharing can be done automatically according to the demand of load. So, this is more better and efficient then Push buttons.

 Angle Drop Control, Distributed Generation, Grid Synchronization, Grid Tie Inverter, Load Sharing, Microcontroller.


1.       H. Hinz, P. Mutschler, and M. Calais, “Control of a single phase three level voltage source inverter for grid connected photovoltaic systems,’’ M.S. Thesis, Department of Power Electronics and Drives, University of Curtin, Australia, 1997.
2.       F. B. Salim, K. M. Venus, “Experiment with a locally developed single phase grid tie inverter,” IEEE Informatics, Electronics & Vision (ICIEV), vol. 21, pp. 916-924, 2012.

3.       Durra, A. Reznik, and S. M. Muyeen, “Performance analysis of a Grid tied Inverter for Renewable energy applications,” IEEE Transections on Power System, USA, Vol. 2, pp, 5-10, 2014.

4.       Y. Beck, D. Medini, “Connecting an Alternative Energy Source to the Power Grid by a DSP Controlled DC/AC Inverter,” M.S. Thesis, Department of Interdisciplinary Engineering, Tel Aviv University, Israel, pp. 14-20, 2005.

5.       Y. Liu, D. Y. Y. Liu, “Potential of Grid connected solar PV without storage,”  IEEE Transections on Renewable energy, pp 1-4, 2010.

6.       T. K. Kwang, S. S. feuding, “Single phase Grid tie inverter for photovoltaic application,” IEEE Transection on Renewable energy, pp 2-6, 2010.

7.       J. Hossain, R. Hasan, M. Hossain and M. R. Islam, “Design and implementation of a Grid connected single phase inverter for photovoltaic system,”  M.S Thesis,  Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Bangladesh, 2015.

8.       F. B. Zia, K. M. Salim, and N. B. Yousuf, “Design and implementation of a single phase Grid tie photo voltaic inverter,” M.S. Thesis, School of Engineering and Computer Science, Independent University Dhaka, Bangladesh, 2015.

9.       S.M. Ali “Performance evaluation of a Grid connected photovoltaic system based on solar cell modelling,” International Conference on Circuit, Power and Computing Technologies(ICCPCT), India, pp. 3-20, 25-28 May,2015.

10.    Jain, B. Singh “Single phase single stage multifunctional Grid interfaced solar photo voltaic system under abnormal Grid conditions IET generation, transmission & distribution special issue on Power Electronic converter systems for integration of Renewable Energy sources,” M.S. Thesis, Department of Electrical Engineering, Indian
Institute of Technology Delhi, India, 2014.

11.    M. Joshi, G. A. Vaidya, “Modeling and simulation of single phase Grid connected solar photovoltaic system,” Annual IEEE India Conference (INDICON), Pune, India, pp. 14-20, 2014.

12.    Datta, “A DSPIC based efficient single-stage Grid connected photovoltaic system,” M.S. Thesis, Department of Electrical & Electronics Engineering, National Institute of Technology, Meghalaya, vol. 3, pp. 1-9, 2015.

13.    Sarwar, M. S. J. Asghar, “Simulation and analysis of a multilevel converter topology for solar PV based Grid connected inverter,” IEEE Transection on Smart Grid and Renewable Energy, vol. 2, pp. 56- 62, 2011.

14.    R.  Hider, R. Alam, and N. B. Yousef, “Design and construction of Single Phase pure sine wave inverter for photovoltaic application,” IEEE International Conference on Informatics Electronics & Vision (ICIEV), pp. 190-194, 2012.

15.    T. Esram, P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transection on Energy Converters, vol. 22, no. 2, pp. 439–449, Jun. 2007.

16.    R. Gonzalez, J. Lopez, P. Sanchez, and L. Marrowy, “Transformer less Inverter for single phase photovoltaic systems,” IEEE Transection on Power Electronics, vol. 22,
no.2, pp. 693-697, 2007.

17.    N. Kasa, T. Iida and H. Iwamoto, “Maximum power point tracking with capacitor identifier for photovoltaic power system” IEEE Transection on Instrumentational Electronics Applications, vol. 147, no. 6, pp. 497–502, November,2000.

18.    M. T. Ho and H. S. H. Chung, “An integrated inverter with maximum power tracking for grid-connected PV systems,” IEEE Transection on Power Electronics, vol. 20, no.
4, pp. 953–962, Jul. 2005.

19.    S. Bahram, V. W. Singh, j. Jatskevich, “Flow for AC-DC networks smart Grid,” IEEE International Optimal power Communicational Conference (IIOPCC), vol. 2, pp. 49
54, 3-6 November,2014.

20.    H. Nikkhajoei, R. Iravani, “Dynamic model AC–DC–AC voltage sourced converted system for distributed resources,” IEEE Transaction on Power Delivery and Control or System, vol. 22, pp.1169-1178, April, 2007.





Arshi Salamat

Paper Title:

A Simple Technique for Obtaining Better Porosity for Improved Performance of a Humidity Sensor

Abstract:  In this paper a method for obtaining better porosity is proposed .The sol gel method and anodisation of aluminium oxide together will result in formation of better pores on alumina. Better porosity will result in enhanced performance of a sensor.

 porosity, sol gel, anodisation, adsorption


1.    B.E. Yoldas, A transparent porous Alumina amer.ceram.soc.Bull. 54(1975)286
2.    Thompson,G.E., Wood G.C.Anodic Films on Aluminium. In treatise on materials Science and technology, Vol.23, 1983

3.    K.k mistry, D.Saha K. Sengupta, Sol gel processed Aluminium oxide thick film template as sensitive capacitive trace moisture sensor, sensor and actuator B,2005.



Volume-5 Issue-6

 Download Abstract Book

S. No

Volume-5 Issue-6, August 2016, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Greeshma T S, Subu Surendren

Paper Title:

Community Detection on Social Network – A Survey

Abstract: Social network is an important application in the internet which represent the geographically dispersed users. Social network provides a variety of methods for explaining patterns and entities. Social networks are mostly represented as graphs,   which contain nodes and edges. Nodes are used to represent actors such as people and organizations whereas edges show the relationship between these nodes. Several data sources involved in the social network forms communities which work in self-descriptive manner.  A collection of nodes which are connected by edges with high similarity is called a community. The community detection in social network, intend to partition the the graph with dense region which correspond to closely related entities. The selection of data sources and determination of community detection approaches can enhance the accuracy, efficiency and scalability of community. In this survey, different community detection approaches are discussed.

social network, community detection, community structure


1.       Michel Plantic, Michel Crampes ,”Survey on Social Community Detection”, Springer Publishers, 25 March 2013.
2.       James P Bagro, “Evaluating Local Community Method in Network “, Journals of Statistical Mechanism Theory and Experience, 2008.

3.       C.C Aggarwal, H.Wang,”Survey of clustering algorithm for graph data managing and Mining Graph Data “,Springer, 2010.

4.       A.Pothen,”Graph Partitioning Algorithm with Application to Scientific Computing “, Springer, 1997.

5.       M.Girvan and M.E.Newman ,”Community Structure in Social and Biological Networks”, Proceeding of National Academy of Science, June 11,2002.

6.       A.Hasian and M.J Zahi,”A Survey of Link Prediction in Social Network”, Springer , March 11,2011.

7.       J.Han,M.Konnber and J.Pei,”Datamining Concepts and Techniques”, Morgen Kaufmann,2006.

8.       R.Xu and D.Wunsch,”Survey of Clustering Algorithm “,Neural Network ,IEEE Transaction, May 2005.

9.       N.F.Chikki,B.Rothenburger and N.Aussenac Gilles,” Combining Link and Content for Community detection : a discriminative approach”, Proceedings of 15th ACM SIGMM workshop on Social Media, June 28,2009.

10.    F.Moser, R.Ge and M. Ester,”Joint Cluster Analysis of Attribute and Relationship Data without a -prior-specification of number of clusters” Proceedings of 13th ACMSIGDD International Conference on Knowledge Discovery andDatamining, August 07, 2007.

11.    S.Fortunate,”Community Detection in Graph”, Physics report ,2009.

12.    S.Papadopacelos, Y.Kompatsiaris,A.Vakali, and P.Spyridones, “Community Detection in Social Media “, Datamining and Knowledge Discovery, June 14,2011.

13.    Yangyang Li,Ruachen Liu and Jiamhe Wu, “A Spectral Clustering Based Adapive Hybrid Multionjective Harmony Search Algorithm for Community Detection “, WCC12012 IEEE World Congress on Computational Intelligance, June 15,2012.

14.    Deepjyoti Chaudhery, Saprativa Bhattachayie , Anirban Das,”An Empirical Study of Community and Sub community Detection in Social Network Applying Newmann Girvan Algorithm,” Emerging Trends and Application in Computer Science ,Sep 14,2013.

15.    Ganjaliyev.F,” New Method for Community Detection in Social Network Extracted from the Web” , Problems of Cyberneties and Information ,Sep 14, 2012.

16.    Michael Ovelganne ,”Distributed Community Detection in Website Network”,Advance in Social Network Analysis and Mining ,IEEE,  Aug 28, 2013.

17.    Guo-Jun Qil,Charu C,Aggarwal and Thomas Huangl ,” Community Detection with Edge Content in Social Media Network “, Data Engineering ,IEEE , April 1,2012.

18.    Yomna M.ElBarawy, Ramedan F Mohammad and Naveen I Ghali,” Improving Social Network Community Detection Using DBSCAN Algorithm” , Computing Application and Research ,Jan 20,2014.

19.    Ahmed Ibrahem Hafez, Abaul Ella Hassanien , Aly A. Fahm and M.F. Talba, “Community Detection in Social Network by Using Bayesian Network and Expectation Maximization Technique”, IEEE , Dec 16,2013.

20.    M .E.J .Newmann ,”Community Structure in Social and Biological Network ” IEEE, April 6, 2002.

21.    Hastic , T.R. Tibshirani and J.H Friedmann,”The elements of Statistical Learning,” IEEE ,Auguest 2008

22.    A.Y.Ng, M.I.Jordan, Weiss,”On Spectral Clustering Analysis and Algorithm “, Stanford Alhab, 2001.





Diejo Jara, Estefania Salinas, Julio Romero, Michael Valarezo

Paper Title:

Mathematical Modeling to Establish the Balance of Heat in a Capacitor

Abstract:  The teaching-learning process in the field of exact sciences strengthened by the practical activity of a technological nature, in which to facilitate the safe reasoning and concise leads to the application of principles of physics and chemistry as well as updating processes industrial in the field of Mining, Pulp, Forest, Food, Chemical and Process. Which have potentiated a high degree of modelling and automation? This automation involves some advantages that have just moved to the quality and improvement of the final product. In this case, establishing the heat balance in a condenser. Includes ensure both a more competitive cost and simultaneously strengthening formation activity and the mathematical model to determine the hot balance in a capacitor means using parameters dependent pressure define variables as the volume of water and the amount of steam saturation entry and quantified by developed and simplified quantification and analysis of material balance equations. Thus, in this article the calculations used are presented to establish the mathematical modeling for the heat balance in a capacitor, for it was selected and implemented, with teams making and data records, pointing to possible strategies to conceive established the study of the processes of heat transfer and control systems as an integral part of an automation project

 Automation, analytical calculation, mathematical modelling, analytical, design and construction.  


1.    Aplein Ingenieros: Diseño y realización de la sala de control y operaciones de la planta Bilbao Bizkaia Gas. En URL: http://www.apleiningenieros.com/bbg.pdf, (2009)
2.    Beyer, H., Hotzblatt, K.: Contextual design. Defining customer-centered systems. Morgan Kaufmann, San Francisco, (1998)

3.    Constantine, L.L., Loockwood, L.A.D.: Software for use: a practical guide to the models and methods of usage-centered design. Addison-Wesley, (1999)

4.    Good, M., Spine, T.M., Whiteside, J., George, P.: User-derived impact analysis as a tool for ACM, (1986)

5.    Granollers, T.: User centred design process model. Integration of usability engineering and software engineering. Proceedings of INTERACT 2003, Zurich, Suiza, (2003)

6.    Smith., Van Ness. Introducción a la Termodinámica en Ingeniería Química. Editorial McGrawHill. México 1981





Michael Valarezo, Estefania Salinas, Julio Romero, Diejo Jara

Paper Title:

Application of an OPC System for Mineral Extraction in a Copper Mine Laboratory Scale

Abstract:   For their importance in the mining industry of copper in the south of the Ecuador, an application of a system of OPC is presented that transfers the copper mineral extracted using the action of a motor of C.A, which moves a transportable band. The programs MATLAB® and LabVIEW® possess an academic profile and for this reason, they have a very limited use inside this technology OPC. Also, these are part of the school formation of the students of the career of Electromechanical Engineering of the National University of Loja (UNL) in the Ecuador. Keeping in mind that pointed out, these programs will use the mark of the technology OPC in the process of extraction of the copper mineral. Finally, a comparison of the found results with these two programs is made

  Data exchange, OLE, OPC Client / Server, PLC. 


1.        Andariz Automation, «Solución de control para molinos SAG,» [En línea]. Available: http://www.andritz.com/de/aa-brainwave-sagmill-spa.pdf. [Último acceso: 2015 Mayo 29].
2.        SIEMENS, «Un Sistema de Control de Procesos a la altura del proyecto minero Spence, Tecnología Minera,» 29 Mayo 2015. [En línea]. Available: http://www.tecnologiaminera.com/tm/novedad.php?id=76. [Último acceso: 2015 Mayo 29].

3.        ¿. s. p. a. a. u. P. S.-1. m. u. P. A. y. q. s. h. d. t. e. cuenta?, «SIEMENS,» 08 Agosto 2014. [En línea]. Available: https://support.industry.siemens.com/cs/document/41928929?dti=0&lc=es-WW. [Último acceso: 2015 Mayo 26].

4.        N. Instruments, «Comunicación del S7200 de SIEMENS con OPC Server,» [En línea]. Available: http://forums.ni.com/t5/Discusiones-sobre-Productos-NI/Comunicaci%C3%B3n-del-S7-200-de-Siemens-con-NI-OPC-Server-2009/td-p/1690440.

5.        M. d. Carmen, «Control PID de la velocidad de una banda transportadora para clasificación de objetos,» 2008.

6.        Mathworks, «Mathworks,» [En línea]. Available: www.mathworks.com.

7.        N. Instruments, «National Instruments,» [En línea]. Available: www.ni.com. [Último acceso: 10 octubre 2013].

8.        J. O. Caizaluisa, Escritor, Dosificador y Comunicación OPC LabView. [Performance]. Universidad Pontificia Salesiana, 2013.

9.        LabView-OPC-PLC. [Performance]. 2011.

10.     N. Instruments, «Comunicación del S7-200 de Siemens con NI OPC Server 2009 mediante el cable USB/PPI,» NI, 31 Agosto 2011. [En línea]. Available: http://forums.ni.com/t5/Discusiones-sobre-Productos-NI/Comunicaci%C3%B3n-del-S7-200-de-Siemens-con-NI-OPC-Server-2009/td-p/1690440. [Último acceso: 18 Mayo 2015].





Jiin-Yuh Jang, Chien-Nan Lin, Sheng-Chih Chang, Chao-Hua Wang

Paper Title:

The 3-D Numerical Simulation of a Walking Beam Type Slab Heating Furnace with Regenerative Burners

Abstract:    This study investigates the furnace thermal efficiency for a walking-beam type slab heating furnace with regenerative burners. The walking-beam type heating furnace is composed of five zones, namely, flameless, preheating, first heating, second heating and soaking zones with regenerator efficiency 90 %. The furnace uses a mixture of coke oven gas as a fuel to reheat the slabs. The numerical model considers turbulent combustion reactive flow coupled with radiative heat transfer in the furnace. It is shown that with regenerator burners, the furnace thermal efficiency is 72%, which is significantly higher than that of a furnace using the conventional burner without regenerator. Comparison with the in-situ experimental data from steel company in Taiwan shows that the present heat transfer model works well for the prediction of thermal behavior of the slab in the reheating furnace with regenerator burners.

   Reheating Furnace, Combustion, Radiative Heat Transfer, Regenerative burner


1.    T. Ishii, C. Zhang, and S. Suglyama, “Numerical simulations of highly preheated air combustion in an industrial furnace,” Transactions of the ASME, Vol. 120, 1989, pp. 276–284. 
2.    Y. Suzukawa, S. Sugiyama, Y. Hino, M. Ishioka, and I. Mori, “Heat transfer improvement and NOx reduction by highly preheated air combustion,” Energy Convers, Mgmt Vol. 38, No. 10–13, 1997, pp. 1061–1071.

3.    J. G. Kim and K. Y. Huh, “Three-dimensional analysis of the walking-beam-type slab reheating furnace in hot strip mills,” Numerical Heat Transfer A38, 2000, pp. 589–609.

4.    T. Ishii, C. Zhang, and Hino. Y, “Numerical study of the performance of a regenerative furnace,” Heat Transfer Engineering, 23:4, 2002, pp. 23–33.

5.    N. Rafidi and W. Blasiak, “Thermal performance analysis on a two composite material honeycomb heat regenerators used for HiTAC burners,” Applied Thermal Engineering, Vol 25, 2005, pp. 2966–2982.

6.    J. P. Ou, A. C. Ma, S. H. Zhan, J. M. Zhou, and Z. O. Xiao, “Dynamic simulation on effect of flame arrangement on thermal process of regenerative reheating furnace,” J. Cent. South Univ. Technol., 2007.

7.    S. H. Han, D. Chang, and C. Y. Kim, “A numerical analysis of slab heating characteristics in a walking beam type reheating furnace,” International Journal of Heat and Mass Transfer, Vol 53, Issue 19–20, 2010, pp. 3855–3861.

8.    S. H. Han, D. Chang, and C. Huh, “Efficiency analysis of radiative slab heating in a walking-beam-type reheating furnace,” Energy, Vol 36, Issue 2, 2010, pp. 1265–1272.

9.    T. Morgado, P. J. Coelho, and P. Talukdar, “Assessment of uniform temperature assumption in zoning on the numerical simulation of a walking beam reheating furnace,” Applied Thermal Engineering, Vol 76, 2015, pp. 496–508.

10. J. M. Casal, J. Porteiro, J. L. Míguez, and A. Vazquez, “New methodology for CFD three-dimensional simulation of a walking beam type reheating furnace in steady state,” Applied Thermal Engineering, Vol 86, 2015, pp. 69–80.





Nithin V G, Libish T M

Paper Title:

Smart Grid State Estimation by Weighted Least Square Estimation

Abstract:     The smart grid is an advanced power grid with many new added functions and more reliability than the traditional grid. More controlled power flow is enabled in the smart grid by use of features from fields of communication, control system, signal processing etc. Knowing the present condition of the system is critical for signal processing applications and hence more accurate state estimation is important. State of the system along with information about the network topology will give complete information about the power grid network. In this paper the network topology is modeled using the MATPOWER package, a powerful software package of MATLAB. Weighted Least Square (WLS) state estimation is used to develop equations and algorithms for state estimation. The linear state estimation problem is formulated with linear methods using phasor measurement unit (PMU) data. The measurements which are included in the observation vector and also the size of the system (given by number of busses in the system) are important and these features affect the accuracy of the system state estimate. In this paper, state estimates of IEEE standard bus system of different size are stimulated using MATPOWER package. Also state estimates are stimulated, with different measurement parameters in the observation vector and the stimulation result obtained are compared.

Smart Grid, State Estimation, Weighted Least Square Estimation, Modeling of Smart Grid.

1.        M. Sasson, S. T. Ehrmann, P. Lynch, and L. S. Van Slyck, Automatic power system network topology determination,” IEEE Trans. Power App. Syst., vol. PAS-92, no. 1, pp. 610–618, Mar. 1973.
2.        A.G. Phadke and J. S. Thorp, Synchronized Phasor Measurements and Their Aplication, Springer Science + Business Media, 2008.

3.        T. L. Baldwin, L. Mili, M. B. Boisen, Jr., and R. A. Adapa, “Power System Observability with Minimal Phasor Measurement Placement,” Power Systems, IEEE Transactions on, vol. 8, pp. 707-715, 1993.

4.        R. D. Zimmerman, C. E. Murillo-S_anchez, and R. J. Thomas, \Matpower: Steady State Operations, Planning and Analysis Tools for Power Systems Research and Education,” Power Systems, IEEE Transactions on, vol. 26, no. 1, pp. 12{19, Feb. 2011.

5.        Abur and A. G. Exposito, Power System State Estimation- Theory and Implementation: CRC, 2004.

6.        Yi Huang, Mohammad Esmalifalak, Yu Cheng, Husheng Li, Kristy A. Campbell, and Zhu Han, “Adaptive Quickest Estimation Algorithm for Smart Grid Network Topology Error”, IEEE systems journal, vol. 8, no. 2, June 2014.

7.        Y. Huang, L. Lai, H. Li, W. Chen, and Z. Han, “Online quickest multiarmed bandit algorithm for distributive renewable energy resources,” in Proc. IEEE Conf. Smart Grid Commun., Tainan, Taiwan, Nov. 2012, pp. 558–563.

8.        The Smart Grid: An Introduction, U.S. Department of Energy (DOE), Washington, DC, USA, Sep. 2010.

9.        J. Chen and A. Abur, “Placement of PMUs to enable bad data detection in state estimation,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1608–1615, Nov. 2006.

10.     Synchrophasor Based Tracking Three Phase State Estimator and It‟s Applications, A.G. Phadke Virginia Tech, Blacksburg, VA. DOE 2010 Transmission Reliability Program Peer Review, October 19-20, 2010.

11.     R. F. Nuqui and A. G. Phadke, “Phasor Measurement Unit   Placement Techniques for Complete and Incomplete Observability,” Power Delivery, IEEE Transactions on, vol. 20, pp. 2381-23





Hamdy Mohamed Soliman

Paper Title:

Sinusoidal PWM to Drive the Induction Motor with Reducing the Torque Ripple and THD

Abstract:   Three phase voltage source inverter are widely used to drive the AC motors as the induction motor. There are many techniques to make the inverter reliable to treatment the AC motor.  From among these techniques, the sinusoidal pulse width modulation. The paper used this technique due to have some advantages as, reduce the total harmonic distortion and torque ripples. Also in this Paper the open and closed loop scalar controls with the sinusoidal pulse width modulation are compared to show the advantages of the closed loop control. The torque ripples and total harmonic distortions is calculated through many modulation index. The PI current controlled is added to the closed loop drive system to minimize the torque ripple and total harmonic distortion this is to show the effect of adding these PIs on the performance overall. 

 Induction motor, PI controller, Scalar control and SPWM.


1.     M.D. Murphy, F.G Turnball: Power electronic control of A.C motors, Pergamon press, 1986.
2.     Bose B.K: Power Electronics and Variable Frequency Drives, IEEE Press, 1997.

3.     W.B Rosink: Analogue control system for A.C motor with PWM variable speed, in proceedings of Electronic Components and Application, Vol. 3, No.1, November 1980, pp. 6-15

4.     B.G. Starr, J.C.F. Van Loon: LSI circuit for AC motor speed control, in proceedings of Electronic Components and Application, Vol. 2, No.4, August 1980, pp. 219-229

5.     Shengxian Zhuang, Xuening Li and Zhaoji Li, “ The application in the speed regulating of asynchronous machine vector frequency changing based on adaptive internal model control (Periodical style),” Journal of University of Electronic Science and Technology of China, vol. 28,no.5, pp.502-504, 1999.

6.     P. L. Jansen and R. D. Lorentz, “Transducerless position and velocity estimation in induction and salient AC machines”, IEEE Trans. Ind. Applicat., vol. 31, pp. 240–247, Mar./Apr. 1995.

7.     Pankaj H Zope, Pravin G.Bhangale, Prashant Sonare ,S. R.Suralkar “Design and Implementation of carrier based Sinusoidal PWM Inverter.” International journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol. 1, Issue 4, October 2012.

8.     “Performance of Sinusoidal Pulse Width Modulation based Three Phase Inverter.” International Conference on Emerging Frontiers in Technology for Rural Area (EFITRA) 2012 Proceedings published in International Journal of Computer Applications® (IJCA).

9.     M. P. Kazmierkowski, and L. Malesani, “Current control techniques for three-phase voltage-source PWM converters: a survey”, IEEE Trans. Ind. Electron., vol. 45, no. 5, October, 1998, pp. 691-703.

10.  B. k. Bose, “An adaptive hysteresis-band current control technique of a voltage – fed PWM inverter for machine drive system”, IEEE Trans., on Ind. Appl., Vol.IA-37, pp.402-408, 1990

11.  Hamdy Mohamed soliman and S. M. EL. Hakim,” Improved Hysteresis Current Controller to Drive Permanent Magnet Synchronous Motors Through the Field Oriented Control”, International Journal of Soft Computing and Engineering , Vol. 2, No. 4, September 2012, pp. 40-46.





Abhishek Pratap Singh, Manoj Gupta

Paper Title:

Robust Performance Comparison of Unstable Videos and their Quality Improvement Implementing Block-Based Frame Matching Technique for Obtaining Digital Video Stabilization

Abstract:  In the context of Digital Image stabilization (DIS), based on morphological frame division and comparing, to estimate matching between local and global motion vectors by the means of averaging pixel information of frames; surprisingly proposes an indispensable Digital video stabilization (DVS) technique which can enhance the quality of an input video stream. Videos captured by hand-held devices (e.g. Cell phones, portable camcorders etc.) sometimes appear remarkably shaky hence Digital video stabilization technique can be implemented to refine the video quality by removing unwanted jitters. It’s an important step for several video processing amenities to acquire video stream without intervening jerkiness, eliminating unnecessary camera movements and withdrawing the superfluous inter frame motion between two successive frames. In order to get the stabilized video sequence, first promising step is to check the validity of local motion vector (LMV), and finally global motion vector (GMV) is obtained by averaging to further enhance the reliability. Here low pass filters and moving average filters are used for smoothing estimated motion vectors to get a stabilized sequence. Experiments show that this video stabilization technique is an efficient method to stabilize the input unstable video stream. In this paper we study the digital video stabilization technique with the use of keen motion estimation and finally performance comparison and conclusion of un-stabilized and stabilized video sequence with the efficacy of our technique of digital video stabilization. . 

Digital Video Stabilization (DVS), Digital Image Stabilization (DIS), Inter Frame Motion, Local Motion Vector (LMV), Global Motion vector (GV).


1.        Hansen M., et.al., “Real time scene stabilization and mosaic construction”. Int. Proc. DARPA Image understanding Workshop, 0-8186-6410-X, 457-465, Monterey, CA, November (1994).
2.        Szeliski R., “Image Alignment and Stitching: A Tutorial,” Technical Report MSR-TR- 2004-92, Microsoft Corp., (2004).

3.        Matsushita Y., et.al., “Full frame video Stabilization with motion inpainting.”Transactions on Pattern Analysis and Machine Intelligence, 28 (7), 1150-1163, IEEE, July (2006).

4.        Hu1 R., et.al., “Video Stabilization Using Scale Invariant Features”.11th International. Conference on Information Visualization IV’07., Zurich, 871-877, IEEE, July 4-6 (2007).

5.        Albu F., et.al., “Low Complexity Global Motion Estimation Techniques for Image Stabilization”, ”, International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 1-2, IEEE, January 9-13 (2008).

6.        Tico M., Vehvilainen M., “Robust Method of Digital Image Stabilization”, International Symposium on Communication, Control and Signal Processing (ISCCSP), St. Julians, 316-321, IEEE, March 12-14 (2008).

7.        Battiato S., et.al., “A Robust Video Stabilization System By Adaptive Motion Vectors Filtering”, International conference on Multimedia and Expo, Hannover, Germany, 373-376, IEEE, June 23-April 26 (2008).

8.        Bosco A., et.al., “Digital Video Stabilization through Curve Warping Techniques” Transactions on Consumer Electronics, 54(2), 220-224, IEEE, May (2008).

9.        Kuo T Y., Wang C H., “Fast Local Motion Estimation and Robust Global Motion Decision for Digital Image Stabilization”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, 442-445, IEEE, August 15-17 (2008).

10.     Liu F., et.al., “Content-Preserving Warps for 3D Video Stabilization,” ACM Transactions on Graphics (Proceedings of SIGGRAPH 2009), 28(3), 44:1-44:9, (2009).

11.     Pang D., et.al., “Efficient Video Stabilization with Dual-Tree Complex Wavelet Transform”, EE368 Project Report, Spring (2010).

12.     Peng X., et.al., “Robust Digital Image Stabilization Based On Spatial-Location Invariant Criterion”, 37th Annual conference on IEEE Industrial Electronics Society, Melbourne, VIC, 2250-2254, IEEE, November 7-10 (2011).

13.     Li C., Liu Y., “Global Motion Estimation Based On Sift Feature Match For Digital Image Stabilization”, International conference on computer science and network technology, Harbin, China ,2264-2267,IEEE, December 24-26 (2011).

14.     Song C., et.al., “Robust Video Stabilization Based on Particle Filtering with Weighted Feature Points”, Transactions on Consumer Electronics, 58(2), 570-577, IEEE May (2012).

15.     Okade M., Biswas P., “Fast Video Stabilization In The Compressed Domain”, International conference on Multimedia and Expo, Melbourne, Australia, 1015-1020, IEEE, July 09-13 (2012).

16.     Mohamadabadi B., et.al., “Digital Video Stabilization Using Radon Transformation”, International conference on Digital Image Computing Techniques and Applications (DICTA),Fremantle, WA, 1-8, IEEE, December 3-5 (2012).

17.     Raimbault F., Incesu Y., “Adaptive Video Stabilization With Dominant Motion Layer Estimation For Home Video And Tv Broadcast”, International conference Image Processing (ICIP), Melbourne, Vic, 3825-3829, IEEE, September 15-18 (2013).

18.     Wang T., Kim T., “An Efficient Video Stabilization System for Low Computational Power Devices”, International Conference on Consumer Electronics (ICCE), Berlin, 73-74,   IEEE, September 9-11 (2013).

19.     Tanakian M., et.al., “Digital Video Stabilization System by Adaptive Fuzzy Kalman Filtering”, Journal of Information Systems and Telecommunication, 1(4), 223-232, October – December (2013).

20.     Blanc-Talon J., et.al., “Automatic Feature-Based Stabilization of video with Intentional Motion through a Particle Filter” , (Eds. Blanc-Talon, J., Kasiniski, A., Philips, W., Popescu, D., Scheunders, P.), Advanced Concepts for Intelligent Vision Systems, Springer International Publishing,356-370,(2013).

21.     Rawat P., Singhai J., “Efficient Video Stabilization Technique for Hand Held Mobile Videos”, International Journal of Signal Processing and Image Processing and Pattern Recognition, 6(3), 17-31, June (2015).

22.     Bhukjwal D., Pawar B., “Review of Video Stabilization Techniques Using Block Based Motion Vectors”, International Journal Of Advanced Research in Science, Engineering and Technology, 6(3), 1741-1747, March (2016).

23.     Chongwu Tang, Xiaokang Yang, Li Chen, “A fast video stabilization algorithm based on block matching and edge completion”, 13th International Workshop on Multimedia Signal Processing (MMSP),  1-5, IEEE, 2011.





Mohammed Khalid, P. Sajith Sethu

Paper Title:

Video Denoising using Surfacelet Transform By Optimised Entropy Thresholding

Abstract: The primary aim of all video denoising systems is to remove noise from a corrupted video sequence. A video is corrupted often due to the limitations of the acquisition and processing devices. Most of the conventional video denoising schemes employ the technique of motion estimation or the optical flow estimation. Motion estimation is mostly an arduous technique particularly in conditions with lighting variations. Motion estimation step is also worsened due to the aperture problem of the optical flow estimation. This limitation of motion estimation paved the way for wavelet transform based video denoising techniques. Unfortunately, those systems resulted in videos with jittery edges and curves. Surfacelet transform is a potential tool used for the processing of multidimensional data. Video signals, which can be dealt as a different type of 3D signal, can be processed using surfacelet transform which preserves the visual quality and edge information. Entropy thresholding optimized using Artificial Bee Colony(ABC) is used to threshold the surfacelet coefficients which can be used to reconstruct the video signal with improved visual quality and with a higher peak signal to noise ratio (PSNR) and structural similarity(SSIM) index.  

 Surfacelet transform, Artificial Bee Colony Algorithm,Entropy  Threshold, NDFB, PSNR, SSIM  


1.     F. A. Mujica, J.-P. Leduc, R. Murenzi, and M. J. T.Smith, “A new motion parameter estimation algorithm based on the continuous wavelet transform,” IEEE Trans. Image Proc., vol. 9, no. 5, pp. 873–888, May2000.
2.     W. Selesnick and K. Y. Li, “Video denoising using2D and 3D dual-tree complex wavelet transforms,” in Proc. of SPIE conference on Wavelet Applications in Signal and Image Processing X, San Diego, USA, August2003.

3.     M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Proc., vol. 14, no. 12, December 2005.

4.     E. Chang and A. Zakhor, “Subband video coding based on velocity filters,” in Proc. IEEE International Symposium on Circuits and Systems, May 1992.

5.     R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional decomposition of images: Theory and design,” IEEE Trans. Signal Process., vol. 40, no. 4, Apr. 1992, pp. 882–893.

6.     Y. M. Lu and M. N. Do, “Multidimensional Directional Filter Banks and Surfacelets,” in IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 918-931, April 2007.

7.     S. Park, “New directional filter banks and their applications in image processing,” Ph.D. dissertation, Georgia Inst. Technol., Atlanta, 1999

8.     Malfait M, Roose D, “Wavelet Based Image Denoising Using a Markov Random Field a Priori Model,” IEEE Transaction on Image Processing, Vol. 6, No. 4, 1997, pp.549-565.

9.     Kazubek M, “Wavelet Domain Image Denoising by Thresholding and Wiener Filtering,” IEEE Signal Processing, Vol. 10, No. 11, 2003, pp. 324-326.

10.  Van De Ville D, Van der Weken D ,Nachtegael M, Kerre E. E., Philips W., and Lemahieu I, “Noise Reduction by Fuzzy Image Filtering,” IEEE Transaction on Fuzzy Systems, Vol. 11, No. 4, 2003,pp. 429-436

11.  Sadhar S. I., and Rajagopalan A. N, “Image Estimation in Film-Grain Noise,”  IEEE Signal Processing Letters, Vol. 12, No. 3, 2005, pp.238-241.

12.  Ozkan M. K., Sezan I., and Tekalp A. M, “Adaptive Motion Compensated Filtering of Noisy Image Sequences,” IEEE Transaction on Circuits and Systems for Video Technology, Vol.3, No. 4, 1993, pp.277-290.

13.  Dugad R., and Ajuha N., “Video Denoising by Combining Kalman and Wiener Estimates,” IEEE International Conference on Image Processing, Kobe, Japan, 1999, pp. 152-161.

14.  Gupta N, Swamy M. N. S, and Plotkin E. I, “Low Complexity Video Noise Reduction in Wavelet Domain,” IEEE 6th Workshop on Multimedia Signal Processing, 2004, pp.  239-242.

15.  Chan T.-W, Au O. C, Chong T. S, and Chau W.S, “A Novel Content-Adaptive Video Denoising Filter,” IEEE ICASSP, Philadelphia, PA, USA, Vol. 2, 2005, pp. 649-652.

16.  S. Das, A Abraham and A Konar, “Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives, “Studies in Computational Intelligence (SCI), 2008.





Madhuri Mhaske, Sachin Patil

Paper Title:

An Image Reranking Model Based on Attributes and Visual Features Eliminating Duplication

Abstract: An image search on internet is increasing day by day. Users type keywords in various search engines like Google, Yahoo, Bing etc for retrieval of relevant images. These search engines search the images from large pool of database. But as the keywords entered by user are generally short and ambiguous, different kinds of images are retrieved and sometimes these results are irrelevant. In this paper, semantic approach is proposed to solve this ambiguity. An image search reranking is definitely a superior approach over the text based image search. Using single modality for image searching is not sufficient as the different images have different features. This paper considers both the textual features as well as visual features for reranking. Attributes of images are classified into the groups. Based on those attributes from classifiers and the visual features of the images, each image is represented. The ranking score is used to evaluate the relevance of the image with query image. Hypergraph models these images based on the ranking scores .Content based image retrieval (CBIR) technique is used for extracting visual features. CBIR focuses on the content of the images such as color, texture, shape or any other information related with the images. Duplicate images found in search results are detected and eliminated by using SURF (Speeded Up Robust Feature) technique.

   Attribute, Hypergraph, CBIR, SURF. Etc


1.       Junjie Cai, Zheng-Jun Zha,Meng Wang, Shiliang Zhang, and Qi Tian,” Attribute Assisted Reranking Model Based on Web Image Search”, In Proceedings of the IEEE Transactions of Image Processing VOL. X, NO. XX, 2015,
2.       Jun. Y, D. Tao and M. Wang.,” Adaptive Hypergraph learning and its application in image classification.”,  IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3262-3272, 2012.

3.       H. Zhang, Z. Zha, Y. Yang, T.-S. Chua, “ Attribute-augmented semantic hierarchy.” In Proceedings of the ACM Conference on Multimedia, 2013.

4.       N. Morioka and J. Wang., “Robust visual reranking via sparsity and ranking constraints.”, Proceedings of ACM Conference on Multimedia,2011.

5.       F. Yu, R. Ji, M-H Tsai, G. Y and S-F. Chang.,” Weak attributes for large-scale image retrieval.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012.

6.       W. H. Hsu, L. S. Kennedy and S.-F. Chang.,” Video search reranking via  information bottle principle.”, In Proceedings of ACM Conference on Multimedia, 2006.

7.       R. Yan, A. G. Hauptmann and R. Jin.,” Multimedia search with pseudorelevance feedback.” In Proceedings of ACM International Conference on Image and Video Retrieval, 2003

8.       X. Tian, L. Yang, J. Wang, Y. Yang, X. Wu and X.-S. Hua.,” Bayesian video search reranking.” Transaction on Multimedia, vol. 14, no. 7, pp.131-140, 2012.

9.       F. Jing and S. Baluja.,”  Visualrank: Applying pagerank to large-scale image search.” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.30, no.7, pp.1877-1890, 2008.

10.    Siddiquie, R.S.Feris and L. Davis.,” Image ranking and retrieval based on multi-attribute queries.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011.

11.    J. Cai, Z.-J. Zha, W.-G. Zhou, Q. Tian.,”  Attribute-assisted reranking for web image retrieval.” In Proceedings of the ACM International Conference on Multimedia, 2012.

12.    N. Kumar, A. C. Berg, P. N. Belhumeur and S. K. Nayar.,” Attribute and simile classifers for face verification.” In Proceedings of the IEEE International Conference on Computer Vision, 2009.

13.    X. Tang, K. Liu, J. Cui, F. Wen and X. Wang.,” IntentSearch: Capturing User Intention for One-Click Internet Image Search.”,  IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.34, no.7, pp.1342-1353, 2012.





Ronald Alexander Reyes Asanza, José Leonardo Benavides Maldonado

Paper Title:

Identification and Control based on PID and Smith Predictor Applied to a Prototype Crushing

Abstract:  This article discusses the identification of the crushing process, which widely used in copper mining discussed; this is done using the ident tool with MATLAB®. Then apply two strategies to control process one based on PID (Proportional out, integral, derivative) and another on the Smith Predictor, mainly by the big current delay in the process. Finally, the best option is chosen, and the results were shown.

Identification Systems, PID control, Smith Predictor Control,


1.       Santos, L. R. (1999). Inexpensive apparatus for control laboratory experiments using advanced control methodolgies. Recuperado el 15 de 11 de 2014
2.       Moriano, P., & Freddy, N. (Julio/Septiembre de 2012). Modelado y control de un nuevo sistema bola viga con levitación magnética. RIAI (Revista Iberoamericana de Automática e Informártica Industrial), 9(3), 258. Recuperado el 08 de Marzo de 2015

3.       Ljung, L. (1999). System Identification Theory for the user (Second ed.). New Jersey: Prentice Hall . Recuperado el 10 de Noviembre de 2014

4.       Duthoit, V. (2000). Crushing and Grinding (Vol. 9). Balkema-Rotterdam: Louis Primel and Claude Tourenq. Recuperado el 22 de Noviembre de 2014

5.       Weiss, N. L. (1985). Jaw Crushers . (N. Weiss, Ed.) New York, EE-UU: SME Mineral Proceessing Handbook. Recuperado el 21 de Noviembre de 2014

6.       Donovan, J. G. (2003). FRACTURE TOUGHNESS BASED MFracture Toughness Based Models For The Prediction Of Power Consumption, Product Size, And Capacity Of Jaw Crushers. Faculty of the Virginia Polytechnic Institute and, Blacksburg, VA. Recuperado el 26 de Noviembre de 2014

7.       Gupta, S. (2003). Elements of Control Sistems. New Delhi: Prentice-Hall of India.

8.       Dorf, R., & Bishop, R. (2008). Sistemas de Control Moderno. Madrid-España: Pearson Prentice-Hall.

9.       Mathworks. (s.f.). Mathworks. Obtenido de www.mathworks.com

10.    Aguado, A. (2010). Temas de Identificación de Control Adaptable. Habana, Cuba: ICIMAF.





Chae-sil. Kim, Jae-min. Kim, Chang-min. Keum, Min-jae. Shin

Paper Title:

A Study on the Vibration Reduction in Manufacturing the Deep Groove Holes with the Tool Holders and Sleeves using Design of Experiment (DOE)

Abstract: Deep hole drilling is a machining process with a high ratio of length to diameter (L / D). If the depth is greater than the diameter, vibration frequently occurs at the end portion of the cutting tool resulting in a product with defective hole surface and size. To solve this problem, dampened bars are installed to absorb vibration. Depending on their length, the dampened bars can lower process efficiency. Instead, a mill turret developed with a holder and sleeve could enhance quality and improve productivity while reducing vibration. In this study, an optimized model of a mill turret holder and sleeve was developed to reduce vibration and replace the dampened bar. To optimize the design parameters, a Design of Experiment (DOE) was used. A finite element analysis was performed using ANSYS. Using Modal analysis and Harmonic analysis, the control factors affecting stress and displacement were examined using a derived signal to noise (S/N) ratio.

 Taguchi Method, Mill turret tool holder, Modal Analysis, Harmonic Analysis


1.    W. S. Yoo, Q. Q. Jin and Y. B. Chung, “A Study on the Optimization for the Blasting Process of Glass by Taguchi Method,” Journal of society of Korea Industrial and Systems Engineering Vol.30, No. 2, pp. 8 – 14, June 2007.
2.    W. G. Jang, “Optimal Design of the Front Upright of Formula Race Car Using Taguchi’s Orthogonal Array,”    Journal of society of Korea Society of Manufacturing Technology Engineers Vol. 22, No. 1, pp. 112 – 118, 2013.





Vineeth Teeda, K.Sujatha, Rakesh Mutukuru

Paper Title:

Robot Voice A Voice Controlled Robot using Arduino

Abstract:  Robotic assistants reduces the manual efforts being put by humans in their day-to-day tasks. In this paper, we develop a voice-controlled personal assistant robot. The human voice commands are taken by the robot by it’s own inbuilt microphone. This robot not only takes the commands and execute them, but also gives an acknowledgement through speech output. This robot can perform different movements, turns, wakeup/shutdown operations, relocate an object from one place to another and can also develop a conversation with human. The voice commands are processed in real-time, using an offline server. The speech signal commands are directly communicated to the server using a USB cable. The personal assistant robot is developed on a micro-controller based platform. Performance evaluation is carried out with encouraging results of the initial experiments. Possible improvements are also discussed towards potential applications in home, hospitals, car systems and industries.

  Robotic assistants, operations, wakeup/shutdown, USB cable, personal assistant and industries, systems, Performance


1.    A Voice-Controlled Personal Robot AssistantAnurag Mishra, Pooja Makula, Akshay Kumar, Krit Karan and V.K. Mittal, IIIT, Chittoor, A.P., India.
2.    H.Uehara,H. HigaandT.Soken,“A Mobile Robotic Armfor people with severe disabilities”, International Conference on Biomedical Robotics and Biomechatronics (BioRob), 3rd IEEE RAS and EMBS , Tokyo, pp. 126- 129, September 2010, ISSN:2155-1774.

3.    David Orenstein, “People with paralysis control robotic arms using brain”, https://news.brown.edu/articles/2012/05/braingate2 (Last viewed on October 23, 2014).

4.    Lin. H. C, Lee. S. T, Wu. C. T, Lee. W. Y and Lin. C. C, “Robotic Arm drilling surgical navigation system”, International conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, pp. 144-147, June 2014.

5.    Rong-Jyue Wang, Jun-Wei Zhang, Jia-Ming Xu and Hsin-Yu Liu, “The Multiple-function Intelligent Robotic Arms”, IEEE International Confer- ence on Fuzzy Systems, FUZZ-IEEE, Jeju Island, pp. 1995 – 2000, August 2009, ISSN:1098-7584.

6.    “Programming Arduino Getting Started with Sketches”. McGraw-Hill. Nov 8, 2011. Retrieved 2013-03-28.

7.    http://www.bitsophia.com/en-US/BitVoicerServer/Overview.aspx.

8.    The National Staff, “Robot arm performs heart surgeries at Sharjah hospital”, http://www.thenational.ae/uae/health/robot-arm-performs-heart- surgeries-at-sharjah-hospital (Last viewed on November 13, 2014).





Nizar Hussain M.

Paper Title:

Analytic Hierarchy Process based Methodology for Ranking Healthcare Management Information Systems

Abstract:  Ranking of Healthcare Management Information System (HMIS) help practitioners to select the best from the trivial many for the success of the organization. The objective of this study is to rank the CSF of HMIS using a suitable Multi-Criteria Decision Making technique (MCDM). Here, Analytic Hierarchy Process (AHP) is the tool used to determine the relative importance of the CSF in influencing the adoption and use of HMIS. In order to rank the factors, this study is planned and performed in two stages. At the first stage to identify the critical success factors of HMIS, a through literature review is made. At the second stage, a pair wise comparison is designed based on AHP method. The weightage got from AHP can also be used for ranking of various HMIS installations in different hospitals.

   Critical Success Factors, Healthcare Management Information System, Multi Criteria Decision Making, Analytic Hierarchy Process

1.          Al Farsi, M., and West, D. J., Jr., Use of electronic medical records in Oman and physician satisfaction. J. Med. Syst. 30:17–22, 2006.
2.          Alquraini, H., Alhashem, A.M., Shah, M.A., Chowdhury, R.I., Factors influencing nurse’s attitudes towards the use of computerized health information systems in Kuwaiti hospitals, J. Adv. Nurs. 57 (4) (2007) 375–378.

3.          Barbeite, F.G., E.M. Weiss, Computer self-efficacy and anxiety scales for an Internet sample: testing measurementequivalence of existing measures and development of newscales, Comput. Hum. Behav. 20 (1) (2004) 1–15.

4.          Barsukiewicz, C. K., Computerized medical records: physician response to new technology. The Pennsylvania State University, Pennsylvania, 1998.

5.          Bedard, J.C., C. Jackson, M.L. Ettredge, K.M. Johnstone, The effect of training on auditors’ acceptance of an electronic work system, Int. J. Account. Inform. Syst. 4 (2003) 227–250.

6.          Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational support and its effect on information technology usage: evidence from the health care sector. The Journal of Computer Information Systems, 48(4), 69-75.

7.          Brady, M. K., Cronin, J. J., & Brand, R. R. (2002). Performance-only measurement of service quality: A replication and extension. Journal of Business Research, 55(1), 17–31. doi:10.1016/S0148- 2963(00)00171-5

8.          Buss, M.D.J., 1983. How to rank computer projects. Harvard Business Review 61 (1), 118–125.

9.          Can U ¨ nal and Mu¨ cella G. Gu¨ner, Selection of ERP suppliers using AHP tools in the clothing industry, International Journal of Clothing Science and Technology, Vol. 21 No. 4, 2009, pp. 239-251.

10.       Chau, P. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of End User Computing, 13(1), 26-33.

11.       Cheng, G. Y., Educational workshop improved information seeking skills, knowledge, attitudes and the search outcome of hospital clinicians: a randomised controlled trial. Health Info. Libr. J. 20(Suppl 1):22–33, 2003.

12.       Chin, K. S., Xu, D. L., Yang, J. B., & Lam, J. P. K. (2008). Group-based ER–AHP system for product project screening. Expert Systems with Applications, 35(4), 1909–1929.

13.       Chisolm, D. J., McAlearney, A. S., Veneris, S., Fisher, D., Holtzlander, M., and McCoy, K. S., The role of computerized order sets in pediatric inpatient asthma treatment. Pediatr. Allergy Immunol. 17:199–206, 2006.

14.       Connelly, D. P., Werth, G. R., Dean, D. W., Hultman, B. K., and Thompson, T. R., Physician use of an NICU laboratory reporting system. Proc. Annu. Symp. Comput. Appl. Med. Care. 8–12, 1992.

15.       Crosson, J. C., Isaacson, N., Lancaster, D., McDonald, E. A., Schueth, A. J., DiCicco-Bloom, B., Newman, J. L., Wang, C. J., and Bell, D. S., Variation in electronic prescribing implementation among twelve ambulatory practices. J. Gen. Intern. Med. 23:364– 371, 2008.

16.       Crowe, B., and Sim, L., Implementation of a radiology information system/picture archiving and communication system and an image transfer system at a large public teaching hospital – Assessment of success of adoption by clinicians. J. Telemed. Telecare 10:25–27, 2004.

17.       Cumbers, B. J., and Donald, A., Using biomedical databases in everyday clinical practice: the Front-Line Evidence-Based Medicine project in North Thames. Health Libr. Rev. 15:255– 265, 1998.

18.       D’Alessandro, D. M., Kreiter, C. D., and Peterson, M. W., An evaluation of information-seeking behaviors of general pediatricians. Pediatrics 113:64–69, 2004.

19.       De Lone, W. H., E.R. McLean, The DeLone and McLean model of information systems success: a ten-year update, Journal of Management Information Systems 19 (4) (2003).

20.       Di Pietro, T., Coburn, G., Dharamshi, N., Doran, D., Mylopoulos, J., Kushniruk, A., Nagle, L., Sidani, S., Tourangeau, A., Laurie- Shaw, B., Lefebre, N., Reid-Haughian, C., Carryer, J., and McArthur, G., What nurses want: diffusion of an innovation. J. Nurs. Care Qual. 23:140–146, 2008.

21.       Eley, D., Hegney, D., Wollaston, A., Fahey, P., Miller, P., McKay, M., and Wollaston, J., Triage nurse perceptions of the use, reliability and acceptability of the Toowoomba Adult Triage Trauma Tool (TATTT). Accident Emerg. Nurs. 13:54–60, 2005.

22.       Firby, P. A., Luker, K. A., and Caress, A. L., Nurses’ opinions of the introduction of computer-assisted learning for use in patient education. J. Adv. Nurs. 16:987–995, 1991.

23.       Galligioni, E., Berloffa, F., Caffo, O., Tonazzolli, G., Ambrosini, G., Valduga, F., Eccher, C., Ferro, A., and Forti, S., Development and daily use of an electronic oncological patient record for the total management of cancer patients: 7 years’ experience. Ann. Oncol. 20:349–352, 2009.

24.       Gillingham, W., A. Holt, J. Gillies, Hand-held computers in health care:what software programs are available? The New Zealand Medical Journal 115 (1162) (2002).

25.       Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. Manage¬ment Information Systems Quarterly, 19(2), 213–236. doi:10.2307/249689

26.       Hains, I. M., Fuller, J. M., Ward, R. L., and Pearson, S. A., Standardizing care in medical oncology: are Web-based systems the answer? Cancer 115:5579–5588,

27.       Hanafizade, P., Ghafori Rayni, S.A., 2007 “Critical Success Factor in Enterprise Strategic Planning for Information Systems “, Iran Economic Bulletin, Vol. 7 No. 26.

28.       Hasan, B., The influence of specific computer experiences on computer self-efficacy beliefs, Comput. Hum. Behav. 19 (4) (2003) 443–450.

29.       Haynes, R. B., McKibbon, K. A., Walker, C. J., Ryan, N., Fitzgerald, D., and Ramsden, M. F., Online access to MEDLINE in clinical settings. A study of use and usefulness. Ann Intern Med. 112:78–84, 1990.

30.       Hier, D. B., Rothschild, A., LeMaistre, A., and Keeler, J., Differing faculty and housestaff acceptance of an electronic health record one year after implementation. Medinfo 11:1300– 1303, 2004.

31.       Hortman, P.A., Thompson, C.B., Evaluation of user interface satisfaction of a clinical outcomes database, Comput. Inform. Nurs. 23 (6) (2005) 301–307.

32.       Hou, I. C., Chang, P., and Wang, T. Y., Qualitative analysis of end user computing strategy and experiences in promoting nursing informatics in Taiwan. Stud. Health Technol. Inform. 122:613–615, 2006.

33.       Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega International Journal of Management Science, 23(6), 587–605. doi:10.1016/0305-0483(95)00035-6

34.       Ioannis N. Lagoudis*, Maria B. Lekakou and Helen A. Thanopoulou, Evaluating ferry services through an AHP estimated KPI system: a focus on central Aegean, Int. J. Decision Sciences, Risk and Management, Vol. 3, Nos. 1/2, 2011.

35.       Joos, D., Chen, Q., Jirjis, J., and Johnson, K. B., An electronic medical record in primary care: impact on satisfaction, work efficiency and clinic processes. AMIA Annu. Symp. Proc. 394–398, 2006.

36.       Jose L. Salmeron, Ines Herrero, An AHP-based methodology to rank critical success factors of executive information systems, Computer Standards & Interfaces 28 (2005) 1 –12

37.       Jousimaa, J., Kunnamo, I., and Makela, M., An implementation study of the PDRD primary care computerized guidelines. Scand. J. Prim. Health Care 16:149–153, 1998.

38.       Kamadjeu, R. M., Tapang, E. M., and Moluh, R. N., Designing and implementing an electronic health record system in primary care practice in sub-Saharan Africa: a case study from Cameroon. Inform. Prim. Care. 13:179–186, 2005.

39.       Keshavjee, K., Troyan, S., Holbrook, A. M., and VanderMolen, D., Measuring the success of electronic medical record implementation using electronic and survey data. Proc. AMIA Symp. 309–313, 2001.

40.       Ketelhohn,W.,1998 “What is a key success factor?” European Management Journal, Vol. 16, No.3, and pp: 335-40.

41.       Kouri, P., Turunen, H., and Palomaki, T., ‘Maternity clinic on the net service’ and its introduction into practice: experiences of maternity-care professionals. Midwifery 21:177–189, 2005.

42.       Lai, F., Macmillan, J., Daudelin, D. H., and Kent, D. M., The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis. Hum. Fact. 48:95–108, 2006.

43.       Lai, V.S., Trueblood, R.P., Wong, B.K., 1999. Software selection: A case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system. Information & Management 36, 221–232.

44.       Lapointe, L., and Rivard, S., Getting physicians to accept new information technology: insights from case studies. Can. Med. Assoc. J. 174:1573–1578, 2006.

45.       Larcher, B., Arisi, E., Berloffa, F., Demichelis, F., Eccher, C., Galligioni, E., Galvagni, M., Martini, G., Sboner, A., Tomio, L., Zumiani, G., Graiff, A., and Forti, S., Analysis of user satisfaction with the use of a teleconsultation system in oncology. Med. Inform. Internet Med. 28:73–84, 2003.

46.       Lee, J.W., Kim, S.H., 2000. Using analytic network process and goal programming for interdependent information system project selection. Computers & Operations Research 27, 367–382.

47.       Lee, T. T., Mills, M. E., and Lu, M. H., The multimethod evaluation of a nursing information system in taiwan. Comput. Inform. Nurs. 27:245–253, 2009.

48.       Lee, T.T., Lee, T.Y., Lin, P.C., Chang, Factors affecting the use of nursing information systems in Taiwan, J. Clin. Nurs. 50 (2) (2005) 170–178.

49.       Lee, T.T., Yeh, C.H., Ho, L.H., Application of a computerized nursing care plan system in one hospital: Experiences of ICU nurses in Taiwan, J. Adv. Nurs. 39 (1) (2002) 61–67.

50.       Lesley Davis and Glyn Williams, Evaluating and Selecting Simulation Software Using the Analytic Hierarchy Process, Integrated Manufacturing Systems, Vol. 5 No. 1, 1994, pp. 23-32.

51.       Likourezos, A., Chalfin, D. B., Murphy, D. G., Sommer, B., Darcy, K., and Davidson, S. J., Physician and nurse satisfaction with an Electronic Medical Record system. J. Emerg. Med. 27:419–424, 2004.

52.       Lu, Y,  Y. Xiao, A. Sears, J. Jacko, Review and a framework of handheld computer adoption in healthcare, International Journal of Medical Informatics 74 (5) (2005).

53.       Lucas, H.C., Moore Jr., J.R., 1976. A multiple-criterion scoring approach to information system project selection. Infor. 14 (1), 1–12.

54.       Magrabi, F., Westbrook, J. I., and Coiera, E. W., What factors are associated with the integration of evidence retrieval technology into routine general practice settings? Int. J. Med. Inform. 76:701–709, 2007.

55.       Marcy, T. W., Kaplan, B., Connolly, S. W., Michel, G., Shiffman, R. N., and Flynn, B. S., Developing a decision support system for tobacco use counselling using primary care physicians. Inform. Prim. Care. 16:101–109, 2008.

56.       Martinez, M. A., Kind, T., Pezo, E., and Pomerantz, K. L., An Evaluation of community health center adoption of online health information. Health Promot. Pract. 2007.

57.       Mehmert, P.A., Dickel, C.A., Mckeighen, R.J., Computerizing nursing diagnosis, Nurs. Manage. 20 (7) (1989) 24–30.

58.       Ngai, E. W. T., & Chan, E. W. C. (2005). Evaluation of knowledge management tools using AHP. Expert Systems with Applications, 29(4), 889–899.

59.       O’Connell, R. T., Cho, C., Shah, N., Brown, K., and Shiffman, R. N., Take note(s): differential EHR satisfaction with two implementations under one roof. J. Am. Med. Inform. Assoc. 11:43–49, 2004.

60.       Ovretveit, J., Scott, T., Rundall, T. G., Shortell, S. M., and Brommels, M., Improving quality through effective implementation of information technology in healthcare. Int. J. Qual. Health Care 19:259–266, 2007.

61.       Pagliari, C., Clark, D., Hunter, K., Boyle, D., Cunningham, S., Morris, A., and Sullivan, F., DARTS 2000 online diabetes management system: formative evaluation in clinical practice. J. Eval. Clin. Pract. 9:391–400, 2003.

62.       Pare, G., Sicotte, C., and Jacques, H., The effects of creating psychological ownership on physicians’ acceptance of clinical information systems. J. Am. Med. Inform. Assoc. 13:197–205, 2006.

63.       Popernack, M. L., A critical change in a day in the life of intensive care nurses: rising to the e-challenge of an integrated clinical information system. Crit. Care Nurs. Q. 29:362–375, 2006.

64.       Pourasghar, F., Malekafzali, H., Koch, S., and Fors, U., Factors influencing the quality of medical documentation when a paperbased medical records system is replaced with an electronic medical records system: an Iranian case study. Int. J. Technol. Assess. Health Care 24:445–451, 2008.

65.       Pugh, G. E., and Tan, J. K., Computerized databases for emergency care: what impact on patient care? Methods Inf. Med. 33:507–513, 1994.

66.       Rahimi, B., Timpka, T., Vimarlund, V., Uppugunduri, S., and Svensson, M., Organization-wide adoption of computerized provider order entry systems: a study based on diffusion of innovations theory. BMC Med. Inform. Decis. Mak. 9:52, 2009.

67.       Saaty, R.W. (1987) ‘The analytic hierarchy process-what it is and how it is used’, Mathematical Modelling, Vol. 9, Nos. 3–5, pp.161–176.

68.       Saaty, T. L. (1994). How to make decision: the analytical hierarchy process.  Interfaces, 24(6), 19-43.

69.       Saaty, T. L. (1996). The analytic hierarchy process: Planning, priority setting, resource allocation (2nd Ed.), Pittsburg, PA: RWS Publications.

70.       Saaty, T. L. (2008). Decision making with analytic hierarchy process. International Journal of Services Sciences, 1(1), pp. 83-98

71.       Saaty, T.L. and Vargas, L.G. (2000). Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, Boston: Kluwer Academic Publishers.

72.       Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw- Hill, New York

73.       Santhanam, R., Kyparisis, G.J., 1995. A multiple criteria decision model for information system project selection. Computers & Operations Research 22 (8), 807

74.       Sarker, S., J.S. Valacich, S. Sarker, Technology adoption by groups: a valence perspective, Journal of the Association for Information Systems 6 (2) (2005).

75.       Schniederjans, M.J., Wilson, R.L., 1991. Using the analytic hierarchy process and goal programming for information system project selection. Information & Management 20, 333–342.

76.       Seddon, P. B., & Kiew, M. Y. (1996). A partial test and development of DeLone and McLean’s model of IS success. Australian Journal of Information Systems, 4(1), 90–109.

77.       Sepahvand, R., Arefnezhad, M., Prioritization of Factors Affecting the Success of Information Systems with AHP (A Case study of Industries and Mines Organization of Isfahan Province), International Journal of Applied Operational Research Vol. 3, No. 3, pp. 67-77, Summer 2013.

78.       Simpson, C.A., Weaver, R.L.,  Administrative application of information technology for nursing managers, in: V.K. Saba, K.A. McCormick (Eds.), Essentials of nursing informatics, McGraw Hill, Boston, 2005.

79.       Soar, J., Ayres, D., and Van der Weegen, L., Achieving change and altering behaviour through direct doctor use of a hospital information system for order communications. Aust. Health Rev. 16:371–382, 1993.

80.       Stahl, Michael J, Encyclopedia of health care management, Sage Publications, 2004.

81.       Sutirtha Chatterjee, Suranjan Chakraborty , Saonee Sarker , Suprateek Sarker , Francis Y. Lau, xamining the success factors for mobile work in healthcare: A deductive study, Decision Support Systems 46 (2009) 620–633

82.       Teltumbde, A., 2000. A framework of evaluating ERP projects. International Journal of Production Research 38, 4507–4520.

83.       Thoman, J., Struk, C., Spero, M. O., and Stricklin, M. L., Reflections from a point-of-care pilot nurse group experience. Home Healthc. Nurs. 19:779–784, 2001.

84.       Torkzadeh, G., T.P. Van Dyke, Effects of training on Internet self-efficacy and computer user attitudes, Comput. Hum. Behav. 18 (5) (2002) 479–494.

85.       Travers, D., and Parham, T., Improving information access with an emergency department system. Proc. AMIA Annu. Fall Symp.121–125, 1997.

86.       Tsai, Y.J., Wu, S., Chiang, B.C., Exploring factors affecting the performance of hospital information systems, J. Inf. Manage. 11 (2) (2004) 191–210.

87.       Vanmeerbeek, M., Exploitation of electronic medical records data in primary health care. Resistances and solutions. Study in eight Walloon health care centres. Stud. Health Technol. Inform.110:42–48, 2004.

88.       Varshney, U., Pervasive healthcare, Computer 36 (2) (2003).

89.       Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi: 10.1287/mnsc.

90.       Venkatesh, V., Morris, M. G., Davis, G. B., & Da¬vis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478.

91.       Verhoeven, F., Steehouder, M. F., Hendrix, R. M., and van Gemert-Pijnen, J. E., Factors affecting health care workers’ adoption of a website with infection
control guidelines. Int. J. Med. Inform. 78:663–678, 2009.

92.       Walji, M. F., Taylor, D., Langabeer, J. R., 2nd, and Valenza, J. A., Factors influencing implementation and outcomes of a dental electronic patient record system. J. Dent. Educ. 73:589–600, 2009

93.       Whittaker, A. A., Aufdenkamp, M., and Tinley, S., Barriers and facilitators to electronic documentation in a rural hospital. J. Nurs. Scholarsh. 41:293–300, 2009.

94.       Wu, S, Lee, A., Tah, J.H.M.  and Aouad, G., The use of a multi-attribute tool for evaluating accessibility in buildings: the AHP approach, Facilities, Vol. 25 No. 9/10, 2007, pp. 375-389.

95.       Yeh, S. H., Jeng, B., Lin, L.W., Ho, T. H., Hsiao, C. Y., Lee, L. N., and Chen, S. L., Implementation and evaluation of a nursing process support system for long-term care: a Taiwanese study. J. Clin. Nurs. 18:3089–3097, 2009.

96.       Zheng, K., Padman, R., Johnson, M. P., and Diamond, H. S., Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system. Int. J. Med. Inform. 74:535–543, 2005.





Nasr Litim, Ayda Baffoun

Paper Title:

Investigation of Acrylic Resin Treatment and Evaluation of Cationic Additive Quality Impact on the Mechanical Properties of Finished Cotton Fabric

Abstract:   Statistical design of experiment (DOE) is an important tool to improve and developed of existing products or processes. This paper investigates the effect of essential finishing factors; curing temperature, curing time, resin, catalyst and cationic additive concentrations on the mechanical properties, especially on 3D ranks of cotton treated fabric with a copolymer acrylic resin. After that, it evaluates the impact of cationic additive class on 3D ranks and mechanical properties loss (breaking strength, breaking elongation and tear strength) of treated fabric with acrylic resin. The results, showed that cationic type effect; firstly (Electroprep) has the best quality on 3D rank of treated fabric and effect a little loss on mechanical properties, secondly (Easy stone super X), whereas (Easystone K) lead to a negatively loss on mechanical properties and gives undesired 3D rank. In order to investigate the causes of resin finish resumption and downgrading of garments in textile industry caused by ingredient concentration in bath resin. The main effect plot, interaction plot and contour plot method applied give to the textile engineer the possibility to predict the effect of resin treatment factors on the final quality desired of 3D rank and preserving the mechanical characteristics of treated fabric.

 Mechanical properties, Cotton, Resin, 3D ranks, Cationic


1.    H. Tavanai1, S. M .Taheri, M. Nasiri. (2005), “Modelling of Colour Yield in Polyethylene Terephthalate Dyeing with Statistical and Fuzzy Regression”, Iranian Polymer Journal, 14 (11), pp 954-967
2.    F. Asim, Mahmood, (2012), “Optimization of process parameters for simultaneous fixation of reactive printing and crease resistant finishing”. Journal of Textile and apparel Technology Management, 7(3).

3.    Y. H. El Hamaky, S. Tawfeek, D. F. Ibrahim, D. Maamoun, S. Gaber (2007), “Printing Cotton Fabrics with Reactive Dyes of High Reactivity from an Acidic Printing Paste”, Coloration Technology, 123(6), pp 365-373.

4.    M . S. Hassan (2009), “Crease Recovery Properties of Cotton Fabrics Modified by Urea Resins under the Effect of Gamma      Irradiation”. Radiation Physics and Chemistry. 78(5), pp 333-337.

5.    W.Udomkichdecha, S.Kittinaovarat, U.Thanasoonthornroek, P. Potiyaraj, and P.Likitbanakorn, (2003), Textile Research Journal. 73, 401.

6.    Pastore and P. Kiekens (2000), “Surface Characteristics of Fibers and Textiles”, Surfactant Science Seriesm Vol. 94, pp.3-30

7.    C.R. Hicks. (1982), ”Fundamental concepts in the design of experiments”, 3rd Ed, CBC College Publishing.

8.    W. Weishu and Y. Charles Q, AIP Conference Proceedings, Athens, August, 1997, Georgia, USA, pp 10-15.

9.    Cooke, T.F. and Weigmann, H.D., (1982).Textile Chemical Coloris. Vol.14, pp 100-106.





Mohammad Reza Elyasi, Mahmoud Saffarzade, Amin Mirza Boroujerdian

Paper Title:

A PLS/SEM Approach Risk Factor Analysis in Road Accidents Caused by Carelessness

Abstract: Many developed countries in line with the increase in road transport, and consequently an increase in the rate of accidents, are searching for effective ways to reduce road accidents. In the area of traffic safety, in order to identify factors contributing to accidents, conventional methods which generally based on regression analysis are used. However, these methods only detect accidents in different roads, but cannot clearly identify the cause of accidents and define the relationship between them. In addition, the methods used have two major limitations: 1- Postulate the structure of the model, and, 2- Observability of all variables. Due to the limitations discussed and also due to the complex nature of human factors, and the impact of road conditions, vehicle and environment on human factors, the aim of this study is to provide a useful tool for defining and measuring road, traffic and human factors, to evaluate the effect of each of them in accidents which caused by carelessness, directly and indirectly by using structural equation modeling with the partial least squares approach. Compared with the regression-based techniques or methods of pattern recognition that only a layer of relationships between independent and dependent variables is determined, the SEM approach provides the possibility of modeling the relationships between multiple independent and dependent structures. Moreover, the ability to use unobservable  hidden variables, by using observable variables would be possible.

  Human factors, Road safety, Road factors; accident analysis; Partial Least Square (PLS); Structural Equation Modeling (SEM).


1.       World health organization(WHO), Road traffic injuries, Fact sheet N°358, Updated October ; 2015.
2.       Fell, J. C., & Freedman, M. (2001). The relative frequency of unsafe driving acts in serious traffic crashes. Washington, DC: National Highway Traffic Safety Administration.‏

3.       Lord D, Geedipally S.R, Guikema  S.  Extension of the application of Conway-Maxwell-Poisson models: analyzing traffic crash data exhibiting under-dispersion. Submitted to the 89th Annual Meeting of the Transportation Research Board, Washington, D.C; 2009. [10] Anastasopoulos P.C, Mannering F.L. A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention 41(1); 2009, 153-159.

4.       Lord D, Washington S.P, Ivan J.N. Further notes on the application of zero inflated models in highway safety. Accident Analysis and Prevention 39(1); 2007, 53-57.

5.       Oh J, Washington S.P, Nam D. Accident prediction model for railway-highway interfaces. Accident Analysis and Prevention 38(2); 2006, 346-56.

6.       Lord D, Mahlawat M. Examining the application of aggregated and disaggregated Poisson-gamma models subjected to low sample mean bias. Transportation Research Record 2136; 2009, 1-10.

7.       Xie Y, Zhang Y, Crash frequency analysis with generalized additive models. Transportation Research Record 2061; 2008, 39-45.

8.       Shankar V.N, Albin R.B, Milton J.C, Mannering F.L. Evaluating median cross-over likelihoods with clustered accident counts: an empirical inquiry using random effects negative binomial model. Transportation Research Record 1635; 1998, 44- 48.

9.       Hauer E. Statistical Road Safety Modeling. Transportation Research Record 1897; 2004, 81–87.

10.    Anastasopoulos P.C, Mannering F.L. A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention 41(1); 2009, 153-159.

11.    Lord M, Mannering F. The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives, Forthcoming in Transportation Research, Part A; 2010.

12.    Wood A.G, Mountain L.J., Connors R.D., Maher M.J., Ropkins K. Updating outdated predictive accident models, Accident Analysis and Prevention 55; 2013, 54-66.

13.    Nelson E , Atchley P, Little T. The effects of perception of risk and importance of answering and initiating a cellular phone call while driving. Accident Analysis and Prevention 41 (3); 2009,  438–444.

14.    Geedipally S.R, Lord D. Investigating the effect of modeling single-vehicle and multi-vehicle crashes separately on confidence intervals of Poisson-gamma models. Submitted for publication in Accident Analysis and Prevention; 2009.

15.    N’Guessan A. Analytical Existence of solutions to a system of nonlinear equations with application. Journal of Computational and Applied Mathematics, 234; 2010, 297

16.    Park E.S, Lord D. Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity. Transportation Research Record 2019; 2007, 1-6.

17.    Park E.S, Park J, Lomax T.J. 2010, A fully Bayesian multivariate approach to before after safety evaluation, Accident Analysis and Prevention; 2009, in press.

18.    Park B.J, Lord D. Application of finite mixture models for vehicle crash data analysis. Accident Analysis and Prevention, 41(4); 2009, 683-691.

19.    Malyshkina N.V, Mannering F.L, Tarko A.P. Markov switching negative binomial models: an application to vehicle accident frequencies. Accident Analysis and Prevention 41(2); 2009, 217–226.

20.    Malyshkina N, Mannering F. Zero-state Markov switching count-data models: An empirical assessment. Accident Analysis and Prevention 42(1); 2010, 122-130.

21.    Xie Y, Lord D, Zhang Y. Predicting motor vehicle collisions using Bayesian neural networks: an empirical analysis. Accident Analysis and Prevention 39(5); 2007, 922- 933.

22.    Gang Ren, Zhuping Zhou. Traffic safety forecasting method by particle swarm optimization and support vector machine, Expert Systems with Applications: An International Journal, Volume 38 Issue 8; 2011, 10420-10424.

23.    Pham M.H, Bhaskar A,  Chung E, Dumont A.G. Random forest models for identifying motorway Rear-End Crash Risks using disaggregate data, Intelligent Transportation Systems (ITSC); 2010.

24.    Hamdar Samer H, Mahmassani Hani S, ChenRoger B. Aggressiveness propensity index for driving behavior at signalized intersections, Accident Analysis and Prevention 40; 2008, 315–326.

25.    Qiu lin, Nixon Wilfrid. PERFORMANCE MEASUREMENT FOR HIGHWAY WINTER MAINTENANCE OPERATIONS, IIHR—Hydroscience and Engineering College of Engineering the University of Iowa; , 2009.

26.    Kim K, P Pant, E Yamashita. Measuring Influence of Accessibility on Accident Severity with Structural Equation Modeling. In Transportation Research Record 2236, TRB,
National Research Council, Washington D.C; 2011, pp. 1-10.

27.    Lai S.F. The Accident Risk Measuring Model for Urban Arterials, Paper Presented at the 3rd International Conference on Road Safety and Simulation, Indianapolis, USA; 2011, 14-16.

28.    Atchley P, Atwood S, Boulton A. The choice to text and drive in younger drivers: behavior may shape attitude. Accident Analysis and Prevention 43 (1); 2011, 134–142.

29.    Hassan H. improving traffic safety and drivers’ behavior in reduced visibility conditions, Ph.d dissertation, University of Central florida; 2011.

30.    American Association of State Highway Transportation Officials (AASHTO).(2009), Highway Safety Manual, 1st Edition, Washington, DC.

31.    Wang K, X Qin. Using structural equation modeling to measure single-vehicle crash severity, Transportation Research Record, Report No. 14-0801. TRB, National
Research Council, Washington, D.C; 2014.

32.    Huang Jun-Chih. Research of the Taiwan Fujian area road traffic accident, National Central University, Department of Graduate Institute of Statistics; 2006, Master paper.

33.    Zhang Guangnan, Yau Kelvin K.W, Gong  Xiangpu, Traffic violations in Guangdong Province of China: Speeding and drunk driving, Accident Analysis & Prevention, Vol. 64; 2014, pp.30-40.

34.    Chiou Yu-Chiun. Hwang Cherng-Chwan, Chang Chih-Chin, Fu Chiang, Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach, Accident Analysis & Prevention, Vol. 51; 2013,  pp.176-184.

35.    Elyasi, M.R., Saffarzadeh M, Boroujerdian,A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement. Transportation Research Part A: Policy and Practice, 91; 2016, 346–357.
36.    Milton John C, Shankar Venky N, Mannering Fred L. Highway accident severities and the mixed logit model: An exploratory empirical analysis, Accident Analysis and
Prevention, Volume: 40, Issue: 1; 2008,  pp. 260-266.

37.    Mahmoud Saffarzadeh, Maghsoud Pooryari, Accident Prediction Model Based on Traffic and Geometric Design Characteristics, International Journal of Civil Engineering, Vol.3, No. 2(9-b- 13); 2005.

38.    Ramirez B Arenas, Izquierdo F Aparicio, Fernández C González, Méndez A Gómez. The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads, Accident Analysis & Prevention, Volume 41, Issue 1; 2009, Pages 15–24.

39.    Kwon O.H, Park M.J, Yeo H, Chung K. Evaluating the performance of network screening methods for detecting high collision concentration locations on highways. Accident Analysis and Prevention 51; 2013, 141– 149.

40.    Mirbaha Babak, Saffarzadeh Mahmoud, Noruzoliaee Mohamad Hossein. A Model for Schoolchildren Accidents in the Vicinity of Rural Roads based on Geometric Design & Traffic Conditions,  International Journal of Transportation, No. 1, Vol. 1 ;2012. (9-b- 22).

41.    Samuel C, keren N, shelley M. C. Freeman S.A, frequency analysis of hazardous material transportation incidents as a function of distance from origin to incident location, Journal of loss prevention in the process Industries, 22, pp; 2009, 783-790.

42.    Anastasopoulos P.Ch, Tarko A.P., Mannering F.L.. Tobit analysis of vehicle accident rates on interstate highways. Accident Analysis and Prevention 40 (2); 2008, 768

43.    Wong K., Kwong K., Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS, Marketing Bulletin, Vol. 24, No. 1, 1-32.

44.    Ralph O.M., Basic Principales of Structural Equation Modeling (An Introduction to LISREL and EQS), Springer, 1996.

45.    Tenenhaus M, Esposito Vinzi, V, Chatelin Y M, Lauro C. PLS path modeling. Computational Statistics & Data Analysis, 48(1); 2005, 159–205.

46.    Mueller, R. O. (1999). Basic principles of structural equation modeling: An introduction to LISREL and EQS. Springer Science & Business Media.


48.    Hair J.F,  Ringle, C. M, Sarstedt M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2); 2011, 139–151.

49.    Henseler Jorg, M Ringle, Christian, Sinkovics  Rudolf R. The use of Partial least squares path modeling in international marketing, New Challenges to International Marketing Advances in International Marketing, Volume 20; 2009, 277–319.

50.    Fornel C, Larcker D.F. Evaluation structural equation models with unobserved variable and variables and measurement error, journal of technology of marketing research; 1981, 39-50. Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 155.

51.    Cunningham WA, Preacher KJ, Banaji MR, 2001, Implicit attitude measures: Consistency, stability, and convergent validity, Psychological science 12 (2), 163-170

52.    Iacobucci, D., & Duhachek, A. (2003). Advancing alpha: Measuring reliability with confidence. Journal of consumer psychology, 13(4), 478-487.‏





Raad Farhood Chisab, Begard Salih Hassen, Aassyia Mohammed Ali Jasim Al-A’assam

Paper Title:

Performance of Single Carrier Frequency Division Multiple Access Under Different Channel Cases

Abstract:  Single Carrier Frequency Division Multiple Access (SCFDMA) is currently a favorable tool for uplink broadcast in 4G mobile communications method. It merges the “single carrier frequency domain equalization (SC-FDE)” and “frequency division multiple access (FDMA)” methods. It inserts DFT before OFDMA modulation to drawing the sign from every operator to a subsection of the existing subcarriers. It is a new system joining best of the benefits of OFDMA with the small “Peak-to-Average Power Ratio (PAPR)”. For that aims, it accepted as a promising technique on the uplink of wireless systems. In this paper the performance of SCFDMA was measured under different variable parameter in order to verify the robustness of the system. The system is tested under parameters like modulation type, subcarrier mapping, Doppler frequency, time of sample, second path gain and roll-off factor.



1.       Wafaa Radi, Hesham Elbadawy and Salwa Elramly, “Peak to Average Power Ratio Reduction Techniques for Long Term Evolution- Single Carrier Frequency Division Multiple Access System”, International Journal of Advanced Engineering Sciences and Technologies, http://www.ijaest.iserp.org. , ISSN: 2230-7818, Vol No. 6, Issue No. 2, 230 – 236, 2011.
2.       Masayuki Nakada, Kazuki Takeda and Fumiyuki Adachi, “Channel Capacity of SCFDMA Cooperative AF Relay Using Spectrum Division & Adaptive Subcarrier Allocation”, Proceedings of IC-NIDC2010, 978-1-4244-6853-9/10/IEEE, 2010.

3.       Tae-Won Yune, Jong-Bu Lim, and Gi-Hong Im, “Iterative Multiuser Detection with Spectral Efficient Relaying Protocols for Single-Carrier Transmission”, IEEE Transactions on Wireless Communications, Vol. 8, NO. 7, July 2009.

4.       Zid Souad and Bouallegue Ridha, “SOCP Approach for Reducing PAPR System SCFDMA in Uplink via Tone Reservation”, International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.6, , DOI : 10.5121/ijcnc.2011.3610 157, November 2011

5.       Yibing LI, Xin GUI and Fang YE, “Analysis of BLER Performance for LTE Uplink Baseband Simulation System” Journal of Computational Information Systems” , http://www.Jofcis.com ,  (2012) 2691–2699,  1553–9105 / 2012.

6.       Pochun Yen and Hlaing Minn, “Low complexity PAPR reduction methods for carrier-aggregated MIMO OFDMA and SC-FDMA systems”, EURASIP Journal on Wireless Communications and Networking 2012, 2012:179, http://jwcn.eurasipjournals.com/content/2012/1/179  , 2012.

7.       Gaurav Sikri and Rajni, “A Comparison of Different PAPR Reduction Techniques In OFDM Using Various Modulations”, International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol.2, No.4, , DOI : 10.5121/ijmnct.2012.2406 53, August 2012.

8.       Md. Masud Rana, Jinsang Kim and Won-Kyung Cho, “An Adaptive LMS Channel Estimation Method for LTE SC-FDMA Systems”, International Journal of Engineering & Technology IJET-IJENS Vol: 10 No: 05, 2015.

9.       Uyen Ly Dang, Michael A. Ruder, Robert Schober andWolfgang H. Gerstacker, “MMSE Beamforming for SC-FDMA Transmission over MIMO ISI Channels”, EURASIP Journal on Advances in Signal Processing, Hindawi Publishing Corporation, Volume 2011, Article ID 614571, 11 pages, doi:10.1155/2011/614571, 2011.

10.    Faisal S. Al-kamali,Moawad I. Dessouky, Bassiouny M. Sallam, Farid Shawki and Fathi E. Abd El-Samie, “Carrier Frequency Offsets Problem in DCT-SC-FDMA System: Investigation and Compensation”, International Scholarly Research Network ISRN Communications and Networking, Volume 2011, Article ID 842093, 7 pages, doi:10.5402/2011/842093, 2011.





Anderson Rigoberto Cuenca S, Jose Leonardo Benavides M, Manuel Augusto Pesantez G

Paper Title:

Comparison PID and MPC Control, Applied to a Binary Distillation Column

Abstract: Using binary distillation column in the industry is currently imperative, the reason why the control parameters that are highly nonlinear necessary to apply classic strategies as advanced control and raised here. These techniques are the PID controller and the MPC; the data that are to perform the calculations are of IFAC event whose mixture is alcohol with water. Finally with the help of software MATLAB® / Simulink simulations for comparing which of the two drivers is the best delivery results when controlling the composition on the bottom, top and pressure in binary distillation column performed.

  Chemical Industry, Distillation Columns, MPC (Predictive Control Method), PID Control.


1.       B. Huick, K. D. (2 de Septiembre de 2011). Identification of a Pilot Scale Distillation Column: A Kernel Based Approach. 18 th IFAC World Congress. Recuperado el 16 de 9 de 2014
2.       Borroto, M. A. (2015). Identificación y Control Predictivo de una columna de destilación Etanol-Agua. CIE2015 (pág. 1). Villa Clara: UCLV. Recuperado el 2 de Julio de 2015

3.       Camacho, E., & Berenguel, M. (1997). Robust adaptive model predictive control of a solar plant with bounded uncertainties. Int. J. Adapt. Control Signal Process, vol 11(4), pp. 311-325.

4.       Camacho, E., & Bordons, C. (2004). Model Predictive Control (2nd ed. ed., Vol. XXII). London, England: Springer-Verlag.

5.       Cruz, P. P. (2010). Intligencia Artificial con Aplicaciones a la Ingeniería. México: Alfaomega.

6.       E.J.Davison. (1967). Control of a distillation column with pressure variation (Vol. 45). Toronto, Ontario, Cánada: Trans Institute of Chemical Engineers. Recuperado el 05 de Julio de 2014

7.       Fernández, D. M. (2014). Torre de Destilación. La Habana, La Habana, Cuba. Recuperado el 10 de Julio de 2014

8.       Frejo, D., & Camacho, E. (2012). Global versus local MPC algorithms in freeway traffic control with ramp metering and variable speed limits. IEEE Trans. Intell. Transport. Syst, vol. 13(no.4), pp. 1556-1565.

9.       Gene F. Franklin, J. D.-N. (1991). Control de Sistemas Dinámicos con Retroalimentación. Wilmington , Delaware , E.U.A: Addison-Wesley Iberoamerica.

10.    Hegrenaes, O., & Gravdahl, J. a. (2005). Spacecraft attitude control using explicit model predictive control. Automática, vol 41(no. 12), pp. 2107-2114.

11.    J.J. Téllez Guzmán, J. F.-C. (23 al 26 de Octubre de Ocutbre de 2012). Event-Based LQR Control for Attitude Stabilization of a Quadrotor. XV Congreso Latinoamericano de Control Automático. Recuperado el 28 de Agosto de 2014

12.    Kuo, B. C. (2012). Sistemas de Control Automático. México.

13.    Macías, M. G. (7 de 10 de 2011). Distillation and Absorption . Obtenido de http://www.youtube.com/watch?v=jeCZ4UKfLKc

14.    Maestre, M., de la Peña, D., & Camacho, E. (2009). Distributed MPC: A supply chain case study. in Proc. Conf. Descion Control, pp. 7099-7104.

15.    Nabais, L., Negenborn, R., C, B., & Botto, M. (Oct. de 2013). Setting cooperative relations among terminals at seaports using a multi-agentsystem. Proc. 16th Int IEEE Conf. Intelligent Transportation Systems.

16.    Negenborn, R., Van Overloop, P., Keviczky, T., & De Scutter, B. (2009). Distributed model predictive control of irrigation canals. Netw. Heterogeneus Media, vol. 4(no. 2), 359-380.

17.    Perez, S. (6 de 3 de 2014). Modelos de Estado. Obtenido de http://www.youtube.com/watch?v=1zcPjcbLPh8

18.    Qin, J., & Badgwell, T. (July de 2003). A survey of industrial model predictive control technology . Control Eng. Pract., vol. 11(no.7), pp. 733-764.

19.    Rawlings, B., & Mayne, D. (2009). Model Predective Control; Theory and Design. Madison: Nob Hill Publishing.

20.    Richalet, A., & Rault, J. (1978). Model Predictive Heuristic control; Applications to industrial processes. Automatica, vol 14(5), pp. 413-428.

21.    Richard C. Dorf, R. H. (2008). Sistemas de Control Moderno. Madrid-España: Pearson Prentice-Hall.

22.    Riverso, R., Farina, M., & Ferrar-Trecate, G. (Oct. de 2013). Plug and Play decentralized model predictive control for linear systems. IEEE Trans. Autom. Control, vol. 58(no. 10), pp.2608-2614.

23.    Rojas, A. (4 de 5 de 2010). Columna de destilación binaria – Parte 1. Obtenido de http://www.youtube.com/watch?v=sYIkyDQVTzA&list=PL995A30D46244C35B

24.    Ruíz, V. M. (Costa Rica-2002). Ecuaciones para Controladores PID Universales. Ingeniería, 11-20.

25.    Werdan, J. P. (08 de Abril de 2016). MODELAGEM EMPIRICA DE COLUNAS DE DESTILAÇÃO UTILIZANDO REDES NEURAIS DE WAVELETS PARA OTIMIZAÇÃO E CONTROLE DE PROCESSOS. BEQ. Recuperado el 10 de Abril de 2016, de http://betaeq.com.br/index.php/2016/04/08/modelagem-empirica-de-colunas-de-destilacao-utilizando-redes-neurais-de-wavelets-para-otimizacao-e-controle-de-processos/

26.    Xia, F., Tian, C., Li, Y., & Sung, Y. (2007). Wireless sensor /actuator network design for mobile control applications. Sensors, vol.7(no. 10), pp.2157-2173.





Sonal Yadav, Sharath Naik

Paper Title:

Shortest Path Computation in Multicast Network with Multicast Capable and Incapable Delay Associated Nodes

Abstract: Multicast transmission results in a bandwidth and cost efficient solution for transmission purpose .If we consider the real life scenario then the nodes considered can either be multicast capable nodes or multicast incapable nodes. In this paper, a method is proposed to increase the success rate of finding the minimum cost path within a given network with both multicast incapable and capable nodes. For this, a real life network is considered with 80 nodes complied within it. The nodes considered can either be multicast capable nodes or multicast capable nodes conforming with real life situations .It is shown that if we make use of algorithm proposed in the paper along with delay association and proper bandwidth consideration then success rate of finding the minimum cost path can be increased up to a significant value

Multicast capable nodes, multicast incapable nodes, minimum cost path


1.       “Cisco Visual Networking Index: Forecast and Methodology, 2009-
2.       2014,”“http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white paper c11-481360 ns827 Networking Solutions White Paper.html”, Cisco Inc., 2010.

3.       L. Tang, W. Huang, M. Razo, A. Sivasankaran, P. Monti, M. Tacca, and A. Fumagalli, “Computing Alternate Multicast Trees with Maximum Latency Guarantee,” 11th International Conference on High Performance Switching and Routing, 2010.

4.       J. Jannotti, D. K. Gifford, K. L. Johnson, M. F. Kaashoek, and J. W. O’Toole, Jr., “Overcast: Reliable Multicasting with an Overlay Network,” in Proceedings of the 4th conference on Symposium on Operating System Design & Implementation – Volume 4, 2000.

5.       “Extensions to Resource Reservation Protocol – Traffic Engineering (RSVP-TE) for Point-to-Multipoint TE Label Switched Paths (LSPs),”“http://tools.ietf.org/html/rfc4875”, IETF, 2007.

6.       X. Zhang, J. Y. Wei, and C. Qiao, “Constrained Multicast Routing in WDM Networks with Sparse Light Splitting,” Journal of Lightwave Technology, 2000.

7.       G. Gutin, A. Yeo, and A. Zverovich, “Traveling Salesman Should not be Greedy: Domination Analysis of Greedy-Type Heuristics for the TSP,” Discrete Applied Mathematics, 2002.

8.       H. Vardhan, S. Billenahalli, W. Huang, M. Razo, A. Sivasankaran, L. Tang, P. Monti, M. Tacca, and A. Fumagalli, “Finding a Simple Path with Multiple Must-include Nodes,” 17th Annual Meeting of the IEEE/ACM International Symposium on Modeling, Analysis and Simulationof Computer and Telecommunication Systems, 2009

9.       Limin Tang,” Multicast tree computation in networks with multicast incapable nodes” High Performance Switching and Routing (HPSR), 2011 IEEE 12th International Conference

10.    Takashima, E.,” A Method for Distributed Computation of Semi-Optimal Multicast Tree in MANET”, Wireless Communications and Networking Conference, 2007.WCNC 2007. IEEE





CH. Bhanu Prakash, M.N.V.S.A. Sivaram.K, G.H. Tammi Raju, CH.N.V.S. Swamy

Paper Title:

Comparative Experimental study on a Photovoltaic Panel with Low Cost Performance Improvement Techniques

Abstract:  The main objective of our project is to increase the efficiency of the solar panel by removing the heat from it. The photovoltaic (PV) cells suffer efficiency drop as their operating temperature increases especially under high insolation levels. The operating temperature is one of the important factors that can affect the efficiency of the PV panels. We rectified this problem by using two techniques which reduces the temperature of the panel. One is cooling the solar panel by water where heat transfer takes place and reduces the panel temperature and the other is placing the Low E-glass which allows only visible light and reflects the non-visible light. In the solar spectrum heat is produced due to non-visible light, temperature of solar panel is reduced by the reflection of non-visible light. Decrease in temperature of the solar panel results increase in the efficiency.

 Photovoltaic (PV) cells, Efficiency, Cooling, Resistance temperature detector, low E-glass.


1.       J.K. Tonui, Y. Tripanagnostopoulos, “Air-cooled PV/T solar collectors with low cost performance improvements”. Solar Energy 81 (4) (2007) 498e511.                                 
2.       W. He, T. T. Chow, J. Ji, et al., “Hybrid Photovoltaic and Thermal Solar-Collector Designed for Natural Circulation of Water,” Applied Energy, Vol. 83, No. 3, 2006, pp. 199-    220.

3.       Z. J. Weng and H. H. Yang, “Primary Analysis on Cooling Technology of Solar Cells under Concentrated Illumination,” Energy Technology, Vol. 29, No. 1, 2008, pp. 16-18.

4.       M. Brogren and B. Karlsson, “Low-Concentrating-Water Cooled PV-Thermal Hybrid Systems for High Latitudes,” 29th IEEE PVSC, New Orleans, May 2002, pp. 1733- 1736.

5.       G. Anderson, P. M. Dussinger, D. B. Sarraf and S. Tamanna, “Heat Pipe Cooling of Concentrating Photovoltaic Cells,” 33rd IEEE Photovoltaic Specialists Conference, San Diego, May 2008, pp.

6.       Raghuraman. P “Analytical predictions of liquid and air photovoltaic/thermal”, flat-plate collector performance. J Solar Energy Eng 1981, 103:291–8.

7.       S. Krauter, “Increased electrical yield via water flow over the front of photovoltaic panels”, Solar Energy Materials & Solar Cells, 82, 2004, 131-137.

8.       Hongbing Chen, Xilin Chen, Sizhuo Li, Hanwan Ding, “Comparative study on the performance improvement of photovoltaic panel with passive cooling under natural ventilation”, International Journal of Smart Grid and Clean Energy, 3(4), 2014, 374-379.

9.       Shiv Lal, Pawan Kumar, Rajeev Rajora, “Performance analysis of photovoltaic based submersible water pump”, International Journal of Engineering and Technology, 5(2), 2013, 552560.

10.    P. Gang, Fu Huide, Z. Huijuan, JiJie, “Performance study and parametric analysis of a novel heat pipe PV/T system”, Energy, 37(1), 2012, 384-395.

11.    H. Bahaidarah, Abdul Subhan, P. Gandhidasan, S. Rehman, “Performance evaluation of a PV (photovoltaic) module by back surface water cooling for hot climatic conditions”, Energy, 59, 2013, 445-453.

12.    H.G. Teo, P.S. Lee, M.N.A. Hawlader, “An active cooling system for photovoltaic modules”, Applied Energy, 90, 2012, 309-3105.





Geethu S S, Sreeletha S H

Paper Title:

An Efficient Depth Segmentation Based Conversion of 2d Images to 3d Images

Abstract:   In the 3D consumer electronics world have a wide increase in demands of more and more 3D technology, so this has led to the conversion of many existing two-dimensional images to three-dimensional images. The depth is an important factor in the conversion process. Determining the depth for a single image is very difficult. There are many techniques widely used for the depth estimation process. In this paper we propose an automatic depth estimation technique. Firstly, we partition the image using graph cut segmentation method. The main goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Then we construct a higher order statistics map. The HOS is mainly used for solving detection and classification problems. We can estimate depth map from HOS mean. Finally, creating left view image and right view image and combined with depth map to generate an enhanced stereoscopic image.

 2D to 3D, Segmentation, Graph cut, HOS, Filtering, Stereoscopic image.


1.       Saravanan Chandran , Novel Algorithm for Converting 2D Image to Stereoscopic Image with Depth Control using Image Fusion, Vol. 2, No. 1, March 2014 J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
2.       J. Konrad, M. Wang, and P. Ishwar, 2D-to-3D image conversion by learning depth from examples, , in Proc. IEEE Comput. Soc. CVPRW, Jun. 2012, pp. 16-22. K. Elissa, “Title of paper if known,” unpublished.

3.       Zeal ganatra, conversion of 2d images to 3d using data mining algorithm, international journal of innovations and advancement in computer science, ijiacs , vol. 22, no. 9, september 2013.

4.       Janusz Konrad, Learning-Based, Automatic 2D-to-3D Image and Video Conversion, Fellow, IEEE, Meng Wang, Prakash Ishwar, Senior Member, IEEE, Chen Wu, and Debargha Mukherjee, 2012.

5.       Raymond Phan, Richard Rzeszutek, Dimitrios Androutsos, semi- automatic 2d to 3d image conversion using scale-space random walks and a graph cuts based depth prior,18th IEEE International Conference on Image Processing, 2011.

6.       Q. Wei,2D to 3D: A Survey, Information and Communication Theory Group (ICT) Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology, the Netherlands, December.

7.       M. H. Feldman and L. Lipton, “Interactive 2D to 3D Stereoscopic Image Synthesis”, in Proc. of the SPIE, Vol.    5664, pp. 186-197 (2005).

8.       Battiato, S.; Capra, A.; Curti, S.; and La Cascia, M, “3D Stereoscopic Image Pairs by Depth-Map Generation”, in           Proc. of 2nd International Symposium on 3D Data Processing Visualization and Transmission, 3DPVT  (2004).

9.       W.J Tam, F. Speranza, L.Zhang, R. Renaud, J. Chan, and C. Vazquez, ” Depth image based rendering for multiview stereoscopic displays: Role of information at object boundaries “, in Proc. of the SPIE, Vol. 6016, pp. 75-85 (2005).

10.    W. J. Tam and L. Zhang, “Non-uniform smoothing of depth maps before image-based rendering”, in Proc. of the  SPIE, Vol. 5599, pp. 173-183 (2004).

11.    Jaeseung Ko, Manbae Kim and Changick Kim, School of Engineering, Information and Communications University  Munji-dong, Yuseong-gu, Deajeon, Korea, Proc. of SPIE Vol. 6696  66962A-1.

12.    Salvatore Curti, Daniele Sirtori, and Filippo Vella, “3D Effect Generation from Monocular View”, in Proc. of the  First International Symposium on 3D Data Processing visualization and Transmission, 3DPVT (2002).

13.    S. A. Valencia and R. M. Rodriguez-Dagnino, “Synthesizing Stereo 3D Views from Focus Cues in Monoscopic 2D  Images”, in Proc of the SPIE, Vol. 5006, pp.377-388 (2003).

14.    Pedro F. Felzenszwalb and Daniel P. Huttenlocher, “Efficient Graph-Based Image Segmentation”, International  Journal of Computer Vision, Vol. 59, Number 2,
Sept. 2004.

15.    O. Chapelle, B. SchÄolkopf and A. Zien. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.





Chanchal Verma, B. Anjanee Kumar

Paper Title:

Improvement of Output Power for Wind Driven Induction Generator using SEPIC Converter

Abstract: This paper deals with dc-dc converter known as SEPIC stands for single ended primary inductor converter. SEPIC is integrated with wind energy in order to maximize the performance of the system. With the help of simple method of tracking maximum power from wind energy to extract maximum power. Basically wind energy is used to generate electricity and the wind is not in uniform speed. So, by using different electronic components .The main part is dc-dc converter and by using SEPIC in place of normal dc-dc converter the output power i.e. THD will enhanced .Here DBR is used to convert AC to DC. The SEPIC can perform both bucks as well as boost converter. It gives the result in microseconds. The simple algorithm is the main advantage of the proposed work. The output is shown in DC microgrid and AC microgrid. It is for the small scale WECS.The work is supported with experimental results and also the output i.e. THD is calculated and compared with Cuk converter.

 MPPT, SEPIC (single ended primary inductor converter), THD, wind energy.


1.    Nayanar, V., Kumaresan, N. and Ammasai   Gounden, N.,”A single sensor based MPPT controller for wind driven Induction Generators Supplying DC Microgrid”, IEEE Transactions on Power Electronics ,Vol.31,  Issue: 2 .pp1161 – 1172,feb. 2016.
2.    A.Yazdani and P.P. Dash,”A control methodology and characterization of dynamics for a photovoltaic (PV) system interfaced with a distribution network,” IEEE Tans. Power Del.,vol.23,no.3,pp.1538-1551.jul 2009.

3.    H.Li and Z. Chen,” Overview of different wind generator systems and their comparisons,” IEEE Renew. Power Gener.,vol.2,no.2,pp123-138,jun.2008.

4.    Monica Chinchilla, Santigo Arnaltes, Juan Carlos Burgos: “Control of permanent magnet generators applied to variable speed wind energy systems connected to the grid”, IEEE  Transaction on energy conversion ,vol.21, NO.1, MARCH 2006.

5.    K. Padmanabham and K. Balaji Nanda Kumar     Reddy:” A New MPPT Control Algorithm for Wind Energy Conversion System”, (IJERT) ISSN: 2278-0181 ,Vol. 4 Issue 03, March-2015

6.    Gayathri Deivanayaki. VP, Dhivyabharathi. R,Surbhi. R and Naveena. P.” comparative analysis of bridgeless CUK and SEPIC converter.”IJICSE, vol.3,issue1,jan-feb 2016,pp15-19.

7.    Notes of IIT, Kharagpur, DC to DC Converters, Module -3.





Joseph Zacharias, Celine George, Vijayakumar Narayanan

Paper Title:

Hybrid Wired and Wireless System Involving Non-upling Technique

Abstract:  A hybrid Radio over Fiber (RoF) system which is compatible with both wired and 90 GHz wireless transmission is proposed in this paper. Baseband and millimeter wave signals are considered as wired and wireless signal respectively. Hybrid signal consisting of wired and wireless signal is generated using a single Dual Drive Mach-Zehnder Modulator (MZM). Using a 10 GHz local oscillator, non-upling (nine times) increase in signal is achieved. As the system uses low frequency local oscillator and a single modulator, overall cost of the system can be reduced considerably. Results obtained show that the system can transmit both wired and wireless signals over a fiber of length 70 km with acceptable bit error rate (BER).

Fiber-to-the-Home, Radio-over-Fiber, W-Band


1.    S. E. Alavi, I. S. Amiri, M. Khalily, N. Fisal, A. S. M. Supa’at, H. Ahmad, and S. M. Idrus., ”W-Band OFDM for Radio-Over-Fiber Direct Detection Link Enabled by Frequency Nonupling Optical Up-Conversion,” IEEE Photon. J., vol. 6, no.6, Dec. 2014.   
2.    C. H. Chang, P. C. Peng, Q. Huang, W. Y. Yang, H. L. Hu, W. C. Wu, J. H. Huang, C. Y. Li, H. H. Lu and H. H. Yee, “FTTH and Two-Band RoF Transport Systems Based on an Optical Carrier and Colorless Wavelength Separators,” IEEE Photon. J., vol. 8, no.1, Feb. 2016.

3.    Tong Shao, F. Paresys, Y. Le Guennec, G. Maury, N. Corrao and B. Cabon, “Convergence of 60 GHz Radio Over Fiber and WDM PON Using Parallel Phase Modulation With a Single Mach-Zehnder Modulator,” IEEE Light Wave Technol. J, vol.30, no.17, Sep. 2012.

4.    C. W. Chow, and Y. H. Lin, “Convergent optical wired and wireless long-reach access network using high spectral efficient modulation,” Opt. Exp., vol. 20, no. 8, pp. 9243-9248, Apr. 2012.

5.    H. T. Huang, Chun-Ting Lin, Chun-Hung Ho, Wan-Ling Liang, Chia-Chien Wei, Yu-Hsuan Cheng and Sien Chi, “High Spectral Efficient W-band OFDM-RoF System with Direct-Detection by Two Cascaded Single-Drive MZMs,” Opt. Exp., vol. 21, no. 14, pp. 16615-16620, Jul. 2013.

6.    G. H. Nguyen, B. Cabon and Y. Le Guennec, “Generation of 60-GHz MB-OFDM Signal-Over-Fiber by Up-Conversion Using Cascaded External Modulators,” Journal of Lightwave Technology, vol. 27, pp. 1496-1502, Jun. 2009.

7.    Jianxin Ma, J.Yu, Chongxiu Yu, Xiangjun Xin, Xinzhu Sang and Qi Zhang, “64 GHz Optical Millimeter-Wave Generation by Octupling 8 GHz Local Oscillator via a Nested LiNbO3 modulator,” Opt. Laser Technol., vol. 42, pp. 264-268, 2010.

8.    Jianjun Yu, Zhensheng Jia, L. Yi, Y. Su, Gee-Kung Chang and Ting Wang, “Optical Millimeter-Wave Generation or Up-Conversion using External Modulator,” IEEE Photon. Technol. Lett., vol. 18, no. 1, pp. 265-267, Jan. 2006.

9.    H. C. Chien, Y. T. Hsueh, A. Chowdhury, J. Yu and G. K. Chang, “Optical millimeter-wave generation and transmission without carrier suppression for single- and multi-band wireless over fiber applications,” J. Lightw. Technol., vol. 28, no. 16, pp. 2230-2237, Aug. 2010.





J. Srinivasan, S. Audithan

Paper Title:

Anonymous Secure Routing Protocol for Multi hop Wireless Mesh Network (ASRP)

Abstract: Anonymous communications are important for many applications of the Wireless Mesh Networks (WMNs) deployed in adversary environments. A major requirement on the network is to provide unidentifiability and unlinkability for nodes and their traffics. The existing protocols are vulnerable to the attacks of fake routing packets or denial-of-service (DoS) broad- casting, even the node identities are protected by pseudonyms. In this paper, we propose Anonymous Secure Routing Protocol for Multi hop Wireless Mesh Network (ASRP) to protect the attacks and multi hop secure data transmission in WMN. ASRP offers anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. The key-encrypted onion routing is designed to prevent intermediate nodes from inferring a real receiver node. Simulation results indicate that the efficiency of the proposed ASRP protocol with improved performance as compared to the existing protocols.

Anonymous, Onion Routing, Encryption, Decryption, Wireless Mesh Networks.


1.       Asad Amir Pirzada a, Marius Portmanna,b, Ryan Wishart a, Jadwiga Indulska, SafeMesh: A wireless mesh network routing protocol for incident area communications, Pervasive and Mobile Computing, vol.5, pp.201-221, 2009.
2.       J. Sun, C. Zhang ; Y. Fang, A Security Architecture Achieving Anonymity and Traceability in Wireless Mesh Networks, IEEE 27th Conference on Computer Communications, 2008.

3.       Yahui Li, Xining Cui, Linping Hu, Yulong Shen, Efficient Security Transmission Protocol with Identity-based Encryption in Wireless Mesh Networks,IEEE, 2010.

4.       D. Benyamina A. Hafid, M. Gendreau b, J.C. Maureira, “On the design of reliable wireless mesh network infrastructure with QoS constraints”, Computer Network, vol.55, pp. 1631-1647, 2011.

5.       Jaydip Sen, “Security and Privacy Issues in Wireless Mesh Networks: A Survey”, Innovation Labs, Tata Consultancy Services Ltd. Kolkata, INDIA.

6.       Kamran Jamshaid  Basem Shihada Ahmad Showail, Philip Levis, Deflating link buffers in a wireless mesh network, Ad Hoc Networks 16 (2014) 266–280.

7.       J. Kong and X. Hong, “ANODR: ANonymous On Demand Routing with Untraceable Routes for Mobile Ad hoc networks,” in Proc. ACM MobiHoc’03, Jun. 2003, pp. 291–302.

8.       MASK: Anonymous On-Demand Routing in Mobile Ad Hoc Networks Yanchao Zhang, Student Member, IEEE, Wei Liu, Wenjing Lou, Member, IEEE, and Yuguang Fang, Senior Member, IEEE.

9.       K. E. Defrawy and G. Tsudik, “ALARM: Anonymous Location-Aided Routing in Suspicious MANETs,” IEEE Trans. on Mobile Computing, vol. 10, no. 9, pp. 1345–1358, Sept. 2011.

10.    Z. Wan, K. Ren, and M. Gu, “USOR: An Unobservable Secure On-Demand Routing Protocol for Mobile Ad Hoc Networks,” IEEE Trans. on Wireless Communication, vol. 11, no. 5, pp. 1922–1932, May. 2012.

11.    Yanchao Zhang, and Yuguang Fang, ARSA: An Attack-Resilient Security Architecture for Multi hop Wireless Mesh Networks, IEEE Journal On Selected Areas in Communications, Vol. 24, no. 10, 2006.





Aleena Xavier T, Rejimoan R.

Paper Title:

A Particle Swarm Optimization Approach With Migration for Resource Allocation in Cloud

Abstract:  Cloud computing is an emerging technology. The main motivation behind the proposed work is to design a Cloud Broker for efficiently managing cloud resources and to complete the jobs within a deadline. The proposed approach intends to achieve the objectives of reducing execution time, cost and workload based on the defined fitness function. The work is simulated in CloudSim and the results prove the effectiveness of the proposed work. A better allocation was achieved when all of the three factors were considered. The analysis of work was done by comparing one of the previous works where only time and cost were the objectives. By plotting a graph against Response time and deadline and another graph depicting the relation between the idle time and deadline this result has been proved.

Resource allocation, Job scheduling, Cloud Computing, IaaS, Particle Swarm Optimization


1.       S. Binitha, S.Siva Sathya, A survey of bio inspired optimization algorithms, Int.J. Soft Comput. Eng. (IJSCE) (ISSN: 2231-2307) 2 (2) (2012).
2.       J. Kennedy, R. Eberhart, Particle swarm optimization (PSO), in: Proc. IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942–1948.

3.       Aman kumar, Emmanueel S.Pilli and R.C.Jshi,” An efficient framework for resource allocation in cloud computing” ,in IEEE 4th ICCCNT – 2013, Tiruchengode, India

4.       M. c. D. Pandit and N. Chaki, “Resource allocation in cloud computing using simulated annealing,” IEEE applications and innovations in mobile computing, 2014.

5.       Suraj Pandey, Linlin Wu, Siddeswara Mayura Guru, Rajkumar Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments, in: AINA’10 Proceedings of the 2010 24th IEEE International Conference on on Advanced Information Networking and Applications

6.       Chandrashekar S.Pawar and Rajnikant B.Wagh, Priority Based Dynamic Resource Allocation in Coud Computing, International Symposium on Cloud ans Services Computing, 2012, pp.1-6.

7.       Biao Song, Mohammad Mehedi Hassan, Eui-nam Huh, A novel heuristicbased task selection and allocation framework in dynamic collaborative cloud service platform, in: CloudCom 2010, pp. 360–367

8.       Eun-Kyu Byuna, Yang-Suk Keeb, Jin-Soo Kimc, Seungryoul Maeng, Cost optimized provisioning of elastic resources for application workflows, Future Gener. Comput. Syst. 27 (2011) 1011–1026.

9.       M. Mezmaz, Choon Lee Young, N. Melab, E.-G. Talbi, A.Y. Zomaya, A bi-objective hybrid genetic algorithm to minimize energy consumption and makespan for precedence-constrained applications using dynamic voltage scaling, in: 2010 IEEE Congress on Evolutionary Computation, CEC, 18–23 July 2010

10.    O.O. Sonmez, A. Gursoy, A novel economic-based scheduling heuristic for computational grids, Int. J. High Perform. Comput. Appl. 21 (1) (2007) 21–29.

11.    S. Chaisiri, Bu-Sung Lee, D. Niyato, Optimization of resource provisionin cost in cloud computing, IEEE Trans. Serv. Comput. 5 (2) (2012) 164–177.

12.    M.F. Tasgetiren, Y.-C. Liang, M. Sevkli, G. Gencyilmaz, A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem, European J. Oper. Res. 177 (3) (2007) 1930–1947

13.    M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a  colony of cooperating agents, IEEE Trans. Syst. Man Cybern. B 26 (1) (1996) 29–41.

14.    Genetic Algorithm, J.H. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Comput. 2 (2) (1973) 88–105.

15.    Thamarai Selvi Somasundaram, Kannan Govindarajan, “CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud”, Future Generation Computer Systems 34 (2014) 47–65.





C. Ramachandra, Sarat Kumar Dash

Paper Title:

ESD Induced Reliability Problems in Space Grade Devices

Abstract: ESD induced reliability problems in an IC have been studied in detail. PEM (Photon Emission Microscopy) analysis has indicated characteristic emission spots at same location from all the failed devices. Reprocessing of the failed device reveals Gate oxide rupture as root cause of the failure. Protection circuits have been designed to prevent ESD induced damage to the devices. The devices are found to be safe till 4500 V stress after protection circuit is implemented.

ESD (Electro Static Discharge), HBM (Human Body Model), PEM (Photon Emission Microscope), BPSG (Boron Phosphorous silicate glass)


1.       Jie Wu,“ Gate Oxide reliability under ESD – like pulse stress” IEEE Transactions on Electron Devices. Vol : 51, Issue : 7, pp : 1192 – 1196; July 2004
2.       Amerasekera and D. Campbell, “ESD pulse and continuous voltage breakdown in MOS capacitor structures”, Proc. EOS/ESD Symp., pp. 208-213, 1986

3.       Y. Fong and C. Hu, “The effect of high electric field transients on thin gate oxide MOSFETs”, Proc. EOS/ESD Symp., pp. 252-257, 1987

4.       H. Wolf, H. Gieser, and W. Wilkening, “Analyzing the switching behavior of ESD-protection transistors by very fast transmission line pulsing”, Proc. EOS/ESD Symp. , pp. 28-37, 1999

5.       J. Wu, P. Juliano, and E. Rosenbaum, “Breakdown and latent damage of ultrathin gate oxides under ESD stress conditions”, Proc. EOS/ESD Symp., pp. 287-293, 2000

6.       S. G. Beebe, “Simulation of complete CMOS I/O circuit response to CDM stress”, Proc. EOS/ESD Symp., pp. 259-270, 1998

7.       P. E. Nicollian, W. R. Hunter, and J. C. Hu, “Experimental evidence for voltage driven breakdown models in ultrathin gate oxides”, Proc. IRPS, pp. 7-15, 2000

8.       E. Wu, A. Vayshenker, E. Nowak, J. Sune, R.-P. Vollertsen, W. Lai, and D. Harmon, “Experimental evidence of ${t}_{\rm BD}$power-law for voltage dependence of oxide breakdown in ultrathin gate oxides”, IEEE Trans. Electron Devices, vol. 49, pp. 2244-2253, 2002

9.       C. Leroux, P. Andreucci, and G. Reimbold, “Analysis of oxide breakdown mechanism occurring during ESD pulses”, Proc. Int. Rel. Phys. Symp., pp. 276-282, 2000

10.    S.-J. Wang, I.-C. Chen, and H. L. Tigelaar, “TDDB on poly-gate single doping type capacitors “, Proc. IRPS, pp. 54-57, 1992

11.    T. Nigam, R. Degraeve, G. Groeseneken, M. Heyns, and H. Maes, “A fast and simple methodology for lifetime prediction of ultrathin oxides”, Proc. IEEE Int. Rel. Phys. Symp., pp. 381-388, 1999

12.    T. Nigam, R. Degraeve, G. Groeseneken, M. Heyns, and H. Maes, “Constant current charge-to-breakdown: Still a valid tool to study the reliability of MOS structures?”, IEEE Int. Rel. Phys. Symp., pp. 62-69, 1998

13.    R. Tu, J. King, H. Shin, and C. Hu, “Simulating process-induced gate oxide damage in circuits”, IEEE Trans. Electron Devices, vol. 44, pp. 1393-1400, 1997





Neethu.M.S, Jayalekshmi.S

Paper Title:

Dependency Based Scheme for Load Balancing in Cloud Environment

Abstract: Cloud computing provides an opportunity to dynamically share the resources among the users through virtualization technology. In this paper, a scheme for load balancing is proposed on the basis of dependency among the tasks. CMS consists of three algorithms including Credit-based scheduling for independent tasks, Migrating Task and Staged Task Migration for dependent tasks. The Credit-based method is used for scheduling the independent tasks considering both user priority and task length. Each task will be assigned a credit based on their task length and its priority. In the actual scheduling of the task, these credits values will be considered. Task Migration algorithm is used to guarantee balancing of loads among the virtual machines. Task migration is done such that the tasks gets migrated from heavily loaded machines to comparatively lighter ones. Thus, no rescheduling is required. For dependent tasks, the dependencies between tasks are considered and the technique termed as data shuffling is used. In data shuffling, a job is divided into several tasks according to the execution order. The method used here is that the tasks in one stage run independently, while the tasks in different stages must be executed serially. Finally the system is simulated and experiments are conducted to evaluate the proposed methods. This work also concentrates on a simulated study among some common scheduling algorithms in cloud computing on the basis of the response times. The algorithms being compared with the work includes: Random, Random Two Choices (R2C) and On-demand algorithms. The evaluations demonstrate that Credit-based scheduling algorithm significantly reduces the response time.

 Load Balancing, Virtual Machine, Task Scheduling, Dependency.


1.       Buyya, R., Ranjan, R., and Calheiros, R.N. “ Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities” , International Conference on High Performance Computing and Simulation, HPCS 2009.
2.       N. Susila, S. Chandramathi, Rohit Kishore, “A Fuzzy-based Firefly Algorithm for Dynamic Load Balancing in Cloud Computing Environment”, Journal of Emerging Technologies in Web Intelligence, vol. 6, no. 4,pp.435-440, IEEE November 2014.

3.       DineshBabu.L.D,P.VenkataKrishna,“HoneyBeeinspiredloadbalancingoftasks in cloud computing environment”, Applied Soft Computing, vol.13,pp.2292-2303 ,Elsevier 2013.

4.       Elina Pacini,Cristian Mateos,Carlos Garcia Garino, “Balancing throughput and response time in online scientific clouds via Ant Colony Optimization”, Advances in Engineering Software, vol.8,pp.31-47 ,Elsevier 2015.

5.       Brototi Mondala, Kousik Dasgupta, Paramartha Dutta,“ Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach”, Procedia Technology, vol.4, pp.783-789, Elsevier 2012.

6.       Kousik Dasgupta, Brototi Mandal, Paramartha Dutta, Jyotsna Kumar Mondal, Santanu Dam,“ A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing”, International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), Elsevier, 2013.

7.       B. R. Kandukuri, R. Paturi V, A. Rakshit, “Cloud Security Issues”, IEEE International Conference on Services Computing, pp. 517-520, IEEE 2009.

8.       Yu Liu, Changjie Zhang, Bo Li, Jianwei Niu .“ DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters”, Journal of Network and Computer Applications, Elsevier 2015.

9.       GaochaoXu, Junjie Pang, and Xiaodong Fu, “A Load Balancing Model Basedon Cloud Partitioning for the Public Cloud”, vol.18 ,pp. 34-39,IEEE 2013.

10.    Aarti Singha, Dimple Junejab, Manisha Malhotraa ,“Autonomous Agent Based Load Balancing Algorithm in Cloud Computing ”, International Conference on Advanced Computing Technologies and Applications (ICACTA2015), vol.45, pp.832-841 , Elsevier 2015.

11.    Michael Mitzenmacher, “The Power of Two Choices in Randomized Load Balancing”, IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 10, pp.1094-1104 ,IEEE 2001.

12.    Antony Thomas, Krishnalal G, Jagathy Raj V P ,“Credit Based Scheduling Algorithm in Cloud Computing Environment”, Procedia Computer Science, vol.46, pp. 913 920, Elsevier 2015.

13.    Venubabu Kunamneni.,“ Dynamic Load Balancing for the Cloud”, International Journal of Computer Science and Electrical Engineering (IJCSEE), ISSN No. 2315-4209, vol-1 issue-1, 2012

14.    L. Wang, GregorLaszewski, Marcel Kunze, Jie Tao, “Cloud Computing: A Perspective Study”, New Generation Computing- Advances of Distributed Information Processing, pp. 137-146, vol. 28, no. 2, 2008.

15.    Ousterhout K, Wendell P, Zaharia M, Stoica I, “Batch sampling: low overhead schedulingforsub-secondparalleljob”, Berkeley: University of California; 2012.

16.    Weiwei Chen, Ewa Deelman, “Work flow Sim: A Toolkit for Simulating Scientific Work flows in Distributed Environments”, The 8th IEEE International Conference on E Science (E Science 2012), Chicago, 2012.





Sharafunisa S, Smitha E S

Paper Title:

Reversible Watermarking Technique for Relational Data using Ant Colony Optimization and Encryption

Abstract:  Data is stored in different digital formats such as images, audio, video, natural language texts and relational data. Relational data in particular is shared extensively by the owners with communities for research purpose and in virtual storage locations in the cloud. The purpose is to work in a collaborative environment where data is openly available for decision making and knowledge extraction process. So there is a need to protect these data from various threats like ownership claiming, piracy, theft, etc. Watermarking is a solution to overcome these issues. Watermark is considered to be some kind of information that is embedded into the underlying data. While embedding the watermark, the data may modify, to overcome this we use reversible watermarking in which owner can recover the data after watermarking. In this paper, a reversible watermarking for relational data has been proposed that uses ant colony optimization and encryption for more accuracy and security.

  Ant colony optimization (ACO), Mutual information (MI), Reversible watermarking, Data recovery, Genetic Algorithm (GA).


1.       Raju Halder, Shanthanu Pal and Agostino Cortesi ,“Watermarking Techniques for Relational Databases: Survey, Classification and Comparison,” Journal of Universal Computer Science, Vol 16 ,2010, Number 21, pp.3164-3190
2.       J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia”, IEEE Trans. Image Process., vol. 6, no. 12, pp. 16731687, Dec. 1997

3.       Ifthikar, M. Kamran and Z. Anwar, “A Survey on Reversible Watermarking Techniques for Relational Databases,” Security and communication networks, 2015.

4.       Marco Dorigo and Thomus Stultze, ”Ant Colony Optimization“, 2004.

5.       T. M. Cover, J. A. Thomas, and J. Kieffer,’Elements of information theory,” SIAM Rev., vol. 36, no. 3, pp. 509510, 1994.

6.       R. Agarwal and J. Kiernan, “Watermarking relational databases”, in Proc. 28th Int. Conf. Very Large Data Bases, 2002, pp. 155166.

7.       G. Gupta and J. Pieprzyk, “Reversible and blind database watermarking using difference expansion,” in Proc. 1st Int. Conf. Forensic Appl. Tech. Telecommun., Inf., Multimedia Workshop, 2008, p. 24.

8.       G. Gupta and J. Pieprzyk, “Database relation watermarking resilient against secondary watermarking attacks,” in Information Systems and Security. New York, NY, USA: Springer, 2009, pp. 222–236.

9.       K. Jawad and A. Khan, “Genetic algorithm and difference expansion based reversible watermarking for relational databases,” J. Syst. Softw., vol. 86, no. 11, pp. 2742–2753, 2013.

10.    M. E. Farfoura and S.-J. Horng, “A novel blind reversible method for watermarking relational databases,” in Proc. IEEE Int. Symp. Parallel Distrib. Process. Appl., 2010, pp. 563–569

11.    Iftikhar S, Kamran M, Anwar Z.,“ RRW-a robust and reversible watermarking technique for relational data, IEEE transactions on Knowledge and Data Engineering , 2015, Volume: 27,Issue: 4, pp: 1132 – 1145

12.    K. Huang, H. Yang, I. King, M. R. Lyu, and L. Chan,”Biased minimax probability machine for medical diagnosis“, AMAI, 2004.





Jasher Nisa A J, Sumithra M D

Paper Title:

Adaptive Minimum Classification Error based KISS Metric Learning for Person Re-identification

Abstract: Person re-identification becoming an interesting research area in the field of video surveillance and is taken as the area of intense research in the past few years. It is the task of identifying a person from a camera image, who is already been tracked by another camera image at different time at different location. Manual re-identification in large camera network is costly and mostly of inaccurate due to large number of camera that he had to simultaneously operate. In a crowded and unclear environment, when cameras are at a lengthy distance, face recognition is not possible due to insufficient image quality. So, visual features based on appearence of people, using their clothing, objects carried etc. can be exploited more reliably for re-identification. A person’s appearence can change between different camera views, if there is large changes in view angle, lighting, background and occlusion, so visual feature extraction is not possible accurately. For solving a person re-identification problem, have to focus on “developing feature representations which are discriminative for identity,but invarient to view angle and lighting”.  Recently, Minimum Classification Error (MCE) based KISS metric learning is considered as one of the top level algorithm for person re-identification. It uses VIPeR feature set as input, which contains the extracted features. MCE-KISS is more reliable with increasing the number of training samples.  It uses the smoothing technique and MCE criteria to improve the accuracy of estimate of eigen values of covarience metrics. The smoothing technique can compensate for the decrease in performance which arose from the estimate errors of small eigenvalues. Here, the value of average number of small eigen values of the covarience metrics is set as a constant. So it does not work well for a large number of samples. In such situation, introduce a new method to find the value of average of such small eigen values by maximizing the likelihood function. The new scheme is termed as Adaptive MCE-KISS and conduct validation experiments on VIPeR feature dataset.

 reidentification, matric learning, covarience matrics, likelihood method.

1.       Vezzani, R., Baltieri, D., Cucchiara, R.: People reidenti_cation in surveillance and forensics: A survey. ACM Computing Surveys (CSUR) 46(2) (2013) 29.
2.       Dapeng Tao, Lianwen Jin, Member, IEEE, Yongfei Wang, and Xuelong Li, Fellow, IEEE “Person Reidentification by Minimum Classification Error-Based KISS Metric Learning”,  ieee transactions on cybernetics, vol. 45, no. 2, february 2015.

3.       H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. Educ. Psychol., vol. 24, no. 6, pp. 417–441, 1933.

4.       McDermott, T. J. Hazen, J. Le Roux, A. Nakamura, and S. Katagiri, “Discriminative training for large-vocabulary speech recognition using minimum classification
error,” IEEE Trans. Audio, Speech, Lang.Process., vol. 15, no. 1, pp. 203–223, Jan. 2007.

5.       Kimura, K. Takashina, S. Tsuruoka, and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 1, pp. 149–153, Jan. 1987.

6.       K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res., vol. 10, pp. 207–244, Feb. 2009.

7.       J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Informationtheoretic metric learning,” in Proc. ICML, Corvallis, OR, USA, 2007, pp. 209–216.

8.       L. Yang, R. Jin, R. Sukthankar, and Y. Liu, “An efficient algorithm for local distance metric learning,” in Proc. AAAI, 2006, pp. 543–548.

9.       B. Prosser, W.-S. Zheng, S. Gong, T. Xiang, and Q. Mary, “Person re-identification by support vector ranking,” in Proc. BMVC, 2010.

10.    D. Tao, L. Jin, Y. Wang, Y. Yuan, and X. Li, “Person re-identification by regularized smoothing KISS metric learning,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 10, pp. 1675–1685, Oct. 2013.

11.    M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof, “Large scale metric learning from equivalence constraints,” in Proc. IEEE CVPR, Providence, RI, USA, 2012, pp. 2288–2295.

12.    M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof “Large scale metric learning from equivalence constraints,” in Proc. IEEE Conf. Comput. Vision Pattern Recogn., Jun. 2012, pp. 2288–2295.

13.    Kimura, K. Takashina, S. Tsuruoka, and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 1, pp. 149–153, Jan. 1987.

14.    B.-H. Juang, W. Hou, and C.-H. Lee, “Minimum classification error rate methods for speech recognition,” IEEE Trans. Speech Audio Process., vol. 5, no. 3, pp. 257–265, May 1997.

15.    D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models for recognition, reacquisition, and tracking,” in Proc. 10th PETS, 2007.

16.    T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.

17.    Shamik Sural, Gang Qian and Sakti Pramanik, “segmentation and histogram generation using the hsv color space for image retrieval”.

18.    D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models for recognition, reacquisition, and tracking,” in Proc. 10th PETS, 2007.





Rita Anitasari, Rizki Fitriani, Erna Triastutik, Alief Makmuri Hartono, Totok R. Biyanto

Paper Title:

Converting Fuel Oil to Gas in Combustion System for CO2 Emission Mitigation at PT. PJB UP Gresik

Abstract:  In environmental point of view, natural gas is the cleanest of the fossil fuels. The combustion of natural gas releases virtually no sulphur dioxide and ash or particulate matter, and very small amounts of nitrogen oxides. Natural gas emits 22% less carbon dioxide than oil and 40% less than coal. NOx is reduced by more than 90% and SOx by more than 95%. This paper will describes the effort of PT. PJB UP Gresik as the owner of the bigest steam power plant in Indonesia to reduce the CO2 emission by converting fuel oil to gas at existing steam power plant fuel system. In order to achive operating conditions that assure mass, energy and momentum balances, some plant modifications and new installation were performed in combustion system area. The effort was performed succesfully. The evidents were compare with the same powerplant in the world. In term of CO2 emission, PT. PJB UP Gressik lay at the best ten compared to others power plant performance in America. It is shown PT. PJB UP Gresik have been performing best green practice especially in reducing CO2 emmision in the steam power plant by utilize fuel gas.

  CO2 Emission, Mitigation, Combustion System, Converting Fuel Oil to Gas


1.       Totok R. Biyanto, Green Concept in Engineering Practice, invited speaker at1St International Seminar on Science and Technology 2015, 5 August 2015, ITS Surabaya, ISSN 2460-6170
2.       EPA, Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion, US Environmental Protection Agency: 2014

3.       E. Dendy Sloan, Fundamental principles and applications of natural gas hydrates, Nature 426, 353-363 (20 November 2003

4.       SA Iqbal, Y Mido, Chemistry of Air & Air Pollution, Discovery Publishing, 2010

5.       Roberts, R. Brooks, P. Shipway, “Internal combustion engine cold-start efficiency: A review of the problem, causes and potential solutions”, Energy Conversion and Management, Volume 82, June 2014, Pages 327–350

6.       D. Sarkar, Thermal power plant, 2015.

7.       Christopher E . Van Atten, Benchmarking Air Emissions, M .J. Bradley & Associates LLC, 2013





Nikhila A, Janisha A

Paper Title:

Lossless Visual Cryptography in Digital Image Sharing

Abstract:   Security has gained a lot of importance as information technology is widely used. Cryptography refers to the study of mathematical techniques and related aspects of Information security. Visual cryptography is a secret sharing scheme which uses images distributed as shares such that, when the shares are superimposed, a hidden secret image is revealed. Visual cryptography schemes (VCSs) generate random and meaningless shares to share and protect secret images. The main issue in visual cryptography is quality of reconstructed image. The secret image is converted into shares; that mean black and white pixel images. There occurs an issue of transmission loss and also the possibility of the invader attack when the shares are passed within the same network. In this paper, a lossless TVC (LTVC) scheme that hides multiple secret images without affecting the quality of the original secret image is considered. An optimization model that is based on the visual quality requirement is proposed. The loss of image quality is less compared to other visual cryptographic schemes. The experimental results indicate that the display quality of the recovered image is superior to that of previous papers. In addition, it has many specific advantages against the well-known VCSs. Experimental results show that the proposed approach is an excellent solution for solving the transmission risk problem for the Visual Secret Sharing (VSS) schemes.

visual cryptography, visual secret sharing.


1.    Kai-Hui Lee and Pei-Ling Chiu “Sharing Visual Secrets in Single Image Random Dot Stereograms” IEEE Transactions on Image Processing, Vol.23, No. 10, October 2014
2.    Ross and A. A. Othman, “Visual Cryptography for Biometric Privacy”, IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 70-81, 2011.

3.    M. Naor and A. Shamir, “Visual cryptography,” in Advances in Cryptology-EUROCRYPT 1994, ser. Lecture Notes in Computer Science, A. De Santis, Ed.

4.    R.-Z Wang and S.-F. Hsu, “Tagged visual cryptography,” IEEE Signal Process. Lett. vol. 18, no. 11, pp. 627-630, 2011.

5.    J.-B. Feng, H.-C. Wu, C.-S. Tsai, Y.-F. Chang and Y.-P. Chu, “Visual secret sharing for multiple secrets, “Patt. Recognition. vol. 41, no. 12, pp.35723581, 2008.





Neenu R S, Greeshma G Vijayan

Paper Title:

Data Mining using Meta Heuristic Approaches for Detecting Hepatitis

Abstract: Clinical Data Mining involves the process of extracting, analyzing and finding the available data for clinical decision making. Mining data from clinical data set is not an easy task as they are inserted manually. In this paper, a solution for accurately predicting the presence or absence of hepatitis is proposed. The proposed technique is applied on clinical data sets taken from University of California at Irvine (UCI) machine learning repository. The proposed system contains two main subsystems for preprocessing and classifying. In the preprocessing subsystem the missing values in the data set is handled using missing data imputation methods like litwise deletion or mean/mode imputation method. If the percentage of missing values in a tuple is greater than 25%, then the tuple is rejected from the dataset else it was imputed by the most frequently used value. After handling the missing value, the relevant attributes are selected using meta-heuristic approaches like Particle Swarm Optimization (PSO) is used for feature selection. The reducts obtained after preprocessing are fed into the classification. In the classification subsystem the selected reducts are trained and tested using back propagation neural network. This paper aims at accurate prediction of diseases by analyzing clinical data sets.

 Back propagation neural network, Clinical Data Mining, Particle Swarm Optimization (PSO), University of California at Irvine (UCI).


1.       Fabricio Voznika and Leonardo Viana, “Data Mining Classifications”.
2.       What is clinical dataming? http://www.slideshare.net/empowerbpo/what-is-clinical-data-mining

3.       Yamille del Valle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez,  and Ronald G. Harley “Particle Swarm Optimization: Basic
Concepts, Variants and Applications in Power Systems”,  IEEE Transactions On Evolutionary Computation, VOL. 12, NO. 2, APRIL 2008

4.       R. C.Chakraborty, “Back Propagation Network: Soft Computing Course Lecture”, 15-20, Aug 10,2010.

5.       Y. Kaya and M. Uyar, “A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease”, ApplieSoft Computing Journal, vol. 13, no. 8, pp. 34293438, 2013.

6.       J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, “Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)”, Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 570579, 2012.

7.       Support Vector  Mechanism.- https://en.wikipedia.org/wiki/Support_vector_machine

8.       D. Çalişir and E. Dogantekin, “A new intelligent hepatitis diagnosis system: PCALSSVM”, Expert Systems with Applications, “vol. 38, no. 8, pp. 1070510708, 2011.

9.       Kindie Biredagn Nahato, Khanna Nehemiah Harichandran and Kannan Arputharaj, “Knowledge Mining from Clinical Datasets Using Rough Sets and
Backpropagation Neural Network”, Hindawi, 2015

10.    K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2013.

11.    Hany M. Harb, and  Abeer S. Desuky , “  Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization “, International Journal of Computer Applications (0975 – 8887) Volume104– No.5, October 2014.

12.    Ezgi Deniz Ülker and Sadık Ülker, “Application of Particle Swarm Optimization To Microwave Tapered Microstrip Lines”, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 4, No. 1, February 2014.





Sibiyakhan M, Sumithra M D

Paper Title:

Fingerprint Classification based on Simplified Rule set and Singular Points with an Image Enhancement Scheme

Abstract:  A rule-based technique using simplified rules is proposed to overcome the challenges faced by previous fingerprint classification techniques. Two features, namely directional patterns and singular points (SPs), are combined to categorize four fingerprint classes: namely Whorl (W); Loop (L); Arch (A); and Unclassifiable (U). The use of directional patterns has recently received more attention in fingerprint classification. It provides a global representation of a fingerprint, by dividing it into homogeneous orientation partitions. With this technique, We can improve the accuracy of the classification by integrating an image enhancement scheme. In addition, incomplete fingerprints are often not accounted for. The proposed technique achieves an accuracy of 93.33% on the FVC 2002 DB1.

  Singular point (SP), Core point, Delta point, Segmentation, Preprocessing.


1.     A, Hong L, Pankanti S (2000) Biometrics: promising frontiers for emerging identification market. Comm ACM Feb:91–98
2.     D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Hand- book of fingerprint recognition. London: Springer, seconded.,2009.

3.     N. Yager and A. Amin, “Fingerprint classification: A review,” Pattern Analysis & Applications, vol. 7, pp. 77–93, Apr.2004.

4.     S. Msiza, B. Leke-Betechuoh, F. V. Nelwamondo, and N.Msimang,“A Fingerprint Pattern Classification Approach Based on  the Coordinate Geometry of Singularities, ”in
Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, (San Antonio, TX, USA), pp.510–517,IEEEComputuerSociety,2009.

5.     Z. Hou, H. Lam, J. Li, H. Wang, L. Chen, and W. Yau, “A Topological Model for Fingerprint Image Analysis,” in 3rd IEEE Conference on Industrial Electronics and Applications,(Singapore),pp.2106–2111,IEEE,2008.

6.     G. Candela, P. Grother, C. Watson, R. Wilkinson, and C. Wilson, “PCASYS-A pattern-level classification automation system for fingerprints,” NIST technical report NISTIR, vol.5647,1995.

7.     J. Guo, Y. Liu, J. Chang, and J. Lee, “Fingerprint classification based on decision tree from singular points and orientation field,” Expert Systems With Applications, vol.
41, no. 2, pp.752–764,2014.

8.     A.K.Jain and S.Minut, “Hierarchical Kernel Fitting for Fingerprint Classification and Alignment, ”in Proceedings of the 16th International on Pattern Recognition, vol. 2, pp. 469– 473,IEEE,2002.

9.     R. Cappelli, A. Lumini, D. Maio, IEEE, and D. Maltoni, “Fingerprint classification by directional image partitioning,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.21,pp.402–421,May1999.

10.  L.Liu, C.Huang, and D.C.D.Hung, “Directional Approach to Fingerprint Classification,” International Journal of Pattern Recognition and Artificial Intelligence,vol.22,pp.347– 365,Mar.2008.

11.  X. Wang, F. Wang, J. Fan, and J. Wang, “Fingerprint Classification Based on Continuous Orientation Field and Singular Points,” in IEEE International Conference on Intelligent Computing and Intelligent Systems, (China), pp. 189–193, IEEE,2009.

12.  Dali Chen, Yang Quan Chen, Dingyu Xue, Feng Pan, “Adaptive Image Enhancement Based on Fractional Differential mask,” in 24 th Chinese Control and Decision Conference(CCDC),2012.

13.  L. Wang, N. Bhattacharjee, G. Gupta, and B. Srinivasen, “Adaptive approach to fingerprint image enhancement,” in Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia, pp. 42–49, 2010.

14.  L. Hong, S. Member, Y. Wan, and A. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation,” vol.20,no.8,pp.777–789,1998.

15.  Kribashnee  Dorasamy,  Leandr Webb, Prof. Jules Tapamo, Nontokozo P.Khanyile, “Fingerprint Classification Using a  Simplified Rule-Set Based on Directional Patterns and  Singularity Features,” 978-1-4799-7824-3/15/ IEEE,2015.

16.  Database-FVC2002,http://bias.csr.unibo.it/fvc2002/.

17.  Database-FVC2004,http://bias.csr.unibo.it/fvc2004/.

18.  K. Karuand A.K.Jain, “Fingerprint Classification,” Pattern recognition,vol.29,no.3,pp.389–404,1996.

19.  H. Jung and J. Lee, “Fingerprint Classification Using the Stochastic Approach of Ridge Direction Information,” in International Conference of Fuzzy Systems, pp. 169–174, IEEE,2009.

20.  L. Webb and M. Mmamolatelo, “Towards a Complete Rule- Based Classification Approach for Flat Fingerprints,” in 2014 Second International Symposium on Computing and Networking, (South Africa, Pretoria), pp. 549–555, IEEE, Dec.2014.





A. Nachev

Paper Title:

Analysis of Irish Labour Market using Predictive Modelling

Abstract:   This study explores empirically Irish labour market and factors affecting employability rate of Irish nationals, using data from the Quarterly National Household Survey and data mining techniques. The research is conducted according to the CRISP-DM methodology and addresses its stages. We perform data cleansing and reduction of dimensionality, analyse data, and build predictive models to measure employability rate. The study uses two statistical techniques to train the models and also provides performance analysis of the models, measures variable significance using sensitivity analysis (SA) and variable effect characteristic (VEC) curves. The paper discusses results and draws conclusions.

   data mining, classification, logistic regression, linear discriminant analysis, labour market.


1.     CSO: QNHS [Online], http://www.cso.ie/en/qnhs/
2.     P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0 – Step-by-step data mining guide,” CRISP-DM Consortium, 2000

3.     Menard, S. (2002). Applied Logistic Regression (2nd ed.). SAGE

4.     Fisher, R., The Use Of Multiple Measurements In Taxonomic Problems. Annals of Eugenics, 1936, pp.179–188

5.     McLachlan, G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition., 2004, Wiley Interscience

6.     Martinez, A., Kak, A., PCA versus LDA,  IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2), 2001, pp.228–233

7.     Tao Li, Shenghuo Zhu, and Mitsunori Ogihara. Using Discriminant Analysis for Multi-Class Classification: An Experimental Investigation. Knowledge and Information Systems, vol. 10 no.4, 2006,  pp.453–72

8.     R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2009, http://www.R-project.org.

9.     Cortez, P. “Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool”. In Proceedings of the 10th Industrial Conference on Data Mining (Berlin, Germany, Jul.). Springer, 2010, LNAI 6171, 572– 583.

10.  P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, “Modeling wine preferences by data mining from physicochemical properties,” Decision Support Systems, vol. 47, no. 4, 2009, pp. 547–553.

11.  P. Cortez, M. Embrechts. Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences vol. 225, 2013, pp.1-17.

12.  R. Kewley, M. Embrechts, C. Breneman “Data strip mining for the virtual design of pharmaceuticals with neural networks,” IEEE Transactions on Neural Networks, vol. 11 (3),  2000, pp. 668–679

13.  T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no.8, 2005, pp. 861–874.

14.  B. Jantavan, C. Tsai, “The Application of Data Mining to Build Classification Model for  Predicting  Graduate Employment”, International Journal of Computer Science and Information Security, vol. 11 No 10, 2013.

15.  T. Mishra, D. Kumar, “Students’ Employability Prediction Model through Data Mining”, International Journal of Applied Engineering Research, vol. 11. No. 4, 2016, pp. 2275-2282.

16.  M. Sapaat, A. Mustapha, J. Ahmad, K. Chamili, R. Muhamad, “A Classification-based Graduates Employability Model for Tracer Study by MOHE”, Digital Information Processing and Communications, Springer Berlin Heidelberg, 2011, pp. 277-287.

17.  J. Kirimi, C. Moturi, “Application of Data Mining Classification in Employee Performance Prediction”, International Journal of Computer Applications, vol. 146,No 7, 2016, pp. 28-35.

18.  Y. Alsultanny, “Labor Market Forecasting by Using Data Mining”, International Conference on Computational Science, Procedia Computer Science 18, Elsevier, 2013, pp.1700-1709.





Bouchra Gourja, Malika Tridane, Said Belaaouad

Paper Title:

Survey on the use of ICT in Physics in Moroccan Schools Survey on the use of ICT in Physics in Moroccan Schools

Abstract:    Morocco, like all developing countries, has understood the importance using and integrating ICT in the education system. The ICT are tools and resources required by the National Education programs to support teachers in their courses while increasing student understanding. The Ministry of Education (MEN) has made significant efforts to equip schools with computers. The objective of this work is to show the level of employment of ICT to Moroccan schools and what can still impede its use. For this reason, we conducted a survey on high school teachers, to measure the degree of use of digital resources. The analysis of our survey showed that more than half of high school teachers use digital resources as a teaching aid for the lessons of physical sciences. However, some teachers who have not benefited from ICT training by the department do not use digital resources in their course or not enough. Despite the MEN having made  some digital resources avaiable, these teachers do not know how to exploit them. Some teachers who have many years of experience in teaching think wasting time using ICT.

ICT, digital resources, secondary education, Moroccan schools.


1.     Charte nationale d’éducation et de formation 1999. Levier 10
2.     M.Mazaudier, &  M. Lambey,  (2009)”L’usage des TICE en Sciences Physiques “–IAIPR De Sciences Physiques. Académie de Besançon,2009, Page1/7.

3.     C. Cleary, A. Akkari,  & D;Corti,D. ,“L’intégration des TIC dans l’enseignement secondaire. Formation et pratiques d’enseignement en questions”, 2008.

4.     A.Biaz, A.Benamar, A. Khyati, M. Talbi, “ Intégration des technologies de l’information et de la communication dans le travail enseignant, état des lieux et perspectives “, Epinet : la revue électronique de l’EPI, n° ,2009. Available: https://www.epi.asso.fr/revue/articles/a0912d.htm

5.     M. Mastafi, “Intégrer les TIC dans l’enseignement : quelles compétences pour les enseignants ?” Formation et profession, 23(2), 2014,29-47. Available: http://dx.doi.org/10.18162/fp.2015.294.





S. S. Sutar, A.V. Sutar, M. R. Rawal

Paper Title:

Torque Measurement in Epicyclic Gear Train

Abstract:     Gears are used to transmit power and rotary motion from the source to its application with or without change of speed or direction. Gears trains are mostly used to transmit torque and angular velocity from one shaft to another shaft, whenever there is large speed reduction requirement within confined space. In epicyclic gear trains there is relative motion between axes which useful to transmit very high velocity ratio with gears of smaller sizes in lesser space.  In this research paper torque calculations are done for epicyclic gear train. Input torque, output torque and holding or braking torque are calculated experimentally using experimental set up and analytically using tabular formulas for rpm range starting from 1000 rpm to 2800 rpm. Finally the experimental and analytical torque values are compared which shows error ranging from 6 % to 8% which is due to some frictional losses and mechanical losses.

Epicyclic gear train, output torque, holding torque.


1.     Balbayev G. and Ceccarelli M., “Design and Characterization of a New Planetary Gear Box”, Mechanisms, Transmissions and Applications, Mechanisms and Machine Science Volume 17, Springer, 2013, pp. 91-98.
2.     Syed Ibrahim Dilawer, Md. Abdul Raheem Junaidi, Dr.S.Nawazish Mehdi ―Design, Load Analysis and Optimization of Compound Epicyclic Gear Trains‖ American Journal of Engineering Research ISSN 2320-0936 Vol.-02, Issue-10, 2013, PP: 146-153.

3.     Ulrich Kissling, Inho Bae, ―Optimization Procedure for Complete Planetary Gearboxes with Torque, Weight, Costs and Dimensional Restrictions‖ Applied Mechanics and Materials Vol. 86 (2011) pp 51-54.

4.     M. Roland, R. Yves, Kinematic and Dynamic simulation of epicyclic gear trains, Mechanisa and Machine Theory, 44(2), 209, 412-424.

5.     Nenad Marjanovic, Biserka Isailovic, Vesna Marjanovic, Zoran Milojevic, Mirko Blagojevic, Milorad Bojic, ―A practical approach to the optimization of gear trains with spur gears‖ Mechanism and Machine Theory 53 (2012) PP:1–16.

6.     P. W. Jensen”Kinematic Space Requirement and Efficiency of Coupled Planetary Gear Trains”, ASME Paper, 68-MECH-45, (1969).

7.     E. Pennestri and F. Freudenstein,”The Mechanical Efficiency of Planetary Gear Trains”, Journal of Mechanical Design, 115, 645-651, (1993).

8.     M. Krstich,”Determination of the General Equation of the Gear Efficiency of Planetary Gear Trains”, International Journal of Vehicle Design, 8, 365-374, (1987).





A.V. Sutar, S.S.Sutar, J.J. Shinde, S.S. Lohar

Paper Title:

Combined Operation Boring Bar

Abstract:  This paper presents a new methodology for the combined operation boring bar. In normal boring operation it requires to replace the tool various operations. We cannot perform multiple operations on one machining tool. So it creates problems: Timeconsumption in changing of tool, cost of different tool, required for various operation. The focus of this research is the operation can be done on the same boring bar. It can able to perform various operation such as rough boring, finish boring, chamfering and spot facing, Which is not possible with conventional machine tool.

 Special purpose machine, Combine operations, Boring Bar


1.     Pradip Kumar, ‘Analysis and Optimization of parameters affecting surface roughness in Boring process’. 2014.
2.     T. Alwarsamy, ‘Theoretical cutting force prediction & analysis of boring

3.     PanyaphirawatPairoj, ‘An Optimization of machine parameters for modified horizontal boring tool using Taguchi method’, 2014.

4.     T. Moriwaki, ‘Multifunctioning machine tools’, 2014.

5.     Prof. Hansini S. Rahate, ‘Methodology of Special Purpose Spot Facing Machine’, 2013.

6.     Sharad Srivastava, ‘Multi-Function Operating Machine: A Conceptual Model’, 2014.

7.     ShivaniP.Raygor, M.S.Tak, K.P.Modi, ‘Selection of Combination of Tool and Work Piece Material using MADM Methods for Turning Operation on CNC Machine’ , 2015.

8.     B. K. Lad,M. S. Kulkarni, ‘Reliability and Maintenance Based Design of Machine Tools’, 2013.

9.     S.V. Kadam, M.G. Rathi, ‘Review of Different Approaches to Improve Tool Life’, 2014.

10.  R. Maguteeswaran,  M. Dineshkumar, ‘Fabrication of multi process machine’, 2014.





M. Raju, N. Seetharamaiah, A.M.K. Prasad

Paper Title:

Characterization of Hydro-Carbon Based Magneto-Rheological Fluid (MRF)

Abstract:   Magneto-rheological fluids (or simply “MR” fluids) belong to the class of controllable fluids. The essential characteristic of MR fluids is their ability to reversibly change from free-flowing, linear viscous liquids to semi-solids having controllable yield strength in milliseconds when exposed to a magnetic field. This feature provides simple, quiet, rapid response interfaces between electronic controls and mechanical systems. MR fluid dampers are relatively new semi-active devices that utilize MR fluids to provide controllable damping forces. The focus of this work is to synthesize and characterize the MR fluids. The first phase of the work (i.e., synthesis) involves the mixture of carrier fluid, iron particles and additives in measured quantities to form an MR fluid. This is then followed by the second phase (i.e., characterization) where the synthesized MR fluids are characterized using a suitable damper to obtain the force-velocity, pressure-velocity and variable input current behavior.

  Synthesis, Characterization, MR Fluids, MR Damper


1.     P. Phulé (2001), Magnetorheological (MR) fluids: principles and applications, Smart Materials Bulletin 2001/2 pp. 7-10.
2.     S.T. Lim, M.S. Cho, I.B. Jang, H.J. Choi (2004), Magneto rheological characterization of carbonyl iron based suspension stabilized by fumed silica, Journal of Magnetism and Magnetic Materials, Vol. 282, pp.170-173.

3.     G. Bossis, E. Lemaire (1991), Yield stresses in magnetic suspensions, Journal of Rheology, Vol.35  pp.1345-1354.

4.     H. Pu, F. Jiang, Z. Yang, (2006), Preparation and properties of soft magnetic particles based on Fe3O4 and hollow polystyrene microsphere composite, Materials Chemistry and Physics, Vol.100  pp.10-14.

5.     M. Kciuk, R. Turczyn (2006), Properties and application of magneto rheological fluids, Journal of Achievements in Materials and Manufacturing Engineering, Vol.18, pp.127-130.

6.     S.P. Rwei, H.Y. Lee, S.D. Yoo, L.Y. Wang, J.G. Lin (2005), Magnetorheological characteristics of aqueous suspensions that contain Fe3O4 nanoparticles, Colloid Polymer Science, Vol.283,  pp.1253-258.

7.     C. Holm, J.J. Weis (2005), The structure of ferrofluids: A status report, Current Opinion in Colloid and Interface Science, Vol.10, pp.133-140.

8.     http://www.mecheng.adelaide.edu.au/avc/publications/publi c/2006/preprint_a06_030.pdf

9.     L.M. Jansen, S.J Dyke (2000), Semi-active control strategies for MR dampers: comparative study, Journal of Engineering Mechanics, American Society of Civil Engineers, Vol.126, pp. 795-802.

10.  S.P. Rwei, H.Y. Lee, S.D. Yoo, L.Y. Wang, J.G. Lin (2005), Magneto rheological characteristics of aqueous suspensions that contain Fe3O4 nanoparticles, Colloid
Polymer Science, Vol.283, pp. 1253-1258.

11.  T. Pranoto, K. Nagaya (2005), Development on 2DOF-type and Rotary-type shock absorber damper using MRF and their efficiencies, Journal of Materials Processing Technology, Vol.161, pp.146-150.

12.  R. Turczyn, M. Kciuk (2008), Preparation and study of model megnetorheological fluids, Journal of Achievements in Materials and Manufacturing Engineering,  Vol.27/2  pp.131-134.

13.  J. Huang, J.Q. Zhang, Y. Yang, Y.Q. Wei (2002), Analysis and design of a cylindrical magnetorheological fluid break, Journal of Materials Processing Technology Vol.129  pp.559-562.

14.  K. Dhirendra, V.K. Jain, V. Raghuram (2004), Parametric study of magnetic abrasive finishing process, Journal of Materials Processing Technology, Vol149, pp. 22-29.

15.  K. Shimada, Y. Wu, Y. Matsuo, K. Yamamoto (2005), Float polishing technique using new tool consisting of micro magnetic clusters, Journal of Materials Processing Technology Vol.162-163, pp.690-695.

16.  Bica (2004), Magnetorheological suspension electromagnetic brake, Journal of Magnetism and Magnetic Materials, Vol.270, pp.321-326.





Hazeena A J, Sumimol L

Paper Title:

An Improved Calibration Specific Self Localization Routing Protocol in Wireless Sensor Networks

Abstract:    Localization problem is inevitable to maintain flawless performance of the Wireless Sensor Networks (WSN) which are typically based on accurate location of the sensor nodes. Sensor nodes are distributed randomly and there is no supporting infrastructure to manage after deployment. Various localization algorithms were implemented to empower the optimized discovery of the node with Maximum Likelihood (ML) and high degree of precision in routing protocols. Typical strategies were employed to improve the sensor location information by discarding the structural errors generated during the position estimation via calibration schemes in localization algorithms. Certain technologies are concentrated on either implementing calibration methods or optional error detection schemes by using Maximum likelihood methods. The proposed scheme uses a calibration method in self Localization algorithm with an augmented routing protocol to obtain the optimized location of the sensor nodes. This method is enhanced from the AODV Routing Protocol provided with an iterative calibration method which accurately estimates the localization information based on the likelihood calculated previously and comparing the relative location  with the reference node position. After ascertaining the minimal error in relativity parameter the routing protocol updates the optimal location and then establishing normal routing with other nodes. The efficiency and throughput analysis is estimated using the network simulator version 3.24. The proposed calibration scheme is efficient for sensitive sensor platforms to improve the performance characteristics of sensor networks.

WSN, Decentralized localization, RSSI, TDoA  ,  AoA , ML, Calibration Scheme ,Node Filtering, AODV


1.       Murat Uney,Bernard Mulgrew, Daniel     E.Clark,”A Cooperative Approach to Sensor Localization in Distributed Fusion Networks,” IEEE Transactions On Signal Processing,10.1109/.March.2015     .
2.       Mustafa Ilhan Akba¸s, Melike Erol-Kantarc and Damla Turgut,”Localization for Wireless Sensor and Actor Networks with Meandering Mobility”,IEEE Transactions On Computers, Vol. 64, No. 4, April 2015.

3.       Nick Iliev and Igor Paprotny,“Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks”,IEEE Sensors Journal,1 0.1109/JSEN.2015.2450742.Vol. 15, No. 10, October 2015.

4.       Aditya Vempaty,Yunghsiang S. Han, and Pramod K. Varshney,”Target Localization in Wireless Sensor Networks Using Error Correcting Codes”,IEEE Transactions On Information Theory, Vol. 60, No. 1, January 2014.

5.       Thomas Anthony and Thomas C. Jannett,“Fault Tolerant and Channel Aware Target Localization in Wireless Sensor Networks that use Multi-bit Quantization”,IEEE-Journals 978-1-4799-6585-4/14/.May 2014.

6.       Gabriele Oliva, Stefano Panzieri, Federica Pascucci, and Roberto Setola,”Sensor Networks Localization: Extending Trilateration via Shadow Edges”, IEEE Transactions On Automatic Control, Vol. 60, No. 10, October 2015.

7.       Nikos Fasarakis-Hilliard, Panos N. Alevizos and Aggelos Bletsas,“Variational Inference Cooperative Network Localization With Narrowband Radios” ,978-1-4673-6997-8/15/,IEEE  ICASSP. 2624, February 2015.

8.       Asma Mesmoudi, Mohammed Feham, Nabila Labraoui,” Wireless Sensor Networks Localization Algorithms:A Comprehensive Survey”,International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013.

9.       Guangjie Han ,Huihui Xu Trung ,Q.Duong Jinfang Jiang ,Takahiro Hara “Localization algorithms ofWireless Sensor Networks: A survey”,Telecom.syst.11235-011-9564-7.Feb.2013.

10.    Ian D. Chakeres,Elizabeth M. Belding-Royer,“AODV Routing Protocol Implementation Design”, Intel Corporation UC Core grantNSF..grant.(EIA0080134).Jan.2011

11.    Giuseppe C. Calafiore, Luca Carlone, Mingzhu Wei, “Network Localization from Range Measurements:Algorithms and Numerical Experiments”,IEEE MACP4LG,grant. (RU/02/26) Piemonte PRIN.grant. 978/10/March.2010.

12.    C. Sivaram Murthy and B. S. Manoj:     Ad Hoc Wireless Networks Architectures and Protocols, Prentice Hall Communications Engineering and Emerging Technologies Series TK5103.2.M89.

13.    D. Helen and D. Arivazhagan,” Applications, Advantages and Challenges of Ad Hoc Networks”, Journal of Academia and Industrial Research (JAIR) ISSN: 2278-5213 Volume 2, Issue 8 January 2014.

14.    Azzedine Boukerche, Horacio A. B,F. Oliveira, Eduardo F. Nakamura and Fucapi Antonio A. F. Loureiro,” Localization Systems For Wireless Sensor Networks”, IEEE Wireless Communications 1536-1284/07 December 2007.

15.    Mohamed Youssef,Aboelmagd Noureldin,Abdel Fattah Yousif and Naser El-Sheimy,”Self-Localization Techniques from Wireless Sensor Networks“,IEEE Journals on Wireless Communication”,-7803-9454-2/06/January.2006.

16.    Koen Langendoen,Niels Reijers,” Distributed localization in wireless sensor networks:A quantitative comparison”, ELSEVIER- Computer Networks.

17.    Murat ¨Uney, Bernard Mulgrew, Daniel E. Clark, “A Cooperative Approach to Sensor Localisation in Distributed Fusion Networks”, IEEE Transactions On Signal Processing, Vol. 59, No. 6, June 2011

18.    Gowrishankar.S , T.G.Basavaraju    Manjaiah D.H , Subir Kumar Sarkar “Issues in Wireless Sensor Networks” Proceedings of the World Congress on Engineering 2008 Vol IWCE 2008, July 2 – 4, 2008, London, U.K.





Kiran Mohan M. S, Jayasudha J. S.

Paper Title:

Prevention of Denial of Service Attacks using Multimatch Packet Classification

Abstract:     The growth of enterprise networks demands better security and quality of service. The denial of service attacks mainly focuses on the network resources or a service of a host, thereby prevent the service is being available to the normal users. This paper contains a method that effectively prevents the denial of service attack with the help of multimatch packet classification.  The method uses multimatch packet classification for identifying the multiple matches and thereby determines the different flow of traffic.  The packet migration is enforced to limit the flow of suspected packets and thus the attacking packet flow can be limited while the normal users unaffected.  The method effectively prevents denial of service attack. The multimatch classification works at high speed by identifying and isolating the attacking flows.

  Routers, packet classification, multiple match, denial of service


1.     Snort, “A free lightweight network intrusion detection system for UNIX and Windows,” 2013 [Online]. Available: http://www.snort.org
2.     P. Gupta and N. McKeown, “Packet Classification on Multiple Fields,” Proceedings Sigcomm, Comp. Commun. Rev., vol. 29, no. 4, pp. 147–60, Sept. 1999.

3.     T. V. Lakshman and D. Stiliadis, “High-Speed Policy-based Packet Forwarding Using Efficient Multi-dimensional Range Matching,” Proceedings ACM Sigcomm, pp. 191–202, Sept. 1998.

4.     V. Srinivasan et al., “Fast and Scalable Layer four Switching,” Proceedings ACMSigcomm, pp. 203–14, Sept. 1998.

5.     P. Gupta and N. McKeown, “Packet Classification using Hierarchical  Intelligent Cuttings”, IEEE Micro, vol. 20:1, pp 34-41, Jan/Feb 2000.

6.     S. Singh, F. Baboescu, G. Varghese, and J. Wang, “Packet  Classification Using Multidimensional Cutting”, ACM SIGCOMM’03, August 2003.

7.     K. Lakshminarayanan, A. Rangarajan, and S. Venkatachary, “Algorithms for advanced packet classification with ternary CAMS,”  Proceedings   ACM SIGCOMM , New York, NY, USA, pp. 193–204, 2005.

8.     M. Faezipour and M. Nourani, “Wire-speed TCAM-based architectures for multimatch packet classification,” IEEE Transaction Computer, vol.  58, no. 1, pp. 5–17, Jan. 2009.

9.     M. Faezipour and M. Nourani, “Cam01–1: a customized TCAM architecture for multi-match packet classification,”  Proceedings IEEE GLOBECOM, pp. 1–5, Dec. 2006.

10.  F. Yu, T. V. Lakshman, M. A. Motoyama, and R. H. Katz, “SSA: a power and memory efficient scheme to multi-match packet classification,” Proceedings ACM ANCS, New York, NY, USA, pp. 105–113, 2005.

11.  F. Yu, R. H. Katz, and T. V. Lakshman, “Efficient multimatch packet classification and lookup with TCAM,” IEEE Micro, vol. 25, no. 1, pp.50–59, Jan. 2005.

12.  Papaefstathiou and V. Papaefstathiou, “Memory-efficient 5D packet classification at 40 Gbps,” Proceedings 26th IEEE INFOCOM, pp. 1370–1378, May 2007.

13.  S. Dharmapurikar, P. Krishnamurthy, D.E. Taylor, “Longest Prefix Matching Using Bloom Filters”, ACM SIGCOMM’03, August 2003.

14.  Yang Xu, Zhaobo Liu, Zhuoyuan Zhang and H. Jonathan Chao, “High-Throughput and Memory-Efficient Multimatch Packet Classification Based on Distributed and Pipelined Hash Tables”,IEEE/ACM TRANSACTIONS ON NETWORKING, vol. 22, no. 3, June 2014.

15.  S. Shin, V. Yegneswaran, P. Porras, and G. Gu. AVANT-GUARD:Scalable and Vigilant Switch Flow Management in Software-Defined Networks. In Proceedings of the 20th ACM Conference on Computer and Communications Security (CCS), 2013.

16.  Haopei Wang, Lei Xu and Guofei Gu, FloodGuard: A DoS Attack Prevention Extension in Software-Defined Networks. In 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2015.





Ramitha A T, Jayasudha J S

Paper Title:

Enhanced Personalized Web Search using Pattern-based Topic Modelling

Abstract:      Personalized Web Search is a method of searching to improve the quality and accuracy of web search. It has gained much attention recently. The main goal of personalized web search is to customize search results that are more relevant and tailored to the user interests. Effective personalization needs collecting and aggregating user information that can be private or general. Personalized search results can be improved by information filtering. Information Filtering is a system to remove irrelevant or unwanted information from an information stream based on document representations which represent users’ interest. Traditional information filtering models assume that one user is only interested in a single topic. In statistical topic modelling documents and collections can be represented by word distributions. But directly applying topic models for information filtering is insufficient to distinctively represent documents with different semantic content. In order to alleviate these problems, patterns are used to represent topics for information filtering. Pattern-based representations are considered more meaningful and more accurate to represent topics than word-based representations. Pattern-based Topic Model (PBTM) combines pattern mining with statistical topic modelling to generate more discriminative and semantic rich topic representations. In the proposed system, user information preferences are acquired as a collection of documents from user browsing history. Latent Dirichlet Allocation is used to perform topic modelling on the collected documents. Word-topic assignments from LDA are used for constructing transactional dataset.  Frequent patterns are discovered from topic models. Maximum matched Pattern-based Topic Model is used to build user interest model representing the user preference information from the collection of documents and filter the incoming documents based on the user preferences by document relevance ranking.

   Topic model, Information filtering, Pattern based mining, User interest model


1.       H. Cheng, X. Yan, J. Han, and C.-W. Hsu, “Discriminative frequent pattern analysis for effective classification,” in IEEE 23rd International Conference on Data Engineering, ICDE’2007. IEEE, 2007, pp.716–725
2.       X. Wei and W. B. Croft, “LDA-based document models for ad-hoc retrieval,” in Proceedings of the 29th annual International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2006, pp. 178–185.

3.       T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1999, pp.50–57

4.       D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” the Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.

5.       Y. Gao, Y. Xu, Y. Li, and B. Liu, “A two-stage approach for generating topic models,” in Advances in Knowledge Discovery and Data Mining, PADKDD’13. Springer, 2013, pp. 221–232.

6.       S. Robertson, H. Zaragoza, and M. Taylor, “Simple BM25 extension to multiple weighted fields,” in Proceedings of the thirteenth ACM International Conference on Information and Knowledge Management. ACM, 2004, pp. 42–49

7.       Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting ranking svm to document retrieval,” in Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2006, pp. 186–193

8.       X. Li and B. Liu, “Learning to classify texts using positive and unlabeled data,” in IJCAI, vol. 3, 2003, pp. 587–592.

9.       J. Furnkranz, “A study using n-gram features for text categorization,”Austrian Research Institute for Artificial Intelligence, vol. 3, no.1998, pp. 1–10, 1998.

10.    W. B. Cavnar, J. M. Trenkle et al., “N-gram-based text categorization,”Ann Arbor MI,vol.48113, no. 2, pp. 161–175, 1994.

11.    Y. Xu, Y. Li, and G. Shaw, “Reliable representations for association rules,” Data & Knowledge Engineering, vol. 70, no. 6, pp. 555–575,2011.

12.    T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1999, pp. 50–57.

13.    Y. Gao, Y. Xu, Y. Li, and B. Liu, “A two-stage approach for generating topic models,” in Advances in Knowledge Discovery and Data Mining, PADKDD’13. Springer, 2013, pp. 221–232.

14.    C. Wang and D. M. Blei, “Collaborative topic modeling for recommending scientific articles,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011, pp. 448–456.

15.    L. Shou,H. Bai,K. Chen and G. Chen, “Supporting Privacy Protection in Personalized Web Search,” IEEE Transaction on Knowledge and Data Engineering,Vol:26,No:2, 2014.





Avinash Tiwari, Anju Malik, C.P. Singh

Paper Title:

Identification of Critical Factors Affecting Construction Labor Productivity in India Using AHP

Abstract:       Construction sector plays a leading role in economic growth for countries all around the world. Since construction is a labor intensive industry, productivity is considered a primary driving force for economic development. In India, the economy is severely challenged by the combined effects of rapid population growth and the closure policy imposed on the area since 2007. Owing to this situation, construction projects are characterized by low profit margin, time and cost overrun making labor productivity a key component of company’s success and competitiveness The main aim of this study is to identify key factors affecting labor productivity in India and to give the ranking to those factors by Analytical hierarchy process. By reviewing the literature and conducting depth  interviews with experienced engineers, twenty five critical factors related to labor productivity were identified and categorized into six groups: Psychological, Human/labor, Design, Technological, Managerial and External factors. Based on the Analytical Hierarchy Process approach, a questionnaire was designed and delivered to 72 construction professionals to elicit the view on how labor  productivity might be affected. A total of 35  feedbacks were analyzed and  the results indicated that Shortage of material, Clarity of technical specifications, payment delay, site layout & construction methods have a significant impact on construction labor productivity in India. DOI:

  keywords: Productivity; CLP; labor productivity; Identification of Critical factor; Critical factors; Construction project; Ranking of factors affecting productivity; Factor affecting productivity; Analytical Hierarchy process.


1.        , J. (1987). “Construction Productivity Improvement”. Elsevier Science Publishing, Amsterdam, Netherlands
2.        Adrian, J. (1990). “Improving Construction Productivity Seminar”, Minneapolis, MN. The Association of General Contractors of America.

3.        A Enshassi, S. Mohamed, Z. Abu Mustafa, P. E. Mayer, “Factors affecting labour productivity in building projects in the Gaza Strip.” Journal of Constuction. Engineering and Management, vol. 13(4), pp. 245-254, 2007.

4.        M. Jarkas, “Critical investigation into the applicability of the learning curve theory to rebar fixing labor productivity,”Journal of Construction Engineering and Management, vol. 136 (12), pp. 1279-1288, 2010.

5.        Abdul Kadir, M. R., Lee, W. P., Jaafar, M. S., Sapuan, S. M., and Ali, A. A. (2005). “Factors affecting construction labor productivity for Malaysian residential projects.” Structure Survey, 23(1), 42-54.

6.        Alarcon, L. F Borcherding, J. D., and. (1991). “Quantitative effects on construction productivity.” The Construction Lawyer, American Bar Association, 11(1), 35-48.

7.        Al-Shahri, M., Assaf S., A., Atiyah S., and AbdulAziz.A, (2001). “The management of construction company overhead costs.” International Journal of Project Management, 19, 295303.

8.        Alum, J., and Lim, E. C. (1995). “Construction productivity: Issues encountered by contractors in Singapore.” International Journal of Project Management, 13(1), 51-58.

9.        Anu V. Thomas and J. Sudhakumar “Factors Influencing Construction Labour Productivity: An Indian Case Study” Journal of Construction in Developing Countries, 19(1), 53–68, 2014 

10.     Anurag Sangole1, Amit Ranit2 “Identifying Factors Affecting Construction Labour Productivity in Amravati” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438

11.     Arditi, D. and Mochtar, K. (2000) “Trends in Productivity Improvement in the US Construction Industry”, Journal of Construction Management and economics, Vol. 18, 15- 27.

12.     Bohrnstedt, G, and Knoke, D (1994). “Statistics for Social Data Analysis (3rd Edition)”. F.E. Peacock Publishers, Inc., Itaska IL.

13.     Borcherding, J. D, and Liou, F.-S. (1986). “Work sampling can predict unit rate productivity.” Journal of Construction Engineering and Management, 112(1), 90-103.

14.     Cheung, S. O., Suen, H. C. H., and Cheung, K. K. W. (2004). “PPMS: A web-based construction project performance monitoring system.” Automation in Construction, 13(3), 361-376.

15.     DeCenzo, D, and Holoviak, S. (1990). “Employee Benefits.” Prentice Hall, City, New Jersey, 5556.

16.     Drewin, F. J. (1982). Construction Productivity: Measurement and Improvementthrough Work Study, Elsevier Science Ltd., NewYork.

17.     Guhathakurta, S. and Yates, J. (1993). “International labor productivity.” Journal of Construction Engineering, 35(1), 15-25.

18.     Hanna, A. S., Taylor, C. S., and Sullivan, K. T. (2005). “Impact of extended overtime on construction labor productivity.” ASCE Journal of Construction Engineering Management, 131(6), 734-740.

19.     Hasan Hamouda, Nadine Abu-Shaaban* “Enhancing Labour Productivity within Construction Industry through Analytical Hierarchy

20.     Process, the Case of Gaza Strip” Universal Journal of Management 3(8): 329-336, 2015

21.     Heizer, J., and Render, B. (1990). Production and Operations Management “Strategic and Tactical Decisions.” Prentice Hall, NJ.

22.     Hinze, J. W. (1999). “Construction Planning & Scheduling.” Prentice Hall, Upper Saddle River, NJ.

23.     Horner, R. M. W., and Talhouni, B. T. (1995). “Effects of Accelerated Working, Delays, and Disruptionson Labor Productivity.” Chartered Institute of Building, London.

24.     Iyer, K. C., and Jha, K. N. (2005). “Factors affecting cost performance: Evidence from Indian construction projects.” International Journal of Project Management, 23, 283-295.

25.     Jarkas, A. M. (2005). “An investigation into the influence of build-ability factors on productivity of in situ reinforced concrete construction.” Ph.D. thesis, University of Dundee, Dundee, UK.

26.     Kaming, P.  F., Olomolaiye, P. O., Holt, G. D., and Harris, F. C. (1997). “Factors influencing craftsmen’s productivity in Indonesia.” International Journal of Project Management, 15(1), 2130. 

27.     Kim, D. H. (1993), “The individual and organizational learning,” Sloan Management Review, 38:49

28.     Leonard, C. A. (1987). “The Effect of Change Orders on Productivity.” The Revay Report, Online. World Wide Web Revay Rep., 6(2), 1-4.

29.     Makulsawatudom, A., and Emsley, M. (2002). “Critical factors influencing construction productivity in Thailand. Proceedings of CIB 10th International Symposium
on Construction Innovation and Global Competitiveness” Cincinnati, OH.

30.     Mistry Soham, Bhatt Rajiv “Critical FactorsAffectin Labour Productivity In Construction Projects: Case Study Of South Gujarat Region Of India”International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013

31.     Mr.C.Thiyagu (Student)1, Mr.M.Dheenadhayalan (Guide)2) “Construction Labor Productivity and its Improvement”    International Research Journal of Engineering and Technology (IRJET) Volume: 02 Issue: 08 Nov-2015   

32.     Olomolaiye, P. O., Wahab, K., and Price, A. (1987). “Problems influencing craftsman productivity in Nigeria.” Building Environment, 22(4), 317-323

33.     Paulson, B. C. (1975). “Estimation and control of construction labor costs”. Journal of Construction Division, 101(CO3), 623-633.  

34.     Rajen B. Mistry1, Mr. Vyom B. Pathak, Dr. Neeraj D. Sharma3 “Evaluation of Factor affecting for Labour Productivity in Construction project by AHP” International Journal of Science and Engineering ISSN: 2454 – 2016

35.     Sanders, S. R. and Thomas, H. R. (1991). “Factors affecting masonry productivity.” Journal of Construction Engineering Management, 117(4), 626-644.

36.     Stall, M. D. (1983). “Analyzing and improving productivity with computerized questionnaires and delay surveys.” Proceedings of the Project Management Institute Annual Seminar

37.     Sumanth, D. J. (1984). “Productivity Engineering and Management.” McGraw-Hill, New York, NY.

38.     Thomas, H. R. (1991). “Labor productivity and work sampling: The bottom line.” Journal of Construction Engineering and Management, 117(3), 423-444.

39.     Thomas, H. R., and Kramer, D. F. (1988). “The manual of construction productivity measurement and performance evaluation.” Source Document 35, Construction Industry Institute, The University of Texas at Austin.

40.     Thomas, H. R., and Sakarcan, A. S. (1994). “Forecasting labor productivity using the factor model.” Journal of Construction Engineering and Management, 120(1), 228-239.

41.     Thomas, H. R., Riley, D. R., and Sanvido, V. E. (1999). “Loss of labor productivity due to delivery methods and weather.” Journal of Construction Engineering and Management, 125(1), 39-46. 

42.     Vaishant Gupta1, R. Kansal2 1M.E. Student Civil Department MITS Gwalior 474005 “Improvement  of Construction Labour Productivity in Chambal Region”   IJRET: International Journal of Research in Engineering and Technology     eISSN: 2319-1163 | pISSN: 2321-7308