International Journal of Engineering and Advanced Technology(TM)
Exploring Innovation| ISSN:2249-8958(Online)| Reg. No.:61902/BPL/CE/2011| Published By BEIESP| Impact Factor: 5.02
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-6 Issue-4 Published on April 30, 2017
Volume-6 Issue-4 Published on April 30, 2017

 Download Abstract Book (It will be upload on April 30, 2017)

S. No

Volume-6 Issue-4, April 2017, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Nidhal Kamel Taha El-Omari, Ahmad H. Al-Omari, Ali Mohammad H. Al-Ibrahim, Tariq Alwada’n

Paper Title:

Text-Image Segmentation and Compression using Adaptive Statistical Block Based Approach

Abstract:  Images and scanned text documents are gradually more used in a vast range of applications. To reduce the needed storage or to accelerate their move through the computers networks, the document images have to be compressed. Traditional compression mechanisms, which are generally developed with a particular image type and purpose, are facing many challenges with mixed documents. This paper describes a statistical block-based technique for an automatic document image segmentation and compression. Based on the number of detected colors in each region of the image, this approach creates a new representation of the image that can produce very highly-compressed document files that nonetheless retain excellent image quality. The proposed algorithm segments the compound document image into blocks of equal size. The blocks are classified into seven different categories. Each category represents an image part that shares the same properties. A new representation of each category is formed and the similar adjacent blocks are merged to form labeled regions sharing the same properties. At the end, to achieve better compression ratio, the different regions of the image are compressed using different compression techniques.

Adaptive Compression, Block-Based Segmentation, Image Document Compression, Image Segmentation, Lookup Dictionary Table (LUD).


1.       Acharyya, M. and Kundu, M.K. (2002). “Document Image Segmentation Using Wavelet Scale-Space Features”, IEEE Transactions Circuits Syst. Video Technol., Volume 12, Issue 12, pp. 1117–1127.
2.       Nidhal Kamel Taha El Omari. (2008). “A Hybrid Approach for Segmentation and Compression of Compound Images”, PhD Dissertation, the Arab Academy for Banking and Financial Sciences.

3.       Nidhal Kamel Taha El-Omari and Arafat A. Awajan. (December 20-22, 2009). “Document Image Segmentation and Compression Using Artificial Neural Network Based Technique”, International Conference on Information and Communication Systems (ICICS09), pp. 320-324, Amman, Jordan.

4.       Kai Uwe Barthel et al., (January 2000). “New Technology for Raster Document Image Compression”, SPIE. The International Society for Optical Engineering, Volume 3967, pp. 286-290, San Jose, CA.

5.       Patrice Y. Simard et al., (March 23-25, 2004). “A Foreground/Background Separation Algorithm for Image Compression”, IEEE Data Compression Conference (DCC), pp. 498–507, Snowbird, UT, USA.

6.       Ricardo L. de Queiroz et al., (February 1999). “Mixed Raster Content (MRC) Model for Compound Image Compression”, SPIE the International Society for Optical Engineering, Volume 3653, pp. 1106-1117.

7.       Ricardo L. de Queiroz. (October 8-11, 2006). “Pre-Processing for MRC Layers of Scanned Images”, Proceedings of the International Conference on Image Processing (ICIP), Atlanta, Georgia, USA, pp.  3093–3096.

8.       Lihong Zheng and Xiangjian He. (2004). “Edge Detection Based on Modified BP Algorithm of ANN”, Conferences in Research and Practice in Information Technology (RPIT), Volume 36, pp. 119–122.

9.       Guotong Feng and Charles A. Bouman. (October 2006). “High Quality MRC Document Coding”, IEEE Transactions Image Processing, Volume 15, Issue 10, pp. 3152-3169.

10.    Leon Bottou, Patrick Haffner et al., (July 1998). “High Quality Document Image Compression with DjVu”, Journal of Electronic Imaging, Volume 07, Issue 3, pp. 410-425.

11.    Wenpeng Ding et al., (January 30, 2007). “Rate-Distortion Optimized Color Quantization for Compound Image Compression”, Visual Communications and Image Processing Conference, SPIE Proceedings, Volume 6508, pp.  65082Q1-65082Q9, San Jose, CA, USA.

12.    Tony Lin and Pengwei Hao. (August 2005). “Compound Image Compression for Real Time Computer Screen Image Transmission”, IEEE Transactions on Image Processing, Volume 14, Issue 8, pp. 993-1005.

13.    Wenpeng Ding et al., (2006). “Block-based Fast Compression for Compound Images”, ICME, paper ID 1722, pp. 809–812.

14.    Debargha Mukherjee et al., (June 2002). “JPEG2000-Matched MRC Compression of Compound Documents”, IEEE International Conference on Image Processing (ICIP), Volume 3, pp.  225-228.

15.    Cheng H. and Bouman C. A. (April 2001). “Document Compression Using Rate-Distortion Optimized Segmentation”, Journal of Electronic Imaging, Volume 10, Issue 2, pp. 460–474.

16.    Nidhal Kamel Taha El-Omari et al., (2012). “Innoviate Text-Image Compression Technique”, European Journal of Scientific Research, © EuroJournals Publishing Inc., Volume 88, Issue 4, pp.  603-616.

17.    Gnana King, G.R.1 and Seldev Christopher, C.2. (2014). “Improved block based segmentation algorithm for compression of compound images”, Journal of Intelligent & Fuzzy Systems, Volume 27, Issue 6, pp.  3213-3225.

18.    Qindong Sun et al., (2015). “A Method of Image Segmentation based on the JPEG File Stream”, Journal of Computational Methods in Sciences & Engineering, Volume
15, Issue 3, pp.  467-475.

19.    Bo Chen et al., (June 2015). “A new image segmentation model with local statistical characters based on variance minimization”, Applied Mathematical Modelling, Volume 39, Issue 12, pp. 3227-3235.

20.    Gagan Jindal and Sikander Singh Cheema, (2016), “Review Paper of Segmentation of Natural Images using HSL Color Space Based on K- Mean Clustering”,
International Journal of Innovations & Advancement in Computer Science, Volume 5, Issue 7, pp.  26-29.

21.    Zhanjiang Zhi et al., (2016), “Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method”, Numer. Math. Theor. Meth. Appl., Volume 9, Issue 3, pp.  451-469.

22.    Haifeng Sima et al., (2016), “Objectness Supervised Merging Algorithm for Color Image Segmentation”, Mathematical Problems in Engineering, Volume 2016, Article ID 3180357, pp.  1-11.

23.    S.Thayammal, and D.Selvathi., (2013), “A Review On Segmentation Based Image Compression Techniques”, Journal of Engineering Science and Technology Review, Volume 6, Issue 3, pp.  134-140.

24.    Ian Sommerville, (2015), “Software Engineering”, 10th Edition, Pearson Education, Inc., ISBN-13: 978-0133943030, New York, USA.

25.    Er. Kuldeep Kaur et al., (2016), “Comparative Analysis of Compression Techniques: A Survey”, International Research Journal of Engineering and Technology (IRJET), Volume 03, Issue: 04, pp. 1042-1046.




Akhilesh Kumar Sharma, Brijendra Kumar Sharma

Paper Title:

Development of Sensors on Android Platform

Abstract: Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. In this research, we propose and demonstrate my DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called ‘Global sensor n/w (GSN)’ with the android platform to capture sensor data. We present the design, implementation, evaluation, and user experiences of the Cence-me application, which represents the first system that combines the inference of the presence of individual using off-the self sensor enabled mobile phones with sharing of this information through social networking applications such as face-book and my-space. An android based application that monitors the vehicle through an On Board Diagnostics (OBD-2) interface, being able to detect accidents. 

 DAM4GSN architecture, Cence-Me application, On Board Diagonstics (OBD-2) interface.


1.       K. Aberer, M. Hauswirth, and A. Salehi. Infrastructure for data processing in large-scale interconnected sensor networks. In Mobile Data Management, 2007 International Conference on, pages 198–205.
2.       L. Cai, S. Machiraju, and H. Chen. Defending   against sensor-sniffing attacks on mobile phones. In Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds, MobiHeld ’09, pages 31–36, New York, NY, USA, 2009. ACM.

3.       Crossbow Technology Inc. Crossbow-manuals getting started guide. Technical report, Crossbow Technology, September 2005.

4.       F. Fitzek, M. Pedersen, G. P. Perrucci, and T. Larsen. Energy and link measurements for mobile phones using ieee802.11b/g. In Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops, 2008. WiOPT 2008. 6th International Symposium on, page 36, april 2008.

5.       Google Inc. Android developer guide: Sensors, 2011. [Accessed on: 2011-12-26].

6.       GSN Team. Global sensors networks. Technical report, Ecole Polytechnique Federale de Lausanne (EPFL), 2009.

7.       GSN Team. Global sensor networks project, 2011. [Accessed on: 2011-12-16].

8.       P. Guillemin and P. Friess. Internet of things strategic research roadmap. echnical report, The Cluster of European Research Projects, 2009.

9.       P. Klasnja, S. Consolvo, T. Choudhury, R. Beckwith, and J. Hightower. Exploring privacy concerns about personal sensing. In Proceedings of the 7th International Conference on Pervasive Computing, Pervasive ’09, pages 176–183, Berlin, Heidelberg, 2009. Springer-Verlag.

10.    G. Kortuem, F. Kawsar, D. Fitton, and V. Sundramoorthy. Smart objects as building blocks for the internet of things. Internet Computing, IEEE, 14(1):44–51, 2010.

11.    N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell. A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9):140 –150, sept. 2010.

12.    M. Lennighan. Total telecom: Number of phones exceeds population of world, May 2011. [Accessed on: 2011-12-30].

13.    Salehi. Design and implementation of an efficient data stream processing system. PhD thesis, Ecole Polytechnique Federale de Lausanne (EPFL), 2010.

14.    H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffl´e. Vision and challenges for realising the internet of things. Technical report, European Commission Information Society and Media, 2010.




Ahmed Sharieh, Raja Masadeh

Paper Title:

Implementing Fair Resource Synchronizer Algorithm for Distributed Mutual Exclusion in Mobile Computing Environment

Abstract:  Mutual exclusion in distributed systems is a critical feature required to coordinate access to shared resources. It is highly needed to be employed in distributed systems including mobile computing environments. Dynamic Resource Synchronizer algorithm (DRS) works on decreasing the amount of messages that transferred in the system by minimizing the amount of sites that are included in the mutual exclusion. In this paper, a DRS algorithm is presented with a simulation study for distributed mutual exclusion that could be used in mobile environments in which nodes communicate with each other based onto specific conditions. Also, ring topology is used, all nodes have a unique identifier, a node failure doesn’t occur, communication links are bi-directional, and First In First Out (FIFO) priority and a partition in a network doesn’t occur. In addition, decreasing the amount of storage which is needed at various sites on the system. The DRS algorithm proved that the mutual exclusion is achieved. Whereas, deadlock and starvation are impossible to occur. Thus development mutual exclusion algorithm is one of the most appropriate for mobile computer systems.

Distributed systems, synchronization, mutual exclusion, mobile computing.


1.       Sharieh, A., Itriq, M., & Dbabat, W. (2008). A dynamic resource synchronizer mutual exclusion algorithm for wired/wireless distributed systems. American Journal of Applied Sciences, 5(7), 829-834.‏
2.       Badrinath, B. R., Acharya, A., & Imielinski, T. (1994, June). Structuring distributed algorithms for mobile hosts. In Distributed Computing Systems, 1994., Proceedings of the 14th International Conference on (pp. 21-28). IEEE.‏

3.       Liu, D., Liu, X., Qiu, Z., & Yan, G. (2003). A high efficiency Distributed Mutual Exclusion algorithm. In Advanced Parallel Processing Technologies (pp. 75-84). Springer Berlin Heidelberg.‏

4.       Erciyes, K. (2004). Distributed mutual exclusion algorithms on a ring of clusters.In Computational Science and Its Applications–ICCSA 2004 (pp. 518-527). Springer Berlin Heidelberg.‏

5.       Ricart, G., & Agrawala, A. K. (1981). An optimal algorithm for mutual exclusion in computer networks. Communications of the ACM, 24(1), 9-17.

6.       Lejeune, J., Arantes, L., Sopena, J., & Sens, P. (2015). A fair starvation-free prioritized mutual exclusion algorithm for distributed systems. Journal of Parallel and Distributed Computing, 83, 13-29.‏

7.       Tamhane, S. A., & Kumar, M. (2012). A token based distributed algorithm for supporting mutual exclusion in opportunistic networks. Pervasive and Mobile Computing, 8(5), 795-809.‏

8.       Lodha, S., & Kshemkalyani, A. (2000). A fair distributed mutual exclusion algorithm. Parallel and Distributed Systems, IEEE Transactions on, 11(6), 537-549.‏

9.       Ding, Z., Zhou, M., & Wang, S. (2014). Ordinary Differential Equation-Based Deadlock Detection. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 44(10), 1435-1454.‏

10.    Du, Y., & Gu, N. (2015, December). Accelerating Reachability Analysis on Petri Net for Mutual Exclusion-Based Deadlock Detection. In 2015 Third International Symposium on Computing and Networking (CANDAR) (pp. 75-81). IEEE.‏

11.    Lodha, S., & Kshemkalyani, A. (2000). A fair distributed mutual exclusion algorithm. IEEE Transactions on Parallel and Distributed Systems, 11(6), 537-549.‏

12.    Lamport, L. (1978). Time, clocks, and the ordering of events in a distributed system. Communications of the ACM, 21(7), 558-565.‏

13.    Jani, K., & Kshemkalyani, A. D. (2004, December). Performance of fair distributed mutual exclusion algorithms. In International Workshop on Distributed Computing (pp. 2-15). Springer Berlin Heidelberg.

14.    Kanrar, S., Chattopadhyay, S., & Chaki, N. (2013). A New Link Failure Resilient Priority Based Fair Mutual Exclusion Algorithm for Distributed Systems. Journal of network and systems management, 21(1), 1-24.

15.    Lejeune, J., Arantes, L., Sopena, J., & Sens, P. (2015). A fair starvation-free prioritized mutual exclusion algorithm for distributed systems. Journal of Parallel and Distributed Computing, 83, 13-29.

16.    Kundu, S. (2005, December). Deadlock-Free distributed relaxed mutual-exclusion without revoke-messages. In International Workshop on Distributed Computing (pp. 463-474). Springer Berlin Heidelberg.‏

17.    Suzuki, I., & Kasami, T. (1985). A distributed mutual exclusion algorithm. ACM Transactions on Computer Systems (TOCS), 3(4), 344-349.

18.    Singhal, M. (1989). A heuristically-aided algorithm for mutual exclusion in distributed systems. IEEE transactions on computers, 38(5), 651-662.‏

19.    Raymond, K. (1989). A tree-based algorithm for distributed mutual exclusion. ACM Transactions on Computer Systems (TOCS), 7(1), 61-77.‏

20.    Itriq, M., Dbabat, W., & Sharieh, P. (2013). Adaptive Dynamic Resource Synchronization Distributed Mutual Exclusion Algorithm (ADRS). Journal of Theoretical & Applied Information Technology, 53(3).‏

21.    Altamony, H., Alshurideh, M., & Obeidat, B. (2012). Information Systems for Competitive Advantage: Implementation of an Organisational Strategic Management Process. Proceedings of the 18th IBIMA Conference on Innovation and Sustainable Economic Competitive Advantage: From Regional Development to World Economic, Istanbul, Turkey, 9th-10th May.

22.    Alkalha, Z., Al-Zu’bi, Z., Al-Dmour, H., & Alshurideh, M. (2012). Investigating the effects of human resource policies on organizational performance: An empirical study on commercial banks operating in Jordan. European Journal of Economics, Finance and Administrative Sciences, 51, 44-64.

23.    Masa’deh, R., Tayeh, M., & Al-Jarrah, I. M. (2015). Accounting vs. Market-based Measures of Firm Performance Related to Information Technology Investments. International Review of Social Sciences and Humanities, 9 (1), 129-145.

24.    Shannak, R., Obeidat, B., & Almajali, D. (2010). Information Technology Investments: A Literature Review. Proceedings of the 14th IBIMA Conference on Global Business Transformation through Innovation and Knowledge Management: An Academic Perspective, Istanbul-Turkey, 23rd-24th June, pp.1356-1368.





Jincy Das, Judith Mercy Praveena S, Mirna Genesia Asian, A. Monisha, R. Sindhuja

Paper Title:

Communication and Obstacle Detection System for the Disabled using Arduino Lilypad

Abstract: This paper proposes the design of a hand glove using Arduino Lilypad and Zigbee for the people with disability in hearing, speaking and vision. There are nearly 900,000 people who are deaf and dumb and 285 million people who are blind. This device would help for   communication by the bending of flex sensors which are fixed on the glove and the obstacles are detected by Ultrasonic sensor. And the combination of input is processed by the microcontroller Arduino Lilypad. The processed value is transmitted through the Zigbee to Microcontroller- AT89S2051 and the sign language is recognized and the corresponding value is obtained through LCD and speaker. The obstacle is sensed by Ultrasonic sensor and the person is alerted through vibration. The proposed system is compact, wireless and easy to use.

Arduino Lilypad, Flex sensor, Hand glove, Microcontroller- AT89S2051, Ultrasonic sensor, Vibration motor, Voice module-WTV040, Zigbee-CC2500.


1.       Arslan Arif, Syed Tahir Hussain Rizvi, Iqra Jawaid, Muhammad Adam Waleed, Muhammad Raheel Shakeel “Techno-talk: An American Sign Language (ASL) Translator.2016 International Conference on Control, Decision and Information Technologies (CoDIT) – IEEE Conference Publications.
2.       P.Vamsi Praveen, K.Satya Prasad “Electronic Voice to Deaf and Dumb People Using Flex Sensor”. International Journal of Innovative Research in Computer and Communication Engineering – Vol.4, Issue 8, August 2016.

3.       Dhiraj Gupta, Pankhuri Singh, Khushbu Pandey, Jaya, Solanki “Design and Development of a Low Cost Electronic Hand Glove for Deaf and Blind”. IEEE-2015, 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

4.       Neha Niharika, Heena, Bhavnesh Jaint “An Electronic Aid for the Mobility of Visual Impaired “ IEEE-2015.

5.       Ranjit A Wagh, Dr.U.B.S. Chandrawat “Hand Gloves for Deaf and Mute Person using Flex Sensor a Survey”. International Conference on Global Trends in Engineering, Technology and Management ICGTETM – 2016.

6.       Thuong N.Hoang, Ross T.Smith, Bruce H.Thomas “Ultrasonic Glove Input Device for Distance Based Interaction”.

7.       Adam Keyes, Mathew D’Souza and Adam Postula “Navigation for the Blind Using a Wireless Sensor Haptic Glove “- 4th Mediterranean Conference on Embedded Computing MECO – 2015.

8.       Mrs.Neela Harish, Dr.S.Poonguzhali “Design and Development of Hand Gesture Recognition System for Speech Impaired People”. - International Conference on Industrial Instrumentation and Control (ICIC) – 2015 May.

9.       J.Thilagavathy, A.Jeyapaul Murugan, S.Darwin “Embedded Based Hand Talk Assisting System for Deaf and Dumb. International Journal of Engineering Research and Technology (IJERT) – Vol.3, Issue 3, 2014 March.

10.    Leah Buchley and Michael Eisenberg. “The Lilypad Arduino: Toward Wearable Engineering for Everyone – IEEE.





Mena Ahmed, Abdul Halim Ghazali, Thamer Ahmed Mohammad, Badronnisa Yusuf, Aminuddin Abdul Ghani

Paper Title:

Hydraulic Simulation of Flow Around Spur Dikes

Abstract:  The morphological changes of rivers, which are manifested by bed and banks deformations, show a direct relationship with water flow and sediment transport. Spur dikes are among the most common structures used to regulate velocity distribution and control sedimentation in a river section. This paper aims to simulate the hydraulic properties of steady turbulent flow in a straight rectangular open channel which has spur dikes with various configurations, such as number, alignment and lengths. The effects of the spur dikes on the velocity distribution have been evaluated three-dimensional (3D) Computational Fluid Dynamic (CFD) method. The simulated results from the model are calibrated and validated using data obtained from physical model. Different scenarios with spur dikes were simulated, and the results were demonstrated using the isovels, velocity magnitudes and mass exchange between spur dikes fields and main flow. Eventually, each scenario gives a better understanding on employing spur dikes for river restoration, enhancing navigation (by increasing water depth and rearranging the thalweg line), and protecting abutments and pump intakes against erosion as well as creating stable aquatic habitat.

 Hydraulic simulation, velocity distribution, spur dike, river restoration.


1.       Azinfar, H., “Flow resistance and associated backwater effect due to spur dikes in open channels,” Thesis, University of Saskatchewan, Saskatoon, Canada, 2010.
2.       Chang, Y., Hsieh, T., Chen, C., & Yang, J., “Two-dimensional numerical investigation for short- and long-term effects of spur dikes on weighted usable area of rhinogobius candidianus (Goby),” Journal of Hydraulic Engineering, (December) 2013, 1297–1303.

3.       Chrisohoides, A., Sotiropoulos, F., & Sturm, T.W., “Coherent structures in flat-bed abutment flow: computational fluid dynamics simulations and experiments” Journal of Hydraulic Engineering, 129(3), 2003, 177-186.

4.       Dargahi, B., “Controlling mechanism of local scouring,” Journal of Hydraulic Engineering, 116 (10), 1990 , 1197-1214.

5.       Engelhardt, C., Kruger, A., Sukhodolov, A., & Nicklisch, A. “A study of  phytoplankton spatial distributions, flow structure and characteristics of mixing in a river reach with groynes” J. Plankton Res., 26, 2004, 1351–1366.

6.       Hinterberger, C. “Three-dimensional and depth-average large eddy simulation of shallow water flows” Ph.D. thesis, Karlsruhe Univ., Karlsruhe, Germany, 2004.

7.       Kuhnle, R. A., Jia, Y., & Alonso, C. V., “Measured and simulated flow near a submerged spur dike” Journal of Hydraulic Engineering, 134 (7), 2008, 916–924.

8.       McCoy, A., Constantinescu, S. G., & Weber, L., “Exchange processes in a channel with two vertical emerged obstructions” Flow Turbul. Combust, 77, 2006, 97–126.

9.       McCoy, A., Constantinescu, S. G., & Weber, L., “A numerical investigation of the dynamics of coherent structures and mass exchange processes in a channel flow with two lateral submerged groynes” Water Resour. Res., 43, 2007, (43)5.

10.    McCoy, A., Constantinescu, G. & Weber, L. J., “Numerical investigation of flow hydrodynamics in a channel with a series of groynes” Journal of Hydraulic Engineering, 134(Feb.), 2008, 157–172.

11.    Melville, B. W. “Pier and Abutment Scour: Integrated Approach” Journal of Hydraulic Engineering, 123(2), 1997, 125-136.

12.    Pagliara, S., & Kurdistani, S. M. “Flume experiments on scour downstream of wood stream restoration structures” Geomorphology, 279, 2017, 141-149.

13.    Reynolds, C. S. “Potomoplankton: Paradigms, paradoxes, prognoses. Algae and aquatic environment” Biopress, Bristol, U.K., 1988, 285–311.

14.    Shi, F., Svendsen, I.A., Kirby, J.T. & McKee Smith, J. “A curvilinear version of a quasi 3D near shore circulation model” Coastal Engineering, 49(1–2), 2003, 99–124.
15.    Shields, F.D. “Fate of Lower Mississippi River habitats associated with river training dikes. Journal of Aquatic Conservation” Marine and Freshwater Conservation, 5(2), 1995, 97-108.
16.    Shields, F. D., Cooper, C. M., and Knight, S. S. 1995. Experiments in stream restoration. Journal of Hydraulic Engineering, 121, 494–502.

17.    Tingsanchali, T. & Maheswaran, S. “2-D depth-averaged flow computation near groyne” ASCE, Journal of Hydraulic Engineering, 116, 1990, 71-86.

18.    Tominaga, A., Ijima, K. & Nakano, Y. “Flow structures around submerged spur dikes with various relative height,” Proc. of 29th IAHR Congress, Beijing, China, Theme D, Hydraulic Structures, 2001, 421-427.

19.    Uijttewaal, W., & Van Schijndel, S. A. H. “The complex flow in groyne fields: Numerical modeling compared with experiments” Proc., River Flow 2004, Naples, Italy, 1331–3838.

20.    Wind, H.G., Vreugdenhil, C.B. “Rip-current generation near structures’ Journal of Fluid Mechanics 171, 1986, 459–476.

21.    Yossef, M. F. M. “The effects of groynes on rivers (literature review),” Delft Cluster Report No. DC1-334-4, Delft University, the Netherlands, 2002, 57-63.





Meenal P.Talekar, Ravindra Kale

Paper Title:

Review on Cryptoleq: Single Instruction Set Abstract Machine

Abstract: Today data communication mainly depends upon digital data communication, where is data security is prior requirement which become crucial now days in every sector. So in order to protect it, various methods and Algorithm have been implemented. Cryptography combines Science, Mathematics, Computer Engineering and Networking. The purpose of this research paper is (i) to find the best cryptographic algorithm for computations (ii) to study the Cryptoleq system which (iii) and finally the comparison of performance of algorithm with Cryptoleq and without Cryptoleq.

single instruction machine, heterogeneous computer, mathematical computations, encryption.


1.       Oleg Mazonka, Nektarios Georgios Tsoutsos, “Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation” in (2016).
2.       S. Halevi and V. Shoup, “Bootstrapping for HElib,” in Advances in Cryptology. Heidelberg, Germany: Springer,, 641–670,2015.

3.       J. Zimmerman, “How to obfuscate programs directly,” in Advances in Cryptology. Heidelberg, Germany: Springer, 2015, pp. 439–467.

4.       S. Halevi and V. Shoup. HElib: Design and Implementation of a Homomorphic-Encryption Library, accessed on Nov. 13, 2015.

5.       D. Apon, Y. Huang, J. Katz, and A. J. Malozemoff, “Implementing cryptographic program obfuscation,” in Proc. IACR Cryptol. ePrint Arch., 2014, p. 779.

6.       S. Garg, C. Gentry, S. Halevi, and M. Zhandry, “Fully secure functional encryption without obfuscation,” in Proc. IACR Cryptol. ePrint Arch.,2014, p. 666.

7.       P. T. Breuer and J. P. Bowen, “A fully homomorphic crypto-processor design,” in Engineering Secure Software and Systems. Heidelberg, Germany: Springer, 2013, pp. 123–138.

8.       Naser A W S and Bin Md Fadli (2013), “ Use of Cryptography in Cloud Computing”, pp. 179-184, proceedings of IEEE International Conference on Control System Malaysia.

9.       Shahzadi Farah et al.”An Experimental Study on Performance Evaluation of Asymmetric Encryption Algorithms”, Recent Advances in information Science, Proceeding of the 3rd European Conf. of Computer Science, (EECS-12)  2012.

10.    Ramgovind S, Eloff M  and smith E “ The management of security in Cloud Computing”, Proceedings of IEEE Conference 2010.




Ananya Kalita, Arnob Bormudoi, Mimi Das Saikia

Paper Title:

Probability Distribution of Rainfall and Discharge of Kulsi River Basin

Abstract: The frequency analysis of daily rainfall data of 24 years was carried out to determine the annual one day maximum rainfall and discharge of Ukiam. For evaluation of observed and expected values Weibull’s plotting position Gumbel, Log Pearson and Log normal probability distribution functions were fitted. For determination of goodness of fit chi square test was carried out by comparing the expected values with the observed values. The results found showed that the Log Pearson and Log Normal were the best fit probability distribution for determination of annual one day maximum rainfall and discharge for different return periods respectively.

  Probability distribution, Chi-Square Value


1.       Benson, M. A. (1968). Uniform flood frequency estimating methods for federal agencies. Water Resources Research, 4(5) : 891-908.
2.       Bhakar  S. R., Iqbal Mohammed,  Devanda Mukesh, Chhajed  Neeraj and  Bansal Anil K.(2008). probablity analysis of rainfall at kota, Indian J. Agric. Res., 42 (3) : 201 -206.

3.       Choudhury P. And Bora Kaushik (2015), “Estimation of annual maximum daily rainfall of Silchar, Assam,” International Conference on Engineering Trends and Science & Humanities ISSN: 2348 – 8352.

4.       Chow, V.T., "Applied Hydrology", McGraw-Hill Book Company Inc., NewYork, N.Y., 1988.

5.       Dingre S, Atre AA(2005). Probability analysis for prediction of annual  maximum daily rainfall of Srinagar region (Kashmir valley). Indian Journal of Soil Conservation , 33(3): 262-263.

6.       Dzubakova,K.(2010),Rainfallrunoffmodeling:Itsdevelopment,classification and possible applications. ACTA Geographical Univerciti Comenianae, 54, 2010, N0. 2, pp 173-181.

7.       Gharagozlou A., “Crisis Management (Flood) and GIS,” Geomatics College of NCC of Iran, Tehran, 2010, pp. 23-29.

8.       Gumbel, E. J. (1958). Statistics of Extremes, Columbia     University Press, New York.

9.       Heywood LAN, S, Cornelius and S. Carver, “Cornelius-An Introduction to Geographic Information Systems,” chap., 1998, pp. 2-5

10.    Jeevarathnam K. Jaykumar K(1979). Probability analysis for prediction of annual maximum daily rainfall for Ootacamund. Indian Journal of Soil Conservation, 7(1): 10-16.

11.    Kumar, D. and Bhattacharya, R. (2011), Distributed Rainfall Runoff Modelling. International Journal of Earth Sciences and Engineering, 4,(6) SPL, pp 270-275.

12.    Kumar, A.(2000). Prediction of annual maximum daily rainfall of Ranichauri (Tehri Garhwal) based on probability analysis. Indian Journal of Soil Conservation, 28 : 178-180.

13.    Kumar, A.,K. K. Kaushal and R.D. Singh (2007). Prediction of annual maximum daily rainfall of Almora based on probability analysis . Indian Journal of Soil Conservation, 35 : 82-83.

14.    Kumar Rajneesh and Bhardwaj Anil (2015). Probability analysis of return period of daily maximum rainfall in annual data set of Ludhiana, Punjab Indian J. Agric. Res., 49 (2) : 160-164.

15.    Prakash C, Rao DH(1986). Frequency Analysis of rain data for crop planning (Kota). Indian Journal of Soil Conservation, 14(2):23-26.

16.    Subramanya, K., "Engineering Hydrology", McGraw-Hill Book Company Inc., New York, N.Y., 1999.

17.    Subudhi, R. (2007). Probability analysis for prediction of annual maximum daily rainfall of Chakapada block of Kandhamal district in Orissa. Indian Journal of Soil Conservation, 35: 84-85.

18.    Upadhaya, A. and S. R. Singh (1998). Estimation of consecutive day's maximum rainfall by various methods and their comparison. Indian Journal of Soil Conservation, 26: 193-201.

19.    Vivekanandan, N. (2012). Intercomparison of Extreme Value Distributions for Estimation of ADMR. International Journal of Applied Engineering and Technology, 2(1) : 30-37.