International Conference on Digital Signal & Image Processing (ICDSIP)-2017
Date of Conference: March 07-08, 2017 | Organised by Maharashtra Institute of Technology, Aurangabad (Maharashtra), India
S. No
ISSN: 2249 – 8958, Volume-6, Issue-ICDSIP17, March 2017
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.
Page No.



Mahendra Gupta, Roshan Varghese, Thomson Johnson, Abhimanyu Gupta, Nilashree W

Paper Title:

Remote Controlled Quadcopter for Image Capturing

Abstract: In this paper, we describe system analysis and implementation of a quadcopter controlled by a RC remote. The motion of the quadcopter is controlled based on   measurements of inertial sensors by controlling the speed of the quadcopter using the flight controller from the accurate values obtained from the IMU (Inertial Measurement Unit). We focus on developing a cost effective system capable of stable flight. Next a camera is coupled with the quadcopter to capture images during the flight. The images are processed by resolution improvement, noise removal and by deblurring techniques.

 Thrust, Pitch, Roll, Yaw, Throttle, Gyroscope,   Quadcopter


1.    Samir and Roland,”full control of a quadcopter’, 2007, IEEE conference on Intelligent Robots and Systems,San Diego.
2.    Jakob Engel, Jurgen, Daniel,”Camera based navigatin of a lowcost quadcopter”, 2012, International conference on intelligent robot and systems, Portugal.

3.    Tommaso Bresciani,” Modelling, identification and control of quadrotor.

4.    D.Bacon,” Instruction Mannual”.

5.    Markus, Tianguang, Kolja, Martin,”Visual tracking and control of quadcopter using stereo camera system and inertial sensor’,2009,IEEE international conference on Mechatronics and automation,Changchun China.


7.    Wikipedia: ZYZ Euler’s form, Newton-Euler equations.

8.    Modelling and control of quadcopter by Teppo Luukkonen.

9.    G.M.Hoffman, H.Huang and C.J.Tomlin,”Quadcopter flight dynamics and control”, 2007, Navigation and international conference.





Jadhav Suhas Sayajirao

Paper Title:

Wireless Electricity or Energy Transmission

Abstract:  Power is very important to consumer product, generally the power is transmitted through wires. By using some techniques we can able to deliver power without wires or transmission lines also we can minimize energy consumption and wire resistances. Wireless power transmission is the transmission of electrical energy without using any conductor wire or transmission lines. Wireless power transfer system use basics of magnetic or inductive resonant coupling known as resonant energy transfer. The coupling consists of an inductor along with a capacitor with its own resonating frequency. Another technique includes transfer of power through microwaves using reactances. This is particularly suitable for long range distances ranging kilometers. Within next few years need of wires for energy transfer will outdated.

 Wireless power, Wireless energy, Inductive resonant coupling, Power or energy transmission, Wireless Electricity Transmission


1.       Benson, Thomas W., “Wireless Transmission of Power now Possible”
2.       U.S. Patent 787, 412, “Art of Transmitting Electrical Energy through the Natural Mediums”.

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4.       H. Khorashadi-Zadeh, M. Sanaye-Pasand. (2006). Correction of saturated current transformers secondary current using ANNs, IEEE Trans. Power Delivery, 21, 1, pp. 73–79.

5.       Zia A. Yamayee and Juan L. Bala, Jr., “Electromechanical Energy Devices and Power Systems”, John Wiley and Sons, 1947, p. 78

6.       Simon Ramo, John R. Whinnery and Theodore Van Duzer, “Fields and Waves in Communication Electronics”, John Wiley & Sons, Inc.; 3rd edition (February 9, 1994)

7.       Tomohiro Yamada, Hirotaka Sugawara, Kenichi Okada, Kazuya Masu, and Akio Oki, “Battery-less Wireless Communication System through Human Body for invivo Healthcare Chip,”IEEE  Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems”

8.       Kurs, A. Karalis, R. Moffatt, J. D. Joannopoulos, P. Fisher, M. Soijacic, “Wireless Power Transfer via Strongly Coupled Magnetic Resonances”, Massachusetts Institute of Technology, 2007 Science, Vol. 317. no. 5834, pp. 83— 86, 2007.

9.       Tesla’s Tower of Power. Information available at the following link,

10.    A.C.M de Queiroz, “The Triple Resonance Network with Sinusoidal Excitation”, EE/COPPE, Universidade Federal do Rio de Janerio, Brazil.

11.    Benjamin L. Cannon, James F. Hoburg, Dabiel D. Stancill, Seth Copen Goldstein, Magnetic Resonant Coupling as a Potential Means for Wireless Power Transfer to Multiple Small Receivers, IEEE Transactions on Power Electronics, Vol. 24, No.7, July 2009.

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Shaikh Mudassar Akhtar, Pandit Anand Purushottam, Khirade Prakash Waghji, Sayyad Shafiyoddin Badruddin

Paper Title:

Support Vector Machine (SVM) Based Supervised Classification Analysis for Microwave SIR-C POL SAR Image

Abstract: The classification of microwave Polarimetric Synthetic Aperture Radar (PolSAR) image analysis has become a very important task after the availability of data from satellite by using different existing technique classification techniques. In the last decade, classification of PolSAR imagery has been deliberate using several different approaches. This paper presents a supervised classification method applied on microwave L-band SIR-C SAR data of Kolkata, West Bengal, India image. Here initially for the preprocessing speckle filter and decomposition technique is applied to original selected SAR image and later the image is classified using supervised classification techniques. In this paper Support Vector Machine (SVM) classification technique is used as a supervised classification. The present paper work using confusion matrix the error like omission and commission and the accuracy was assessed for the selected dataset. The accuracy for SVM classified image is improved up to 99.58% and hence it is observed that among the supervised classifiers SVM classification technique is one of the best technique for classification.

  Classification, PolSAR, SIR-C, Support Vector Machine (SVM).


1.       Jensen R. J. 2014. Remote sensing of the environment an earth resource perspective. (2nd ed.) Pearson.
2.       M. A. Shaikh, P. W. Khirade, S. B. Sayyad. 2016. Classification of Polarimetric SAR (PolSAR) image analysis using decomposition techniques. International Journal of Computer Application (IJCA) Proceedings on National Conference on Digital Image & Signal Processing (NCDISP 2016).  1, 20-23.

3.       Chi, M., Feng, R., Bruzzone, L. 2008. Classification of hyperspectral remote sensing data with primal SVM for small-sized training dataset problem. Advances in Space Research. 41, (11), 1793–1799.

4.       M. A. Shaikh, P. W. Khirade, S. B. Sayyad. 2016, July. Unsupervised and supervised classification of PolSAR image using decomposition techniques: an analysis from L- band SIR-C data. Asian Journal of Multidisciplinary Studies (AJMS). 4, (8), 140-144.

5.       V.N. Vapnik. 1998. Statistical learning theory. John Wiley and Sons.

6.       G. Mercier, M. Lennon. 2003. Support vector machines for hyperspectral image classification with spectral-based kernels. In Proceedings of IGARSS, Toulouse, France. 1, 288-290.

7.       M. Chi, R. Feng, L. Bruzzone. 2008. Classification of hyperspectral remote-sensing data with primal SVMS for small-sized training data. Advances in Space Research. 41, 1793-1799.

8.       C. Lardeux, P.L. Frison, J.P. Rudant, J.C. Souyris, C. Tison, B. Stoll. 2006. Use of the SVM classification with polarimetric SAR data for land use cartography. In IGARSS 2006, Denver, Colorado.

9.       J.S. Lee, M.R. Grunes, E. Pottier, L. Ferro-Famil. 2004. Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Transactions on Geoscience and Remote Sensing. 42, (4), 722-731.

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11.    R. Shah, R. Hosseini, S. Homayouni. 2009. A SVMS-based hyperspectral data classification algorithm in a similarity space. In 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS2009), Grenoble, France.

12.    M. Ouarzeddine, B. Souissi. Unsupervised classification using wishart classifier. USTHB, F.E.I, BP No 32 EI Alia Bab Ezzouar, Alger.

13.    R. Touzi, W.M. Boerner, J.S. Lee, E. Lueneburg. 2004. A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction. Can. Journal. Remote Sensing. 30, (3), 380–407.

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16.    Lillesand T. M., Kiefer R.W. (1999). Remote Sensing and Image Interpretation. (4th ed.). John Wiley & Sons, Inc.

17.    V. Vapnik. 1979. Estimation of Dependences Based on Empirical Data. Nauka, Moscow, 27, 5165–5184.
18.    G. Zhu, D.G. Blumberg. 2002. Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel. Remote Sensing of Environment. 80 (2), 233–240.
19.    P. Mantero, G. Moser, S.B. Serpico, 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing. 43 (3), 559–570.

20.    L- Band SIR-C dataset download from





G.V. Avhale, A.D. Shaligram, D.C. Gharpure

Paper Title:

Design and Development of Internet of Things (IoT) based Data Acquisition System for Milk Quality Monitoring in the Dairy Industry

Abstract: The dairy industry is of crucial importance to India as 13% of the world’s total milk production. In the field of dairy industry in India, there is a need to acquire the latest technologies in order to improve the quality of milk. The Dairy industry must have a reliable Data Acquisition System for monitoring the milk quality at various stages in the dairy industry. Nowadays there is a necessity to do the long distance monitoring or monitoring from anywhere at any time. The internet is available worldwide, hence the first choice is the technology which monitors different parameters based on the internet called internet of things (IoT) technology. This paper presents an Internet of things (IoT) based data acquisition system for monitoring the quality of milk in the dairy industry. The system used an Arduino Uno board as processor and GSM SIM 900 module for internet connectivity. The data monitoring is possible by Thingspeak open IoT platform and can be accessed through any device e.g. Computer, laptop, or smart mobile phone which has internet connectivity.

  Dairy industry, Data acquisition system, end to end monitoring, Internet of Things (IoT). it is the world’s largest milk producer, accounting for more than


1.       M. F.AL. Faisal, S Bakar, PS Rudati, “The Development of A Data Acqusition System Based on Internet of Things Framework”, ICT for Smart Society (ICISS), 2014 International Conference on, 24-25 Sept. 2014.
2.       Jie ZHANG, Aicheng LI, Jianlong LI, Qi YANG, Chengcheng GANG, “Research of Real-time Image Acquisition System Based on ARM 7 for Agricultural Environmental Monitoring”, 978-1-4244-9171-1/11/2011 IEEE, pp. 6216 – 6220.

3.       Tengfei Xing, “IoT Identifier Service based-Public Food Quality Safety Traceability Platform and its Social Impacts”, Proc. of World Symposium on Computer Networks and Information Security, DOI: 02. WSCNIS.2014.1.15, pp. 93 – 98.

4.       M. S. Patil, Dr. M. A. Shinde, “SWOT Analysis: Problems and Prospects of Dairy Industry in India”, INDIAN JOURNAL OF APPLIED RESEARCH, Volume: 6, Issue: 3, March 2016, ISSN – 2249-555X, IF: 3.919, pp. 494 – 497.

5.       Feng Tian, “A Quality and Safety Control System for China’s Dairy Supply Chain Based on HACCP & GS1”, 978-1-5090-2842-9/16/Q016 IE.

6.       Gh. R. Jahed Khaniki, “Chemical contaminants in milk and public health concerns: A Review”, International journal of dairy science 2 (2): pp. 104-115, 2007. ISSN

7.       Article on “Milk quality incentives” – John H. kirk.

8.       Wu Li, Guofang Nan, Ting Yang, “Service-oriented Sensor Data Query System for Monitoring Milk Storage, Production and Delivery”, 978-1-4244-2972-1/08/ 2008 IEEE.

9.       Jun Jiao, Huimin Ma, Yan Qiao, Yulin Du, Wen Kong and Zhongcheng Wu,  “Design of Farm Environmental Monitoring System Based on the Internet of Things”, Advance Journal of Food Science and Technology 6 (3), 2014 ISSN: 2042-4868; e-ISSN: 2042-4876, pp. 368 – 373.

10.  //used for Arduino Uno  board information

11.    Chao-Hsi Huang, Pin-Yin Shen, Yueh-Cheng Huang, “IoT-Based Physiological and Environmental Monitoring System in Animal Shelter”, ICUFN 2015, 978-1-4799-8993-5/15/ 2015 IEEE, pp. 317-322.

12.  //used for SIM 900 module information

13.  //used for information about TCPIP AT commands for SIM 900 module

14.   //used for information about ThingSpeak IoT platform.





Chaitanya Ahire, Pramod Ambhore

Paper Title:

Noise Reduction Techniques for Medical Images using Transform Domain Approach

Abstract: Medical images suffers from noise introduced during its capturing process. There are two approaches to remove this noise .i. e Transform domain and spatial domain .Spatial domain techniques leads to loss of details or important information. In this paper we have proposed a transform based denoising algorithms. We have used joint approach to denoise the medical images which uses Dual tree DWT and Rotated version of Dual Tree DWT to improve denoising results. We are mainly focusing on the mixed diagonal information which we are able to separate by using the concept of rotated filters. Results which we have obtained with the joint approach are very exciting.

  Medical images, noise, Dual tree DWT, Rotated Dual Tree DWT, PSNR, SSIM, Rotated Wavelet Filters


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Dipak Shelar, Arvind Shaligram, Damayanti Gharpure

Paper Title:

The Design of Low Power Wireless Sensor node for Food grain Warehouse Monitoring

Abstract:  Grain storage is an important issue related to the National economy and people’s livelihood, and all are valued through the ages. Pest free storage is needed for handling crops at harvest time and to carry over reserve from year to year. Considerable losses both in quality and quantity of food grains take place due to a several factors. Organisms directly responsible for causing loss in store food grains are insects, mites, rodents, fungi and bacteria. Grain temperature and moisture content are the primary factors that affect the grain storage. As temperature and moisture content increase, grain will deteriorate faster. Therefore, it necessary to monitor the physical parameters such as temperature and humidity inside food grain storages in time and take effective measures. In this paper, we have designed low power and high performance wireless sensor node using MSP430G2553 microcontroller, XBee series 1 transceiver and DHT11 temperature and Humidity sensor. Temperature and relative humidity were measured and transmitted wirelessly. We have measured and analyzed the current consumption of sensor node in active mode. The experimental result shows that the developed sensor node has good low power characteristics.

  MSP430, Wireless Sensor Networks, lifetime, Current Consumption, sensor node


1.       Xuan He, QunHao and Longhai Zhao, “The Design of Low-power Wireless Sensor Node”, 2010 IEEE Instrumentation & Measurement Technology Conference.
2.       He Jianfeng, Qu Jinhui,Wang Yuan, Pan Hengya, “The Designing and Porting of Temperature &Humidity Sensor Node Driver Based”, 2014 IEEE Workshop on Electronics, Computer and Applications with ARM-Linux.

3.       LiqiangZhengy, Jingxuan Li, Michael Hayes, Brendan O’Flynn, “Design of a Low Power Wireless Sensor Node for Environmental Monitoring”, ISSC 2011.

4.       Robert Faludi, “Building Wireless Sensor Networks”, Published by O’Reilly Media.

5.       Datasheet of MSP430G2x53 microcontroller,Avilable:

6.       Datasheet of XBee,Avilable:

7.       Datasheet of Osong DHT11 Sensor,Avilable:





S. N. Patil, A. M. Pawar, B. P. Ladgaonkar

Paper Title:

Synthesis and Deployment of Nanoferrites to Design Embedded System for Monitoring of Ammonia Gas

Abstract: Keeping pace with facets of nanotechnology and its applications in the field of development of sensors of promising features, nanoparticle spinel Manganese-Zinc ferrites have been synthesized by co-precipitation method. The formation of the materials is confirmed by characterization of the same with X-ray powder diffraction, FTIR absorption and SEM as well. These materials were used for development of the sensor for monitoring of ammonia gas. The detection of ammonia gas is essential in various industries and for environmental prediction as well. The sensing elements of the sensors are developed using thick film technology and ammonia gas sensitive electrical properties are investigated. An operating temperature was optimized to 125 0C. Ammonia gas is reducing gas and hence it plays remarkable role on the electrical properties. The electrical resistance R, measured against in concentration of ammonia, shows decreasing trend with increase in concentration of ammonia. From sensitivity data, it is confirmed that, the materials are mostly suitable for sensing of ammonia gas. Employing embedded technology and using thus prepared sensor, a sensor module is designed about AVR ATmega 8L microcontroller and deployed for monitoring of the ammonia gas. Analog part of the system is wired about TLC 271. Deployment of on-chip ADC of 10-bit resolution causes to increase in the preciseness of the results. The system is calibrated ammonia gas in %. From performance analysis, it is found that sensor module is suitable for further development of measurement instrumentation. Results of implementation are interpreted in this paper.

 Polycrystalline ferrite, X-ray, FTIR spectroscopy, ammonia gas sensor, embedded system, on-chip ADC


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4.       S.N. Patil, A.M. Pawar, S.K. Tilekar and B.P. Ladgaonkar “Investigation of magnesium substituted nano particle zinc ferrites forrelative humidity sensors” Sensors and Actuators A 244 (2016) 35–43.

5.       T. G. G. Maffeis, “Nano-Crystalline SnO2 Gas Sensor Response to O2 and CH4 At Elevated Temperature Investigated by XPS”, Surface Science, 520 1 (2002)29–34 -1.

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8.       J. Chargles, N. O’Connor, E. Kolesnichenko, C. Carpenter, S. W. Zheu, A. Kumbhar, S. Jessica and A. Fabrice, “Fabrication and Properties of Magnetic Particles with Nanometer Dimensions”, Synthetic Metals, 122 3 (2001)547-555.                                

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14.    S.N. Patil and B.P. Ladgaonkar, “Synthesis and Implementation of NiZnFe2O4 Ferrites to Design Embedded System for Humidity Measurement”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August 2013,3813-3821.

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16.    A.M. Pawar, S. N. Patil, A. S. Powar and B. P. Ladgaonkar, “Wireless Sensor Network To Monitor Spatio-Temporal Thermal Comfort of Polyhouse Environment”, International Journal of Innovative Research in Science, Engineering and Technology, 2 10 (2013) 4866-4875.

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Anilkumar C. Korishetti, V.S. Malemath

Paper Title:

Comparison of Different Motion Estimation Algorithms for H.264 Video Compression

Abstract: This paper is the review of the different block matching motion estimation algorithms including the basic algorithms to the new motion estimation algorithms, which are accepted by all the people who are working in this field. The main goal of this paper is to understand the basic and new motion estimation algorithms which help the new researchers to understand the algorithms and help them to develop the new algorithms in this field. The algorithms that are evaluated in this paper are accepted by the video compressing community for different input standards like MPEG1 to H.261 and MPEG4 to H.263, and H.264. This paper also gives the brief introduction of video compression flow.

 block matching; video compression; MPEG; H.261; H.263 and H.264.


1.    E.Richardson ,“ H.264 Advanced video compression standard” (Second edition), 2010, John Wiley & Sons, Ltd.
2.    Study materials and tutorials from website.

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4.    Faizul Jamil, Ali Chekima, Rosalyn R. Porle, Othman Ahmad, and Nor-farariyanti Parimon, “BMA Performance of Video Coding for Motion Estimation”, IEEE 2012 3rd International Conference on Intelligent Systems Modelling and Simulation.

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8.    Lai-Man Po, and Wing-Chung Ma, “A Novel Four-Step Search Algorithm for Fast Block Motion Estimation “, IEEE Trans. Circuits And Systems For Video Technology, vol 6, no. 3, pp. 313-317, June 1996.





Nikita P.Nagori, Vandana Malode

Paper Title:

Gesture Recognition for Communication between Physically Challenged People and Normal People

Abstract: Sign Language is the most essential communication path between hearing impaired group and ordinary people. There are roughly three hundred Sign Languages that are utilized the world over today and it is thought to be the 6th biggest language utilized around the world. The Sign Languages are created relying upon the country and area of the hard of hearing individuals. Since there are a wide range of Sign Dialects, it represents a trouble for a hard of hearing individual to speak with ordinary people from various areas. The point is to build up a Sign Language interpreter that facilitates the correspondence of the hard of hearing individuals. Advances in information technology inspirit the development of systems that can facilitate the automatic transcription between sign language and spoken language, and these lines help in expelling obstructions confronting the incorporation of hard of hearing individuals with the typical individuals in the general society. In this arose devices which are used capable of capturing the movements of a person and, through it, control these gestures. One of the devices that came with this purpose is Microsoft Kinect. The objective is to develop a communication interface between a normal person and a deaf-mute person using Kinect. In this paper a set of experiments is used to develop a statistical system for translating sign language to text for deaf people and then text to speech for normal people, which helps to reduce communication gap.

 MATLAB, Microsoft Kinect, Sign Language, Skeletal Tracking


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6.       C. Wang, W. Gao, and Z. Xuan, ―A Real-Time Large Vocabulary Continuous Recognition System for Chinese Sign Language,‖ Proc. IEEE Pacific Rim Conf. Multimedia, 2001, pp. 150-157.

7.       K Assaleh, M Al-Rousan, ―Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers,‖ EURASIPJournal on Applied Signal Processing, 2005, vol. 2005, no. 13, pp. 2136-2145.

8.       T. Shanableh, K. Assaleh and M. Al-Rousan, ―Spatio-Temporal feature extraction techniques for isolated gesture recognition in Arabic sign language,” IEEE Trans. Syst., Man, Cybern. B, 37(3), June 2007.

9.       Hernandez-Rebollar, J. L., Kyriakopoulos, N., & Lindeman, R. W. (2004, May). A new instrumented approach for translating American Sign Language into sound and text. In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on (pp. 547-552). IEEE.





Dipak V. Sose, Sayyad Ajij D.

Paper Title:

Data Acquisition of Weather Parameters and Wireless Monitoring

Abstract:  Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Human beings have attempted to predict the weather informally for millennium and formally since the nineteenth century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere on a given place and using scientific understanding of atmospheric processes to project how the atmosphere will evolve on that place. Wireless technology has been tremendously growing day by day. The need of wireless technology is that it replaces the conventional methods including long wires for communication and thereby increasing the redundancy of the whole system. The measurements of temperature, atmospheric pressure and relative humidity remotely by using the appropriate sensors are not only important in environmental or weather monitoring but also crucial for many industrial processes. A system for weather monitoring is described in this project to monitor and display the, Temperature (0C), Humidity (RH%), Light Intensity (lx), Atmospheric Pressure (Kpa), Vibration or Acceleration (g), Wind Speed(Km/h), Rainfall (mm), LPG leakage (ppm) and CO2 contents in air (ppm) using analogue and digital components. The analogue outputs of the sensors are connected to a microcontroller through an ADC for digital signal conversion.

 Microcontroller, Sensors, Wireless, ADC etc.


1.          Nisha Gahlot, Varsha Gundkal, Sonali Kothimbire, Archana Thite,  Zigbee based weather monitoring system, The International Journal Of Engineering And Science (IJES) Volume 4 Issue 4 Pages  PP.61-66, 2015 
2.          Luis Diego Bricen, Anthony A. Maciejewski, Heuristics for robust resourse allocation of satellite weather data processing on heterogeneous parallel system, IEEE transactions on parallel and distributed systems, vol. 22, no. 11, November 2011.

3.          Dushyant Pande, Jeetender Singh Chauhan, A real time hardware design to automatically monitor and control light & temperature, International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013.

4.          Iswanto And Helman Muhammad, Weather Monitoring Station With Remote Radio Frequency Wireless Communications, International Journal of Embedded Systems and Applications (IJESA) Vol.2, No.3, September 2012.

5.          Nisha singh, Prof. Ravi Mishra, Microcontroller Based Wireless Temperature And Heart Beat Read-Out, International Organization of Scientific Research Journal of Engineering (IOSRJEN), Vol. 3, Issue 1,  PP 01-06, Jan. 2013.

6.          K C Gouda1, Preetham V R and M N Shanmukha Swamy, Microcontroller Based Real Time Weather Monitoring Device With GSM, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014.

7.          P.Susmitha and  G.Sowmyabala, Design and Implementation of Weather Monitoring and Controlling System, International Journal of Computer Applications Volume 97– No.3, July 2014.

8.          Mr. P. K. Jayalaxmi Mr. A. Pritviraj, A Real Time Weather Monitoring System With Fm Channel, International Journal of Advanced Information and Communication Technology  Vol 1, Issue-1, May 2014”.

9.          Pranita Bhosale and V.V.Dixit, Agricon-Weather Monitoring System and Irrigation Controller, International Organization of Scientific Research Journal of Electronics and Communication Engineering (IOSRJECE), Volume 1, Issue 6, PP 05-11, July-Aug 2012.

10.       Nitant Sabharwal, Rajesh Kumar, Abhishek Thakur, Jitender Sharma, A  low cost Zigbee based automatic wireless weather station with GUI and web hosting facility, ICRTEDC, Vol. 1, Spl. Issue 2, May 2014.

11.       Karthik Krishnamurthi, Suraj Thapa, Lokesh Kothari, Arun Prakash, Arduino based weather monitoring system, International Journal of Engineering Research and General Science, Volume 3, Issue 2, March-April 2015.

12.       Keshav kumar singh, S.styline chirmaxo, Design of wireless weather monitoring system, Bachelor of Technology, National Institute of Technology Rourkela, India, May 2013.

13.       National Space Weather Program Council, Space weather observing system.

14.       Muthoni Masinde and Antoine Bagula, A Calibration Report for Wireless Sensor-Based Weatherboards, Journal of Sensor and Actuator Networks, Central University of Technology Free State, Private Bag X20539, Bloemfontein 9300, Republic of South Africa 2015.

15.       Anna Maria Sempreviva, Sven-Erik Gryning and Gregor Giebel, Weather intelligence for Renewable Urban Areas Gaps, Challenges and future perspectives, National Council of Research of Italy Wind Energy Department, Technical University of Denmark, 2nd 3rd June 2014. 

16.       Kamrul AriffinNoordin Chow chee onn and Mohamad faizal ismail, A low cost microcontroller based weather monitoring system, Carnegie Mellon University, vol 5 pp 33-38, 2006

17.       Kefei Zhang, Toby Manning, Suqin Wu, Witold Rohm, D. Silcock, and Suelynn Choy, Capturing the signature of severe weather events in austrilia using GPS measurement, Institute of Electrical and Electronics Engineers journal of selected topics in applied earth observations and remote sensing, vol. 8, no. 4, april 2015

18.       Field visit, Hydrology Project, Godavari Pathbandhare Vikas Mahamandal, Aurangabad (MS), India, 22nd December 2015.

19.       Field visit, Department of Meteorology, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani (MS) India, 24th December 2015





Kajal Patel, Bijith Marakarkandy

Paper Title:

Feature Selection using Clusters

Abstract:   Feature selection involve identifying a subset of the most useful feature that produces compatible results as the original entire set of features. The feature selection algorithm can be evaluated on the basis of two criteria: efficiency and effectiveness. In fact feature selection, as a pre-processing step which is effective in reducing dimensionality, removing irrelevant data, removing redundant data etc. Feature selection clustering mechanism works in five stages. In the first stage, T-relevance test is used to eliminate irrelevant features. In the second stage, F-correlation is calculated between attributes. In third stage, map and reduce function accomplishes groups of features. In fourth stage, actual clusters are evaluated. And in the final stage, the most representative feature from each cluster are considered. Features in different clusters are comparatively efficient and independent features. For the lack of density-based clustering algorithm in dealing with large data sets, MapReduce programming model is proposed to accomplish the clustering of DBSCAN. Map function completes the data analysis, and gets clustering rules in different data attributes; Then Reduce functions merge these clustering rules to get a final result. Experimental results shows the DBSCAN with MapReduce running on the cloud computing platform Hadoop has good speed-up and scalability approach. An analysis has been done between the most representative feature of DBSCAN clustering using MapReduce with the existing FAST feature subset algorithm on the basis of execution time & number of clusters performed. Experimentally it has been verified that proposed algorithm alleviates the problem of time delay caused by large data sets.

 Big Data, DBSCAN, Fast Clustering, Feature Selection, Hadoop, MapReduce.


1.       F. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Hoboken, N.J, USA: Wiley, 2013.
2.       Q. Song, J. Ni and G. Wang, “A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data”, IEEE transaction on Knowledge and Data Engineering ,vol. 25, no. , pp. 1-14, Jan. 2013.

3.       M. Noticewala and D. Vaghela,”MR-IDBSCAN: Efficient Parallel Incremental DBSCAN algorithm using MapReduce”. International Journal of Computer Applications 93(4):13-18, May 2014

4.       R. Shettar Professor, B.V.Purohit,”A Review on Clustering Algorithms Applicable for Map Reduce”, Proceedings of the International Conference , Computational Systems for Health & Sustainability, 17-18, April, 2015.

5.       V. Bawane and S. Kale ,” Clustering Algorithms in MapReduce: A Review,” National Conference on Recent Trends in Computer Science & Engineering (MEDHA 2015).

6.       V.A.Kokane and A.C.Lomte,”CBFAST- Efficient Clustering Based Extended Fast Feature Subset Selection Algorithm for High Dimensional Data”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),Volume 4 Issue 5, May 2015.

7.       J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun ACM, 51(1), pp. 107-113, 2008.

8.       V. Kumar and S. Minz, ”Feature Selection: A literature Review,” Smart Computing Review, vol. 4, no. 3, Jun 2014.

9.       Hadoop [online]. Available:





G. S. Sable, M. R. Rajput, H. R. Gite

Paper Title:

Analogy between Statistical and CHT Segmentation for Human Iris

Abstract: As Iris is the most safe and reliable biometric pattern, Iris biometrics provides high recognition rate and high accuracy for person identification as compared with the other biometric techniques. The crucial stage in iris recognition is iris segmentation. Segmentation of iris from captured eye image is a difficult task as iris images are affected by different factors such as poor illumination, motion blur, improper focus, large standoff distances and occlusion by eyelashes and eyelids etc. This ultimately affects the iris recognition rate. In this  paper two methods of iris localization are discussed The first is a statistical method and the second is a CHT method. Paper shows that CHT method overcomes the limitations of statistical method and is better suited for iris segmentation.

 CHT segmentation, Iris segmentation, recognition rate


1.       Adegoke, B. O. , Omidiora, E.  , Falohun, S. A.  Oja, J.A “Iris Segmentation: a survey ”,International Journal of Modern Engineering Research (IJMER) ,Vol.3, Issue.4, Jul – Aug. 2013 pp-1885-1889 ISSN: 2249-6645
2.       John Daugman, “How Iris Recognition Works” IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1, january 2004.

3.       Richard P. Wildes,”Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE , Vol. 85, No. 9, September 1997.

4.       Lim, K. Lee, O. Byeon and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier”, ETRI Journal,Vol. 23, No. 2 Korea, pp 1220-1225, 2001.

5.       W. Kong and D. Zhang, “Accurate iris segmentation based on novel reflection and eyelash detection model”, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 2001.

6.       L. Ma, Y. Wang and T. Tan, “Iris recognition using circular symmetric filters”, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 2002.

7.       Jiali Cui, Yunhong Wang, Tieniu Tan, Li Ma, Zhenan Sun, “An Iris Recognition Algorithm Using Local Extreme Points”,

8.       Zhonghua Lin, “A Novel Iris recognition method based on the natural      open eyes.”, IEEE sponsored (ICSP) 2010 conference Proceedings , pp 1090-   1093.

9.       Bowyer, Kevin W. , Burge, Mark J.   “Introduction to the Handbook on Iris Recognition ” , Springer Publication.
10.    Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson  Prentice Hall Publication
11.    Min Han ,Weifeng Sun, Mingyan Li., “Iris recognition based on a Novel Normalization method and contourlet transform , Proceedings of    2nd International Congress on Image and Signal Processing 2009.

12.    Chia-Te Chou, Sheng-Wen Shih, “on Orthogonal View Iris Recognition system” IEEE transactions on circuit and systems for video technology, 2010

13.    Vishnu Boddeti, B.V.K. Vijay Kumar, “Extended depth of field Iris  Recognition using unrestored wavefront coded Imagery,” IEEE transactions on systems, MAN and cybernetics Part A: system   and humans, vol.40, no.3, May, 2010, pp495-508.

14.    Amir shahram, Hematian, Aziazh, Abdul Manaf, “Field  programmable gate array system for real time-Iris Recognition”, IEEE conference on open systems , 2012.

15.    Hiew Moi, Hishammudi Asmuni, Rohyanti, Hassan, RazibM.Othman,  “A Unified approach for unconstrained off angle Iris Recognition”IEEE sponsored International Symposium on biometrics and security technologies,2014,pp-39-44 .

16.    Osman M .Kurtunui, Mahmut Karkaya, “Limbus Impact removal for off- angle Iris Recognition using eye models” IEEE 7th International conference on Biometrics theory, applications and Systems,pp-1-6 .

17.    Kumal Hajari ,Kishor Bhoyar, “A review of issues and challenges in   designing Iris recognition systems for noisy image environment, IEEE transaction on International conference on Pervasive computing 2015.

18.    Chun-wei,Tan, Ajaykumar, “Accurate Iris recognition at a distance using  stabilized Iris encoding and zernike moment phase features”,IEEE Transactions on image processing ,vol-23,no-9,September 201,pp-3962-3974.

19.    Dongdong Zhao, WenjianLuo, RanLiu,LihuaYue, “Negative Iris recognition”, Proceedings of IEEE transactions on dependable and secure computing.,2015 pp-1-15.

20.    Kiennguyen, Sridha Sridharan, Simon Denman, “Quality driven super resolution for less constrained iris recognition at distance and on the move.” , IEEE transactions on information forensics and security,vol-6,no-4, December2011.

21.    Karen P. Hollingsworth ,Kelvin W.Bower, Patrick J. Flynn. “ Improved Iris recognition through fusion of hamming distance and fragile bit distance”,IEEE transactions on pattern analysis and machine intelligence,vol-33,no-12,December2011,pp-2465-2476.

22.    Jaishankar K. Pillai, Vishal M.Patel, Rama Chellappa , Nalini Ratha, “Secure and robust Iris Recognition using random projections and sparse representations”,IEEE transactions on pattern analysis and machine intelligence.vol-33,no.9,2011.

23.    Arjun Agarwal, Gundeep Singh Bindra,Priyanka Sharma, “Feature based  Iris Recognition system functioning on extraction of 2D features”,IEEE sponsored International conference on System engineering and technology  2012,pp-1-5.

24.    Sudeep Thepade, Pushpa Manchal , “Energy compaction based novel iris recognition techniquesusing partial energies of transformed Iris images with Cosine,Walsh,Harr,Kekre,Hartley transforms and their wavelet transforms”, Annual  IEEE India conference,2014.

25.    S.Hariprasath, V.Mohan, “Biometric Personal Identification based on Iris recognition using complex wavelet transforms. ”,IEEE International conference on computing, communication and networking,2008.

26.    Min Han ,Weifeng Sun, Mingyan Li., “Iris recognition based on a Novel Normalization method and contourlet transform , Proceedings of   2009 2nd International Congress on Image and Signal Processing 2009.

27.    Mrinalini I.R., Pratusha B.P.,Manikanton K. S. Ramachandran, “Enhanced Iris Recognition using Discrete cosine Transform and radon transform.”, IEEE sponsored,2nd International conference on

28.    R.Meenakshi Sundaram, Bibhas Chandra Dhara, “Neural network based Iris recognition system using Haralick features, Procededings of 3rd International Conference on Electronics Computer Technology (ICECT) 2011, pp-19-23,2011.

29.    Mrunal M.Khedakar, S.A. Ladhake, “Neural network based Iris pattern  recognition system using Discrete Walsh Hadamard transform features”, IEEE sponsored International conference on Advances in computing ,communications and Informatics,2013 ,pp-388-393

30.    Naresh Babu NT, Vaidehi V., “Fuzzy based Iris recognition system for person identification.”, IEEE sponsored International conference on recent trends in information technology, 2011 pp-1005-1010.

A.      Aldhahab, G. Atia and W. Mikhail, “Supervised Facial Recognition based on Multiresolution Analysis with Radon Transform,” 48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove CA, Nov 2014.

31.    Yuanning Liu and FeiHe, Yonghua Zhao, Ning Wang, “An Iris recognition based on GHM multiwavelet transformation”, IEEE 4th International conference on Innovative computing ,Information and control,2009,pp-793-796.

32.    Aalaa Albadarneh, Israa Albadarneh, Jafar Alquatawna, “Iris recognition system for secure authentication based on texture and shape feature.”IEEE International Jordan conference on applied electrical engineering and computing technologies, 2015.

33.    B.V.Bharath,Vilas, S.,K.Manikantan,S.Ramachandran. “Iris Recognition using Radon transform thresholding based feature extraction with gradient based isolation as a preprocessing technique.”, 9th International Conference on Industrial and Information Systems,2014,pp.1-8.

34.    Lin Zhonghua, Lu Bibo, “Iris recognition method based on the coefficients of morlet wavelet transform,” IEEE sponsored International conference on Intelligent Computation technology and automation,2010 pp-576-580.

35.    Izem Hamouchene, Saliha,Aoqat, “Texture matching using local and global descriptor.”, IEEE sponsored 5th European workshop on Visual image processing, (EUVIP),  2014.

36.    Mohammad Javed, Aligholizadeh Shahram Javedi, Kareh Kangarloo., “Eyelid and eyelash segmentation based on wavelet transform for Iris recognition.”, IEEE sponsored  4th International congress on Image and signal processing, 2011, pp-1231-1235.

37.    Lydia Elizabeth, Duraipandi C, AnjuPratap, Rhymed Uthariraj W. “ A Grid based Iris Biometric watermarking using wavelet transform.”, IEEE sponsored International conference on recent trends in Information Technology 2014 ,pp.1-6,.

38.    Salim Lahmiri, Mounir Bhukadoum, “DWT and DT based approach for feature extraction and classification of mammograms with SVM”,IEEE sponsored International conference on Biomedical Circuits and Systems Conference , 2011, pp-412-415.

39.    Ameya Deshpande, Sumitkumar Dubey, Satishkumar Chavan, Aditya Potnis, “Iris Recognition system using Block based approach with DWT and DCT”, Annual IEEE India Conference, 2014.

40.    Prajoy Poddev, Tanvir Zaman Khan, Mamdulul Haque Khan, Rafi Ahmed ,MdSaifur Rahman, “An efficient Iris segmentation model based on eyelid and eyelashes detection in Iris recognition systems”, IEEE sponsored International conference on Computer communication and Informatics,Coimbatore,India,2015.

41.    Jianxu Chen, Feng Shen, DannyZ.Chen, Patrick J.Flynn , “Iris recognition system based on Human Interpretable features”, IEEE  Transactions on Identity, Security , and Behaviour analysis, 2015,”pp 1-6.

42.    V.V. Satyanarayana, Tallapragada, Dr.E.G.Rajan, “Morphology based Non-ideal Iris recognition using decision tree classifier.”, IEEE sponsored International conference on pervasive computing.,” 2015,pp 1-14.

43.    Libor Masek, “Recognition of Human Iris Patterns for BiometricIdentification”, libor/Libor  MasekThesis.pdf    

44.    Nitin K. Mahadeo, Gholamreza Haffari, Andrew P. Paplinski “Predicting Segmentation errors in an Iris recognition.” International conference on Biometrics  2015,pp23 -30.

45.    Zhonghua Lin, “A Novel Iris recognition method based on the natural   open eyes.”, IEEE sponsored (ICSP) 2010 conference Proceedings , pp 1090-    1093.

46.    Prateek Verma, Maheedhar Dubey, Somak Basu, Praveen Verma , “Hough Transform Method for Iris Recognition-A Biometric Approach “, International Journal of Engineering and Innovative Technology (IJEIT) ,Volume 1, Issue 6,   June 2012




Bhagyashree V. Shivpuje, Ganesh S. Sable, Kartik Raman

Paper Title:

Feature Extraction of Dental Radiographic Images for Disease Identification and Classification using ANN

Abstract:  The early detection of disease is one of important matter of diagnostic imaging. In this paper we developed a system to analysis the dental x-ray images and diagnosis the tooth which has abnormalities of caries. Enhancement applied to improve the quality of x-ray images and Thresholding method performed to simplify the images. Segmentation has been done by applying the integral projection technique to extract the individual tooth and therefore feature map of tooth generated and detection and classification process can be done by using artificial neural network. Nevertheless, experiments show the accurate segmentation and caries detection with proposed method which achieves high accuracy for diseased images and promising result.

 ANN, caries detection, integral projections, segmentation, feature extraction


1.       J. Solanki, K. R. Jain & N. P. Desai, “ISEF Based Identification of RCT/Filling in Dental Caries of Decayed Tooth”, International Journal of Image Processing (IJIP), Volume (7) : Issue (2) : 2013
2.       Ştefan OPREA1, Costin MARINESCU 2, Ioan Liţă, Daniel Alexandru Vişan, Ion Bogdan Cioc, “Image Processing Techniques used for Dental X-Ray Image Analysys”.

3.       Jincy Raju, Dr. Chintan K. Modi,” A Proposed Feature Extraction Technique for Dental X-Ray Images Based on Multiple Features”, Conference Paper July 2011 DOI: 10.1109/CSNT.2011.116 • Source: IEEE Xplore

4.       Vijayakumari Pushparaj, Ulaganathan Gurunathan, Banumathi Arumugam, ” An Effective Dental Shape Extraction Algorithm Using Contour Information and Matching by Mahalanobis Distance”, Journal of Digit Imaging. 2013 Apr; 26(2): 259–268.

5.       Eyad Haj Said, Diaa Eldin M. Nassar, Gamal Fahmy, Hany H. Ammar,” Teeth Segmentation in Digitized Dental X-ray Films using Mathematical Morphology”, IEEE, ISSN: 1556-6013 Transactions on Information Forensics and security

6.       Sharmila. M, Dr. R. Ganesan, R. Kartika Devi, “Detection of Dental Plaque using Image Processing”, IJAIST, Vol.18, No. 18, October 2013

7.       M.Thamarai, M.Kalpa, “Automated Diagnosis of Periodontal Diseases Using Image Processing Techniques”, IJIRSET, Vol. 3 Issue 1, January 2014

8.       A.Farzana Shahar Banu, M. Kayalvizhi, Dr. Banumathi Arumugam, “Texture Based Classification of Dental Cysts”, ICCICCT, 2014

9.       M.V. Bramhananda Reddy, Varadala Sridhar, M. Negendra, “Dental X-Ray Image Analysis by using Image Processing Techniques”, IJARCSSE, Vol.2,Issue 6, June 2012

10.    Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, AlirezaNorouzi, “Digital Dental X-Ray Image Segmentation and feature Extraction”, TELKOMNIKA, Vol.11, No.6, June 2013

11.    Dipali Rindhe, Ganesh Sable,“Teeth Feature Extraction and Matching for Human Identification using SIFT Algorithm”, EJAET, Vol.2 No.1, 2015

12.    Kavindra R. Jain, Narendra C. Chauhan, “Efficacy of Digital Image Processing Techniques in intra Oral Dentistry”, TROI, Vol. 2, Issue 2, 2015

13.    Adhar Vashishth, Bipan Kaushal, Abhishek Srivastava, “Caries Detection Technique for Radiographic and Intra Oral Camera”, IJSCE Vol-4, Issue-2, May 2014

14.    Chintan K. Modi and Nirav P. Desai, “A Simple and Novel Algorithm for Automatic Selection of ROI for Dental Radiograph Segmentation”, IEEE CCECE 2011
15.    Azam Amini Harandi, Hossein Pourghassem, ” A Semi Automatic Algorithm Based on Morphology Features for Measuring of Root Canal Length”, IEEE 2011, 978-1-61284-486-2/11.
16.    Omaima Nomir  and Mohamed Abdel-Mottaleb, ” Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth”,  IEEE 2007, 556-6013.

17.    Martin L. Tangel, Chastine Fatichah, and Fei Yan, ” Dental Classification for Periapical Radiograph based on Multiple Fuzzy Attribute”,  IEEE 2013, 978-1-4799-0348-1/13.

18.    Khin Mya Mya Tun, Aung Soe Khaing, “Feature Extraction and Classification of Lung Cancer Nodule using Image Processing Techniques”, IJERT 2014, 2278-0181-3/3.

19.    Abdolvahab Ehsani Rad, Ismail Bin Mat Amin, Mohd Shafry Mohd Rahim, and Hoshang Kolivand, ” Computer-Aided Dental Caries Detection System from X-Ray Images”, Springer 2015, 978-3-319-13513-5_23

20.    A.N Shaikh, G.S.Sable, “Clinical Applications and Importance of Cone Beam Computed Tomography (CBCT) artifacts in Dental Imaging”,  IEEE ICETECH, 2015.

21.    Bhagyashree V. Shivpuje, Dr. G. S. Sable, “A Review on Digital Dental Radiograph Images for Disease Identification and Classification”, Int. Journal of Engineering Research and Application, ISSN : 2248-9622, Vol. 6, Issue 7, ( Part -5) July 2016.

22.    White and Pharoah , “Oral Radiology-Principles and Interpretation”.

23.    Gonzalez, Woods, and Eddins, “Digital Image Processing using MATLAB”, 3rd Edition.

24.    Rafael Gonzalez, Richard Woods, “Digital Image Processing”, 2nd Edition

25.    Prof. D. Cavouras, ” Medical Image Processing”.




P. S. Tiwari, S. R. Patil

Paper Title:

Principal Component Analysis Based Face Recognition System

Abstract: This paper focuses on a software framework to support face recognition and Security and authentication, specific area of image processing. For the processing approach, we use principal component analysis (PCA), a data dimensionality reduction approach.   There are many techniques used for this purpose. One of them is face recognition. Face recognition is an effective means of authenticating a person. The advantage of this approach is that, it enables us to detect changes in the face pattern of an individual to an appreciable extent. The recognition system can tolerate local variations in the face expression of an individual. Hence face recognition can be used as a key factor in crime detection mainly to identify criminals. The system consists of a database of a set of facial patterns for each individual. The characteristic features called ‘Eigen faces’ are extracted from the stored images using which the system is trained for subsequent recognition of new images The goal of this study is to understand the entire face recognition process with PCA and to present a software framework supporting multiple variations, which can be used to help users create customized face recognition applications efficiently. The proposed method is tested using FERET database

 PCA, Face Recognition, Eigen faces.


1.       Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Ric hard Russell,   “Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About” Proceedings of the IEEE, Vol. 94, No. 11, November 2006
2.       G. Shakhnarovich, J. Fisher, and T. Darrell. Face recognition from long-term observations. In ECCV, 2002.

3.       K. Chang, K. Bowyer, and P. Flynn, “Multi-modal 2d and 3d biometrics for face recognition,” to appear in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003.

4.       B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intell, 19:696–710, 1997.

5.       N. Rajagopalan, K. S. Kumar, J. Karlekar, R. Manivasakan, M. M. Patil, U. B. Desai, P. G. Poonacha, and S. Chaudhuri. Locating human faces in a cluttered scene. Graphical Models in Image Processing, 62:323–342, 2000.

6.       D. A. Socolinsky and A. Selinger, “A comparative analysis of face recognition performance with visible and thermal infrared imagery,” in International Conference on Pattern Recognition, pp. IV: 217–222, August 2002.

7.       A.J. GoldStein, L.D Harmon, and A.B Lesk, “Identification of human faces”, Proc IEEE, May 1971, Vol. 59, No. 5, 748-760.

8.       L. Sirovich and M Kirby, “A low dimensional Procedure for the characterization of human faces,” J Optical. Soc. Am. A, 1987, Vol. 4, No. 3, 519-524

9.       M. A .Turk and A. P. Pentland, “Face Recognition using Eigen Faces,” Proc. IEEE, 1991, 586-591.

10.    Kwang In Kim, Keechul Jung, and Hang Joon Kim, “Face recognition using kernel principal component analysis,” Signal Processing letters IEEE, vol. 9 Issue. 2 page 40-42 Feb, 2002.

11.    Xin Chen, Patrick J. Flynn, Kevin W. Bowyer, “PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative   Studies,” amfg, pp.127, IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003

12.    Peng, Peng, and Yehu Shen. ”Efficient face verification in mobile environment using component-based PCA.” Image and Signal Processing (CISP), 2013 6th International Congress on. Vol. 2. IEEE, 2013.

13.    The Code Project, “EMGU Multiple Face Recognition using PCA and Parallel Optimisation”, 05 October 2011.

14.    L. Sirovich and M Kirby, “A low dimensional Procedure for the characterization of human faces,”

15.    Wikipedia, the free encyclopedia, “Natural language processing”, 15 September.




Kalyanee S. Patil

Paper Title:

Wireless Body Area Network

Abstract:  The project in designing a system which is capable of tracking the location of patients and also monitoring of heart rate and health parameters also alerts in case of emergency through SMS to predefined number. This aims in design and present the novel wearable system with the bio recognition sensors based on the GPS and GSM communication Network. In this project sensors (3-axis accelerometer, temperature sensor, heartbeat sensor, humidity sensor, ECG sensor) added and located at patient body for getting the postures information and activity by them always monitored. The system sends alert messages in emergency times.

 ARM7, ECG Sensor, GPS, GSM, Heart Beat, MEMs Sensor


1.    A.Sagahygroon, F.Aloul, Al-Ali, M. S. Bahrololoum, F. Makhsoos, N. Hussein “Monitoring patients signs wirelessly” 97 8-1-4244-7000-6/11/$26.00 ©2011 IEEE
2.    S. M. Mahalle P.V.Ingole. “Design and implementation of wireless body area sensor network based health monitoring system” International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 2 Issue 6, June – 2013

3.    MirHojjat Seyedi, Student Member, IEEE, Behailu Kibret, Student Member, IEEE, Daniel T. H. Lai, Member, IEEE, and Michael Faulkner, Member, IEEE. “A survey on intrabody communications for body area network applications” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 8, AUGUST 2013

4.    Malika haru rana. “An indoor wireless zigbee based patient monitoring system for hospitals” International Journal of Engineering Sciences Research-IJESR, ISSN: 2230-8504, Vol 04, Issue 02; March-April 2013
5.    Milenkovic. Otto and E. Jovanov “Wireless Sensor Networks for Personal Health Monitoring: Issues and an Implementation” in Computer Communications, vol. 29 (13-14), August 2006.

6.    “Interoperability and Security in Wireless Body Area Network Infrastructures” Steve Warren1, Jeffrey Lebak1, Jianchu Yao3,





Mahender G. Nakrani, Ganesh S. Sable, Ulhas B. Shinde

Paper Title:

A Review: Automatic and Semi-Automatic Detection of Pulmonary Lung Nodules in Computed Tomography Images

Abstract: Lung nodules which are also called as “Spot on the lung”, a “shadow” or a “coin lesions” are caused by scar tissue, a healed infection or some air irritant but sometimes they are an early sign of lung cancer. The detection of a cancerous lung nodule will facilitate early treatment for lung cancer of patients. Radiologists can detect lung nodules by examining CT scan or X-ray images. Radiologists can be provided with vital information by using automatic lung nodule detection system to assist them in their decision making and treatment suggestion.  This paper presents a study of different methods and algorithms used in automatic lung nodule detection. It gives a generalized structure of lung nodule detection that are commonly used in the existing system. It also describes methods and algorithms used for lung nodule detection. The structure includes components which are image acquisition, lung segmentation, nodule candidate detection and segmentation, nodule candidate feature extraction and false positive reduction/classifier. This paper describes methods and algorithms used in every components.

 Automatic detection, Computed Tomography, Lung Images, Pulmonary Nodule.


1.       Amal A. Farag, Hossam E. Abd El Munim, James H. Graham, and Aly A. Farag, “A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 12, DECEMBER 2013, PP. 5202 – 5213.
2.       D. Cascio, R.Magro, F.Fauci, M.Iacomi, and G.Raso, “Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models,” Computers in Biology and Medicine, Vol. 42, 2012, PP. 1098–1109.

3.       Temesguen Messay, Russell C. Hardie, and Timothy R. Tuinstra, “Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset,” Medical Image Analysis, Vol. 22, 2015, PP. 48–62.

4.       Mohsen Keshani, Zohreh Azimifar, Farshad Tajeripour, and Reza Boostani, “Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system,” Computers in Biology and Medicine, Vol. 43, 2013, PP. 287–300.

5.       G. Deep, L. Kaur, and S. Gupta, “Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary Pattern,” ACEEE Int. J. on Signal & Image Processing, Vol. 4, No. 1, Jan 2013, PP. 20-23.

6.       Stelmo Magalhaes Barros Netto, Aristofanes Correa Silva, Rodolfo Acatauassu Nunes, and Marcelo Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Computers in Biology and Medicine, Vol. 42, 2012, PP. 1110–1121.

7.       T. K. Senthil Kumar, Ganesh Narasimhan, and R. Umamaheswari, “Texture Pattern Based Lung Nodule Detection (TPLND) Technique in CT Images,” International Review on Computers and Software (I.RE.CO.S.), Vol. 9, N. 3, March 2014, PP. 415-426.

8.       Sheng Chen and Kenji Suzuki, “Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography,” IEEE Transaction on Biomedical Engineering, Vol. 60, N. 2,  February 2013, PP. 369–378.

9.       Saleem Iqbal, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum, “Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images,” Hindawi Publishing Corporation, Computational and Mathematical Methods in Medicine, Volume 2014.

10.    Pablo G. Cavalcanti, Shahram Shirani, Jacob Scharcanski, Crystal Fong, Jane Meng, Jane Castelli, and David Koff, “Lung nodule segmentation in chest computed tomography using a novel background estimation method,” Quantitative Imaging in Medicine and Surgery, Vol 6, No 1, February 2016, PP. 16-24.

11.    Shiwen Shen, AlexA.T.Bui, Jason Cong, and William Hsu, “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy,” Computers in Biology and Medicine, Vol. 57, 2015, PP. 139–149.

12.    S.Sivakumar, and Dr.C.Chandrasekar, “Lung Nodule Detection Using Fuzzy Clustering and Support Vector Machines,” International Journal of Engineering and Technology, Vol 5, No 1, Feb-Mar 2013, PP. 179-185.

13.    Tong Jia, Hao Zhang, and Haixiu Meng, “A novel lung nodules detection scheme based on vessel segmentation on CT images,” Bio-Medical Materials and Engineering, Vol. 24, 2014, PP. 3179–3186.

14.    Ayman El-Baz, Ahmed Elnakib, Mohamed Abou El-Ghar, Georgy Gimel’farb, Robert Falk, and Aly Farag, “Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans,” Hindawi Publishing Corporation, International Journal of Biomedical Imaging, Volume 2013.

15.    Ernst Th. Scholten, Colin Jacobs, Bram van Ginneken, Sarah van Riel, Rozemarijn Vliegenthart, Matthijs Oudkerk, Harry J. de Koning, Nanda Horeweg, Mathias Prokop,  Hester A. Gietema,  Willem P. Th.M. Mali, Pim A. de Jong, “Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation,” Eur Radiology,Vol. 25, 2015, PP. 488–496.

16.    Xueqian Xie, Martin J. Willemink, Pim A. de Jong, Peter M. A. van Ooijen, Matthijs Oudkerk, Rozemarijn Vliegenthart, and Marcel J. W. Greuter, “Small Irregular Pulmonary Nodules in Low-Dose CT: Observer Detection Sensitivity and Volumetry Accuracy,” AJR, Vol. 202, March 2014.

17.    Sasidhar B, Ramesh Babu D R, Ravi Shankar M, and Bhaskar Rao N, “ Automated Segmentation of Lung Regions using morphological Operators in CT scan,” International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September 2013, PP. 1114-1118.

18.    Atiyeh Hashemi, Abdol Hamid Pilevar, Reza Rafeh, “Mass Detection in Lung CT Images Using Region Growing Segmentation and Decision Making Based on Fuzzy Inference System and Artificial Neural Network,” I.J. Image, Graphics and Signal Processing, Vol.6, 2013, PP. 16-24.

19.    Ji-Seok Yoon, and Tae-Sun Choi, “ Pulmonary Nodule Segmentation by using 3D Deformable Model in CT Images,” Advanced Science and Technology Letters, Vol.51, 2014, pp.1-4.

20.    Kyung Nyeo Jeon, Jin Mo Goo, Chang Hyun Lee, Youkyung Lee, Ji Yung Choo, Nyoung Keun Lee, Mi-Suk Shim, In Sun Lee, Kwang Gi Kim, David S. Gierada, and Kyongtae T. Bae, “Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT,” Invest Radiology, Vol.47, No. 8, August 2012, PP. 457–461.

21.    William J. Kostis, Anthony P. Reeves, David F. Yankelevitz, and Claudia I. Henschke, “Three-Dimensional Segmentation and Growth-Rate Estimation of Small Pulmonary Nodules in Helical CT Images,” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 10, OCTOBER 2003, PP. 1259-1274.

22.    Yongbum Lee, Takeshi Hara, Hiroshi Fujita, Shigeki Itoh, and Takeo Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching Technique,” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 20, NO. 7, JULY 2001, PP. 595-604.

23.    Binsheng Zhao, Gordon Gamsu, Michelle S. Ginsberg, Li Jiang, and Lawrence H. Schwartz, “Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm,” JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, VOLUME 4, NUMBER 3, SUMMER 2003, PP. 248-260.

24.    William Mullally, Margrit Betke, Jingbin Wang, and Jane P. Ko, “Segmentation of nodules on chest computed tomography for growth assessment,” Medical Physics, Vol. 31, No.4, April 2004, PP. 839-848.

25.    Bram van Ginneken, “Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans,” Springer-Verlag Berlin Heidelberg 2006, PP. 912-919.

26.    Stefano Diciotti, Giulia Picozzi, Massimo Falchini, Mario Mascalchi, Natale Villari, and Guido Valli, “3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images,” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 12, NO. 1, JANUARY 2008, PP. 7-19.

27.    Artit C. Jirapatnakul, Yury D. Mulman, Anthony P. Reeves, David F. Yankelevitz, and Claudia I. Henschke, “Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate,” Hindawi Publishing Corporation, International Journal of Biomedical Imaging, Volume 2011.

28.    Jiantao Pu, Bin Zheng, Ken Leader, Xiao-Hui Wang, and David Gur, “An Automated CT Based Lung Nodule Detection Scheme Using Geometric Analysis of Signed Distance Field,” Medicaal Physics, Vol. 35, No. 8, August 2008, PP. 3453–3461.

29.    Kazunori Okada, Dorin Comaniciu, and Arun Krishna, “Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CT,” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 24, NO. 3, MARCH 2005, PP. 409-423.

30.    Ren Yuan, Patrick M. Vos, and Peter L. Cooperberg, “Computer-Aided Detection in Screening CT for Pulmonary Nodules,” American Roentgen Ray Society, Vol. 186, May 2006, PP. 1280–1287.

31.    Qiang Li, Feng Li, and Kunio Doi, “Computerized Detection of Lung Nodules in Thin-Section CT Images by use of Selective Enhancement Filters and an Automated Rule-Based Classifier,” Academic Radiology, Vol. 15, No. 2, February 2008, PP. 165–175.

32.    Sumit K. Shah, Michael F. McNitt-Gray, Sarah R. Rogers, Jonathan G. Goldin, Robert D. Suh, James W. Sayre,  Iva Petkovska, Hyun J. Kim, and Denise R. Aberle, “Computer Aided Characterization of the Solitary Pulmonary Nodule Using Volumetric and Contrast Enhancement Features,” Academic Radiology, Vol 12, No 10, October 2005, PP. 1310-1319.

33.    Serhat Ozekes, Onur Osman, and Osman N. Ucan, “Nodule Detection in a Lung Region that’s Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding,” Korean Journal Radiology, Vol. 9, No. 1, February 2008.

34.    Xujiong Ye, Gareth Beddoe and Greg Slabaugh, “Graph Cut-based Automatic Segmentation of Lung Nodules using Shape, Intensity, and Spatial Features,” MICCAI, Workshop on Pulmonary Image, 2009.

35.    Joao Rodrigo Ferreira da Silva Sousa, Aristofanes Correa Silva,Anselmo Cardoso de Paiva, and Rodolfo Acatauassu Nunes, “Methodology for automatic detection of lung nodules in computerized tomography images,” computer methods and programs in biomedicine, Vol. 98, 2010, PP. 1–14.

36.    S. L. A. Lee, A. Z. Kouzani, and E. J. Hu, “Automated Identification of Lung Nodules,” Proceedings of IEEE 10th International Workshop on Multimedia Signal Processing, 2008, PP. 497-502.

37.    Asem M. Ali,  Ayman S. El-Baz, and  Aly A. Farag, “A NOVEL FRAMEWORK FOR ACCURATE LUNG SEGMENTATION USING GRAPH CUTS,” IEEE International Symposium on Biomedical Imaging • January 2007, PP. 908-911.

38.    Wook-Jin Choi and Tae-Sun Choi, “Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach,” Entropy, Vol. 15, February 2013, PP. 507-523.

39.    Omid Talakoub, Javad Alirezaie, and Paul Babyn, “LUNG SEGMENTATION IN PULMONARY CT IMAGES USING WAVELET TRANSFORM,” IEEE International Conference on Acoustics, Speech and Signal Processing, April 2007, PP. 453-456.

40.    Wook-Jin Choi, and Tae-Sun Choi, “ Genetic programming based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images,” Information Science, Vol. 212, 2012, PP. 57-78.




A.N. Shaikh, G.S. Sable, Huma Mohd. S.

Paper Title:

Conventional to 3D Digital Radiography: An Essential Element to Diagnose Dental Abnormalities

Abstract:  As far as concerned with technological development in medical domain, detection and diagnosis of abnormalities are becoming more easy and accurate. Digital image processing has an important role in our day today life as well in different technical domain. Medical imaging is a broad area for researchers to work on different aspects of medical like X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Doppler ultrasound, PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), CBCT (Cone Beam Computed Tomography), Radio visiography (RVG) and Denta scan. In case of dentistry there is an important role of radiographs to detect and diagnose dental abnormalities, apart from conventional 2D imaging to recently 3D imaging techniques. Though latest technologies are updating in clinics but not replaced conventional film-based radiography completely. There are number of diseases pertinent to teeth are identified and diagnose by means of analyzing 2D and or 3D images. These images captured by means of respective machines. This Paper focuses on different techniques used form Conventional to 3D imaging. Also shows the importance of radiographs by means of detection and diagnosis of dental disease, dental clinical applications, advantages and disadvantages of the digital radiographs.

  Medical imaging, CT, CBCT, RVG, 2D, 3D imaging.


1.       N. R. Diwakar, S. Swetha Kamakshi, “ Recent advancements in dental digital radiography”, Journal of Medicine, Radiology, Pathology & Surgery (2015), 1, 11–16
2.       White SC, Pharoah MJ “A Digital Imaging In: Oral Radiology Principles and Interpretation”, 6th ed. Elsevier; 2009. 78-99

3.       Muhammed Ajmal, Mohamed I. Elshinawy, “Subjective image quality comparison between two digital dental radiographic systems and conventional dental film”, The Saudi Dental Journal, Production and hosting by Elsevier, pp.145–150, 2014

4.       Belma Muhamedagic, Lejla Muhamedagic, “Digital Radiography versus Conventional Radiography in Dentistry”, ScopeMed, Acta Inform Med. 2009; 17(2): 85-89

5.       Perth Radiological Clinic, “A General X-rays”, Guide for patient.

6.       Axel Bumann, “”

7.       R.Pauwels, K.Araki, J.H Siewerdsen, S.S Thongvigitmanee “Technical aspects of dental CBCT: state of the art”, Dentomaxillofacial Radiology (2015) 44, 20140224, 1-20.

8.       Suprijanto, Gianto, Juliastuti E, Azhari, “Image contrast enhancement for film-based dental panoramic radiography”, Published in IEEE Int. Conference on System Engineering and Technology (ICSET), Bandung, ISBN: 978-1-4673-2375-8, pp. 1-5, 11-12 Sept. 2012.

9.       Association Report, “The use of dental radiographs Update and recommendations”, American Dental Association Council on Scientific Affairs, JADA, Vol. 137, September 2006, pp.1304-1312.

10.    Arpah Bt Ahmad S, Taib M.N, Khalid N.E.A, Taib H, “Analysis of image quality based on dentists’ perception cognitive analysis and statistical measurements of intra-oral dental radiographs”, Published in IEEE Int. Conference on Biomedical Engineering (ICoBE), Penang, ISBN: 978-1-4577-1990-5, pp. 379-384, 27-28 Feb.

11.    McAuliffe M.J., Lalonde F.M., McGarry D., Gandler W, “Medical Image Processing, Analysis and Visualization in clinical research”, Proceeding of 14th IEEE Symposium on Computer-Based Medical Systems, CBMS, ISSN : 1063-7125, Print ISBN: 0-7695-1004-3, pp. 381 – 386, 26-27 July 2001.

12.    Olsen G.F, Brilliant S.S, Primeaux D., van Najarian K, “An image-processing enabled dental caries detection system”, IEEE International Conference on Complex Medical Engineering (ICME), E-ISBN : 978-1-4244-3316-2, Print ISBN:978-1-4244-3315-5, pp. 1-8, 9-11 April 2009.

13.    Mertz L., “Medical Imaging: Just What the Doctor (and the Researcher) Ordered: New Applications for Medical Imaging Technology”, IEEE Journal, Volume: 4, Issue: 1, pp. 12-17, 2013.

14.    Motohiro Uo, Takahiro Wada, Tomoko Sugiyama, “Applications of X-ray fluorescence analysis (XRF) to dental and medical specimens”, Japanese Association for Dental Science. Published by Elsevier Ltd., JDSR-139, pp.1-8. 2014.




Kalyanee S Patil

Paper Title:

Wireless Body Area Network

Abstract: The project in designing a system which is capable of tracking the location of patients and also monitoring of heart rate and health parameters also alerts in case of emergency through SMS to predefined number.This aims in design and present the novel wearable system with the bio recognition sensors based on the GPS and GSM communication Network .In this project sensors (3-axis accelerometer, temperature sensor, heartbeat sensor, humidity sensor, ECG sensor) added and located at patient body for getting the postures information and activity by them always monitored. The system sends alert messages in emergency times.

 ARM7, ECGSensor, GPS, GSM, Heart Beat, MEMs Sensor.


1.    A.Sagahygroon, F.Aloul, Al-Ali, M. S. Bahrololoum, F. Makhsoos, N. Hussein “Monitoring patients signs wirelessly” 97 8-1-4244-7000-6/11/$26.00 ©2011 IEEE
2.    S. M. Mahalle P.V.Ingole. “Design and implementation of wireless body area sensor network based health monitoring system” International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 2 Issue 6, June – 2013

3.    MirHojjat Seyedi, Student Member, IEEE, Behailu Kibret, Student Member, IEEE, Daniel T. H. Lai, Member, IEEE, and Michael Faulkner, Member, IEEE. “A survey on intrabody communications for body area network applications” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 8, AUGUST 2013

4.    Malika haru rana. “An indoor wireless zigbee based patient monitoring system for hospitals” International Journal of Engineering Sciences Research-IJESR, ISSN: 2230-8504, Vol 04, Issue 02; March-April 2013

5.    Milenkovic . Otto and E. Jovanov “Wireless Sensor Networks for Personal Health Monitoring: Issues and an Implementation ” in Computer Communications, vol. 29 (13-14), August 2006.

6.    “Interoperability and Security in Wireless Body Area Network Infrastructures” Steve Warren1, Jeffrey Lebak1, Jianchu Yao3,




Ajit Kumar Yadav, Hemant L. Jadhav, Rohit Paithane

Paper Title:

Simulation of Topology Recovery Algorithms with Preventive Measure in WSANs

Abstract:  In Wireless Sensor-Actor Networks (WSANs), a node might get failed and a network can get partitioned into two disjoint blocks. Partitioning can cause the network to violate the need of having strong connectivity all the time in the network. The problem of connectivity can be overcome by repositioning of a node present in the network. A Least Disruptive Topology Repair (LeDiR) algorithm is proposed to restore the connectivity in the network. Furthermore, a preventive measure of rotating a cluster head is taken into consideration to reduce the chances of a node failure in the network by conserving the energy of each node. Another contemporary scheme of node repositioning, DARA, is used to compare the performance of LeDiR. It is shown finally that how LeDiR restores connectivity better than DARA without significantly changing the pre-failure status of the network.

 Connectivity restoration, DARA, LeDiR, Network topology, Node, Node failure, WSANs.


1.       Ameer A. Abbasi, Mohamed F. Younis, and Uthman A. Baroudi, “Recovering from node failure in wireless     sensor-actor networks with minimal topology changes” in IEEE transactions on vehicular technology, vol. 62, No. 1 January 2013, pp. 256-271. 
2.       M. Younis and K. Akkaya, “Strategies and techniques for node placement in wireless sensor networks: A survey,” J. Ad Hoc Netw., vol. 6, no. 4, pp. 621–655, Jun. 2008

3.       V. Sri Jahnavi & Shaik Fayaz Ahamed (2015): Smart Wireless Sensor Network for Automated Greenhouse, IETE Journal of Research, DOI: 10.1080/03772063.2014.999834.

4.       Sashigaran Sivathasan & Dominic O’Brien (2011) Hybrid Radio and Optical Communications for Energy efficient Wireless Sensor Networks, IETE Journal of Research, 57:5, 399-406.

5.       Abbasi, M. Younis, and K. Akkaya, “Movement-assisted connectivity restoration in wireless sensor and actor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 9, pp. 1366–1379, Sep. 2009.

6.       M. Younis, S. Lee, S. Gupta, and K. Fisher, “A localized self-healing algorithm for networks of moveable sensor nodes,” in Proc. IEEE GLOBECOM, New Orleans, LA, Nov. 2008, pp. 1–5.

7.       K. Akkaya, F. Senel, A. Thimmapuram, and S. Uludag, “Distributed recovery from network partitioning in movable sensor/actor networks via controlled mobility,” IEEE Trans. Comput., vol. 59, no. 2, pp. 258–271, Feb. 2010.

8.       K. Akkaya and M. Younis, “COLA: A coverage and latency aware actor placement for wireless sensor and actor networks,” in Proc. IEEE VTC, Montreal, QC, Canada, Sep. 2006, pp. 1–5.

9.       K. Akkaya, A. Thimmapuram, F. Senel, and S. Uludag, “Distributed recovery of actor failures in wireless sensor and actor networks,” in Proc. IEEE WCNC, Las Vegas, NV, Mar. 2008, pp. 2480–2485.

10.    F. Senel, M. Younis, and K. Akkaya, “Bio-inspired relay node placement heuristics for repairing damaged wireless sensor networks,” IEEE Trans. Veh. Technol., vol. 60, no. 4, pp. 1835–1848, May 2011.

11.    S. Lee and M. Younis, “Recovery from multiple simultaneous failures in wireless sensor networks using minimum Steiner tree,” J. Parallel Distrib. Comput., vol. 70, no. 5, pp. 525–536, May 2010.

12.    J Niharika Gorre, Prudhvi Raj Kyatham and Mohamed El-Sharkawy, “Performance Evaluation of Least Disruptive Topology Repair Algorithm(LEDIR) using NS2”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 6, No. 4, August 2014.  

13.    Liu, Xiao Yang; Zhu, Yanmin; Kong, Linghe; Liu, Cong; Gu, Yu; Vasilakos, Athanasios V.; Wu, Min You, “CDC: Comprehensive Data Collection for Wireless Sensor Networks” in IEEE Transactions on Parallel and Distributed Systems, Vol.26, No.8, 6870490, 01.08.2015, p.2188-2197.

14.    Mohammad Ahmadinia, Mohammad Reza Meybodi, Mahdi Esnaashari, Hamid Alinejad-Rokny, “Energy- efficient and multi- stage clustering algorithm in wireless sensor networks using cellular learning automata”, IETE Journal of Research 2013, Vol. 59, Issue:6, pp. 774-782.

15.    M. Sir, I. Senturk, E. Sisikoglu, and K. Akkaya, “An optimization based approach for connecting partitioned mobile sensor/actuator networks,” in Proc. 3rd Int. Workshop WiSARN, Shanghai, China, Apr. 2011, pp. 525– 530.

16.    K. R. Kasinathan and M. Younis, “Distributed approach for mitigating coverage loss in heterogeneous wireless sensor networks,” in Proc. 3rd IEEE Int. Workshop MENS, Houston, TX, Dec. 2011.

17.    S. Lee and M. Younis, “QoS-aware relay node placement in a segmented wireless sensor network,” in Proc. IEEE ICC, Dresden, Germany, Jun. 2009, pp. 1–5.

18.    G. Wang et al., “Sensor relocation in mobile sensor networks,” in Proc. 24th Annu. Joint Conf. INFOCOM, Miami, FL, Mar. 2005, pp. 2302–2312.

19.    Alfadhly, U. Baroudi, and M. Younis, “Least distance movement recovery approach for large scale wireless sensor-actor networks,” in Proc. Workshop FedSenS, Istanbul, Turkey, Jul. 2011.




Nandini S. Gore, G. S. Sable

Paper Title:

A Review: Personal Authentication using Finger Knuckle Surface

Abstract: The various biometric traits are used to accurately identify a person. Personal identification is more popular because it is more reliable. Many traits like fingerprint, face, palm print, vein, DNA and many others used personal authentication. Now- a-days finger knuckle surface is used for personal authentication and identification. The texture pattern produced by finger knuckle bending is highly unique and make surface as biometric identifier. The image processing used for recognition of collected data, increasing matching accuracy. The motivation of this topic is to develop system for biometric purpose with high accuracy and with less consuming time. This review papers represents survey on various methods are used for database collection and technique used for recognition.

 Biometric, Finger Knuckle Surface, Database Collection, Recognition


1.       Miguel A. Ferrer, Carlos M. Travieso, “Using hand knuckle texture for biometric identification” IEEE A&E Systems Magazine, June 2006.
2.       Ajay Kumar, “Personal authentication using finger knuckle surface”, IEEE Transactions On Information Forensics And Security, Volume. 4, No. 1, March 2009.

3.       Lin zhang, “Ensemble of local and global information for finger knuckle print recognition”, Biometrics Research Center, Department of Computing, The HongKong
PolytechnicUniversity,HongKong,11 June 2010.

4.       Damon L. Woodard, Patrick J. Flynn, “Finger knuckle print: biometric identifier” Journal of Pattern Intelligence ISSN: 2230-9330 & E-ISSN: 2230-9349, Volume 2, Issue 1, 2012.

5.       Guangwei Gao, Lei Zhang, “Reconstruction Based Finger-Knuckle-Print Verification With Score Level” IEEE Transactions On Image Processing, Vol. 22, No. 12, December 2013

6.       K Usha, “A Hybrid Model for Biometric Authentication using Finger Back Knuckle Surface based on Angular Geometric Analysis” I.J. Image, Graphics And Signal Processing, 2013, 10, 45-54 Published Online August 2013 In MECS (Http://Www.Mecs-Press.Org/)

7.       Sivaranjani, “A Literature Study on Finger Knuckle Matching Techniques” International Journal For Research In Emerging Science And Technology, Volume-1, Issue-6, November-2014.

8.       Ajay Kumar, “Importance of Being Unique From Finger Dorsal Patterns: Exploring Minor Finger Knuckle Patterns in Verifying Human Identities” IEEE Transactions On Information Forensics And Security, Vol. 9, No. 8, August 2014.

9.       P. Diviya, “Identification of suspect using finger knuckle pattern in biometric fusion” International journal of research in computer science, Volume: 02, issue:02, 2015.

10.    Hongyang Yu, Gongping Yang, “A New Finger-Knuckle-Print ROI Extraction Method Based on Two-Stage Center Point Detection” International Journal of Signal Processing, Image Processing and Pattern Recognition Volume: 8, No. 2 (2015)

11.    Shubhada Sonawane, “Verifying Human Identities Using Major and Minor Finger Knuckle Pattern” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 2, February 2016.




Pranali Anant Nalange, Manisha Madhukar Ambekar

Paper Title:

A Review on Multimodal Biometric Databases

Abstract:  In biometric technology physical or behavioral characteristics are used to identify user or particular person. Personal authentication system using biometric helps to establish the identity of an actual user. From the various biometric traits, face and fingerprint are most commonly used as well as easily available. There are so many unimodal biometric databases are available on internet. But only few databases are available for multimodal biometric. But in all multimodal biometric databases, only few of them are freely available as well as only some of them having face and fingerprint biometric traits commonly present. So, in this paper we study the multimodal biometric database in which face and fingerprint biometric traits are commonly present, which is created by us for serial multimodal biometric technique research purpose. 

Biometric, Database, Face, Fingerprint, Multimodal


1.       Marcos Faundez-Zanuy, Julian Fierrez-Aguilar, Javier Ortega-Garcia, Joaquin Gonzalez-Rodriguez, “Multimodal Biometric Databases: An Overview” IEEE A&E Systems Magazine, August 2006
2.       Kieron Messer, Josef Kittler, Mohammad Sadeghi, Sebastien Marcel, Christine Marcel, Samy Bengio, et al. “Face Verifcation Competition on the XM2VTS Database”

3.       Kieron Messer, Josef Kittler, Mohammad Sadeghi, Miroslav Hamouz, Alexey Kostin, Fabien Cardinaux, et al., “Face Authentication Test on the BANCA Database”

4.       Sonia Garcia-Salicetti, Charles Beumier, Gerard Chollet, Bernadette Dorizzi, Jean Leroux les Jardins, Jan Lunter, et al. “BIOMET: a Multimodal Person Authentication Database Including Face, Voice, Fingerprint, Hand and Signature Modalities ” Conference Paper in Lecture Notes in Computer Science, June 2003

5.       Bruno Dumas, Catherine Pugin, Jean Hennebert, Dijana Petrovska-Delacretaz, Andreas Humm, Florian Evequoz, et al. “MYIDEA – Multimodal Biometrics Database, Description Of Acquisition Protocols”

6.       Doroteo T. Toledano, D. Hernandez-Lopez, C. Esteve-Elizalde, J. Fierrez, J. Ortega-Garcia, D. Ramos, J. Gonzalez-Rodriguez, “BioSec Multimodal Biometric Database in Text-Dependent Speaker Recognition”

7.       J. Fierrez, J. Galbally, J. Ortega-Garcia, M. R. Freire, F. Alonso-Fernandez, D. Ramos, et al. “BiosecurID: a multimodal biometric database” Springer-Verlag London Limited, February 2009

8.       Yilong Yin, Lili Liu, Xiwei Sun, “SDUMLA-HMT: A Multimodal Biometric Database” Springer-Verlag Berlin Heidelberg 2011

9.       Pranali Anant Nalange, Prof. Manisha Madhukar Ambekar, “Identification of Face and Fingerprint Using serial Multimodal Biometric Technique” International Journal of Pure and Applied Research in Engineering and Technology ISSN – 2319-507X volume 4 (9): 1236-1243, 2016

10.    Pranali Nalange, Prof. M. M. Ambekar, “A Comparative Study of Pixel by Pixel and PCA Technique in Serial Multimodal Biometric” International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 4 Issue: 10, ISSN: 2321-8169, 165-167




Gajanan Tudavekar, Sanjay R. Patil

Paper Title:

Video In Painting using Wavelets

Abstract: Video in-painting is the technique of filling the holes or recovering the damages in a video. In this paper, video in-painting frame work is proposed to recover the damaged frame. This framework consists of Discrete Wavelet Transform based in-painting algorithm, and Papoulis-Gerchberg based super resolution algorithm. The framework performs first the in-painting on sub band level of coarse version of the input frame. A super-resolution algorithm is then used to enhance the image. The proposed approach is compared with state-of-art algorithms. The results demonstrate that the proposed work is well suited for filling the gap in a frame of a video sequence.

In-painting, Discrete Wavelet Transform, Super resolution, PSNR, SSIM


1.       C. Guillemot and O. Le Meur, “Image inpainting: Overview and recent advances,” IEEE Signal Process. Mag., vol. 31, no. 1, pp. 127–144, Jan. 2014.
2.       C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman, “Patch Match: A randomized correspondence algorithm for structural image editing,” ACM Trans. Graph., vol. 28, no. 3, pp. 24:1–24:11, Jul. 2009.

3.       Liu, Yunqiang, and Vicent Caselles. “Exemplar-based image inpainting using multiscale graph cuts.” Image Processing, IEEE Transactions on 22.5 (2013): 1699-1711.

4.       Xia, Aijuan, et al. “Exemplar-Based Object Removal in Video Using GMM.”Multimedia and Signal Processing (CMSP), 2011 International Conference on. Vol. 1. IEEE, 2011.

5.       Ghanbari, Amanna, and Mohsen Soryani. “Contour-based video inpainting.”Machine Vision and Image Processing (MVIP), 2011 7th Iranian. IEEE, 2011.

6.       Chan T.F, Shen J. Mathematical models for local nontexture inpainting. SIAM Journal of Applied Mathematics.2002.

7.       Chan T.F, Shen J. Non-texture inpainting by curvature-driven diffusions. Journal of Visual Communication and Image Representation. 2001; 12:436–449. doi: 10.1006/jvci.2001.0487

8.       Bertalmio M, Sapiro G, Caselles V, Ballester C. Image inpainting. Proc ACM SIGGRAPH Computer Graphics (SIGGRAPH). 2000; p. 417–424.

9.       M. Granados, J. Tompkin, K. I. Kim, J. Kautz, and C. Theobalt, “Background inpainting for videos with dynamic objects and a freemoving camera,” in Proc. Eur. Conf. Comput. Vis., 2012, pp. 682–695.

10.    M. BERTALMIO, A.L. BERTOZZI AND G. SAPIRO, “Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting”, Proc. IEEE Computer Vision and Pattern Recognition (CVPR’01), Hawaii, December 2001.

11.    S. MASNOU AND J.M. MOREL, “Level Lines Based Disocclusion” Proceedings of the fifth IEEE International Conference on Image Processing, New York, 1998.

12.    Criminisi,P. P´erez and K. Toyama, “Region Filling and Object Removal by Exemplar-Based Image Inpainting,”IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 9, SEP 2004

13.    Sanjay Patil and Sanjay Talbar, “Multiresolution analysis using Wavelet and Curvelet Features for CBIR,” International Journal of Computer Applications, Vol. 47 No.17, pp. 6-10 June 2012.

14.    Subhasis Chaudhuri, “Super Resolution Imaging,” Kluwer Academic Publishers, 2002.

15.    Anand Deshpande, Prashant Patavardhan,  D. H. Rao, “Super-Resolution for Iris Feature Extraction,”, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2014.

16.    Peleg, T. and Elad, M.,”A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution,” IEEE Transactions on Image Processing, Volume:23, Issue: 6, pp. 2569 – 2582, June 2014.

17.    Priyam Chatterjee and Sujata Mukherjee, “Application of Papoulis-Gerchberg Method in Image Super-resolution and Inpainting,” The Computer Journal, Vol.1, 2007.

18.    Soheil Darabi, Eli Shechtman, Connelly Barnes, Dan Goldman, and Pradeep Sen, Image melding: combining inconsistent images using patch-based synthesis, ACM Trans. Graph., 31 (2012).

19.    Yunqiang Liu and Vicent Caselles, Exemplar-based image inpainting using multiscale graph cuts, IEEE Trans. Image Proc., 22 (2013), pp. 1699–1711.

20.    Wang, “Image quality assessment: from error visibility to structural similarity’, IEEE, proceedings on Image Processing. 2004.

21.    Sheikh, et. al., “An information fidelity criterion for image quality assessment using natural scene statistics”, IEEE Transaction on Image Processing, 2005.

22.    Lukes, et. al., “Performance evaluation of image quality metrics with respect to their use for super-resolution enhancement,” Qualcomm Multimedia, 2013.




Gangadhar V. Shinde, Vinodpuri R. Gosavi

Paper Title:

Design & Development of Digital Panel Meter

Abstract:  Measurement of electrical parameter is an essential aspect in today’s world, as electricity has become an indispensible part of our lives. Earlier electrical parameters used to be measured using analog meters but later several manufacturers started developing digital meters to overcome the drawbacks of analog meters. Today with several manufacturers in market Cost and also Accuracy have become major parameters to be considered before taking up design of any project. Digital Panel Meter for short DPM’s are absolutely necessary in any electrical industry we find. Digital Panel Meters to be designed in proposed project will be Cost effective, efficient and also accurate in measurement, which are the features found desirable in any DPM that customer wish to purchase.

True RMS, DPM, SMPS, Accuracy Class, MCU, ADC.


1.    ‘Using Microcontrollers for High Accuracy Analogue Measurements’ by M. Jaanus A. Udal, V. Kukk, K. Umbleja, Tallinn University of Technology 2013.
2. 4.pdf






Mohtashim Alam Khan, A.V. Nikalje

Paper Title:

PLC-SCADA Based Boiler Automation

Abstract: This paper deals with operation and control of boiler system using PLC (Programmable Logic Controller).It focuses on automation of various parameters like temperature, steam pressure, water levels at various stages in a boiler assembly and its synchronization with boiler operation. With technological advancement need for improvements in boiler system has increased ,this system tries to fulfill the emerging demand in existing boiler systems. Using PLC automation of boiler is done , different sensors like RTD ,float switches, pressure sensors give input to the PLC which accordingly regulates different values and control parameters of this system. Monitoring and control of this operation is done using SCADA (Supervisory Control and Data Acquisition System) connected through various communication cables making it more easy to operate through a computer. This automation and control in turn improves efficiency of the system. The emergency system which is also incorporated in this system comes into action once the predefined values exceeds hence providing safety of life and protects our system from failure.

 PLC, Boiler, SCADA, Automation.

1. George Boltan , “Programmable Logic Controllers: Programming Methods & Applications”, Pearson India.
2. Webb John. W, “Programmable Logic Controllers,” PHI learning Pvt Ltd.
3. Gary Anderson, “Industrial Network Basics: Practical Guides for the Industrial Technician,”TMH India.
4. Francis G.L, “PLC & SCADA systems: Quick Reference Guide”. 
5. Frank D.Petruzella, “Programmable Logic Circuits,” Tata McGraw- Hills.
6. Gary Dunning, “Introduction to Programming logic controllers 3rd Edition,” Thomas Delmar learning.
7. George Bolton, “Programmable logic Controllers(English) 5th Edition,” Elsevier India.
8. Stuart A. Boyer “Supervisory Control and Data Acquisition,” 4th Edition Instrument Society of Automation.
9. “SCADA System Selection Guide Allen-Bradley,” Rockwell Automation Publication AG-2.1.1998
10. Madhuchhandan Mitra & Samarjit Sen Gupta, “Programmable Logic Controllers & Industrial Automation,” 2nd Edition Penram International Publishing Pvt.Ltd.
11. Engin Ozdemir, Mevlut Karacor, “Mobile phone based SCADA for industrial automation”, Research gates The instrumentation, systems and automation society (ISA),  vol. 45,no.1,Jan 2006.
12. P.Kiruthika, P.Navaseelam,L.Senthilnathan, “Automated control system design for ultra supercritical thermal power plant” IEEE International conference on technological innovations in ICT for agriculture and rural development(TIAR),2015.
13. Dongwang, Xian-Li Su,“The Boiler Design of remote monitoring system based on SCADA”,IEEE The National Natural Science Founds,2013.
14. P.K.Bhowmik,S.K.Dhar, “Boiler gas burner management system automation using PLC”,IEEE 7th International Conference on Electrical and computer Engineering, Dec 2012.
15. A.Archana ,B.Yadav, “PLC-SCADA based automation of filter house , a section of water treatment plant1”1st International Conference on Emerging Technology Trends in Electronics Communication and Networking IEEE, Dec 2012.
16. Programmable logic controllers (PLC’s) details are Available:
17. Ezell,Barry, “Supervisory control and data acquisition system for water supply and its vulnerability to cyber risks “Available:




Shrikant J. Upase, R. P. Labade

Paper Title:

Design of Encrypted SDR and analysis of Noise in High Level System Architecture using MATLAB

Abstract:  The aim of this project is to design and implement a software defined radio based wireless communication system using MATLAB. Software defined radio is a feasible solution for reconfigurable radios, which can carry out distinctive functions at different times on the same hardware. The baseband area of a remote communication system is first simulated and then implemented in program. The implementation of the baseband transmitter is analyzed utilizing constellation and eye diagrams for different modulation techniques and different signal-to noise ratios, while considering an additive white Guassian noise channel. The execution of the receiver is analyzed by comparing the input and yield waveforms. The performance of the system in real time is also analyzed by implementing the system in MATLAB. A comparison of the simulation results with the results obtained from implementing the system in the form of plotted graph. It is expected that the simulation results and experimental results will be similar. Apart from this the parameters like encoder and decoder will make an optimum solution with minimum noise in transmission for software define radio. To avoid the noise we have to adjust various parameter like frequency, width, delay, bandwidth etc. accordingly we will compare the result with existing system and proposed system with encoder. We expect the minimum noise level as compared to normal.

Additive White Gaussian Noise, Analog-to Digital Converter, Binary Phase Shift Keying, Software Defined Radio, quadrature phase shift keying.


1.       . Rappaport, Wireless Communications – Principles & Practice, 2nd edition. Prentice-Hall, Upper Saddle River, NJ, 1996.
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4.       J. Noll and E. Buracchini, “Software radio – A key technology for adaptive access,” Wireless Communn  and Mobile Computing, vol. 2, issue 8, Dec. 2002, pp. 789-798.

5.       Vanu Software Radio. Retrieved on June 14, 2005 from

6.       O. Ugweje and M. Souaré, Software Defined Radio in Communication Systems, Research Brief, Department of Electrical and Computer Engineering, The University of Akron, Akron, OH.

7.       S. Srikanteswara, Design and Implementation of Soft Radio Architecture for Reconfigurable  Platforms, Ph.D Dissertation, Virginia Polytechnic Institute and State University, July 2001.

8.       Atul Adya , Paramvir Bahl, Ranveer Chandra, Lili Qiu,” Architecture and Techniques for   Diagnosing Faults in IEEE 802.11 Infrastructure Networks”, Proceedings of the Tenth Annual International Conference on Mobile Computing and Networking (MobiCom 2004),pp 1-10R.

9.       Schiporst, Demonstration of Software Radio Concept, Master’s Thesis,  University of   Twente, Netherkands, June 2000.

10.    M. Laddomada, “Reconfiguration issues of future mobile software radio platforms,” Wireless Commun. and Mobile Computing, vol. 2, issue 8, Dec. 2002, pp. 815-826.

11.    Vikram Iyer, Vamsi Talla, Bryce Kellogg, Shyamnath Gollakota and Joshua R. Smith,”  Inter-Technology Backscatter: Towards Internet Connectivity for Implanted Devices”,pp 1-16

12.    Buracchini, “The software radio concept,” IEEE Commun. Mag., vol. 38, no. 9, Sep. 2000,  pp. 138-143.

13.    Wiesler and F. Jondral, “A software radio for second- and third-generation mobile systems,”  IEEE Trans. on Vehicular Technology, vol. 51, no. 4, July 2002, pp. 738-748.

14.    S. Weiss, A. Shligersky, S. Abendroth, J. Reeve, L. Moreau, T. E.Dodgson andD. Babb, “A  software defined radio testbed implementation,” In Proceedings of IEE Colloquium on DSP Enable Radio, Livingston, Scotland, 2003, pp. 268-274.

15.    N. W. Anderson, H. R. Karimi, P. Mangold and M. Wezelenburg, “Software definable implementation of a dual mode TD-CDMA/DCS1800 transceiver.” Retrieved on June 13, 2005 from

16.    N. W. Anderson, H. R. Karimi and P. Mangold, “Software-definable implementation of a TDMA/CDMA transceiver.” Retrieved on June 13, 2005 from

17.    Software Defined Radio Forum FAQs. Retrieved on June 14, 2005 from

18.    R. Baines, “The DSP bottleneck,” IEEE Commun. Mag., vol. 33, issue 5, May 1995, pp. 46-54.

19.    Z. Kostic and S. Seetharaman, “DSPs in cellular radio communications,” IEEE Commun. Mag.,  vol. 35, issue 12, Dec. 1997, pp. 22-35.

20.    J. Mitola III, “Technical challenges in the globalization of software radio,” IEEE Commun. Mag., vol. 37, no. 2, Feb. 1999, pp. 84-89.

21.    Mohebbi, E. Filho, R. Maestre, M. Davies and F. Kurdahi, “A case study of mapping a  software defined radio application on a reconfigurable DSP core,”  International Symposium on Systems Synthesis, Newport Beach, CA, 2003, pp. 103-108.

22.    Jo, M. Sheen, S. Lee and K. Cho, “A DSP-based reconfigurable SDR platform for 3G systems,” IEICE Trans. on Commun., Feb. 2005, pp. 678-683.

23.    Aqsa Malik, Junaid Qadir, Basharat Ahmad, Kok-Lim Alvin Yau, Ubaid Ullah,” QoS in IEEE   802.11-based Wireless Networks: A Contemporary Survey”,pp 1-25

24.    Abdeldime M.S. Abdelgader, Wu Lenan,” The Physical Layer of the IEEE 802.11p WAVE Communication Standard:  The Specifications and Challenges”, the World Congress on Engineering and Computer Science 2014 Vol II WCECS 2014, 22-24 October, 2014, San Francisco, USA, pp 1-8   



27.    J. Gunn, K. Baron and W. Ruczczyk, “A low power DSP core-based software radio architecture,”  IEEE Journal on Selected Areas in Commun., vol. 17, no. 4, Apr. 1999, pp. 574-590.

28.    Truter, E.Wolmarans, “A software defined radio architecture with power control for 3GW-CDMA systems,” Alcatel Altech Telecoms, Boksburg, South Africa.

29.    K. Moessner, D. Bourse, D. Greifendorf and J. Stammen, “Software radio and reconfiguration  management,” Computer Commun., vol. 26, issue 1, Jan., 2003,  pp. 26-.

30.    N. Apostolou, Signal Synthesis with Dynamically-Charged Power Spectral Density in a  Software Defined Radio Transmitter, Master’s Thesis, Naval Postgraduate School, Monterey,  California, Sept. 2003.

31.    Lei Yang, Zengbin Zhang, Wei Hou, Ben Y. Zhao, Haitao Zheng,” A Software Platform for   Distributed Dynamic Spectrum Sharing Using SDRs”, pp 1-7

32.    Rini Supriya .L,Mr.Senthil Murugan, Dr.R.C.Biradar,” Design and Implementation of Software Defined Radio Using Xilinx System Generator”, International Journal of Scientific and   Research Publications, Volume 2, Issue 12, December 2012  ISSN 2250-3153, pp 1-6

33.    Benjamin Drozdenko, Ramanathan Subramanian, Kaushik Chowdhury, and Miriam Leeser,” Implementing a MATLAB-based Self-Configurable Software Defined Radio Transceiver”, pp       1-12

34.    Shriram K Vasudevan,Sivaraman R,Z.C.Alex,” Software Defined Radio Implementation (With simulation & analysis)”, International Journal of Computer Applications (0975 – 8887)     Volume 4– No.8, August 2010, pp 1-7

35.    Noa Zilberman,, Philip M. Watts,Charalampos Rotsos, and Andrew W. Moore, “Reconfigurable Network Systems and Software-Defined Networking” Vol. 103, No. 7, July 2015 | Proceedings of the IEEE

36.    Ramanathan Subramanian, Benjamin Drozdenko, Eric Doyle, Rameez Ahmed,  Miriam  Leeser, and Kaushik Roy Chowdhury, “High-Level System Design of IEEE 802.11b  Standard-Compliant Link Layer for  MATLAB-Based  SDR” IEEE Trans.  Volume 4, 2016 pp 1494-1509.




Bhagyalaxmi T.Tejinkar, R.K.Kanhe

Paper Title:

A Review Paper on Facial Expression Recognition

Abstract: Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human–computer interaction and data-driven animation. Facial expression reflects not only emotions but also other mental activities, social interaction and physiological signals. Facial expression recognition usually performed in three-stages consisting of face detection, and expression classification. This paper presents a survey of the current work done in the field of facial expression recognition techniques with various face detection, feature extraction and classification methods used by them and their performance.

Facial Expression Detection, Feature Extraction, Expression classification, Human Machine Interface


1.       Tanjea Ane, Md. Fazlul Karim Patwary, “Performance analysis of similarity coefficient feature vector on facial expression recognition”, 12th International conference on vibration problems, ICOVP 2015, pp.444-451.
2.       Anurag De, Ashim Saha, Dr.M.C.Pal, “A Human facial expression recognition model based on eigen face approach”, International conference on advanced computing technologies and applications, ICACTA 2015,pp.282-289.

3.       Jyoti Kumari, R.Rajesh, KM.Pooja, “Facial expression recognition: A survey”,Second international symposium on computer vision and the internet,2015,pp.486-491.

4.       Veena Mayya et al, “Automatic facial expression recognition using DCNN”, 6th International conference on computing and communication, ICACC 2016, PP.453-461.

5.       Jun Ou, “Classification algorithms research on facial expression recognition”International conference on solid state devices and materials science, 2012, pp.1241-1244.

6.       Hung-Fu Huang,Shen-Chuan Tai, “Facial expression recognition using new feature extraction algorithm”,Electronics letters on computer vision and Image analysis,2012

7.       Nazia Perveen,Nazir Ahmad, “Facial expression recognition through machine learning”,International journal of scientific and technology research,Vol.5,no.03,pp.2277-8616,March 2016.

8.       Amira E.Youself, Sherin F.Aly, “Auto-optimized multimodal expression recognition framework using 3D Kinect data for ASD therapeutic aid”International journal of modeling and optimization ,Vol.3,No.2,April 2013.

9.       Mandeep Kaur,Rajeev Vashisht, “Facial expression recognition using a novel approach and its application”,International journal of computer and electrical engineering,Vol.3,no.2,April 2011.

10.    Mu-chun Su, Chun-Kai Yang et al., “An SOM –based automatic facial expression recognition system”, International journal on soft computing using artificial intelligence and applications,vol.2,no.4,August 2013.

11.    Evangelos Sariyanid,Hatice Gunes, “Automatic analysis of facial affect :A survey of registration ,representation and recognition”, IEEE transactions on intelligence,vol.37,no.6,June 2015.

12.    Harish Kumar Dogra,Zohaib Hasan, “Face expression recognition using scaled –conjugate gradient back- propogation algorithm”, International journal of modern engineering research,vol.3,no.4,pp.1919-1922,August 2013.

13.    V.Goomathi,Dr.K .Raman, “Huamn facial expression recognition using MANFIS model” International journal of computer electrical automation control and information engineering,vol.3,no.2,2009

14.    Shalini Mahto,Mrs.Yojana Yadav, “Effectual approach for FER system” International journal of advanced research in computer and communication engineering,vol.4,no.3,March 2015.

15.    Nivedita Singh,Chandra Mani Sharma, “Real time automatic facial expression recognition video sequence” International journal of computer science , vol.12,no.2,pp.1694-0784,January 2015.

16.    Anchal Garg,Dr.Rohit Bajaj, “Facail expression recognition and classification using hybridization of ICA,GA,and Neural Network for Human Computer Interaction”,Journal network communication and emerging technologies,vol.2,no.1,May 2015.




Suvarna S. Kale, R. K. Kanhe

Paper Title:

A Review on Image Defogging

Abstract:  Digital image processing is the method to perform some operation on the image, in order to get an enhanced image and extract the useful information from that image. Mostly in winter season the visibility of outdoor images captured in inclement weather is often degraded due to the presence of fog. Because of this problem clear image is not obtained. In this paper, we first presented a review of the detection and classification method of a foggy image. Literature survey is an important for understanding and gaining much more knowledge about the specific area of a subject there is an improvement in terms of contrast, visible range and color fidelity. All these techniques are widely used in many applications such as outdoor Surveillance, object detection, underwater images, etc.

Image processing, image defogging, dark channel prior, Improved DCP (IDCP).


1.        R. Sharma and V. Chopra, “A review on different image dehazing methods,’‘ Int. J. Comput. Eng. Appl., vol. 6, no. 3, pp. 7787, Jun. 2014.
2.        N. Hautière, J.-P. Tarel, and D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2007, pp. 18.

3.        S. G. Narasimhan, C. Wang, and S. K. Nayar, “All the images of an Outdoor scene,” in Computer Vision (Lecture Notes in Computer Science), vol. 2352. Heidelberg, Germany: Springer, 2002, pp. 148162.

4.        S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” Int. J. Comput. Vis., vol. 48, no. 3, pp. 233254, 2002.

5.        S. G. Narasimhan and S. K. Nayar, “Removing weather effects from monochrome images,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), vol. 2. Dec. 2001, pp. II-186II-193.

6.        J. John and M. Wilscy, “Enhancement of weather degraded video sequences using wavelet fusion,” in Proc. 7th IEEE Int. Conf. Cybern. Intell. Syst., Sep. 2008, pp. 16.

7.        Ramya and D. S. S. Rani, “Contrast enhancement for fog degraded video sequences using BPDFHE,” Int. J. Comput. Sci. Inf. Technol., vol. 3, no. 2, pp. 34633468, 2012.

8.        Z. Xu, X. Liu, and X. Chen, “Fog removal from video sequences using contrast limited adaptive histogram equalization,” in Proc. IEEE Int. Conf. Comput. Intell. Softw. Eng., Dec. 2009, pp. 14.

9.        K. He, J. Sun and X. Tang, “Single Image Haze Removal Using Dark Channel Prior” , IEEE Int. Conf. on Computer Vision and Pattern reorganization, 2009.

10.     Vinkey Sahu and Vinkey Sahu, “A Survey Paper On Single Image Dehazing”, IJRITCC Volume: 3 Issue: 2 February 2015.

11.     Yan Wang and Bo Wu, “Improved Single Image Dehazing using Dark Channel Prior”, IEEE 2010.

12.     Kaiming He, Jian Sun, and Xiaoou Tang, “Single Image Haze Removal Using Dark Channel Prior”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, December 2011.




Aravind Jadhav, Sanjay Pujari

Paper Title:

Resolution Enhancement of CT Images Based on Histogram Equalization

Abstract: In this paper, we will discuss the development of the image enhancement techniques and their application in the field of image processing. The principle objective of image enhancement techniques is to process an input image so that the resultant image is more suitable than the original image for specific application. The histogram equalization (HE) is one of the most popular methods for image contrast enhancement. The idea of image resolution enhancement concerns with the improvement of image resolution based on the fusion of several acquisitions of low resolution observations by the imaging sensor. The result presented in this paper demonstrates that the application of the histogram equalization (HE) technique on a dark medical image yields a better quality, which makes it suitable for pre-processing of visibility for a majority of images and medical image applications.

Resolution Enhancement, histogram equalization (HE), low resolution (LR)


1.       R.Gonzalez & R.Wood , Digital Image Processing,3rd ed. Englewood Cliffs, NJ: Prentice Hall, 2007
2.       Jindal, K. ET AL. “Bio-medical image enhancement based on spatial domain technique,” IEEE International Conference on Advances in Engineering and Technology Research, 2014

3.       Ankit Aggarwal, et al. “An Adaptive Image Enhancement Technique Preserving Brightness Level Using Gamma Correction” Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 9 (2013),

4.       Fan Yang, et al. “An Improved Image Contrast Enhancement in Multiple-Peak Images Based on Histogram Equalization” 201O International Conference On
Computer Design And Appliations (ICCDA 2010)

5.       Nazia Afroz Choudhury, et al. “Resolution Enhancement of CT images Using Multiple Low Resolution Sub-pixel Shifted Images”. IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing

6.       Torre, A. M. Peinado, J. C. Segura, J. L. Perez-Cordoba, M. C. Benitez, and A. J. Rubio, “Histogram equalization of speech representation for robust speech recognition,” IEEE Trans. Speech Audio Processing, Vol. 13, pp. 355–366, 2005.

7.       S.–D. Chen, and A. R. Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, pp.1301-1309, 2003.

8.       Md. Foisal Hossain, Mohammad Reza Alsharif, and Katsumi Yamashita, “Medical Image Enhancement Based on Nonlinear Technique and Logarithmic Transform Coefficient Histogram Matching”, 2010.

9.       Renjie He, Sheng Luo, Zhanrong Jing, Yangyu Fan “Adjustable Weighting Image Contrast Enhancement Algorithm and Its Implementation”, 2011 6th IEEE Conference on Industrial Electronics and Applications. Huda Mustafa Rada Al-Bayati, “Skeleton Medical Image Enhancement”, 2011.

10.    S. Park, M. Park, and M. Kang, “Super-resolution image reconstruction, a technical overview,” IEEE Signal Processing Magazine, vol. 20, pp. 21-36, 2003.

11.    K. Hyunwoo, J. Jeong-Hun and H. Ki-Sang, “Edge-Enhancing SuperResolution Using Anisotropic Diffusion”, in Proc. IEEE Conf Image Process., Thassaloniki, Greece, vol. 3, pp. 130-133, 2001.

12.    M. Irani, and S. Peleg, “Super resolution from image sequences,” Proc. of the International Conference on Pattern Recognition, pp.115-120, Atlantic City, Jun 1990.

13.    H. Shen, M.K. Ng, P. Li, and L. Zhang, “Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images,” COMPJ, vol. 52, pp. 90-100, 2009.

14.    Y. T. Kim, “Contrast enhancement using brightness preserving bihistogram equalization,” IEEE Trans. Consumer Electron., Vol. 43, No.1, pp. 1–8, 1997.




B. S. Sonawane, Ratadeep Dehsmukh, Swapnil Waghmare, Pushpedra Chavan

Paper Title:

Disk less Client: Dracut for Boot & Initialization for Standalone Systems

Abstract:  The Basic Objective of this paper is to highlight the diskless client using the upcoming open source term i.e. Dracut for boot initialization for standalone systems for cost efficiency and reliability of the performance of operating systems connected over the network. The major advantage of the diskless client using Dracut initialization is to optimize the bandwidth, network recourses and bandwidth as per the user requirement.  The experiment was conducted during the lab work,  as  we  have  connected  three  computer  systems  and check   above   said   parameters   i.e.   network   resources, bandwidth and booting time, it gives us good result. The main parameter is a virtualization; it implies the good result during the  accession of  operating system  over  the  network.  This paper explores how  to  configure and access the  operating systems over  the  network and minimize the  booting time. Effective solution required to the mass education like schools, colleges, government offices, industries and many more area where number of computer systems are connected with network. Due to lack of funds government schools like Zilha Parishad,   not accord to purchase computer systems, in this case our system can help them to provide the I T infrastructure with low cost, by connecting more than 1000 systems with single network and access the operating system using cloud. General Terms: The Dracut image is a replacement for initial ram disk images used in traditional Linux boot mechanism. The Disk Less Client is a technology used for ease of administration. The scope of this paper will be only for Software oriented Virtual Disk Less clients.

Dracut is referred for the initramfs.img which is a replacement to initrd.img for traditional Linux machine booting. Linux, Operating Systems, DHCP Server, TFTP server, NFS Server, DRACUT Network, IAAS, PAAS, SAAS.


1.       Marisol  Garcia-Valls,  Tommaso  Cucinotta,  Chenyang Lu, Challenges in real-time virtualization and predictable cloud computing, Journal of System Architecture 60 (2014) 726-740. @2014 Elsevier B. V. All rights reserved.
2.       Kulthida  phapikhor,  suchart  khummanee,  panida songram, chatklaw jareanpon, Performance Comparison of the Diskless Technology, 10 th Internaional Joint Conference on Computer Science and Software Engineering (JCSSE). @2012 IEEE.

3.       Zhang  haiming,  WU  kaichau,  LI  Jianhui,  Zhang  Bo, XUE Zhenghua, YAN Baoping, “Parallel file system- supported  server  virtual  environment  in  data  center”, First international conference on networking and distributed computing. @2010 IEEE

4.       Shingo Takada*, Akira Sato*, Yasushi Shinjo*, Hisashi Nakai, Akiyoshi Sugiki* and kozo Itano*,” A P2P Approach to Scalable Network-Booting, Third International Conference on Networking and Computing.

5.       @2013 IEEE

6.       Noki Tanida, Kei Hiraki, Mary Inaba*, “Efficient disk- to- disk copy through long-distance high-speed networks with   background   traffic”,   Fusion   Engineering   and Design, www.

7.       Yaoxue  zhang,  Yuezhi  Zhou,  “separating  and computation and  storage  with  storage  virtualization “ computer communication @2011,

8.       G. Clarco*, M. Casoni, “On the Effectiveness of Linux Containers for network virtualization. @2012 Simulation modeling practice and theory.

9.       Jinqian   Liang,   Xiaohong   Guan,   “A   Virtual   Disk Envoirment for providing file system recover” @2006 Science      Direct.      www.




Kriti Shrivastava, M.D. Pawar

Paper Title:

Necessity of Starters for DC Shunt Motors

Abstract: DC motor has a very high starting current which is capable of damaging the armature winding of motor, if not controlled to certain limited value. This limitation to the starting current of dc motor can be given by the means of a starter. The speed of any motor has to be increased from zero and should be brought to the operating speed, this is called as starting of a motor. DC motors speed can be controlled over wide range either by changing the voltage or by changing strength of current in its winding. This starting of a dc motor is controlled with the help of a starter, which is connected in series to the armature winding of a motor so as to limit its starting current to finest value, taking into consideration the safety aspect of the motor. A starter allows a DC motor or a DC motor controlled device to turn ON or OFF. This paper gives the study of different types of DC motors, need of an external starter for these DC motors and different techniques that can be involved in the construction of the starters.

 DC Motors, Speed, Armature, Field winding, IGBT.  


1.    Rohit Kumar, 3- COIL STARTER USE FOR STARTING D.C   MOTOR. International Journal of Scientific Research Engineering & Technology (IJSRET) March, 2015
2.    Rakesh J. Waghmare, Dr. S.B Patil Mr. Uddhav S Shid2 and Uttam Y Siddha, NEED OF ELECTRONIC STARTER FOR DC MOTOR. International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 12, December 2013


4.    Universal motors” ( /2012/02 /universal-motors-construction-working)

5.    Laughton M.A and Warne D.F, Editor. Electrical engineering reference book

6.    B.L Theraja, A.K Theraja, Electrical Technology, AC & DC Machines, Volume II  




Pranjal Lokhande, M.D. Pawar

Paper Title:

Garbage Bins Collection and Management by using Zigbee and GSM Technology

Abstract:  the pollution affected especially growing region as well as the most populated cities. Our environment is being spoiled by nescience of cleanliness. The main aim of this paper is being eradicated this issue and to reduce it. Now days, there are a number of techniques, which are used for the collection and management of the garbage. set of carefully chosen sensors to monitor the status of garbage bin. The smart garbage bin consist sensors namely ultrasonic sensor, gas sensor and moisture sensor. The ultrasonic sensor is placed inside the garbage bin at lead position, gas sensor will sense the toxic gases and moisture sensor will sense moist in bin. hence, Sensor is an ideal detecting device to solve this problem. In order to make this process more economical sensor based system is used in real time. Now days, there are a number of techniques, which are used for the collection and management of the garbage. Zigbee and GSM technologies are not only latest trends but also one of the best combinations to use in the project. The ultrasonic sensor is placed inside the garbage bin at lead position, gas sensor will sense the toxic gases and moisture sensor will sense moist in bin then that indication will give to PIC micro-controller. The controller will give indication to the cleaning authority and needs urgent attention. The Pic-micro-controller will indication by sending SMS using GSM technology. These Dustbins are interfaced with the central system showing status of garbage in Dustbin on GUI.

 Ultrasonic sensor, Gas Sensor, Moisture sensor, GSM, Zigbee, PIC-controller..  


1.       Md. Abdulla Al Mamun, Hannan, Aini Hussain, Hassan Basri “Wireless sensor Network Prototype for solid Waste Bin Monitoring with Energy Efficient Sensing Algorithm ,”  16th international conference on computational Science and engineering,December 2013.
2.       Md.Shafique Islam,M.A. Hannan, “An overview for Solid Waste Bin Monitoring System,”Journal of Applied Science Research,8(2):879-886,February 2012

3.       Kanchan Mahajan, Prof.J.S.Chitode “Waste Bin monitoring system using Integrated Technology”,International Journal of Innovative Research in Science engineering and Technology”,vol 3,Issue 7,july 2014.

4.       Pavithra, “Smart trash system: An applicating using Zigbee,” International and Technology , vol. 1,Issue 8 october 2014.

5.       Priya B.K.,T.Lavanya,V.Samyakta Reddy “Bin That Think’s ,” The international journal of science and technology(ISSN 2321-919x)vol 3 Issue 6,June 2015 .


7.       Myke Predkoion, programming And customizing the PIC microcontroller.

8.       Electronic maker, January2016  Issue236, Vol No.21

9.       Muhammad Ali Mazidi, Rolin D. Mckinlay, Danny Causy PIC Microcontroller and embedded systems.

10.    EMBEDDED For You, A Research Journal on Embedded Technology, November/December 2015, Vol.9 No.6

11.    Electronics for you, Volume 46, Issue 3



14.    AT%20 COMMANDS_SIM900_ATC_V1_00.PDF






Vilas A Jadhav, S M Jagade

Paper Title:

Review on Optimized Utilization of Bandwidth in FM Radio

Abstract:   This review paper focuses on increasing the communication channels of an FM Radio. It comments on need to use bandwidth at optimize level, to achieve more number of channel’s in available limited spectrum. The previous methods used are studied on theoretical basis. It discusses on advantages of spectrum saving, full-filing increased need of communication and disadvantages of distortion, noise interference by narrowing the bandwidth. The primary resources of any communication system are transmission bandwidth and transmitted power which must be used effectively. It is well known that FM has been always superior over AM. The major drawback in FM is need of increased transmission bandwidth as compared to AM. If the bandwidth is used at optimum level then we can enjoy advantages of FM at a large scale in terms of high quality reception and increased number of channels.

Bandwidth, Communication Channels, Frequency Modulation.


1.    Frequency Modulation (FM) Tutorial Lawrence Der, Ph.D. Silicon Laboratories Inc.
2. › RF topics‎

3. › DIY Digital/Analog Electronics.

4.    System and method for reduced deviation time domain FM/PM discriminator to achieve a reduced bandwidth frequency or phase modulation communications channels US Patent no. 7272368 B2  




S. K. Tilekar, S. C. Pathan, P. V. Mane-Deshmukh, S. V. Chavan, B. P. Ladgaonkar

Paper Title:

Synthesis of AMS Based System-on-Chip for Measurement of Physicochemical Parameters of Water

Abstract: An innovative technology, Analog Mixed Signal (AMS) based VLSI design, is realizing commendable dynamic reconfigurability of on-chip resources, analog as well as digital, which furnishes needs ubiquitous System-on-Chip (SoC) design. It reveals wide spectrum of applicability, particularly in the field of precision measurements and controlling of various parameters such as pH, Electrical Conductivity (EC), concentration of Dissolved Oxygen (DO), etc., of liquid. Further, the deployment of AMS based Programmable SoC (PSoC) overcomes constraints in the configurability and has ultra-low power consumption, which is otherwise exhibited by traditional VLSI. Therefore, deploying PSoC5 device the SoC for temperature compensated EC and pH measurement of water is synthesised and presented in this paper. The AD590, standard EC electrode and standard pH electrode are wired off the chip for the synthesis of this SoC. Deploying the on-chip resources of the CY8C55 series PSoC device and ensuring co-development process, the necessary DAS is configured and routed in PSoC device by PSoC Creator2.1 environment. Configuring 10-bit ƩΔADC, the preciseness of parameter measurement is achieved. The SoC is calibrated to the standard units by employing scientific method and standardize with sophisticated instrument from standard Hanna make meter and results are interpreted in this paper.

 Analog Mixed Signal, PGA, Conductivity, pH, PSoC..


1.        K. Paramsivam and K. Gunavathi, “Recording algorithm for minimizing Test Power in VLSI Circuits”, Engineering letters, 14 (2007) 1-6.
2.        B. Srihari, R. Prabhakar and  K. V. Murli Mohan, “ Applications of MEMS in Robotics using PSoC5”, Global Journal of Advanced Engineering Technologies, 1, 2 (2012) 54.

3.        O. Postolache, D. Richebon, J.M.D. Pereira and P. Girao, “Microcontroller Based Multi-sensing System For Water Quality Measurement”, 17th Symposium IMEKO TC4, 3rd Symposium IMEKO TC 19 and 15th IWADC Workshop Instrumentic for the ITC Era, (2010) 47-52.

4.        Rajendran and P. Neelamegam, “Design and development of microcontroller based conductivity measurement system”, Indian J. of Pure and Appl.Phy., 42 (2004) 182-188.

5.        R. P. Uhlig, M. Zec, M. Ziolkowski, H. Brauer, and A. Thess, “Lorentz force sigmometry: A contactless method for electrical conductivity measurements”, J. Appl. Phys., American Institute of Physics, 111, 094914 (2012).

6.        G. R. Helena ,O. Postolache and M. Pereira, “ Distributed Water Quality Measurement system Based on SDI-12”, IEEE AFRICON, (2004).

7.        B. Massot, C. Gehin, R. Nocua, A. Dittmar and E. McAdams, “A wearable, low-power, health-monitoring instrumentation based on a Programmable System-on-Chip”, 31st Annual International Conference of the IEEE EMBS, 2-6 September, 2009, Hilton Minneapolis, Minnesota, USA.

8.        M. Rathode, “Design and prototyping of PSoC based pulse oximeter”, Int. J. of Scientific and Engg. Research, ISSN 2229-5518, 3 10 (2012) 1-5.

9.        M. Hrgetic, I Krois and M Cifrek, “Accuracy analysis of dissolved oxygen measurement system realized with Cypress PSoC configurable mixed signal array”, IEEE ISIE 2005, (2005) 1105-1110.

10.     B. P. Ladgaonkar, S. N. Patil and S. K. Tilekar, “Development Of Ni-Zn Ferrite Based Smart Humidity Sensor Module By Using Mixed Signal Programmable System-On-Chip”, Applied Mechanics and Materials, Trans Tech Publications, Switzerland, Vol. 310 (2013) 490-493.


12.     S. K. Tilekar and B. P. Ladgaonkar, “Designing of Mixed Signal based Programmable System on Chip for temperature compensated pH Measurement”, International Journal of Scientific & Engineering Research, 4 6 (2013) 672-678.

13.     B. Saleha Begum, B. Ashraf Ahamed, A. Suresh Kumar, B. RamaMurthy, P. Thimmaiah and K. K. Azam Khan, “Embedded Based Soil Electrical Conductivity Measurement System”, IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS), Vol 26 (2013) 17-20.

14.     O. Postolache, P. S. Girao, G. Patricio, J. Sacramento, P. Macedo and M. D. Pereira, “Distributed Instrumentation and Geographic Information System for Dolphins’ Environment Assessment” IEEE Instrumentation and Measurement Technology Conference Proceedings, IMTC, (2008) 1777-1782.

15.     H. Willard, L. Merritt, J. Dean  and F. Settle, “Instrumental Methods of Analysis”, CBS Publisher, New Delhi 7thEdn (1986).

16.     W. Y. Chung, C. H. Yang, Y. F. Wang, Y. J. Chan, W. Torbicz, D. G. Pijanowska, “A signal processing ASIC for ISFET based Chemical sensors”, Microelectronic J.,
35(2004) 667-675.

17.     Temperature compensation for pH meters,

18.     G.R. Helena, O. Postolache and M. Pereira, “Distributed Water Quality Measurement system Based on SDI-12”, IEEE AFRICON, (2004).




P. V. Mane-Deshmukh, S. K. Tilekar, B. P. Ladgaonkar

Paper Title:

Designing of Wireless Sensor Network for Real-Time Patroling of the border

Abstract:  Real time patroling at the border is the challenging task to the soldiers. During worst environmental conditions it become more tedious. Therefore one can opt for deployment of innovative technology to assist the Border Security Force (BSF) to provide real time patrolinng. The wireless sensor network is most suitable technology to overcome this problem. The wireless network is designed to ensure real time patroling at the border and presented in this paper. Based on embedded technology, the wireless sensor node have been designed by deploying advance microcontroller PIC 18F4550. To identify the movement of the intruder crossing the border, the sensors based on PIR technology is employed. The wireless sensor node can be distributed systematically on the border. It collects information of the intruder and disseminates towards the base station located at main control office. From the database and alarming facilities, the commanders can immediate tack action for border security.

  PIC18F4550, PIR technology, Wireless Network, Patroling.


1.       C. K. Ho, A. Robinson, David R. Miller and Mary J. Davis, “Overview of Sensors and Needs for Environmental Monitoring”, Sensors, 5 (2005) 4-37.
2.       Fidelis C. Obodoeze1, Hyacinth C. Inyiama and V. E. “Wireless Sensor Network in Niger Delta Oil And Gas Field Monitoring: The Security Challenges And Countermeasures”, Int. J. of Distributed and Parallel Systems (IJDPS), 3 6 (2012) 65-77.

3.       R. Venkataraman, M. Pushpalatha, and T. Rama Rao, “Implementation of a Regression-based Trust Model in a Wireless Ad hoc Testbed”, Def. S. Journal, 62 3 (2012) 167-173.

4.       H. J. Kang, D. Lee, J. G. Shin, and B. J. Park, “Location Tracking of Moving Crew Members for Effective Damage Control in an Emergency”, Defence Science Journal, Vol. 61, No. 1, pp:57-61, 2011.

5.       Z. Qian, Y. Xiang-long, Z. Yi-ming, W. Li-ren and G. Xi-shan, “A wireless solution for greenhouse monitoring and control system based on ZigBee technology”, J Zhejiang Univ Sci, vol. 8, pp:1584-1587,2007.

6.       R. Anandan, B. Karthik and Dr.T.V.U.Kiran Kumar, Wireless Home and Industrial Automation Security System Using GSM”, Journal of global research in computer science, 4 4 (2013) 126-132.

7.       IR sensor Datasheet available Online;

8.       F. C. Obodoezel, H. C. Inyiama and V.E. “Wireless Sensor Network In Niger Delta Oil And Gas Field Monitoring: The Security Challenges And Countermeasures”, Int. J. of Distributed and Parallel Systems (IJDPS), 3 6 (2012) 65-77.

9.       F. Akyildiz, W.Su, Y. Sankarasubramaniam and E. Cayirci, “A Survey on Wireless Networks”, IEEE Communications Magazine, (2002) 102-114.

10.    R. Srvidya, C. Manjula and S. Gama, “Wireless Real Time Sensor”, World Journal of Science and Technology, 2 5 (2012) 104-110.

11.    G. Song, A. Song and W. Huang, “Distributed Measurement System Based on Network Smart Sensors with Standardize Interface”, Sensors and Actuators A, 120
1 (2005) 147-153.

12.    “Datasheet of PIC 18F4550 microcontroller”,

13.    Simple-3-Resistor-40-pins-PIC-Programmer, available online: http://www.instructables .com/id.

14.    S.  Sarkar and B. Kumari, “Effect of Radiofrequency Electromagnetic Field on Human DNA”, Defence Science Journal, 56 2 (2006) 199-208.

15.    G. Song, Y. Zhou, W. Zhang, A. Song, “Amulti-interface gateway architecture for home automation networks”, IEEE Transactions on Consumer Electronics, 54 3 (2008) 1110–1113.

16.    802.15.4-2003 IEEE Standard for Information Technology-Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), 2003.

17.    XBee/XBee-PRO, ZB RF Modules dataseet available online, /products/wireless /zigbeemesh/ xbee -smt.jsp#docs.

18.    G. Ismaeel, R. Zuhair, Y. Essa and F. Abdallh,  “GUI Based Automatic Remote Control of Gas Reduction System using PIC Microcontroller”, IRACST– Engineering Science and Technology: An International Journal (ESTIJ), 3 2 (2013) 217-227.

19.    V. Markova1, T. Trifonova, V. Draganov, “Design and implementation of data collection module for WSN application”, International Journal of Practical Electronics, 11 (2013) 27-36.

20.    S. S. Shaikh, S. C. Pathan, P. V. Mane-Deshmukh, S. K. Tilekar and B. P. Ladgaonkar, “Development of Smart Fusion Technology Based Customizable System-on-Chip For Monitoring of Polyhouse Parameters”, Int. J. of Scientific & Engineering Research, 5 9 (2014) 100-108.

21.    C.O.Iwendi and A.R Allen, “Wireless Sensor Network Nodes: Security and Deployment in Niger-Delta Oil and Gas Sector”, Int. J. of Network Security and Its Applications (IJNSA), 3 1 (2011) 68.




Bhagyashri A. Jadhav, Vinodpuri R. Gosavi, Amitesh Ghatak

Paper Title:

A Review: Global Virtual Private Network

Abstract: Global Virtual Private Network (GVPN) is growing technology which plays important role in wireless local area networks (WLAN) by providing safe and secure data transmission. VPN provides safe and secure data transmission by creating tunnels between two different pair of CPE (customer premises equipments), once tunnel created data transfer can take place. It provides facility to users to send and receive data across shared or public networks as if their computing devices were directly connected to the private network, and thus are benefiting from the functionality, security and management policies of the private network. A VPN is created by establishing a virtual point-to-point connection through the use of dedicated connections, virtual tunneling protocols, or traffic encryption. From a user perspective, the extended network resources are accessed in the same way as resources available within the private network. Traditional VPNs are characterized by a point-to-point topology, and they do not tend to support or connect broadcast domains. This paper presents a study of GVPN architecture, MPLS based layer -3 VPN and protocols used together with their advantages and disadvantages.



1.    TU-T Recommendation Y. 1.311, Network based VPNs- generic architecture and service requirements.
2.    T Recommendation Y. 1.311.1, Network based IP VPN over MPLS architecture.

3.    R. Callon (ed.), a framework for layer 3 provider provisioned virtual private networks, IETF Draft draft-ietf-framework-06.txt (October 2002)

4.    V. Alwayn, Advanced MPLS design and implementation (Cisco Systems, 2001)

5.    S. Kent, R, Atkinson, security architecture for the Internet protocols, IETFRFC 2401, (November 1998).

6.    M. Leech, SOCKS protocol version 5, IETF RFC 1928, (March 1996)

7.    Rosen, at al., Multiprotocol label switching architecture, IETFRFC 3031, (January 2001).

8.    E. Rosen, Y. Rekhter, BGP/ MPLS VPNs, IETF RFC 2547, (March1999).

9.    W. Townsley, et al., Layer two tunneling protocol “L2TP”, IETF RFC 2661, (August 1999).




Mayuri N. Joshi, Abhilasha D. Mishra, Suhas S. Chate

Paper Title:

A Review: Submarine Optical Cables in Undersea Telecommunications

Abstract:  Undersea Fiber Optic Cables are spread throughout the world. The main advantage is that the underwater optical communication is more reliable and secure. It is connecting entire globe through submarine cables, submarine cables have their network throughout the world. It carries more than 95% of transoceanic voice and data traffic. This paper presents various Submarine Optical Cables in Undersea Telecommunications with their bandwidth capacities and technologies used in these cables and also their advantages over previously used cable systems in communication. This paper also describes the effects of environments on submarine cables and troubleshooting over them.



1.    M. Arumugam, ―Optical Fibre Communication – An Overview‖ Pramana journal of physics, VOL. 57, Nos5 & 6, PP. 849–869, 2001.
2.    Mehdi Malekiah, Dony Yang ,and Shiv Kumar, ―Comparison of optical back propagation scheme for fibre optic communication‖ optical Fibre Technology,19, pp. 4-9,2013.

3.    Bell, 1970. Bell System Technical Journal 49 (5), Part 5, May/June

4.    Sui Meihong and Yu Xinsheng and Zhang Fengli, “The Evaluation of Modulation Techniques for Underwater Wireless Optical Communications”, International Conference on Communication Software and Networks, pp. 138-142, 2009.

5.    M. Kordahi, “New tools for multilayered undersea telecommunication networks,” Sea Technology Magazine,Volume 15, No 7, 2010.

6.    Realising the benefits of SDH technology for the delivery of services in the access network [IEEE- 06 August 2002]

7.    Use of TMN for SONET/SDH network management[IEEE- 06 August 2002]




Sarfraz Khan, Shaikh Aamer

Paper Title:

Smart Glasses: Technological Perspective and Modern Vital Applications in Diverse Vicinity

Abstract:   With wide spread and development of technology, the human being is more comfort to accomplish day to day task and schedules. There is no issue to adopt and used to the updated, recent and or innovative technology by human being with less efforts. Smart glasses are nothing but wearable head-mounted display (HMD) devices or imaging system having computer to add information what the wearer see. Basically Smart glasses used to execute task by hand freely. A user can check information without using hands. Recently the use of smart glasses is at par. There are various applications where these smart glasses are playing an important role such as technical and medical education, remote sensing, record handling and storage, route tracking, face recognition, human machine interfacing, tracking and control, forensic, safe navigation of ships etc. This paper shows technological overview of smart glasses in various field, their importance and key applications. It also shows the knowledge regarding future scope and compatibility of technology with human being.

 (HMD), various applications, development of technology freely, schedules.


1.       Nikitha Kommera, Faisal Kaleem, Syed Mubashir Shah Harooni, “Smart augmented reality glasses in cyber security and forensic education”, IEEE International Conference on Intelligence and Security Informatics (ISI), 17 November 2016.
2.       Buti Al Delail, Chan Yeob Yeun, “Recent advances of smart glass application security and privacy”, IEEE International Conference on Internet Technology and Secured Transactions (ICITST), 25 February 2016.

3.       Jay Kim, Daniel Zimmer, “The History of Smart Glasses by SAP Startup Focus Member APX Labs 2011-present”,, March 31, 2015.



6.       Babak Taraghi , Mahdi Babaei, “Object detection using Google Glass”, IEEE International Conference on Open Systems (ICOS), 2015

7.       Xiaohui Wu, Jibo He, Jake Ellis, William Choi, Pingfeng Wang, Kaiping Peng, “Which is a Better In-Vehicle Information Display? A Comparison of Google Glass and Smartphones”, IEEE International Journal of Display Technology, Volume: 12, Issue: 11, Nov. 2016, pp.1364-1371

8.       Nikitha Kommera, Faisal Kaleem, Syed Mubashir Shah Harooni, “Smart augmented reality glasses in cyber security and forensic education”, IEEE International Conference on Intelligence and Security Informatics (ISI), 17 November 2016.

9.       Buti Al Delail, Chan Yeob Yeun, “Recent advances of smart glass application security and privacy”, IEEE International Conference on Internet Technology and Secured Transactions (ICITST), 2015

10.    Roger Schaer, Fanny Salamin, Oscar Alfonso, Jiménez del Toro, Manfredo Atzori, Henning Müller, Antoine Widmer,  “Live ECG readings using Google Glass in emergency situations”, IEEE International Conference on Engineering in Medicine and Biology Society (EMBC), 2015.

11.    Hermann Schweizer, “Smart glases: Technology and applications”, Ubiquitous computing seminar, FS2014.

12.    Brian L. Due, “The future of smart glasses: An essay about challenges and possibilities with smart glasses”, Working papers on interaction and communication, 1(2), 1-21, 2014.




S. P. Bhosale, D. M. Yadav

Paper Title:

Image Resolution Enhancement Using Wavelet Transform

Abstract:    Discrete wavelet transform (DWT) used for image resolution enhancement suffers artifacts due to shift variance property of DWT. To overcome these drawbacks dual tree complex wavelet transform (DTCWT) is proposed to enhance input image. In this paper implementation using dual tree complex wavelet transform, discrete and stationary wavelet transform is carried out using Lanczos interpolation and Wiener filtering techniques .The values of peak signal to noise (PSNR) are presented in order to differentiate the discrete and stationary wavelet transform, dual tree complex wavelet transform technique.

  image enhancement, discrete wavelet transform, stationary wavelet transform, dual tree complex wavelet


1.       Turgay Celik and Tardi Tjahjadi, “Image Resolution Enhancement Using Dual Tree Complex Wavelet Transform,” IEEE Geosciences and Remote Sensing Letters, Vol. 7, No. 3, July 2010.
2.       Muhamad Zafar Iqbal, Abdul Ghafoor, Adil Masood Siddiqui, “Satellite Image Resolution Enhancement using Dual Tree Complex wavelet Transform and Non Local Means,” IEEE geoscience and remote sensing letters, Vol. 10, No. 3, May 2013.

3.       Hasan Demirel and Gholamreza Anbarjafari, “Satellite Image Resolution Enhancemnt using Complex Wavelet Transform,” IEEE Geosciences and Remote Sensing Letters, Vol. 7, No. 1, January 2010.

4.       Ivan .Selesnick, Richard G.Baraniuk and Nick G. Kings Bury,”The Dual Tree Complex Wavelet Transform,” IEEE signal processing magazine, November 2005.

5.       Hasan Demirel and Gholamreza.

6.       “Image Resolution Enhancement by using Discrete and Stationary wavelet Decompositions,” IEEE Transaction on Image Processing, Vol. 20, No.5, May 2011.

7.       Hasan Demirel and Gholamreza Anbarjafari ,“ Discrete Wavelet Transform Based Satellite Image Resolution Enhancement,” IEEE Transaction on Geosciences and Remote sensing, Vol. 49, No. 6 , June 2011.

8.       Sung Cheol Park, Min Kyu Park and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, Vol. 20, No. 3, pp. 21-36, May 2003.

9.       R. Ramya, Dr. M. Senthil Murugan, “Comparative Study on Super Resolution Image Reconstruction Techniques,” Indian Journal of Applied Research, Volume 3, Issue 10, October 2013.

10.    S.S. Qureshi, xue Ming Li, T. Ahmad, “Investigating Image super resolution Techniques: What to choose”, Advance communication Technology (ICACT) 2012, International conference.

11.    Massimo, Fierro, Ho-gun Ha and yeong –Ho Ha, “Noise reduction based partial reference dual tree complex wavelet transform shrinkage”. IEEE transaction on image processing Vol. 22, No.5 may 2013




Madhuri Y Sonule, Sangeeta R Chougule

Paper Title:

Detection of Microaneurysm Symptom of Diabetic Retinopathy using Slant Stacking Algorithm and Artificial Intelligence

Abstract: In this paper the focus is on to detect automated MAs to prevent DR due to which blindness  occurs in a diabetic patient. There are several pathologies has complications that lead to the deterioration, or even loss of the sight. Among them, one of the most important is the DR, which is, at an eye level, the most frequent and serious complication of diabetes mellitus. This complication is the leading cause of blindness for adults between 20 to 74 years old. So there is a need of an effective automated MAs detection method so that DR can be treated at early stage and blindness due to DR can be prevented. So it is helpful to the health professionals can provide an adequate treatment to the patient.

   DR: Diabetic Retinopathy, pathologies MAs: Microaneurysms, Diabetes mellitus,


1.       Mohammed Hafez, Sherif Abdel Azeem    “Using Adaptive Edge Technique for Detecting Microaneurysmsin Fluorescein Angiograms of the Ocular Fundus”IEEE MELECON 2002, May 7-9,2002, Cain,, EGYPT.
2.       Rumano A. Simandjuntakl”Development of Computer-Aided Diagnosis System for     Early Diabetic Retinopathy based on MicroAneurysms Detection from Retina Images”,Department of Electrical Engineering, InstitutTeknologi Bandung, Indonesia.

3.       Thomas P. Karnowski, “Retina Lesion and Microaneurysm Segmentation using Morphological Reconstruction Methods with Ground-Truth Data”30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008

4.       Meinder tNiemeijer,Bram van Ginneken”Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs”Ieee Transactions On Medical Imaging, Vol. 29, No. 1, January 2010

5.       B´alintAntal, Istv´an Lazar, AndrasHajdu, ZsoltTorok, Adrienne Csutak, T¨undePeto”Evaluation of the grading performance of an ensemble-based microaneurysm detector”33rd Annual International Conference of the IEEE EMBS, September 3, 2011.

6.       Giancardo, Student Member, IEEE, F. Meriaudeau, Member, IEEE, T. P. Karnowski, Member, IEEE,Y. Li, Member, IEEE, K. W. Tobin Jr, Senior Member, IEEE and E. Chaum, Member, IEEE “Microaneurysm Detection with Radon Transform-based Classification on Retina Images”33rd Annual International Conference of the IEEEEMBS, September 3, 2011

7.       Ankita Agrawal, Charul Bhatnagar, Anand Singh Jalal”A Survey on Automated Microaneurysm Detection in Diabetic Retinopathy Retinal Images”978-1-4673-5986-3/13/$31.00 ©2013 IEEE

8.       L. Giancardo, F. Meriaudeau” Validation of Microaneurysm-based Diabetic Retinopathy Screening acrossRetinaFundus Datasets”,978-1-4799-1053-3/13/$31.00 c 2013 IEEE.

9.       Jorge Oliveira, Grac¸a Minas and Carlos Silva “Automatic Detection of Microaneurysm Based on the Slant Stacking”978-1-4799-1053-3/13/$31.00 c 2013 IEEE.

10.    Tsuyoshi Inoue, Yuji Hatanaka, Chisako Muramatsu, and Hiroshi Fujita”Automated Microaneurysm Detection Method Based on Eigenvalue Analysis Using Hessian Matrix in Retinal Fundus Images” Osaka, Japan, 3 – 7 July, 2013.

11.    R. Vidyasari1 , I. Sovani2, and T.L.R. Mengko3 ,H. Zakaria “Vessel Enhancement Algorithm in Digital RetinalFundusMicroaneurysms Filter for Nonproliferative Diabetic Retinopathy Classification”2011 International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering8-9 November 2011, Bandung, Indonesia .

12.    Gwenole Quellec, Stephen R. Russell, and Michael D. Abramoff,”Optimal Filter Framework for Automated,Instantaneous Detection of Lesions in Retinal           Images” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 2,  FEBRUARY 2011.

13.    V. VijayaKumari, N. Suriyanarayanan, C.ThankaSaranya,”Feature Extraction  for Early Detection of Diabetic Retinopathy”2010 International Conference on Recent  Trends in Information.

14.    KanikaVerma, Prakash Deep and A. G. Ramakrishnan, “Detection and   Classification of Diabetic Retinopathyusing Retinal Images” Department of Electrical Engineering, Indian Institute of Science.

15.    Ramon Pires, Herbert F. Jelinek, Jacques Wainerand Anderson Rocha“Retinal Image Quality Analysis for Automatic Diabetic Retinopathy Detection”2012 XXV SIBGRAPI Conference on Graphics, Patterns and Image..

16.    MukundDesai , Rami Mangoubi , Lloyd Paul AielloLloyd M. Aiello“Retinal             Venous   Caliber Abnormality:Detection and Analysis Using Matrix Edge Fields-Based Simultaneous Smoothing and Segmentation”Cambridge, MA, USA.

17.    MohdFazliHashim,SitiZaitonMohdHashim “Comparison of Clinical and Textural Approach for Diabetic Retinopathy Grading” 2012 IEEE International Conference on Control System, Computing and Engineering, 23 – 25 Nov. 2012.

18.    R.V.Prasannap.G.Scholars.A, “Enhancement Of Retinal Blood Vessel segmentation And Classification”.

19.    Kevin Noronha, Jagadish Nayak, S.N. Bhat “Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes” 1-4244-0549-1/06/$20.00 ©2006 IEEE.

20.    P. Burlina, D.E. Freund, B. Dupas, and N. Bressler “Automatic Screening of Age-Related Macular Degeneration and Retinal Abnormalities” 33rd Annual International Conference of the IEEE EMBS.

21.    Pallavi Kahai Kamesh Rao Namuduri “Decision Support For Automated Screening Of Diabetic Retinopathy” 0-7803-8622-1/04/$20.00 ©2004 IEEE.

22.    Chanjira Sinthanayothin, ViravudKongbunkiat, Suthee Phoojaruenchanachai, Apichart Singala vanija’“Automated Screening System For Diabetic Retinopathy”Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (2003).




Shrishail H. Patale, M. S. Potdar

Paper Title:

Analysis of Faults in HVDC Transmission System using Wavelet Transform and Neural Network

Abstract:  HVDC Transmission line fault classification and location has been one of primary concern of the power system. Accurate location of fault on HVDC transmission system can save time and major problems of the power utility. Based on wavelet transform and ANN a fault classification and location for HVDC system is proposed.

HVDC Transmission System, Discrete Wavelet Transform (DWT), Artificial Neural Network (ANN).


1.          ZHANG Jie, WANG Chao, WU Na, et al. Study of HVDC transmission line fault location method[J]. Yunnan Electric Technology, 2005, 13-18.
2.          ZHANG Xiaoli, ZENG Xiangjun, MA Hongjiang, et al. The grid of traveling wave fault location based the Hilbert-Huang transform [J]. Automation of Electric Power System, 2008, 32(8): 64-68.

3.          YANG Cunxiang, GAN Zhanying, WANG Yuhao, et al. The extraction analysis and research of power system transient compound disturbance single based the HHT[J]. Power System Protection and Control, 2009, 37(11): 6-9.

4.          YANG Fusheng, HONG Bo. The principle and application of independent component analysis[M]. Beijing: Tsinghua University Press, 2006.

5.          LI Zhixiong, YAN Xinping. Independent Component Analysis And Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC System[J]. Xian Jiaotong University, 2011, 45(2): 44-48(in Chinese).

6.          SHU Hongchun, TIAN Xincui, ZHANG Guanbin, et al. Fault location for HVDC transmission lines using natural frequency of single terminal voltage data[J]. Proceedings of the CSEE, 2011, 31(25): 104-111(in Chinese).

7.          Ruchita Nale, P. Suresh Babu “Distance Protection of HVDC Transmission line with Novel fault location technique” IJRET, Volume: 03 Issue: 04, Apr-2014.

8.          Baina He, Yunwei Zhao, Hengxu Ha, “ Research of Bipolar HVDC Transmission line based on Travelling Wave Differential Protection”, TELKOMNIKA, Vol.11, No12, December 2013.

9.          Swehta, P. Krishna Murthy, N. Sujata and Y.Kiran, “A Novel Technique for the Location of Fault on A HVDC Transmission Line”, ARPN Journal of Engineering and Applied Science, Vol. 6, No.11, NOVEMBER 2011.

10.       Zheng Xiao-Dong, Student Member, IEEE, TaiNeng-Ling, Member, IEEE, James S. Thorp, Life Fellow, IEEE, and Yang Guang-Liang, “A Transient Harmonic Current Protection Scheme for HVDC Transmission Line “, IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 27, NO. 4, OCTOBER 2012

11.       Sherin Tom, Jaimol Thomas, “A Transient Scheme for the Identification of Fault in HVDC Transmission Line “,IJAREEIE, Vol. 4, Issue 10, October 2015.

12.       Priyanka Giradkar, Ashwini Deshmukh, “Identifying Fault in a Transient Harmonic Current Protection Scheme for HVDC Transmission Line”, IJSR, Vol. 03, Issue 12, December 2014.

International Conference, 3rd May 2015, Chennai, India.

14.       Kasun Nanayakkara, A.D. Rajapakse, Randy Wachal, “Fault Location in Extra Long HVdc Transmission Lines using Continuous Wavelet Transform”, International Conference on Power Systems Transients (IPST2011) in Delft, the Netherlands, June 2011.

15.       L.Shang, G.Herold, Senior Member, IEEE, J.Jaeger, R.Kebs, A.Kumar, “ High-Speed Fault Identification and Protection for HVDC Line Using Wavelet Technique”, 2001 IEEE Porto Power Tech Conference, 10th – 13th September, Porto, Portugal.

16.       Sheng Lin *, Shan Gao, Zhengyou He and Yujia Deng, “A Pilot Directional Protection for HVDC Transmission Line Based on Relative Entropy of Wavelet Energy”, Entropy 2015, 17, 5257-5273.

17.       L. Shang, G. Herold; J. Jaeger, R. Krebs, A. Kumar, Friedrich-Alexander University of Erlangen–Nuremberg; Siemens AG, Germany “Analysis and identification of HVDC system faults using wavelet modulus maxima”, Conference Paper, December 2001.

18.       K.Satyanarayana, Saheb Hussain MD, B.Ramesh, “ Identification of Faults in HVDC System using Wavelet Analysis”, IJECE, Vol.2, No.2,April 2012.

19.       Rahil Abrol, Mr. Anshul Mahajan, “HVDC System Fault Analysis through Wavelet Analysis Technique”, IJERT, Vol. 4 Issue 05, May-2015.

20.       Yew Ming YEAP, Abhisek UKIL, “Wavelet Based Fault Analysis in HVDC System”, Nanyang Technological University, Singapore.

21.       Madhuri S Shastrakar, Manisha B Gaikwad, “ Fault identification of HVDC Converter Using Artificial Neural Network”, IJERT, Vol.2, Issue 3, March-2011.

22.       Jebu John Mathew, Anish Francis, “HVDC Transmission Line Fault Location Using Wavelet Feeded Neural Network Bank”, International Journal of Enhanced Research in Science Technology & Engineering, Vol. 2 Issue 11, November-2013.




Mohammed Zeeshan, Deshmukh R.R, Syed Shafiuddin B

Paper Title:

An Overview of Different Optical Remote Sensing Techniques

Abstract:   Remote sensing is a field to study an object or phenomena without being in the physical contact of the object or phenomena under study. This paper overviews the different remote sensing techniques like Panchromatic remote sensing, Multispectral remote sensing, hyper spectral remote sensing an ultra spectral remote sensing and their significance in the study of different objects over the inaccessible are in the earth. In this paper the comparison of different techniques is done to understand the basic idea and role of remote sensing is to propose solutions for different problems like change detections, disaster management, rural and urban planning, agricultural forestry, weather forecasting and many more.

 Remote sensing, Phenomena, Panchromatic, Multispectral, hyperspectral, spectral signature.


1.    A review of Hyperspectral remote sensing and its application in vegetation and water resource studies M Govender , K Chetty and H Bulcock
2.    Remote Sensing Third Class First Edition (2010) Laser Branch Department Of Applied Sciences University Of Technology Dr. Abdulrahman K. Ali

3.    Spectral and Spatial Classification of Hyperspectral Data  Pedram Ghamisi University of Iceland2015

4.    Multi spectral, thermal and hyper spectral sensing and usage Chandan Roy Department of Computer and Information Science Linköping University 2016

5.    Hyperspectral Image Processing for Automatic Target Detection Applications Dimitris Manolakis, David Marden, and Gary A. Shaw

6.    Hyperspectral Data Processing Chain Development Perspectives for Vegetation Studies Kçroly Bakos Telecommunication and Remote Sensing Laboratory University of Pavia, Department of Electronics




Siddheshwar P. Mahale, Ram K. Kanhe

Paper Title:

Brain Tumor Segmentation using K-Means Clustering for Detection and Tumor Area Calculation

Abstract: The most challenging and emerging field is medical image processing. For the detection and identification of tumor in MRI of brain is implemented here. The traditional method for brain medical resonance imaging and the detection of tumor is done by human inspection which is operator dependent. Segmentation is done by operator in the clinical environment which is very tedious task and time consuming work. Segmentation of MRI of brain is complicated in this medical imaging field except few presented methods. Brain MRI can be divided into various regions mainly soft tissues like white matter, cerebrospinal fluid, gray matter etc. Using this process of segmentation, the location and size of tumor may be found evaluated. The present methodology proposed here consisting of preprocessing like removal of noise, process of segmentation and some morphological operations which are the basic steps in the image processing tool. Extraction and detection of tumor from brain MRI scan images is done by using k-means clustering method in MATLAB software.

  MRI (Magnetic Resonance Imaging), K-means clustering.


1.       Kailash Sinha, G.R.Sinha. “Efficient Segmentation Methods for Tumor Detection in MRI Images” 2014 IEEE Student’s Conference on Electrical, Electronics and Computer.
2.       Rajesh C. Patil, Dr. A. S. Bhalchandra. “Brain Tumour Extraction from MRI Images Using MATLAB” International Journal of Electronics, Communication & Soft Computing Science and Engineering    ISSN: 2277-9477, Volume 2, Issue 1

3.       Divya Kaushik, Utkarsha Singh, Paridhi Singhal, Vijai Singh. “Brain Tumor Segmentation using Genetic Algorithm” International Journal of Computer Applications® (IJCA) (0975 – 8887) International Conference on Advances in Computer Engineering & Applications (ICACEA-2014) at IMSEC, GZB

4.       Sneha Khare, Neelesh Gupta, Vibhanshu Srivastava. “Genetic algorithm employed to detect brain tumor in MRI image” International Conference on Cloud, Big Data and Trust 2013, Nov 13-15, RGPV

5.       Prof.B.K.Saptalakar, Miss. Rajeshwari.H. “Segmentation based detection of brain tumor” B.K Saptalakar, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013].

6.       Kailash Sinha, G. R. Sinha. “Comparative Analysis of Optimized k-Means and c-Means Clustering Methods for Segmentation of Brain MRI Images for Tumor Extraction” Proceedings of international conference on “Emerging research in computing, information, communication and applications” ERCICA 2013 ISBN:

7.       Gauri Anandgaonkar, Dr. Ganesh Sable. “Detection and Identification of Brain Tumor in Brain MR Images Using Fuzzy C-Means Segmentation” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 10, October 2013.

8.       Gauri Anandgaonkar, Dr. Ganesh Sable. “Brain Tumor Detection and Identification from T1 Post Contrast MR Images Using Cluster Based Segmentation.” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064

9.       Shital Agrawal, Dr. S. R. Gupta. “Detection of brain tumor using different edge detection algorithm” Research article/ April 2014, International Journal of Emerging Research in Management & Technology

10.    Sindhu, S. Meera. “A Survey on Detecting Brain Tumor in MRI Images Using Image Processing Techniques” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization)

11.    Muhd. Mudzakkir Mohd. Hatta, Shoon Lei Win. “Brain Tumor Detection and Localization in Magnetic Resonance Imaging” International Journal of Information Technology Convergence and Services (IJITCS) Vol.4, No.1, February 2014.




Swarupa S. Vedak, Mukesh D. Patil, Vishwesh Vyawahare

Paper Title:

Design of Band-Pass FIR filter using Fractional Fourier Transform

Abstract: In this paper, the new scientific model is designed for registering the rectangular windowed band-pass FIR filter exchange capacity where the rotation angle of fractional Fourier transform is utilized as a free parameter. The excellence of the strategy is by changing the rotation angle of fractional Fourier transform, the transition width and stop band attenuation can be tuned. Using proposed fractional Fourier transform technique, magnitude response of FIR can be tuned by changing rotation angle.

Window function, Fractional Fourier Transforms (FrFT), Rectangular window..


1.       Pooja Mohindru , Rajesh Khanna , S. S. Bhatia, “New tuning model for rectangular windowed FIR filter using fractional Fourier transform”, Signal, Image and Video Processing, pages: 761-767, vol.9, NO.4, 2015.
2.       Er. Mukesh Kumar, Rohit Patel, Er. Rohini Saxena , Saurabh Kumar,“Design of Band pass Finite Impulse Response Filter Using Various Window Method”,Int. Journal of Engineering Research and Applications, Pages: 1057-1061, vol. 3, Issue. 5, Sep-Oct 2013.

3.       Amer Ali Ammar, Dr. Mohamed. K. Julboub and Dr.Ahmed. A. Elmghairbi,“Digital Filter Design (FIR) Using Frequency Sampling Method”, University Bulletin, ISSUE No.15 ,vol. 3, 2013.

4.       Er. Sandeep Kaur and Er. Sangeet Pal Kaur,“Design of FIR filter using hanning window, hamming window and modified hamming window.”, International Journal of Advanced Research in Computer Engineering Technology (IJARCET), Pages: 2440-2443, vol. 4 , Issue 5, May 2015.

5.       Ervin Sejdic, Igor Djurovic and LJubisa Stankovic,“Fractional Fourier transform as a signal processing tool: An overview of recent developments”, Signal Processing, Pages: 1351-1369, vol. 91, No. 6, June 2011.

6.       V. Ashok Narayanan and K.M.M. Prabhu,“The fractional Fourier transform: theory, implementation and error analysis”, Microprocessors and Microsystems 27, Pages: 511521,June 2003.

7.        Imam S, amil Yetik and Arye Nehorai,“Beam forming using the Fractional Fourier Transform”, IEEE Transactions on signal processing, Pages: 16631668, vol. 51, no.6, June 2003

8.       Shushank Dogra and Narinder Sharma,“Comparison of Different Techniques to Design of Filter”, International Journal of Computer Applications, vol. 97, No.1, July

9.       Somesh Chaturvedi, Mahendra Kumar, Girish Parmar and Pankaj Shukla, “Implementation of Different Non-Recursive FIR Band pass Filters using Fractional Fourier Transform”, Fourth International Conference on Computational Intelligence and Communication Networks Mathura, Pages: 343-347, Nov. 2012.

10.    John G. Proakis, Dimitris G. Manolakis, “Digital Signal Processing Principles, Algorithams, and Applications”, Third Edition, Prentice-Hall International, INC.




Prachi S. Kulkarni,  R. P. Labade

Paper Title:

Review On Vehicle Tracking System

Abstract:  Situating and following a vehicle turns out to be increasingly vital to empower inescapable and setting mindful administration. The broad research has been performed in physical restriction and consistent confinement for satellite, GSM and Wi-Fi correspondence systems where settled reference focuses are thickly sent, the situating and following strategies in a thick system have not been all around tended to. This venture builds up a strategy for vehicular situating. The fundamental kinematics factors redesigned in every progression are the introduction and relative position of the vehicle. A discrete stretched out Kalman channel is utilized to foresee and upgrade the conditions of the vehicle and their vulnerabilities. A few strategies are talked about to refine the area estimation in which the vehicle utilizes a direct corresponding route law to track the objective. Recreation of the movement kinematics of vehicle and the position is performed utilizing MATLAB. It is demonstrated utilizing numerous recreation situations that the vehicle can track and achieve the moving objective effectively.

Kalman Filter, CP, GPS, RSS, VANET, DSRC


1.       Soo Siang Teoh and Thomas Bräunl, “A Reliability Point and Kalman Filter-based Vehicle Tracking Technique”, University of Western Australia, pp. 1-5
2.       Tuan Le, Meagan Combs, and Dr. Qing Yang,“Vehicle Tracking based on Kalman Filter Algorithm”, Computer Science Department at Montana State University, 2008, pp. 1-7

3.       Priya Gupta, S.S.Sutar, “Study of Various Location Tracking Techniques for Centralized Location, Monitoring & Control System”, IOSR Journal of Engineering, Vol. 04, Issue 03, March. 2014, pp. 27-30

4.       Sheldon Xu and Anthony Chang, “Robust Object Tracking Using Kalman Filters with Dynamic Covariance”, Cornell University, pp. 1-5

5.       C.Nandhini, S. Satheesbabu, “Reduction of Noise in GPS Using Kalman Filter”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 11, November 2015, pp. 11398-11404

6.       Lubna Farhi, “Dynamic Location Estimation By Kalman Filter”, Ubiquitous Computing and Communication Journal, Volume 7 Number 5, pp. 1309-1315

7.       Nathan Funk, “A Study of the Kalman Filter applied to Visual Tracking”, December 7, 2003, pp. 1-26

8.       Thomas Ristenpart, Gabriel Maganis, Arvind Krishnamurthy, Tadayoshi Kohno “Privacy-Preserving Location Tracking of Lost or Stolen Devices: Cryptographic Techniques and Replacing Trusted Third Parties with DHTs”, University of Washington, pp. 1-16

9.       Jean-Pierre Dubois, Jihad S. Daba, M. Nader, C. El Ferkh, “GSM Position Tracking using a Kalman Filter”, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering Vol:6, No:8, 2012, pp. 867-876

10.    Karthika. G, Ramalakshmi. K, “Accurate Target Tracking using Kalman Filtering and Location Estimation in Wireless Sensor Networks”, International Journal of Science and Research (IJSR), Volume 2 Issue 4, April 2013, pp. 381-385

11.    Steven R. Bible, Michael Zyda, Don Brutzman, “Using Spread-Spectrum Ranging Techniques for Position Tracking in a Virtual Environment”, Department of Computer Science, pp. 1-16

12.    Erin-Ee-Lin Lau, Boon-Giin Lee, Seung-Chul Lee, Wan-Young Chung, “Enhanced RSSI-Based High Accuracy Real-Time User Location Tracking System For Indoor
And Outdoor Environments”, International Journal On Smart Sensing And Intelligent Systems, Vol. 1, No. 2, June 2008, pp. 534-548

13.    Y.T. Chan and K.C. Ho, “A simple and efficient estimator for hyperbolic location,” IEEE Trans. Signal Process. Vol. 42, no. 8, pp. 1905- 1915, Aug. 1994.

14.    X. Wang, Z. Wang and B. O’Dea, “TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation,” IEEE Trans. Vehicle. Technol., vol.52, no. 1, pp. 112-116, Jan. 2003.

15.    Pankaj verma 1, J.S Bhatia 2, “Design and development of GPS-GSM based tracking system with Google Map based monitoring,” International Journal of
Computer science, Engineering and Applications (IJCSEA) vol.3, no.3, June 2013.

16.    Ceder, “Urban transit scheduling: Framework, review and examples,” J. Urban Planning Develop., vol.128, no.4, pp.225-244, Dec. 2002.

17.    Jeongyeup Paek Joongheon Kim Ramesh Govindan, “Energy-Efficient Rate-Adaptive GPS-based Positioning for Smartphones,” Embedded Networks Laboratory
Computer Science Department University of Southern California, pp. 15-18, June 2010.

18.    Swati katwal, Ravinder Nath and Govind Murumu, “A simple Kalman Channel Equalizer using Adaptive Algorithms for Time Variant Channel, in Proc. Of the IEEE ICSCCN’, pp.178-181, jul.2011.

19.    Praneet Sonil, Agya Mishra2, “Kalman Filter Based Channel Equalizer: A Literature Review,” International Journal of Emerging Technology and Advanced Engineering vol.4, Issue 5, May 2014.

20.    Cheng-Tse Chiang, Po-Hsuan Tseng, Student Member, IEEE, “Hybrid Unified Kalman Tracking Algorithms for Heterogeneous Wireless Location System,” IEEE Trans. Vehicular Technology, vol.61, no.2, Feb.2012.  




Sanjay B. Dhumal, Vinodpuri R. Gosavi

Paper Title:

Design and Development of Digital Energy Meter

Abstract: Electricity is one of the vital requirements for sustainment of comfort of life. It should be used very judiciously for its proper utilization. The main purpose of this project is to measure the energy with high accuracy for industrial applications. In industry, There are lots of control panels in which the energy meters are used to measure the amount of energy consumed. Today with several manufacturers in market , There are lots of parameters has to be consider like accuracy, cost, size of meter etc. while designing the product . In the market, many types of energy meters are available for industrial applications but the high accuracy meters are not available that’s why it is necessity of the market to propose a energy meter with high accuracy.

 MSP430 microcontroller; Energy meter; ΣΔ ADC; MAX 3232 IC; Real-Time Clock.


1.    S.Arun, Dr.Sidappa “Design and Implementation of Automatic Meter Reading System Using GSM, ZIGBEE through GPRS”. International Journal of Advanced Research in Computer Science and Software Engineering Research Paper. Volume 2, Issue 5, May 2013.
2.    Bharat Kulkarni “GSM Based Automatic Meter Reading System Using ARM Controller “International Journal of Emerging Technology and Advanced Engineering Website, Volume 2, Issue 5, May 2012.

3.    Tariq Jamil, “Design and Implementation of a Wireless Automatic Meter Reading System” WCE 2008, July 2-4, 2008, London, U.K. IAENG Processing of the World Congress on Engineering 2008 Vol I. July 2-4, 2008, London.

4.    B.S.Koay, S.S. Cheah, Y.H. Sng, P.H.J. Chong, P.Shum, Y.C. Tong, X.Y. Wang, Y.X. Zuo and H.W Kuek, “Design and Implementation of Bluetooth Energy Meter” ICICS – PCM – 2003, 15-18 December 2003.

5.    Subhashis Maitra, “Embedded Energy Meter- A new concept to measure the energy consumed by consumer and to pay the bill”, Power System echnology and IEEE Power India Conference, 2008.




Rajesh Kunte, M. D. Pawar

Paper Title:

License Checking System for Auto-Mobile using Fingerprint

Abstract: Driving license system is a very difficult task for the government to monitor. In this project, all the citizens’ images will scan and recorded. Whenever a citizen crosses the traffic rules, the police can scan his image and can collect penalty / fine from the defaulter. Using this method, the police can track the history of the driver. This biometric based driving license monitoring system is very easy and convenient to monitor. According ancient Greek scripts BIOMETRICS means study of life. Biometrics studies commonly include fingerprint, face, iris, voice, signature, and hand geometry recognition and verification. Many other modalities are in various stages of development and assessment. Among these available biometric traits Finger Print proves to be one of the best traits providing good mismatch ratio and also reliable. Registering the attendances of students has become a hectic work as sometimes their attendance may be registered or missed. To overcome this problem i.e. to get the attendances registered perfectly we are taking the help of two different technologies viz

Authentication, Fingerprint, License, Matching, Registration, Sensor


1.    National Science and Technology Council Subcommittee on Biometricsand Identity Management,
2.    Biometrics in Government Post-9/11: Advancing Science, Enhancing Operations, Aug 2008.

3.    A.K. Jain, P. Flynn, and A.A.Ross, eds., Handbook of Biometrics, Springer, 2007.

4.    H.C.Lee and R.E.Gaensslen. eds.,advences  in Fringerprintn Technology, 2nd., CRC press 2001.

5.    J. Feng, “Combining Minutiae Descriptors for Fingerprint Matching,” Pattern Recognition, Jan. 2008, pp. 342-352

6.    A.A. Ross, K. Nandakumar, and A.K. Jain, Handbook of Multibiometrics, Springer, 2006.




Hari Mohan Rai, Arpita Gupta, Prachi Mohan Kulshreshtha, Arun Vashishtha, Lalit Kr Gupta

Paper Title:

Design of Smart Solar Inverter System

Abstract:  This system is designed for outdoor application in un-electrified remote rural areas. The system is an ideal application for household. The system is provided with battery storage backup sufficient to operate the inverter for 7-8 hours. The project is about to develop and fabricate the circuit that can charge the lead acid battery of the inverter during day time by using the solar energy as the source. The battery can be charged by the mains connection in the absence of solar energy. To control the circuit for charging, we have used the circuit charging that can implement the condition of the charging whether it’s in charging condition or in float condition. The design consists of a PV array, a 12-volts lead acid battery, a control section that uses the PIC16F676 microcontroller. The control section obtains the information from the PV array through microcontroller’s Analog and Digital (A/D) ports and hence to perform the pulse width modulation (PWM) to the converter through its D/A ports. Battery’s state of charge is also controlled by the microcontroller to protect the battery from over charging. During the charging of the battery, red LED is turned on until the battery reach the full charge state that is in floating condition and when the condition is reached, green LED will turn on. The PIC16F676 will determine whether it is daytime or night time by using the sensing circuit. The light will automatically turn ON when the sensor circuit give the input to the PIC and PIC will gives the output to the relay to switch on the light. In the absence of solar energy, the relay automatically shifts the charging of the battery from the solar to the mains.

 solar inverter, battery charger, PIC16F676, EEPROM,

1.       “An Intelligent DC-DC/AC Converter.” [Online]. [Accesed: Feb 10,2010]
2.       “Intelligent Power Conversion Functions and Benefits.” [Online].

3.       “What is a Smart Battery Charger.” [Online]. [Accesed: Apr 11, 2010].

4.       “What is a Smart Battery.” [Online]. [Accesed: Apr 11, 2010].

5.       “Smart Battery Charger Specification.” [Online]. [Accesed: Apr 11, 2010]

6.       JoeAir Jiang, TsongLiang Huang, YingTung Hsia and ChiaHong Chen, Maximum Power Tracking for Photovoltaic Power Systems, Perturb and Observe Algorithm  Flowchart  




Majed Ahmed Khan, Mazher Khan, Sayyad Ajij

Paper Title:

Automation of Inter-Networked Banking and Teller Machine Operations

Abstract: In this article about biometric systems the general idea is to use of facial recognition to reinforce  security on one of the oldest and most secure piece of technology that is still in use to date thus an Automatic  Teller Machine. The main use for any biometric system is to authenticate an input by Identifying and verifying it in an existing database. Security in ATM’s has changed little since their introduction in the late 70’s. This puts them in a very vulnerable state as technology has brought in a new breed of thieves who use the advancement of technology to their advantage. With this in mind it is high time something should be done about the security of this technology beside there cannot be too much security when it comes to people’s money.

Biometrics, Facial Recognition, GSM Standards, Biometric Standards, Automatic Teller Machine Technology, Biometric Predecessors.


1.       S. C. Dass and A. K. Jain. Markov face models. In Proceedings, Eighth IEEE International Conference on Computer Vision (ICCV), pages 680–687, July 2001.
2.       C.-C. Han, H.-Y. M. Liao, G.-J. Yu, and L.-H. Chen. Fast face detection via morphology-based pre-processing. In Proceedings, Ninth International Conference on Image analysis and Processing (ICIAP), volume 2, pages 469–476, 1998.

3.       J. Huang and H. Wechsler. Eye location using genetic algorithm. In Proceedings, Second International Conference on Audio and Video-Based Biometric Person Authentication, pages 130–135, March 1999.

4.       T. Leung, M. Burl, and P. Perona. Finding faces in cluttered scenes using labeled random graph matching. In Proceedings, Fifth International Conference on Computer Vision, pages 637–644, June 1995.

5.       P. Kiran Kumar, Sukhendu Das and B. Yegnanarayana. One- Dimensional processing of images. In International Conference on Multimedia Processing Systems, Chennai, India, pages 181 185, August 13-15, 2000.

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Priyanka N. Bande

Paper Title:

Automatic Measurement and Monitoring System of Water Quality with Wireless GUI

Abstract: Water is an essential factor for sustaining life on earth and its adequate and safe supply must be accessible to all. As improved water quality is beneficial for health. Every possible effort should be made in that direction. Water quality depends on various physical and chemical standards such as color, turbidity, pH, temperature, DO, conductivity and TDS. The conventional method of measuring the water is to collect the samples manually and send it to laboratory for analysis. But this method is time consuming and require too much manpower, material resource and has limitation of the sample collecting, longtime analyzing, aging of experimentation equipment and other issues hence, it is not efficient.So, in order to make this process more economical it is beneficial to go for modern approach. The system consists of multiple sensors of water quality testing, PIC microcontroller, LCD display and CC2500 Zigbee module. The water parameters are automatically measured under the control of sensor array and PIC controller. The measured values will be displayed on LCD and also on wireless GUI with the help of CC2500 wireless Zigbee module. If the values exceed the threshold value red light will be indicated in front of the parameter name which exceeds on GUI.  This is the real time system which measures the water quality by continuously measuring the water parameters and values are displayed on LCD as well as wireless GUI for remote monitoring purpose.

 DO (dissolved oxygen), GUI (graphical user interface), pH, TDS (total dissolved solids), temperature.


1.       Mo Deqing, Zhao Ying and Chen Shangsong, “Automatic Measurement and Reporting System of Water Quality Based on GSM”, International Conference on Intelligent System Design and Engineering Application, IEEE Comp. society pub., April  2012.
2.       Theofanis P. Lambrou, Christos C. Anastasiou, Christos G. Panayiotou and Marios M. Polycarpou, “A Low Cost Sensor Network for Real Time Monitoring and Contamination Detection in Drinking Water Distribution Systems”, IEEE Sensors Journal, Vol. 14, Issue 8, August 2014.

3.       Theofanis P. Lambrou, Christos C. Anastasiou, Christos G. Panayiotou and Marios M. Polycarpou, “A Low Cost Sensor Network for Real Time Monitoring and Assessment of Potable Wtaer Quality at Consumer Sites”, IEEE Sensors, October 2012.

4.       Mithila Barabde, Shruti Danve, “Real Time Water Quality Monitoring System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 6, June 2015.

5.       Zulhani Rasin and Mohd Rizal Abdullah, “Water Quality Monitoring System Using Zigbee Based Wireless Network”, International Journal of Engineering & Technology, Vol.9, December 2009.

6.       Mr. Kiran Patil, Mr. Sachin Patil, “Monitoring of Turbidity, PH and Temperature of Water Based on GSM”, International Journal for Research in Emerging Science and Technology, Vol. 2, Issue 3, March 2015.

7.       Vinay Swethi Korvi, Narendra Kumar, “Design and Implementation of Aqua Quality Monitoring System”, International Journal of Research in Advent Technology, Vol. 1, Issue 5, December 2013.

8.       T. Surya, M. Manoj Kumar, SD. Karmulla, CH. GopiNadh ,P. Venkatesh and B.SitaMadhuri, “A Low Cost Sensor Network for Real Time Monitoring and Contamination Detection in Drinking Water Distribution Systems”, Vol. 4,Issue 7,March 2015.

9.       ThamaraiSelvi D.,Anitha S.R, “Potable Water Quality Monitoring and Automatic Billing System”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4,Issue 4,April 2015.

10.    Akansha Purohit and U. M. Gokhale, “Design and Implementation of Real Time Water Quality Measurement Using GSM”, IOSR Journal of Electronics And Communication Engg., Vol. 9,Issue 3,June 2014.

11.    Mrs. Sarita Vijay Verma, Pramod Bhaiyasaheb Shinde, “ARM based Low Cost Sensor Network for Real Time Contamination Detection in Drinking Water Distribution System”, International Journal of Engineering Research & Technology, Vol. 5 Issue 03, March 2016.

12.    H. Chea, S. Liua , “Contaminant Detection using Multiple Conventional Water Quality Sensors in an Early Warning System”, Elsevier, 16th Conference on Water Distribution System Analysis, WDSA 2014.







B. M. Kharat, G. V. Sonatkar, A. A. Chaudhari

Paper Title:

“Synthesis, Characterization and Gas Sensing Applications of Spinel Ferrite Thin Films Prepared by Spray Pyrolysis Technique: Engineering Approach for the Improvement of Sensor Parameters”



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Vaidehi Bukka, Rushikesh Bhagat, Ashok Bhalerao

Paper Title:

Automated Detection of Traffic Violations using Image Processing in MATLAB

Abstract: The traffic signal system is probably the most important kind of transportation facility in operation today, considering the perspectives of both safety and efficiency. A traffic signal system at its core has two major tasks: move as many users through the intersection as possible doing this with as little conflict between these users as possible. The first task relates to efficiency and capacity while the second relates to safety. Now-a-days, we face many problems at traffic signal, like increased traffic and breaking traffic rules and regulation. Traffic control also includes the use of CCTV and other means of monitoring traffic by local or State roadways authorities to manage traffic flows and providing advice concerning traffic congestion. It is necessary to control and monitor this traffic and breaking of rules.

 Region of Interest, Optical Character Recognition.


1.    Sorin Draghici, “A neural network based artificial vision system for license plate recognition”, International Journal of Network Security, International Journal of Neural Systems.
2.    Christos-Nikolaos E. Anagnostopoulos, “License Plate Recognition. A Brief Tutorial”, Intelligent Transportation Systems Magazine IEEE.

3.    Isack Bulugu, “Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers”, International Journal of Science and Research (IJSR).




Vaidehi Bukka, Rushikesh Bhagat, Ashok Bhalerao

Paper Title:

Automated Detection of Traffic Violations using Image Processing in MATLAB

Abstract: The traffic signal system is probably the most important kind of transportation facility in operation today, considering the perspectives of both safety and efficiency. A traffic signal system at its core has two major tasks: move as many users through the intersection as possible doing this with as little conflict between these users as possible. The first task relates to efficiency and capacity while the second relates to safety. Now-a-days, we face many problems at traffic signal, like increased traffic and breaking traffic rules and regulation. Traffic control also includes the use of CCTV and other means of monitoring traffic by local or State roadways authorities to manage traffic flows and providing advice concerning traffic congestion. It is necessary to control and monitor this traffic and breaking of rules.

 Region of Interest, Optical Character Recognition.


1.    Sorin Draghici, “A neural network based artificial vision system for license plate recognition”, International Journal of Network Security, International Journal of Neural Systems.
2.    Christos-Nikolaos E. Anagnostopoulos, “License Plate Recognition. A Brief Tutorial”, Intelligent Transportation Systems Magazine IEEE.

3.    Isack Bulugu, “Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers”, International Journal of Science and Research (IJSR).




Swati S. Vaidya, M. B. Kadu

Paper Title:

Review on MIMO Communication with Coherent and Non Coherent Detection

Abstract:  This paper explains the  study of  coherent and non coherent detection technique, SISO,   MIMO,wireless communication,  some  non  coherence  detection  technique  as grassmannian   signals,   STBC,   Temporal   correlation   etc. By Studying  the  different  papers  generalized  likelihood  test  like detector at receiver side and an arbitrary correlation structure is for estimating additive Gaussian noise, probability of error for both high level SNR, low level SNR. Also we estimate how to calculate and evaluate parameters   such    as SNR, FER, FRR, JITTER, DELAY, correlation between channels etc.

Coherent and non coherent detection technique, coding technique, parameters of communication, Grassmannian signaling, DUSTM, differential STBC, MIMO, temporal correlation.


1.          Jorge Cabrejas, Sandra Roger, Member, Daniel Calabuig, Member, ,  Yaser M. M. Fouad, Ramy H.Gohary, Senior Member, , Jose F. Monserrat, Senior Member, and Halim Yanikomeroglu, Senior Member, “Non-Coherent Open-loop MIMO Communications Over Temporally-Correlated Channels, IEEE,2015,p.p.1-7
2.          Henning Z ¨orlein and Martin Bossert, “Coherence Optimization and Best Complex Antipodal Spherical Codes”, IEEE Transactions on Signal Processing, Vol. 63, December 2015, pp. 6606 –   6615

3.          Shaoshi Yang and Lajos Hanzo, “Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs”, IEEE Communications Surveys & Tutorials, 2015, pp. 1-49

4.          Marko Beko, João Xavier and Victor A. N. Barroso, “Noncoherent Communication in Multiple-Antenna Systems: Receiver Design and Codebook Construction”, IEEE Transactions on Signal Processing, Vol. 55, No. 12, December 2007, pp. 5703-5715

5.          Kareem M. Attiah, Karim Seddik, Ramy H. Gohary and Halim Yanikomeroglu, “A Systematic Design Approach for Non-coherent Grassmannian Constellations”, Department of Systems and Computer Engineering, 2016, pp. 1-5

6.          Philip R. Balogun, Ian D. Marsland, Ramy H. Gohary, and Halim Yanikomeroglu, “Polar Codes for Noncoherent MIMO Signalling”, Ontario Ministry of Economic Development and Innovations, 2016, pp. 1-6

7.          Daifeng Wang and Brian L. Evans, “Codebook Design for Non-coherent MIMO Communications via Reflection Matrices”, Wireless Netw,16 pp.

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Nitin S. Zope, Kishan Mahamuni

Paper Title:

Implementation of Power Transformer Differential Protection Scheme using Advanced DSP Techniques

Abstract: This paper presents the differential protection algorithm when performing simplifications in certain calculations. Advancements in digital technology have allowed relay manufacturers to include more and more relay functions within a single hardware platform. This paper presents schematic logic of numeric differential protection relay used for power transformer. The analog input signal is converted to its digital equivalent through a suitable 16-bit ADC and is further processed using complex Fast Fourier Transform algorithm to extract the fundamental frequency magnitude. Once this magnitude is obtained, it is fed to the internal relay algorithm to initiate appropriate actions. In this paper, implementation of differential relay and its associated PSL is explained.

Analog to Digital Converter (ADC), Digital Signal Processing (DSP), Programmable Sequence Logic (PSL), Fast Fourier Transform (FFT), Power System Protection.


1.       Md. A. Rahman, Kazi Main Uddin Ahmed, Md. R. Sakib; (2012); “Modeling of a Novel Fuzzy Based Overcurrent Relay using Simulink”; International Journal of Scientific & tehnology Research; 1; 24-29.
2.       N. Paliwal; A. Trivedi; (2014); “Modeling and Simulation of Modern Digital Differential Protection Scheme of Power Transformer based on FIS”; Intrenational Journal of Enhanced Research in Science Technlogy & Engineering; 3; 69-75.

3.       D. Barbosa, U. C. Netto, Denis V. Coury, Member, IEEE, and Mario Oleskovicz, Member, IEEE, “Power Transformer Differential Protection Based on Clarke’s Transform and Fuzzy System”, IEEE Transactions On Power Delivery, VOL. 26, NO. 2 APRIL 2011.

4.       S. Hodder; B. Kasztenny; N. Fischer, and Y. Xia; (2014); “Low Second-Harmonic Content in Transformer Inrush Currents – Analysis and Practical Solutions for Protection Security”; Texas A & M Conference for Protective Relay Engineers.

5.       Xiang- Ning Lin and Pei Liu, “The Ultra-Saturation Phenomenon of Loaded Transformer Energization and Its Impacts on Differential Protection”, IEEE Transactions On Power Delivery, VOL. 20, NO. 2 APRIL 2005

6.       Z. Moravej; D. N. Vishwakarma; S. P. Singh; (2003); “Application of radial basis function neural network for differential relaying of a power transformer”; Computers & Electrical Engineering; 29; 421-434.

7.       P. L. Mao; R. K. Aggarwal; (2000); “A wavelet transform based decision making logic method for discrimination between internal faults and inrush currents in power transformers”; International Journal of Electrical Power & Energy Systems; 22; 389-395.




 B. M. Kharat, A. A. Chaudhari, Malode V. B.

Paper Title:

Engineering Approaches for the Improvement of Sensor Parameters of Spinel Ferrite Based Gas Sensors

Abstract: Engineering approaches designed to improve parameters of conduct metric gas sensors are being considered in this survey. In particular, in this paper we are going to analyze engineering approaches used for improvement of sensor stability and reliability, sensitivity and selectivity and for decrease of power dissipated by conductometric gas sensors. Analysis has shown that those engineering approaches can eliminate some genetic disadvantages of conductometric gas sensors, provide a significant improvement of their exploitation parameters, and expand their application in various fields.

Keywords: conductometric, fields. Engineering,


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  2. Duane Lindner, The μchem Lab TM project: micro total analysis system, R&D at Sandia National Laboratories, Lab on a chip, 1 (2001), pp. 15-19.
  3. F M Burkle, Measures of effectiveness in large-scale bioterrorism events. Prehosp. Disast. Med. 18 (2003),pp. 258–262.
  4. Eden-Firstenberg, and B J Schaertel, Biosensors in the food industry present and future. J. of Food Protection. 51 (1988), pp 811-820.
  5. B. Gadkari, T.J. Shinde, P.N. Vasambekar, Ferrite gas sensors, IEEE Sensors, Journal 11 (2011) 849–861.
  6. V. Bangale, D.R. Patil, S.R. Bamane, Nanostructured spinel ZnFe2O4 for the detection of chlorine gas, Sensors and Transducers Journal 134 (2011) 107–119.
  7. O. Meltzer, J. Szwarcrcberg, M.W. Pill, Allergic rhinitis, asthma, and rhinosinusitis: diseases of the integrated airway, Journal of Managed Care Pharmacy, 10 (2004) 310–317.
  8. T Gregory Drummmond, Electrochemical DNA sensors. Nature Biotechnol. 21(2003), pp. 1192-1199.
  9. Robert M Umek, Electronic detection of Nucleic acids. J. of Mol. Diagnostics 3(2001), pp. 74-84.
  10. A A. Yassi and E. Nieboer. In Chromium in the Natural and Human Environments, ed. J. O.Nriagu and E. Nieboer. Wiley and Sons, New York, 20 (1988).pp. 443.
  11. Ferrary, D. Marioli, A. Taroni, E. Ranucci. Multisensor Array of Mass Microbalances for Chemical Detection Based on Resonant Piezo-Layers of Screen-Printed PZT. In Proceedings of 13th European Conference on Solid-State Transducers (EUROSENSORS XIII), The Hague, The Netherlands, 12-15 September, (1999). pp.949-952.
  12. Goldman, Modern Ferrite Technology (2006) Springer US, pp 375-386.
  13. H. Yang, Z. W. Li, Y. H. Yang, L. Liu, L. B. Kong, Journal of Magnetism and Magnetic Materials 331 (2013) 232-236.
  14. Xiaofei Cao, Kangning Sun, Chang Sun, Liang Leng, Journal of Magnetism and Magnetic Materials 321 (2009) 2896-2901.
  15. J. Iqbal, Zahoor Ahmad, Turgut Meydan, Yevgen Melikhov, Journal of Magnetism and Magnetic Materials 324 (2012) 3986-3990.
  16. F. Hochepied, M. P. Pileni, J. Appl. Phys. 87 (2000) 2472.
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