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Volume-1 Issue-5

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Volume-1 Issue-5, November 2012, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Kareemullah Shaik, Mohammad Mohiddin, Md. Zabirullah

Paper Title:

A Reduced Latency Architecture for Obtaining High System Performance

Abstract: Microprocessor performance has improved rapidly these years. In contrast, memory latencies and bandwidths have improved little. The result is that the memory access time has been a bottleneck which limits the system performance. As the speed of fetching data from memories is not able to match up with speed of processors. So there is the need for a fast memory controller. The responsibility of the controller is to match the speeds of the processor on one side and memory on the other so that the communication can take place seamlessly. Here we have built a memory controller which is specifically targeted for SDRAM. Certain features were included in the design which could increase the overall efficiency of the controller, such as, searching the internal memory of the controller for the requested data for the most recently used data, instead of going to the Memory to fetch it. The memory controller is designed which compatible with Advanced High-performance Bus (AHB) which is a new generation of AMBA bus. The AHB is for high-performance, high clock frequency system modules. The AHB acts as the high-performance system backbone bus. AHB supports the efficient connection of processors, on-chip memories and off-chip external memory interfaces with low-power peripherals.

   SDRAM, Memory controller, AMBA, FPGA, Xilinx, Modelsim.


1.        Ching - SDRAM Controller Applications”.IEEE J. Solid-State Circuits, Vol.39, Nov. 2004.Che Chung, Pao-Lung Chen, and Chen-Yi Lee “Delay-Locked Loop for DDR
2.        Micron Technology Inc.Synchronous DRAM Data Sheet,2001.

3.        ARM, AMBA Specification Rev.2.0, 1999.

4.        “Memory Controllers for Real-Time Embedded systems” Benny Akesson Kees Goossens vol. 3, no. 3, pp. 75–77, Mar1999.

5.        Hynix Semiconductor Inc., SDRAM Device operationRev.1.1, Sep. 2003.

6.        Samir Palnitkar, Pearson 2nd edition “Verilog HDL, A Guide to Digital Design and Synthesis.






N. Prabhakar Reddy, K.Sasidha

Paper Title:

Design and Development of Can Sniffer

Abstract:  Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured over the last 8 years. This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools. The CAN Sniffer Tool is a simple to use low cost CAN bus monitor which can be used to develop and debug a high speed CAN network. The tool supports CAN 2.0b and ISO11898-2 and a broad range of functions which allow it to be used across various market segments including automotive, industrial, medical and marine. The toolkit comes with all the hardware and software required to connect a CAN network to a PC. In CAN bus, the two CAN channels can send/receive CAN messages either with extended or standard ID.  All messages received by the CAN interface are sent via UART to the serial port of PC. On the PC the CAN-messages get collected and ordered by CAN-ID.  In CAN the communication is done in two-wire, the CAN sniffer can receives the messages based on arbitration process.



1.        N.C. Audsley, "Optimal priority assignment and feasibility of static priority tasks with arbitrary start times", Technical Report YCS 164, Dept. Computer Science, University of York, UK, December 1991.
2.        R.J. Bril. “Existing worst-case response time analysis of real-time tasks under fixed-priority scheduling with deferred pre-emption is too optimistic”. CS-Report 06-05, Technische Universiteit Eindhoven (TU/e) The Netherlands, February 2006.

3.        L. George, N. Rivierre, and M. Spuri. “Pre-emptive and non- pre- emptive real-time uni-processor scheduling. Technical Report 2966, Institut National de Recherche et Informatique et en Automatique (INRIA), France, September 1996

4.        S. Punnekkat, H. Hansson, C. Norstrom. “Response time analysis under errors for CAN”. In Proceedings 6th Real-Time Technology and Applications Symposium, pp. 258-265, IEEE Computer Society Press May/June 2000.

5.        J. Lehoczky. “Fixed priority scheduling of periodic task sets with arbitrary deadlines”. In Proceedings 11th IEEE Real-Time Systems Symposium, pp. 201–209, IEEE Computer Society Press, December 1990.

6.        K.W. Tindell and A. Burns. “Guaranteeing message latencies on Controller Area Network (CAN)”, In Proceedings of 1st International CAN Conference, pp. 1-11, September 1994.






Leman Dewangan, Mangal Singh, Neelam Dewangan

Paper Title:

A Survey of PAPR Reduction Techniques in LTE-OFDM System

Abstract:  Orthogonal Frequency Division Multiplexing (OFDM) is one of the most promising technique for today’s wireless broadband communication systems.3GPP’s LTE was the first to adopt OFDM as its downlink technique. One of the major disadvantageisitshighpeak-to- averagepowerratio(PAPR). In this paper various PAPR Reduction Techniques are discussed along with their advantages, disadvantages and improvements done so far. Techniques like clipping, Companding, Selective Mapping (SLM), Interleaving, Tone Reservation (TR), Tone Injection (TI), Partial Transmit Sequence (PTS), etc.



1.        SeungHee Han, Jae Hong Lee, “An overview of peak-to-average power ratio reduction techniques for multicarrier transmission”, Wireless Communications, IEEE, Vol.12, Issue 2, pp.56–65, April, 2005
2.        RashidaAkter, Mohammad Rakibul Islam and Ju Bin Song , “PAPR in 3rd Generation Partnership Project Long Term Evolution : An Overview to find the Impact” IETE Technical Review , vol 27 ,issue 6  , Nov-Dec 2010

3.        Suma M N, Kanmani.B, “Developments in Orthogonal Frequency Division Mutiplexing (OFDM) system – A Survey”, IEEE, 2011

4.        Hyung G. Myung, Junsung Lim, and David J. Goodman, "Single Carrier FDMA for Uplink Wireless Transmission"; IEEE Vehicular Technology Magazine, September 2006, pp. 30-38

5.        Ramjee Prasad, “OFDM for Wireless Communication System”, Arctech House, 2004

6.        Satoshi Kimura, Takashi Nakamura, Masato Saito and Minoru Okada, “PAR Reduction for OFDM signals based on Deep Clipping” ISCCSP 2008, Malta, 12-14 March

7.        Jean Armstrong, “New Peak to Average Power Reduction Technique,” Proc IEEE VTC 2001 .Spring , Rhodes Greece,2001

8.        Jean Armstrong, “New Peak to Average Power Reduction Technique,”  IEEE Electronic Letters vol .38 No.5 , February 2008

9.        M. M. Rana, Md. Saiful Islam and Abbas Z. Kouzani, “Peak to Average Power Ratio Analysis for LTESystems” EEESecond International Conference on Communication Software and Networks, 2010

10.     Josef Urbaf, Roman Marsalek, “PAPR Reduction by Combination of Interleaving with Repeated Clipping and Filtering in OFDM” IEEE Explore, 2007

11.     Deng Qing, ZhongHongsheng, “An Improved Algorithm to Reduce PAPR BasedClipping-and-Filtering” IEEE Explore, 2008

12.     Tao Jiang, Member, IEEE, and YiyanWu, Fellow, IEEE, “An Overview: Peak-to-Average Power RatioReduction Techniques for OFDM Signals”IEEE Transactions on
Broadcasting, vol. 54, no. 2, June 2008

13.     Dae-Woon Lim, Seok-JoongHeo, and Jong-Seon , “An Overview of Peak-to-Average Power Ratio Reduction Schemes for OFDM Signals”, Journal of Communications and Networks, vol. 11, no. 3, June 2009 229

14.     YasirRahmatallah, NidhalBouaynaya and Seshadri Mohan, “On The Performance Of Linear And Nonlinear Companding Transforms In Ofdm Systems” IEEE 2011

15.     Shiann-ShiunJeng, Member, IEEE, and Jia-Ming Chen, Student Member, IEEE, “Efficient PAPR Reduction in OFDM Systems Based on a Companding TechniqueWith Trapezium Distribution”, IEEE Transactions on Broadcasting, vol. 57, no. 2, June 2011

16.     Zhongpeng Wang, “Combined DCT and Companding for PAPR Reduction in OFDM Signals”, Journal of Signal and Information Processing, 2011, 2, 100-104

17.     Sulaiman A. Aburakhia, Ehab F. Badran, and Darwish A. E. Mohamed, Member, IEEE, “Linear Companding Transform for the Reduction ofPeak-to-Average Power
Ratio of OFDM Signals”IEEE Transactions on Broadcasting, vol. 55, no. 1, March 2009

18.     Yuan Jiang, “New Companding Transform for PAPR Reduction in OFDM”, IEEE Communications Letters, vol. 14, no. 4, April 2010

19.     Jun Hou, JianhuaGe, DeweiZhai, and Jing Li, “Peak-to-Average Power Ratio Reduction of OFDM SignalsWith Nonlinear Companding Scheme”, IEEE Transactions on Broadcasting, vol. 56, no. 2, June 2010

20.     Tao Jiang, Yang Yang, Member, IEEE, and Yong-Hua Song, Senior Member, IEEE, “Exponential Companding Technique for PAPR Reduction in OFDM Systems”, IEEE Transactions on Broadcasting, vol. 51, no. 2, June 2005

21.     Bauml, R., Fischer, R., and Huber, J., "Reducing the peak-to-average power ratioof multicarrier modulation by selected mapping," IEE Electronics Letters, vol. 32, pp. 2056 -2057, Oct. 1996.

22.     Robert F. H. Fischer, Member, IEEE, and Christian Siegl, Student Member, IEEE, “Reed–Solomon and Simplex Codes for Peak-to-Average Power Ratio Reduction in OFDM”, IEEE Transactions on Information Theory, vol. 55, no. 4, April 2009

23.     Zhongpeng Wang, Shaozhongzhang, binqingqiu, “PAPR Reduction of OFDM Signal by UsingHadamard Transform in Companding Techniques” IEEE Explore, 2010

24.     Kee-Hoon Kim, Hyun-BaeJeon, Jong-Seon No, and Dong-Joon Shin, “A New Low-Complexity Selected Mapping Scheme Using Cyclic Shifted IFFT for PAPR Reduction in OFDM Systems” IEICE International Symposium on Information Theory and its Applications, March 2012

25.     Ehab F. Badran and Amr. M. El-Helw, “A Novel Semi-Blind Selected Mapping TechniqueforPAPRReductioninOFDM” IEEE Signal Processing letters, vol. 18, no. 9, September 2011

26.     N.V. Irukulapati, V.K. Chakka and A. Jain, “SLM based PAPR reduction of OFDM signal using new phase sequence”, Electronics letters 19th November 2009 vol. 45 no. 24

27.     Stephane Y. Le Goff, Samer S. Al-Samahi, Boon KienKhoo, charalampos C. Tsimenidis, and Bayan S. Shari, “Selected mapping without side information for PAPR reduction in OFDM”, IEEE Transactions on Wireless Communications, vol. 8, no. 7, July 2009

28.     Mahmoud FerdosizadehNaeiny and FarokhMarvasti, Senior Member, IEEE, “Selected Mapping Algorithm for PAPR Reductionof Space-Frequency Coded OFDM SystemsWithout Side Information”IEEE Explore, 2008

29.     Yuh-Ren Tsai, Member, IEEE, Chi-Hung Lin and Yen-Chen Chen, Student Member, IEEE, “A Low-Complexity SLM Approach Based on Time-domainSub-block Conversion Matrices for OFDM PAPR Reduction”IEEE Explore, 2011

30.     Hyun-BaeJeon, Jong-Seon No, Senior Member, IEEE, and Dong-Joon Shin, Senior Member, IEEE, “A Low-Complexity SLM Scheme UsingAdditive Mapping Sequences for PAPRReduction of OFDM Signals”, IEEE Transactions on Broadcasting, vol. 57, no. 4, December 2011

31.     ThitaphaChanpokapaiboon, PotcharaPuttawanchai, and PrapunSuksompong, “Enhancing PAPR Performance of MIMO-OFDMSystems Using SLM Technique with CenteringPhase Sequence Matrix”, Communication Systems Wireless Mobile Communications & Technologies

32.     Y. Wu, IEEE member, K. L. Man, IEEE member, Y. Wang, IEEE student member, “Optimum Selective Mapping for PAPRReduction” IEEE Explore, 2011

33.     Jingru Zhou, XiaodongXu, and Xuchu Dai, “A Constellation Extension Based SLM Scheme forPAPR Reduction of OFDM Signals” IEEE Explore, 2011

34.     Sang -Woo Kim, Jin-Kwan Kim and Heung-GyoonRyu, “A Computational Complexity Reduction Scheme UsingWalsh Hadamard Sequence in SLM Method” IEEE Explore, 2006

35.     Athinarayanan Vallavaraj1, Brian G Stewart2, David K Harrison2, Francis G McIntosh1, “Reducing the PAPR of OFDM Using a Simplified Scrambling SLM
Techniquewith No Explicit Side Information”, 14th IEEE International Conference on Parallel and Distributed Systems, 2008S. Mohammady, R. M. Sidek, P. Varahram, M. N. Hamidon, and N. Sulaiman, “A new DSI-SLM method for PA_P R reduction in OFDM systems”, IEEE International Conference on Consumer Electronics (ICCE), 2011

36.     Amr M El-Helw, Ehab F. Badran  andHesham Y. Al-Kafrawy, “A New Sequence for Embedding Side Informationin SLM for PAPR Reduction in OFDM” Japan-Egypt Conference on Electronics, Communications and Computers, 2012

37.     HimanshuBhusanMishra,Madhusmita Mishra, Sarat Kumar Patra, “Selected Mapping Based PAPR Reduction inWiMAX Without Sending the Side Information” 1st Int’l Conf. on Recent Advances in Information Technology ,RAIT-2012

38.     Jamal Mountassir, AlexandruIsar, “Precoding Techniques in OFDM systemsFor PAPR Reduction”IEEE Explore, 2012

39.     Robert J. Baxley, “Analyzing Selected Mapping for Peak-to-Average PowerReduction in OFDM”, School of Electrical and Computer Engineering Georgia Institute of
Technology,May 2005

40.     KitaekBae, Student Member, IEEE, Jeffrey G. Andrews, Senior Member, IEEE,and Edward J. Powers, Life Fellow, IEEE, “Adaptive Active Constellation Extension Algorithm forPeak-to-Average Ratio Reduction in OFDM”, IEEE Communications letters, vol. 14, no. 1, January 2010

41.     B. S. Krongold and D. L. Jones, “PAR reduction in OFDM via active constellation extension,” IEEE Trans. Broadcast., vol. 49, no. 3, pp. 258–268, Sep. 2003.

42.     Kamal Singh, ManoranjanRaiBharti, SudhanshuJamwal, “A modified PAPR reduction scheme based on SLM and PTS Techniques” IEEE Explore 2012.

43.     Di-xiao Wu, “Selected Mapping and Partial Transmit Sequence Schemes to Reduce PAPRin OFDM Systems” IEEE Explore 2011.

44.     Alok Joshi, Davinder S. Saini, “PAPR Analysis of Coded- OFDM System andMitigating its Effect with Clipping, SLM and PTS” Proceedings of the 5th International Conference onIT & Multimedia at UNITEN (ICIMU 2011) Malaysia

45.     Stefan H. Muller and Johannes B. Huber, “A Comparison of Peak Power Reduction Schemes For Ofdm” IEEE Explore 1997.

46.     Josef URBAN, Roman MARSALEK, “OFDM PAPR Reduction by Partial Transmit Sequences and Simplified Clipping with Bounded Distortion”IEEE Explore 2008

47.     ByungMooLee ,RuiJ.P.deFigueiredo, YoungokKim, “A computationally Efficient Tree-PTS Technique for PAPR Reduction of OFDM Signals” Wireless PersCommun (2012) 62:431–442

48.     Robert J. Baxley and G. Tong Zhou, “Comparing Selected Mapping and Partial Transmit Sequence for PAR Reduction”, IEEE Transactions on Broadcasting, vol. 53,no. 4, December 2007 797

49.     G. Lu, P. Wu and C. Carlemalm-Logothetis, “Peak-to-average power ratio reduction in OFDM based on transformation of partial transmit sequences” Electronics Letters 19th January 2006 Vol. 42 No. 2

50.     Bader HamadAlhasson, and Mohammad A. Matin, Senior Member, IEEE, “PAPR Distribution Analysis of OFDM signals with Partial Transmit Sequence”, Proceedings of 14th International Conference on Computer and Information Technology (ICCIT 2011) 22-24 December, 2011, Dhaka, Bangladesh

51.     LingyinWang and Ju Liu, Senior Member, IEEE, “PAPR Reduction of OFDM Signals by PTS With Grouping and Recursive Phase Weighting Methods”, IEEE Transactions on Broadcasting, vol. 57, no. 2, June 2011

52.     UmerIjaz Butt, “A Study On The Tone-Reservation Technique For Peak-To-Average Power Ratio Reduction In Ofdm Systems”, Univeraal Publication, 2008

53.     Yong Soo Cho, JaekwonKim , Won Young Yang , Chung Gu Kang, “MIMO-OFDM WIrelessCOmmunication s with MAtlab”Jhon Wiley and Sons, 2010

54.     SaeedGazor and RuhallahAliHemmati, “Tone Reservation for OFDM Systems byMaximizing Signal-to-Distortion Ratio” IEEE Transactions on Wireless Communications, vol. 11, no. 2, February 2012






S. Ramya, T. Manokaran

Paper Title:

Analysis and Design of Multi Input Dc–Dc Converter for Integrated Wind PV Cell Renewable Energy Generated System

Abstract: The objective of this paper is to propose a multi-input power converter for the hybrid system in order to simplify the power system and reduce the cost. The proposed converter interfaces two unidirectional input ports and a bidirectional port for storage element in a unified structure. It   also utilizes four power switches that are controlled independently with four different duty ratios. The renewable power system hybridizes PV and Wind as main source & Battery Power for backup energy source. Three different power operation modes are defined for the converter based on utilization state of the battery as follows: 1) An operation type wherein power is delivered to load from hybrid renewable energy sources; 2)A single type wherein only one renewable energy source supplies power to the load with battery discharging; 3) An operation  type wherein power is delivered to load  from renewable sources along with battery charging. A simple and cost effective control with DC-DC converter is used for maximum power point tracking (MPPT) and hence maximum power is extracted from the source .The integration of the hybrid renewable power system is implemented and simulated using MATLAB/SIMULINK.

Photovoltaic (PV)/Wind/Battery sources, hybrid power system, State Of Charge(SOC), Multi input  power  converter, Maximum Power Point Tracking (MPPT).


1.        J. L. Duarte, M. Hendrix, and M. G. Simoes, “Three-port bidirectional converter for hybrid fuel cell systems,” IEEE Trans. Power Electron., vol. 22, No. 2, Mar. 2007.
2.        Y-C. Kuo, T-J. Liang, and J-F. Chen: Novel Maximum-Power-Point- Tracking Controller for Photovoltaic Energy Conversion System, IEEE Transactions On Industrial Electronics, Vol. 48, No. 3, June 2001

3.        F. Valencaga, P. F. Puleston, and P. E. Battaiotto, “Power control of a solar/wind generation system without wind measurement: A passivity/ sliding mode approach,” IEEE Trans. Energy Convers., vol. 18, No. 4,  Dec. 2003.

4.        X. Huang, X. Wang, T. Nergaard, J. S. Lai, X. Xu, and L. Zhu, “Parasitic ringing and design issues of digitally controlled high power interleaved boost converters,” IEEE Trans. Power Electron., vol. 19, No. 5, pp. 1341–1352, Sep. 2004.

5.        K. Rajashekara, “Hybrid fuel-cell strategies for clean power generation,” IEEE Trans. Ind. Appl., vol. 41, No. 3,June 2005.

6.        F. Valenciaga and P. F. Puleston, "Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy," IEEE Trans. Energy Conversion, vol. 20, June 2005.

7.        J. M. Carrasco, L. G. Franquelo, J. T. Bialasiewicz, E. Galvan, R. C. PortilloGuisado, M. A. M. Prats, J. I. Leon, and N.Moreno-Alfonso, “Power-electronic systems for the grid integration of renewable energy sources: A survey,”  IEEE Trans. Ind. Electron., vol. 53, No. 4, June . 2006.

8.        K. N. Reddy and V. Agrawal, “Utility-interactive hybrid distributed generation scheme with compensation feature,” IEEE Trans. Energy Convers., vol. 22, No. 3, Sep. 2007.

9.        H. Tao, J. L. Duarte, andM. A.M. Hendrix, “Three-port triple-half-bridge bidirectional converter with zero-voltage switching,” IEEE Trans. Power Electron., vol. 23, No. 2, Mar. 2008.

10.     O. C. Onara,M. Uzunoglu, andM. S. Alam, “Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra capacitor hybrid power system,” J. Power Sources., vol. 185, No. 2, Apr.2008.

11.     Khaligh, J. Cao, and Y. J. Lee, “A multiple-input DC–DC converter topology,” IEEE Trans. Power Electron., vol. 24, no. 3, Mar. 2009.

12.     S. H. Hosseini, S. Danyali, F. Nejabatkhah, and S. A. K. Mozafari Niapour, “Multi-input DC boost converter for grid connected hybrid PV/FC/battery power system,” in Proc. IEEE Elect. Power Energy Conf., 2010

13.     R. J. Wai, Ch. Y. Lin, J. J. Liaw, and Y. R. Chang, “Newly designed ZVS multi-input converter,” IEEE Trans. Ind. Electron., vol. 58, No. 2, Feb. 2011

14.     Farzam Nejabatkhah, Saeed Danyali, Seyed Hossein Hosseini,Mehran Sabahi, and Seyedabdolkhalegh Mozaffari Niapour, “Modeling and Control of a New Three-Input DC–DC Boost Converter for Hybrid PV/FC/Battery Power System” IEEE Trans . Power Electron., vol .27, NO. 5, May    2012.






Senthil Ragavan Valayapalayam Kittusamy, Venkatesh Chakrapani

Paper Title:

Extraction of Expressions from Face Images using Neuro Fuzzy Approach

Abstract:  Body language is a form of communication between human beings. Facial expressions are a form of nonverbal communication. Facial expressions can often communicate a person's mood than a word. Here, the authors extract the facial features from facial points. Extracted feature points are tracked using a cross-correlation based optical flow to extract feature vectors. These vectors are used to categorize expressions, using RBF neural networks and Fuzzy Inference System. Recognition results from two classifiers are compared with each other.

 Facial Expression, FIS, Neuro Fuzzy, RBF


1.        P. Ekman and W.V. Friesen, Facial Action Coding System (FACS) (Consulting Psychologists Press, Inc., 1978).
2.        M. Yoneyama, Y. Iwano, A. Ohtake, and K. Shirai, “Facial Expression Recognition using Discrete Hopfield  Neural Networks” (Proc. Int'l Conf. Information Processing, Vol. 3, 1997), pp. 117-120.

3.        M. J. Black and Y. Yacoob, “Recognizing Facial Expression in Image Sequences using Local Parameterized Models of Image Motion” (Int'l J. Computer Vision, Vol. 25, No.1, 1997), pp.23-48. 

4.        H. Kobayashi and F. Hara, “Facial Interaction between Animated 3D Face Robot and Human Being” (Proc. Int'l Conf.  Systems, Man, Cybernetics, 1997), pp. 3732-3737. 

5.        J. F. Cohn, A.J. Zlochower, J.J. Lien, and T. Kanade, “Feature-Point Tracking by Optical Flow Discriminates subtle Difference in Facial Expression” (Proc. Int'l Conf. Automatic Face and Gesture Recognition).

6.        Senthil Ragavan V K and Venkatesh C, “Facial Expressions Recognition using Eigenspaces”, (Journal of Computer Science, Vol 8, No. 10, 2012), pp.1674-1679.

7.        Senthil Ragavan V K and Venkatesh C, “Emotion Classification from the Analysis of Facial Expressions based on Gabor Wavelets Using Radial Basis Function Neural Network”, (European Journal of Scientific Research, Vol. 84, No. 4, 2012), pp.609-615

8.        T. Kanade, J. Cohn and  Y. Tian. Comprehensive database for facial expression analysis, 2000. 

9.        J. Moody and C. Darken, “Learning with Localized receptive fields” (Proc. 1988 Connectionist Models Summer School. San Matco. CA: Morgan-Kaufmann 1988).

10.     H. Seyedarabi, A. Aghagolzadeh and S. Khanmohammadi, “Facial Expression Recognition from Static Images using RBF Neural Networks and Fuzzy Logic” (2 nd
Iranian conf. on Machine Vision and Image Processing (MVIP 2003) ,Tehran, 2003).





Devendra Thakore, Torana Kamble

Paper Title:

Application of Genetic Algorithm in Software Security

Abstract:   Assigning access specifier is not an easy task as it decides over all security of any software .Though there are many metrics tools available in a market to measure the security at early stage. But in this case assignment of access specifier is totally based on the human judgment and understanding .Objective of proposed tool is  to generate all possible solutions by applying Genetic Algorithm (GA). Our Secure Coupling Measurement Tool (SCMT) uses coupling, feature of OO design to determine the security at design level. It Takes input as a UML class diagram with basic constraints and generates alternate solutions i.e. combinations. Tool also provides metrics at code level to compute the security at code level. Result of both the metrics give proof of secure design with the help of spider chart as well as scope to change the design

   Coupling, Genetic Algorithm, Quality, Security, Software Metrics.


1.        j. Bansiya and C. G. Davis, “A hierarchical model for object-oriented design quality assessment,” IEEE Transactions on Software Engineering, vol. 28, pp. 4–17, 2002 ..
2.        P K. Manadhata, K. M. C. Tan, R. A. Maxion, and J. M. Wing, “An approach to measuring a system’s attack surface,” Tech. Rep. CMU-CS- 07-146, Carnegie Mellon
3.        B. Alshammari, C. J. Fidge, and D. Corney, “Security metrics for object-oriented class designs,” in Proceedings of the Ninth International Conference on Quality Software (QSIC 2009), (Jeju, Korea), pp. 11–20, IEEE, 2009
4.        Chowdhury, B. Chan, and M. Zulkernine, “Security metrics for sourcecode structures,” in Proceedings of the Fourth International Workshop onSoftware Engineering for Secure Systems, (Leipzig, Germany),  ACM, 2008..

5.        Smriti Jain, “A Review of Security Metrics in Software Development Process” et al / (IJCSIT) International Journal of Computer Science and Information Technologies, 2011.

6.        IstehadChowdhury, Mohammad Zulkernine “Can Complexity, Coupling, and Cohesion Metrics be Used as Early Indicators of Vulnerabilities?” ACM 2010.

7.        S. Chidamber and C. Kemerer, “A metrics suite for object oriented design,” IEEE Transactions on Software Engineering, vol. 20, pp. 476–493, 1994.,

8.        M. Fowler, Refactoring: Improving The Design of Existing Code. Reading, MA: Addison-Wesley, 1999     

9.        Payal Khurana&Puneet Jai Kaur DYNAMIC METRICS AT DESIGN LEVEL ,International Journal of Information Technology and Knowledge Management July-December 2009, Volume 2, No. 2, pp. 449-454

10.     AmjanShaik,C. R. K. Reddy, BalaManda, Prakashini. C, Deepthi. K, “An Empirical Validation of Object Oriented Design Metrics in Object Oriented Systems” Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) ,(ISSN: 2141-7016).

11.     John Lloyd1 and Jan Jürjens2,‘Security Analysis of a Biometric Authentication System ‘Using UMLsec and JML*, A. Schürr and B. Selic (Eds.): MODELS 2009, LNCS 5795, pp. 77–91, 2009.,© Springer-Verlag Berlin Heidelberg 2009

12.     M. Y. Liu and I. Traore, “Empirical relation between coupling and attackability in software systems: a case study on DOS,” in Proceedings of the 2006 Workshop on Programming Languages and Analysis for Security Ottawa. Ontario, Canada: ACM, 2006, pp. 57–64

13.     Rüdiger Lincke, Jonas Lundberg and Welf Löwe,“Comparing Software Metric Tools”, 2008 ACM 978-1-59593-904-3/08/07.

14.     Lionel C. Briand Jie Feng Yvan Labiche," Using Genetic Algorithms and Coupling Measures to Devise Optimal Integration Test Orders" SEKE '02, July 15-19, 2002, Ischia, Italy. ACM 1-58113-556-4/02/0700.





Snehal S. Shinde, P. R. Devale

Paper Title:

Automated Entity Alias Evocation from Web

Abstract:    Identifying the correct reference to an entity among a list of references is required in lots of works such as information retrieval, sentiment analysis, person name disambiguation as well as in biomedical fields. More previous work had been done on solving lexical ambiguity here we proposed a method that is based on referential ambiguity. In this paper we proposed a method which is based on referential ambiguity to extract correct alias for a given name. Given a person name and/or with context data such as location, organization retrieves top K snippets and depth up to level two from a web search engine. With the help of Lexical pattern extract candidate aliases. As to find correct alias from a list of aliases we used n-depth crowling method. This method is useful to improve the precision and minimize the recall than the previous baseline method.

    Web mining, web text analysis, text mining, n-depth crawling.


1.              Danushka Bollegala, YutakaMatsuo and IitsuruIshizuka, Member , IEEE, Automatic Discovery of Personal Name Aliases from the Web, IEEE Transaction on knowledge and data engineering, vol. 23, no. 6, June 2011.
2.              Dmitri V. Kalashnikov Zhaoqu Chen Rabia Nuray – Turan Sharad Mehrotra Zheng Zhang, Web People Search via connection Analysis, IEEE International Conference on Data Engineering, 2009.

3.              Bagga and B. Baldwin, Entity-Based Cross-Document Coreferencing using the vector space model, Proc. Int’s Conf. Computational linguistics (COLING ’98), pp. 79-85, 1998.

4.              T. Hokama and H. Kitagawa, Extracting Mnemonic Names of People from the Web, Proc. Ninth Int’l Conf. Asian Digital Libraries (ICADL ‘ 06), pp. 121-130, 2006.

5.              C. Galvez and Fg. Moya-Anegon, Approximate Personal Name Matching through Finite State Graphs, J. Am. Soc. Fro Information Science and Technology, vol. 58, pp. 1-17, 2007.

6.              Christian Borgelt, Graph Mining: An Overview, Proc, 19th GMA/GI Workshop Computational Intelligence, Germany, 2009.






G. Pydiraju, M. Daivaasirvadam

Paper Title:

Sensorless Speed Control of Induction Motor Using MRAS

Abstract: In order to implement the vector control technique, the motor speed information is required. Tachogenerators, resolvers or incremental encoders are used to detect the speed. These sensors require careful mounting and alignment and special attention is required with electrical noises. Speed sensor needs additional space for mounting and maintenance and hence increases the cost and the size of the drive system .These problems are eliminated by speed sensorless vector control by using model reference adaptive system. Model reference adaptive system is a speed estimation method having two models namely reference and adaptive model .The error between two models estimates induction motor speed. This project proposes a Model Reference Adaptive System (MRAS) for estimation of speed of induction motor. An Induction motor is developed in stationary reference frame and Space Vector Pulse Width Modulation (SVPWM) is used for inverter design. PI controllers are designed controlling purpose. It has good tracking and attains steady state response very quickly which is shown in simulation results by using MATLAB/SIMULINK.

Sensorless vector control, Model Reference Adaptive System (MRAS), Induction motor, stationary reference frame, Speed estimation


1.        Abbondanti, A. and Brennen, M.B. (1975). “Variable speed induction motor drives use electronic slip calculator based on motor voltages and currents”. IEEE Transactions on Industrial Applications, vol. IA-11, no. 5: pp. 483-488.
2.        Nabae, A. (1982). “Inverter fed induction motor drive system with and instantaneous slip estimation circuit”. Int. Power Electronics Conf., pp. 322-327.

3.        Jotten, R. and Maeder, G. (1983). “Control methods for good dynamic performance induction motor drives based on current and voltages as measured quantities”. IEEE Transactions on Industrial Applications, vol. IA-19, no. 3: pp. 356-363.

4.        Amstrong, G. J., Atkinson, D. J. and Acarnley, P. P. (1997). “A comparison of estimation techniques for sensorless vector controller induction motor drives”. Proc. Of IEEE-PEDS.

5.        Wang yaonan,lu jintao,haung shoudao(2007).”speed sensorless vector control of induction motor based on MRAS theory”.

6.        Dao hung anh; pham dinhtruc(2005) .”Model reference adaptive system based sensorless control of induction motor”.

7.        “Modern power electronics and ac drives” by BIMAL K.BOSE

8.        “Electric motor drives modeling, analysis and control” by  R.KRISHNAN.






Satish R. Billewar, D. Henry Babu

Paper Title:

Approach to Improve Quality of E-Commerce

Abstract:  E-Commerce is the purpose of Internet and the web to conduct business. E-Commerce is the future of the businesses of 21st Century. But E-Commerce companies are facing big problems at the time of providing products to customers online. The problem is not about the quality of the products, but the information is not reaching to the customers easily and whatever information is available on the web sites of the companies that are not satisfying the traditional product purchase habit of the customer. Now the need arise to redefine the quality in the applications of the web sites as well as the implementation issues that become hurdle in E-Commerce business activities. The global and Indian E-Commerce sales statistics shows the internet penetration worldwide and E-Commerce Users World Statistics to address the reasons why the people have not accepted E-Commerce in India. The study addresses to various quality issues of the web sites which are neglected to fulfill the requirements of thee customers, and propose Total Quality Management (TQM) implementation as the best solution to sort out the issues.

 Commerce; E-Commerce Applications; Total Quality Management (TQM); Quality issues


1.        Besterfield, Carol Besterfield-Michna, “Total Quality Management, Third Edition”, Professor Emeritus, Southern Illinois University, Pearson Education
2.        Gary P. Schneder , “Electronic Commerce – Fourth Annual Edition”, Thomson Course Technology

3.        IMRGWorld“, B2C Global e-Commerce Overview ,April 2011

4.        US Census Bureau Satistics, US Department of Commerce, Economic and Statistics Administration, May 2012

5.        Econsultancy Newyork “B2B Internet Statistics Compendium” Aug 2011

6.        J.J.Oschman, E.C.Stroh, “A Conceptual Analysis of Total Quality Management(TQM)”, Department of Public Administration and Management, University of South Africa.

7.        Dr. Japhet E. Lawrence, Dr. Usman A. Tar, “Barriers to E-Commerce in Developing Countries” January,2010

8.        Md. Mahbubur Rahim,  “A Qualitative Evaluation of an Instrument for Measuring the Influence of Factors Affecting Use of Business-to-Employee (B2E) Portals” Feb, 2008

9.        Mukesh Purohit and Vishnu Kant Purohit, “E-Commerce on Economic Development” Foundation for Public Economics and Policy Research.

10.     QIN Denzi, ZOU Lifang, “Discussion of Information Asymmetry in B2C E-Commerce”, School of Business and Tourism Management, Yunnan University

11.     IAMAI Report,  “India e-commerce market to cross Rs 46,000 crore in 2011: Study”, March 2011

12.     Zoltan Veres and Erzsebet Hetesi, “Bottlenecks in B2B Quality Management and Their Impact on Marketing Research”, Regional Development in Hungary, JATEPress, Szeged: 130-142

13.     Osama Mohammed Ahmad Rababah and Fawaz Ahmad Masoud, “ Key Factors for Developing a Successful E-commerce Website”,The University of Jordan, Amman, Jordan, 2010

14.     Ankita Pahuja, “E-Commerce in India and the potential competition issues”, TERI University

15.     IMRB Report, “Consumer E-Commerce in India “, May 2007

16.     Sami I. Makelinen, “From B2C to C2C E-Commerce”, Department of Computer Science, University of Helsinki, May 2006

17.     Aashit Shah and Parveen Nagree, “Legal Issues of E-Commerce”, Nishith Desai Associates.

18.     Rhetta L. Standifer, James A. Wall, Jr.,  “Managing conflict in B2B e-commerce” , MU Distinguished Professor of Management, University, March-April 2003

19.     YANG Hongbin, CAO Jingjing, “B2E Portal Integration Conceptual Architecture Framework”, Economics and Management School, North University of China.

20.     Ariadi Nugroho, Michael R.V.Chaudron, “ Managing the Quality of UML Models in Prctice”, Leiden University, The Netherlands

21.     Sarah Spiekermann, Jens Grosslags, Bettina Berendt(), “E-privacy in 2nd Generation E-Commerce: Privacy Preferences versus actual Behavior”, The School of Business and Economics, Hamboldt Univerity, Germany

22.     Osama Mohammed Ahmad Rababah and Fawaz Ahmad Masoud(), “Key Factors for Developing a Successful E-commerce Website”, Journal of International Business Information Management Association (IBIMA), Vol. 2010 (2010), Article ID 763461, pp 1-9

23.     Iren Gyoker and Henrietta Finna(2010), “Social Domain”, International Cross-Industry Journal, Vol. 5 (2), pp 55-58

24.     J.J.Oschman(2004), “A Framework for The Implementation of Total Quality Management in The South African Air Force”, A Thesis submitted to University of South Africa.

25.     Hendrik Voiht, Baris Guldali and Gregor Engels(2008)“Quality Plans for Measuring Testability of Models”, 11th International Conference on Quality Engineering in Software Technology, Vol. 15, pp 353-370.






G.Satheesh, T. Bramhananda Reddy, Ch. Sai Babu

Paper Title:

SVPWM based DTC of Three Level Voltage fed Open End Winding Induction Motor

Abstract:  A Space Vector Pulse Width Modulation (SVPWM) based Direct Torque Control (DTC) of Dual Inverter Fed Open End Winding Induction Motor is analyzed in this paper. A SVPWM based, 3 level phase voltages are generated with two individual two level inverters. In this method, first inverter pulses are generated normally and second inverter pulses are generated with 180 degrees phase shift. But at a particular state of switching first inverter is switched in all states and second inverter is clamped to that active state. In the next state of switching the second inverter is switched in all states and first inverter is clamped to corresponding active state. One inverter output is superimposed on the other inverter, resulting a 3-level line voltage waveform for the induction motor. The imaginary switching time concept is used in the proposed method. It does not require any procedures for calculation of regions in space voltage vector and angle calculations sector identification. The imaginary switching time greatly reduces the complexity of the algorithm. Simulation studies have been carried out for the proposed scheme and results are presented.

   DTC, Dual Inverter, NSHC Algorithm, OEWIM, SVPWM.


1.        EG Shivakumar, K Gopakumar, SK Sinha, VT Rangnathan, “Space Vector PWM Control of Dual Inverter Fed Open-End Winding Induction Motor Drive,” IEEE-APEC, Vol.1, 2001, pp 399-405.
2.        I Takahashi and T Noguchi, “A New Quick- Response and High-Efficiency Control of an Induction Motor,” IEEE Trans. Industry Applications, Vol. IA-22, No.5, 1986, pp 820-827.

3.        I Takahashi and Youchi Ohmori, “High- Performance Direct Torque Control of an Induction Motor,” IEEE Trans. Industry Applications, Vol. IA-25, No.2, 1989, pp 257-264.

4.        Janssen, M.  Steimel, A.  “Direct Self Control With Minimum Torque Ripple and High Dynamics for Double three-level GTO Inverter Drive,” IEEE Trans. On Industrial Electronics, Vol.49, No.5, 2002, pp 1065-1071.

5.        Brain A Welchko and James M Nagashima, “A Comparative Evaluation of Motor Drive Topologies for Low-Voltage, High-Power EV/HEV Propulsion Systems,” IEEE International Symposium on Industrial Electronics, ISIE’03, Brazil, 2003, pp 1-6.

6.        Arbind Kumar, BG Fernandes, K Chatterjee, “DTC of Open-End Winding Induction Motor Drive Using Space Vector Modulation With Reduced Switching Frequency,” IEEE-PESC, 2004, pp 1214-1219.

7.        Arbind Kumar, BG Fernandes, K Chatterjee,“SVPWM-DTC OF Open-End Winding Induction Motor Drive With Complete Elimination of Common Mode Voltage”, Second India International Conference on Power Electronics, IICPE04, 2004,

8.        G.Satheesh, T. Bramhananda Reddy  and  Ch. Sai Babu, “Novel SVPWM Algorithm for Open end Winding Induction Motor Drive Using the Concept of Imaginary  switching Times” IJAST, Vol. 2, No.4, 2011, pp 44- 92.

9.        G.Satheesh, T. Bramhananda Reddy  and  Ch. Sai Babu.” Three Level Voltage Generation for Dual Inverter Fed Open End Winding Induction Motor drive. ” IJEST, Vol. 3 No. 5 May 2011, pp 3982-3991.

10.     Nabae, A., Takahashi, I., and Akagi, H.: ‘A neutral-point clamped PWM inverter’, IEEE- Trans. Ind. Appl., 1981, 17, (5), pp. 518–523

11.     D. W. Chung, J. S. Kim and S. K. Sul, “Unified Voltage Modulation Technique for Real-Time Three-Phase Power Conversion”, IEEE-Trans. on Ind.Appl, Vol.34, No.2, pp.374-380 (1998).

12.     S.Srinivas and V.T.Somasekhar, “Space Vector Based PWM switching strategies for a 3 level dual inverter fed open end winding induction m otor drive and their comparative evaluation” IET-Electr. Power Appl., VOl2, No.1, January 2008, PP19-31.

13.     V.T. Somasekhar, MR.Baiju, KK Mohapatra and K gopakumar, “A multi level Inverter System for an Induction Motor with Open End Windings” Proc. IEEE-2002, PP 973-978

14.     J.S.Kim, S.Kltage Modulation technique of the space vector PWM”, IPEC Yokohama-95, pp742-747.






Hadi Alipour, Mohammad Reza Noorbakhsh, Zahra Mansourian

Paper Title:

A Study on Modeling of MIMO Channel by Using Different Neural Network Structures

Abstract: Recognition of Radio Channel (channel Parameters) is one of Main Challenges in Signal Transformation, and has important role in cognitive radio approach. Goal of this paper is “Channel modeling” to estimate coefficients of transmission functions affected on data being transformed in the channel. We use Multilayer perceptron(MLP) Neural Network with Back-propagation learning algorithm, block-structured Neural Network with Least Squares(LS) method(cost function) and a multilayer neural network with multiple back-propagation(MBP) learning algorithm for error estimation. These networks will be trained with received signals to be compatible with channel, then give us an estimation of these coefficients. Simulation will show that this MBP method is better than the other two method in error estimation. It has good performance and also consume less execution time. Then, we will use this network for estimating coefficients of non-linear transmission functions of actual radio channel.

Cognitive Radio, Channel Recognition, Channel Modeling, Least Squares, Multiple Back-propagation (MBP), Neural Network, Transmission function.

1.        E. Hossain, D. Niyato, and Z. HAN, Dynamic Spectrum Access and Management in Cognitive Radio Networks, Cambridge University Press, 2009,USA.
2.        M. Ibnkahla, Adaptive Signal Processing in Wireless Communications, CRC Press, Talor & Francis Group, LLC, 2009, USA.

3.        M. Biguesh, and Alex. B. Gershman, “Training-Based MIMO Channel Estimation: A Study of Estimator Tradeoffs and Optimal Training Signals”, IEEE Transactions on Signal Processing, Vol.54, No.3, pp 1-5, March 2006.

4.        H. Minn, and N. G. Al-Dhahir, “Optimal Training Signals for MIMO OFDM Channel Estimation”, IEEE, pp 2-3, 2004.

5.        Omri, and R. Bouallegue, R. Hamila, and M. Hasna, “Channel Estimation for LTE Uplink System by Perceptron Neural Network”, International Journal of Wireless & Mobile Networks(IJWMN), Vol 2., No 3., pp 2-7, August 2010.

6.        S. Theodorodis, and K. Koutroumbas, Pattern Recognition, Second Edition, Elsevier Academic Press, 2003, USA.






Bhawana Agarwal

Paper Title:

Some Rules to Transform Activity Diagrams into Colored Petri Nets

Abstract:  This paper presents a set of rules that allows software engineers to transform the behavior described by a UML 2.0 Activity Diagram (AD) into a Colored Petri Net (CPN). ADs in UML 2.0 are much richer than in UML 1.x, namely by allowing several traces to be combined in a unique diagram, using high-level operators over interactions. The main purpose of the transformation to Petri nets is to use the theoretical results in the Petri nets domain to analyze the equivalent Petri nets and infer properties of the original workflow. Thus, non-technical stakeholders are able to discuss and validate the captured requirements. The usage of this model is an important topic , since it permits the user to discuss the system behavior using the problem domain language. A small control application from industry is used to show the applicability of the suggested rules.

 Activity Diagram, Petri Nets, Colored Petri Nets, Verification and Validation.


1.        , K.: Coloured Petri Nets. Basic Concepts, Analysis Methods and Practical Use. Brauer, W. and Gozenberg, G. and Salomaa edn. Volume Volume 1, Basic Concepts of Monographs in Theoretical Computer Science. Springer-Verlag (1997) ISBN: 3-540-60943-1.
2.        Fowler, M.: UML Distilled: A Brief Guide to the Standard Object Modelling Language. Addisson-Wesley (2003)

3.        Billington et al., The Petri Net Markup Language: Concepts,Technology, and Tools [Online]. Available:

4.        http://www.informatik.huberlin.de/top/pnml/download/about/P NML_CTT.pdf

5.        Harald Storrle, Semantics of UML 2.0 Activities Workflow management coalition [Online].

6.        http://www.wfmc.org/standards/docs/TC-1011_term_glossary_v3.pdf

7.        Machado, R.J., Lassen, K.B., Oliveira, S., Couto, M., Pinto, P.:  Execution of UML Models with CPN Tools for Workflow Requirements Validation. In: Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools.(2005).

8.        Zhou CH,The modeling of UML diagrams based on the Petri Net[M], Shandong University of Science and Technology. 2004: 19-31.

9.        Adamski, M.: Direct Implementation of Petri Net Specification. In:7th International Conference on Control Systems and Computer Science. (1987) 74–85.

10.     Carl Adam Petri and Wolfgang Reisig (2008) Petri net. Scholarpedia, 3(4):6477.

11.     P. Küngas. Petri Net Reachability Checking Is Polynomial with Optimal Abstraction Hierarchies. In: Proceedings of the 6th International Symposium on Abstraction, Reformulation and Approximation, SARA 2005, Airth Castle, Scotland, UK, July 26–29, 2005.

12.     G. Rozenburg, J. Engelfriet, Elementary Net Systems, in: W. Reisig, G. Rozenberg (Eds.), Lectures on Petri Nets I: Basic Models - Advances in Petri Nets, volume 1491 of Lecture Notes in Computer Science, Springer,1998, pp. 12-121

13.     J.L. Peterson. Petri net theory and the modeling of systems. Prentice Hall, Englewood Clis, 1981.

14.     R.E. Barlow and F. Proschan. Statistical Theory of Reliability and Life Testing. Holt, Rinehart and Winston, New York, 1975






N.K. Nakum, A.M.Kothari

Paper Title:

A Review paper on Implementation &Comparative Analysis of Motion Estimation Algorithm in Video Compression

Abstract:   This paper is a review of the block matching algorithms. The motion estimation algorithm is one of the most important issues in the video coding standards. To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. This paper shows implementations and comparison of  different types of block matching algorithms that range from the very basic Exhaustive Search to the recent fast adaptive algorithms.

   Block matching, motion estimation, video compression, H.261. .


1.        Aroh Barjatya, Student Member, IEEE “Block Matching Algorithms For Motion Estimation”,DIP 6620 Spring 2004 Final Project Paper 2.
2.        T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T. Ishiguro, “Motion compensated interframe coding for video conferencing,” in Proc. NTC 81, pp. C9.6.1-9.6.5, New Orleans, LA, Nov./Dec. 1981.

3.        Amish Tankariya , Prof. Mukesh Tiwari and Prof. Jaikaran Singh Department of Electronics & Communication Engineering, SSSIST- Sehore, Bhopal,, (M.P), “International Journal on Emerging Technologies” (IJET)(0975-8364).

4.        S. Zhu and K.-K. Ma, “A New Diamond Search Algorithm. for Fast Block-Matching Motion Estimation,” IEEE. Transactions on Image Processing, vol. 9, no. 2, pp.287-290, Feb. 2000.

5.        K. H.-K. Chow and M. L. Liou, “Genetic motion search algorithm for video compression,” IEEE Trans. Circuits Syst. Video Technol., vol. 3, pp. 440–445, Dec. 1993.

6.        Liang-Wei Lee, Jhing-Fa Wang, Jau-Yien Lee, andJung-Dar Shie,” Dynamic Search-Window Adjustment and Interlaced Search for Block-Matching Algorithm” IEEE Transactions on Circuits and Systems for video  Technology. VOL. 3. NO I . FEBRUARY 1093.






V. B. Katariya, Y. N. Makwana, P. A. Goswami

Paper Title:

A Review on Implementation of Automatic Movement Controlled Using Gesture Recognition

Abstract: Nowadays, computer interaction is mostly done using dedicated devices. Abundant amount of input devices are used to interact with the computer world or more precisely saying to digital world and very less through gestures made by body movements. Concepts of assistive technology are one of them used for controlling the input from mouse movements, like by detecting the eye, hand, face etc movements of a user with the help of eye tracking system, hand gestures through wearable devices, etc. Our focus is in moving mouse cursor on the screen without using any hardware which is used very often now-a-days i.e. mouse. We use the newly born technology for this purpose. We implement computer mouse movement through finger by image processing using latest Technology which gets processed in MATLAB without and with using gesture recognition.

    Color Recognition, camera, Image Processing, Keyboard, MATLAB, Mouse


1.          Sushmita Mitra and Tinku Acharya, “Gesture Recognition: A Survey”, IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, Vol.n 37(3), pp. 56-68, May 2007.
2.          Akhil Gupta, Akash Rathi, Dr. Y. Radhika, “Hands-free pc control, controlling of mouse cursor using eye movement”, International Journal of Scientific and Research Publications, vol. 2, issue 4, ISSN 2250-3153. pp. 1-5, April 2012.

3.          Prof. R. W. Jasutkar, Ms. Shubhangi J. Moon, “ A Real Time Hand Gesture Recognition Technique by using Embedded device”. International Journal Of Advanced Engineering Sciences And Technologies, vol. 2, issue no.1, pp. 043–046 may 2005.

4.          Zhi-gang XuHong-lei Zhu,“Vision-based Detection of Dynamic Gesture”, International Conference on Test and Measurement, vol. no. 6 issue no.8 pp. 89-90, may 2010.

5.          Michal Lech, Bozena Kostek,“Gesture-based Computer Control System applied to the Interactive Whiteboard” Proceedings of the 2nd International Conference on Information Technology, vol. no.06  pp. 28-30, June 2010.

6.          Prateek Agrawal,  Kunal Gupta. “Mouse Movement Through Finger By Image Grabbing Using Sixth Sense Technology”, International Journal Of Engineering Science & Advanced Technology vol-2, Issue-2, pp.245 – 249, march-april 2012.

7.          Hae Jong Seo, Peyman Milanfar “A Review on Action Recognition from One Example”, IEEE Transactions on Pattern Analysis And Machine Intelligence, vol. 33(5), may 2011.

8.          M. A. MONI and A B M Shawkat Ali., “HMM based Hand Gesture Recognition: A Review on Techniques and Approaches”.

9.          S.B. Wang et al. “Hidden Conditional Random Fields for Gesture Recognition”. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, 2006.

10.       Denis Amelynck, Maarten Grachten, Leon Van Noorden, and Marc Leman. “Toward E-Motion-Based Music Retrieval a Study of Affective Gesture Recognition”, IEEE transactions on affective computing, vol. 3, no. 2, april-june 2012.

11.       http://www.youtube.com/watch?v=k-rSWM6h3Aw

12.       http://www.youtube.com/watch?v=1GhNXHCQGsM

13.       https://github.com/zk00006/OpenTLD

14.       http://touchless.codeplex.com/releases/view/17986

15.       http:// Wikipedia, the free encyclopedia gesture recognition based on matlab simulation






Uma Shankar Modani, Gajanand Jagrawal

Paper Title:

A survey on Application of Ferroelectric Materials for Fabrication of Microstrip Patch Antennas

Abstract:  Ferroelectric materials (FEM’s) are very attractive because their dielectric constant can be modulated under the effect of an externally applied electric field perpendicular to the direction of propagation of a  signal. In this paper, classification, properties and application of ferroelectric material for the fabrication of microstrip patch antennas is discussed.

     Ferroelectric materials and Microstrip patch antenna.


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6.        Z.P. Cao, A.L. Ding, X.Y. He Zhang and X. Zeng, “new lead- free ferroelectric materials for high-temperature applications: Nb-doped Bi3.5Nd0.5Ti3O12” keyEngineering Materials Vols.280-283(2005) pp247-250.

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15.     K.A. Muller, Y. Luspin, J.L. Servoin and F. Gervais, “Displacive-order-disorder crossover at ferroelectric-paraelectric phase transitions of BaTiO3 and LiTiO3,” J. physique letters 43(1982),L-537- L-542.

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