Volume-1 Issue-1

S. No

Volume-1 Issue-1, October 2011, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Anubhuti Khare, Manish Saxena, Neha Parmar

Paper Title:

Satellite Communication Networks Performance

Abstract:   A number of serious consortiums develop satellite communication networks. The objective of these communication projects is to service personal communication users almost everywhere on earth. The inter satellite links in those projects use microwave radiation as the carrier. Free-space optical communication between satellites networked together can make possible high-speed communication between different places on earth. Some advantages of an optical communication system over a microwave communication system in free space are 1) smaller size and weight, 2) less transmitter power, 3) larger bandwidth, and 4) higher immunity to interference. The pointing from one satellite to another is a complicated problem due to the large distance between the satellite, the narrow beam divergence angle, and vibration of the pointing system. Such vibration of the transmitted beam in the receiver plane decreases the average received signal, which increases the bit error rate.

Keywords:  Laser communication, optical networks, satellite optical communication, vibrations.

References:
1.       R. J. Leopold and A. Miller, “The IRIDIUM communications system,” IEEE Potentials, vol. 12.
2.       “The IRIDIUM communications system,” in IEEE Conf. TT-S Int. Microwave Symp. Dig., vol. 2,
3.       F. Ananasso, “System market and regulatory aspects for satellite personal communications,” in Mobile and Personal Satellite Communications: Proceeding of the First European Workshop on Mobile/Personal Satcoms (EMPS’94), F. Ananasso and F. Vatalaro, Eds. Berlin: Springer-Verlag.
4.       P. P. Giusto and G. Qualione, “Technical alternative for satellite mobile networks,” in Mobile and Personal Satellite Communications: Proceeding of the First European Workshop on Mobile/ Personal Satcoms (EMPS’94), F. Ananasso and F. Vatalaro, Eds. Berlin: Springer-Verlag,
5.       B. I. Edelson and G. Hyde, “Laser satellite communications, program technology and applications,” IEEE-USA Aerospace Policy Committee Rep.
6.       D. K. Paul, F. Faris, R. Garlow, T. Inukai, B. Pontano, R. Razdan, A. Ganz, and L. Caudill, “Optical intersatellite links: Application to commercial satellite communications,”
in Proc. 14th AIAA Int. Communication Satellite Systems, Washington, D.C.
7.       D. K. Paul, “Optical cross links for advanced Satcom networks,” presented at the Asia Pacific Microwave Conf., New Delhi India.
8.       M. Fujise, M. Nohara, K. Uehara, and W. Chujo, “Broadband mobile satellite communication system by LEO–SAT and optical ISL’s,” in Proc. IEEE GLOBECOM, London, vol. 1.
9.       M. Wittig, L. van Holtz, D. E. L. Tunbridge, and H. C. Vermeulen, “In orbit measurements of microaccelerations of ESA’s communication satellite OLYMPUS,” in Selected Paper on Free-Space Laser Communication II—SPIE Ms 100, D. L. Begly and B. J. Thompson, Eds. Bellingham, WA: SPI.
10.     S. Dyne, P. P. Collins, and D. Tunbridge, “Satellite mechanical health monitoring,” in IEE Colloquium Advanced Vibration Measurements, Techniques and Instrumentation for the Early Predication of Failure.
11.     S. J. C. Dyne, D. E. L. Tunbridge, and P. P. Collins, “The vibration environment on a satellite in orbit,” in IEE Colloquium High Accuracy Platform Control in Space.
12.     K. J. Held and J. D. Barry, “Precision pointing and tracking between satellite-borne optical systems,” Opt. Eng., vol. 27.

1-5

2.

Authors:

Anubhuti Khare, Manish Saxena, Bhagawati Patil

Paper Title:

Voice Data Compression and Decompression

Abstract:   An efficient and minimum hardware implementation for the Voice data compression and decompression will be presented in this paper. Voice data compression and decompression is about a process which reduces the data rate or file size of digital audio signals. This process reduces the dynamic range (withoutchanging the amount of digital data) of audio signals [1]. The Huffman coding is used to have lossless audio compression. Very High Speed Integrated Circuit Hardware Description Language (VHDL) is used for to code the Huffman encoder and decoder and Actel’s ProASIC kit is used for hard ware implementation of it. This system is minimal model of real time audio compression and decompression system.

Keywords:  FPGA, VLSI, ADC, Huffman Coding.

References:
1.       FPGA based architecture of MP3decoding core for multimedia systems.
2.       C. Murthy and P. Mishra. Bit mask-based control word compression for NISC architectures. In Proceedings of ACM Great Lakes Symposium onVLSI (GLSVLSI), 2009.
3.       K. Basu and P. Mishra. A novel test-data compression technique using application-aware bit mask and dictionary selection methods. In Proceedings of ACM Great Lakes Symposium on VLSI (GLSVLSI),pages 83–88, 2008.
4.       S. Seong and P. Mishra. Bit mask-based code compression for embedded systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 27(4):673–685, April 2008.
5.       B. Gorjiara and D. Gajski. FPGA-friendly code compression for horizontal micro coded custom IPs. In Proceedings of Field Programmable Gate Arrays (FPGA), 2007.
6.       S. Seong and P. Mishra. An efficient code compression technique using application-aware bit mask and dictionary selection methods. In Proceedings of Design Automation and Test in Europe (DATE), pages 582–587, 2007.
7.       M.-B. Lin, J.-F. Lee, and G. E. Jan, “A lossless data compression and decompression algorithm and its hardware architecture,” IEEE Trans.Very Large Scale Interation (VLSI) Syst., vol. 14, no. 9, pp. 925–936,Sep. 2006.8.       K. Pagiamtzis and A. Sheikholeslami, “Content-addressable memory (CAM) circuits and architectures: A tutorial and survey,” IEEE J. Solid-State Circuits, vol. 41, no. 3, pp. 712–727, Mar. 2006.
9.       S. Seong and P. Mishra. A bit mask-based code compression technique  for embedded systems. In Proceedings of International Conference on Computer-Aided Design (ICCAD), pages 251–254, 2006.
10.     V. Sklyarov, I. Skliarova, B. Pimentel, J. Arrais, “Hardware/Software Implementation of FPGA-Targeted Matrix-Oriented SAT Solvers”,Proceedings of the 14th International Conference on Field-Programmable Logic and Applications –FPL‟2004, Antwerp, Belgium,August/September, 2004, pp. 922-926.
11.     V. Sklyarov, “Hierarchical Finite-State Machines and Their Use for Digital Control”, IEEE Transactions on VLSI Systems, Vol. 7, No 2,1999, pp. 222-228.
12.     B. Pimentel, J. Arrais, “Implementación de Algoritmo de Compressão Descompressão de Dados para Modelo de Coprocessamento basead on FPGA‟s”, Electrónica e Telecomunicações, vol. 4, no. 10, Jan. 2004, pp. 215-220.
13.     V. Sklyarov, “FPGA-based implementation of recursive algorithms”,Microprocessors and Microsystems, Special Issue on FPGAs:Applications and Designs, 2004, vol. 28/5-6 pp 197-211.
14.     M. Guthaus, J. Ringenberg, D. Ernst, T. Austin, T. Mudge, and R.Brown. MiBench: A free, commercially representative embedded benchmark suite. In Proceedings of International Workshop onWorkload Characterization (WWC), 2001.
15.     NiosEmbeddedProcessorhttp://www.altera.com/products/devices/excalibur/excniosindex.html. http://www.xilinx.com/partinfo/databook.htm.
16.     Xilinx Micro Blaze. http://www.xilinx.com/xlnx/xil product.jsp? Title=micro blaze.

6-10

3.

Authors:

Anubhuti Khare, Manish Saxena, Vijay B. Nerkar

Paper Title:

ECG Data Compression Using DWT

Abstract:   Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted; the amount that grows (depending upon the sampling rate, quantization levels and number of sensors) at the rate of 7.5-540 KB per minute per patient, depending upon the time and amplitude, sampling rate and number of sensors. Besides the increased storage capacity for archival purposes, ECG compression allows real-time transmission over telephone networks, economic off-line transmission to remote interpretation sites, improves Holter monitor systems and enables efficient ECG rhythm analysis algorithms. A wide range of compression techniques based on different transformation techniques like DCT, FFT; DST & DCT2 were evaluated to find an optimal compression strategy for ECG data compression. Wavelet compression techniques were found to be optimal in terms of compression.

Keywords:  ECG, Compression, DCT, DWT, CR and PRD

References:
1.       R. S. Khndpur, “Biomedical Engineering and Instrumentation”.
2.       S. Jalaleddine, C. Hutchens, R. Strattan, and W. Coberly, “ECG data compression techniques – a unified approach”, IEEE Trans. On Biomedical Engineering, vol. 37, pp. 329-343, 1990.

11-13

4.

Authors:

Anubhuti Khare, Manish Saxena, Pravin J Chaudhari

Paper Title:

Signal Code Modulation for Broadband Wireless Systems

Abstract:  This paper seeks to present ways to eliminate the inherent quantization noise component in digital communications, instead of conventionally making it minimal. It deals with a new concept of signaling called the Signal Code Modulation (SCM) Technique. The primary analog signal is represented by: a sample which is quantized and encoded digitally, and an analog component, which is a function of the quantization component of the digital sample. The advantages of such a system are two sided offering advantages of both analog and digital signaling. The presence of the analog residual allows for the system performance to improve when excess channel SNR is available. The digital component provides increased SNR and makes it possible for coding to be employed to achieve near error-free transmission

Keywords:  SCM, Hybrid Modulation, Quantized residual amplification.

References:
1.        B.P. Lathi, Modern Digital and Analog Communication Systems, Oxford Press, 1998 : p.711
2.        Simon Haykin, Communication Systems, 4th Edition, John Wiley & Sons, 2000 : p.542
3.        Simon Haykin, Communication Systems, 4th Edition, John Wiley & Sons, 2000 : p.151 and p.164, Fig. 2.55
4.        B. Friedlander and E. Pasternak’s published work at the Asilomar Conference on Signals, Systems and Computers, November 2001.

14-19

5.

Authors:

Mohd. Shahnawaz, Ashish Ranjan, Mohd Danish

Paper Title:

Temporal Data Mining: An Overview

Abstract:   To classify data mining problems and algorithms we used two dimensions:  data type and type of mining operations.  One  of  the  main issue  that  arise  during  the  data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.

Keywords:  Temporal Data; Temporal Data Mining; TDM Task; Temporal Sequence Mining.

References:
1.       Han J, Kamber M 2001 Data mining: Concepts and techniques (San Fransisco, CA: Morgan Kauffmann)
2.       Kanak Saxena, Efficient Mining Weighted Temporal Association Rules. 2009 World Congress on Computer Science and Information Engineering pp  421-425 IEEE Computer Society.
3.       Garofarakasi, Rajeev Rastogi and K Shim, in 1999, design SPIRIT: Sequential Pattern Mining with Regular Expression Constraints. Bell Labs Tech. Memorndum BL0112370-990223-03TM, February 1999.
4.       SRIVATSAN LAXMAN and P S SASTRY, A survey of temporal data mining, Sadhana Vol. 31, Part 2, April 2006, pp. 173–198.
5.       A.K. Pujari 2007, Data Mining Techniques, University Press ISBN 8173713804.
6.       WuY-L, AgrawalD, AbbadiAE 2000Acomparison of DFT and DWT based similarity search in time series databases. In Proc. Ninth Int. Conf. on Information and Knowledge Management, McLean,VA, pp 488–495
7.       Gray R M, Buzo A, Gray Jr. A H, Matsuyama Y 1980 Distortion measures for speech processing. IEEE Trans. Acoust., Speech Signal Process. 28: 367–376
8.       Haselsteiner E, Pfurtscheller G 2000 Using time-dependent neural networks for EEG classification. IEEE Trans. Rehab. Eng. 8: 457–463.
9.       Yule G 1927 On a method of investigating periodicity in distributed series with special reference to Wolfer’s sunspot numbers. Philos. Trans. R. Soc. London A226
10.     Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. ACM SIGMOD Int. Conf. on Management of Data (2000) 1-12.
11.     Claudia m M. Antunes and Arlindo L. Oliveira: Temporal Data Mining: an overview. Lecture Notes in Computer Science.
12.     Coiera,  E.:  The  Role  of Knowledge  Based  Systems  in  Clinical  Practice.  In Barahona, P.,  Christensen,  J.:  Knowledge  and  Decisions  in  Health  Telematics   –  The Next Decada. IOS Press Amsterdam (1994) 199-203

20-24

6.

Authors:

Anubhuti Khare, Manish Saxena, Heena A Jain

Paper Title:

Next Generation Micro-power Systems

Abstract:   Emerging micro-systems such as portable and implantable medical electronics, wireless micro-sensors and next-generation portable multimedia devices demand a dramatic reduction in energy consumption. The ultimate goal is to power these devices using energy harvesting techniques such as vibration-to-electric conversion or through wireless power transmission. A major opportunity to reduce the energy consumption of digital circuits is to scale supply voltages to 0.5V and below. The challenges associated with ultra-low-voltage design will be presented. These include variation-aware design for logic and SRAM circuits, efficient DC-DC converters for ultra-low-voltage structuring to support extreme parallelism. This paper also addresses micro-power analog and RF circuits, which require the use of application specific structures and highly digital variation-aware architectures.

Keywords:  Micro-System, SRAM, RF Circuit

References:
1.       N. Guilar et al., “Integrated Solar Energy Harvesting and Storage,” IEEE ISLPED, pp. 20-24, Oct. 2006
2.       R. Das et al., “Integration of Photosynthetic Protein Molecular Complexes in Solid-State Electronic Devices,” Nano Letters, vol. 4, no. 6, pp. 1079-1083, 2004.
3.       H. Lhermet et al., “Efficient Power Management Circuit: Thermal Energy Harvesting to Above-IC Microbattery Energy Storage,” IEEE ISSCC, pp. 62-63, Feb. 2007.
4.       V. Leonov et al., “Thermoelectric Converters of Human Warmth for Self-Powered Wireless Sensor Nodes,” IEEE Sensors Journal, vol. 7, no. 5, pp. 650-657, May 2007.
5.       I. Stark, “Thermal Energy Harevsting with Thermo Life,” IEEE Intl. Workshop on Wearable and Implantable Body Sensor Networks, pp. 19-22, Apr. 2006.
6.       M. Renaud et al., “Piezoelectric Harvesters and MEMS Technology: Fabrication, Modeling and Measurements,” IEEE Intl. Conf. Solid-State Sensors, Actuators and Microsystems, pp. 891-894, June 2007.
7.       S. Roundy, P.K. Wright and J. Rabaey, Energy Scavenging for Wireless Sensor Networks with Special Focus on Vibrations, Kluwer Academic Press, 2003.
8.       Y.K. Ramadass and A.P. Chandrakasan, “Minimum Energy Tracking Loop with Embedded DC-DC Converter Delivering Voltages down to 250mV in 65nm CMOS,” IEEE ISSCC, pp. 64-65, Feb. 2007.
9.       J. Kwong et al., “A 65nm Sub-Vt Microcontroller with Integrated SRAM and Switched-Capacitor DC-DC Converter,” IEEE ISSCC, pp. 318-319, Feb. 2008.
10.     V. Sze and A. Chandrakasan, "A 0.4-V UWB Baseband Processor," IEEE ISLPED, pp. 262-267, Aug. 2007.
11.     A. Srivastava, D. Sylvester, D. Blaauw, Statistical Analysis and Optimization for VLSI: Timing and Power, New York: Springer, 2005, pp. 79-132.
12.     D. Blaauw, S. Kalaiselvan, K. Lai et al., “Razor II: In Situ Error Detection and Correction for PVT and SER Tolerance,” IEEE ISSCC, pp. 400-401, Feb. 2008.
13.     N. Verma and A. P. Chandrakasan, “A 256kb 65nm 8T Subthreshold SRAM Employing Sense-Amplifier Redundancy,” IEEE JSSC, vol. 43, no. 1, pp. 141-149.
14.     N. Verma and A. P. Chandrakasan, "A 25μW 100kS/s 12b ADC for Wireless Micro-Sensor Applications," IEEE ISSCC, pp. 222-223, Feb. 2006.
15.     M. Elzakker, E. Tuijl, P. Geraedts, et al., “A 1.9μW, 4.4fJ/Conversion-step 10b 1MS/s Charge-Redistribution ADC,” IEEE ISSCC, pp. 244-245, Feb. 2008.
16.     D. C. Daly and A. P. Chandrakasan, "A 6-bit, 0.2V to 0.9V Highly Digital Flash ADC with Comparator Redundancy," IEEE ISSCC, pp. 554-555, Feb. 2008.
17.     B. P. Ginsburg and A. P. Chandrakasan, "Highly Interleaved 5b 250MS/s ADC with Redundant Channels in 65nm CMOS," IEEE ISSCC, pp. 240-241, Feb. 2008.
18.     T. S. Cho et al., "A Low Power Carbon Nanotube Chemical Sensor System," IEEE CICC, pp. 181-184, Sept. 2007.
19.     H.-S. Lee and C. G. Sodini, “Analog-to-Digital Converters: Digitizing the Analog World,” Proceedings of the IEEE, vol. 96, no. 2, pp. 323-334, Feb. 2008.
20.     K. Muhammad et al., “Digital RF Processing: Toward Low- Cost Reconfigurable Radios,” IEEE Communications Magazine, vol. 43, no. 8, pp. 105-113, Aug. 2005.
21.     D. D. Wentzloff and A. P. Chandrakasan, "A 47pJ/pulse 3.1-to-5GHz All-Digital UWB Transmitter in 90nm CMOS," IEEE ISSCC, pp. 118-119, Feb. 2007.
22.     F. S. Lee and A. P. Chandrakasan, "A 2.5nJ/b 0.65V 3-to- 5GHz Subbanded UWB Receiver in 90nm CMOS," IEEEISSCC, pp. 116-117, Feb. 2007.
23.     N. M. Pletcher et al., "A 2GHz 52μW Wake-Up Receiver with -72dBm Sensitivity Using Uncertain-IF Architecture," IEEE ISSCC, pp. 524-525, Feb. 2008.

25-29

7.

Authors:

Sanjay Sharma, P. S. Patheja, Akhilesh A. Waoo, Rahul Gour

Paper Title:

A Survey on Different Security Techniques of Mobile Code

Abstract:   Mobile agents are software which moves autonomously through a computer network with aim to perform some computation or gather information on behalf of its creator or an application. In the last several years, mobile agents have proved its numerous applications including e-commerce. In most applications, the security of mobile agents is a burning issue. There are plenty of techniques to protect mobile code. There need a brief discussion about each method including strength and limitation so it may guide to choose best techniques for individual application. This paper presented a overview of various security techniques with their strength and limitation. This article presents comparison of different aspects of mobile code security, namely the protection of hosts receiving a malicious mobile code and the protection of a mobile code within a malicious host.

Keywords:  Security, Mobile agents, Mobile code, malicious host, Electronic commerce.

References:
1.       Najmus Saqib Malik, David Ko and Harry H. Cheng, “A secure migration process for mobile agents”, Published online 30 August 2010, U.S.A.
2.       Wayne A. Jansen, “Countermeasures for Mobile Agent Security” March 01,2010
3.       Sandhya Armoogum, Asvin Caully,” Obfuscation Techniques for Mobile Agent code confidentiality”, March 2010
4.       AHMADI-BROOGHANI, ZAHRA, Proceedings of the 11th WSEAS International Conference on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greece, July 26-28, 2007
5.       Marc Joye,Thomson R&D France,” On White-Box Cryptography” ,Published in A. El»ci, S.B. Ors, and B. Preneel, Eds, Security of Information and Networks, pp. 7{12,Tra®ord Publishing, 2008.
6.       Li Gong,”Secure java class loading,” IEEE Internet Computing, pages 56-61, 1998.
7.       L. Gong, “Java Security Architecture (JDK1.2),” Technical Report, Sun Microsystems, Inc., 901 San Antonio Road, Palo Alto, California 94303, U.S.A, 1998.
8.       Bo Chen1, Harry H. Cheng1, and Joe Palen2, SOFTWARE—PRACTICE AND EXPERIENCE published online, 13 July 2006,U.S.A
9.       “Signed Code,” (n.d.). Retrieved December 15, 2003, from James Madison University, IT Technical Services Web site:
10.     http://www.jmu.edu/computing/infosecurity/ engineering/issues/signedcode.shtml
11.     “Introduction to Code Signing,” (n.d.). Retrieved December 15, 2003, from Microsoft Corporation, Microsoft Developer Network (MSDN) Web site: http://msdn.microsoft.
12.     Robert Fischer, Ming-Yang Kao,” Multi-Domain Sandboxing: An Overview” Sep. 2000
13.     Chow, P. Eisen, H. Johnson, P.C. van Oorschot,” White-Box Cryptography and an AES Implementation”, Annual Workshopon Selected Areas in Cryptography (SAC'02), Aug. 15-16, 2002.
14.     R. Levin (1998). “Security Grows Up: The Java 2 Platform,” Retrieved December 21, 2003, from Sun Microsystems, Inc. Sun Developer Network (SDN) Web site: http://java.sun.com/features/1998/11/jdk.security.html
15.     P. Lee and G. Necula, “Research on Proof-Carrying Code on Mobile-Code Security,” In Proceedings of the Workshop on Foundations of Mobile Code Security, 1997.
16.     S. Loureiro, R. Molva, and Y. Roudier, "Mobile Code Security," Institut Eurecom, 2001.
17.     P. Lee. (n.d.), “Proof-carrying code,” Retrieved December 28, 2003, from Web site: http://www-2.cs.cmu.edu/~petel/papers/pcc/pcc.html
18.     D. Chess, J. Morar, “Is Java still secure?,” IBM T.J. Watson Research Center, NY, 1998.
19.     G. Wroblewski, “General Method of Program Code Obfuscation,” PhD Dissertation, Wroclaw University of Technology, Institute of Engineering Cybernetics, 2002, (under final revision).
20.     F. Hohl, “Time Limited Blackbox Security: Protecting Mobile Agents from Malicious Hosts,” To appear in Mobile Agents and Security Book edited by Giovanni Vigna, published by Springer Verlag 1998.
21.     B. Barak, O. Goldreich, R. Impagliazzo, S. Rudich, A. Sahai, S. Vadhan, and K. Yang, “On the (Im)possibility of Obfuscating Programs,” in Advances in Cryptology, Proceedings of Crypto'2001, Lecture Notes in Computer Science, Vol. 2139, pages 1-18.
22.     G. Hachez, “A Comparative Study of Software Protection Tools Suited for Ecommerce with Contributions to Software Watermarking and Smart Cards,” Universite Catholique de Louvain, 2003.
23.     C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” Technical Report 148, Department of Computer Science, University of Auckland, July 1997.
24.     L. Gong, M. Mueller, H. Prafullchandra, and R. Schemers, “Going Beyond the Sandbox: An Overview of the New Security Architecture in the Java Development Kit 1.2,” In Proceedings of the USENIX Symposium on Internet Technologies and Systems, Monterey, California, Dec. 1997.
25.     Gary McGraw and Edward Felten (1996-9). Securing JAVA [Electronic version]. John Wiley and Sons. http://www.securingjava.com/
26.     M. Hauswirth, C. Kerer, and R. Kurmanowytsch, “A secure execution framework for Java,” In Proceedings of the 7th ACM conference on computer and communications security (CCS 2000), pages 43--52, Athens, Greece, Nov. 2000.
27.     M. Dageforde. (n.d.). “Security Features Overview,” Retrieved December 21, 2003, from Sun Microsystems, Inc. The JavaTM Tutorial Web site: http://java.sun.com /docs/books/tutorial/security1.2 /overview/

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8.

Authors:

Dipak R.Pardhi, Akhilesh A.Waoo

Paper Title:

An Efficient Ranking Based Clustering Algorithm

Abstract:   There are several databases, which contain large information about research publications in various fields, for examples, DBLP in computer science and PubMed in medical science. US Census data set which contains information with 68 categorical attributes, which is very complex to get the information. Zoo data set which having information with 17 attributes, Plant Cell Signalling data set which describes the interactions of the nodes within the plant signalling network by considering 43 different attributes. Each such database forms an immense size of information network connecting in very complex ways. In this work, we are proposing an approach for “information network mining” on such a database. We consider DBLP as an example.The database contains information about research papers, authors, conferences and journals. It also includes the date, year and the place of publication of particular journals and conferences. Various users have very specific personalized search criteria for profiling such patterns and verifying the interest, we are proposed an algorithm RBC_A, so that- (1) In-depth information about research, such as the clustering of conferences due to their sharing of many common authors can be categorized; (2) The reputation of a conference can be evaluated; finally (3) Time relevant information can be inferred. The above have been addressed in the design and development of this work.

Keywords:  Information Network, Data Mining, Profiling, Ranking, Clustering, Classifications, Associations, User interface

References:
1.       Yizhou Sun, Tianyi Wu, Zhijun Yin, Hong Chen Jiawei Han, Xiaoxin   Yin, Peixiang Zhao, BibNetMiner: Mining Bibliographic Information Networks Canada.June9–12, 2008 in SIGMOD’08, Vancouver, BC, Canad
2.       X.Yin, J.Han, and P.S.Yu.Linkclus: Efficient clustering via      heterogeneous semantic links. In  VLDB’06, Seoul, Korea, Sept. 2006
3.       DBLP. http://www.informatik.uni-trier.de/ ley/db/.The dblp computer science bibliography
4.       Xi. Yin, Ji. Han, and Ph. S. Yu. Truth discovery with multiple conflicting   information providers on  the web. In KDD’07, San Jose, CA, Aug. 2007
5.       D. Xin, J. Han, H. Cheng, and X. Li. Answering top-k queries with multi-dimensional selection: The ranking cube approach. In VLDB’06 Seoul, Korea, Sept. 2006.
6.       Y. Sun, J. Han, P. Zhao, Z. Yin, H. Cheng, and T. Wu. Rankclus: Integrating clustering with ranking for heterogeneous information network analysis. In EDBT’09, 2009.
7.       Yizhou,Sun Yintao,Yu Jiawei Han: Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema. KDD’09, June 28–July 1, 2009, Paris, France.
8.       Chengkai Li, Mohamed A. Soliman, Kevin Chen-Chuan Chang, Ihab F. Ilyas: RankSQL: Supporting Ranking Queries in Relational Database Management Systems
9.       Arun K. Pujari: Data Mining Techniques, Universities Press, Computer Science Book.
10.     Max Bramer: Principles of Data Mining Book , Springer.
11.     Jiawei Han : Data Mining Concepts and Techniques Book . University of Illinois at Urbana-Champaign , Micheline Kamber

35-40

9.

Authors:

Ashwini Waoo, Swati Khare, Sujata Ganguly

Paper Title:

Unconventional Plant-based Remediation Technologies for Soil pollution at Contaminated Sites in Bhopal

Abstract:  The purpose of this study was to emphasize the Vegetation-enhanced bioremediation or phytoremediation plan for the surroundings of industrial area of Bhopal. This regions contain many industries and therefore there is a big risk of environmental pollution which is very hazardous to our health. Heavy metal accumulation in agricultural soils is potentially hazardous to human and livestock health. Excessive accumulations also present the risks of elevated heavy metal uptake by crops which could affect food quality and safety. Conventional remediation technologies are used to clean the vast majority of metal-polluted sites but they also tend to be clumsy, costly, and disruptive to the surrounding environment. In contrast, plants are known to sequester certain metal elements in their tissues and may prove useful in the removal of metals from contaminated soils. Over the past decade there has been increasing interest for the development of plant-based remediation technologies which have the potential to be low-cost, low-impact, visually benign, & environmentally sound, a concept called phytoremediation. Efforts should be made for dense vegetation of heavy metal bioaccumulation plants at the industrially contaminated sites. So that they reduce pollution and also give eco friendly aesthetic sense to enhance the beauty of the Bhopal city. Growing and, in some cases, harvesting plants on a contaminated site must be compulsory condition to setup or run a specific industry because this remediation method is an aesthetically pleasing, solar-energy driven, passive technique that can be used to clean up sites with shallow, low to moderate levels of contamination. This technique can be used along with or, in some cases, in place of mechanical cleanup methods. This paper attempted to provide a brief review on recent progresses in research and practical applications of phytoremediation for soil. Numerous plant species have been identified and tested for their traits in the uptake and accumulation of different heavy metals. Mechanisms of metal uptake at whole plant and cellular levels have been investigated. Progresses have been made in the mechanistic and practical application aspects of phytoremediation. They were reviewed and reported in this paper

Keywords:  Phytoremediation, Heavy Metals, Soil pollution, Contaminated Site, Phytoextraction, Rhizofiltration, phytostabilization, phytovolatilization

References:
1.       Phytoremediation: Transformation and Control of Contaminants (Environmental Science & Technology: A Wiley Interscience Series of Texts and Monographs); McCutcheon, S.C., Schnoor, J.L., Eds.; John Wiley & Sons, Inc.:
2.       United States Protection Agency Reports (2000): Introduction to Phytoremediation. – EPA 600/R-99/107.
3.       Chaudhry, T.M., Hayes, W.J., Khan, A.G. and Khoo, C.S. (1998): Phytoremediation - focusing on accumulator plants that remediate metalcontaminated soils. – Austraaslian Journal of Ecotoxicology. 4; 37-51.
4.       Mueller, B., Rock, S., Gowswami, Dib, Ensley, D. (1999): Phytoremediation Decision Tree. – Prepared by - Interstate Technology and Regulatory Cooperation Work Group
5.       Anon. 1997. Phytoremediation Becoming Quite "Poplar". The Hazardous Waste Consultant 15(3):1.16-1.20 (May-June 1997).
6.       Raskin, I. and Ensley, B. D. (2000): Phytoremediation of Toxic Metals: Using Plants to Clean Up the Environment. – John Wiley & Sons, Inc., New York.
7.       Dushenkov, S., Vasudev, D., Kapolnik, Y., Gleba, D., Fleisher, D., Ting K. C. and Ensley, B. (1997): Environmental Science and Technology. 31(12); 3468- 76.
8.       Gerhardt, K.E.; Huang, X.-D.; Glick, B.R.; Greenberg, B.M. Phytoremediation and rhizoremediation of organic soil contaminants; Potential and challenges. Plant Sci.
9.       Pilon-Smits, E.A.H. Phytoremediation. Annu. Rev. Plant Biol. 2005, 56, 15-39.
10.     Abia, A. A., Horsfall, M., & Didi, O. (2003). The use of chemically modified and unmodified cassava waste for the removal of Cd, Cu and Zn ions from aqueous solution. Bioresource Technology, 90, 345–348.
11.     Adriano, D. C., Wenzel, W. W., Vangronsveld, J., & Bolan, N. S. (2004). Role of assisted natural remediation in environmental cleanup. Geoderma, 122, 121–142.
12.     Albasel, N., & Cottenie, A. (1985). Heavy metal contamination near major highways, industrial and urban areas in Belgium grassland. Water, Air and Soil Pollution, 24, 103–109.
13.     Al-Chalabi, A. S., & Hawker, D. (2000). Distribution of vehicular lead in roadside soils of major roads of Brisbane,
14.     Prabha K. Padmavathiamma & Loretta Y. Li Phytoremediation Technology: Hyper-accumulation  of Metals in Plants Received: 13 October 2006 / Accepted: 1 April 2007 / Published online: 22 May 2007
15.     WHO (1997). Health and environment in sustainable development. Geneva: WHO
16.     Morikawa, H., Erkin, O.C.(2003). Basic processes in phytoremediation and some applications to air pollution control. Chemosphere 52(9):1553–1558.
17.     Champion: Stuart Lunn, Ph.D. Imperial Oil Resources  R&D Providers: R.E. Farrell, Ph.D. & J.J. Germida, Ph.D. Assessment of Phytoremediation as an In-Situ Technique for Cleaning Oil Contaminated Sites      
18.     Cummingham, S. D. Kruger, E. L. Anderson, T. and Coasts, J. R.  1997). Phytoremediation of Contaminated Soil and Water. ACS Symposium Series 604. American Society, Washington, DC.23pp.
19.     Ross SM (1994). Toxic Metal in Soil–Plant Systems. Wiley, Chichester.
20.     Benavides, M.P., Gallego, S.M and Tomaro, M.L, 2005. Cadmium toxicity in plants.Braz. J Plant Physiol., 17: 21-34.
21.     Hrishikesh Upadhyaya1, Sanjib Kumar Panda2, Mrinal Kanti Bhattacharjee1 and Sakhi Dutta1 “Role of arbuscular mycorrhiza in heavy metal tolerance in plants:
prospects for phytoremediation”.
22.     Streit B., Strumm W., Chemical properties of metals and the process of bioaccumulation in terrestrial plants, in Plants as Biomonitors, in: Indicators for Heavy Metals in the Terrestial Environment, VCH, Weinheim, 381, 1993.
23.     Pollard AJ, Powell KD, Harper FA, Smith JAC. The genetic basis of metal hyperaccumulation in plants. Crit Rev Plant Sci. 2002; 21(6): 539–566. doi: 10.1080/0735-260291044359.

41-44

10.

Authors:

K. Prahlada Rao, D. P Girish, M. Krishna, Madhu. B. V

Paper Title:

Thermal Mismatch Stresses in a Metal Matrix Composite - A Finite Element Analysis

Abstract:   The coefficients of thermal expansion (CTEs) of aluminum and aluminum/Al2O3 metal matrix composites (MMCs) are measured using a dilatometer and analysis of residual thermal stresses by Finite Element Analysis (FEA).  The MMCs were prepared by liquid metallurgy technique for varying percentages of reinforcement in steps of 0, 5, 10, and 15% by weight. The CTE is expected to vary with relative residual strains which in turn are dependent on the percentage of reinforcement when cooled from 500C to room temperature. The experimental CTE values were compared with developed model. FEA has been used to investigate the distribution of residual thermal stresses in the interfacial region. The result indicates that the properties of the interfacial region affect the stress distribution.

Keywords:  Al 6061, CTE, FEA, Liquid Metallurgy, TMA

References:
1.       P. K. Rohatgi, N. Gupta, & Simon Alaraj, “Thermal expansion of aluminum–fly ash cenosphere composites synthesized by pressure infiltration technique,” Journal of Composite Materials, Vol. 40, No. 13, 2006, pp. 1163-1174.
2.       Beffort. O, Vaucher. S, Khalid F. A, “On the thermal and chemical stability of diamond during processing of Al/diamond composites by liquid metal infiltration (squeeze casting),” Diam Relat Mater, Vol. 13, 2004, pp.1834–43.
3.       C. Edtmaier, T. Steck, R.C. Hula, L. Pambaguian, F. Hepp, “Thermo-physical properties and TEM analysis of silver based MMCs utilizing metalized multiwall-carbon nanotubes,” Composites Science and Technology, Vol. 70, Issue 5, May 2010, Pages 783-788.
4.       K. Prakasan, S. Palaniappan, S. Seshan, “Thermal expansion characteristics of cast Cu based metal matrix composites,” Composites Part A: Applied Science and Manufacturing, Vol. 28, Issue 12, 1997, pp. 1019-1022.
5.       Q. Fang, P.S. Sidky, M.G. Hocking, J.Y. Zhang,  “Cracking behavior of carbon coating on SiC fibre and residual stresses in Ti/SiC MMCs,”  Surface and Coatings Technology, Volumes 100-101, March 1998, pp. 264-270.
6.       M. M. Aghdam, A. Kamalikhah, “Micromechanical analysis of layered systems of MMCs subjected to bending effects of thermal residual stresses,”Composite Structures, Vol. 66, Issues1-4, December 2004, pp. 563-569.
7.       Francis Delannay, “Thermal stresses and thermal expansion in MMCs,” Comprehensive Composite Materials, Vol.  3, 2000, pp. 341-369.
8.       S. Skirl, M. Hoffman, K. Bowman, S. Wiederhorn, and J. Rodel: Acta. Mater., Vol. 46, 1998, pp.2493.
9.       D. K. Balch, T. J. Fitzgerald, V. J.  Michaud, A. Mortensen, Y. L. Shen, and S. Suresh: Metall. Trans., Vol. 27A, 1996, pp.3070.
10.     S. C. Sharma, B. M. Satish, B. M. Girish, Rathnakar Kamath, and Hiroshi Asanuma: Tribology International, Vol.31, No.4, (1998), pp.183.
11.     M. Hoffman, S. Skirl, W. Pompe, J. Rödel, “Thermal residual strains and stresses in Al2O3/Al composites with interpenetrating networks,” Acta Materialia, Vol. 47, Issue 2, 15 January 1999, pp. 565-577.
12.     Z. R. Xu, K. K. Chawla, R. Mitra, M. E., “Fine effect of particle size on the thermal expansion of TiCAl XD™ composites ,” Scripta Metallurgica et Materialia, Vol. 31, Issue 11, 1 December 1994, pp. 1525-1530.
13.     Denoath, Ramnarayan, K. Pradeep, and Rohatgi: Jour. of Mat. Sci. Vol. 1024, 1981, pp.3026.
14.     S. Elomari, M. D. Skibo, Sundarajan, and D. Richards: Comp. Sci. and Tech., Vol. 58, 1998, pp.369.

45-51

11.

Authors:

Anubhuti Khare, Manish Saxena, Shweta Tiwari

Paper Title:

Multimedia networks based Dynamic WCDMA System Proposal for QoS

Abstract:    In WCDMA 3rd Generation (3G) systems although there is no need for frequency planning as the case with second generation (2G) systems, the limitation of both capacity and coverage by the interference level in the system rendered the planning task for 3G systems far more complex than 2G systems. As the 3G radio networks now more sensitive to radio environment and traffic conditions, the advantage of dropping frequency planning could be cumbersome due to difficulties linked to what is called Cell-Breathing Phenomenon (CBP). In this work we propose a Dynamic WCDMA system that adjusts the spreading factor adaptively to overcome this problem. The simulation design will have the output of mapping the network load in terms of the number of users to the Eb/N0 required; this will give the network controller an idea about the number and type of users that may cause coverage to shrink i.e. start cell breathing. Therefore the network controller will be able to decide on the number and type of users admitted to the network at specific times.

Keywords:  D-WCDMA, Coverage, cell breathing, Multimedia services

References:
1.       O. Alani and H. Ali, “Power control scheme to combat cell breathing phenomenon in 3G mobile systems”, Int. J. Mobile Network Design and Innovation, vol. 1, Nos. 3/4, pp.234-239,   2006.
3.       M. Al-Akaidi, H. Ali, and O. Alani,”Performance analysis of antenna sectorisation in cell breathing”, MESM03, IEEE UKRI SPC, Publisher EUROSIS, January, 2003.
4.       F. Adachi, M. Sawahashi, and H. Suda, “Wideband DS-CDMA for next generation mobile communication systems,” IEEE Commun. Mag., vol. 36, pp. 56–69, Sept. 1998.
5.       E. Dahlman, B. Gudmundson, M. Nilsson, and J. Skold, “UMTS/IMT-2000 based on wideband CDMA,” IEEE Communications Mag., vol. 36, pp. 70–81, Sept. 1998.
6.       K. Tutschku, , K. Leibnitz, and T-G Phuoc, () ‘Teletraffic issues in mobile communication network planning’, Presented at the 11th ITC Specialised Seminar on Multimedia and Nomadic Communications, Japan., October, 1998.
7.       S. Saunders, ‘Antennas and Propagation for Wireless Communication Systems’, John Wiley& Sons, 1999.
8.       I. Siomina, P. Värbrand, and Di Yuan, “Automated optimization of service coverage and Base Station antenna configuration in UMTS networks,” IEEE Communications Mag., vol. 13, Dec., pp.. 16–25, 2006.
10.     E. Amaldi,; A. Capone; F. Malucelli, “Optimizing UMTS radio coverage via base station configuration,” Proc. The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 1 pp. 315 – 319, 2002.
11.     Y.C. Yee, K.N. Choong, Andy L.Y. Low, S.W. Tan and S.F. Chien “A conservative approach to adaptive call admission control for QoS provisioning in multimedia wireless networks”
12.     Elsevier Computer Communications, Vol. 30, pp 249-260, 2007.
13.     F. Prihandoko, M.H. Habaebi, B.M. Ali () “Adaptive call admission control for QoS provisioning in multimedia wireless networks”, Elsevier Computer Communications, Vol. 26, pp 1560

52-55

12.

Authors:

Basil Hamed, Walid Issa

Paper Title:

A Modified Internal Model Control for Unstable – Time Delayed System

Abstract:   A new approach of control design of internal model controller is proposed in this paper. The proposed design method focuses on modifying the old general structure of IMC and develops a new model structure while saving the same general concept of using the invertible version of the system in the controller design. The new approach combines the IMC structure and the traditional structure of a control problem and this demonstrates an excellent performance and behavior against different disturbance inputs and model uncertainty presented in model parameter mismatch. Beside that a smith predictor is added to promote the design to compensate the delayed time systems. Also a proposed stabilizer has mentioned to deal with unstable systems.

Keywords:  IMC, Unstable, Time Delay, Pendulum System, Smith predictor.

References:
1.        Jeffrey E. Arbogast, Douglas J. Cooper. “Extension of IMC tuning correlations for non-self regulating processes”. ISA Transactions, 2007, 46(4), 303-311.
2.        Garcia, C. E. and Morari, M., “Internal Model Control - Unifying Review and Some New Results”, Industrial Engineering Chemical Process Design and Development, vol. 21,1982.
3.        Scott A. Geddes , Thesis, “Internal Model Control (IMC) of a Fruit Drying System”, University of Southern Queensland,2006.
4.        Jiliang Shang, Guangguang Wang, “Application Study on Internal Model Control in Boiler Burning System”, 2010.
5.        Caifen Fu, Wen Tan, “Active control of combustion instability via IMC”, 2008.
6.        JIN Qi-bing，FENG Chun-lei，LIU Ming-Xin, “Fuzzy IMC for Unstable Systems with Time Delay”  IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008.
7.        Wen Tan, Horacio J. Marquez, Tongwen Chen, “IMC design for unstable processes with time delays”,2003, accessed on: April 2011, Online: http://dsp.vscht.cz/konference_matlab/MATLAB09/prispevky/035_hanta.pdf  .
8.        Kou Yamada, “Modified Internal Model Control for unstable systems”, Proceedings of the 7th Mediterranean Conference on Control and Automation (MED99) Haifa, Israel - June 28-30, 1999
9.        Smith, O. J. M., “Closer Control of Loops with Dead Time” Chem. Eng. Progress, 1975.
10.     Byronic company, “Pendulum system model manual”, 2001, accessed on: April 2011, online: http://www.lehigh.edu/inconsy/lab/experiments/PCS_Manual.pdf .
11.     M. Shamsuzzohal, Moonyong Lee, “IMC Based Control System Design of PID Cascaded Filter”, SICE-ICASE International Joint Conference, 2006.

56-62

13.

Authors:

Pramodini DV, AG Ananth, HM Mahesh

Paper Title:

Study of the Performance of 3x3 MIMO Transmission System Using MMSE and Ml Detectors

Abstract:   Analysis of full-rate linear and space-time block code for 3X3 multiple-input multiple-output (MIMO) communication systems under a Rayleigh flat-fading environment has been carried out.  The design targets specifically use a Linear Minimum Mean-square Error (MMSE) and Maximum Likelihood (ML) receivers which minimizes the average Symbol error rate (SER) for a QPSK transmitted signal.  The optimization problem is solved by minimizing a lower bound of the SER.  The performance of MIMO systems has been studied by changing the number of transmitters and receivers.  A comparison has been made between MMSE and ML receivers in terms SER as a function of SNR. The simulation results show that the higher MIMO system exhibits a SNR~4 dB improvement for ML receiver in comparison to linear MMSE receiver.

Keywords:  MIMO, MMSE, ML, QPSK, SER, SNR.

References:
1.        Enhanced detection with new ordering schemes for V-BLAST systems  by Sang-Rim Lee Seok-Hwan Park Sung Won Kim Inkyu Lee Sch. of Electr. Eng., Korea Univ., Seoul in Communications, IEEE Transactions on, June 2009, Issue: 6, 1648 – 1651.
2.        On the Design of Minimum BER Linear Space-Time Block Codes for MIMO Systems Equipped With MMSE Receivers Jing Liu;   Jian-Kang Zhang;   Wong, K.M.; Dept. of Electr. &Comput. Eng., McMaster Univ.,Hamilton, Ont. In Signal Processing, IEEE Transactions on Aug. 2006, Volume: 54 Issue:8, 3147 - 3158
3.        Spatial Modulation - A New Low Complexity Spectral Efficiency Enhancing Technique Mesleh, R.  Haas, H.  Chang Wook Ahn  Sangboh Yun Sch. of Eng. & Sci., Int. Univ. Bremen  in  Communications and Networking in China, 2006. ChinaCom '06. First International Conference on 25-27 Oct. 2006, 1 – 5, Beijing, 10 April 207.
4.        Performance Analysis of M X N Equalizer Based Minimum Mean Square Error (MMSE) Receiver for MIMO Wireless Channel N.Sathish Kumar and Dr.K.R.Shankar Kumar, 2011. International Journal of Computer Applications 16(7):47–50, doi: 10.5120/2021-2726.
5.        Uniform channel decomposition for MIMO communications, Y. Jiang, J. Li, and W. Hager, IEEE Transactions on Signal Processing, vol. 53, pp. 4283-4294, November 2005.
6.        Probability of error in MMSE multiuser detection, H. V. Poor and S. Verdu, IEEE Transactions on Information Theory, vol. 43, pp. 858-871, May 1997.
7.        On the diversity multiplexing for ordered SIC receivers over MIMO channels, H. Zhang, H. Dai, Q. Zhou, and B. L. Hughes, IEEE International Conference on Communications (ICC), Istanbul, Turkey, June 2006.

63-66

14.

Authors:

Manish Kansal, Hardeep Singh Saini, Dinesh Arora

Paper Title:

Designing & FPGA Implementation of IIR Filter Used for detecting clinical information from ECG

Abstract:   This paper describes an approach to design and implementation of digital filter algorthims based on field programmable gate arrays (FPGAs).The advantages of FPGA approach to digital filter implementation include higher sampling rates than are available from traditional DSP chips, lower cost than ASIC for moderate volume applications An ECG is a simple and useful test which records the rhythm and electrical activity of the heart of the patient that suffers from any heart disease While recording ECG signal it gets corrupted due to different noise interferences and artefacts. Noise and interference are usually large enough to obscure small amplitude features of the ECG hat are of physiological or clinical interest. The bandwidth of the noise overlaps that of wanted signals, so that simple filtering cannot sufficiently enhance the signal to noise ratio...We have used MATLAB for this purpose as it is the most advanced tool for DSP applications. Also it helps to verify the design and results that comes from the hardware.

Keywords:  FIR, IIR, FPGA,Mat lab, VHDL.

References:
1.        ShanthalaS, S.Y.Kulkarni, “High speed and low power FPGA Implementation for DSP applications”,European Journal of Scientific Research, ISSN 1450-216X, vol. 31, no.1 , pp. 19-28, November 2009.
2.        Maheshs.Chavan,AGARWALA,“Comparative Study of Chebyshev I And Chebyshev II Filter used For Noise Reduction in ECG Signal”, International  Journal  of Circuits, Systems and Signal Processing Issue 1,vol. 2, December 2008.
3.        Appealed and B. Liu. “A New Hardware Realization of Digital Filters”, IEETrans. On Acoust. Speech, Signal Process., vol. 22, pp. 456-462, December 2007.
4.        Mahesh S. Chavan, R.A. Agarwala, M.D. Uplane,”Application of  Chebyshev II  digital filter for noise reduction in ECG Signal”, WSEAStransactions on Circuits and Systems,vol. 4, no.10, pp. 1260-1267, Oct 2005.
5.        X. Hu, L. S. DeBrunner, and V. DeBrunner, “An efficient design for FIR filters with Variable precision”, IEEE Int. Symp. On Circuits and Systems, vol. 4, pp. 365-368, May 2002.
6.        S. C. Chan, W. Liu, and K. L. Ho.,“Multiplier less perfect reconstruction Modulate filterbanks with sum-of-powers-of-two coefficients”, IEEE Signal Processing Letters, vol. 8, no. 6, pp. 163-166, June 2001.
7.        Christov II., “Dynamic power line interference subtraction from biosignals”, J Med Technology, vol. 24, pp. 169-172, July2000.
8.        Y. C. Lim, R. Yang, D. Li and J. Song., “Signed-power-of-two term  allocation scheme for the design of digital filters”, IEEE Transactions on Circuits and Systems II, vol. 46, pp. 577-584, May 1999.
9.        Sanjit K. Mitra James F Kaiser, “Handbook for Digital Signal Processing”, John Wiley & Sons, Inc 1993.
10.     VHDL, Douglas L. Perry, Second Edition, McGraw Hill, 1993.
11.     Escalon OJ, Mitchell RH, BaldersonDE, Harron DW, “Fast and reliable QRS alignment technique for high frequency analysis of signal-averaged ECG”, Med Biology Suppl, pp. 137-146, october1993.
12.     Yi-Shang, Hemet al., “P-wave detection by an adaptive QRS-T cancellation technique”, IEEE. Friesen GM, Jannett TC, Jadallah MA, 1987.

67-72

15.

Authors:

Sandeep Patil, Shailendra Dewangan

Paper Title:

Neural Network-based Offline Handwritten Signature Verification System using Hu’s Moment Invariant Analysis

Abstract:   Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using NN architecture. Various static (e.g., area covered, number of elements, height, slant, etc.) [1] and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN [2]. Several Network topologies are tested and their accuracy is compared. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature matching, feature comparison and classification) allowing us to gain a better control over the precision of different components. This paper focuses on classification, the last phase in the process, covering some of the most important general approaches in the field. Each approach is evaluated for applicability in signature verification, identifying their strength and weaknesses. It is shown, that some of these weak points are common between the different approaches and can partially be eliminated with our proposed solutions. To demonstrate this, several local features are introduced and compared using different classification approaches.

Keywords:  Handwritten Signature Verification (HSV), Hu’s moment invariants, Neural Networks (NN),  offline, Signature Recognition, etc.

References:
1.        Bence Kovari, Benedek Toth, Hassan Charaf, “Classification Approaches in Off-Line Handwritten Signature Verification”, WDET Transactions on Mathematics ,Issue 9, Volume 8, September 2009, p.p. 500-509
2.        Dr. S.A Daramola and Prof. T.S Ibiyemi, “Efficient on-line signature verification system”, International Journal of Engineering & Technology IJET-IJENS Vol: 10 No: 04, p.p. 48-52
3.        M. R. Teaque, "Image Analysis via the General Theory of Moments," Journal of the Optical Society of America, vol. 70, pp. 920-930, 1980.
4.        S. N. Srihari, A. Xu, and M. K. Kalera, "Learning Strategies and Classification Methods for Off-line Signature Verification," Proceedings of the 9th Int’l Workshop on Frontiers in Handwriting Recognition (IWFHR-9 2004), 2004.
5.        W. Nelson, W. Turin, T. Hastie, “Statistical methods for on-line signature verification,” International Journal of Pattern Recognition and Artificial Intelligence, 8, 1994
6.        A. Alizadeh, T. Alizadeh, Z. Daei, “Optimal Threshold Selection for Online Verification of Signature”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, Vol.I, p.p. 17-21, IMECS 2010
7.        G. F. Russell and A. B. Jianying Hu, "Dynamic Signature Verification Using Discriminative Training," in Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05), 2005.
8.        Y. S. Abu-Mostafa and D. Psaltis, "Recognitive Aspects of Moment Invariants," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-6, pp. 698-706, 1984.
9.        Mr. Bhupendra M. Chaudhari, Mr. Atul A. Barhate, Mrs. Anita A. Bhole: Signature Recognition Using Fuzzy Min-Max Neural Network: Proceeding of International conference of Control Automation Communication & Energy Conversation, pp- 242-249, (ISBN 978-81-8424-439).
10.     W. Nelson and E. Kishon, Use of Dynamic Features for Signature Verification, in Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 1, Charlottesville, Virginia, pp. 201-205, October 1991.
11.     V.A. Baradi, H.B. Kakere, “Offline Signature Recognition System”, International Journal of Computer Applications (0975 - 8887) Vol.1, p.p. 48-56, 2010
12.     Ronny Martens, Luc Claesen, “On- Line Signature Verification by Dynamic Time-Warping”, IEEE Proceedings of ICPR'96, 1996
13.     J.R. Parkar, “Simple Distances Between Handwritten Signatures”, Laboratory for Computer Vision, Department of Computer Science, University of Calgary
14.     Alexandra Boldyreva, “Efficient threshold signature, multi-signature and blind signature schemes based on the Gap-Diffie-Hellman-group signature scheme”, Dept. of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA, 2002 Quen-Zong Wu, I-Chang Joe, and Suh-Yin Lee, “On-Line Signature Verification Using LPC Cepstrum and Neural Networks”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 27(1):148-153, 1997.
15.     Olaf Henniger, Katrin Franke, “Biometric user authentication on smart cards by means of handwritten signatures”, Fraunhofer Institute for Secure Telecooperation Rheinstr, Darmstadt, Germany Amac Herdagdelen, Ethem Alpaydm “Dynamic Alignment Distance Based Online Signature Verification”, 13th Turkish Symposium on Artificial Intelligence & Artificial Neural Networks, Izmir, Turkey, p.p. 119-127, June 2004
16.     G. K. Gupta, “The State of the Art in On-line Handwritten Signature Verification”, Faculty of Information Technology, Monash University, Building 75 Clayton, Victoria 3800, Australia, May, 2006
17.     Pave1 Mautner, Ondrej Rohlik, Vaclav Matousek, Juergen Kempp, “Signature Verification Using ART-2 Neural Network”, Proceedings of the 9th International Conference on Neural information Processing (ICONIP'OZ) ,2: 636-639, 2002
18.     J. F. Boyce and W. J. Hossack, “Moment Invariants for Pattern Recognition," Pattern Recognition Letters, vol. 1, pp. 451-456, 1983.
19.     A. Jain, F. Griess and S. Connell, “On-line signature Verification: Pattern Recognition”, WSEAS Transactions on Mathematics, Issue 9, Volume 8, 2010
20.     H. Ming-Kuei, "Visual pattern recognition by moment invariants," Information Theory, IRE Transactions, vol. 8, pp. 179-187, 1962.

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16.

Authors:

Kavita Mahajan, M. R. Vargantwar, Sangita M. Rajput

Paper Title:

Classification of EEG using PCA, ICA and Neural Network

Abstract:   The processing and analysis of Electroencephalogram (EEG) within a proposed framework has been carried out with DWT for decomposition of the signal into its frequency sub-bands and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Reduction of the dimension of the data is done with the help of Principal component analysis and Independent components analysis. Then these features were used as an input to a neural network for classification of the data as normal or otherwise. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a normal and abnormal prediction method on data from individual petit mal epileptic patients.

Keywords:  ANN. DWT, Electroencephalogram (EEG), Independent components analysis (ICA), Principal component analysis (PCA),

References:
1.       Subasi A., M. Ismail Gursoy (2010) EEG signal classification using PCA, ICA, LDA and support vector machines Expert Systems with Applications ,37 ,8659–8666.
2.       Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123, 69–87.
3.       Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in timeseries of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64, 061907.
4.       Bronzino, J. D. (2000). Principles of electroencephalography (2nd ed.). In J. D.
5.       Bronzino (Ed.). The biomedical engineering handbook. Boca Raton: CRC Press LLC.
6.       Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., & Gu, Q. M. (2003). A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55, 321–336.
7.       D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, A., & Litt, B. (2003). Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients. IEEE Transactions on Biomedical Engineering, 50(5), 603–615.
8.       Duda, R. O., Hart, P. E., & Strok, D. G. (2001). Pattern classification (2nd ed.). John Wiley & Sons.
9.       Semmlow, J. L. (2004). Biosignal and biomedical image processing: MATLAB-based applications. New York: Marcel Dekker, Inc..
10.     Hyvärinen A. and Oja E., (1997) A fast fixed-point algorithm for independent component analysis, Neural Computing, 9, 1483–1492.
11.     Subasi, A. (2007). Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Expert Systems with Applications, 32, 227–244

80-83

17.

Authors:

Anubhuti Khare, Manish Saxena, Jagdish Patel

Paper Title:

FPGA Based Efficient Implementation of Viterbi Decoder

Abstract:   It is well known that data transmissions over wireless channels are affected by attenuation, distortion, interference and noise, which affect the receiver’s ability to receive correct information. Convolutional encoding with Viterbi decoding is a powerful method for forward error detection and correction. It has been widely deployed in many wireless communication systems to improve the limited capacity of the communication channels. In this paper, we present a Spartan XC3S400A Field-Programmable Gate Array efficient implementation of Viterbi Decoder with a constraint length of 3 and a code rate of 1/3. The Viterbi Decoder is compatible with many common standards, such as DVB, 3GPP2, 3GPP LTE, IEEE 802.16, Hiperlan, and Intelsat IESS-308/309.

Keywords:  Convolutional encoder, FPGA, Register Exchange, Spartan XC3S400A Board, Viterbi decoder.

References:
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18.

Authors:

Naveen Choudhary

Paper Title:

Distributed Routing Simulation for Generic Network-on-Chip Topologies

Abstract:   Networks-on-Chip (NoC) is recently proposed as an alternative to the on-chip bus to meet the increasing requirement of complex communication needs in Systems-on-Chip (SoC). Most researchers advocate the use of traditional regular networks like meshes as architectural templates which gained a high popularity in general-purpose parallel computing.  However, most SoC platforms are special-purpose tailored to the domain-specific requirements of their application. They are usually built from a large diversity of heterogeneous components which communicate in a very specific, mostly irregular way.  In such systems the size and nature of cores may vary quite widely making the topology irregular. Moreover regular topologies can become irregular due to faults in links and switches. In such scenario topology agnostic routing algorithms are generally required. In this paper, we have analyzed the performance and applicability distributed table based routing for irregular NoC on an Network-on-Chip simulation framework.

Keywords:  Distributed routing, Interconnection networks, NoC, SoC.

References:
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