Volume-2 Issue-3

S. No

Volume-2 Issue-3, July 2013, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Krutika A Veerapur, Ganesh V. Bhat

Paper Title:

Colour Object Tracking On Embedded Platform Using Open CV

Abstract:  Object tracking in real time is one of the most important topics in the field of computer Vision. Detection and tracking of moving objects in the video scenes is the first relevant step in the information extraction in many computer vision applications. This idea can be used for the surveillance purpose, video annotation, traffic monitoring, human-computer interaction, intelligent transportation, and robotics and also in the field of medical. In this paper, we are discussing color object tracking using OpenCV software on Eclipse platform and the implementation of the tracking system on the Pandaboard ES. CAMShift algorithm is used for object tracking which is based on Meanshift algorithm. The proposed approach is demonstrated for real-time multiple object tracking system.

Keywords:   CAMShift algorithm, Meanshift algorithm, OpenCV, Pandaboard-ES.

References:
1.        Afef Salhi and Ameni Yengui Jammoussi, “Object tracking system using Camshift, Meanshift and Kalman filter”, World Academy of Science, Engineering and Technology, 2012.
2.        Alok K. Watve ,IndianInstitue of Technology, Kharagpur, seminar on “Object tracking in video scenes”, 2005.
3.        Amir Salarpour and Arezoo Salarpour and Mahmoud Fathi and MirHossein Dezfulian, “Vehicle tracking using kalman filter and features”, Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.2, June 2011.
4.        C. Lakshmi Devasena, R. Revathi, “ Video surveillance system-A survey”, IJCSI International journal of computer science Issues, vol 8, issue 4, no.1, Jult 2011
5.        Flavio B. Vidal and Victor H. Casanova Alcalde (2010). “Object Visual Tracking Using Window-Matching Techniques and Kalman Filtering”, Kalman Filter, VedranKordic (Ed.), ISBN: 978- 953-307-094-0.
6.        Greice Martins de Freitas, Clésio Luis Tozzi, “Object Tracking by Multiple State Management and Eigen background Segmentation”, International Journal of Natural Computing Research, 1(4), 29-36, October-December 2010.
7.        Hamidreza Rashidy Kanan and ParastoKarimi, “Visual Object Tracking Using Fuzzy-based Thresholding and Kalman Filter”, International Journal of Modeling and Optimization, Vol. 2, No. 3, June 2012.
8.        Jiyan Pan, Bo Hu, and JianQiu Zhang, “An Efficient Object Tracking Algorithm with Adaptive Prediction of Initial Searching Point”, 2006 IEEE Pacific-Rim Symposium on Image and Video Technology (PSIVT'06), December 2006.
9.        Marek Chovanec, “Computer vision vehicle tracking using background subtraction”, Journal of Information, Control and Management Systems, Vol. 1,(2005),No.1.

1-3

2.

Authors:

E.Saranya and S.Arulselvi

Paper Title:

Design and Simulation of Conventional and Intelligent Controllers for Temperature Control Of Shell and Tube Heat Exchanger System

Abstract:   Heat exchanger system is widely used in chemical plants because it can sustain wide range of temperature and pressure. The main purpose of a heat exchanger system is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is of prime importance. The designed controllers will regulate the temperature of the outgoing fluid to a desired set point in the shortest possible time irrespective of load and process disturbances, equipment saturation and nonlinearity. To control the temperature of outlet fluid of the heat exchanger system, a conventional P,PI and PID controller can be used.  Due to nonlinear nature, shell and tube heat exchanger system is hard to model and control using conventional methods. The intelligent controllers are effective for nonlinear processes. In this paper, conventional P,PI,PID and IMC based PID controllers are designed and simulation results are presented and discussed. From the results it is observed that IMC based PID controller gives better results when compared to other controllers. To improve the performance the fuzzy controller and model based neuro controllers (inverse and internal model controllers) are designed and simulated. To develop model based neuro controllers forward and inverse neuro model are developed, trained and validated. Simulation studies are carried out with fuzzy logic controller and model based neuro controllers  for servo and regulatory problems. The results are presented and discussed. It is observed that ,fuzzy logic controller and IMC based PID controllers are giving better results when compared to conventional PID controller and model based neuro controllers.

Keywords: Shell and tube heat exchanger,IMC based PID controller, fuzzy, inverse controller , neuro IMC controller.

References:
1.        Subhransu Padhee “Performance Evaluation of Different Conventional and Intelligent Controllers for Temperature Control of Shell and Tube  Heat Exchanger System”,MS Thesis Thapar University, India, July 2011.
2.        Warne Bequette ,“Process Control, Modelling Design and Simulation” Prientice-Hall of India Private Limited, 2004.
3.        George Stephanopoulos , “Chemical Process Control, ”PHI Learning New Delhi ,2010.
4.        M. Gopal,“Control Systems Principles and Design,” Tata McGraw Hill, 2007.
5.        Vikas Gupta, Kavita Khare and R.P Singh, “Efficient FPGA Design and Implementation of Digital PID Controllers In Simulink,” International Journals of Recent Trends in Engineering, Vol. 2, No. 6, Nov 2009, pp. 147-150.
6.        Wen Tan, Horacio J. Marquez, Tongwen Chen, IMC design for unstable processes with time delays Journal of Process Control ,13 (2003) 203–213.
7.        D. Driankov, H. Hellendorn, and M. Reinfrank, “An Introduction to Fuzzy Control”, Narosa publishing house ,New Delhi.1993.
8.        C.Ahilan,S.Kumanan,N.Sivakumaran,“ Prediction of Shell And Tube Heat Exchanger Performance Using Artificial Neural Networks” The International Conference on Advanced Computing and Communication Technologies, 2011, pp. 307-312.
9.        Yuvraj Bhushan Khare, Yaduvir Singh,''PID Control of Heat Exchanger System'', International Journal of Computer Applications (0975 – 8887),Volume 8– No.6, October 2010.
10.     Mohamed Azlan Hussaina, Paisan Kittisupakornb and Wachira Daosudb,''Implementation of Neural-Network-Based Inverse-Model Control Strategies on an Exothermic Reactor'', ScienceAsia 27 (2001) : 41-50.
11.     Afraa H. Al-Tae, Dr. Safa A. Al-Naimi,''Comparative Study of Temperature Control in a Heat Exchanger Process'',Eng.& Tech.Journal,vol.30,No.10.2012.

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3.

Authors:

Raghvendra Patel, Deepak Kumar Dixena

Paper Title:

Self Monitoring E-mail Organizer with Information Management and Text Mining Application

Abstract: Email is one of the most ubiquitous applications used regularly by millions of people worldwide.  Professionals have to manage hundreds of emails on a daily basis, sometimes leading to overload and stress.  Lots of emails are unanswered and sometimes remain unattended as the time pass by. Managing every single email takes a lot of effort especially when the size of email transaction log is very large. This work is focused on creating better ways of automatically organizing personal email messages. In this paper, a methodology for automated event information extraction from incoming email messages is proposed.  The proposed methodology/algorithm and the software based on the above, has helped to improve the email management leading to reduction in the stress and timely response of emails.”

Keywords:  Information management; periodic access; mail organizer; email client; text mining; EIA algorithm

References:
1.     http://en.wikipedia.org/wiki/Email_client
2.     http://www.multiemailnotifier.com/
3.     http://support.microsoft.com/kb/835830
4.     http://products.secureserver.net/email/email outlookexpres s.htm
5.     http://www.procmail.org/
6.     http://en.wikipedia.org/wiki/Procmail
7.     http://userpages.umbc.edu/~ian/procmail.html
8.     Mia K. Stern, “Dates and Times in Email Messages” published in ACM digital library, 2004.
9.     D. S´anchez, M.J. Mart´ın-Bautista, I. Blanco, C. Justicia de la Torre, “Text Knowledge Mining: An Alternative to Text Data Mining”, published in IEEE, 2008.
10.  Jan-Peter Kramer, “PIM-Mail: Consolidating Task and Email Management”, published in ACM digital library, 2010.

13-15

4.

Authors:

M.Sakthi, Antony Selvadoss Thanamani

Paper Title:

An Enhanced K Means Clustering using Improved Hopfield Artificial Neural Network and Genetic Algorithm

Abstract:  Due to the increase in the quantity of data across the world, it turns out to be very complex task for analyzing those data. Categorize those data into remarkable collection is one of the common forms of understanding and learning.  This leads to the requirement for better data mining technique. These facilities are provided by a standard data mining technique called Clustering. The key intention of this technique is to categorize a dataset into a set of clusters that contains similar data items, as computed by some distance function. One of the widely used clustering techniques is K-Means clustering. K-Means clustering is very simple and effective for clustering. But, the main disadvantage of this technique is when the large dataset is used for clustering. To overcome this difficulty, various researchers focus on suggesting better alteration in K-Means clustering. This paper provides a new technique to modify K-Means clustering which can result in better performance.  For initialization, this paper uses an improved version of Hopfield Artificial Neural Network (HANN) algorithm. Also, the Genetic Algorithm (GA) is in combined with K-Means algorithm. The experimental result indicates that the proposed K-Means clustering algorithm results in better clustering result.

Keywords:   K-Means, Genetic Algorithm, Hopfield Artificial Neural Network

References:
1.          Zhang Zhe, Zhang Junxi and Xue Huifeng, "Improved K-Means Clustering Algorithm", Congress on Image and Signal Processing, Vol. 5, Pp. 169-172, 2008.
2.          Hai-xiang Guo, Ke-jun Zhu, Si-wei Gao and Ting Liu, "An Improved Genetic k-means Algorithm for Optimal Clustering", Sixth IEEE International Conference on Data Mining Workshops, Pp. 793-797, 2006.
3.          Yanfeng Zhang, Xiaofei Xu and Yunming Ye, "NSS-AKmeans: An Agglomerative Fuzzy K-means clustering method with automatic selection of cluster number", 2nd International Conference on Advanced Computer Control, Vol. 2, Pp. 32-38, 2010.
4.          Xiaoyun Chen, Youli Su, Yi Chen and Guohua Liu, "GK-means: an Efficient K-means Clustering Algorithm Based on Grid", International Symposium on Computer Network and Multimedia Technology, Pp. 1-4, 2009.
5.          Trujillo, M., Izquierdo, E., "Combining K-means and semivariogram-based grid clustering", 47th International Symposium, Pp. 9-12, 2005.
6.          Huang, J.Z., Ng, M.K., Hongqiang Rong and Zichen Li, "Automated variable weighting in k-means type clustering", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, Pp. 657-668, 2005.
7.          Yi Hong and Sam Kwong “Learning Assignment Order of Instances for the constrained k-means clustering algorithm” IEEE Transactions on Systems, Man, and Cybernetics, Vol 39, No 2. April, 2009.
8.          Davidson,M. Ester and S.S. Ravi, “Agglomerative hierarchical clustering with constraints: Theoretical and empirical results”, in Proc. of Principles of Knowledge Discovery from Databases, PKDD 2005.
9.          Wagstaff, Kiri L., Basu, Sugato, Davidson, Ian “When is constrained clustering beneficial, and why?” National Conference on Aritficial Intelligence, Boston, Massachusetts 2006.
10.       Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrodl “Constrained K-means Clustering with Background Knowledge” ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning, 2001.
11.       Davidson, M. Ester and S.S. Ravi, “Efficient incremental constrained clustering”. In Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, August 12-15, San Jose, California, USA.
12.       Davidson, M. Ester and S.S. Ravi, “Clustering with constraints: Feasibility issues and the K-means algorithm”, in proc. SIAM SDM 2005, Newport Beach, USA.
13.       D. Klein, S.D. Kamvar and C.D. Manning, “From Instance-Level constraintes to space-level constraints: Making the most of Prior Knowledge in Data Clustering”, in
proc. 19th Intl. on Machine Learning (ICML 2002), Sydney, Australia, July 2002, p. 307-314.
14.       N. Nguyen and R. Caruana, “Improving classification with pairwise constraints: A margin-based approach”, in proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’08).
15.       K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl, “Constrained Kmeans clustering with background knowledge”, in: Proc. Of 18th Int. Conf. on Machine Learning ICML’01, p. 577 - 584.
16.       Y. Hu, J. Wang, N. Yu and X.-S. Hua, “Maximum Margin Clustering with Pairwise Constraints”, in proc. of the Eighth IEEE International Conference on Data Mining (ICDM) , 253-262, 2008.
17.       Merz C and Murphy P, UCI Repository of Machine Learning Databases, Available: ftp://ftp.ics.uci.edu/pub/machine-Learning-databases.
18.       Text Documents Clustering using Genetic Algorithm and Discrete Differential Evolution. International Journal of Computer Applications 43(1):16-19, April 2012. Published by Foundation of Computer Science, New York, USA. BibTeX

16-21

5.

Authors:

CH.V.T.E.V.Laxmi, K.Somasundaran

Paper Title:

Peer to Peer Scheduling in PBS and Implementing Multi-Level Feedback Queues

Abstract:   Grid computing is the federation of pooling resources in order to solve various large problems. Grid computing has gained remarkable importance in the last decade as the resource requirements for large applications increased drastically. Scheduling is the main issue in grid computing and it is the process of making scheduling decisions over multiple grid resources. In this paper we proposed a approach for Portable Batch System (PBS) to implement peer to peer scheduling. This mainly defines that a site can have multiple PBS Pro cluster, each cluster has its server, scheduler and one or more execution systems. This paper focuses on designing of new architecture for Portable Batch System to implement Peer to Peer scheduling system. In this research of Peer to Peer scheduling in PBS we proposed new approach for selecting the jobs from the job pool through ready queue data structure mechanism and we proposed Grid scheduling algorithm Multi Level Feedback Queues Scheduling algorithm for the execution of jobs.

Keywords:  Grid Computing, Grid scheduling, Resource discovery, Portable Batch System, Peer to Peer Scheduling, Pro cluster

References:
1.        Ian Foster and Carl Kesselman, “The Grid: Blueprint for a New Computing Infrastructure,” Elsevier Inc., Singapore, Second Edition, 2004.
2.        Hamscher, V., Schwiegelshohn, U., Streit, A. and Yahyapour, R. Evaluation of Job-Scheduling Strategies for Grid Computing. GRID 2000, 191–202, 17–20
December 2000, Bangalore, India. Lecture Notes in Computer Science, Springer-Verlag.
3.        PBS Pro, http://www.pbspro.com/.
4.        Cactus, http://www.cactuscode.org/. 300 GRID SCHEDULING AND RESOURCE MANAGEMENT
5.        PBS Pro, http://www.pbspro.com
6.        Edi.Laxmi Scheduling in Grid Computing International Journal of Computer Science and Management Research Vol 2 Issue 1 January 2013
7.        Goux, Jean-Pierre, Kulkarni, Sanjeev, Yoder, Michael and Linderoth, Jeff. Master-Worker: An Enabling Framework for Applications on the Computational Grid. Cluster Computing, 4(1): 63–70 (2001).
8.        Cactus, http://www.cactuscode.org/. 300 GRID SCHEDULING AND RESOURCE MANAGEMENT Spooner, D., Jarvis, S., Cao, J., Saini, S. and Nudd, G. Local Grid Scheduling Techniques using Performance Prediction, IEE Proc. – Comp. Digit. Tech., 150(2): 87–96 (2003
9.        Enterprise Edition policy,

22-25

6.

Authors:

Suraj Kumar Sahu,  Sandeep Kumar Gonnade

Paper Title:

QR Code and Application in India

Abstract:  This paper examines QR Codes and how they can be composed and scan and decode by a camera. QR code is 2-dimensional barcode used for quick response in promotional and marketing purpose. The paper describes about QR code, how QR code is different from barcode, It’s formation, Capacity and Error correction code.  It’s application in India and worldwide.

Keywords:   QR Code, Cryptography, Data Hiding, encryption, decryption, code generation

References:
1.     http://en.wikipedia.org/wiki/QR_code
2.     http://gizmodo.com/5969312/how-qr-codes-work-and-why-they-suck-so-hard
3.     H. S. Al-Khalifa. Utilizing qr code and mobile phonesfor blinds and visually impaired people. In ICCHP,
4.     Alapetite. Dynamic 2d-barcodes for multi-device web session migration including mobile phones. Personal and Ubiquitous Computing,
5.     M. Canadi, W. Hopken, and M. Fuchs. Application of qr codes in online travel distribution.
6.     J. Gao, V. Kulkarni, H. Ranavat, L. Chang, and H. Mei. A 2d barcode-based mobile payment system.
7.     J. Z. Gao, H. Veeraragavathatham, S. Savanur, and J. Xia. A 2d-barcode based mobile advertising solution.
8.     S. Lisa and G. Piersantelli. Use of 2d barcode to access multimedia content and the web from a mobile handset.
9.     I. Reed and G. Solomon. Polynomial codes over certain finite fields. Journal of the Society for Industrial and Applied Mathematics,

26-28

7.

Authors:

Nagarathna N, Preeti.G.Biradar, Himanshi Budhiraja,Susham K.Rao

Paper Title:

BER Analysis of FSK Transceiver for Cognitive Radio Applications

Abstract:   In modern wireless communication, the radio spectrum is the most vital resource for a mobile operator. Most of the prime spectrums (licensed bands) are already allocated for license users for exclusive use. Few, unlicensed bands are left open for unlicensed users. Cognitive radios (CR) offer a solution to this spectrum scarcity problem and the demand for reliable high data rate transmission had been increased significantly these days, which leads the way to modulation techniques.The main objective of this paper is designing and analyzing of   FSK Transceiver using Lab-VIEW and to measure the graphical representation of BER VsEb/No in the presence of Additive White Gaussian Noise (AWGN) of digital modulation schemes. FSK is chosen as modulation scheme for design of CR system is widely used for data transmission applications over band pass channels such as Cordless and paging systems, Telephone-line modems, Caller ID, Microcomputers, Audio cassettes, Radio control etc. FSK Transceiver is used to control the power for CR system. It is easy to implement and widely used for the wireless communication in VHF and UHF Frequency bands giving very good BER with high data rates.

Keywords:  Bit error Rate (BER), Cognitive Radio, Digital communication, Frequency Shift Keying, Lab-VIEW graphical programming, Signal-to-Noise Ratio (SNR).

References:
1.     RituKhullar, Sippy Kapoor, Naval Dhawan, “Modulation   Technique For Cognitive Radio, Applications”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 3, May-Jun 2012, pp.123- 125.
2.     Amanpreet Singh Saini, “The Automated Systems For Spectrum Occupancy Measurement And Channel Sounding In Ultra-Wideband, Cognitive Communication, And Networking” Master of Science in Electrical Engineering, August 2009.
3.     Ahmed Barnawi,“Wideband Sensing for Cognitive Radio Systems in Heterogeneous Next Generation Networks” International Journal of Computer Networks (IJCN), Volume (3): Issue (2): 2011.
4.     Hans-PetterHalvorsen,”Introduction to LabVIEW” 012.04.20
5.     N.Kim, N.Kehtarnavaz, M. Torlak,”LabVIEW-Based Software-Defined   Radio: 4-QAM Modem”systemics, cybernetics and informatics volume 4 - number 3.
6.     Md. AbirHossainKhandokar, Md. GolamRashed, “Performance Comparison of M-Ary Modulation Schemes for an Efficient Wireless Communication System with Digital Color Image Transmission”, Department of Information and Communication Engineering, University of Rajshahi, Rajshahi, Bangladesh.
7.     Eric NiiOtorkunorSackey, “Performance Evaluation of M-ary Frequency Shift Keying Radio Modems via Measurements and Simulations “Department of Electrical Engineering,Blekinge Institute of Technology, Karlskrona, Sweden, September 2006.
8.     A. S Kang, Vishal Sharma, “Pulse Shape Filtering in Wireless Communication-A Critical Analysis”,(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.3, March 2011.

29-34

8.

Authors:

Seema Kolkur, K.Jayamalini

Paper Title:

Web Data Extraction Using Tree Structure Algorithms – A Comparison

Abstract:    Nowadays, Web pages provide a large amount of structured data, which is required by many advanced applications. This data can be searched through their Web query interfaces. The retrieved information is also called ‘deep or hidden data’. The deep data is enwrapped in Web pages in the form of data records. These special Web pages are generated dynamically and presented to users in the form of HTML documents along with other content. These webpages can be a virtual gold mine of information for business, if mined effectively. Web Data Extraction systems or web wrappers are software applications for the purpose of extracting information from Web sources like Web pages. A Web Data Extraction system usually interacts with a Web source and extracts data stored in it. The extracted data is converted into the most convenient structured format and stored for further usage. This paper deals with the development of such a wrapper, which takes search engine result pages as input and converts them into structured format. Secondly, this paper proposes a new algorithm called Improved Tree Matching algorithm, which in turn, is based on the efficient Simple Tree Matching (STM) algorithm.  Towards the end of this work, there is given a comparison with existing works.  Experimental results show that this approach can extract web data with lower complexity compared to other existing approaches.s.

Keywords:   About Web Data Extraction, Document Object Model (DOM), Improved Tree Matching algorithm.

References:
1.        http://arxiv.org/pdf/1207.0246.pdf  (Accessed: May, 2013).
2.        C. H. Chang, M. Kayed, M. R. Girgis, and K. Shaalan, “A survey of web information extraction systems,” IEEE Transactions on  Knowledge and Data Engineering, 2006, pp. 1411-1428.
3.        Hua Wang, Yang Zhang, “Web Data Extraction Based on Simple Tree Matching,”, IEEE, 2010,  pp. 15-18.
4.        M. E. Califf, and R. J. Mooney, “Relational learning of pattern-match rules for information extraction,” Proceedings of the ACL Workshop on Natural Language Learning, Spain, July 1997, pp. 9-15.
5.        N. Kushmerick, D. S. Weld, and R. Doorenbos, “Wrapper induction for information extraction,”  Proceedings of the 15th International Conference on Artificial Intelligence (IJCAI), pp. 729-735, 1997.
6.        N. Kushmerick, D. S. Weld, and R. Doorenbos, “Wrapper induction for information extraction,”  Proceedings of the 15th International Conference on Artificial Intelligence (IJCAI), pp. 729-735, 1997.7.        C. N. Hsu, and M. T. Dung, “Generating finite-state transducers for semi-structured data extraction from the web,” Journal of Information Systems, vol. 23(8), pp. 521-538, 1998.
8.        J. Shanmugasundaram, J. Kiernan, E. Shekita, F. Catalina, and F.John, “Querying XML views of relational data,”  Proceedings of the 27th VLDB Conference, Rome, Italy, 2001, pp. 261-270.
9.        L. Liu, C. Pu, and W. Han, “XWRAP: An XML-Enabled Wrapper Construction System for Web Information Sources,” Proceedings of the 16th International Conference on Data Engineering (ICDE), San Diego, California, CA, USA, 2000, pp. 611-621.
10.     V. Crescenzi, G. Mecca, and P. Merialdo, “RoadRunner: towards automatic data extraction from large Web sites,” Proceedings of the 27th VLDB Conference, Roma, Italy, 2001, pp. 109-118.
11.     Sahuguet, and A. Fabien, “Building intelligent web applications using lightweight wrappers,” Data and Knowledge Engineering, vol. 36(3), pp. 283-316, 2001.
12.     Wei Liu, Xiaofeng Meng, Weiyi Meng, “ViDE: A Vision-Based Approach for Deep Web Data Extraction,” IEEE, 2010 pp. 447- 460.
13.     http://www.w3.org/TR/xpath/ (Accessed: May, 2013).
14.     Haikun Hong, Xiaoxin Chen, Guoshi Wu, Jing Li, “Web Data Extraction Based on Tree Structure Analysis and Template Generation,” IEEE, 2010, pp. 1-5.

35-39

9.

Authors:

Faizan Ahmed Sheikh, Raminder Preet Pal Singh, Parveen Lehana

Paper Title:

Effect of High Voltage on the Resistance of Aloe Vera Leaves

Abstract:   Every plant has a property to react to internal as well as external stimuli. There are many electromagnetic radiations present in environment to which plants react such as electrical fields, magnetic fields, and electromagnetic (EM) fields. A large number of products and applications in our day to day life make use of various forms of electromagnetic energy. One such form of energy is high voltage and high frequencies EM waves. High voltage means electrical energy at voltages which is sufficient to cause harm or death upon living things. The electromagnetic field from high power transmission lines affects the growth of plants. The growth and health status of plants can be accessed from the electrical properties such as resistance or impedance of its leaves. In this paper, the effect of high voltage on Aloe Vera leaves has been reported. Aloe Vera plant is chosen due to its various properties and uses in dermatology. Investigations were carried out to study the effect of high voltage on the resistance of the Aloe Vera leaves. The analysis of the results showed considerable amount of change in the resistance of the leaves due to high voltage and high frequencies.

Keywords:    Aloe Vera, D.C. resistance, high voltage, Tesla coil.

References:
1.     C. Plieth, “Temperature sensing by plants Calcium permeable channels as primary sensors, a model,” The Journal of Membrane Biology, vol. 172, 1999, pp. 121–127.
2.     Available online: www.wikipedia.org/high_voltage.
3.     N. Cherry, “Evidence that electromagnetic fields from high voltage power lines and in buildings, are hazardous to human health, especially to young children,”
Department of human science, Lincoln University, New Zealand, 2001.
4.     Z. Demir, “Proximity effects of high voltage electric power  transmission lines on ornamental plant growth,” African Journal of Biotechnology, vol. 9, 2010, pp. 6486-6491.
5.     K. Trebacz, H. Dziubinska, and E. Krol, “Electrical signals in long distance communication in plants,” Communication in Plants, Springer  Berlin Heidelberg, 2006, pp. 277–90.
6.     L. A. Gurovich, P. Hermosilla, “Electric signalling in fruit trees in response to water applications and light–darkness conditions,” Journal of Plant Physiology, vol. 166, 2009, pp. 290-300.
7.     M. F. Desrosiers, and R. S. Bandurski, “Effect of a longitudinally applied voltage upon the growth of Zea mays seedlings,” Journal of Plant Physiology, vol. 87, 1988, pp. 874-877.
8.     J. Fromm, S. Lautner, “Electrical signals and their physiological significance in plants,” Plant Cell Environment, vol. 30, 2007, pp. 249–57.
9.     E. Vorobiev, N. Lebovka, “Electrotechnologies for Extraction from Food Plants and Biomaterials,” Springer, vol. 5996, 2008.
10.  Available online: www.wikipedia.org/Aloe_Vera.
11.  M. A. Saeed, I. Ahmad, U. Yaqub, Akbar, M.A. Waheed, A. Saleem, and Nasir-ud-Din. “Aloe vera a plant of vital significance,” Science vision, vol. 9, 2004, pp.1-2.
12.  T. A. Waller, “Comparative studies of Aloe from commercial sources,” Aloe Today Spring, 1993, pp. 22-26.
13.  J. E. Crew, “Aloes in the treatment of burns and scalds,” Minnesota Medicine, vol. 22, 1939, pp. 538-539.
14.  F. A. Sheikh, R. P. P. Singh, J. B. Singh, P. Lehana, “Effect of microwaves on the resistance of Aloe Vera leaves,” International Journal of Engineering Research and Applications, vol. 3, 2013, pp. 242-247.
15.  Available online: www.fragrantica.com.
16.  F. A. Sheikh, R. P. P. Singh, J. B. Singh, P. Lehana, “Effect of microwaves on texture, colour, and growth of Aloe Vera plant,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013, vol.2, pp. 2403-2410.

40-43

10.

Authors:

Upwinder Kaur, Rajesh Mehra

Paper Title:

Optimization of CMOS 8-bit Counter using SLA and Clock Gating Technique

Abstract:    The development of digital integrated circuits is challenged by higher power consumption. Scaling helps to improves transistor density, increase speed and frequency of operation and hence higher performance. As voltages scale downward with the geometries threshold voltages must also decrease to gain the performance advantages of the new technology but leakage current increases exponentially. Power consumption has a static component coming from the leakage of inactive devices and a dynamic component coming from the switching of active devices. It has been proved that clock signal consumes a high dynamic power as the clock net has one of the highest switching densities. The clock signal keeps changing its state in certain time points according to its frequency even if the logic output doesn’t change “hold mode”. Switching power dissipation may eventually dominate total power consumption in sub micron technology. Glitches or unwanted transitions consume about 20%-70% of Dynamic Power and needs to be eliminated. Therefore, the main aim of this thesis work is to reduce power consumption due to both glitches and Clock switching. In this thesis work, the novel approaches are proposed for reducing dynamic power with minimum possible power consumption and delay trade off. A novel approach for reducing dynamic power i.e. SLA (State Look Ahead) and Clock Gating has been proposed. Proposed parallel counter shows 66.04% power improvement as compared to Kakarountas counter and 54.3% power reduction as compared to Alioto’s counter. Abdel’s counter with proposed parallel counter shows 29.9% power reduction. Maximum operating frequency is also improved in proposed parallel counter. By using pass transistors drawback of large area in Abdel’s counter is optimized in proposed circuit. So the proposed parallel 8-bit counter is optimized in terms of speed, power and area as compared to previous counter designs. Use of Pass Transistor is helpful in reducing or eliminating the glitches from circuit. Clock Switching power reduction designs are also proposed which are more power efficient and have less delay as compared to existing techniques.

Keywords: Dynamic power, Integrated clock gating, Pass transistor, State look ahead logic, Switching activity.

References:
A.       P. Kakarountas, G. Theodoridis, K.S. Papadomanolakis, C.E. Goutis, “A novel high-speed counter with counting rate independent of the counter’s length”,  IEEE International Conference on Electronics, Circuits, and Systems(ICECS), UAE, pp. 1164-1167,Dec, 2003.
1.        SwartzlanderE.E,Jr.,“A review of large parallel counter designs”, IEEE Computer Society Annual Symposium on VLSI, pp.89-98, 2004.
2.        M.R Stan, “Systolic Counters with Unique Zero State”, IEEE International symposium on Circuits and Systems (ISCAS), Vol.2, pp. 909-912, 2005.
3.        X.P. Yu, M.A. Do, Jia. L., Ma. J.G., “Design of a low power wide-band high resolution programmable frequency divider”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol.13, No.9, pp. 1098-1103, 2005.
4.        Ma.J.G., Yeo, K.S, Wu.R, Zhang Q.X., “A 2GHz programmable counter with new re-loadable D flip-flop”, IEEE Conference on Electron Devices and Solid-State Circuits,         pp. 269-272, 2005.
5.        Aguirre-Hernandez M, Linares-Aranda M, “A Clock Gated pulse Triggered D-flip flop for Low power High Performance VLSI Synchronous Systems”, IEEE International Caribbean Conference on Devices, Circuits and Systems, pp. 293-297, 2006.
6.        M. Alioto, R. Mita, G. Palumbo, “Design of High Speed Power Efficient MOS Current- Mode Logic Frequency Dividers”, IEEE Transactions on Circuits and Systems II, Vol. 53, No. 11, pp. 1165-1169, 2006.
7.        Ko-Chi Kuo, Feng-Ji Wu,  “ A 2.4GHz/5GHz Low Power Pulse swallow Counter in 0.18µm CMOS Technology”, IEEE Asia Pacific Conference on  Circuits and Systems    (APCCAS ), pp.214-217, 2006.
8.        M. Dastjerdi-Mottaghi, A. Naghilou, M. Daneshtalab, A. Afzali-Kusha, Z. Navabi, “Hot Block Ring Counter: A Low Power Synchronous Ring Counter”, IEEE International Conference on Microelectronics, pp. 58-62, 2006.
9.        D. Lee, G. Han, “High-speed, low-power correlated double sampling counter for column-parallel CMOS imagers”, IEEE Electronics Letters, Vol. 43, No. 24, pp. 1362-1364, 2007.
10.     Jie Pan, Haigang Yang, Li-wu Yang,“ A High Speed Low Power Pulse Swallow Divider with Robustness Considerations”, IEEE International Conference on  Solid-State and Integrated Circuit Technology( ICSICT), pp.2168-2171, 2008.
11.     Abdel-Hafeez S, Harb S.M, Eisenstadt W.R, “High Speed Digital CMOS Divide-by-N Frequency Divider”, IEEE International Symposium on Circuits and Systems(ICSICT), pp.592-595, 2008.
12.     Ogunti E, Frank M, Foo S, “Design of a Low Power Binary Counter using Bistable Storage Element”, IEEE International Conference on Electronic Design(ICED),
pp.1-5, 2008.
13.     Hui Zhang, Hai-gang Yang, Jia Zhang, Fei Liu, “ High Speed Programmable Counter Design for PLL based on a Delay Partition Technique”, IEEE International Symposium on Radio Frequency Integration Technology(RFIT ), pp.100-103, 2009.
14.     Young-Won Kim, Joo-Seong Kim, Jae-Hyuk Oh, Yoon-Suk-Park, Jong-Woo Kim, Kwang-II Park, Bai-Sun Kong, Young-Hyun Jun, “Low Power Synchronous Counter With Clock Gating Embedded Into Carry Propagation”, IEEE Transactions on Circuits and Systems Part II: Express Briefs, Vol .56, No. 8, pp .649-653, 2009.
15.     Mukherjee N, Pogiel A, Rajski J, Tyszer J, “High-Speed On-Chip Event Counters for Embedded Systems”, IEEE International Conference on VLSI Design, pp.275-280, 2009.
16.     Tezaswi Raja, Vishwani D.Agrawal and Michael L.        Bushnell “Variable Input Delay CMOS Logic for Low Power Design”, IEEE Transactions on Very Large Scale Integration (VLSI) System, Vol. 17, No. 10, pp. 1534-1545, 2009.
17.     Salendra. Govindarajulu, Dr. T. Jayachandra Prasad, “Low Power Energy Efficient Domino Logic Circuits”, IEEE Journal of Recent Trends in Engineering, Vol. 2, No. 7, pp. 30-33, November 2009.
18.     Zhiqiang Gao, Yuanxu Xu, Peng Sun, Enyi Yao, Yongshuang Hu, “A programmable high-speed pulse swallow divide-by-N frequency divider for PLL frequency synthesizer”, IEEE International Conference on Computer Application and System Modelling (ICCASM), Vol.6, pp. 315-318, 2010.
19.     O. Fathy, A. Abdallah, A.Wassal, Y. Ismail, “Counter Based CMOS Temperature Sensor for Low Frequency Applications” IEEE International Conference on Thermal Issues in Emerging Technologies Theory and Applications, pp. 103-109, 2010.
20.     Heung Jun Jeon, Yong-Bin Kim, and Minsu Choi, “Standby Leakage Power Reduction Technique for Nanoscale CMOS VLSI Systems”, IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 5, pp. 1127-1133, May 2010.
21.     Jae Woong      Chun and C. Y. Roger Chen, “A Novel Leakage Power Reduction Technique for CMOS Circuit Design”, IEEE International Conference on SoC Design
Conference (ISOCC), pp. 119-122 , 2010.
22.     M. S. Islam, M. Sultana Nasrin, Nuzhat Mansur and Naila Tasneem, “Dual Stack Method: A Novel Approach to Low Leakage and Speed Power Product VLSI Design”, IEEE 6th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, pp. 18-20, December 2010.
23.     Ashoka Santhanur, Luca Benini, “Row–Based Power– Gating: A Novel Sleep Transistor Insertion Methodology for Leakage Power Optimization in Nanometer CMOS Circuits”, IEEE Transactions on VLSI Systems, Vol. 19, No. 3,pp. 469-482, March 2011.
24.     Shuzhe Zhou, Hailong Yao, Qiang Zhou, “Minimization of Circuit Delay and Power through Gate Sizing and Threshold Voltage Assignment”, IEEE Computer SocietyAnnual Symposium on VLSI, pp. 212-217, 2011.
25.     Philippe          Matherat, Mariem Slimani “Multiple Threshold Voltage for Glitch Power Reduction”, IEEE Faible Tension Faible Consommation (FTFC), pp. 67¬70, 2011.
26.     Ajane. Avinash, Furth. Paul M, Johnson. Eric E, Subramanyam Rashmi Lakkur, “Comparison of Binary and LFSR counter and Efficient Decoding Algorithm”, IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), pp.1-4, 2011.
27.     Abdel-Hafeez. S, Gordon Ross. A, “A Digital CMOS parallel counter Architecture based on State Look Ahead Logic”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol.13, No.6, pp.1023-1033, 2011.
28.     S. Vinod Kumar, M. Malathi, “Low Power Synchronous Counter using Improvised Conditional Capture Flip-Flop”, IEEE International Conference on Sustainable Energy and Intelligent Systems, pp. 589-592, 2011
29.     Mohamed O. Shaker, Magdy A. Bayoumi, “Clock Gated Flip Flop for Low Power Applications in 90nm CMOS”, IEEE International Symposium on Circuits and Systems (ISCAS), pp.558-562, 2011.
30.     Huimin Liu, Xiaoxing Zhang, Yujie Dai, Yingjie Lv, “Low Power CMOS High Speed Dual-Modulus 15/16 Prescaler for Wireless communications”, IEEE International Conference on Communications and Mobile Computing (CMC), pp. 397-400, 2011.
31.     Dake Liu, Christer Svensson, “Power Consumption Estimation in CMOS VLSI Chips”, IEEE Transactions Solid State Circuits, Vol.46, No. 6, pp .663-670, 2011.
32.     Johnson, M., Somasekhar, D., Chiou, L.Y., Roy, “Leakage Control with Efficient Use of Transistor Stacks in Single Threshold CMOS”, IEEE Transactions on VLSI Systems, Vol. 20, No. 1, pp. 1–5, February 2011..
33.     S. Datta, S. Nag, K. Roy, “ASAP: A Transistor Sizing Tool for Area, Delay and Power Optimization of CMOS Circuits”, IEEE International Symposium on Circuits and Systems, pp. 61 – 64, May 2011.
34.     F.Hu and V.D.Agrawal, “Input-Specific Dynamic Power Optimization for VLSI circuits”, IEEE International Symposium on Low Power Electronics and Design, pp. 232-237, 2012.
35.     James T. Kao and Anantha P. Chandrakasan, “Dual-Threshold Voltage Techniques for Low-Power Digital Circuits”, IEEE Journal of Solid-State Circuits, Vol. 45, No. 7, pp. 1009-1018, July 2012.

44-50

11.

Authors:

Gopi Krishna S, P.Anantha Christu Raj, K.Gerard Joe Nigel

Paper Title:

Web based Remote Accessing of Medical Devices with ARM Cortex-M3

Abstract:     Remote monitoring of a medical device in critical conditions is an emerging trend where cost reduction, portability and mobility are the main focuses. Integrating web servers to these medical devices will aid in monitoring them over the Internet and also in creating effective user interfaces in the form of web pages. This paper explores the topic of an efficient and low-weight embedded Web server for Web-based medical monitoring management. In this paper, the controller that is used is an Arm cortex M3 (LM3S9B92), which support all the networking protocol. CGI script will act as a bridge between the controller and the browser, helps for accessing the devices that are connected to the controller from the browser.

Keywords: Webit’s, HTML, LwIP, CGI script.

References:
1.        Joby Antony,  Sachin Sharma, Basanta Mahato, Gaurav Chitranshi, “Distributed data acquisition and control system based on low cost Embedded Web servers”, International Journal of Instrumentation, Control & Automation (IJICA), Volume 1, Issue 1,Pg no. 53-56, 2011
2.        Manivannan  M, Kumaresan  N “EMBEDDED WEB SERVER & GPRS BASED ADVANCED INDUSTRIAL AUTOMATION USING LINUX RTOS ” International Journal of Engineering Science and Technology Vol. No.2,Issue No.11,Pg no 6074-6081, 2010
3.        Yilhyeong Mun, Dongsub Cho “Users Access Discrimination and Remote Control Study of Embedded System using Mini Web Server” International Conference on Advanced Language Processing and Web Information Technology, Vol. No.2,Issue No.08,Pg no 341-346, 2010
4.        Rui Li, and Xiang Qiang Xiao,“Application Research of Embedded Web Technology in Traffic Monitoring System”, Proceedings of the Second Symposium International Computer Science and Computational Technology, ISCSCT, Pg no.94, 26 December 2009.
5.        V.Billy Rakesh Roy, Sanket Dessai, and S. G.Shiva Prasad Yadav, “Design and Development of ARM Processor Based Web Server”, International Journal of Recent Trends in Engineering, Vol. No.1, Issue No. 4, Pg.No. 94-98., May 2009.
6.        Fioretti, S. Pasqualini, A. Andreoli, P. Pierleoni, “Permanent Switchboard Monitoring using Embedded Web Server” International Conference on Renewable Energies and PowerQuality (ICREPQ), Vol. No.1, Issue No. 1, Pg.No. 01-06., 17th April, 2009.
7.        Guangjie Han, Deokjai Choi and Tam Van Nguyen, “A Lightweight Embedded Web Server For non-Internet Devices” , International Conference of Network and Computer Applications, Issue No. 5, Pg. No. 01-05., Sept 2007 (IEEE)
8.        M. Can Filibeli, Oznur Ozkasap, M. Reha Civanlar, “Embedded web server-based home appliance networks”, Journal of Network and Computer Applications, Vol. No.2, Issue No.30, Pgs. no 499-514, 2007
9.        Yanzheng LI, Shuicai WU, Jia LI, Yanping BAI “ The ECG Tele-monitor Based on Embedded Web Server”, International Conference on Bioinformatics and Biomedical Engineering, Digital Object Identifier :  10.1109/ICBBE.2007.196,  Pg no. 752 - 755 , 8 July 2007.

51-54

12.

Authors:

P.Ramya, K.Radha

Paper Title:

Design of Efficient Transport Protocol for Multimedia through Wireless Networks

Abstract:  Wireless Internet enables wireless connectivity to the Internet via radio waves. The rapid growth of wireless Internet, and broad band networking infrastructures, such as 3G and 3.5G, WLAN and WLAN-mesh and WiMAX, makes multimedia (audio and video) information available to us anytime, anywhere, on any device. The versatility of wireless internet has consumers demanding the service at an increasing rate. Therefore it is important to develop an efficient rate control protocol for the transmission of multimedia data through wireless networks which reduces all types of losses by satisfying multimedia constraints such as delay jitter, and to achieve a high throughput. In this approach, we make use of adaptive end to end loss differentiation algorithm for differentiating congestion losses from wireless losses and a function to differentiate burst wireless losses from transient wireless losses. Loss proportion increase and loss proportion decrease (PIPD) algorithm will be used for defining burst period in order to increase efficiency. Also evaluation will be performed by taking different decision factors into consideration and thus choose a suitable decision factor for reducing losses. The proposed approach can achieve high throughput, low losses, and good energy efficiency.

Keywords: Wireless Internet, multimedia, rate control, loss differentiation, RTT, throughput, TFRC, burst loss,Energy efficiency.

References:
1.        M. Handley, S. Floyd, J. Padhye, and J. Widmer, “TCP Friendly Rate Control (TFRC):      Protocol Specification,” Internet Standards Track RFC 3448, IETF, Jan. 2003.
2.        G. Cheung, T.Yoshimura, ”Streaming Agent: A network proxy for Media Streaming in 3G Wireless networks”, IEEE Packet video workshop,2002.
3.        Kamal D. Singh, David Ros, Laurent Toutain, César Viho: Improving Multimedia Streaming Over Wireless Using End-to-End Estimation of Wireless Losses. VTC Fall 2006: 1-5
4.        Chang-Hyeon Lim, Ju-wook Jang “An adaptive end-to-end loss differentiation scheme for TCP over wired/wireless networks”, IJSNS, VOL.7 No.3, March 2007.
5.        O. B. Akan and I. F. Akyildiz, “ARC: the analytical rate control scheme for real-time traffic in wireless networks,” IEEE/ACM Transactions on Networking, vol. 12, no. 4, pp. 634–644, 2004.
6.        S. McCanne and S. Floyd, “ns Network Simulator,” http://www.isi.edu/nsnam/ns/.
7.        F. Yang ,Q.Zang, W.Zhu ,Y.Q Zhang,” An end-to-end TCP-friendly streaming protocol for multimedia over Wireless Internet”, ICME,2003.
8.        E.N. Gilbert, “capacity of a burst-noise channel”, Bell Systems Technical journal vol.39, pp.1253-1265, sept.1960.
9.        Eric Hsiao-Kuang Wu, Yu-Chen Cheng, “JRC: jitter-based rate control scheme for wired-wireless hybrid network” international Journal of Pervasive Computing
and Communications; Vol: 3; 2007.
10.     S. Floyd and T. Henderson, The NewReno Modification to TCP's Fast Recovery Algorithm," RFC 2585, April 1999.
11.     C.Kun Tan;Qian Zhang;Wenwu Zhu; ”An end-to-end rate control protocol for multimedia streaming in wired-cum-wireless environments”In proceedings of International symposium on circuits and system ,ISCAS 2003,Beijing, China.
12.     L. Mamatas and V. Tsaoussidis. “Protocol Behavior: More Effort, More Gains?, PIMRC 2004”, September 2004, Barcelona, Spain.’
13.     Chiping Tang and Philip K. McKinley, “Modeling Multicast Packet Losses in Wireless LANs”, MSWIM '03  Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems Pages 130 – 133, NY, USA ©2003
14.     Ben Milner and Alastair James ,An Analysis of Packet Loss Models for Distributed Speech Recognition” , INTERSPEECH 2004
15.     Wajahat Abbas,Misbah Zareen, Asim Shahzad, Lala Rukh “Investigation of Packet Loss Patterns in Audio/Video Content Distribution over RTP in Wireless Networks”,  International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 10 No: 01
16.     S. Kontogiannis, L. Mamatas, I. Psaras, and V. Tsaoussidis “Measuring Transport Protocol Potential for Energy Efficiency”, Lecture Notes in Computer Science, wired/wireless internet communications 2005, Volume 3510/2005, 574-576.
17.     ITU-T recommendation G.114, February 1996.

55-58

13.

Authors:

D.D. Suryawanshi, S.T. Gaikwad, A.D. Suryawanshi A.S. Rajbhoj

Paper Title:

Ultrasound Synthesis and Antimicrobial Screening of Metal Complexes of 1– (5 – Chloro – 2–Hydroxyphenyl) – 3 – (2, 4 – Dichlorophenyl) Propane – 1, 3 – Dione

Abstract:  1-(5-chloro-2-hydroxyphenyl)-3-(2,4-dichlorophenyl) propane-1,3-dione and its metal complexes Cu(II), Ni(II), Co(II), Cr(III) and Fe(III) have been synthesized by ultrasound irradiation method. The diketone is offered by employing Baker-Venkatraman rearrangement. The synthesized compounds were confirmed by the spectroscopic analysis such as IR, 1H-NMR, 13C-NMR, mass, elemental analysis, magnetic susceptibility and evaluated for antibacterial screening.

Keywords:  β-diketone, Baker-Venkatraman rearrangement, metal complexes, magnetic susceptibility, antimicrobial screening, ultrasound irradiation..

References:
1.          Siedle in comprehensive Coordination chem.,1987, Wilkinson, pergamon press,Oxford, vol.2 cha15,4,pp 365
2.          S.T. Heller, S.R. Natrajan, S. Org. Lett; 2006 8, 2675    
3.          L. Tang, S. Zhang, J.Yang, w. Galo, J Cui,      T. Zhuang, Molecules;             2004,9,842.
4.          R. Kumar, Y.Joshi, Arkivoc; 2007, 9, 142
5.          D.Simoni, F. Invidiata, R. Rondanin, S. Grimaudo, G. Cannizzo, E. Barbusca, F. Porretto, N. Alessandro, M.Tolomeo, J. Med. Chem;1999,42,4961
6.          O.Kuzueva, Y. Burgart, V. Saloutin, O. Chupakhin, Chem. Hetero. Compds; 2001; 37,1130
7.          L. Tchertanov and J. Mouscadet, J. Med. Chem, 2007, 50, 1133
8.          K.Sato, S. Yamazoe, R. Yamamoto, S.,Ohata and A. Ando, org Lett, 2008, 10:2405-2408
9.          Andrae, A. Bringhen, F. Bohm, H. Gonzenbach, T. Hill, L. Mulroy and T. Truscott, J. photochemistry and photobiol, 1997, 37:147
10.       I.Bennett J. Broom, R. Cassels, J. Eleder, N. Masson and J. Hanlon, Bioinorganic and medicinal Chem Lett, 1999,9,1847
11.       T. Dziemboska, Z. Rozwadowski, Curr. Org. Chem; 2001; 5, 289-313
12.       V. Chate, S.S. Bhagat C.H. Gill, J. Heterocyclic Chem, 2011, DOI10.1002/955
13.       K. Singletary, C. Macdonald, M. Lovinelli, C. Fisher, M. Wallig Carcinogenesis; 1998; 19, 1039
14.       Lin, G. wei, M. Huang, J. Food and drug analysis; 2005; 13, 284
15.       C.Lin , Y. Tsai, M. Huang, Y. Lu, S. Tseng, Carcinogenesis; 2006; 27, 131-136
16.       Manuel, R. Silva and M. Luis, J. Chem. Thermodynamics 2005, 38:817
17.       R. Rajgopal, D. Jarikote, K. Srinivasan, Chem. Commun. 2002, 616
18.       A.V. Chate, R.Joshi, P. Mandhane., C.H. Gill, J. Korean Chemical Soci. 2011, 55, 419.       A.V. Chate, R.S. Joshi, V.Badadhe and C.H. Gill, Bull. Korean Chem. Soc, 2011,.vol.32,no.11,3887
20.       K.Singh, M.S. Barwa, and P. Tyagi European Journal of Medicinal Chemistry, 2006, vol. 41, no. 1, pp. 147
21.       C.Ballhausen, an introduction to Ligand Field Theory. New York, NY: Mc Graw Hill;1962.
22.       P.Mane, S. Shirodkar, T. Chondhekar, J. Indian Chemical Society. 2002, 79(A), 15, 4.
23.       N. Rao, D. Rao, M. Ganorkar, Indian J. Chemi 1982, 27(a), 839.
24.       Z. H. Chohan, M. Arif, M. A. Akhtar and C. T. Supuran, J. Bioinorganic Chemistry and Applications, 2006, Article ID 83131, 1-13.
25.       Sharma O, Singla R, Shrivastava B, Bhat V, Shenoy G, Sreenivasan K. Indo Global J. of Pharmaceutical Sciences; 2012; 2(1), 70-75.

59-61

14.

Authors:

Ankit Awasthi, Vipul Awasthi

Paper Title:

A New Approach To Analyse Qosparameters In Cognitive Radio

Abstract:   Cognitive radio is a technology for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. The spectrum sharing network consists of a pair of primary users (PUs) and a pair of cognitive users (CRs).The pair of PUs establishes a wireless link as the PU link. The PU link and CR link utilize spectrum simultaneously with different priorities. The PU link has a higher priority to utilize spectrum with respect to the CR link. The proposed work focusses on different spectrum allocation techniques for the secondary users, based on Genetic Algorithms and an analysis of the performance of these techniques with an assumption that the radio environment has already been sensed and the QoS requirements for the application have been specified either by the sensed radio environment or by the secondary user itself. The proposed work focusses towards the technique that not only work on the QoS of cognitive radio but also covers all the parameters for efficient communication like power and bandwidth. When the PU link utilizes spectrum, a desired quality of service (QoS) is to be assured and the CR utilizes spectrum with an opportunistic power scale under this constraint, assuring the desired QoS on the PU link. To compute an optimal opportunistic power scale for the CR link, a fuzzy-based opportunistic power control strategy is proposed based on the Mamdani fuzzy control model using four input variables: QoS,RSSI, bandwidth as well as noise delay with the opportunistic power management along with bandwidth allocation.

Keywords:   Cognitive radio networks, Fuzzy control, Power control, Bandwidth allocation ..

References:
1.          “A New Fuzzy Rule Based Power Management Scheme for Spectrum Sharing in Cognitive Radio” , AnileshDey,Susovan Biswas, SaradinduPanda,IEEE 2011.
2.          “Performance Evaluation of Qos Parameters in Cognitive Radio Using Genetic Algorithm” ,ManinderJeet Kaur, Moin Uddin and Harsh K. Verma,“World Academy of Science, Engineering and Technology 46 2010.
3.          “Fuzzy-based Opportunistic Power Control Strategy in Cognitive Radio Networks” Wail  MustafaandJhang Shih Yu, Elisabeth Rakus-Andersson, Abbas Mohammed and   Wlodek J. Kulesza IEEE 2010
4.          “An efficient power control scheme for cognitive radios” H. S. T. Le and Q. Liang, IEEE.WCNC 2007.
5.          “Optimal power allocation for cognitive radio under primary user’s outage loss constraint”, X. Kang, R. Zhang, Y. C. Liang, and H. K. Garg, in Proc. IEEEICC 2009.
6.          “Analysis and Enhancement of QoS in Cognitive Radio Network for Efficient VoIP Performance”, Tamal Chakrabortyl, AtriMukhopadhyay, SumanBhunia, ItiSahaMisra, Salil Kumar SanyalIEEE 2011.
7.          “OPTIMIZATION  OFQoS PARAMETER IN COGNITIVE RADIO USING ADAPTIVE GENETIC ALGORITHM”, Maninderjeetkaur,Moin Uddin & Harsh verma,  International journal of NEXT- GENERATION NETWORK (IJNGN) VOL.4,no 2,june 2012.
8.          “Cognitive radio: making software radios more personal, Personal Communications”, Mitola, J., III; Maguire, G.Q.Jr,  IEEE Volume 6, Issue 4, Aug1999.
9.          “The End of Spectrum Scarcity, Gregory Staple and Kevin Werbach”, IEEE Spectrum Online, March 2004.
10.       “Secondary User Selection Scheme Using Adaptive Genetic Algorithms for Cooperative Spectrum Sensing Under Correlated Shadowing”, DefengRen • JianhuaGe• Jing Li,SpringerScience+Business Media, LLC. 2012.

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15.

Authors:

Pratik D. Shah, Anil L. Wanare

Paper Title:

Semi Blind Restoration of Diversified Field Images

Abstract:    This paper is concerned with critical performance analysis of spatial linear restoration techniques for still images from various fields (Medical, Natural and Arial images).The performances of the linear restoration techniques are provided with possible combination of various additive noises and images from diversified fields. Efficiency of linear restoration techniques according to difference distortion and correlation distortion metrics is computed. Tests performed on monochrome images, with various synthetic and real-life degradations, without and with noise, in single frame scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio (ISNR) measure. The comparison of the present approach with previous individual methods in terms of mean square error, peak signal-to-noise ratio, and normalized absolute error is also provided. In comparisons with other state of art methods, our approach yields better to optimization, and shows to be applicable to a much wider range of noises. We discuss how experimental results are useful to guide to select the effective combination.

Keywords: About Additive noise, Correlation distortion metrics, linear image restoration, Monochrome image denoising, Wiener filter.

References:
1.          T.Weissman, E,Ordentlich, G.Seorussi, S.Verdi, and M.J.Weinberger, “Universal discrete denoising :known channel,” IEEE trans. Info. Theory, Jan.2005.
2.          Buades, B.Coll, and J.M.Morel, “ A new image denoising , with new one,” SIAM of multiscale Modeling (MMS)
3.          Anil L. Wanare, Dr. Dilip D. Shah, “ Performance Analysis and Optimization of Nonlinear Restoration Techniques in Spatial Domain,” International Journal of Image Processing, Volume 06, Issue 02,pp 123-137,April. 2012
4.          T.Weissman,E. Ordentlich, S. Verdi, and M.J.Weinberger, “A discrete universal denoiser and its application,” in proc. Of ICIP’2003, Barcelona, Sept.2003.
5.          A.kubota, K.Aiaiwa, “A reconstruction  arbitrarily focused images from two diff. focused images using linear  filters,” IEEE trans.IP, Vol. 14,no. 11, Nov.2011..
6.          S.chang, S.W.Yun, and P. Prak, “PSF search algorithm for dual exposer type blurred,” IEEE Trans., SP, Vol.15, no.02, Feb.2003. 
7.          M.Blume, D.Zikic, W.Wain, and N. Nawab, “A new and general methods for blind SV deconvolution of biomedical images,” in proc. MICCAI, 2007, pp.743-750.
8.          Kundur, D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Processing Mag., pp46-64, May1996.
9.          M. Sindhant Devi, V. Radhika “Comparative approach for speckle reduction in medical images,” International Journal of ART, Vol.01, Issue 01, pp-7-11, 2011.
10.       J. Umamaheshwari, “Hybrid denoising methods for mixed noise in medical images,” IJACSA, Vol.03, no. 05 pp-44-47, 2011.
11.       Mariana S.C Almeida and Luis B. Almeida, “Blind and semi blind deblurring of natural images,” IEEE Trans. Vol.19, no.01, Jan. 2011.
12.       R.Deriche, “Fast algo. For low level vision,” IEEE trans. MI, vol.12,no. 01, pp. 78-87, Jan. 1990.
13.       Anil K. Jain, “fundamental of digital image processing.” Englewood Cliffs, New Jersey, 1989.
14.       Peter J. Rausseeuw, and Christiph Croux, “ altenative to the median absolute deviation” Journal of American statistical association,” vol. 88, no. 424, pp 1273-1283.
15.       Tang Sang cho, Sing bing Kang, “Image restoration by matching gradient distribution,” IEEE trans. 
16.       Jean Tarel, Nicolas, “Fast visibility restoration from a single gray level images,” LCPC-INRETS (LEPSIS) , Paris France.
17.       Srinivasa and K. Nayar, “Contrast restoration of weather degraded images,” IEEE Trans. PAMI, Vol. 25, No. 06.
18.       M. D Grassberg and S.K.Nayar, “Modeling the space response function of camera,” IEEE trans. PAMI,vol. 26. Oct. 2004.
19.       Donoho, “Denoising by soft thresholding,” IEEE trans IT, vol. 03, 1995.
20.       J Astola and P. Kaosmanen, fudamentals of nonlinear and linear filtering, Boca Raton, CRC press, 1997.
21.       L.Yin, R. Yang M Gabbouj, and Y Neuvo, “Weighted median filtering for speckle suppression in medical imaging,” IEEE trans, CS, vol. 36.
22.       Stefano, P.White, “training method for image noise level estimation on wavelet component,” JASP, vol.16,2004.
23.       T.Chen, K.K.Ma, and L.H.Chen, “Tri state median filter for denoising,” IEEE Trans. IP. Vol.8, pp.1834-1838.
24.       Jong Sen Lee, “refined filtering of image noise using local statistics,” Computer Graphics and Image Processing, Vol.15, issue 04,pp.380-89.
25.       Chang Shing Lee, You Thang kuo, “Fuzzy set and system,” Vol.89, issue 02, pp. 157-180.
26.       E.Areu, M. Lightstone, S.K.Mitra, and K.Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Trans. IP, Vol.05, no06,pp 1012-1025.
27.       E.Abreu, “Signal dependent rank-ordered Mean filters,” Linear-Nonlinear Image processing, S.K.Mitra, Academic Press, 2000.
28.       P.S.Heckbert, “Filtering by repeated integration,” in proc. Int. conf. CGIT, 1986, vol20, no.04,pp. 315-321.
29.       Arrate Munoz-Barrutia, Michel Unser, “Fast Space-Varient Elliptical Filtering using Box Splines,” IEEE Trans, IP, Vol.19, No.9, Sept.2010.pp. 2290-2305

66-70

16.

Authors:

T.Sairam Vamsi,  K.Radha

Paper Title:

ARM Based Stair Climbing Robot Controlling Through DTMF Technology

Abstract: In today’s technology development, mobile robotics plays a vital role in many aspects because they are used to operate in hazardous and urban environments, for surveillance as well as military operations. Some of the mobile robots are designed to operate only on natural terrains, but some other also for rough terrains and artificial environments including stairways. In the age of ubiquitous systems it is necessary to be able to monitor the robots from everywhere. Although many methods to remotely control robots have been devised, the methods have the problems such as the need for special devices or software to control the robots. This paper suggests an advanced method for robotic control using the DTMF technology. Until recent years, the stair climbing robots are designed with vast hardware and robots are equipped with wheels to climb stairs or to move on a flat surface. The controlling mechanism has been advanced with the development of mobile robots. This paper addresses the design and implementation of DTMF controlled stair climbing robot. This climbing robot is operated with ARM7TDMI (LPC2148) controller. The mechanical design of the robot contains the roller chains instead of wheels and rubber blocks are attached to the roller chains to generate high friction with ground. Experimental trials showed that the implementation of the behavior control systems was successful.

Keywords:  Control, mobile Robot, PIC LPC2148, DTMF technology, roller chains

References:
1.     Nan Li, Shugen Ma,” An Online Stair-Climbing Control Method for a Transformable Tracked Robot,” Senior Member, IEEE, Bin Li, Minghui Wang, and Yuechao Wang, 2012.
2.     P. Ben-Tzvi, S. Ito, and A. A. Goldenberg, “Autonomous stair climbing with re-configurable tracked mobile robot,” in Proc. IEEE Workshop Robot. Sens. Environ, 2007, pp. 1–6.
3.     Shatnawi, A. Abu-El-Haija, A. Elabdalla, “A Digital Receiver for Dual-Tone Multi-frequency (DTMF) Signals”, Technology Conference, Ottawa, CA, May 1997.
4.     http://seminarprojects.com/Thread-mobile-controlled-robot-using-dtmf-technology.
5.     Dr. Basil Hamed, “Design and Implementation of Stair-Climbing Robot for Rescue Applications,” in International Journal of Computer and Electrical Engineering,
Vol. 3, No. 3, June 2011.
6.     Saleh Ahmad, Hongwei Zhang, and Guangjun Liu, Senior Member, IEEE“Multiple Working Mode Control of Door-Opening With a Mobile Modular and Reconfigurable Robot” IEEE/ASME TRANSACTIONS ON MECHATRONICS, 2012.
7.     http://www.nxp.com/documents/data_sheet/LPC2141_42_44_46_48.pdf
8.     Junke Li1, YujunWang2, TingWan3Department of C&IS, Southwest University, Chongqing, China “Design of A Hexapod Robot”.2008.
9.     R. C. Luo, K. L. Su, “Amultiagentmulti sensor based real-time sensory control system for intelligent security robot” IEEE International Conference on Robotics and Automation, vol. 2, 2003, pp.2394 –2399.
10.  Nicolai Dvinge, Ulrik P. Schultz, and David Christensen Maersk Institute University of Southern Denmark. “Roles and Self-Reconfigurable Robots”2010 IEEE.
11.  Raúl A. Gonzales, Federico A. Gaona,”An Autonomous Robot Based on a Wheelchair,” IEEE International Conference on Robotics and Automation, vol. 5, ©2012 IEEE.
12.  http://users.ece.utexas.edu/~valvano/Datasheets/L293D_ST.pdf.
13.  Raúl A. Gonzales, Federico A. Gaona, Raúl R. Peralta.“An Autonomous Robot Based on a Wheelchair”, 2012 IEEE.
14.  Michael Fair, “Autonomous Stair climbing With A Mobile Robot,” A Thesis Report Submitted To University Of Oklahoma Graduate College, Norman, Oklahoma 2000.

71-74

17.

Authors:

K. Gafoor Raja, G. Ramakrishna

Paper Title:

Wireless Control of Serpentine like Robot for Industrial Inspection and Surveillance

Abstract:  This paper focuses on a Robot which is biologically inspired from nature. Snakes are unique because they utilize the irregularities in the terrain and make an effective motion. This robot is designed to visualize the situation and to measure the environment parameters. Snake is composed of segments, those are individually controlled. In particular the locomotion of snake is controlled by CAN-bus. Among the all available buses the CAN-bus is faster and provides real time data transfer. A wireless technology (ZigBee) is introduced between robot section and monitoring section. The measured values are updated on the PC.

Keywords:   Snake Robot, CAN-bus, locomotion control, surveillance, sensing.

References:
1.        Hirose, S.; Yamada, H., "Snake-like robots [Tutorial]," Robotics & Automation Magazine, IEEE , vol.16, no.1, pp.88,98, March 2009.
2.        S. Hirose, Biologically inspired robots / Snake-like locomotors and manipulators, Oxford University Press, 1993.
3.        Peteris Apse-Apsitis, “CAN bus elements in robotic snake-like movement device control”, 10th International Symposium, Topical Problems in the Field of  Electrical and Power Engineering, Pärnu, Estonia, January 10-15, 2011.
4.        Maruyama, H.; Ito, K., "Semi-autonomous snake-like robot for search and rescue," Safety Security and Rescue Robotics (SSRR), 2010 IEEE International Workshop on , vol., no., pp.1,6, 26-30 July 2010.
5.        Murai, R.; Ito, K.; Nakamichi, K., "Proposal of a snake-like rescue robot designed for ease of use -Improvement of operability for non-professional operator-," Industrial Electronics, 2008. IECON 2008. 34th Annual Conference of IEEE , vol., no., pp.1662,1667, 10-13 Nov. 2008.
6.        Bayraktaroglu, Z.Y.; Kilicarslan, A.; Kuzucu, A., "Design and Control of Biologically Inspired Wheel-less Snake-like Robot," Biomedical Robotics and Biomechatronics, 2006. BioRob 2006. The First IEEE/RAS-EMBS International Conference on , vol., no., pp.1001,1006, 20-22 Feb. 2006.
7.        Zhang Yue; Li Xin; Zhou Xianli, "A Reconfigurable Snake Robot Based on CAN-Bus," Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on , vol.1, no., pp.493,497, 23-25 March 2012
8.        FANG Haijun. The control system of CAN-bus-based independent mimic robot [J]. ROBOT, 2002, 24(1): 58-61.
9.        Rashid, M.T.; Ali, A.A.; Ali, R.S.; Fortuna, L.; Frasca, M.; Xibilia, M.G., "Wireless underwater mobile robot system based on ZigBee," Future Communication
Networks (ICFCN), 2012 International Conference on , vol., no., pp.117,122, 2-5 April 2012
10.     Varga, M.; Pogar, I.; Varga, P.; Mate, A.; Vegh, J., "Developing sensors and real-time controlling software for a robot," Carpathian Control Conference (ICCC), 2012 13th International , vol., no., pp.752,756, 28-31 May 2012
11.     Chen Li, Wang Yuechao, Ma Shugen, Li Ben ; The Research of a Reconfigurable Snake Robot, China Mechanical Engineering Phase 1, 2003.
12.     Philips LPC 2000 CAN interface driver manual.  http://www.nxp.com/documents/application_note/AN10438.pdf
13.     ARM7 controller (LPC 2148) data sheet http://www.keil.com/dd/docs/datashts/philips/lpc2141_42_44_46_48.pdf‎
14.     Motor driving circuit IC L293D data sheet

75-78

18.

Authors:

Harvesh Singh Panwar, Firoz Khan, Puneet Khanna

Paper Title:

Design & Analysis of Square Microstrip Patch Antenna

Abstract: This research aims at designing and analysis of Square Microstrip patch Antenna, Communication between humans was first by sound through voice. With the desire for slightly more distance communication came, devices such as drums, then, visual methods such as signal flags and smoke signals were used. These optical communication devices, of course, utilized the light portion of the electromagnetic spectrum. It has been only very recent in human history that the electromagnetic spectrum, outside the visible region, has been employed for communication, through the use of radio. One of humankind’s greatest natural resources is the electromagnetic spectrum and the antenna has been instrumental in harnessing this resource. This paper provides a detailed study of how to design and fabricate a probe-fed Square Microstrip Patch Antenna using IE3D software and study the effect of antenna dimensions Length (L), and substrate parameters relative Dielectric constant (ε_r), substrate thickness (t) on the Radiation parameters of Bandwidth and Beam-width. The proposed antenna is designed at the height of 1.59mm from the ground plane and this design is operated at 3.0GHz.

Keywords:    Square Patch Antenna, VSWR, Return Loss.

References:
1.     R. Garg, P. Bhartia, I. Bahl, A. Ittipiboon, “Microstrip Antenna Design   Handbook”, ARTECH HOUSE, Boston 2001.
2.     Yasir Ahmed, Yang Hao and Clive Parini, “A 31.5 GHZ Patch Antenna Design for Medical Implants”, University of London, International Journal of Antennas & Propagation”, volume 2008, (2008), article ID 167980.
3.     F. E. Gardiol, “Broadband Patch Antennas,” Artech House.
4.     Shivnarayan & Babu R Vishvakarma “Analysis of notch-loaded patch for dual-band operation”, Indian Journal of Radio & Space Physics.Vol.35, pp.435-442.
5.     S. Silver, “Microwave Antenna Theory and Design”, McGraw-HILL BOOK COMPANY, INC, New York 1949
6.     C.A. Balanis, Antenna Theory, 2nd Ed., John wiley & sons, inc., New York.1982

79-81

19.

Authors:

Ruta V. Mulajkar, A.S. Devare

Paper Title:

Failure Detection Using E-ARS in WMN

Abstract:  Wireless  Mesh  Networks  (WMNs)  consist  of  mesh  routers  and  mesh  clients for  their  lifetime,  multihop  wireless  mesh networks  (WMNs)  experience  frequent  link  failures  caused  by  channel  interference,  dynamic  obstacles, and/or applications’ bandwidth demands. Dynamic channel allocation for effective autonomous network reconfiguration system (E-ARS), by analyzing E-ARS, it shows that by using E-ARS alone it wont provide a sufficient result such as network quality, leader assigning problems etc, so in order improve the network performance we going to implement a new concept Breadth First Search Channel Assignment (BFS-CA) algorithm against with E-ARS so that it will multi radio configuration for mesh network and channel assignment problems. We demonstrate our solution’s through the evaluation of a prototype implementation in an IEEE 802.11 in ns2. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance more gains compared E-ARS.

Keywords:  IEEE 802.11, multiradio wireless mesh networks (mr-WMNs), E-ARS, BFS-CA networks, wireless link failures.

References:
1.        K. Ramanchandran, E. Belding-Royer, and M. Buddhikot, “Interference- aware channel assignment in multi-radio wireless mesh networks,” in Proc. IEEE INFOCOM, Barcelona, Spain, Apr. 2006.
2.        M. Alicherry, R. Bhatia, and L. Li, “Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks,” in Proc. ACM MobiCom, Cologne, Germany, Aug. 2005
3.        P. Subramanian, H. Gupta, S. R. Das, and J. Cao, “Minimum interference channel assignment in multiradio wireless mesh networks,”IEEE Trans. Mobile Comput., vol. 7, no. 12, pp. 1459–1473, Dec. 2008
4.        K.-H. Kim and K. G. Shin, “On accurate and asymmetry-aware measurement of link quality in wireless mesh networks,” IEEE/ACMTrans.Netw., vol. 17, no. 4, pp. 1172–1185, Aug. 2009.
5.        P. Kyasanur and N. Vaidya, “Capacity of multi-channel wireless networks:Impact of number of channels and interfaces,” in Proc. ACM MobiCom, Cologne, Germany, Aug. 2005, pp. 43–57.
6.        Brzezinski, G. Zussman, and E. Modiano, “Enabling distributed throughput maximization in wireless mesh networks: A partitioning approach,” in Proc. ACM MobiCom, Los Angeles, CA, Sep. 2006, pp.26–37.
7.        S. Chen and K. Nahrstedt, “Distributed quality-of-service routing in ad hoc networks,” IEEE J. Sel. Areas Commun., vol. 17, no. 8, pp.1488–1505, Aug. 1999
8.        R. Draves, J. Padhye, and B. Zill. Routing in Multi-radio, Multihop Wireless Mesh Networks. In ACM MobiCom, Philadelphia, PA ,September 2004
9.        P. Bahl, R. Chandra, and J. Dunagan. SSCH: Slotted Seeded Channel Hopping For Capacity Improvement in IEEE 802.11 Ad Hoc Wireless Networks. In ACM MobiCom, Philadelphia, PA, September 2004
10. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Comput. Netw., vol. 47, no. 4, pp. 445–487, Mar. 2005.

82-85

20.

Authors:

B.Suresh, M.V.Srikanth

Paper Title:

DDS Based Multi Channel Radar Waveform Generator

Abstract:  A radar waveform generator is designed and it is implemented by using directly digital modulation method based on DDS. It can generate arbitrary signals whose amplitude, frequency or phases are controlled by the description words given from an external computer. Accurate waveforms can be generated. We can generate waveforms digitally by using Direct digital synthesis technique. DDS principles of the technique are simple and widely applicable. Direct Digital Synthesizer (DDS) is a frequency synthesizer  which can generate arbitrary waveforms from a single, fixed-frequency reference clock. DDS Applications include:  function generators, modulators. Here in order to implement radar waveform generator along with DDS we need FPGA and microcontroller. In order to generate the waveform using DDS we need to store the hexadecimal data into the internal registers of DDS chip along with that we need some control signals which will be generated using FPGA . First from PC we need to send the hexadecimal data through serial port for that we need design a Graphical User Interface (GUI).In GUI we will enter the amplitude, phase and frequency of the waveform to be generated and then it has to covert it into particular hexadecimal data based on the formulas mentioned in the datasheet, then it has to send the data through serial port. Now, microcontroller has to receive the data from PC and then it has to send the data to the FPGA. FPGA has to receive that data, along with that it has to generate some control signals based on the control signals, received data will be send to the internal registers of DDS chip.

Keywords:   Direct Digital Synthesizer (DDS), FPGA microcontroller

References:
1.        “Development of an Eight Channel Waveform Generator for Beam-forming Applications”,john ledford.
2.        “FPGA-Based Design, Implementation, and Evaluation of Digital Sinusoidal Generators”, 2008 IEEE.
3.        Analog Devices, A technical tutorial on digital signal synthesis, 1999.
4.        Microchip, MPLAB_User_Guide_51519c
5.        Digital frequency synthesis demystified / Bar- Giora Goldberg, 1999.
6.        Digital modulation techniques / Fuqin Xiong,2000.
7.        Xilinx Spartan-3E Evaluation Kit User Guide
8.        Analog Devices, AD9910 Data Sheet.
9.        Analog Devices, ADCLK846 Data Shee.t
10.     Analog Devices, AD9520 Data Sheet.
11.     Analog Devices, CN0121 Circuit Note.
12.     Spartan3E_FPGA_User Guide
13.     Cadence Layout_Tutorial
14.     Microchip, dsPIC33F Product Overview

86-88

21.

Authors:

Ritesh B. Meshram

Paper Title:

Motion Control of Wheeled Mobile Robots Using Fuzzy Logic

Abstract:   In this paper, we propose a fuzzy logic controller for the motion control of a wheeled mobile robot (WMR) in simulation environment. We address the problem of mobile robot tracking and formation control. The leader mobile robot is controlled to reach the desired position, and the follower mobile robot keep constant relative distance and constant angle to the leader robot. Algorithms  for  controlling  robot  formations  have  been inspired  by  biological  and  organizational  systems. Simulation is conducted in Matlab to investigate the performance of the proposed fuzzy controller.

Keywords:    Mobile wheeled robot, fuzzy logic controller, leader- follower,formation.

References:
1.        DongbingGu, “A Differential Game Approach to Formation Control” IEEE Transactions On Control Systems Technology, Vol. 16, No. 1, January 2008
2.        MiˇselBrezak, Ivan Petrovi´c and NedjeljkoPeri´c,”Experimental Comparison of Trajectory Tracking Algorithms for Nonholonomic Mobile Robots”, Industrial Electronics, IECON '09. 35th Annual Conference of IEEE, 2009.
3.        E. Camponogara, D. Jia, B. Krogh, and S. Talukdar, “Distributed model predictive control,” IEEE Control Syst. Mag., vol. 22, no. 1, pp. 44–52, Feb. 2002
4.        T. Balch and R. Arkin, “Behavior-based formation control for multirobot systems,” IEEE Trans. Robot. Autom., vol. 14, no. 2, pp. 926–939, 1998
5.        AltamiroVeríssimo da SilveiraJúnior, Elder Moreira Hemerly ,”Kinematic Control Of The Magellan-Isr Mobile Robot” ABCM Symposium series in mechanics-Vol. 1-pp.48-57, 2004
6.        T. Basar and G. Olsder, Dynamic Noncooperative Game Theory. Warrendale, PA: SIAM, 1995.
7.        J. C. Engwerda, LQ Dynamic Optimization and Differential Games. New York: Wiley, 2005
8.        Shamsi F.*, Abdollahi F.*, Nikravesh K.Y.*,“Time varying Formation Control Using Differential Game Approach”,18th IFAC World Congress Milano (Italy) August 28 - September 2, 2011
9.        J. Shinar and V. Glizer, “Application of receding horizon control strategy to pursuit-evasion problems,” Opt. Control Appl. Methods,vol. 16, no. 2, pp. 127–142, 1995.
10.     Sunil Surve, Thesis on “Coordination Control And Synchronization Of Multi‐Agent System”, VJTI Nov‐2010
11.     GregorKlanˇcar_, Igor ˇ Skrjanc, “Tracking-error model-based predictive control for mobile robots in real time”, ScienceDirect,Robotics and Autonomous
Systems 55,page 460–469 (2007)
12.     Jian CHEN1 , Dong SUN 2 and Jie YANG3 “A Receding-Horizon Formation Tracking Controller With Leader-Follower Strategies” Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11, 2008
13.     Zachary Lamb thesis on “Model Predictive Control of a Wheeled Mobile Robot with Nonlinear, Parametric Model of Wheel Slip” Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, August 2011.
14.     Yanyan Dai, Viet-Hong Tran, ZhiguangXu, and Suk-Gyu Lee “Leader-Follower Formation Control of Multi-robots by Using a Stable Tracking Control Method” ICSI 2010, Part II, LNCS 6146, pp. 291–298, 2010
15.     DongbingGu∗ and Huosheng Hu “A model predictive controller for robots to follow a virtual leader” Cambridge University Press,Robotica: page 1 of 9, 2009
16.     Xiaohai LI and Jizhong XIAO “Robot Formation Control in Leader-Follower Motion Using Direct Lyapunov Method”,International Journal Of Intelligent Control And Systems, Vol. 10, No. 3, September 2005
17.     Seung-IkLee,Sung-BaeCho “Emergent Behaviors of a Fuzzy Sensory-Motor controller Evolved by Genetic Algorithm”, Journals & Magazines, Volume: 31 , Issue: 6, Dec 2001.
18.     Ching-Chang Wong, Hoi-Yi Wang, Shih-An Li, and Chi-Tai Cheng “Fuzzy Controller Designed by GA for Two-wheeled Mobile Robots”,International Journal of Fuzzy Systems, Vol. 9, No. 1, March 2007 
19.     Oscar Castillo, Luis T. Aguilar, and S´eleneC´ardenas”Fuzzy Logic Tracking Control for Unicycle Mobile Robots” Engineering Letters, 13:2, EL_13_2_4 (Advance online publication: 4 August 2006).
20.     ZenonHendzel “Fuzzy Reactive Control O Fwheeled Mobile Robot”Journal Of The Oretical And Applied Mechanics42, 3,pp.503-517,Warsaw 2004
21.     O. Ob e, I. Dumitrache”AdaptiveNeuro-Fuzzy Controler With Genetic Training For Mobile Robot Control” Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 Vol. VII (2012), No. 1 (March), pp. 135-146
22.     Rajeev  Kumar Sharma and  M.K Muju “Methodology For Kinematics Modeling Of Articulated  Rovers, Enhanced With Fuzzy Logic System”,XXXII National Systems Conference, NSC  2008, December 17-19, 2008
23.     SitiNurmaini ,AngginaPrimanita  “Modeling of Mobile Robot  System with Control Strategy Based  on Type-2 Fuzzy Logic”International Journal of Information and Communication Technology Research, Volume 2 No. 3,  March  2012.
24.     SouravDutta “Obstacle Avoidance of mobile robot using PSO based Neuro Fuzzy Technique” International Jour nal on Computer Science and Engineering Vol. 02,
No. 02, 2010, 301-304
25.     M. Mucientes* D.L. Moreno, A. Bugarı´n, S. Barro”Design of a fuzzy controller in mobile robotics using genetic algorithms”,Elsevier,Volume 7, Issue 2, Pages 540–546, March 2007,
26.     M. Hellmann ”Fuzzy Logic Introduction” ,Epsilon Nought Radar Remote Sensing Tutorials, 2001.
27.     Fuzzy Logic - Algorithms, Techniques and Implementations Edited by Elmer P. Dadios, ISBN 978-953-51-0393-6, Hard cover, 294 pages, Publisher: InTech, Published: March 28, 2012

89-93

22.

Authors:

Gurjeet Singh, O. P Gupta, B.K. Sawhney, Salam Din

Paper Title:

A Circular Cache Layer Based Caching Algorithm in Wireless Sensor Network

Abstract:   This technique proposes Circular Cache Layer Based (CCLA) Algorithm to divide the load of query from single cluster head1. All sensor nodes are commonly resource constrained and have limited amount of energy .In wireless sensor networks, BS (Base Station) is generally set away from the target area like base research. In base research single CH transmits data directly to BS. Single CH(cluster head) there is heavy load on CH because CH  broadcast query to all nodes in cluster, receive data from nodes ,aggregate data, amplify signal and transmit to base station. So sensor nodes become dead after working as CH but in proposed technique there are two cluster heads CH1 and CH .CHs are not stationary means CHs changes within cluster according to available battery .CH2 is responsible for collecting data from sensor nodes, store this data and transmit to CH1 when required.CH2 contain data time bounded interval according to TTL (time to live) process CH1 transmit information to BS. This technique provides load balancing. In this research calculate average energy of CHs and number of time rounds in which sensor nodes become dead. Simulation results comparing with previous protocol prove that our new algorithm is able to extend the network lifetime observably.

Keywords: About four key words or phrases in alphabetical order, separated by commas.

References:
1.        Cao Guohong, Yin Liangzhong and. Das Chita R (2004) Cooperative based data access in Ad Hoc Networks. IEEE Computer Society.
2.        I.F A and Su W and Sankarasubramaniam Y and Cayirci E (2002) Wireless sensor networks: a survey. IEEE Communications Magazine 40(8) : 102- 114.
3.        Kaur N and Bansal M (2012) Energy Efficient and Intelligent Clustering Protocol for WSN Based on STBC transmission: IEEE.
4.        Li Jinbao and Ji Shouling (2008) Data Caching Based Queries in Multi-Sink Sensor Networks. IEEE Fifth International Conference on mobile Ad-hoc and Sensor
Networks: 9-16.
5.        Lim H,  Kim S, Yeo H, Kim S and  Ahn K (2007) Maximum Energy Routing Protocol based on Strong Head in Wireless Sensor Networks. Sixth International Conference on Advanced Language Processing and Information Technology pp. 414-19.
6.        Rahman A Md and Hussain S (2007) Effective Caching in Wireless Sensor Network. 21st International Conference on Advanced Information Networking and Applications Workshops 1: 43-47.
7.        Sharma T P and Joshi R C (2008) Dual Radio Based Cooperative Caching for Wireless Sensor Networks. 16th IEEE International Conference on Digital Object Identifier pp. 1-7.
8.        Wei H, Chen L and Zhang Y (2012) proposes a protocol, Expected Number of Cluster Members clustering algorithm in International Conference on Systems and Informatics (ICSAI 2012)
9.        Xu Junfeng, Li Keqiu and Shen YanMing (2008) An Energy-Efficient Waiting Caching Algorithm in Wireless Sensor Networks. IEEE/IFIP International Conference on Embedded and Ubiquitous Computing 1: 323-29.
10.     Ye F, Luo H, Cheng J and Lu S (2002) A Two Tier Data Dissemination Model for Largescale Wireless Sensor Networks. 8th annual international conference on Mobile computing and networking pp. 148-59.
11.     Zhang Y, Kuhn L and Fromherz  M(2004) Improvements on Ant Routing for Sensor Networks. Ant Colony Optimization and Swarm Intelligence, LNCS, 3172, pp. 289-313.
12.     Zungeru A M, Ang L M, Prabaharan S and Seng K (2011) Improved energy efficient ant-based routing algorithm in wireless sensor networks.  Ragab K, Abdullah A B, Zaman N (ed.). Wireless Sensor Networks and Energy Efficiency: Protocols, Routing and Management, 20. Hershey, PA 17033, USA: IGI Global,  pp. 420–44.

94-95

23.

Authors:

Prashant H. Band, Seema R. Chaudhary, Radhakrishna Naik

Paper Title:

KVM Based Virtualization for Low Cost Computing

Abstract:   In this paper, we propose a Virtual Remote environment to user over a LAN by using open source system and virtualization tools. Today’s Desktop processors offer great computation functionality, fast processing power and memory. The aim is to provide low cost computing using open source technology and various free remote desktop environments. We used free Terminal Server [1], [2] to provide desktop, remote desktop tools and KVM (Kernel-based Virtual Machine) hypervisor [3], [4] to create and run virtual machines. Users can get a personalized OS (Operating System) which is the VM (Virtual Machine) running over a Local server. User gets an independent OS through the various graphical desktop sharing systems, which is platform independent. Complete resource utilization is possible because VMs run on Remote Server. As we are using open source OS and tools, this implementation becomes inexpensive. Some solutions like Oracle VM server for SPARC [5], CtrixXEN Desktop [6], and VM ware View [7], [8] are available but they are costly and proprietary. By using this method we can make use of very low configured system as a client system to access VM's desktop for best performance.

Keywords: OS, Virtualization, Hypervisor, KVM, and Remote Desktop. .

References:
1.        LTSP”, https://help.ubuntu.com/community /UbuntuLTSP.
2.        “Linux Terminal Server Project”, www.ltsp.org.
3.        www.linux_kvm.org/page/HOWTO.
4.        www.linuxnix.com/2013/02.
5.        “Running Oracle Real Application Clusters on Oracle SPARCE”, An oracle white paper, July 2012.
6.        en.wikipedia.org/wiki/Citrix Systems.
7.        www.vmware.com/products/view/overview.html.
8.        www.vmware.com/in/products/desktop_virtualization/workstation/ overview.html.
9.        en.wekipedia.org/wiki/Virtual_machine.
10.     en.wekipedia.org/wiki/Virtualization.
11.     en.wekipedia.org/wiki/ Desktop_Virtualization.
12.     Paul Barham, Boris Dragonic, “Xen and the art of virtualization,” in ACM 2003.
13.     “A performance Analysis of XEN and KVM hypervisors for hosting XEN Worlds Project” (2011) Charles David Graziano, Graduate Theses & Dissertation paper 12215.
14.     en.wikipedia.org/wiki/Hypervisor.
15.     en.wikipedia.org/wiki/VMware_view.
16.     https://access.redhat.com/site/documentation/en-US/Red_Hat_Enterprise_Linux/5/html/Virtualization/.
17.     “Low Cost Computing Using Virtualization For Remote Desktop”, Dhaval Manvar, Mayank Mishra, Anirudha Saho, Computer Science And Engineering Department, Indian Institute of Technology Bombay, 978-1-4673-0298 2012 IEEE.
18.     en.wikipedia.org/wiki/X86_virtualization.
19.     en.wikipedia.org/wiki/Virtual_Network_Computing.

96-98

24.

Authors:

M. V. Yadav, G. T. Chavan

Paper Title:

Predictive Location-Based QoS Routing with Admission Control in Mobile Ad-hoc Networks

Abstract:   This paper is aimed at identifying the issues and challenges involved in providing QoS in MANETs, overcoming these issues by using predictive location based QoS routing with admission control which are required to ensure high levels of QoS, improving bandwidth, throughput and minimizing packet loss rate, end to end delay, jitter, throughput QoS metrics. Distributed Admission Control Mechanism (DACME) applies on the valid route to check whether that route satisfies QoS or not if it satisfies QoS requirement then only route is selected otherwise rejected.The aim here is to improve peer to peer communication in wireless mobile ad hoc networks by identifying the location of mobile node using Predictive Location Based Routing Protocol (PLBRP) and Admission Control mechanism. This project can adapt to applications with bandwidth, delay and jitter constraints .This application proposes optimizations based on interactions between routing, and admission control layers which offer important performance improvements

Keywords:  Predictive Location Based QoS Routing Protocol ( PLQRP ),  Quality of Service (QoS), Medium Access Control (MAC), QoS Specification (QSPEC).  .

References:
1.        Boomarani Malany et. al., “Throughput and Delay Comparison of MANET Routing Protocols”, Int. J. Open Problems Compt. Math., Vol. 2, No. 3, September 2009 ISSN 1998-6262, 2009, www.i-csrs.org.
2.        R. Lin and J. S. Liu, “QoS Routing in Ad-Hoc Wireless Networks.”, IEEE Journal On Selected Areas In Communications, Vol.17, No.8, pages 1426-1438, August 2009.
3.        G. Santhi and Alamelu Nachiappan, “A Survey of QoS Routing Protocols for Mobile Ad Hoc Networks”, International journal of computer science & information
Technology (IJCSIT) Vol.2, No.4, Au-gust 2010.
4.        JooSang Youn, Sangheon Pack and Yong-Geun Hong, “ Distributed admission control protocol for end-to- end QoS assurance in ad hoc wireless net-works ”, youn et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:163.
5.        L Hanzo II and R. Tafazolli, “Admission Control Schemes for 802.11-Based Multi-Hop Mobile Ad Hoc Networks: A Survey”, IEEE Comm. Surveys and Tu-torials, vol. 11, no. 4, pp. 78-108, Oct.- Dec 2009.
6.        L. Hanzo II and R. Tafazolli, “A survey of QoS Routing Solutions for Mobile Ad Hoc Networks”, IEEE comm., vol 9, no. 2, 2nd MAY 2007.
7.        L. Hanzo II and R. Tafazolli, “QoS-Aware Routing and Admission Control in Shadow-Fading Environments for Multirate MANETs”, Wiley J. Wireless Comm. and Mobile Computing, vol 10,no.5, May. 2011.
8.        M. P. Malumbres, C. T. Calafate, “QoS Support in MANETs: A Modular Architecture Based on the IEEE 802.11e Technology”, IEEE Transaction on Circuits and systems, vol. 19, NO. 5, MAY 2009.
9.        Samarth H. Shah, Klara Nahrstedt,  “Predictive Location Based QoS Routing in Mobile Ad Hoc Net-works”, Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, U.S.A, 2002.
10.     Y. Yang and R. Kravets, “Contention-Aware Admission Control for Ad Hoc Networks”, IEEE Trans. Mo-bile Computing, vol. 4, no. 4, pp. 363-377, July/Aug. 2005..

99-103

25.

Authors:

Ashwini S. Mane, P. M. Kamde

Paper Title:

Video Classification using SVM

Abstract: In today’s world, information is indispensable in each and every activity. Precise retrieval of information according to the user’s requirement is the dire need of the day. A Content Based Video Retrieval System, in a nutshell, aims at assisting a user to retrieve a video sequence target within a potentially large database. Content-based Video Retrieval Systems (CBVRS) are less common and is even now  a research area. There are no existing systems of CBVRS  in  use because of  various restrictions like video size, characteristics, high error rate etc. Search engines like Google etc use textual annotations to retrieve videos for the user which has very high error rate. Content based video retrieval  has lots of applications in varied areas like medical sciences, news broadcasting, advertizing, video archiving and will surely revolutionize the field of information technology. A consequence of the growing consumer demand for visual information is sophisticated technology that is needed for representing, modeling, indexing and retrieving multimedia data. In particular, we need robust techniques to index/retrieve and compress visual information. Therefore this system Video Classification using SVM will play an important role in information retrieval and information storage.

Keywords:   Direct Frame Difference, FFT , Mean and Standard Deviation ,Support Vector Machine .

References:
1.        L. Chaisorn, et. al.,”The segmentation of news video into story units”,in Proc. of IEEE Int. Conf.on Multimedia and Expo (ICME), vol. 1, pp.73 - 76, 2002.
2.        M.R. Naphade et. al.,”A probabilistic framework for semantic video indexing, filtering, and retrieval”,IEEE Transactions on Multimedia, vol. 3(1),pp. 141-151, 2001.
3.        G. Xinbo et. al.,”A graph-theoretical clustering based anchorperson shot detection for news video indexing”,in Proc IEEE Int. Conf On Computational Intelligence and Multimedia Applications (ICCIMA),pp 108 - 113, 2003.
4.        Stefan Eickeler et. al.,”Content-based video indexing of tv broadcast news using hidden markov models”, in Proc. IEEE ICASSP, 1999, pp. 2997-3000.
5.        Weiming Hu et. al.,”A Survey on Visual Content-Based Video Indexing and Retrieval”,ieee transactions on systems, man, and cybernetics part c, applications and
reviews, vol. 41, no. 6, november 2011.
6.        Vincent S. Tseng et. al,”Integrated Mining of Visual Features,Speech Features, and Frequent Patterns for Semantic Video Annotation”,ieee transactions on multimedia, vol. 10, no. 2, february 2008.
7.        Liuhong Liang et. al.,”Enhanced Shot Boundary Detection Using Video Text Information”, IEEE Transactions on Consumer Electronics, Vol. 51,No. 2, MAY 2005.
8.        Yingying Zhu et. al,” Video Scene classification and Segmentation based on Support Vector Machine”,978-1-4244-1821-3/08.
9.        Wan Jianping et. al.,”News Video Story Segmentation Based on Nave Bayes Model”,978-0-7695-3736-8/09 25.00 2009 IEEE.
10.     X. Li et. al., ”Content-Based News Video Mining”,ADMA 2004, LNAI 3584, pp. 431438, 2005.Springer-Verlag Berlin Heidelberg 2005 .

104-106

26.

Authors:

Monika Rana, Sudesh Kumar, Surbhi Sangwan

Paper Title:

Combinatorial Auction for Grid Computing

Abstract:  Grid computing, emerging as a new paradigm for next-generation computing, enables the sharing, selection, and aggregation of geographically distributed heterogeneous resources for solving large-scale problems in science, engineering, and commerce. The resources in the Grid are heterogeneous and geographically distributed. The paper demonstrates the capability of economic-based systems for wide-area parallel and distributed computing by using auction-oriented approach. In this paper combinatorial auction  has been discussed. This  auction model allows biders to bid on various  attributes beyond the price .The overall utility of a deal for the buyer must consider a combination of the different attributes. This model has also been implemented. The auctioneer selects winners based on the price as well as on other attributes.

Keywords:  Combinatorial Auction, Grid computing, Resource management, Economic models and  Auction Models.

References:
1.       Berman F, Wolski R. The AppLeS Project: A status report. Proceedings of the 8th NEC Research Symposium, Berlin,Germany, May 1997.
2.       Casanova H, Dongarra J. NetSolve: A network server for solving computational science problems. International Journal of Supercomputing Applications and High Performance Computing 1997; 11(3):212–223.
3.       Chapin S, Karpovich J, Grimshaw A. The Legion resource management system. Proceedings of the 5th Workshop on Job Scheduling Strategies for Parallel Processing, San Juan, Puerto Rico, 16 April 1999. Springer: Berlin, 1999.
4.       Fipa dutch auction interaction protocol specification. FIPA - Foundation for Intelligent Physical Agents (http://www.fipa.org/), August (2001).
5.       Fipa english auction interaction protocol specification. FIPA - Foundation for Intelligent Physical Agents (http://www.fipa.org/), August (2001).
6.       Foster, I. and Kesselman, C, “The Grid: Blueprint for a New Computing Infrastructure,” Morgan   Kaufmann, 1999.
7.       Kapadia N, Fortes J. PUNCH: An architecture for Web-enabled wide-area network-computing. Cluster Computing:The Journal of Networks, Software Tools and
Applications 1999; 2(2):153–164.
8.       Litzkow M, Livny M, Mutka M. Condor—a hunter of idle workstations. Proceedings 8th International Conference of Distributed Computing Systems (ICDCS 1988), San Jose, CA, January 1988. IEEE Computer Society Press: Los Alamitos,CA, 1988.
9.       R. JR. Cassady. Auctions and Auctioneering. University of California Press, Berkley and Los Angeles, California, (1967).
10.    Rajesh Bauya Economic-based Distributed Resource Management and Scheduling for Grid Computing,April(2002)
11.    wikipedia.org/wiki/Grid_computing

107-109

27.

Authors:

Harjeet Singh Chauhan

Paper Title:

Comparative Study of Clustering Based Algorithm in Mobile Ad-Hoc Network

Abstract:  Today Energy efficiency is a critical issue in mobile ad hoc networks (MANETs) for increasing the lifetime of the individual nodes as well as the overall network. Mobile ad-hoc network has become a very important field of study for students and researchers owing to its wide application. In mobile ad-hoc network all nodes are responsible for routing and forwarding of packets, hence all nodes are required to act selflessly for proper functioning of mobile ad-hoc network. In the we present the study of low power clustering algorithm. In this paper present the survey of clustering algorithms in MANET. In particular, the algorithms under consideration are single hop representatives of the energy constrained ad hoc network. The emphasis is given mostly on the cluster formation principles and the cluster maintenance parameters of the algorithms. Simulation results are discussed to describe the effect of transmission range and the size of the network on the parameters like cluster density, frequency of reelection, frequency of cluster changes in the dynamic network. Partitioning the mobile nodes is a NP-hard problem. The lowest id, highest connectivity and weighted cluster algorithm to make it more intrusting.

Keywords:   MANET, Lowest id theorem, highest connective algorithm, Weight cluster algorithm.

References:
1.       Luc Hogie. Mobile Ad Hoc Networks: Modelling, Simulation and Broadcast-based  Applications. PhD thesis, University of Luxembourg, April 2007.
2.       Q. Gao, K. J. Blow, D. J. Holding, I. W. Marshall, and X. H. Peng. Radio range adjustment for energy efficient wireless sensor networks. Ad Hoc Networks, 4(3):75–82, 2006.
3.       G. Noubir G. Lin and R. Rajaraman. Mobility models for ad hoc network simulation. In proceedings of IEEE INFOCOM 04, 2004
4.       M. R. Garey and D. S. Johnson. Computers and Intractability: A guide to the theory of NP-completeness. Freeman, San Francisco, CA, 1979.
5.       J. Baker, A. Ephremides, and J. A. Flynn. The design and simulation of a mobile radio network with distributed control. IEEE Journal on selected areas in communication, SAC-2(1):226–237, January 1984.
6.       Ephremides, J. E. Wieselthier, and D. J. Baker, "A design concept for reliable mobile radio networks with frequency hopping signaling," presented at the Proceedings of the IEEE  , Jan. 1987.
7.       Suchismita Chinara “Analysis and Design of Protocols for Clustering in Mobile Ad Hoc Networks” Doctor of Philosophy thesis at National Institute of Technology Rourkela 2011

110-113

28.

Authors:

G.Veena, V.Uma, Ch. Ganapathy Reddy

Paper Title:

Contrast Enhancement for Remote Sensing Images with Discrete Wavelet Transform

Abstract:   Contrast Enhancement is very important for better visual perception and color reproduction. In this paper we explained the base enhancement techniques Histogram Equalization, Bi-histogram Equalization, Contrast Enhancement using Discrete Wavelet Transform(DWT) and Singular Value Decomposition (SVD),Discrete Cosine Transform(DCT) and Singular Value Decomposition(SVD) and the proposed technique Contrast enhancement based on Dominant Brightness and Adaptive Transformation. The performance of every method is evaluated with parameters such Mean Square Error (MSE), Measure of Enhancement (EME), Peak Signal to Noise Ratio (PSNR) and Mean Absolute Error (MAE).

Keywords: Bi Histogram Equalization (BHE), Discrete Cosine Transform (DCT),Discrete Wavelet Transform (DWT), Histogram Equalization (HE),  Singular Value Decomposition (SV)D.

References:
1.     R. Gonzalez and R. Woods, Digital Image Processing, 3rd ed.      Englewood Cliffs, NJ: Prentice-Hall, 2007.
2.     Y. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consum. Electron., vol. 43, no. 1, pp. 1–8, Feb. 1997.
3.     H. Demirel, C. Ozcinar, and G. Anbarjafari, “Satellite image contrast      enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geosci. Reomte Sens. Lett., vol. 7, no. 2, pp. 3333–337, Apr. 2010.
4.     H. Demirel, G. Anbarjafari, and M. Jahromi, “Image equalization based on singular value decomposition,” in Proc. 23rd IEEE Int. Symp. Comput. Inf. Sci., Istanbul, Turkey, Oct. 2008, pp. 1–5.
5.     Eunsung Lee, Sangjin Kim, Wonseok Kang, Doochun Seo, and Joonki Paik, “Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images”, in IEEE geoscience and remote sensing letters, vol. 10, no. 1, January 201.3
6.     Multi Resolution And Contrast Enhancement Using Wavelet Transforms and Singular Value Decomposition International Journal of Advanced Trends in Computer Science and Engineering, Vol.2 , No.2, Pages : 209- 215 (2013).
7.     K. Bhandari, A. Kumar and P. K. Padhy, “Enhancement of Low Contrast Satellite Images using Discrete Cosine Transform and Singular Value Decomposition”, in  World Academy of Science, Engineering and Technology.

114-120

29.

Authors:

Garima Chawla, Santosh Kr Thakur

Paper Title:

A Fault Analysis based Model for Software Reliability Estimation

Abstract:    When a software system is designed, the major concern is the software quality. The quality of software depends on different factors such as software reliability, efficiency, cost etc. In this paper,  we have defined the software reliability as the measure of software quality. There are different available models that estimate the reliability of software based on type of faults, fault density etc. In this paper, a study on different aspects related to software reliability are discussed..

Keywords:  Software Faults, Reliability, Quality.

References:
1.        G Sultan Aljahdali,”Predicting the Reliability of Software Systems Using Fuzzy Logic”.
2.        Khalaf Khatatneh,” Software Reliability Modeling Using Soft Computing Technique”, European Journal of Scientific Research ISSN 1450-216X
3.        GUO JUNHONG,” Software Reliability Nonlinear Modeling and Its Fuzzy Evaluation”, 4th WSEAS Int. Conf. on NON-LINEAR ANALYSIS, NON-LINEAR SYSTEMS and
CHAOS
4.        Sultan H. Aljahdali,” Employing four ANNs Paradigms for Software Reliability Prediction: an Analytical Study”, ICGST-AIML Journal, ISSN: 1687-4846
5.        Ajeet Kumar Pandey,” Fault Prediction Model by Fuzzy Profile Development of Reliability Relevant Software Metrics”, International Journal of Computer Applications (0975 – 8887)
6.        K. Krishna Mohan,”  Selection of Fuzzy Logic Mechanism for Qualitative Software Reliability Prediction”.
7.        Jie Yang,” Managing knowledge for quality assurance: an empirical study”.
8.        Michael R. Lyu,” Optimal Allocation of Test Resources for Software Reliability Growth Modeling in Software Development”, IEEE TRANSACTIONS ON RELIABILITY 0018-9529/02© 2002 IEEE
9.        Yadav,” Critical Review on Software Reliability Models”, International Journal of Recent Trends in Engineering
10.     Sultan H. Aljahdali,” Employing four ANNs Paradigms for Software Reliability Prediction: an Analytical Study”, ICGST-AIML Journal, ISSN: 1687-4846
11.     Michael R. Lyu,” Optimization of Reliability Allocation and Testing Schedule for Software Systems”.
12.     J. O. Omolehin,” Graphics to fuzzy elements in appraisal of an in-house software based on Inter-failure data analysis”, African Journal of Mathematics and Computer Science Research
13.     P.C. Jha,”  A fuzzy approach for optimal selection of COTS components for modular software system under consensus recovery block scheme incorporating execution time”, TJFS: Turkish Journal of Fuzzy Systems (eISSN: 1309–1190)
14.     Qiuying Li,” A Software Reliability Evaluation Method”, 2011 International Conference on Computer and Software Modeling IPCSIT

121-123

30.

Authors:

Rohini R. Mergu, Shantanu K. Dixit

Paper Title:

Investigation of Transform Dependency in Speech Enhancement

Abstract:  Noise is an unwanted signal. One of the most common type of noise is a background noise which is always present. The paper presents speech enhancement scheme for suppression of  background noise. The objective of speech enhancement is to improve the perceptual aspect such as quality and intelligibility of the processed speech. The main objective of this paper is to investigate the use of different transforms for speech enhancement. Speech enhancement using wiener filtering approach is proposed and implemented using DFT, DCT and DWT thus showing the feasibility of utilization of the different transforms.

Keywords:   Speech enhancement, DFTF, DCTF, DWTF, wiener filter, transform

References:
1.     Ephraim Y. and Malah D.,” Speech enhancement using a minimum mean-square error log-spectral amplitude estimator”, Volume: 33 , Issue 2 , pp. 443-445, IEEE trans. On Acoustics, Speech and Signal Processing, 1985
2.     Boll S.,” Suppression of acoustic noise in speech using spectral subtraction”, Volume 27 ,Issue 2 ,pp. 113 - 120 , IEEE trans. On Acoustics, Speech and Signal Processing, 1979
3.     B.Yegnanarayana ,Carlos Avendano ,Hynek Hermansky, P.Satyanarayana, Murthy, “Speech enhancement using linear prediction residual”, Elsevier, Speech
Communication 28, 25-42, 1999
4.     Jensen J. , Hansen  J.H.L., “Speech enhancement using a constrained iterative sinusoidal model”, IEEE trans. On speech and Audio Processing, Vol 9, Issue 7, pp. 731 – 740, 2001
5.     I.Y. Soon and S.N. Koh, “Speech enhancement using 2-D Fourier transform”, IEEE trans. On speech and Audio Processing, vol 11 , Issue: 6,pp. 717 - 724 , 2003
6.     Evans, JS Mason, MJ Roach ,” Noise Compensation using SpectrogramMorphological Filtering”, Proceedings  4th IASTED, 2002
7.     Mrs. R. R. Mergu , Dr. S.K. Dixit , “A new paradigm for Plotting Spectrogram ”, Journal of Information Systems & Communication, vol-3, Issue-1,pp.158-161, Feb 2012
8.     Mrs. R. R. Mergu , Dr. S.K. Dixit , “Multi-Resolution Speech Spectrogram”, International Journal of  Computer Applications , vol 15, No. 4, Pgs. 28-32, Feb 2011
9.     I.Y. Soon, S.N. Koh, C.K. Yeo,Noisy speech enhancement using Discrete Fourier transform”, Elsevier, Speech Communication, Vol-24, pp.249-257, 1998
10.  Joon Hyuk Chang, ”Warped Discrete Cosine Transform Based Noisy Speech Enhancement”, IEEE Trans on circuits & Systems-II Expressbriefs,Vol.52,No.9, pp.535-539, September2005
11.  Yi Hu and Philipos C. Loizou,, ”Evaluation of Objective Quality measures”, IEEE trans. On speech and Audio Processing, vol 16, Issue 1, 2008

124-128

31.

Authors:

Tanutdech Rotjana Kusol 

Paper Title:

Development of Algorithm for Assessment of Cloud Properties

Abstract:   The aim of this study is to development of algorithm for assessment of cloud properties in the atmosphere using satellite data. The work began by analyzing cloud cover in Thailand. To reduce possible confusion between high clouds and precipitation-causing clouds, the high clouds were removed using the spectral method based on the a priori knowledge-based threshold. This study successful development of algorithm for assessment of cloud properties in the atmosphere using satellite data, which will be use for early warnings of precipitation-causing clouds in Thailand.

Keywords:    Development of algorithm, satellite data, a priori knowledge-based threshold.   

References:
1.        G M.P. Jensen, A.M. Vogelmann, W.D. Collins, G.J. Zhang, E.P. Luke, Investigation of regional and seasonal variations in marine boundary layer cloud properties from MODIS observations. Journal of Climate, 21, 2008, pp. 4955–4973.
2.        P. A. Richard. Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere. Department of Meteorology/National
Centre for Atmospheric Science, School of Physical and Mathematical Sciences, University of Reading, Reading, Berks RG6 6AL, UK, 2011.
3.        B.J. Sohn, Cloud-induced infrared radiative heating and its implications for the large-scale tropical circulation. Journal of the Atmospheric Sciences, 56, 1999, pp. 2657–2672.
4.        W. Su, A. Bodas-Salcedo, K.M. Xu, T.P. Charlock, Comparison of the tropical radiative flux and cloud radiative effect profiles in a climate model with Clouds and the Earth’s Radiant Energy System (CERES) data. Journal of Geophysical Research, 115, 2010. 
5.        Y. J. Kaufman, A. Smirnov, B. N. Holben, and O. Dubovik, Baseline maritime aerosol: Methodology to derive the optical thickness and scattering properties, Geophys. Res. Lett., 28, 2001, pp. 3251–3254.
6.        Kim, and V. Ramanathan, Solar radiation budget and radiative forcing due to aerosols and clouds, J. Geophys.Res., 113, 2008, D02203, doi: 10.1029/2007J D0 08434.
7.        F. J. Turk, P. Arkin, E.E. Ebert, and M. Sapiano, Evaluating high-resolution precipitation products, B. Am. Meteorol. Soc., 89, 2008, pp. 1911–1916.
8.        M. Lensky, and D. Rosenfeld, Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT), Atmos. Chem. Phys., 8, 2008, pp. 6739-6753.
9.        M. Grecu, and W. S. Olson,   Precipitating snow retrievals from combined airborne cloud radar and millimeter-wave radiometer observations, J. Appl. Meteorol. Climatol. 47, 2008, pp. 1634–1650.
10.     Capacci, and F. Porcu, Evaluation of a satellite multispectral VIS–IR daytime statistical rainrate classifier and comparison with passive microwave rainfall estimates, J. Appl. Meteorol. Climatol, 48, 2009, pp. 284–300.
11.     D. Fraser, Massom, R. A. and K. J.  Michael, Generation of high-resolution East Antarctic land fast sea-ice maps from cloud-free MODIS satellite composite imagery, Remote Sensing of Environment, 114, 2010, pp.  2888-2896.
12.     R. Frey, S. A. Ackerman, Y. Liu, K. I. Strabala, H. Zhang, J. Key, and X. Wang. Cloud detection with MODIS: Part I. Improvements in the MODIS Cloud Mask for Collection 5, J. Atmos. Oceanic Technol., 25, 2008, pp.  1057– 1072.
13.     Tang, and Z.L. Li, Estimation of instantaneous net surface long wave radiation from MODIS cloud-free data. Remote Sensing of Environment, 112, 2008, pp. 3482-3492
14.     Z. Yao, Z. Han, Z. Zhao, L. Lin, and X. Fan, Synergetic use of POLDER and MODIS for multilayered cloud identification. Remote Sensing of Environment, 114, 2010, pp. 1910-1923.
15.     Monitoring the earth from the MTSAT, 2013, online available at http://mscweb.kishou.go.jp/index.htm
16.     Rainfall data, 2011, Thai Meteorological Department, online available at http://www.tmd.go.th/en/
17.     P.A. Burrough, & R.A. McDonnell, 1998, Principles of Geographical Information Systems. Oxford: Oxford University Press.
18.     R.B. Smith, 2005, Computing the Planck Function, Yale University, online available at http://www.yale.edu/ceo/ Documentation/
ComputingThePlanckFunction.pdf
19.     Teerawong Laosuwan, Singthong Pattanasethanon, and Worawat Sa-ngiamvibool, Automated Cloud Detection of Satellite Imagery Using Spatial Modeler Language and ERDAS Macro Language, IETE Technical Review, 30 (3), pp 183-190, 2013. 
20.     T. Inoue, Features of clouds over the tropical pacific during northern hemispheric winter derived from split window measurements. J. Meteor. Soc. Japan, 67, 1989, pp. 621-637.
21.     T. Inoue, M. Satoh, Y. Hagihara, H. Miura, and J. Schmetz, Comparison of high-level clouds represented in a global cloud system–resolving model with CALIPSO/CloudSat and geostationary satellite observations, J. Geophys. Res., 115, 2010, D00H22, doi: 10.1029/2009JD012371, [printed 116(D4), 2011].

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32.

Authors:

M. K. Bhaskar, S. S. Mehta, N. S. Lingayat, Guneet Singh Mehta, D. A. Shete

Paper Title:

Probabilistic Neural Network Based Delineation of QRS-complexes in Single-Lead ECG using Slope Criterion

Abstract:    This paper presents the delineation of QRS-complexes in electrocardiogram using Probabilistic Neural Network (PNN) and Slope criteria. Before going to the step of delineation of QRS-complexes, first follow the step of detection of QRS-complexes. The results of detection rate of QRS-complexes are obtained is quite encouraging i.e. 99.23% using slope criteria and PNN. The delineation is process to determine the onset and offset of QRS-region. The delineation performance of the proposed algorithm is validated using referees annotations and the combined program median provided in the CSE multi-lead measurement library. The results of delineation are presented using BA- plots and they are found to be well within tolerance limits as specified by CSE working group.

Keywords:  BA-Plot, Combined Program Median, Delineation, Probabilistic Neural Network (PNN), QRS-complexes, Refree’s Annotation

References:
1.        U. Kohler, C. Henning, and R. Orglmeister, “The principles of software QRS detection,” IEEE Eng. in Med. and Bio., vol. 21, pp. 42-47, 2002.
2.        F. Gritzali, “Towards a generalized scheme for QRS Detection in ECG waveforms,” in Signal Processing, vol. 15, 1988, pp. 183-192.
3.        O. Pahlm and L. Sornmo, “Software QRS detection in ambulatory monitoring- A review,” Med. Biol. Eng. Comp., vol. 22, pp. 289-297, 1984.
4.        G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “ A Comparison of noise sensitivity of nine QRS detection algorithms”, IEEE trans on Biomed. Engg., vol. 37, pp. 85-98, 1990.
5.        P. J. M. Fard, M. H. Moradi, and M. R. Tajvidi, “A novel approach in R peak detection using Hybrid Complex Wavelet,” Int. J. Card., 2007, doi:10.1016/j.ijcard.2006. 11.136.
6.        M. B. Messaoud, “On the algorithm for QRS complex localization in Electrocardiogram”, I. J. of Computer Science and Network Security, vol. 7, pp. 28-33, 2007.
7.        M. P. S. Chawla, H. K. Verma, V. Kumar, “A new statistical PCA-ICA algorithm for location of R-peaks in ECG,” Int. J. Card., 2007, doi: 10.1016/j.ijcard.2007.06.
036.
8.        Ghaffari, H. Golbayni, and M. Ghasemi, “A new mathematical based QRS detectorsusing continous wavelet transform,” Computers and Electrical Engg. 2007. 
9.        F. Zhang and Y. Lian, “Electrocardiogram QRS detectionusing multiscale filtering using wavelet transform,” Proc. 29th Annual Int. Conf. of the IEEE EMBS, Lyon,
France, pp. 3196-3199, Aug. 2007.
10.     S. S. Mehta and N. S. Lingayat, “Development of Entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram,” Sig. Proc., vol. 87, pp. 3190-3201, 2007.
11.     S. S. Mehta and N. S. Lingayat, “Combined Entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM,” Comp. in Biol. and Med, vol. 38, pp. 138-145, 2008.
12.     S. S. Mehta and N. S. Lingayat “Detection of QRS Complexes in Electrocardiogram using Support Vector Machine,” Journal of Medical Engineering and Technology, vol. 32, no. 3, pp. 206-215, 2008.
13.     S. S. Mehta and N. S. Lingayat “SVM-based algorithm for recognition of QRS complexes in Electrocardiogram,” ITBM-RBM (2008),doi:10.1016/j.rbmret.2008.03.006
14.     V. S. Chouhan and S. S. Mehta, “Detection of QRS-complexes in 12-lead ECG using adaptive quantized threshold,” IJCSNS International Journal of Computer Science and Network Security, vol.8, no.1, pp. 155-163, 2008
15.     F. Specht, “Probabilistic neural networks for classification, mapping, or associative memory,” Proc. IEEE Int. Conf. Neural Networks, San Diego, CA, vol. 1, pp. 525-532, July 1988.
16.     J. A. Van Alste, and T.S. Schilder, “Removal of base-line wander and power line interference from the   ECG by an efficient FIR filter with a reduced number of taps,” IEEE Trans. Biomed. Eng., vol. 32, pp.1052- 1059, 1985.
17.     G. S. Furno, and W. J. Tompkins, “A learning filter for removing noise interference,” IEEE Trans. Biomed. Eng., vol. 30, pp. 234-235, 1983.
18.     J.L. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing Company MA, 1974
19.     J. L. Willems, P. Arnaud, J. H. Van Bemmel, P. J. Bourdillon, R. Degani, B. Denis, I. Graham, F. M. A. Harms, P. W. Macfarlane, G. Mazzocca, J. Meyer, and C. Zywietz, “A reference database for multilead electrocardiographic computer measurement programs,” J. Amer. Coll. Cardiol., vol. 10, pp. 1313-1321, 1987.
20.     S. S. Mehta, S.C. Sexana and H.K Verma, Computer-aided interpretation of ECG for diagnostics, Int. J. of System Science, vol. 27, pp. 43-58, 1996.
21.     S. C. Saxena, V. Kumar, S. T. Hamde, Feature extraction from ECG signals using wavelet transform for disease diagnostics, Int. J. of System Science, vol . 33, pp. 1073- 1085, 2002.
22.     Gritzali, Towards a generalized scheme for QRS Detection in ECG waveforms, Signal Processing, vol.15, pp. 183-192, 1988.
23.     Kyrkos, E. A. Giakoumakis, G. Carayannis, QRS detection through time recursive prediction technique, Signal Processing, vol. 15, pp. 429-436, 1988.
24.     P. Trahanias, E. Skordalalkis, Bottom up approach to the ECG pattern-recognition problem, Med. Bio. Eng. and Compu., vol . 27, pp. 221-229, 1989.
25.     P. E. Trahanias, An approach to QRS complex detection using mathematical morphology, IEEE Trans. Biomed. Eng., vol . 40, pp. 201-205, 1993.
26.     Vijaya G, Kumar V, and Verma H. K., Artificial Neural Network Based Wave Complex Detection in Electrocardiograms, Int. J. of System Science, vol. 28, 1997, pp125-132.
27.     Zahia Zidelmal, Ahmed Amirou, Mourad Adnane, Adel Belouchrani, “QRS detection based on wavelet coefficients”, Computer Methods and Programs in Biomedicine,Volume 107, Issue 3, pp. 490-496, September 2012.
28.     Shih-Chin Fang, Hsiao-Lung Chan, “QRS detection-free electrocardiogram biometrics in the reconstructed phase space”,  Pattern  Recognition  Letters, Volume 34, Issue 5, 1 pp. 595-602, April 2013.
29.     C. Cros,   M. Skinner,   J. Moors,   P. Lainee,  J.P. Valentin,  “Detecting  drug-induced prolongation   of   the   QRS-complex:  New insights  for  cardiac  safety  assessment”,  Toxicology    and   Applied   Pharmacology,   Volume 265,   Issue 2,    pp. 200-208,  December 2012.
30.     A. Ghaffari,   M.R. Homaeinezhad,  Y. Ahmadi,  “An  open-source  applied  simulation framework   for   performance   evaluation   of   QRS-complex   detectors”,   Simulation Modelling Practice and Theory, Volume 18, Issue 6,  pp. 860-880, June 2010.
31.     Takeshi Tsutsumi, Nami Takano, Narihisa Matsuyama, Yukei Higashi, Kuniaki Iwasawa, Toshiaki Nakajima, “High-frequency powers hidden within QRS-complex as an additional predictor of lethal ventricular arrhythmias to ventricular late potential in post–myocardial infarction patients”,  Heart Rhythm, Volume 8, Issue 10,  pp.
1509-1515,October-2011.
32.     S.S. Mehta, D.A. Shete, N.S. Lingayat, V.S. Chouhan, “K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram”, IRBM, Volume 31, Issue 1, pp. 48-54, February 2010.
33.     João P.V. Madeiro, Paulo C. Cortez, Francisco I. Oliveira, Robson S. Siqueira, “A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique”, Medical Engineering & Physics, Volume 29, Issue 1, pp. 26-37, January 2007.
34.     Indu Saini, Dilbag Singh, Arun Khosla, “QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases”, Journal of Advanced Research, Volume 4, Issue 4, pp. 331-344, July 2013.                 
35.     João P.V. Madeiro, Paulo C. Cortez, João A.L. Marques, Carlos R.V. Seisdedos, Carlos R.M.R. Sobrinho, “An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms”, Medical Engineering & Physics, Volume 34, Issue 9, pp. 1236-1246. November 2012.
36.     Ali Ghaffari, Mohammad R. Homaeinezhad, Mohammad M. Daevaeiha, “High resolution ambulatory holter ECG events detection-delineation via modified multi-lead wavelet-based features analysis: Detection and quantification of heart rate turbulence”, Expert Systems with Applications, Volume 38, Issue 5, pp. 5299-5310, May
2011.
37.     Ghaffari, M.R. Homaeinezhad, M. Akraminia, M. Atarod, M. Daevaeiha, “A robust wavelet-based multi-lead electrocardiogram delineation algorithm”, Medical Engineering & Physics, Volume 31, Issue 10, pp. 1219-1227, December 2009.
38.     M. K. Bhaskar, S. S. Mehta and N. S. Lingayat, “Probabilistic  Neural Network for the Automatic Detection of QRS-complexes in ECG using Slope”, International Journal of Emerging Technology and Advance Engineering, Volume 3, Issue 6, pp. 255-261, June-2013.
39.     Bland J. M., Altman D. G., “Statistical Methods for assessing agreement between two methods of clinical Measurement”, Lancet, pp. 307-310, 1986.
40.     Willems J.L., “The CSE Multilead atlas measurement results data set 3,” Common Standards for Quantitative Electrocardiography, Leuven, Belgium, 1988.

133-138

33.

Authors:

Asmeeta Mali

Paper Title:

Spam Detection Using Baysian with Pattren Discovery

Abstract:  Text mining is nothing but the discovery of interesting knowledge in text documents. But there is a big challenging issue that how to guarantee the quality of discovered relevant features. And that are in the text documents for describing user preferences because of the large number of terms, patterns and noise. For text mining there are basically two types of approaches; one is term based approach and another is phrase based approach. But term based approach suffered with the problem of polysemy and synonymy. And phrase based approach suffered with low frequency occurrence. But phrase based approachs are better than the term based approachs. But pattern based approach is better than the term based and phrase based approach. The proposed method is an innovative and effective pattern discovery technique. This method includes two main processes pattern deploying and inner pattern evaluation. This paper presents an effective technique to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Using Baysian filtering algorithm and effective pattern Discovery technique we can detect the spam mails from the email dataset with good correctness of term.    

Keywords:   Text mining, information filtering, pattern mining, sequential pattern, closed sequential patterns. 

References:
1.        Yuefeng Li, Abdulmohsen Algarni, Ning Zong ,âA.Mining Positive and Negative Patterns for Relevance Feature DiscoveryâAI , ACM Tranaction, KDDâAZ10 , July 2010.
2.        K. Aas and L. Eikvil, âA.Text Categorisation: A Survey,âAI Technical Report Raport NR 941, Norwegian Computing Center, 1999.
3.        R. Agrawal and R. Srikant, âA.Fast Algorithms for Mining Association Rules in Large Databases,âAI Proc. 20th IntâAZl Conf. Very Large Data Bases (VLDB âAZ94), pp. 478-499, 1994.
4.        H. Ahonen, O. Heinonen, M. Klemettinen, and A.I. Verkamo, âA.Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collections,âAI Proc. IEEE IntâAZl Forum on Research and Technology Advances in Digital Libraries (ADL âAZ98), pp. 2-11, 1998.
5.        Y. Huang and S. Lin, âA.Mining Sequential Patterns Using Graph Search Techniques,âAI Proc. 27th Ann. IntâAZl Computer Software and Applications Conf., pp. 4-9, 2003.
6.        T. Joachims, âA.A Probabilistic Analysis of the Rocchio Algorithm with tfidf for Text Categorization, âAI Proc. 14th IntâAZl Conf. Machine Learning (ICML âAZ97), pp. 143-151, 1997.
7.        T. Joachims, âA.Text Categorization with Support Vector Machines: Learning with Many Relevant Features,âAI Proc. European Conf. Machine Learning (ICML âAZ98)„ pp. 137-142, 1998.
8.        W. Lam, M.E. Ruiz, and P. Srinivasan, âA.Automatic Text Categorization and Its Application to Text Retrieval,âAI IEEE Trans. Knowledge and Data Eng., vol. 11, no. 6, pp. 865-879, Nov./Dec. 1999.
9.        D.D. Lewis, âA.Feature Selection and Feature Extraction for Text Categorization,âAI Proc. Workshop Speech and Natural Language, pp. 212-217, 1992.
10.     Y. Li and N. Zhong, âA.Mining Ontology for Automatically Acquiring Web User Information Needs,âAI IEEE Trans. Knowledge and Data Eng., vol. 18, no. 4, pp. 554-568, Apr. 2006.
11.     Y. Li, X. Zhou, P. Bruza, Y. Xu, and R.Y. Lau, âA.A Two-Stage Text Mining Model for Information Filtering,âAI Proc. ACM 17th Conf. Information and Knowledge Management (CIKM âAZ08), pp. 1023-1032, 2008.
12.     Moulinier, G. Raskinis, and J. Ganascia, âA.- Text Categorization: A Symbolic Approach,âAI  Proc. Fifth Ann. Symp. Document Analysis and Information Retrieval (SDAIR), pp. 87-99, 1996.
13.     J.S. Park, M.S. Chen, and P.S. Yu, âA.An Effective Hash-Based Algorithm for Mining Association Rules,âAI Proc. ACM SIGMOD IntâAZl Conf. Management of Data (SIGMOD âAZ95), pp. 175- 186, 1995.
14.     J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, âA.Prefixspan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,âAI Proc. 17th IntâAZl Conf. Data Eng. (ICDE âAZ01), pp. 215-224, 2001.
15.     M.F. Porter, âA.An Algorithm for Suffix Stripping, âAI Program, vol. 14, no. 3, pp. 130-137, 1980.
16.     S. Robertson and I. Soboroff, âA.The Trec 2002 Filtering Track Report,âAI TREC, 2002, trec.nist.gov/pubs/trec11/papers/OVER. FILTERING. ps.gz.
17.     J. Rocchio, Relevance Feedback in Information Retrieval. chapter 14, Prentice-Hall, pp. 313-323, 1971.
18.     G. Salton and C. Buckley, âA.Term-Weighting Approaches in Automatic Text Retrieval,âAI Information Processing and Management: An IntâAZl J., vol. 24, no. 5, pp. 513-523, 1988.
19.     S. Scott and S. Matwin, âA.Feature Engineering for Text Classification,âAI Proc. 16th IntâAZl Conf. Machine Learning (ICML âAZ99), pp. 379- 388, 1999.
20.     F. Sebastiani, âA.Machine Learning in Automated Text Categorization,âAI ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
21.     M. Seno and G. Karypis, âA.Slpminer: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraint,âAI Proc. IEEE Second
IntâAZl Conf. Data Mining (ICDM âAZ02), pp. 418-425, 2002.
22.     K. Sparck Jones, S. Walker, and S.E. Robertson, âA.A Probabilistic Model of Information Retrieval: Development and Comparative ExperimentsâATPart 2,âAI Information Processing and Management, vol. 36, no. 6, pp. 809-840, 2000.
23.     R. Srikant and R. Agrawal, âA.Mining Generalized Association Rules,âAI Proc. 21th IntâAZl Conf. Very Large Data Bases (VLDB âAZ95), pp. 407-419, 1995.
24.     S.-T. Wu, Y. Li, and Y. Xu, âA.Deploying Approaches for Pattern Refinement in Text Mining, âAI Proc. IEEE Sixth IntâAZl Conf. Data Mining (ICDM âAZ06), pp.
1157-1161, 2006.
25.     S.-T. Wu, Y. Li, Y. Xu, B. Pham, and P. Chen, âA.Automatic Pattern- Taxonomy Extraction for Web Mining,âAI Proc. IEEE/WIC/ACM IntâAZl Conf. Web Intelligence (WI âAZ04), pp. 242-248, 2004.
26.     X. Yan, J. Han, and R. Afshar, âA.Clospan: Mining Closed Sequential Patterns in Large Datasets,âAI Proc. SIAM IntâAZl Conf. Data Mining (SDM âAZ03), pp. 166-177, 2003.
27.     M. Zaki, âA.Spade: An Efficient Algorithm for Mining Frequent Sequences,âAI Machine Learning, vol. 40, pp. 31-60, 2001.

139-143