Volume-3 Issue-5

S. No

Volume-3 Issue-5, November 2013, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

A. Ravi, Nirmala

Paper Title:

Critical Parameters for Coupling and Cohesion of S/W Reusability Problem Domain

Abstract: Interdependency between different s/w modules that are linked in specific application areas and/or dependent functions within single module, carry risks while they are adapted for s/w reusability. Coupling and cohesion are related terms that contribute a lot in s/w reusability. While well structured programs are risk free when used for specific application, huge s/w when developed for similar applications, then reusability of part of or full modules cause risks. Features like hardware capacity, operating system, file structure, network capability, interoperability, scalability and security (popularly known as ‘ilities’) are parameters that put in influences on the coupling and cohesion. This paper attempts to determine criticality of some of the parameters so as they form critical elements causing the risks on coupling and cohesion. Even though the paper does not present optimization techniques to consider these parameters for s/w reuse, the parametric study results will be of immense use to s/w reusability for obtaining optimum solutions. Experiments with four s/w modules written in Java have been carried out with different entities that form different coupling and cohesions. Observance from the results has yielded to identifying critical elements.

Keywords: e reusability, coupling and cohesion, critical parameters

References:
1. Wikipedia, 2012,
2. Pressman, Roger S. Ph.D. (1982). Software Engineering - A Practitioner's Approach – Fourth Edition. ISBN 0-07-052182-4
3. Lawrence Chung and Brian A. Nixon, (1995), “Dealing with Non-Functional Requirements: Three Experimental Studies of a Process-Oriented Approach”, Proc. of IEEE, 17th International Conference on Systems, Seattle, April 24-28, 1995, pp 25-37.
4. Andrea Capiluppi and Cornelia Boldyreff (2007), ‘Coupling Patterns in the Effective Reuse of Open Source Software’, Proceedings of International Conference on Software Engineering ‘07, Vol. 1, p.9.
5. Basili V.R., Bri L.C. and Thomas W.M. (1994), ‘Domain Analysis for the Reuse of Software Development Experiences’, Experimental Software Engineering Group (ESEG), Vol. 11, pp. 86-95.
6. Fenton Norman and Neil Martin (1999), ‘Software Metrics and Risk’, Proceeding of second European Software Measurement Conference, pp.1-17.
7. F. Beck, S. Diehl (2011), “On the Congruence of Modularity and Code Coupling”, Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering (SIGSOFT/FSE '11), Szeged, Hungary, September 2011. doi:10.1145/2025113.2025162

1-3

2.

Authors:

Jaya Bhatt, Naveen Hemrajani

Paper Title:

Effective Routing Protocol (DSDV) for Mobile Ad Hoc Network

Abstract: An ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration. In such a network, each node acts as both router and host simultaneously, the nodes can leave or join the network anytime. The routers are free to move. DSDV is developed on the basis of Bellman Ford routing algorithm with some modifications. In this routing protocol, each mobile node in the network keeps a routing table listing all the other nodes it has known either directly or through some neighbours. Every node has a single entry in the routing table. The entry will have information about the node’s IP address, last known sequence number and the hop count to reach that node. Along with these details the table also keeps track of the next hop neighbour to reach the destination node, the timestamp of the last update received for that node. Information updates might either be periodic or event driven. DSDV protocol requires each mobile node in the network to advertise its own routing table to its current neighbours. The advertisement is done either by broadcasting or by multicasting.

Keywords: Mobile Ad hoc Network (MANET), Destination-Sequenced Distance Vector Routing Protocol (DSDV).

References:
1. Current Research Work on Routing Protocols for MANET: A Literature Survey by G.Vijaya Kumar, Y.Vasudeva Reddyr , Dr.M.Nagendra CSE Dept, Asst Prof,G.Pulla Reddy Engg.College (Autonomous), Kurnool-2,AP,India
2. Analysis and Improvement of DSDV Protocol by Nayan Ranjan Paul, Laxminath tripathy and Pradipta Kumar Mishra, Deptt. Of Computer Science and Engg., KMBB college of Engg. and Technology, Odisha, India.
3. Destination-Sequenced Distance Vector (DSDV) Protocol by Guoyou He, Networking Laboratory Helsinki University of Technology
4. The Destination Sequenced Distance Vector (DSDV) protocol by Dr. R.B. Patel
5. Routing Protocols for Mobile Ad Hoc Network
6. Highly Dynamic Destination Sequenced Distance Vector Routing DSDV for Mobile Computers by Charles E Perkins IBM T J Watson Research Center Hawthorne, NY

4-7

3.

Authors:

Okonkwo V. O, Onyeyili I. O, Aginam C. H., Chidolue C. A

Paper Title:

Formulation of the Internal Stress Equations of Pinned Portal Frames Putting Shear Deformation into Consideration

Abstract: In this work the internal stress equations for pinned portal frames under different kinds of loading was formulated using the equilibrium method. Unlike similar equations in structural engineering textbooks these equations considered the effect of deformation due to shearing forces. This effect was captured in a dimensionless constant α, when α is set to zero, the effect of shear deformation is removed and the equations become the same as what can be obtained in any structural engineering textbook. An investigation into the effect of shear deformation on the internal stresses and its variation with the ratios of second moment of areas of the horizontal and vertical members of the frame ( I_2⁄I_1 ) and the ratio of height to length of the portal frame ( h⁄L) showed that the effect of shear deformation is generally small and can be conveniently neglected in manual calculations except for pinned portal frames under concentrated horizontal forces where the effect was considerable.

Keywords: Flexural rigidity, Pinned Portal frames, shear deformation, stiffness matrix

References:
1. R. Graham, P. Alan. “Single Storey Buildings” in Steel Designer’s Manual Sixth Edition, United Kingdom: Blackwell Science Ltd, 2007, pp. 1
2. Samuelson, O. C. Zienkiewi O., “Review: History of the Stiffness Method”, International Journal for Numerical Methods in Engineering, Vol. 67, 2006,
pp.149 – 157
3. Saikat, Tips and Tricks for Computer-Aided Structural Analysis, India, Ensel Software, 2001, pp.7 – 8
4. E. Reynolds, J. C. Steedman, Reinforced Concrete Designer’s Handbook, 10th Edition, London: E&FN Spon, Taylor & Francis Group, 2001
5. R. C. Hibbeler, Structural Analysis. Sixth Edition, New Jersey: Pearson Prentice Hall, 2006, pp. vii
6. Victor Dias da Silva, Mechanics and Strength of Material, New York: Springer-Verlag Heidelberg, 2006, pp. 258 – 268
7. J. D. Renton, Elastic Beams and Frames, 2nd Edition, England: Horwood Publishing Limited, West Sussex, 2002, Pg 65
8. S. P. Timoshenko, J. M. Gere, Mechanics of Materials, New York: Van Nostrand 1972, pp. 372
9. Ghali , A. M. Neville, Structural Analysis: A Unified Classical and Matrix Approach. 3rd Edition, London: Chapman & Hall, 1996, pp. 151
10. W. Nash, (1998), Schaum’s Outline of Theory and Problems of Strength of Materials, Fourth Edition, New York: McGraw-Hill Companies, 1998, pp. 177-181

8-15

4.

Authors:

M.Suresh, R.Parthasarathy, M.Prabakaran, S.Raja

Paper Title:

Big Data Challenges for E-governess System in Distributing Systems

Abstract: This paper discusses the challenges that are imposed by E-governess on the modern and future infrastructure. This paper refers to map reducing algorithm to define the requirements on data management, access control and security. This model that includes all the major stages and reflect specifying data management in modern E-government. This paper proposes the map reducing architecture model that provides the basic for building interoperable data. The paper explain how the implemented using Distributed structures and provisioning model.

Keywords: Big Data, Map reducing, Distributed structures.

References:
1. , V. and J. Bhattacharya. E-governance solution for government of Maharashtra. Technology whitepaper, India Research Lab, IBM, 2000.
2. Batra, V.; J. Bhattacharya; H. Chauhan; A. Gupta; M.Mohania; U. Sharma. 2002. “Policy Driven Data.
3. Administration”. In POLICY 2002, IEEE 3rd International Workshop on Policies for Distributed Systems and Networks.
4. Gillick et al., 2006, Gillick D., Faria A., DeNero J., and MapReduce: Distributed Computing for Machine Learning, Berkley, and December 18, 2006.
5. K. Shvachko, Hairong Kuang, S. Radia and R Chansler – The Hadoop Distributed File System. Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium 3-7 May 2010.
6. F. Chang, J. Dean, S. Ghemawat, W. Hsieh, D. Wallach,M. Burrows, T. Chandra, A. Fikes, and R. Gruber, “Bigtable: A distributed structured data storage system,” in 7th OSDI,2006, pp. 305–314.
7. Stupar, S. Michel, and R. Schenkel, “Rankreduce–processing k-nearest neighbor queries on top of mapreduce,” in Proceedings of the 8th Workshop on Large-Scale Distributed Systems for Information Retrieval, 2010, pp. 13–18.
8. J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S. Bae, J. Qiu, and G. Fox, “Twister: a runtime for iterative mapreduce,” in Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. ACM, 2010, pp. 810–818.
9. J.S. Chase et al., “Dynamic Virtual Clusters in a Grid Site Manager,” Proc. 12th IEEE Int’l Symp. High Per¬formance Distributed Computing (HPDC 03), IEEE CS Press, 2003, p. 90.

16-19

5.

Authors:

Bharti Panjwani, Vijay Mohan

Paper Title:

Comparative Performance Analysis of PID Based NARMA-L2 and ANFIS Control for Continuous Stirred Tank Reactor

Abstract: This paper deals with two intelligent control schemes based on artificial neural network for temperature control in a jacketed Continuous Stirred Tank Reactor. Objective is to regulate the reactor temperature for an exothermic reaction taking place in the CSTR by manipulating the thermal condition of jacket. PID based NARMA-L2 and PID based ANFIS controller are designed and their performances are analyzed and compared. The simulation results show the priority of ANFIS control over NARMA-L2 control to achieve better response.

Keywords: Continuous Stirred Tank Reactor (CSTR); Nonlinear Auto Regressive Moving Average (NARMA); Adaptive-Network-Based Fuzzy Inference System (ANFIS); PID.

References:
1. T. Zhang and M. Guay, “Adaptive Nonlinear Control of Continuously Stirred Tank Reactor Systems”, Proceedings of the American Control Conference Arlington, VA June 25-27, 2001, pp.1274-1279.
2. Martin T. Hagan, Howard B. Demuth, “Neural Networks for Control”, Proceedings of the American Control Conference, 1999, pp. 1642-1656.
3. C. Jeyachandran and M. Rajaram, “Comparitive Performance Analysis of Various Training Algorithms for Control of CSTR Process Using Narma-L2
Control,” International Conference on Trendz in Information Sciences and Computing, 2011, pp. 5-10.
4. Hamza Chaal, “A Chemical Reactor Benchmark for Adaptive Control using V-Model and NARMA-L2 Techniques”, International Conference on Control, Automation and Systems 2007, Oct. 17-20, 2007 in COEX, Seoul, Korea, pp.2871-2875.
5. T. Procyk and E. Mamdani, "A linguistic self-organizing process controller," Automatica, vol. 15, no. I, 1979, pp. 15-30.
6. Nejaud Almalki Al-Jehani, Hazem N. Naunau and Mahamed N. Naunau, “Fuzzy Control of a CSTR Process”, International Symposium on Mechatronics and its Applications, 2012, pp. 1-6.
7. Lenka Blahova, Jan Dvoran, “Neuro-Fuzzy Control of Chemical Reactor with Disturbances”, LATEST TRENDS on SYSTEMS (Volume I), 2010, pp.336-340.
8. Glan Devadhas.G & Dr.Pushpakumar.S, “Intelligent Controller Design for a Chemical Process”, International Journal of Engineering, (IJE), vol. 4(5), pp. 399-410.
9. B. Wayne Bequette, "Process Control-Modeling Design and Simulation",Prentice Hall of India, 2003.
10. Jyh-Shing Roger Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, May/June, 1993.
11. Hagan, M.T., Menhaj, M.B., “Training feed forward networks with the Marquardt algorithm”, IEEE Transactions on Neural Networks, vol. 5, no. 6, Nov, 1994.
12. Jang, J.-S.R.; Chuen-Tsai Sun, “Neuro Fuzzy Modelling and Control”, IEEE Proc., vol.83, pp. 378-406, March1995.
13. Neural Network Toolbox, MATLAB 2010.

20-23

6.

Authors:

I A. Kamani C Samarasinghe, Saluka Kodituwakku, Roshan D. Yapa

Paper Title:

Data Mining and Service Customization in Leisure and Hospitality

Abstract: This study provides insights into the relationship between data mining activities and the customization of the packages offered to tourists by hotels. Data mining provides a method of understanding the needs and habits of the tourists so that hotels can provide targeted value propositions to tourists from different geographies. For the study, ten prominent hotels around the country were selected and three employees from each hotel with information were interviewed to collect information about the data mining activities they perform. The study identifies that hotels give a high level of attention to data mining. Many of the hotels have suitable systems to capture the data of the guests, and have suitable staff to operate them. This indicates that data mining capabilities are either being built or are already in place in many of the hotels. It is also seen that hotels actively seek to customize the packages they offer to customers. All these eventually result in increased levels of long term customer loyalty. The regression analysis indicated a very strong relationship between data mining and the customization of service packages. The also study indicates that there is a significant correlation between the data mining activities of the hotels and the customized value and service offerings. This indicates that the hotels of the country are using data mining to develop customized service offerings. This is likely to benefit the hotels as well as Sri Lanka as a whole due to increased repeat visits by the tourists.

Keywords: Customized service packages, Data mining, , Hotels, Tourism.

References:
1. Byrd, E.T., and Gustke, L., (2011). Using decision trees to identify tourism stakeholders. Journal of Place Management and Development. Vol. 4 Iss: 2, pp.148 – 168.
2. Carvão, S., (2010). Embracing user generated content within destination management organizations to gain a competitive insight into visitors' profiles. Worldwide Hospitality and Tourism Themes. Vol. 2 Iss: 4, pp.376 – 3.
3. France, T., Yen, D., Wang, J., and Chang, C.M., (2002). Integrating search engines with data mining for customer-oriented information search. Information Management & Computer Security. Vol. 10 Iss: 5, pp.242 – 254.
4. Golmohammadi, A., Ghareneh, N.S., Keramati, A., and Jahandideh, B., (2011). Importance analysis of travel attributes using a rough set-based neural network: The case of Iranian tourism industry. Journal of Hospitality and Tourism Technology. Vol. 2 Iss: 2, pp.155 – 171.
5. Jayawardena, C., and Ramajeesingh, D., (2002). Performance of tourism analysis: a Caribbean perspective. International Journal of Contemporary Hospitality Management. Vol. 15 Iss: 3, pp.176 – 179.
6. Jayawardena, C., and Sinclair, D., (2010). Tourism in the Amazon: conclusions and solutions. Worldwide Hospitality and Tourism Themes. Vol. 2 Iss: 2, pp.201 – 210.
7. Koh, H.C., and Low, C.K., (2004). Going concern prediction using data mining techniques. Managerial Auditing Journal. Vol. 19 Iss: 3, pp.462 – 476.
8. Lejeune, M., (2001). Measuring the impact of data mining on churn management. Internet Research. Vol. 11 Iss: 5, pp.375 – 387.
9. Low, B., (2005). The evolution of China's telecommunications equipment market: a contextual, analytical framework. Journal of Business & Industrial Marketing, Vol. 20 Iss: 2, pp.99 – 108.
10. Min, H., Min, H., and Emam, A., (2002). A data mining approach to developing the profiles of hotel customers. International Journal of Contemporary Hospitality Management. Vol. 14 Iss: 6, pp.274 – 285.
11. Miori, V.M., (2009). Econometric count data forecasting and data mining (cluster analysis) applied to stochastic demand in truckload routing.
12. Patrick, X., and Ypsilanti, D., (2008). Switching costs and consumer behaviour: implications for telecommunications regulation. info, Vol. 10 Iss: 4, pp.13 – 29.
13. Rafalski, E., (2002). Using data mining/data repository methods to identify marketing opportunities in health care. Journal of Consumer Marketing. Vol. 19 Iss: 7, pp.607 – 613.
14. Ranjan, J., and Malik, K., (2007). Effective educational process: a data-mining approach. VINE. Vol. 37 Iss: 4, pp.502 – 515.
15. Reyes, M.V., (2010). Tourism strategies for “advantaging” the Amazon rainforest region: The Ecuador model . Worldwide Hospitality and Tourism Themes. Vol. 2 Iss: 2, pp.163 – 172.
16. Ritchie, J.R., Tung, V., and Ritchie, R., (2011). Tourism experience management research: Emergence, evolution and future directions. International Journal of Contemporary Hospitality Management. Vol. 23 Iss: 4, pp.419 – 438.
17. Segall, R.S., (2004). Incorporating data mining and computer graphics for modeling of neural networks. Kybernetes. Vol. 33 Iss: 8, pp.1258 – 1276.
18. Shannon, T., and Zwick, M., (2004). Directed extended dependency analysis for data mining. Kybernetes. Vol. 33 Iss: 5/6, pp.973 – 983.
19. Sri Lanka Tourism Development Authority (a), (2013). Arrival of Tourists by Country, Available: http://www.sltda.gov.lk/sites/default/files/Page_2_Dec_2011.pdf.
20. Sri Lankan Tourism Development Authority (b), (2013). Statistic. Available: http://www.sltda.gov.lk/statistics
21. TTG Digital, (2013). Q&A: Is it still safe to sell Thailand?, Available: http://www.ttgdigital.com/news/qa-is-it-still-safe-to-sell-thailand/4686270.article
22. [Uzama, (2009). Marketing Japan's travel and tourism industry to international tourists. International Journal of Contemporary Hospitality Management. Vol. 21 Iss: 3, pp.356 – 365.
23. Wang, T., (2012). An efficient algorithm for mining condensed sequential pattern bases. Kybernetes. Vol. 41 Iss: 9, pp.1289 – 1296.
24. Data mining. Available: http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
25. Data Mining in Tourism Demand Analysis: Available: http://link.springer.com/chapter/10.1007%2F978-3-540-73871-8_47#page-1
26. Data Mining Example: Tourism. Available: http://www.easydatamining.com/index.php?option=com_content&view=article&id=59&Itemid=79&lang=en
27. Wei Z., (2008), Data mining procedures in generalized Cox regressions
28. Viktor, H.L., and Arndt, H., (2000). Combining data mining and human expertise for making decisions, sense and policies, Journal of Systems and Information Technology, Vol. 4 Iss: 2, pp.33 – 56
29. Pendharkar, P.C., and Rodger, J.A., (2000) .Data mining using client/server systems, Journal of Systems and Information Technology, Vol. 4 Iss: 2, pp.72 – 82
30. Data Mining in Tourism Demand Analysis: A Retrospective Analysis. Available: http://dl.acm.org/citation.cfm?id=1421221
31. The Application of Data Mining in Tourism Information. Available: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5199787&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5199787
32. S.Daskslaki,I.Kopanas,M.Goudara,N.Avouris.Data Ming for decision support on customer insolvency in telecommunications business. European Journal of Operational Research,2003,145:239-255
33. Data mining using SAS/EM:A Case Study Approach,SAS,Institute Inc.2003.4
34. Jiawei Han and Micheline Kamber Data Mining Concepts and Techniques BeiJing Higher Education Press, 2001:279-299.
35. Elder J F & Abbott D W , A Comparison of Leading Data Mining Tools, KDD-98.
36. Jiawei Han, Michelin Kamber;Fan Ming, Meng Xiaofeng(interpret),Data Mining: Concepts and Technologies [M] ,Beijing Machinery Industry Press,2001,8
37. Xin Haitao, Analysis of Mining Technology of Web Data and Application in Tourism E-Commerce, Journal of Harbin University of Commerce (Natural Sciences Edition). Aug. 2010, No. 4
38. Ren Mingshu, Study on Web Mining and Electronic Commerce, Shandong University of Science and Technology.2002.

24-29

7.

Authors:

S, R. Singh, Pinky Saxena

Paper Title:

A Two-Warehouse Production Inventory Model with Variable Demand and Permissible Delay in Payment under Inflation

Abstract: In this paper, a two-warehouse production inventory model is developed for deteriorating items with variable demand .The effect of permissible delay in payment is considered under inflation. Since, the capacity of any warehouse is limited, it has to rent warehouse (R.W) for storing the excess units over the fixed capacity of the own warehouse (O.W). On the basis of this fact, we have developed a two-warehouse production inventory model for deteriorating items under inflation & permissible delay in payment. The objective of this study is to derive the retailer’s optimal replenishment policy that minimizes the total relevant inventory costs. The necessary and sufficient conditions for an optimal solution are characterized. An algorithm is developed to find the optimal solution. Finally, numerical examples are provided to illustrate the proposed model. Sensitivity analysis is made and some managerial inferences are presented.

Keywords: Inventory, Two-warehouse, Deterioration, Permissible delay in payment, Inflation

References:
1. Y. Yang, F. Zhou, A two-warehouse inventory model for deteriorating items under conditionally permissible delay in payment , Applied Mathematical modeling 35 (2011) 2221– 2231.
2. H.L.Yang, Two-warehouse partial backlogging inventory models for deteriorating items under inflation, International Journal of Production Economics 103 (2006) 362–370.
3. S.K. Goyal, Economic order quantity under conditions of permissible delay in payments, Journal of the Operational Research Society 36 (1985) 335–338.
4. N.H. Shah, Probabilistic time-scheduling model for an exponentially decaying inventory when delay in payments is permissible, International Journal of Production Economics 32 (1993) 77–82.
5. S.P. Aggarwal, C.K. Jaggi, Ordering policies of deteriorating items under permissible delay in payments, Journal of the Operational Research Society 46(1995) 658–662.
6. A.M.M. Jamal, B.R. Sarker, S. Wang, An ordering policy for deteriorating items with allowable shortages and permissible delay in payments, Journal of the Operational Research Society 48 (1997) 826–833.
7. H. Hwang, S.W. Shinn, Retailer’s pricing and lot sizing policy for exponentially deteriorating products under the conditions of permissible delay in Payments, Computers & Operations Research 24 (1997) 539–547.
8. H.C. Liao, C.H. Tsai, C.T. Su, An inventory model with deteriorating items under inflation when a delay in payment is permissible, International Journal of Production Economics 63 (2000) 207–214.
9. H.J. Chang, C.Y. Dye, An inventory model for deteriorating items with partial backlogging and permissible delay in payments, International Journal of Systems Science 32 (2001) 345–352.
10. H.J. Chang, C.H. Hung, C.Y. Dye, An inventory model for deteriorating items with linear trend demand under the condition of permissible delay in payments, Production Planning & Control 12 (2001) 274–282.
11. N.H. Shah, Inventory model for deteriorating items and time value of money for a finite time horizon under permissible delay in payments, International Journal of Systems Science 37 (2006) 9–15.
12. H. Soni, A.S. Gor, N.H. Shah, An EOQ model for progressive payment scheme under DCF approach, Asia-Pacific Journal of Operational Research 23 (2006) 500– 524.

30-35

8.

Authors:

Ashvini A. Patil, Swapnil V. Suryawanshi

Paper Title:

Run Time Evaluation by using Object Oriented Debugging Tool

Abstract: In the process of Software Development and evolution, Developer has to answer multiple questions about how the code or software behaves at runtime and already many options available for debugging. Debugging is an essential part of programming language and what sets great programmers apart from average ones. Beginners are often pleased if a bug/virus that was seen earlier inexplicably disappears. Inexperienced programmers have a tendency to shy away from error messages or be frightened by observable errors, whereas skilled programmers rely heavily on error messages and he is aware about fixing of bugs by using different debugging tool. And programmer can easily detect and remove it at run time. The traditional or classical debugger while debugging gives developer bunch of breakpoints in the source code. Object based debugging offer, interruption when a given or a particular object is accessed or modified. Programmers, who try to find violations in such source code, need new tool that allows them to explore objects in the system effectively. The implementation of the proposed debugging described here offers programmers an effective tool which will allows searching of objects even for programs that have huge number of objects. Therefore Successful debugging tool involve efficient exploratory ability and a proper understanding of troubleshooting in programming code.

Keywords: Object based debugging offer, interruption when a given or a particular object is accessed or modified. Programmers,

References:
1. Adrian lienhard, tudor Girba and Oscar Nierstrasz ”Practical Object Oriented Back-In-Time Debugging”LNCS 5142, pp 592-615.
2. Raimondas Lencevicius, Urs Holzle And Ambuj K. Singh, “Query-based Debugging of Object-Oriented Programs” OOPSLA 97 Atlanta, USA.
3. Mark Minas “Cyclic Debugging For pSather, a Parallel Object-Oriented Programming Language” Jan 31 2002
4. Tanja Mayerhofer,”Testing and Debugging UML Models Based On Fuml” ICSE 2012.
5. G. Pothier, E. Tanter, and J. Piquer, “Scalable omniscient Debugging, “Proceedings of the 22nd Annual SCM SIGPLAN Conference on Object-Oriented Programming Systems, Languages And Applications (OOPSLA’07), vol. 42, no. 10, pp.535–552, 2007.
6. C. Hofer,M. Denker, and S. Ducasse, “Design and implementation of a backward-in-time debugger,” in Proceedings of NODE’06, ser. Lecture Notes in Informatics, vol. P-88 (GI), Sep 2006, pp. 17-32.
7. J. KO and B. A. Myers, “Designing the whyline: a debugging interface for asking questions about program behaviour,” in Proceedings of the 2004 conference on Human factors in computing systems. ACM Press, 2004, pp. 151–158.
8. Sai Zhang; Zhongxian Gu; Yu Lin; Jianjun Zhao “AutoFlow: An automatic debugging tool for AspectJ software” ICSM 2008. IEEE International Conference on 2008, pp. 470 – 471.
9. Rossler, J. “How helpful are automated debugging tools?” User Evaluation for Software Engineering Researchers (USER), 2012 IEEE Conference Publications, pp. 13 – 16.
10. Chi-Neng Wen;shu-hsuan Chou;chih Chen ;tien-fu chen. ”NUDA: A Non-Uniform Debugging Architecture and Nonintrusive Race Detection For Many core system” IEEE transaction, vol.61, 2012, pages.199-212.
11. Engblom, J. ”A Review of Reverse debugging” System, Software, SC and Silicon Debug Conference (S4D), 2012, pp. 1 – 6.
12. Chris parnin and alessandro orso, “Are automated debugging techniques actually helping programmers” ISSTA’ July 2011
13. Jorge ressia, Alexandre Bergel and Oscar Nierstrasz “object centric debugging” ICSE 2012
14. Renee McCauley, Sue Fitzgerald, Gary Lewandowski, Laurie Murphy, Beth Simon, Lynda Thomas and Carol Zander “Debugging: a review of the
literature from an educational perspective” June 2008
15. K. Glerum, K. Kinshumann, S. Greenberg, G. Aul, V. Orgovan, G. Nichols, D. Grant, G. Loihle, and G. Hunt, “Debugging in the large: ten years of implementation and experience,” Proc. SOSP, 2009, pp. 103-116.
16. Noor Fazlida Mohd Sani, Noor Afiza Mohd Arifin and Rodziah Atan “Design of object-oriented debugger model using unified modeling language” JCSSP 2013, pp 15-18.
17. Potanin, A., Noble, J., Biddle, R.: Snapshot query-based debugging. In: Proceedings of the 2004 Australian Software Engineering Conference (ASWEC’04), Washington, DC, USA, IEEE Computer Society (2004) 251
18. P. Iyenghar, C. Westerkamp, J. Wuebbelmann, E. Pulvermueller, A Model Based Approach for Debugging Embedded Systems in Real-time, in 10th
19. Jorge Ressia, Alexandre Bergel, Oscar Nierstrasz “Object-Centric Debugging” ICSE 2012, IEEE, Zurich, Switzerland.
20. D.M.Thakore, Tanveer S Beg “An Automatic Debugging Tool Extension for Object Oriented Software” IJSCE.

36-39

9.

Authors:

Swapan Kumar Mondal, Hitesh Tahbildar

Paper Title:

Automated Test Data Generation Using Fuzzy Logic-Genetic Algorithm Hybridization System for Class Testing Of Object Oriented Programming

Abstract: In this paper we have explained automatic test data generation particularly for class testing of object oriented programming. During test data generation we have implemented the Genetic program - Fuzzy logic control auxiliary hybridization techniques. Some cases genetic algorithm has been used for optimized the desired results. As a future challenges we have made comments on the utilization of this new proposed technique. This proposed technique can be used for testing of industrial production oriented software. Production oriented software is use in Computer numerical control (C.N.C) machine.

Keywords: Binary tree, Fuzzy logic control (FLC), Genetic programming (GP), Genetic algorithm (GA), Mutation testing.

References:
1. M. Prasanna and K.R. Chandran, “Automatic Test Case Generation for UML Object diagrams using Genetic Algorithm ” Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 1, July 2009, PP. 19-32.
2. Andrea Arcuri and Xin Yao (Advisor), “On Test Data Generation of Object-Oriented Software” 0-7695-2984-4/07 $25.00 © 2007 IEEE, PP. 72-76.
3. A.V.K.Shanthi and Dr. G. Mohankumar, " Automated test cases generation for object oriented software", Int.j.of Computer science and engineering,
IJCSE Vol.2 No.4 Aug-Sep 2011.
4. Andrea G. B. Tettamanzi, “Fuzzy Logic Based Objective Function Construction for Evolutionary Test Generation ” Dagstuhl Seminar Proceedings 08351, http://drops.dagstuhl.de/opus/volltexte/2009/2016 PP. 1-11.
5. Rajasekaran, s. and G.A.Vijayalakshmi Pai (2012) Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Application, PHI Learning Private Limited,New Delhi. pp.187-429.
6. Yun Y., Mitsuo Gen, and Seunglock Seo (2003) Various hybrid methods based on genetic algorithm with fuzzy logic controller, Kluwer Academic Publishers, joul. Of intelligent manufacturing, 14, 401-419, September 2002 ,pp. 401-419.
7. Gursaran (2003), “ Software Test Data Generation Using Evolution ”,PPT.
8. Janet Clegg, ” A new crossover technique in Genetic Programming ”, PPT
9. Sudarshan K. Valluru & T.Nageswara Rao,” Indroduction to Neural Networks, Fuzzy Logic & Genetic Algorithms”.
10. Chayanika Sharma1, Sangeeta Sabharwal2, Ritu Sibal3, “A Survey on Software Testing Techniques using Genetic Algorithm “,IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013, pp.381-393.
11. Masoud Ahmadzade, Davood Khosroanjom, Toufiq Khezri, Yusef Sofi, ” Test Adequacy Criteria for UML Design Models Based on a Fuzzy - AHP Approach ”, American Journal of Scientific Research ISSN 1450-223X Issue 42(2012), pp. 72-84
12. Hitesh Tahbildar1 and Bichitra Kalita2,” AUTOMATED SOFTWARE TEST DATA GENERATION: DIRECTION OF RESEARCH”, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.1, Feb 2011.

40-49

10.

Auths:

Gowtham Mamidisetti, T.Divya

Paper Title:

A Three Engine Application Level Firewall for Web Servers

Abstract: Due to insufficient checks on input data in many web applications web servers remain prone to external tampering. This paper proposes ALF (application level firewall) to protect web systems with three new mechanisms. First, ALF provides a fine grained access control policy. Second, ALF allows web application developers to specify the restriction on application running parameters. Finally, ALF collects web user behavior statistics.

Keywords: ALF (Application Level Firewall), Attack Signature, CGI (Common Gateway Interface)

References:
1. Scott, D. and R. Sharp. Abstracting Application-Level Web Security. in Proceeding of the eleventh international conference on World Wide Web (WWW'2002). 2002.
2. 2003 ACM Press: Washington D.C., USA p. 251-261.
3. CERT Center, Microsoft Internet Information Server (IIS) vulnerable to cross-site scripting via HTTP TRACK method, 2004. CERT Advisory, "Code Red" Worm Exploiting Buffer Overflow In IIS Indexing Service DLL, 2001.
4. Anley, C., Advanced SQL Injection In SQL Server Applications, 2002.
5. SAINT Corp., SAINT vulnerability scanner. Nikto, Nikto 1.32. Symantec Corp., Symantec NetRecon.
6. Kruegel, C. and G. Vigna, Anomaly detection of web-based attacks in Proceedings of the 10th ACM conference on Computer and communications security.
7. Ristic,I.,Introducingmod_security,2003. http://www.onlamp.com/pub/a/apache/2003/11/26/mod_ security.html
8. Vigna, G., et al. A Stateful Intrusion Detection System for World-Wide Web Servers. in Proceedings of the 19th Annual Computer Security Applications Conference. 2003.
9. Nessus, NESSUS Scanner, 2004. Forristal, J. and G. Shipley, Vulnerability Assessment Scanners.

50-53

11.

Auths:

Shaymaa Mohammed Jawad Kadhim, Manjusha Joshi, Shashank Joshi

Paper Title:

Increase the Security of Web Service without Effect on Its Performance by Using Blowfish Cryptography Algorithm

Abstract: Day by day network and internet applications are becoming very popular. Sensitive information requires ensured information security and safety measures. Security is the most challenging aspect in the internet and network applications.Encryption algorithm offers the necessary protection against the data intruders’ attacks by converting information from its normal form into an unreadable form. In the first part of the work described in this paper, we are going to use the Blowfish algorithm and apply it on a web service (employing management system) and in second part we will provide a fair comparison between two web services one without applying on it the algorithm and the other using Blowfish.The comparison is made on the basis of these three parameters :Response time, MTBF(mean time between failure) and MTTR(mean time to repair).

Keywords: blowfish, cryptography, performance, Web Service.

References:
1. Daemen, J., and Rijmen, V. “Rijndael: The Advanced Encrption Standard. ”Dr. Dobb’s Journal, March 2001,PP.137-139.
2. Bill Gatliff, courtesy of “Embedded systems programming” jul 15 2003 (11:00 AM).
3. http://www.design-reuse.com/articles/5922/encrypting-data-with-the- blowfish-algorithm.html.
4. jawahar Thakur, Nagesh Kumar “DES,AES and Blowfish: Symmetric key cryptography algorithms simulation based performance analysis” .
5. Kevin Allison , Keith Feldman , Ethan Mick “ Blowfish”.
6. Gurjeevan Singh*, Ashwani Kumar** , K.S.Sandha*** “A Study of New Trends in Blowfish Alogrithm”.
7. Bill Gatliff “Embedded Systems Programming”.
8. Ch Panchamukesh , Prof.T.Venkat Narayana Rao , A.Vijay Kumar “An Implementation of Blowfish Encryption Algorithm using KERBEROS Authentication Mechansim”.

54-56

12.

Auths:

Gowtham Mamidisetti, P.N.S.L.Sravani, P.Anusha

Paper Title:

Mnfc-Operation Modes and Risks

Abstract: This paper explains about MNFC(Mobile Near Field Communication ) .Near Field Communication (NFC) is wireless communication technology to communicate with other NFC enabled devices . NFC builds upon Radio-Frequency Identification (RFID). In smart phones NFC created sensational things such as less time complexity for connection between the NFC enabled devices and data transfer rate is so high. The main advantage of this technology is, NFC enabled devices can work even when the devices is in switch off mode. This paper mainly focuses on different modes of operation of MNFC and their risks.

Keywords: MNFC (Mobile Near Field Communication), NFC (Near Field Communication), RFID (Radio-Frequency Identification).

References:
1. NFC Forum: www.nfc-forum.org.
2. NFC World: www.nfcworld.com.
3. Dr.Francesco Prato, Near Field Communication (NFC) Marketing Introduction by PHILIPS, Business Development Manager – NFC, Marketing and Sales.
4. Ben Dodson Hristo Bojinov Monica S. Lam, Touch and Run with Near Field Communication (NFC).
5. B. Joan, “Difference Between RFID and NFC,” Difference Between. Retrieved September 26, 2011, at
6. Harley Geiger, Center for Democracy and Trust, NFC Phones Raise Opportunities, Privacy And Security Issues (April 2011), at: www.cdt.org/blogs/harley-geiger/nfc-phones-raise-opportunities-privacy-and-security-issues Collin Mulliner,
7. Kevin Curran, Amanda Millar, Conor Mc Garvey, Near Field Communication, International Journal of Electrical and Computer Engi-neering (IJECE) Vol.2, No.3, June 2012.
8. 8.G. Broll, S. Siorpaes, E. Rukzio, M. Paolucci, J. Hamard, M. Wagne and A. Schmidt. Supporting mobile service usage through physical mobile interaction. In In Proceedings of PerCom 2007, White Plains, pages 262–271. IEEE Computer Society, 2007.

57-60

13.

Auths:

Er. Amarjeet Kaur, Er. Kumar Saurabh, Er. Gurpreet Singh

Paper Title:

A Combined Approach of Data Mining Algorithms Based on Association Rule Mining and Rule Induction

Abstract: Association rule learning is a popular method for discovering interesting relations between variables in large database. It is often used in market basket analysis domain e.g. if a customer buys onions and potatoes then he buys also beef. But, in fact, it can be implemented in various application areas where we want to discover the association between variables. The A PRIORI approach is certainly the most popular[1]. But, despite its good properties, this method has a drawback: the number of obtained rules can be very high. The ability to underline the most interesting rules, those which are relevant, becomes a major challenge. In this research work titled a hybrid approach based on Association Rule mining and Rule Induction in Data Mining we using induction algorithms and Association Rule mining algorithms as a hybrid approach to maximize the accurate result in fast processing time. This approach can obtain better result than previous work. This can also improves the traditional algorithms with good result. In the above section we will discuss how this approach results in a positive as compares to other approaches.

Keywords: Association Rule mining, A priori algorithm, Rule Induction, Decision list induction, Data mining

References:
1. http://en.wikipedia.org/wiki/Inductive_Logic_Programming.
2. KhurramShehzad(2012)” EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 8, AUGUST 2012.
3. NingZhong, YuefengLi(2012)” Effective Pattern Discovery for Text Mining”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 1, JANUARY 2012.
4. Anil Rajput, S.P. Saxena(2012)” Rule based Classification of BSE Stock Data with Data Mining”, International Journal of Information Sciences and Application. ISSN 0974-2255 Volume 4, Number 1 (2012), pp. 1-9.
5. K. Shehzad(2011)” Simple Hybrid and Incremental Post-pruning Techniques for Rule Induction”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.
6. Alexander Borisov(2011)” Rule Induction for Identifying Multilayer Tool Commonalities”, IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 24, NO. 2, MAY 2011.
7. Alexander Borisov(2011)” Rule Induction for Identifying Multilayer Tool”,IEEE.
8. Fernando E. B. Otero (2011)” A New Sequential Covering Strategy for Inducing Classification Rules with Ant Colony Algorithms”,IEEE.
9. Thomas R. Gabriel and Michael R. Berthold(2010)” Missing Values in Fuzzy Rule Induction”, IEEE.
10. Nick F Ryman-Tubb(2010)” SOAR – Sparse Oracle-based Adaptive Rule Extraction: Knowledge extraction from large-scale datasets to detect credit card fraud”, IEEE.
11. Alberto Fern´andez(2010)” Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study”, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 14, NO. 6, DECEMBER 2010.
12. Jeremy Davis (2010)” Methods of Information Hiding and Detection in File Systems”, 2010 Fifth International Workshop on Systematic Approaches to Digital Forensic Engineering.
13. Richard Jensen, Chris Cornelis(2009)” Hybrid Fuzzy-Rough Rule Induction and Feature Selection”, R. Jensen and Q. Shen are with the Department of Computer Science, Aberystwyth University, UK.

61-62

14.

Auths:

Ricardo Pérez, S. Jöns, Arturo Hernández

Paper Title:

Study of a Queue Model Using an Estimation of Distribution Algorithm

Abstract: An analysis of a queue model M/M/1 for an outpatient clinic was done considering no-shows from the patients. In order to detect how no-shows affect the performance measure, i.e., the doctor’s idle time on the patient-doctor system we consider analyzing the behavior of the patients when they have an appointment with a previous diagnostic successfully. The alternative approach was validated using a simulation model that was built through Delmia Quest® R20 and an Estimation of Distribution Algorithm to model the workflow in a small health clinic in México.

Keywords: Queue Models, Appointment Scheduling, Estimation of Distribution Algorithms.

References:
1. H. Mühlenbein, andG. Paaß, “From recombination of genes to the estimation of distributions: I. binary parameters”, in Parallel Problem Solving from Nature PPSN IV, H. Voigt, W. Ebeling, I. Rechenberg, and H. Schwefel Eds.,Berlin: Springer, 1996, pp. 178–187.
2. S.H. Chen, M.C. Chen, P.C.Chang, Q. Zhang, and Y.M. Chen, “Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems”,Expert Systems with Applications, 37, 2010,pp. 6441-6451.
3. H. Liu, L. Gao, and Q. Pan, A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Experts Systems with Applications, 38, 2011, pp. 4348-4360.
4. Q.K. Pan and R. Ruiz, “An estimation of distribution algorithm for lot-streaming flow shop problems with setup times”,Omega, 40, 2012,pp. 166-180.
5. Chen, S.-H., Chang, P.-C., Cheng, T., & Zhang, Q. (2012b). A Self-guided Genetic Algorithm for permutation flowshop scheduling problems. Computers and Operations Research, 39, 1450-1457.
6. Wang, L., Wang, S., Xu, Y., Zhou, G., & Liu, M. (2012). A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers and Industrial Engineering, 62, 917-926.
7. Chen, Y.-M., Chen, M.-C., Chang, P.-C., & Chen, S.-H. (2012c). Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems. Computers & Industrial Engineering, 62, 536-545.
8. De Bonet, J., Isbell, C., & Viola, P. (1997). MIMIC: Finding Optima by Estimation Probability Densities. Advances in Neural Information Processing Systems, 9.

63-65

15.

Auths:

Saraswati Mishra, Abhishek Kalra, Kavita Choudhary

Paper Title:

Influence of Information and Communication Technology in Health Sectors

Abstract: This paper represents that how Information and Communication Technology (ICT) is influencing the health services. Emergence of Internet has boosted the use of this technology and now this has become an imitable source of healthcare services. We have listed the technologies that are being used to cure the patient in various medical sectors and hospitals. Further, comparison between traditional methods of treatment and methods that include ICT, shows that it is beneficial to implement ICT to provide e-health services which helps developing countries to reduce cost of treatment. As obvious that installation cost is more but after implementation it becomes more facilitating solution. Conclusion can be made that if technology is easy to understand, learn and implement as well as solves various problems at a one stretch then no harm in its procurement even though installation cost is a bit high because after effects are more beneficial.

Keywords: Cybermedicine, e-health, telemedicine.

References:
1. Connecting people, improving health: the role of ICTs in the health sector of developing Edited by Andrew Chetley, et al. chetley.a@healthlink.org.uk InfoDev Task Manager: J Dubow February 2006 infoDev
2. Newsletter LAC 2010, Number 12, July 2010 ICT and HEALTH
3. Impact of Information and Communication Technologies (ICT) on Health Care Robert Rudowski, Department of Medical Informatics and Telemedicine, Medical University of Warsaw, Poland
4. wordnetweb.princeton.edu/perl/webwn
5. National Knowledge Commission Management of Health Sector in India Recommendations for the NKC “Report of the working group on health information network”

66-68

16.

Auths:

Vaijinath V. Bhosle, Vrushsen P. Pawar

Paper Title:

Texture Segmentation: Different Methods

Abstract: Image Segmentation is an important pixel base measurement of image processing, which often has a large impact on quantitative image analysis results. The texture is most important attribute in many image analysis or computer vision applications. The procedures developed for texture problem can be subdivided into four categories: structural approach, statistical approach, model based approach and filter based approach. Different definitions of texture are described, but more importance is given to filter based methods. Such as Fourier transform, Gabor, Thresholding, Histogram and wavelet transforms. These filters are used to VisTex images and Brodatz Textures Database. The main objective of this paper is to study different methods for texture segmentation.

Keywords: Texture segmentation, Gabor Filter, Thresholding, VisTex, Brodatz.

References:
1. Jianguo Zhang, Tieniu Tan “Brief review of invariant texture analysis” The Jurnal of the Pattern Recognition Society 35 (2002) 735–747.
2. B.S. Manjunathi and W.Y. Ma “Texture Features for Browsing and Retrieval of Image Data” IEEE Transaction on Pattern Aivalysis and Machine Intelligence, VOL. 18, NO. 8, AUGUST 1996.
3. George Paschos “Perceptually Uniform Color Spaces for Color Texture Analysis: An Empirical Evaluation” IEEE Transactions On Image Processing, Vol. 10, No. 6, June 2001.
4. Amanpreet Kaur (2012) “Texture Based Image Segmentation using Gabor filters” [IJESAT] International Journal of Engineering Science & Advanced Technology Vol-2, Issue-3, 687 – 689.
5. Khaled Hammouda, Prof. Ed Jernigan “Texture Segmentation Using Gabor Filters” University of Waterloo, Ontario, Canada.
6. Anjali Goswami “For Image Enhancement And Segmentation By Using Evaluation Of Gabor Filter Parameters” International Journal of Advanced Technology & Engineering Research (IJATER).
7. Ms. Priyanka S. Chikkali* et al. “FPGA based Image Edge Detection and Segmentation” International Journal of Advanced Engineering Sciences And Technologys Vol No. 9, Issue No. 2, 187 – 192.
8. Y.Ramadevi, T.Sridevi, B.Poornima, B.Kalyani “Segmentation And Object Recognition Using Edge Detection Techniques” International Journal of Computer Science & Information Technology (IJCSIT), Vol 2, No 6, December 2010.
9. S.Selvarajah and S.R. Kodituwakku “Analysis and Comparison of Texture Features for Content Based Image Retrieval” International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) 108 Vol 2, Issue 1, March 2011.
10. Mohammad Faizal Ahmad Fauzi, Paul H. Lewis “Automatic Texture Segmentation for Content-Based image Retrieval Application” Pattern Anal Applic (2006) 9:307–323 DOI 10.1007/s10044-006-0042-x.
11. Magdolna Apro1, Szabolcs Pal2, Sandra Dedijer1 “Evaluation of single and multi-threshold entropy-based algorithms for folded substrate analysis” Journal of Graphic Engineering and Design, Volume 2 (2), 2011.
12. Ralf Reulke, Artur Lippok “Markov Random Fields (MRF)-Based Texture Segmentation For Road Detection” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008.
13. Erdogan Çesmeli and DeLiang Wang “Texture Segmentation Using Gaussian–Markov Random Fields and Neural Oscillator Networks” IEEE Transactions On Neural Networks, Vol. 12, No. 2, March 2001.
14. A Materka, M. Strzelecki, “Texture Analysis Methods – A Review” Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998.
15. P.Lakshmi Devi, S.Varadarajan “Image Segmentation and Techniques: A Review” International Journal of Advanced Research Technology, Vol. 1 Issue 2, 2011,118-127 ISSN NO: 6602 3127.
16. Hui Zhang a,*, Jason E. Fritts b, Sally A. Goldman a “Image segmentation evaluation: A survey of unsupervised methods” Computer Vision and Image Understanding 110 (2008) 260–280.
17. C.Umarani1, L.Ganesan2, S. Radhakrishnan3 “Combined Statistical and Structural Approach for Unsupervised Texture Classification” International Journal Of Imaging Science And Engineering (IJISE).
18. Timo Ojala, Matti Pietikainen “Unsupervised texture segmentation using feature distributions” The Jurnal of the Pattern Recognition Society 32 (1999) 477-486.
19. Yong Yang “Image Segmentation by Fuzzy C-Means Clustering Algorithm with A Novel Penalty Term” Computing and Informatics, Vol. 26, 2007, 17–31.
20. Tomasz Adamek, Noel E. O’Connor, Noel Murphy “Region-Based Segmentation of Images Using Syntactic Visual Features”.
21. Xiuwen Liu, Member, IEEE, and DeLiang Wang, Fellow, IEEE “Image and Texture Segmentation Using Local Spectral Histograms” IEEE Transactions On Image Processing, Vol. 15, No. 10, October 2006

69-74

17.

Auths:

Shalini Batra, Amritpal Singh

Paper Title:

A short survey of Advantages and Applications of Skip Graphs

Abstract: Although the enormous growth of Internet, Social Network, Cloud Computing, etc. has brought the world closer and faster, major concern for computer experts is how to store such enormous amount of data especially in form of graphs. Further, data structure used for storage of such type of data should provide efficient format for fast retrieval of data as and when required. Although adjacency matrix is an effective technique to represent a graph having few or large number of nodes and vertices but when it comes to analysis of huge amount of data from site likes like face book or twitter, adjacency matrix cannot do this. This paper provides a special kind of data structure, skip graph which can be efficiently used for storing such type of data resulting in optimal storage, space utilization and retrieval.

Keywords: Advanced Data Structure, Skip List, Skip Graph, Efficient and fast search.

References:
1. L. Arge, D. Eppstein, and M.T. Goodrich. Skip-webs: efficient distributed data structures for multi-dimensional data sets. Proceedings of the annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing, pages 69–76, 2009.
2. J. Aspnes and G. Shah. Skip graphs. Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, pages 384– 393, 2003.
3. James Aspnes and Gauri Shah. Skip graphs. ACM Transactions on Algorithms, 3(4):37, November 2007.
4. James Aspnes and Udi Wieder. The expansion and mixing time of skip graphs with applications. In SPAA ’05: Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures, pages 126–134, New York, NY, USA, 2005. ACM.
5. Thomas Clouser, Mikhail Nesterenko, Christian Scheideler : Tiara: A self-stabilizing deterministic skip list and skip graph . 2012 Elsevier
6. Hammurabi Mendes , Cristina G. Fernandes - A Concurrent Implementation of Skip graphs . Electronic Notes in Discrete Mathematics 35 (2009 ) page no .-263-268 .
7. James Aspnes , Udi Wieder -The expansion and mixing time of skip graphs with applications. page no 385-394 , Springer-Verlag 2008
8. Michael T. Goodrich, Michael J. Nelson , Jonathan Z. Sun -The Rainbow Skip Graph: A Fault-Tolerant Constant-Degree P2P Relay Structure . ArXiv - 2009
9. Fuminori Makikawa, Tatsuhiro Tsuchiya, Tohru Kikuno - Balance and Proximity-Aware Skip Graph Construction. 2010 First International Conference on Networking and Computing .
10. Shabeera T P, Priya Chandran, Madhu Kumar S D - Authenticated and Persistent Skip Graph: A Data Structure for Cloud Based Data-Centric Applications . CHENNAI, India , 2012 , ACM
11. Ian Munro and Patricio V. Poblete. Fault tolerance and storage reduction in binary search trees. Information and Control, 62(2/3):210-218, August 1984.
12. Jianjun Yu, Hao Su, Gang Zhou, Ke Xu - SNet: Skip Graph based Semantic Web Services Discovery . Seoul, Korea. 2007 ACM
13. James Aspnes, Guari Shah, ppt in SODA 2003." http://www.cs.yale.edu/homes/aspnes/papers/skip-graphs-soda03.ppt"

75-78

18.

Auths:

E.M.C.L Ekanayake, J.V Wijayakulasooriya

Paper Title:

Time Varying Moving Object Acceleration/ Deceleration Detection in Region of Interest Using Motion of Center of Gravity

Abstract: The basic focus of this research is to design an improved algorithm upon the existing algorithm to detect and index moving objects on the road. The algorithm in use involves both the image subtraction approach and image dilation method. The image sequences acquired through a digital video during the day time was processed to analyze the characteristics of object movement in the Region of Interest (ROI). Basically this developed program can index moving objects and remove unwanted noisy shadows within the ROI. In addition it also represents how objects enter into the ROI and how they exit from ROI. Moreover it can characterize a particular object’s moving directions and its characteristics such as velocity, acceleration and deceleration. Those features could also be derived from analyses of the images

Keywords: Image Processing, Video image analysis, image morphology, image dilation, image thresholding.

References:
1. Cohem,I. and Medioni,G. “Detection and Tracking moving object for video Survillance”. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, CO.1999.
2. M.Yokoyama, T. Poggio,” A Contour-Based Moving Object Detection and Tracking”, In: Proceedings of Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (in conjunction with ICCV 2005), Beijing, China,2005.
3. Thompson, W.B. and Pong ,T..”Moving Objects”, International Journal of Computer Vision,4, Netherland, Kluwer Academic Publisher, pp 39-57,1990.
4. Fragkiaduki,K., and Shi , J.”Detection free Tracking : Exploiting motion and topology for segmenting and tracking under entanglement”.In IEEE Conference on Computer Vision and pattern recognition(CVPR),2011.
5. Hibo ,H., and Hong ,Z.”Real-time tracking in image sequences based-on parameters updating with temporal and spatial neighborhoods mixture Gaussian Model”.World Academiy of Science,Engineering and Technology.Vol : 43, pp 551-556 ,2010.
6. Prabhakar ,N et al.,” Object Tracking Using Frame Differencing and Template Matching”, Research Journal of Applied Sciences, Engineering and Technology 4(24): pp. 5497-5501, 2012.
7. R.C. Gonzalez, R.E. Woods,”Digital image processing (2nd Edition)”, New Delhi: Prentice-hall of India private limited, 2005.
8. R.C. Gonzalez, R.E. Woods, S.L. Eddings ,”Digital image processing using MATLAB”, New Delhi: Pearson Education Inc, 2004.

79-82

19.

Auths:

Alex J Kalume, Stephen Diang’a

Paper Title:

Development of a Model for Determining Realistic Contract Period

Abstract: The construction industry is very important to the economy in terms of employment and wealth but it is faced with numerous challenges which necessitate the need of taking measures to improve the management of construction projects. Building projects are normally realized by inputs from different experts in their areas of specialization. The many parties involved in the construction process shows how difficult it is to manage the process to a successful completion. Majority of projects are not completed within the agreed time due to unforeseen circumstances not included at the estimating stage. The study focused on developing tools for effective management of building construction projects in Kenya, as a means of helping to bring about improvements in project executions. Specifically, this is realized by developing regression models for realistic estimate of contract period. A survey of construction practitioners in Mombasa was conducted. The sample consists of architects, engineers, contractors and quantity surveyors. Information was obtained on past building projects performance in terms of time and determination of the degree of influence of various factors at every stage of a building project. Multiple regression technique was used in the analysis of data for the study.Findings include regression models for realistic estimation of project time for residential houses, institutional buildings (education), industrial (factories), Hotel/motel and commercial (offices).

Keywords: Time, Cost, Construction, Project, Management

References:
1. Burke, R. (2000). Project management planning and control technique, 4th ed. Hong Kong. Burke publishing.p27-39
2. Chang, A. (2002).Reasons for cost and schedule increase for engineering design projects, p29-36.
3. Frank and Maccaffer, R. (1990). modern construction management. 8th edition, Prenttice Hall, New Jersey, Ohio.p25-35
4. Harry, F., & Stevens, c. (1991). Statistics Concepts and Applications. Cambridge.p65-98.
5. Harper, W. (1991). Statistics. 6th edition. Prentice Hall.p 23-89
6. Ivor, s. (1987). civil engineering contract administration and control.p67-89.
7. Jason. (2009). A guide to the project management body of knowledge, PMBOK guide, project p34-87
8. Kaming, P. O. (1997). Factors influencing construction time and cost overruns on high-rise projects in Indonesia, construction management and economics, p83-94.
9. Love, P. &. (2005). Time-Cost relationships in Australia building construction projects, Journal of construction engineering and management, pg 187-194.
10. Peter, M. (1969). Contracting for engineering Management and Construction Projects. 5th edition. Gower Publishing Company.p34-56
11. Silverman, D. (2000). Doing qualitative research: A practical handbook, London. sage publication.p35-57
12. Susong, M. K. (2005). the construction project: phases, people, terms, paperwork processes. In m. susong. ABA publishing. 3rd ed. p125-142
13. Talukhaba, A. (1998). An investigation into factors causing construction project delays in kenya, a case study of high rise building projects in Nairobi. unpublished ph.D thesis, university of Nairobi.121-145
14. Twort. (1985). civil engineering supervision and management. 4th ed. (pp. 50-67). John Wiley & sons.

83-90

20.

Auths:

Eltyeb E. A bed Elgabar

Paper Title:

Comparison of LSB Steganography in BMP and JPEG Images

Abstract: The literature on information security has to consider innovative and continuously developing ways of protecting data from infiltration taking into consideration the speed of the processors and the cryptanalysis. New steganographical technologies have been created to provide security with or without data encryption including data hiding. Etymologically, the term ‘steganography’, which means ‘covered writing’, originates from the Greek words “stegos” (cover), and “grafia” (writing). Steganography operates to conceal a secret message rooted in various forms of media including image, video, audio, and text. The domain of information hiding utilizes several algorithms. The easiest and widely known technique is Least Significant Bit (LSB). This paper compares and analyses Least Significant Bit algorithm using the cover object as an image with a focus on two types: BMP and JPEG. The comparison and analysis are done with respect to a number of criteria to understand their strengths and weaknesses.

Keywords: Robustness, Steganalysis, Steganography, Steganographic, Unsuspicious.

References:
1. Eltyeb E.Abed Elgabar, Haysam A. Ali Alamin, “Comparison of LSB Steganography in GIF and BMP Images ”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-4, September 2013.
2. "Watermarking Application Scenaros and Related Attacks ", IEEE international Conference on Image Processing, Vol. 3, pp. 991 – 993, Oct. 2001.
3. Moerland, T., “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science, www.liacs.nl/home/ tmoerl/privtech.pdf.
4. Henk C. A. van Tilborg (Ed.), "Encyclopedia of cryptography and security", pp.159. Springer (2005).
5. Johnson, N.F. & Jajodia, S., “Exploring Steganography: Seeing the Unseen”, Computer Journal,February 1998.
6. Krenn, R., “Steganography and Steganalysis”, http://www.krenn.nl/univ/cry/steg/article.pdf
7. Pallavi Hemant Dixit, Uttam L. Bombale, " Arm Implementation of LSB Algorithm of Steganography", International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-3, February 2013.
8. “Reference guide:Graphics Technical Options and Decisions”, http://www.devx.com/ /Article/1997.
9. Artz, D., “Digital Steganography: Hiding Data within Data”, IEEE Internet Computing Journal, June 2001.
10. NXP & Security Innovation Encryption for ARM MCUs ppt.
11. Owens, M., “A discussion of covert channels and steganography”, SANS Institute, 2002.
12. “MSDN:About Bitmaps” <http://msdn.microsoft. com/library/default.asp?url=/library/enus/gdi/bitmaps_99ir.asp?frame=tru>, 2007,M Corporation.
13. V. Lokeswara Reddy, Dr. A. Subramanyam and Dr.P. Chenna Reddy, " Implementation of LSB Steganography and its Evaluation for Various File Formats", Int. J. Advanced Networking and Applications, Volume: 02, Issue: 05, Pages: 868-872 (2011).
14. Neeta Deshpande, Snehal Kamalapur and Jacobs Daisy, “Implementation of LSB steganography and Its Evaluation for Various Bits”, 1st International Conference on Digital Information Management, 6 Dec. 2006 pp. 173-178.
15. J. E. Boggess III, P. B. Nation, M. E. Harmon, “Compression of Colour Information In Digitized Images Using an Artificial Neural Network”, Proceedings of the IEEE 1994 National Aerospace and Electronics Conference, Issue 23-27 May 1994 Page(s):772 - 778 vol.2.
16. Atallah M. Al-Shatnawi, "A New Method in Image Steganography with Improved Image Quality", Applied Mathematical Sciences, Vol. 6, 2012, no. 79, 3907 - 391.
17. Ze-Nian Li and Marks S.Drew, "Fundamentals of Multimedia, School of computing Science Simon Faster University, Pearsoll Education, Inc, 2004.
18. J. Fridrich, M. Goljan, and R. Du, “Detecting LSB steganography in color, and gray-scale images,” IEEE Multimedia, vol. 8, no. 4, pp. 22–28, Oct. 2001.
19. Priya Thomas," Literature Survey On Modern Image Steganographic Techniques", International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 5, May - 2013 ISSN: 2278-0181.
20. V. Lokeswara Reddy, Dr. A. Subramanyam, Dr.P. Chenna Reddy ,"Implementation of LSB Steganography and its Evaluation for Various File Formats", Int. J. Advanced Networking and Applications Volume: 02, Issue: 05, Pages: 868-872 (2011).
21. Roshidi Din and Hanizan Shaker Hussain, “The Capability of Image In Hiding A Secret Message”, Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing, September 2006.
22. D. Llamas, C. Allison, and A. Miller, Covert channels in internet protocols: A survey," in Proceedings of the 6th Annual Postgraduate Symposium about the Convergence of Telecommunications, Networking and Broadcasting, 2005.
23. Neil R. Bennett, JPEG STEGANALYSIS & TCP/IP STEGANOGRAPHY, University of Rhode Island , 2009.
24. H. Wu, N. Wu, C. Tsai, and M. Hwang, “Image steganographic scheme based on pixel-value differencing and LSB replacement methods,” IEE Proceedings-Vision, Image and Signal Processing, vol. 152, no. 5, pp. 611–615, 2005.
25. R. Chandramouli and N. Memon, “Analysis of LSB based image steganography techniques,” in Image Processing, 2001. Proceedings. 2001 International Conference on, vol. 3, 2001.
26. Y. Lee and L. Chen, “High capacity image steganographic model,” IEE Proceedings-Vision, Image and Signal Processing, vol. 147, no. 3, pp. 288–294, 2000.
27. W. Pennebaker and J. Mitchell, JPEG still image data compression standard. Kluwer Academic Publishers, 1993.

91-95

21.

Auths:

Aziz ORICHE, Abderrahman CHEKRY, Mohamed KHALDI

Paper Title:

Intelligent Agents for the Semantic Annotation of Educational Resources

Abstract: Our objective is to describe the content of educational resources semantically annotating with unambiguous information to facilitate the exploitation of these resources by software agents, these resources are delimited by tags (XHTML, XML), and are well structured. We propose a semantic annotation system based on three intelligent agents to manage semantic annotations educational resources, these annotations are guided by domain ontology. The domain ontology contains a set of concepts, relationships between these concepts and their properties. Each concept of ontology some have one or more (synonyms) to represent words. All these terms are used to describe instances of a domain concept; these terms are more focused in the description of concepts. All these concepts are validated by the domain expert. Taking into account that the teaching materials are in HTML or XML whose structure is a DOM (Document Object Model) we seek to identify the terms of educational concepts in the nodes of the tree, extract all these concepts while respect of the domain ontology. The terms or educational concepts candidates are associated with terms and concepts of the ontology to determine appropriate concepts to annotate nodes in which they are located.

Keywords: Semantic Annotation, Metadata, Multi-agent systems, Ontology

References:
1. Lenne, D., Abel, M.H., Benayache, A. & Moulin, C. (2005). “E-MEMORAe: an e-Learning Environment Based on an Organizational Memory”. In P. Kommers & G. Richards (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2005 (pp. 4651-4658).
2. Neepa K. Shah, “E-Learning and Semantic Web”, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 2, April 2012
3. Anand Tamrakar, Kamal K.Mehta, “Analysis of Effectiveness of Web based E-Learning through Information Technology”, International Journal of Soft Computing and Engineer ing (IJSCE) Volume-1, Issue-3, July 2011
4. Stojanovic, L., Staab, S. and Studer, R. “eLearning based on the Semantic Web”. WebNet2001, World Conference on the WWW and Internet, October 23-27, 2001, Orlando, Florida, USA
5. Ljiljana Stojanovic, “eLearning based on the Semantic Web”, FZI Research Center for Information Technologies at the University of Karlsruhe, 76131 Karlsruhe, Germany
6. Berdjouh Chafik, “agent approach for service discovery”, Centre de Formation professionnelle El-Meghaier Wilaya El-OUED, ALGERIE, Special Issue on ICIT 2009 Conference – “Web and Agent Systems”
7. Berners-Lee T., “Weaving the Web”, Harper Eds, San Francisco, 1998, p.226.
8. Handschuh S. et Staab S. (Eds).. “Annotation for the semantic web”. Vol. 96 of Frontiers in Artificial Intelligence and applications”, IOS Press. 2003.
9. Sbaa.A, R. El Bejjet and H.Medromi, “Architecture design of a virtualized embedded system “, International Journal on Computer Science and Engineering (IJCSE), Vol. 5 No. 01 Jan 2013.
10. CORCHO O., “Ontology based document annotation: trends and open research problems”, in International Journal of Metadata, Semantics and Ontologies, Inderscience, 2006, pp. 47-57.
11. DZBOR M., DOMINGUE J. & MOTTA E., “Magpie – towards a semantic web browser”, in Proceedings of the 2nd International Semantic Web Conference (ISWC’03), International Handbooks on Information Systems, Springer-Verlag, 2003, pp. 690-705.
12. DOMINGUE J., DZBOR M. & MOTTA E., “Semantic Layering with Magpie”, in Handbook on Ontologies, STAAB S. & STUDER R. (Eds.), Frontiers in Artificial Intelligence and Applications, Volume 96, IOS Press, Springer-Verlag, 2003, pp. 533-554.
13. DILL S., EIRON N., GIBSON D., GRUHL D., GUHA R., JHINGRAN A., KANUNGO T., RAJAGOPALAN S., TOMKINS A., TOMLIN J. A. & ZIEN J. Y., “SemTagand Seeker: Bootstrapping the Semantic Web via Automated Semantic Annotation”, in Proceedings of the 12th International World Wide Web Conference (WWW’03), ACM Press, Budapest, Hongrie, 2003, pp. 178-186.
14. DILL S., EIRON N., GIBSON D., GRUHL D., GUHA R., JHINGRAN A., KANUNGO T., MCCURLEY K. S., RAJAGOPALAN S. & TOMKINS A., “A Case for Automated Large-Scale Semantic Annotation”, in Journal of Web Semantics, Science, Services and Agents on the World Wide Web, Elsevier, 2003, pp. 115-132.
15. CIRAVEGNA F., DINGLI A., GUTHRIE D. & WILKS Y., “Integrating Information to Bootstrap Information Extraction from Web Sites, in Proceedings of the workshop on Information Integration on the Web” (IJCAI’03), Acapulco, Mexique, 2003.
16. DINGLI A., CIRAVEGNA F. & WILKS Y., “Automatic Semantic Annotation using Unsupervised Information Extraction and Integration”, in Proceedings of the Workshop on Knowledge Markup and Semantic Annotation (KCAP’03), Sanibel, Floride, 2003.
17. CIRAVEGNA F., CHAPMAN S., DINGLI A. & WILKS Y., “Learning to Harvest Information for the Semantic Web, in Proceedings of the 1st European Semantic Web” Symposium (ESWS’04), SpringerVerlag, Heraklion, Crête, Grèce, 2004.
18. Popov B., Kiryakov A., Manov D., Kirilov A., Ognyanoff D. & Goranov M. (2003). “Towards semantic web information extraction”. In Proceedings of the Human Language Technologies Workshop (ISWC’03), p. 1–22, Sanibel, Floride.
19. Amardeilh F., Laublet P. &Minel J.-L. (2005). « Annotation documentaire et peuplement d’ontologie à partir d’extractions linguistiques ». In Actes des 16èmes journée francophones d’Ingénierie des Connaissances, p. 25–36.
20. AUSSENAC N. & SEGUELA P., ₺ Les relations sémantiques : du linguistique au formel, in Cahiers de grammaire ₺, Numéro spécial ₺ Sémantique et Corpus ₺, Volume 25, Presses de l’UTM, Toulouse, 2000, pp. 175-198.
21. Martins B., Silva M.J., ₺The WebCAT Framework - Automatic Generation of Meta-Data for Web Resources₺, Web Intelligence 2005, Compiegne- France, p. 236-242.
22. Shchekotykhin K. M., Jannach D., Friedrich G., Kozeruk O., ₺ AllRight: Automatic Ontology Instantiation from Tabular Web Documents ₺, The 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference, 2007, Busan-Korea, p. 466-479.

96-104

22.

Auths:

Akhila Devi B V, S.Suja Priyadharsini

Paper Title:

Diagnosis of Neuromuscular Disorders Using Softcomputing Techniques

Abstract: Biomedical signals are collection of electrical signals which generated from any organ that signal represents a physical variable of interest. Electromyography (EMG) is a technique for evaluating and recording of electrical activities produced from skeletal muscles. There are so many applications of EMG signals. Major interests lies in the field of clinical as well as biomedical engineering.EMG is used as a diagnostic tool for identifying neuromuscular disorders .Motor unit action potentials (MUPS) provides information about neuromuscular disorders. Traditionally neurophysiologist can access MUPs information from their shapes and patterns using an oscilloscope. But MUPs from different motor neurons will overlap leads to the formation of interference pattern and it is difficult to detect individual shapes accurately. For this reason a number of computer based quantitative EMG analysis algorithm have been developed. In this work, different types of learning methods were used to classify EMG signals. The model automatically classifies EMG signals into normal, myopathy and neuropathy. In order to extract useful information from the EMG signals different feature extraction methods such as discrete wavelet transform(DWT) and auto regressive modeling(AR)are implemented. Adaptive neuro-fuzzy inference system (ANFIS) with hybrid learning algorithm, support vector machine (SVM) and fuzzy support vector machine (FSVM) were compared in relation to their accuracy in the classification of EMG signals. Based on the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques.

Keywords: Adaptive neuro-fuzzy inference system (ANFIS), Discrete Wavelet Transform (DWT), Electromyography (EMG) Fuzzy SVM (FSVM), Support vector machine (SVM)

References:
1. Angkoonphinyomark, Pornchai phukpattaranont, Chusak Limsakul, "Feature reduction and selection for EMG signal classification”, Expert Systems with Applications,vol. 39 pp(7420–7431),2012
2. A.Subasi,Classification of EMG signals using combined features and soft computing techniques, Application software computing vol.12,2012,pp 2188-2198
3. A. Subasi, M. Yilmaz, H.R. Ozcalik, “Classification of EMG signals using wavelet neural network”, Journal of Neuroscience Methods ,156 ,pp( 360–367),2006
4. Constantinos s. Pattichis,, Christos N. Schizas,and Lefkos T. Middleton, “Neural Network Models in EMG Diagnosis” IEEE Transaction on biomedical engineering. Vol. 42(5), 1995
5. DeLuca CJ, Towards understanding the EMG signal, 4th ed., Baltimore: Williams & Wilkinson, 1978.
6. Inan Guler , Elif Derya Ubeyli , “Adaptive neuro-fuzzy inference system for classification of signals using wavelet coefficients,” Journal of Neuroscience Methods, 49 ,pp(640–650),2005.
7. Jit Muthuswamy,’ Biomedical signal analysis’, Standard Handbook Of Biomedical Engineering And Design vol.14,pp(18.1-18.27),2004
8. J.S.R. Jang, “ANFIS: adaptive network based fuzzy inference system”, IEEE Transactions on Systems, Man and Cybernetics, vol .3 ,pp(665–683),1993.
9. Kandaswamy, C.S. Kumar, R.P. Ramanathan, S. Jayaraman, N. Malmurugan, Neural classification of lung sounds using wavelet coefficients, Computers in Biology and Medicine 34 (6) (2004) 523–537.
10. Krarup C, Pitfalls in electrodiagnosis, J Neurophysiol, vol. 81, 1999, pp. 1115-1126.
11. L.J. Pino, D.W. Stashuk, S.G. Boe, T.J. Doherty, Motor unit potential characterization using pattern discovery, Med. Eng. Phys. Vol.30 pp( 563–573),2008.
12. N. Anand and D. Chad, “The clinical Neurophysiology primer”,Humana Press, 2007.
13. R.Polikar The wavelet tutorial http://users.rowan.edu/polikar/WAVELETS/ WT tutorial.html (accessed at 28.08.11).
14. Rajesh Ku.Tripathy, Ashutosh Acharya, Sumit Kumar Choudhary,”Gender Classification Fro ECG Signal Analysis Using Least Square Support Vector Machine”American Journal of Signal Processing,2(5),pp(145-149),2012
15. Sivarit Sultornsaneea, Ibrahim Zeida, Sagar Kamarthia, “ Classification of Electromyogram Using Recurrence Quantification Analysis”, Proceediang of Computer Science,Elsiever, vol.6 ,pp( 375–380),2011.
16. V.Vapnik,StatisticalLearningTheory,Wiley,NewYork, 1998.
17. Xiufeng Jiang ,Zhang Yi ,Jian Cheng Lv, “Fuzzy SVM with a new fuzzy membership function” Neural Comput & Applic ,Vol 15:pp( 268–276),2006
18. Examples of elecromyograms(emgdb)
19. http://www.physionet.org

105-111

23.

Auths:

Sachin Lonare, Rutuja Paripagar, Roshani channe

Paper Title:

Multi-Language Compiler on Private Cloud Based System

Abstract: Cloud Computing is an attractive concept in IT field, that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Using cloud computing resources to be is provide according to the user needs can share the resources, device and software. Private cloud services are very expensive so normal user cannot buy them are use that application for experimental. So we are developing such application that normal user can buy them and experiment on that product. We implementing such product by using the Open sources software such Ubuntu 10.04 operating system. By using that student and normal users can also use an experiment on that product.

Keywords: an open source system has been used to implement a private cloud using the hardware and software.

References:
1. http://www.cloudsherpas.com/resources/learn/saas-paas-and-iaas/
2. Dr. G.R.Karpagam, J.Parkavi “Setting up of an Open Source based Private Cloud” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, May 2011
3. https://help.ubuntu.com/community/KVM/Installation.
4. http://www.webopedia.com/TERM/V/virtualization.html.
5. www.ubuntugeek.com/
6. https://help.ubuntu.com/community/g++.
7. Paper cloud_computing_IJCSI-8-3-1-354-359.

112-115

24.

Auths:

Yogesh M. Gajmal, Vandana M. Gaikwad

Paper Title:

MDT: Middle Tire Automation Testing Framework

Abstract: Web Services innovations empower adaptable and alert interoperation of independent programming and informative data frameworks. An approach that could be utilized to test the strength and other identified quality of Web Services, Software. Analyzers are defied with extraordinary challenges in testing Web Services particularly while mixing to Services possessed by different outlets. Testers have multiple options to pick licensing or open source automation tools such as SOAP UI, Quick Test Professional (QTP) etc. based on their requirement. They are losing its market because of its limitations like supporting only windows environment, single database support and limited functionality. Additionally for using SOAP UI, Quick Test Professional (QTP) the tester must have knowledge of programming language to Create Project, Test cases, Method and Class. Automated testing has particularly been a standout amongst the most complicated, yet most beneficial technologies for testing Web Services. This paper describes Middle-Tire Testing Tool for to automate the process of testing Software, Web Services with extra features. The system tries to build a tool on the top of the SOAP UI which will be the testing tool for Web Services.

Keywords: Java, Middle Tire Testing, SOAP UI, Testing Tool, Web Services.

References:
1. Fei Wang,Wencai DU, A Test Automation Framework Base on WEB, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science
2. Navaraj Javvaji, Anand Sathiyaseelan, Uma Maheswari Selvan, Data Driven Automation Testing of Web Application using Selenium, STEP-AUTO 2011
3. F. Ricca and P. Tonella, “Analysis and Testing of Web Applications,” Proc. Int‟l Conf. Software Eng., pp. 25-34, May. 2001.
4. Kumar, A, S.; Kumar, P, G.; and Dhawan, A. (2009), “Automated Regression Suite for Testing Web Services" International Conference on Advances in Recent Technologies in Communication and Computing, pp.590-592.
5. Sneed, H, M.; Huang, S.( 2006), " WSDLTest – A Tool for Testing Web Services", Eighth IEEE International Symposium on Web Site Evolution .
6. Conroy, K, M.; Grechanik, M.; Hellige, M.; Liongosari, E, S.; and Xie, Q.( 2007), " Automatic Test Generation From GUI Applications For Testing Web Services", Software Maintenance 2007,ICSM 2007,IEEE International Conference on 2-5 Oct 2007, pp.345-354 .
7. Siblini, R.; Mansour, N. (2005), "Testing Web Services", aiccsa, pp.135-vii, ACS/IEEE 2005 International Conference on Computer Systems and Applications .
8. Xun Yuan, Atif M. Memon, Generating Event Sequence-Based Test Cases Using GUI Runtime State Feedback, VOL. 36, NO. 1, Feb 2010.
9. CARLOGANU, A., AND RAGUIDEAU, J. Claire: An event-driven simulation tool for test and validation of software programs. In Proceedings of the 2002 International Conference on Dependable Systems and Networks (2002), IEEE Computer Society, p. 538.
10. Boris Beize, “Software Testing Techniques” [M] 2nd. Ed.New York: Van Nostrand Einhold, 1990.
11. EATON, C., AND MEMON, A. Improving browsing environment compliance evaluations for Websites. In Proceedings of the International Workshop on Web Quality (WQ'04) (Munich, Germany, July 2004).
12. D. C. Kung, C.-H. Liu, and P. Hsia, “An object-oriented Web test model for testing Web applications,” in The First Asia-Pacific Conf.on Quality Software. Singapore: IEEE Computer Society, Oct. 2000, pp. 111–120.
13. Jett Offutt, Ye wu, Xiaochen Du and Hong Huang, “Web application Bypass testing”
14. "SOAP UI Tool”,http://www.SOAP UI.org.

116-120

25.

Auths:

Bhavneet Dhindsa, Malika Narang, Kavita Choudhary

Paper Title:

Benefits And Challenges of E-Governance Portal

Abstract: ICT i.e. information and communication technology provides communication i.e. faster and accurate, and further provides adequate storage with the use of computer, electronics devices and software application. Basically e-governance is an application of ICT. E-Governance provides citizens to access government services and information by electronics means. There are various dimensions of e-Governance. One of them is through e-Portal. E-Portal is specially designed webpage at website which brings all the information together related to specific field of interest or industry from different sources in a uniform way. In this paper we have focused on e-Governance portal which provides users with easy access to all government information and service, while downplaying the structure of government, or which ministry provides the service.

Keywords: ICT, e-governance, e-portal, e-governance portal.

References:
1. Dr. Sanjay Kumar Dwivedi, Ajay Kumar Bharti,” E-governance in India-problems and acceptability”, Journal of Theoretical and Applied Information Technology.
2. Subhash Bhatnagar,” Opportunities and Challenges in E- Governance”, http://www.iimahd.ernet.in/egov/documents/opportunities-and-challenges-in-egovernance.pdf
3. Prof. T.P. Rama Rao,” ICT and e-Governance for Rural Development”, http://www.iimahd.ernet.in/egov/documents/ict-and-egovernance-for-rural-development.pdf
4. Promoting e-Governance – The SMART Way Forward “, e-Governance: Conceptual Framework”, second administrative reform commission, eleventh report.
5. Jyoti charade, Dr Gulnar Sharma,” Study of Status of ICT Use and Awareness in E - Governance: A Case of Pune Division”, Volume: 3 | Issue: 4 | April 2013 | ISSN - 2249-555X.
6. Nugi Nkwe,” E-Government: Challenges and Opportunities in Botswana”, International Journal of Humanities and Social Science Vol. 2 No. 17; September 2012.
7. Ernst and Young,”e-governance 2020”, FICCI Ernst and young report.
8. HISTORY AND DEVELOPMENT OF e-GOVERNANCE.
9. Nikita Yadav, V. B. Singh,” E-Governance: Past, Present and Future in India”, International Journal of Computer Applications (0975 – 8887) Volume 53– No.7, September 2012.
10. Bhudeb Chakravarti,M. Venugopal,”Citizen centric Service Delivery through e-Governance Portal”, A White Paper published by National Institute for Smart Government Hyderabad, India, 2008.

121-123

26.

Auths:

Bhagyasree. P , C.Silpa, M.J.C.Prasad

Paper Title:

A Novel RISC Processor with Crypto Specific Instruction Set

Abstract: Old-time necessity for security and data protection against unauthorized access to classified information in many industries especially in military application is undeniably sobering. Hence, Cryptography plays a significantly important role in, the security of data transmission. On one hand, with developing computing technology, implementation of sophisticated cryptographic algorithms has become feasible. On the other hand, stronger cryptographic specifications are needed in order to be reluctant to possible threats. Some well-known examples of cryptographic algorithms are DES and AES. One of the main concerns in designing cryptographic algorithms is efficiency in either software or hardware implementation. General purpose processors are mostly used to speed up data manipulation and information processing in systems. Nevertheless, these processors are not performance efficient when they are utilized for data encryption and decryption. A novel RISC processor with Crypto Specific Instruction Set has been designed such that the processor is a Crypto Instruction-Aware RISC Processor, that makes the encryption and decryption processes of data faster, with the help of techniques like pipelining, register windows and a special architecture of barrel shifter. The main goal of this paper is to present a novel processor architecture being feasible for high speed implementation of low throughput cryptographic algorithms.

Keywords: Cryptographic Algorithms, Pipeline Technique, Register Windowing Technique, RISC Processor.

References:
1. Computer Organization and Design: The Hardware/Software Interface, 3rd ed., Morgan Kaufmann, 2005.
2. A. Kalambur and M. J. Irwin, “An extended addressing mode for low power,” In Proc. of the IEEE Symposium on Low Power Electronics,pp. 208-213, August 1997.
3. S. Jourdan, R. Ronen, M. Bekerman, B. Shomar, and A.Yoaz, “A Novel Renaming Scheme to Exploit Value Temporal Locality through Physical Register Reuse and Unification,” In Proc. of the 31st MICRO, pp. 216- 225, 1998.
4. S. Balakrishnan and G.S. Sohi, “Exploiting Value Locality in Physical Register Files,” In Proc. of 36th MICRO, pp. 265-276, Dec 2003.
5. J.-L. Cruz, A. Gonzalez,M. Valero, N.P. Tophanm, “Multiple-Banked Register File Architectures,” In Proc. of 27th ISCA, pp. 316-325, June 2000.
6. The SPARC Architecture Manual Version 8, SPARC International, Prentice Hall, 1992.
7. K. Nakano, Y. Ito, “Processor, Assembler, and Compiler Design Education Using an FPGA,” IEEE International Conference on Parallel and Distributed Systems, pp. 723-728, Dec. 2008.

124-128

27.

Auths:

K. Sreelaxmi, B. Srinivas, M.J.C.Prasad

Paper Title:

Implementation Of High Reliable Fine Grain Fault Tolerance Redundant Technique For FPGA

Abstract: SRAM based FPGAs are attractive to use in space applications because of more flexibility and reprogram ability. As technology size decreases below nanometer SRAM based FPGAs are more susceptible to radiation. These effects can cause transient or permanent bit flipping on SRAM cells and respectively change the function of logic elements within FPGAs. Fault-masking methodologies are essential, because it is vital for the system to work always properly irrespective of various faults that occurs in Complex digital circuitry. Due to this fact, redundancy techniques, which target fault masking and fault tolerance are in our scope.In this project we are proposing Quadruple Force Decide Redundancy (QFDR) a new approach in fault tolerance for mitigation problems in digital circuits, as simply replicating complete systems in Triple Modular Redundancy (TMR) technique may not be sufficient anymore when especially applies to the space applications, failure rate increases because of second instance occurs before the first one recovers. It QFDR makes SRAM-based FPGAs effectively immune from SEU (Single Event Up-set) mitigation challenges.The proposed QFDR is operated at an abstraction level of CLBs of FPGA. The Quadruple Force Decide Redundancy (QFDR) is a redundant logical structure which quadruplicates logical functions and defines two different Force and Decide rules for different quadruple logic functions based on their level in design and then connects them together using special connection patterns. The complete logic of QFDR is implemented in VHDL. Modelsim Xilinx edition (MXE) will be used for simulation and functional verification. Xilinx ISE will be used for synthesis. Xilinx FPGA board will be used for testing and demonstration of the implemented system.

Keywords: (QFDR) a new approach in fault tolerance for mitigation problems in digital circuits,

References:
1. J. Barth, K. LaBel, “Radiation assurance for the space environment,” in ICICDT ’04. International Conference on, 2004, pp. 323 – 333.
2. D. Mavis, P. Eaton, and M. Sibley, “Multiple bit upsets and error mitigation in ultra-deep submicron SRAM,” Nuclear Science, IEEE Transactions on, vol. 55, no. 6, pp. 3288 –3294, Dec. 2008.
3. M. Berg, https://nepp.nasa.gov/mafa/talks /mafa0734berg.pdf.
4. N. R. Effects and A. home page, in http://radhome.gsfc.nasa.gov.
5. N. R. J., R. P. A., K. K., S. W. J., M. P. T., and H. W., “Radiation characterization of a hardened 0.22 956;m anti-fuse field programmable gate array,” Nuclear Science, IEEE Transactions on, vol. 53, no. 6, pp. 3525–3531, dec. 2006.
6. C. M., G. P., J. E., and Wirt, “Single event upsets in sram fpgas,” Proc of MAPLD, Sep. 2002

129-132

28.

Auths:

Sibarama Panigrahi, Ashok Kumar Bhoi, Yasobanta Karali

Paper Title:

A Modified Differential Evolution Algorithm trained Pi-Sigma Neural Network for Pattern Classification

Abstract: In this paper a modified differential evolution (DE) algorithm trained Pi-Sigma network (PSN) is used for classification. The used DE algorithm is a modification of traditional DE/rand/1/bin algorithm and novel mutation as well as crossover strategies are followed considering both exploration and exploitation. The performance of proposed methodology for pattern classification is evaluated through three well-known real world classification problems from UCI machine learning data library. The results obtained from the proposed method for classification is compared with results obtained by applying the two most popular variants of differential evolution algorithm (DE/rand/1/bin and DE/best/1/bin) and Chemical Reaction Optimization (CRO) algorithm. It is observed that the proposed method provides better classification accuracy than that of other methods.

Keywords: Differential Evolution, Higher Order Neural Network, Pi-Sigma Network, Classification.

References:
1. G. P. Zhang, “Neural Networks for Classification: A Survey”, IEEE Transaction on Systems, Man, and Cybernetics- Part C: Applications and Reviews, vol. 30, no. 3, 2000, pp. 451-462.
2. Y. Shin and J. Ghosh, “Efficient higher-order neural networks for classification and function approximation”, In: International Journal on Neural Systems, vol. 3, 1992, pp.323–350.
3. M. G. Epitropakis, V. P. Plagianakos, M. N. Vrahatis, “Hardware-friendly Higher-Order Neural Network Training using Distributed Evolutionary Algorithms”, Applied Soft Computing, vol. 10, 2010, pp. 398-408.
4. Y. Shin, J. Ghosh, “The pi–sigma network: An efficient higher-order neural network for pattern classification and function approximation”, International Joint Conference on Neural Networks, 1991.
5. S. Panigrahi, S. Pandey, R. Singh, “A Novel Evolutionary Higher Order Neural Network for Pattern Classification”, International Journal of Engineering Research and Technology, vol. 2, no. 9, 2013, pp.2561-2566.
6. D. S. Huang, H. H. S. Ip, K. C. K. Law and Z. Chi, “Zeroing polynomials using modified constrained neural network approach”, IEEE Transactions on Neural Networks, vol. 16, no. 3, 2005, pp. 721–732.
7. S. Perantonis, N. Ampazis, S. Varoufakis and G. Antoniou, “Constrained learning in neural networks: Application to stable factorization of 2-d polynomials”, Neural Processing Letter, vol.7, no. 1, 1998, pp. 5–14.
8. Y. Shin and J. Ghosh, “Realization of Boolean functions using binary pi-sigma networks”, in: C. H. Dagli, S. R. T. Kumara, Y. C. Shin (Eds.), Intelligent Engineering Systems through Artificial Neural Networks, ASME Press, 1991, pp. 205–210.
9. R. Storn and K.Price, “Differential evolution- A simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, vol. 11, no.4, 1997, pp. 341-359.
10. S. Das, P. N. Suganthanam, “Differential Evolution: A Survey of the state-of-the-Art”, IEEE Transaction on Evolutionary Computation, vol. 15, no.1, 2011, pp. 4-31.
11. S. Das, A. Abraham, U. K. Chakraborty, A. Konar, “Differential evolution using a neighbourhood based mutation operator”, IEEE Transaction on Evolutionary Computation, vol. 13, no. 3, 2009, pp. 526-553.
12. S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama, “Opposition based differential evolution”, IEEE Transaction on Evolutionary Computation, vol. 12, no.1, 2008, pp. 64-79.
13. D. Goldberg, “Genetic Algorithms in Search”, Optimization and Machine Learning. Reading, MA:Addison-Wesley, 1989.
14. J. Kennedy, R. C.Eberhart and Y.Shi, “Swarm intelligence”, San Francisco, CA:Morgan Kaufmann, 2001.
15. K. Socha and M. Doringo, “Ant colony optimization for continuous domains”, Europian Journal of Operation Research, vol. 185, no. 3, 2008, pp. 1155-1173.
16. D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim and M. Zaidi, “The bees algorithm- A novel tool for complex optimization problems”, in IPROMS Oxford, U.K.: Elsevier, 2006.
17. H.G. Beyer and H.P. Schwefel, “Evolutionary Strategies: A Comprehensive introduction”, Nat. Comput., vol. 1, no. 1, 2002, pp. 3-52.
18. K. H. Han and J.H. Kim, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization”, IEEE Transactions on Evolutionary Computation, vol. 6, 2002, pp. 580–593.
19. Y. S. Lam and V. O. K. Li, “Chemical-Reaction-inspired metaheuristic for optimization”, IEEE Transactionson on Evolutionary Computation, vol. 14, no.3, 2010, pp. 381–399.
20. A.Y.S. Lam, “Real-Coded Chemical Reaction Optimization”, IEEE Transaction on Evolutionary Computation, vol. 16, no. 3, 2012,pp. 339-353.

133-136

29.

Auths:

Manoj Kumar

Paper Title:

A Secure And Efficient Authentication Protocol Based On Elliptic Curve Diffie-Hellman Algorithm And Zero Knowledge Property

Abstract: Elliptic curves have been extensively studied for over hundred years, originally pursued mainly for aesthetic reasons; elliptic curves have recently become a tool in several important applied areas, including coding theory, pseudo-random bit generation and number theory algorithms. Actually ECC is an alternative approach for traditional public key cryptography like RSA, DSA and DH. It provides the highest strength-per-bit of any cryptosystem known today with smaller key size resulting in faster computations, lower power assumption and memory. Another advantage is that authentication protocols based on ECC are secure enough even if a small key size is used. It also provides a methodology for obtaining high speed, efficient and scalable implementations of protocols for authentication and key agreement. The present paper consists of an introduction to elliptic curves and an authentication protocol based on ECC and zero knowledge property. The protocol is developed for group communication where every person of the group has a secret information and the communication starts when all this information is put together. If one person is not online, the others cannot communicate.

Keywords: and phrases-Elliptic Curves, Cryptosystem, Authentication Protocols, Public key Cryptography, Finite Fields

References:
1. R. Afreen and S.C. Mehrotra, A review on elliptic curve cryptography for embedded systems, International Journal of Computer Science and Information Technology, Vol. 3, No. 3, June 2011.
2. ANSI X9.62, Public Key Cryptography for the Financial Services Industry, The Elliptic Curve Digital Signature Algorithm, 1999.
3. M. Aydos, B. Sunar and C. K. Koc, An elliptic curve cryptography based authentication and key agreement protocol for wireless communication, Proceedings of the 2nd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Dallas, Texas, 1-12, 1998.
4. K. Chalkias, G. Filiadis and G. Stephanides, Implementing authentication protocol for exchanging encrypted messages via an authentication server based on ECC with ElGamal’s algorithm, World Academy of Science Engineering and Technology, 7, 137-142, 2005.
5. N. Constantinescu, Authentication protocol based on elliptic curve cryptography, Annals of the University of Craiova, Mathematics and Computer Science Series, Volume37(2), 2010, 83-91.
6. S. Goldwasser, S. Micali and C. Rackoff, The knowledge complexity of interactive proof systems, Society for Industrial and Applied Mathematics, Journal on Computing, vol. 18, no. 1, 186-208, 1989.
7. D. R. Hankerson, A. J. Menezes and S.A. Vanstone, Guide to Elliptic Curve Cryptography, Springer-Verlag, 2003.
8. IEEE P1363, Standard Specifications for Public Key Cryptography, Draft Version 7, Sep, 1998.
9. Introduction from http://www.device forge.com /articles/ AT4234154468.html.
10. V. S. Iyenger, Novel elliptic curve scalar multiplication algorithms for faster and safer public-key cryptosystems, International Journal on Cryptography and Information Security, Vol. 2, No. 3, September 2012.
11. N. Koblitz, Elliptic curve cryptosystem, Mathematics of computation, 48:203-209, 1987.
12. N. Koblitz, A course in Number Theory and Cryptography, New York, NY: Springer-Verlag, Second edition, 1994.
13. K. Mangipudi, N. Malneedi and R. Katti, Attacks entific papers and solutions on Aydos-Savas-Koc’s wireless authentication protocol, Proceedings of 2004 IEEE Global Telecommunications conference, 2229-2234, 2004.
14. J. Menzes, Elliptic Curve Public Key Crypto System, Boston, MA: Kluwer Academic Publishers, 1993.
15. V. Miller, Uses of elliptic curves in cryptography, Crypto 1985, LNCS 218: Advances in Cryptography, Springer-Verlag, 1986.
16. NIST/ U. S. Dept. of Commerce, Digital Signature Standards (DSS), FIPS Publication 186-2 Feb, 2000.
17. NIST, Special Publication 800-57: Recommendation for key management Part 1: General Guideline, Draft Jan, 2003.
18. M. Rosing, Implementing ECC, Manning Publications Co., 1999.
19. C. P. Schnorr, Efficient signature generated by smart cards, Journal of Cryptography, 4, no.3, 161-174, 1991.
20. W. Stallings, Crytography and Network Security, Prentice Hall, Third Edition, 2003.
21. L. Yongliang, W. Gao, H. Yao and X. Yu, Elliptic curve cryptography based wireless authentication protocol, International Jouranl of Network Security, 5, no.3, 327-337, 2007.

137-142

30.

Auths:

Mohammed Aboud Kadhim, Hamood Shehab Hamid, Nooraldeen Raaoof Hadi

Paper Title:

Improvement of Fixed WiMAX OSTBC–OFDM Transceiver Based Wavelet Signals by Non-Linear Precoding Using SDR Platform

Abstract: This paper inquire, a new Non-Linear precoding method to the acclimatization for the Worldwide Interoperability for fixed Microwave Access (WiMAX) baseband, in the physical layer performance of multi-antenna techniques, All cases are based on the IEEE 802.16d standard using orthogonal frequency division multiplexing (OFDM) based discrete wavelet transform (DWT) and 16-Quadrature amplitude modulation (QAM), ½ of coding rates and using SFF SDR development platform. The proposed Non-Linear Precoding Tomlinson-Harashima Precoding (THP) in WiMAX baseband consider a new way to further reduce the level of interference signals achieved much lower bit error rates and increase spectral efficiency. The proposed model was modeled-tested, and its performance was found to comply with International Telecommunications Union channel models (ITU) that have been elected for the wireless channel in the simulation process

Keywords: THP, WIMAX, SFF SDR, OFDM, RS, Coding, OSTBC, IDWT, DWT.

References:
1. H. Heiskala and J. Terry, “OFDM Wireless LANs: A Theoretical and Practical Guide,” SAMS, 2002.
2. Jeffrey G. Andrews, PhD., Arunabha Ghosh, Ph.D,Rias Muhamed., ed, Fundamentals of WiMAX Understanding Broadband Wireless Networking. Theodore S. Rappaport, Series Editor, ed. P.H.C.E.a.E.T. Series. 2007.
3. D. Gesbert, M. Shafi, D. Shiu, P. J. Smith, and A. Naguib, “From theory to practice: an overview of MIMO space-time coded wireless systems,” IEEE J. Select. Areas Commun., vol. 21, no. 3, pp. 281–301, Apr. 2003.
4. J. Paulraj, D. A. Gore, R. U. Nabar, and H. B¨olcskei, “An overview of MIMO communications - a key to Gigabit wireless,” Proc. of IEEE, invited paper, vol. 92, no. 2, pp. 198–218, Feb. 2004.
5. R. T. Derryberry, S. D. Gray, D. M. Ionescu, G. Mandyam, and B. Raghothaman, “Transmit diversity in 3G CDMA systems,” IEEE Commun. Magazine, vol. 40, no. 4, pp. 68–75, Apr.2002.
6. S. Dakdouki, V. L. Banket, N. K. Mykhaylov, and A. A. Skopa, “Downlink processing algorithms for multi-antenna wireless communications,” IEEE Commun. Magazine, vol. 43, no. 1, pp. 122–127, Jan. 2005.
7. G. L. St¨uber, J. R. Barry, S.W. McLaughlin, Y. G. Li, M. A. Ingram, and T. G. Pratt, “Broadband MIMO- OFDM wireless communications,” Proc. of IEEE, invited paper, vol. 92, no. 2, pp. 271–294, Feb. 2004.
8. H. Yang, “A road to future broadband wireless access: MIMO-OFDM-based air interface,” IEEE Commun. Magazine, vol. 43, no. 1, pp. 53–60, Jan. 2005.
9. M. Tomlinson, “New automatic equalizer employing modulo arithmetic,” Electron. Lett. vol. 7, pp. 138–139, Mar. 1971.
10. H. Harashima and H. Miyakawa, “Matched-transmission technique for channels with inter symbol interference,” IEEE Trans. Commun., vol. COM-20, pp. 774–780, Aug. 1972.
11. G. D. Forney and M. V. Eyuboˇglu, “Combined equalization and coding using pre-coding,” IEEE Commun. Magazine, vol. 29, no. 12, pp. 25–34, Dec.
1991.
12. C. Wind passinger, R. F. H. Fischer, T. Vencel, and J. B. Huber, “Pre-coding in multi antenna and multiuser communications,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1305– 1315, Jul. 2004.
13. R. F. H. Fischer, Pre-coding and Signal Shaping for Digital Transmission. New York: Wiley, 2002.
14. Martin Vetterli, Jelena Kovacevic, “Wavelets and Subband Coding”, Prentice-Hall PTR, New Jersey, 1995
15. B.G.Negash and H.Nikookar, “Wavelet Based OFDM for Wireless Channels,” International Research Center for Telecommunication Transmission and Radar.
16. Haixia Zhang, Dongfeng Yuan Senior Member IEEE, Mingyan Jiang, Dalei Wu, “Research of DFT-OFDM and DWT-OFDM on Different Transmission Scenarios,” Proceedings of the 2nd International Technology for Application (ICITA 2004).
17. Dereje Hailemariam, “Wavelet Based Multicarrier Code Division multiple Access communication for wireless Environment,” M.Sc. Thesis, Addis Ababa University, 2003.
18. Imed Ben Dhaou and Hannu Tenhunen, “Comparison of OFDM and WPM for Fourth Generation Broadband WLAN,” Electronic System Design Laboratory, Dept.of electronics.
19. Manish J. Manglani, “Wavelet Modulation in Gaussian and Rayleigh Fading Channels,” Msc. Thesis, Faculty of the Virginia Polytechnic Institute and State University, July 2001.
20. Mohammed Aboud Kadhim ,Widad Ismail, (2011), Implementation Transmitter Diversity Tomlinson-Harashima Precoding (THP) for WiMAX OSTBC-OFDM–FFT Baseband Transceiver on Multi-Core Software Defined Radio Platform, World Applied Science Journal.12(9): 1482-1491, 2011.
21. Yu Fu “Transmitter Precoding for Interference Mitigation in Closed-Loop MIMO OFDM” thesis Doctor of Philosophy University of Alberta 2009
22. Mohammed Aboud Kadhim and Widad Ismail (2010) Implementation of WiMAX OSTBC-OFDM (IEEE802.16.d) Baseband Transceiver on a Multi-Core Software-Defined Radio Platform, Australian Journal of Basic and Applied Sciences, 4(7): 2125-2133, 2010
23. Mohammed Aboud Kadhim and Widad Ismail (2010) Implementation of WiMAX IEEE802.16d Baseband Transceiver Based Wavelet OFDM on a Multi- Core Software-Defined Radio Platform, European Journal of Scientific Research, 42, 303-31
24. SFF SDR Development Platform - Quick start guide
25. International Telecommunication Union, Recommendation ITU-R M.1225, Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000, Feb. 2007.

143-150

31.

Auths:

Sharad R.Mahajan, Rahul D.Rajopadhye

Paper Title:

Transportation Noise and Vibration-Sources, Prediction, and Control

Abstract: A major part of the world energy consumption is related to transportation. The extensive use of automobile vehicles causes harmful effects on the surrounding environment. The 20-25% of the total greenhouse gas emission in industrialized countries is generated by transportation. Vehicle noise (NVH) is becoming the most important factor to customers. The transportation noise is one of the major sources of noise exposure in residential areas and causes substantial annoyance during night. Noise is unwanted sound; vibration is the oscillation that is typically felt rather than heard. Harshness is generally used to describe the severity and discomfort associated with unwanted sound and/or vibration, especially from short duration events. Considering this, many countries have implemented legislation limiting the noise levels in residential areas. For today’s compact era the trend towards compact power units is substantially increased resulting in components/vehicles running at higher level of noise and vibrations.Vehicle manufacturers work with noise and vibration control to fulfil legislation demands and to meet customer requirements. The exterior noise control work is mainly motivated by legislation demands while interior noise and vibration control work is motivated by driver and passenger noise and vibration comfort requirements.The motivation for reducing traffic noise is that it is the most important environmental noise source in Europe and in the rest of the world. About 25 % of the population in Europe is exposed to transportation noise with an equivalent sound level over 65 dB(A). At this sound level sleep is seriously disturbed and most people become annoyed .

Keywords: Transportation, Vehicle noise, vibrations, Vibration isolation, surface pavement.

References:
1. Noise and Vibration Control of Combustion Engine Vehicles,by Mathias Winberg Ronneby, Doctoral Dissertation Series No. 2005:03,ISBN: 91-7295-062-5 ,Published 2005,Printed by Kaserntryckeriet AB,Karlskrona, Sweden.
2. Automotive Noise Control: Thirty Years of Changing Perspectives, Jim Thompson, National Institute for Occupational Safety and Health
3. Myer Kutz, Handbook of Transportation Engineering, McGraw-Hill (2004) ISBN 978-0-07-139122-1
4. The Noise Pollution (Regulation And Control) Rules, 2000, Published In The Gazette Of India, Extraordinary, Pt.II, Sec. 3 (Ii) Dated 14th February, 2000.
5. Environmental Noise Pollution and its Control, Proceedings of Specialist Course, Institution of Engineers, Roorkee, India 1992.
6. Jeva, R. et al, Noise Impact Assessment - an approach, Energy Environment Monitor,Vol.: 12, September 96.
7. Mathur, J.S.B., Noise Control : Legislation, Planning and Design, Industrial Effluent Treatment, Vol.:2, Applied Science Publishers Ltd., London, 1981 ed.
8. 5. Rao, P.R., Noise Pollution and Control, Encyclopedia of Environmental Pollution and Control, Vol.-2, Environmedia Publications, India, 1995 ed.

151-155

32.

Auths:

Jayashree D. Jadhav, Manjusha Joshi, S.D.Joshi, R. M Jalnekar

Paper Title:

Distributed Transaction System Application Using Design Pattern

Abstract: Distributed Systems are helpful in gathering and processing information about customers or users of a particular domain by means of efficient communication techniques between Models for an effective sharing of resources. Extraction of relevant data from enormous volume of data has become a tedious task in today’s world. Distributing System reduces the complexity of the task by assisting the users to obtain the relevant information from various data sources. Distributed System that fills the gap between a customer and resource provider through various Models communications in the specific domain is proposed which has a service requestor module which can access the services offered by the system by making request with Interface. Also this system is made up of a number of role based Models which provide services such as user registration, authorization,&after successful validation, user access the functions. In this paper we have discussed detail working of a distributed system application that we have developed using different design patterns.

Keywords: SOAP, DS

References:
1. F.Dabous, F.Rabhi, H.Yu, Using Software Architectures and Design Patterns For Developing Distributed applications,IEEE-(ASWEC-2004)
2. E. Burmakin, B. Krassi, Design Patterns for development of Dynamic Distributed Automation System, Helsinki university of technology.
3. J.S.Fant, Building Domain specific Software architecture from Software architectural design Patterns, ICSE-2011
4. C. Tianhuang, H. Feifei, Design Patterns Application in a distributed system, IEEE-2010
5. Thomas Gschwind, Jana Koehler, and Janette Wong, Applying Patterns during Business Process Modelling
6. DuaneNickul, Laurel Reitman, JamesWard, Jack Wilber, Service Oriented Architecture (SOA) and Specialized Messaging Patterns-2007
7. Hatem M. Hamad, Distributed electronic health care system (DEHCA), IslamicUniversity-2003
8. LadanTahvildari, accessing the impact of using design pattern based system-1999
9. PetruEles, Distributed system.
10. Krishna Nadiminti, Marcos Dias de Assunção, and RajkumarBuyya,Distributed Systems and Recent Innovations: Challenges and Benefits.

156-158

33.

Auths:

Mehare Suraj, Paliwal Poonam, Pardeshi Mangesh, Begum Shahnaz

Paper Title:

Private Cloud Implementation for Centralized Compilation

Abstract: A private cloud is a cloud computing model that involves a distinct and secure cloud based environment in which only the specified client can operate. The paper aims at creating a private cloud over intranet for centralized compilation of codes of languages. Separate compilers of Java, C, C++ and assembly language will be installed on the cloud and the clients can use those compilers for compilation of codes. A web Application is designed for the User authentication and personalized task distribution is provided i. e. the faculty will be able to assign user-id, password & personalized tasks to all the clients. The codes will be compiled centrally and the results will be displayed at client-side application. It is efficient for conducting practical examinations, since every client will be assigned a different login id and password. The faculty may create, edit and delete client profiles anytime.

Keywords: Centralized Compilation, Intranet, Private Cloud.

References:
1. Rabiyathul Basariya, and K.Tamil Selvi, “Centralized C# compiler using cloud computing,” International Journal of Communications and Engineering, vol. 06-no.6, Issue: 02, pp. 148-151, Mar. 2012.
2. Mayank Patel, “Online Java Compiler Using Cloud Computing,” International Journal of Innovative Technology and Expolring Engineering, ISSN: 2278-3075, vol. 2, Issue-02, pp. 116-118, Jan. 2013.
3. Z. Pantic A and M.A. Babar, “Guidelines for building a private cloud infrastructure,” IT Univ. of Copenhagen, Denmark, Tech. Rep. 153, 2012.
4. “Web Service to Web Service Communication”. Available:http://www.wstutorial.com/web-service-to-web-service-communication/
5. Aamir Nizam Ansari, Siddharth Patil, Arundhati Navada, Aditya Peshave, Venkatesh Borole, Online C/C++ Compiler using Cloud Computing, Multimedia Technology (ICMT), July 2011 International Conference, pp. 3591-3594.
6. Vouk, M. A., “Cloud Computing – Issues, Research and Implementations" - ITI 2008 - 30th International Conference on Information Technology Interfaces.

159-161

34.

Auths:

Ishani Bishnu, Jyoti Vimal, Neha Kumari

Paper Title:

Using Artificial Neural Network(ANN) Machinability Investigation Of Yttria Based Zirconia Toughness Alumina (Y-ZTA) Ceramic Insert

Abstract: A back propagation neural network model has been developed for the machinability evaluation i.e flank wear, cutting force and surface roughness prediction of Zirconia Toughness Alumina(ZTA) inserting in turning process. Numerous experiments have been performed on AISI 4340 steel using developed yttria based ZTA inserts. These inserts are constructed through wet chemical co-precipitation route followed by powder metallurgy process. Process parametric conditions such as cutting speed, feed rate and depth of cut are nominated as input to the neural network model and flank wear, surface roughness and cutting force of the inserts corresponding to these conditions has been selected separately as the output of the network. The experimentally calculated values are used to train the feed forward back propagation artificial neural network(ANN) for forecasting. The mean square error both in training and testing results positively. The performance of the trained neural network has been confirmed with experimental data. The results reveal that the machining model is acceptable and the optimization technique satisfies practical prospects.

Keywords: Zirconia Toughness Alumina(ZTA), Artificial Neural Network(ANN), Flank Wear, Cutting Force, Surface Roughness, Back Propagation.

References:
1. Ihsan Korkut, Adem Acir, Mehmet Boy ”Application of regression and artificial neural network analysis in modelling of tool chip interface temperature in machining”.
2. E.O Ezugwu, D.A.Fadare, J.Bonney, R.B.Da Silva, W.F.Sales “Modelling the correlation between cutting and process parameters in high speed machining of Inconel 718 alloy using an artificial neural network”.
3. S.Aykut, M.Golcu, S.Semiz, H.S.Ergur “Modelling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network ” received 26 June 2006.
4. S.Mitra “An artificial neural network approach of parametric optimization of laser micro-machining of die-steel”.
5. Surjya K.Pal, Debabrata Chakraborty “Surface roughness prediction in turing using artificial neural network” received 12 August 2004.
6. J.Paulo Davim, V.N.Gaitonde, S.R.Karnik“Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models”published under Journal of Materials Processing Technology 205(2008).
7. Ulas Caydas, AhmetHascalik “A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method”published under Journal of Materials Processing Technology 202(2008)574-582.
8. Muammer Nalbant, Hasan Gokkaya, Ihsan Toktas “Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC Turning” received 29 October 2006.

162-166

35.

Auths:

Ata Jahangir Moshayedi, Damayanti C Gharpure, Arvind D Shaligram

Paper Title:

Design and Development Of MOKHTAR Wind Tracker

Abstract: This work presents the design and development of a wind tracker (MOKHTAR ) with implementation of Zigzag and Spiral algorithms for detecting the wind source. For odor tracking research the Wind tracking is important. A novel wind flow and direction sensor based on an array of Evaporative Heat Rejection Devices (EHRD) such as the ones used in cooling towers is developed during the present work. The developed sensor is installed on the wind tracker (MOKHTAR). MOKHTAR seeks the location of the wind source with Zig-Zag and Spiral tracking algorithms. Both the Zig Zag and Spiral algorithms are implemented and their performance is compared with respect to step size, time taken and the probability of reaching the wind source. Finally, results of path monitoring for comparison between the two algorithms are shown. The proposed system is used on a robot vehicle as the initial stage of plume tracking activity. Moreover, the proposed wind sensor array can be used in the meteorological and environmental studies

Keywords: Mokhtar wind tracker, Wind sensor, Spiral movement, Zig zag movement.

References:
1. Tien-Fu Lu; Chee Liang Ng ,"Novel Wind Sensor for Robotic Chemical Plume Tracking," Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on , vol., no., pp.933-938, 25-28 June 2006
2. J. Moshayedi and D. C. Gharpure, “Priority based algorithm for Plume tracking Robot,” in Physics and Technology of Sensors (ISPTS), 2012 1st International Symposium on, 2012, pp. 51–54.
3. Lochmatter, T ; Martinoli, A: Simulation Experiments with Bio-Inspired Algorithms for Odor Source Localization in Laminar Wind Flow, Presented at: Seventh International Conference on Machine Learning and Applications (ICMLA 2008), San Diego, CA, USA, December 11-13, 2008
4. Grütz, A.: Jahrbuch Elektrotechnik ’98, VDE-VER-LAG, Berlin, Offenbach 1997
5. Stewart, R, L; Russell, A; and Kleeman, L;‘’recognition and discrimination of ethanol and methanol odour with applications for robotic swarm control’’irrc, australia, 2003
6. Lochmatter, T.; Raemy, X.; Matthey; Martinoli,A;”Odor source localization with mobile robots “,bulletin of the Swiss Society for Automatic Control, number 46 (2007), p. 11 – 14
7. Lochmatter, T.; Raemy, X.; Matthey, L.; Indra, S.; Martinoli, A.; , "A comparison of casting and spiraling algorithms for odor source localization in laminar flow," Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on , vol., no., pp.1138-1143, 19-23 May 2008
8. Loutfi, A.; Coradeschi, S.; "Relying on an electronic nose for odor localization," Virtual and Intelligent Measurement Systems, 2002. VIMS '02. 2002 IEEE International Symposium on , vol., no., pp. 46- 50, 2002
9. H Ishida: K Suetsugu.: T Nakamoto.: T Moriizumi Study of autonomous mobile sensing system for localization of odor source using gas sensors and anemometric sensors, Sensors and Actuators A: Physical Volume: 45, Issue: 2, Pages: 153-157, 1994
10. Mark A. Willis*, Jennifer L. Avondet and Andrew S. Finnell; Effects of altering flow and odor information on plume tracking behavior in walking cockroaches, Periplaneta americana (L.); The Journal of Experimental Biology 211, 2317-2326 ,2008
11. Hiroshi Ishida1,Takamichi Nakamoto2, Toyosaka Moriizumi2, Timo Kikas1 and Jiri Janata1; Plume-Tracking Robots: A New Application of Chemical Sensors, 2001
12. Ishida,H;”Robotic systems for gas/odor source localization: gap between experiments and real-life situations”, Robotica,2009
13. Hayes,A,T; Martinoli,A; Goodman,R,J; “Swarm robotic odor localization off-line optimization and validation with real robots”,journal of robotica, volume 21 issue 4, august 2003
14. Lochmatter, T.; Martinoli, A.;,"Simulation experiments with bio-inspired algorithms for odor source localization in laminar wind flow," machine learning and applications, 2008. icmla '08. seventh international conference on , vol., no., pp.437-443, 11-13 dec. 2008.
15. Kuzu,A;Bogosyan,S;Gokasan,M;“Survey: odor source localization”, applied computer and applied computational science, acacos '08, hangzhou, china, april 6-8, 2008
16. Ishida,H;Nakamoto,T; Moriizumi,T; et al,“Plume-tracking robots: a new application of chemical sensors”, The Biological bulletin , 200: 222-226.,April 2001
17. Sahyoun, S.S.; Djouadi, S.M.; Hairong Qi; , "Dynamic plume tracking using mobile sensors," American Control Conference (ACC), 2010 , vol., no., pp.2915-2920, June 30 2010-July 2, 2010
18. Ishida, H.; Nakayama, G.; Nakamoto, T.; Moriizumi, T.; , "Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors," Sensors, 2002. Proceedings of IEEE , vol.2, no., pp. 1665- 1670 vol.2, 2002
19. Lilienthal, A,; Streichert, F; Zell, A. .”gas source tracing with a mobile robot using an adapted moth strategy”, Proceedings of the AMS, 2003
20. Llomas, c. “Fundamentals of hot wire anemometry. “cambridge university press, 1986.
21. N.-T. Nguyen, “A novel wind sensor concept based on thermal image measurement using a temperature sensor array,” Sensors and Actuators A: Physical, vol. 110, no. 1, pp. 323–327, 2004.
22. Gouveia,F,J; Baskett,R.L; “evaluation of a new sonic anemometer for routine monitoring and emergency response applications”. Technical
23. Russell, R.A;,” Survey of robotic applications for odor-sensing technology”. the international journal of robotics research, 20(2):144–162, 2001.
24. Shibaura. “Air flow sensor,f6201-1 specifications”, 2002.
25. Harvey,D; Lu,T,F; Keller,M,” Wind sensor and robotic model wasp development” Proceedings of the Australian Conference on Robotics and Automation, 1-3 December, 2003
26. Bruun,H.H;,”Hot-wire anemometry:principles and signal analysis”.oxford science publications. Oxford university press, Oxford, 1995.
27. Marques, L.; Tomaszewski, G.; de Almeida, A.T., "Switched Thermal Anemometer," Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE , vol., no., pp.1783-1786, 12-15 May 2008
28. Hernandez, B.A.J.; Hernandez, E.M,.; et al Perez, J.Y.m.; lopez, m.r.c.; "arrangement of temperature sensors as a wind sensor, " electrical and electronics engineering, 2006 3rd international conference on , vol., no., pp.1-4, 6-8 sept. 2006
29. Chapman,T;Hayes,A; and Tilden,M.”Reactive maze solving with a biologically-inspired wind sensor’. in proceedings of the 6th international conference on simulation of adaptive behavior, paris, france, pages 81–87. mit press, 2000
30. Russell,R.A.; Purnamadjaja, A.H, "Odor and airflow: complementary senses for a humanoid robot," robotics and automation, 2002. proceedings. icra '02. ieee international conference on , vol.2, no., pp. 1842- 1847 vol.2, 2002
31. Thomas Lochmatter, Nicolas Heiniger , Alcherio Martinoli ;Localizing an odor source and avoiding obstacles: Experiments in a wind tunnel using real robots ; 13th International Symposium on Olfaction and Electronic Nose (ISOEN ),2009
32. Waphare, S.; Gharpure, D.; Shaligram, A.; Botre, B.; "Implementation of 3-Nose Strategy in Odor Plume-Tracking Algorithm," Signal Acquisition and Processing, 2010. ICSAP '10. International Conference on, vol., no., pp.337-341, 9-10 Feb. 2010
33. J. Moshayedi and D. Gharpure, “Path and Position Monitoring Tool for Indoor Robot Application,” International Journal of Applied Electronics in Physics & Robotics, vol. 1, no. 1, 2013
34. National Semiconductor,Precision Centigrade Temperature Sensors, LM35,data sheet,November 2000
35. A. J. Lilienthal, A. Loutfi, and T. Duckett, “Airborne chemical sensing with mobile robots,” Sensors, vol. 6, no. 11, pp. 1616–1678, 2006.
36. “ROBOMokhtar”,Available:http://mokhtarrobo.webs.com/platform, [Sep 2013]
37. “Mokhtar wind tracker,” YouTube.[Online] Available:https://www.youtube.com/watch?v=YMe3DxpaFfI:[September,1,2013].
38. T.-F. Lu, “Indoor odor source localization using robot: Initial location and surge distance matter?,” Robotics and Autonomous Systems, 2013.

167-173

36.

Auths:

Gowtham Mamidisetti, B.Venkatesh

Paper Title:

Predicton With Smart Phones

Abstract: This paper predicts two aspects of human behavior using smart phones as sensing devices. This paper introduces a method for predicting where users will go and which application they will use next by exploiting the rich contextual information from smart phone sensors. Our first goal is to understand which smart phone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting user independent behavioral patterns and study how user independent behavior models can improve the predictive performance.

Keywords: smart phone data, human behavior, mobility prediction, app usage prediction.

References:
1. G. Adomavicius, A. Tuzhilin, Context-aware recommender systems, Recommender Systems Handbook (2011) 217–253.
2. T. Liu, P. Bahl, I. Chlamtac, Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks, Selected Areas in Com- munications 16 (6) (1998) 922–936.
3. J. Krumm, E. Horvitz, Predestination: Inferring destinations from par- tial trajectories, in: Proc. UbiComp, 2006, pp. 243–260.
4. N. Eagle, A. (Sandy) Pentland, Reality mining: sensing complex social systems, Personal and Ubiquitous Computing 10 (4) (2006) 255–268.
5. Oulasvirta, R. Petit, M. Raento, S. Tiitta, Interpreting and acting on mobile awareness cues, Human–Computer Interaction 22 (1-2) (2007).
6. S. N. Patel, J. A. Kientz, G. R. Hayes, S. Bhat, G. D. Abowd, Farther than you may think: An empirical investigation of the proximity of users to their mobile phones, Ubiquitous Computing 4206 (2006) 123–140.
7. K. Farrahi, D. Gatica-Perez, Discovering routines from large-scale hu- man locations using probabilistic topic models, Transactions on Intelli- gent Systems and Technology 2 (1) (2011) 3.
8. A. Peddemors, H. Eertink, I. Niemegeers, Predicting mobility events on personal devices, Pervasive and Mobile Computing 6 (2010) 401–423.

174-176

37.

Auths:

Tso-Bing Juang, Hsin-Hao Peng, Han-Lung Kuo

Paper Title:

Parallel and Digit-Serial Implementations of Area- Efficient 3-Operand Decimal Adders

Abstract: In this paper, parallel and digit-serial implementations of area-efficient 3-operand decimal adders are proposed. By using proposed analyzer circuits and the generation of correction terms with recursive schemes, our proposed decimal adders could perform efficient additions with three operands. Unit gate estimates and synthesis results show that our proposed adders are more area-efficient than those previously proposed decimal adders with three operands under the same delay constraints. Also the power consumptions for our decimal adders are lesser. In addition to parallel implementations, the digit-serial 3-operand adders are easily developed to increase the throughput and the operating frequency due to area efficiency. Our proposed decimal adders could be applied to ease the tremendous computation efforts for decimal computations such as multi-operand decimal additions, decimal multiplications and divisions.

Keywords: Computer arithmetic, Decimal additions, Parallel-prefix adders, VLSI design,

References:
1. Draft IEEE Standard for floating-point arithmetic. New York: IEEE, Inc., 2004, http://754r.ucbtest.org/drafts.
2. M. S. Schmookler and A.W.Weinberger. “High speed decimal addition,” IEEE Transactions on Computers, Vol. 20, No. 8, pp. 862–867, Aug. 1971.
3. M. A. Erle, M. J. Schulte, and J. M. Linebarger, “Potential speedup using decimal floating-point hardware,” Proc. of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1073–1077, Nov. 2002.
4. M. F. Cowlishaw, “Decimal floating-point: algorism for computers,” Proc. of 16th IEEE Symposium on Computer Arithmetic (ARITH-16), pp. 104–111, June 2003.
5. R.D. Kenney and M.J. Schulte, “Multioperand decimal addition,” Proc. IEEE Computer Society Ann. Symp. VLSI, pp. 251-253, Feb. 2004.
6. R.D. Kenney and M.J. Schulte, “High-speed multioperand decimal adders,” IEEE Transactions on Computers, pp. 953-963, Vol. 54, No. 8, Aug. 2005.
7. Thapliyal, H, Kotiyal. S, Srinivas, M.B., “Novel BCD adders and their reversible logic implementation for IEEE 754r format”, Proc. 19th IEEE International Conference on VLSI Design, pp. 3-7, Jan. 2006
8. Sreehari Veeramachaneni, M.Kirthi Krishna, Lingamneni Avinash, Sreekanth Reddy P, M.B. Srinivas, “Novel, high-speed 16-digit BCD adders conforming to IEEE 754r format,” Proc. IEEE Computer Society Ann. Symp. VLSI (ISVLSI'07), pp. 343-350, May 2007.
9. Bayrakci and A. Akkas, “Reduced delay BCD adder,” Proc. IEEE 18th International Conference on Application-specific Systems, Architectures and Processors, (ASAP), pp. 266-271, July 2007.
10. G. Bioul, M. Vazquez, J. P. Deschamps, and G. Sutter, "Decimal addition in FPGA," Proc. SPL. 5th Southern Conference on Programmable Logic, pp. 101-108, 2009.
11. Vazquez and E. Antelo, "A high-performance significand BCD adder with IEEE 754-2008 decimal rounding," Proc. 19th IEEE Symposium on Computer Arithmetic (ARITH-19), pp. 135-144, 2009.
12. E. Cornea, J. Harrison, J. C. Anderson, P. Tang, E. Schneider, and E. Gvozdev, “A software implementation of the IEEE 754r decimal floating-point arithmetic using the binary encoding format,” IEEE Transactions on Computers, Vol. 58, No. 2, pp. 148-162, Feb. 2009.
13. M.A. Erle and M.J. Schulte, “Decimal multiplication via carry-save aAddition,” Proc. IEEE 14th Int’l Conf. Application-Specific Systems, Architectures, and Processors, pp. 348-358, June 2003.
14. M. A. Erle, E. M. Schwarz, and M. J. Schulte, "Decimal multiplication with efficient partial product generation," Proc. 17th IEEE Symposium on Computer Arithmetic (ARITH-17), pp. 21-28, 2005.
15. M. A. Erle, M. J. Schulte and B. J. Hickmann, "Decimal floating-point multiplication via carry-save addition," Proc. 18th IEEE Symposium on Computer Arithmetic (ARITH-18), pp. 46-55, 2007.
16. B. J. Hickmann, A. Krioukov, A.M. J. Schulte and M. A. Erle, "A parallel IEEE P754 decimal floating-point multiplier," Proc. 25th International Conference on Computer Design (ICCD), pp. 296-303, 2007.
17. G. Jaberipur, and A. Kaivani, "Binary-coded decimal digit multipliers," IET Computers & Digital Techniques, Vo1. 1, No. 4, pp. 377-381, 2007.
18. Vazquez, E. Antelo and P. Montuschi, “A new family of high performance parallel decimal multipliers," Proc. of the 18th IEEE Symposium on Computer Arithmetic (ARITH-18), pp. 195-204, 2007.
19. R. K. James, T. K. Shahana, K. P. Jacob, and S. Sasi, “Decimal multiplication using compact BCD multiplier,” Proc. International Conference on Electronic Design (ICED), pp. 1-6, 2008.
20. G. Jaberipur and A. Kaivani, “Improving the speed of parallel decimal multiplication,” IEEE Transactions on Computers, Vol. 58, No. 11, pp. 1539-1552, Nov. 2009
21. Vazquez, E. Antelo and P. Montuschi, “Improved design of high-performance parallel decimal multipliers,” IEEE Transactions on Computers, Vol. 59, No. 5, pp. 679-693, May 2010.
22. H. Nikmehr, B. Phillips, and C. -C. Lim, “Fast decimal floating-point division,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 14, No. 9, pp. 951-961, Sept. 2006.
23. T. Lang, and A. Nannarelli, “Division unit for binary integer decimals,” Proc. 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 1-7, 2009.
24. T. Lang and A. Nannarelli, “A radix-10 digit-recurrence division unit: algorithm and architecture,” IEEE Transactions on Computers, Vol. 56, No. 6, pp. 727-739, June 2007.
25. L. -K. Wang, M. A. Erle, C. Tsen, E. M. Schwarz and M. J. Schulte, “A survey of hardware designs for decimal arithmetic,” IBM Journal of Research and Development, Vol. 54, Issue 2, pp. 8:1-8:15, 2010.
26. P. Parhami, Computer arithmetic: algorithms and hardware designs. New York: Oxford Univ. Press, 2000.
27. Tyagi, “A reduced-area scheme for carry-select adders,” IEEE Transactions on Computers, Vol. 42, No. 10, pp. 1163–1170, Oct. 1993.
28. R. Brent and H. Kung, “A regular layout for parallel adders,” IEEE Transactions on Computers, Vol. 31, No. 3, pp. 260-264, 1982.
29. C. Sundaresan, CVS Chaitanya, PR Venkateswaran, Bhat Somashekara, and J. Mohan Kumar, “Modified reduced delay BCD adder,” Proc. 4th International Conference on Biomedical Engineering and Informatics pp. 2148-2151, 2011.

177-182

38.

Auths:

Swarnendu Jana, Jaydeb Bhaumik, Manas Kumar Maiti

Paper Title:

Survey on Lightweight Block Cipher

Abstract: With the rapid advances in wireless networks low-end devices, such as RFID tags, wireless sensor nodes are deployed in increasing numbers each and every day. Such devices are used in many applications and environments, leading to an ever increasing need to provide security. When choosing security algorithms for resource-limited devices the implementation cost should be taken into account, In order to satisfy these need, secure and efficient encryption and authentication schemes have to be developed. Symmetric-key algorithms, especially lightweight block ciphers, still play an important role to provide confidentiality in the said applications. In this paper, a survey of several existing light weight block ciphers has been provided.

Keywords: Symmetric Key Cryptography, Lightweight Block Cipher.

References:
1. G. Leander et al., "New Lightweight DES Variants," Proc. of Fast Software Encryption , LNCS, vol. 4593, Mar. 2007, pp. 196-210.
2. D. Hong, J. Sung, S. Hong, J. Lim, S. Lee, B. Koo, C. Lee, D. Chang, J. Lee, K. Jeong, H. Kim, J. Kim, and S. Chee,” HIGHT: A New Block Cipher Suitable for Low-Resource Device, ” Proc of Cryptographic Hardware and Embedded Systems, LNCS, vol. 4249, Oct. 2006,pp. 46-59.
3. C. Canniere, O. Dunkelman, and M. Knezevic , “Katan and ktantan - a family of small and effcient hardware-oriented block ciphers,” Proc. of Cryptographic Hardware and Embedded Systems, LNCS, vol. 459, Sept. 2009,pp. 272-288.
4. Z. Gong, S. Nikova, and Y. W. Law, “KLEIN: A New Family of Lightweight Block Ciphers,” Proc. of RFID. Security and Privacy, LNCS, vol. 6, Jun. 2011, pp. 1–18.
5. J. Guo, T.Peyrin, A.Poschmann, and M. Robshaw, “The led block cipher Cryptographic Hardware and Embedded Systems, LNCS, vol. 6917, Sept. 2011, pp. 326-341.
6. Y. Wang, W. Wu, X. Yu, and L. Zhang,” Security on lblock against biclique cryptanalysis,” Proc. of Information Security Applications, LNCS , vol. 7690, Aug. 2012, pp.1-14.
7. Chae Hoon Lim and Tymur Korkishko,” mCrypton - A Lightweight Block Cipher for Security of Low-Cost RFID Tags and Sensors,” Proc. of Information Security Applications, LNCS, vol. 3786, Aug. 2005,pp. 243-258.
8. Bogdanov, L.R. Knudsen, G. Leander, C. Paar, A. Poschmann, M. J. B. Robshaw, Y. Seurin, and C. Vikkelsoe, “PRESENT: An Ultra-Lightweight Block Cipher,” Proc. of Cryptographic Hardware and Embedded Systems, LNCS, vol. 4727, Sept. 2007,pp. 450-266.
9. J . Borgho,A. Canteaut T. uneysu, S.S. Thomsen and T. Yalcin, ”Prince - a low-latency block cipher for pervasive computing applications - extended abstract,” Proc. of Advances in Cryptology, LNCS, vol.7658, Dec. 2012,pp. 208-225.
10. T. Suzaki, K. Minematsu, S. Morioka, and E. Kobayashi, “TWINE: Lightweight Block Cipher for Multiple Platforms,” Proc. of Selected Areas in Cryptography, LNCS , vol. 7707, Aug. 2013,pp. 339-354.
11. R. Needham, D. Wheeler, ” eXtended Tiny Encryption Algorithm,” Technical Report, Cambridge University, England, Oct. 1997.
12. D. Wheeler and R. Needham, ” TEA, a Tiny Encryption Algorithm,” Proc. of the Second International Workshop on Fast Software Encryption, 1995, pp. 97-110.
13. Juels, “RFID Security and Privacy ,“ IEEE Journal on Selected Areas in Communications, vol. 24, 2006, pp.381-394.
14. K. Kim, K. Chung, J. Shin, H. Kang, S. Oh, C. Han, and K. Ahn, “A Lightweight RFID Authentication Protocol using Step by Step Symmetric Key Change,” Proc. of 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009
15. S. Ojha,N. Kumar,K. Jain and S.Lal, “TWIS - A Lightweight Block Cipher,” Information Systems Security, LNCS, vol. 5905, Dec. 2009, pp. 280-291.
16. Arora, and S. Pal,” A Survey of Cryptanalytic Attacks on Lightweight Block Ciphers,” Proc. of International Journal of Computer Science and Information Technology & Security (IJCSITS), vol. 2, Apr. 2012.
17. T. Suzaki and K. Minematsu, “Improving the Generalized Feistel,” Proc. of Fast Software Encryption, LNCS, vol. 6147, Feb. 2010, pp. 19-39.
18. Poschmann. “Lightweight Cryptography - Cryptographic Engineering for a Pervasive World,” Cryptology ePrint Archive, Report 2009/516, 2009.
19. O. Ozen, K. Varici, C. Tezcan and C. Kocair, “Lightweight Block Ciphers Revisited: Cryptanalysis of Reduced Round PRESENT and HIGHT,” Proc. Of Information Security and Privacy, LNCS, vol. 5594, July 2009, pp. 90-107.
20. K. Minematsu, T. Suzaki, and M. Shigeri, “On Maximum Differential Probability of Generalized Feistel,” Proc. Of Information Security and Privacy, LNCS, vol. 6812, July. 2011, pp. 89-105.
21. M. Izadi, B. Sadeghiyan, S. S. Sadeghian, and H. A. Khanooki, “MIBS: A New Lightweight Block Cipher,” Proc. of Cryptology and Network Security, LNCS, vol. 5888, Dec. 2009, pp. 334-348.
22. Bogdanov, D. Khovratovich and C. Rechberger, “Biclique Cryptanalysis of the Full AES,” Advances in Cryptology – ASIACRYPT, LNCS, vol. 7073, Dec. 2011, pp. 344-371.
23. Biryukov, “DES-X (or DESX). In Encyclopedia of Cryptography and Security (2nd Ed.),” 2011, pp. 331.
24. H.Yue-chao and W.Yi-ming, “Secure RFID system based on lightweight block cipher algorithm of optimized S-box,” Proc. of IEEE International Conference, June 2010,pp. 17-19.
25. T. Shirai, K. Shibutani, T. Akishita, S. Moriai, and T. Iwata, ” The 128-bit blockcipher clefia (extended abstract),” Proc. of Fast Software Encryption , LNCS, vol.4593, Feb.2007,pp. 181-195.
26. Rebeiro, R. Poddar, A. Datta, and D. Mukhopadhyay, "An Enhanced Differential Cache Attack on CLEFIA for Large Cache Lines," Proc. of Indocrypt, vol. 7107, 2011, pp. 55-75.
27. S. Khurana, S. Kolay, C. Rebeiro and D. Mukhopadhyay, ”Lightweight Cipher Implementations on Embedded Processors”’, Proc. of DTIS , Mar.2013.
28. T. Eisenbarth, S. Kumar, C. Paar, A. Poschmann, and L. Uhsadel, “A Survey of Lightweight-Cryptography Implementations,” IEEE Design & Test, vol. 24, Dec. 2007,pp. 522–533.
29. T. Suzaki, K. Minematsu, S. Morioka, and E. Kobayashi. TWINE: A Lightweight, Versatile Block Cipher. Available: www.nec.co.jp/rd/media/code/research/images/twine_LC11.pdf.
30. G. Gaubatz , "State of the Art in Ultra-Low Power Public Key Cryptography for Wireless Sensor Networks," Proc. of 3rd IEEE Int'l Conf. Pervasive Computing and Communications (PERCOMW 05), pp. 146-150, 2005.
31. M. Wang, “Differential cryptanalysis of reduced-round PRESENT,” Proc. of Progress in Cryptology AFRICACRYPT, vol. 5023, Jun. 2008, pp. 40–49.
32. J.Y.Cho, “Linear cryptanalysis of reduced-round PRESENT,” Proc. of Topics in Cryptology, LNCS, vol. 5985 Mar. 2010,pp.302-317.
33. Rajashekarappa, K M Sunjiv Soyjaudah and Sumithra Devi K A, “Study on Cryptanalysis of the Tiny Encryption Algorithm,” International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278-3075, vol. 2, Feb. 2013

183-187

39.

Auths:

Anne Srijanya. K, N. Kasiviswanath

Paper Title:

Data Integrity Verification by Third Party Auditor in Remote Data Cloud

Abstract: Cloud Computing gets its name as a metaphor for the internet. It is the next generation platform to provide resources as the services to the end users. In cloud storage system, the clients store their data in the server without keeping a local copy. Clients store their data in the private clouds but when storage expansion is needed they move to public clouds. Security is the major concern in the public clouds. The security mechanisms for private and public clouds are different. It may be possible that an unauthorized user can access the data from the public clouds. Hence, it is of critical importance that the client should be able to verify the integrity of the data stored in the remote un-trusted server. There may be security services offered by public clouds but they are not sufficient. In order to address the security issues, Trusted Third Party Auditing (TPA) is used as a service for private and public clouds, which offers various services to check for the integrity of the data. TPA mechanisms offer various auditing mechanisms such as read, write, update to verify the integrity of the data stored in the public clouds. We present such an auditing model based on Merkle Hash Tree. In this work we will conduct a study on possible auditing mechanisms which can be offers as a service over hybrid/public clouds. Such services can be subscribed by the users to verify the integrity of the data stored in the public clouds.

Keywords: TPA, Data Storage, Public auditability, Cloud Computing, Data Dynamics.

References:
1. Qian Wang, Cong Wang, Kui Ren, Wenjing Lou and Jin Li, “Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing”
2. K.Govinda, V.Gurunathaprasad, H.Sathishkumar, “Third Party Auditing for Secure Data Storage in Cloud through Digital Signature using RSA” International Journal of Advanced Scientific and Technical Research (Issue 2, Volume 4- August 2012) ISSN 2249-9954.
2.
3. Wang Shao-hui, Chang Su-qin,Chen Dan-wei,Wang Zhi-we, “Public Auditing for Ensuring Cloud Data Storage Security With Zero Knowledge Privacy”.
4. S. Sivachitralakshmi,T. Judgi, “A Flexible Distributed Storage Integrity AuditingMechanism in Cloud Computing”, International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012.
5. R.Ushadevi, V. Rajamani, “A Modified Trusted Cloud Computing Architecture based on Third Party Auditor (TPA) Private Key Mechanism”, International Journal of Computer Applications (0975 – 8887) Volume 58– No.22, November 2012.
6. Boyang Wang,Baochun Li,Hui Li, “Public Auditing for Shared Data with EfficientUser Revocation in the Cloud”.
7. Nandeesh.B.B, Ganesh Kumar R, Jitendranath Mungara, “Secure and Dependable Cloud Services for TPA in Cloud Computing”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-1, Issue-3, August 2012.
8. Irfan Gul, Atiq ur Rehman, M Hasan Islam, “Cloud Computing Security Auditing.”
9. Mortaza Mokhtari Nazarlou,Javad Badali, “A New Case for Trusted Third PartyAuditor in Cloud Computing”, European Journal of Scientific Research ISSN: 1450-216X / 1450-202X Vol. 95 No 1 January, 2013, pp.152-157 © EuroJournals Publishing, Inc. 2012
10. Balakrishnan.S, Saranya.G, Shobana.S, Karthikeyan.S, “Introducing Effective Third Party Auditing (TPA) for Data Storage Security in Cloud”, IJCST Vol. 2, Iss ue 2, June 2011
11. Gayatri.R , “Privacy Preserving Third Party Auditing for Dynamic Data”, International Journal of Communications and Engineering Volume 01– No.1, Issue: 03 March2012.
12. Abhishek Mohta and Lalit Kumar Awasthi, “Cloud Data Security while using Third Party Auditor”, International Journal of Scientific & Engineering Research,Volume 3, Issue 6, June-2012 1 ISSN 2229-5518.

188-193

40.

Auths:

Ramesh Kumar Ayyasamy

Paper Title:

Organizing Information in the Blogosphere: The Use of Unsupervised Approach

Abstract: This study covers the learning approaches discussed by the information retrieval community in categorising texts with a specific focus given to blogs within the last ten years. Early research studies were solely focused on general text classification and these techniques were later improved, and applied to classify webpages, and then to blogs due to the similarity of textual content present in these items. We review how blog classification techniques have evolved from the foremost text classification techniques to the recent ones and discuss the future research directions.

Keywords: Blog classification, Blogosphere, Supervised, Semi-supervised, Unsupervised classification.

References:
1. N. Agarwal, M. Galan, H. Liu, and S. Subramanya, 'Clustering blogs with Collective Wisdom', In 8th International Conference on Web Engineering, 2008, pp. 336-339.
2. Bayoudh, N. Bechet, and M. Roche, ‘Blog classification: Adding linguistic knowledge to improve the K-NN algorithm', Intelligent Information Processing IV, 2008, pp. 68-77.
3. D. Cao, X. Liao, H. Xu, and S. Bai, ‘Blog post and comment extraction using information quantity of web format', Information Retrieval Technology, 2008, pp. 298-309.
4. E. Elgersma, and M. De Rijke, 'Learning to recognize blogs: A preliminary exploration', EACL Workshop: New Text - Wikis and blogs and other dynamic text sources, 2006, pp. 24-73.
5. E. Elgersma, and M. De Rijke, 'Personal vs Non-Personal Blogs: Initial classification experiments', In ACM SIGIR conf., 2008, pp. 723-724.
6. D. Ikeda, H. Takamura, and M. Okumura, 'Semi-supervised learning for blog classification', In AAAI, 2008, pp. 1156-1161.
7. P. Kolari, T. Finin, and A. Joshi, ‘SVMs for the blogosphere: Blog identification and splog detection', In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, vol. 4, 2006. Available: http://aisl.umbc.edu/resources/213.pdf.
8. T. Nanno, T. Fujiki, Y. Suzuki, and M. Okumura, 'Automatically collecting, monitoring, and mining Japanese weblogs', In 13th International World Wide Web Conference on Alternate Track Papers and Posters, 2004, pp. 320-321.
9. Ounis, C. Macdonald, and I. Soboroff, 'On the TREC Blog Track', In ICWSM conference, 2008. Available: http://terrierteam.dcs.gla.ac.uk/publications/ounis08trecblog.pdf.
10. F. Sebastiani, 'Machine learning in automated text categorization', ACM Computing Surveys, vol. 34, no. 1, 2002, pp. 1-47.
11. P. Berkhin, 'A survey of clustering data mining techniques', Grouping multidimensional data, 2006, pp. 25-71.
12. R. Xu, and D. Wunsch, 'Survey of clustering algorithms', IEEE Transactions on Neural Networks, vol. 16, no. 3, 2005, pp. 645-678.
13. B. Choi, and Z. Yao, 'Web page classification*', Foundations and Advances in Data Mining, 2005, pp. 221-274.
14. X. Qi, and B.D. Davison, 'Web Page Classification: Features and Algorithms', ACM Computing Surveys, vol. 41, no. 2, 2009. Available: http://www.cse.lehigh.edu/~xiq204/pubs/classification-survey/LU-CSE-07-010.pdf.
15. Fürnkranz, J., 'Web Mining', Data mining and knowledge discovery handbook, 2005, pp. 899-920.
16. Y. Yang, S. Slattery, and R. Ghani, 'A study of approaches to hypertext categorization', Journal of Intelligent Information Systems, vol. 18, no. 2, 2002, pp. 219-241.
17. H. Zha, X. He, C. Ding, H. Simon, and M. Gu, ‘Bipartite graph partitioning and data clustering', In CIKM ’01, 2001, pp. 25-32.
18. H. Qu, A.L. Pietra, and S. Poon, 'Automated blog classification: Challenges and pitfalls', In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 2006, pp. 184-185.
19. D. Ikeda, H. Takamura, and M. Okumura, 'Semi-supervised learning for blog classification', In AAAI conference, 2008, pp. 1156-1161.
20. I.S. Dhillon and Y. Guan, 'Information theoretic clustering of sparse co-occurrence data', In ICDM conference, 2003, pp. 517-520.
21. P. Chesley, B. Vincent, L. Xu, and R.K. Srihari, 'Using verbs and adjectives to automatically classify blog sentiment', In AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, 2006, pp. 27-29.
22. T. Li, Y. Zhang, and V. Sindhwani, 'A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge', In AFNLP, vol. 1, 2009, pp. 244-252.
23. B. Pang, and L. Lee, 'Opinion mining and sentiment analysis', Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, 2008, pp. 1-135.
24. P. Kolari, A. Java, T. Finin, J. Mayfield, A. Joshi, and J. Martineau, 'Blog track open task: spam blog classification', Technical Report, TREC 2006 Blog Track Notebook, 2006.
25. S.C. Herring, L.A. Scheidt, S. Bonus and E. Wright, 'Bridging the gap: A genre analysis of weblogs', In 37th Hawaii International Conference on System Sciences, 2004, pp. 1-11.
26. C.R. Miller, and D. Shepherd, 'Blogging as social action: a genre analysis of the weblog', Into the blogosphere: Rhetoric, community, and culture of weblogs, 2004. Available:
27. http://blog.lib.umn.edu/blogosphere/blogging_as_social_action_a_genre_analysis_of_the_weblog.html.
28. G. Mishne, 'Experiments with Mood Classification in Blog Posts', In ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, 2005. Available:
29. http://staff.science.uva.nl/~gilad/pubs/style2005-blogmoods.pdf.
30. N. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton and T. Tomokiyo, 'Deriving marketing intelligence from online discussion', In 11th ACM SIGKDD conference, 2005, pp. 419-428.
31. B. Liu, Web data mining: Exploring hyperlinks, contents, and usage data, Springer Verlag, 2007, pp. 1-532.
32. E. Fix, and J.L. Hodges Jr, Discriminatory analysis-nonparametric discrimination: small sample performance, USAF School of Aviation Medicine, Randolph Field, Tex., Project 21-49-004, Rept. 4, Contract AF41(128)-31, 1952.
33. V.N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York Inc, 1995.
34. McCallum, and K. Nigam, 'A comparison of event models for naive bayes text classification', In AAAI-98 Workshop on Learning for Text Categorization, vol. 752, 1998, pp. 41-48.
35. J.D. Rennie, L. Shih, J. Teevan, and D. Karger, 'Tackling the poor assumptions of naive bayes text classifiers', In ICML conference, vol. 20, 2003, pp. 616-623.
36. H.T. Ng, W. B. Goh, and K.L. Low, 'Feature selection, perceptron learning, and a usability case study for text categorization', In ACM SIGIR Conference, vol. 31, 1997, pp. 67-73.
37. N. Friedman, D. Geiger, and M. Goldszmidt, 'Bayesian network classifiers', machine learning, vol. 29, no. 2, 1997, pp. 131-163.
38. O. Chapelle, M. Chi, and A. Zien, 'A continuation method for semi-supervised SVMs', In ICML conference, 2006, pp. 185-192.
39. X. Zhu, 'Semi-supervised learning literature survey', University of Wisconsin – Madison, 2005, pp. 1-60.
40. L.D. Baker, and A.K. McCallum, 'Distributional clustering of words for text classification', In ACM SIGIR conference, 1998, pp. 96-103.
41. T. Hofmann, 'Probabilistic Latent Semantic Analysis', In 15th Conference on Uncertainty in Artificial Intelligence, 1999, pp. 289-296.
42. K. Bennett and A. Demiriz, A, 'Semi-supervised support vector machines', Advances in Neural Information processing systems, 1999, pp. 368-374.
43. V. Vapnik, Statistical learning theory, Wiley, New York, 1998, pp. 1-768.
44. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, 'Text classification from labeled and unlabeled documents using EM', Machine learning, vol. 39, no. 2, 2000, pp. 103-134.
45. S. Basu, A. Banerjee, and R. Mooney, 'Semi-supervised clustering by seeding', In ICML conference, 2002, pp. 19-26.
46. D. Merkl and A. Rauber, 'Document classification with unsupervised artificial neural networks', Soft Computing in Information Retrieval: Techniques and Applications, vol. 50, 2000, pp. 102-121.
47. I.S. Dhillon, Y. Guan, and B. Kulis, 'Kernel k-means: Spectral clustering and normalized cuts', In ACM SIGKDD conference, 2004, pp. 551-556.
48. Hotho, S. Staab, and G. Stumme, 'Wordnet improves text document clustering', In ACM SIGIR Semantic Web Workshop, 2003, pp. 1-9.
49. J. Sedding, and D. Kazakov, 'WordNet-based text document clustering', In Proc. of the COLING 2004 3rd Workshop on Robust Methods in Analysis of Natural Language Data, 2004, pp. 104-113.
50. F. Shahnaz, M.W. Berry, V. P. Pauca, and R. J. Plemmons, 'Document clustering using nonnegative matrix factorization', Information Processing and Management, vol. 42, no. 2, 2006, pp. 373-386.
51. W. Xu, X. Liu, and Y. Gong, 'Document clustering based on non-negative matrix factorization', In ACM SIGIR conference, 2003, pp. 267-273.
52. S. Dhillon, 'Co-clustering documents and words using bipartite spectral graph partitioning', In ACM SIGKDD conference, 2001, pp. 269-274.
53. B. Gao, T.Y. Liu, X. Zheng, Q. S. Cheng, andW.Y. Ma, 'Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data
co-clustering', In ACM SIGKDD conference, 2005, pp. 41-50.
54. T. Kohonen, S. Kaski, K. Lagus, J. Salojarvi, J. Honkela, V. Paatero, and A. Saarela, 'Self organization of a massive document collection', IEEE Transactions on Neural Networks, vol. 11, no. 3, 2000, pp. 574-585.
55. D. Merkl, 'Text classification with Self-Organizing maps: Some lessons learned', Neurocomputing, vol. 21, no. 1-3, 1998, pp. 61-77.
56. N. Slonim, N. Friedman, and N. Tishby, 'Unsupervised document classification using sequential information maximization', In ACM SIGIR conference, 2002, pp. 129-136.
57. C. Hung, S. Wermter, and P. Smith, 'Hybrid neural document clustering using guided self-organization and WordNet', IEEE Intelligent Systems, vol. 19, no. 2, 2004, pp. 68-77.
58. G.T. Lakshmanan, and M. A. Oberhofer, 'Knowledge discovery in the blogosphere: Approaches and challenges', IEEE Internet Computing, vol. 14, no. 2, 2010, pp. 24-32.
59. Kyriakopoulou, and T. Kalamboukis, 'Text classification using clustering', In Proceeding of the ECML-PKDD The Discovery Challenge Workshop, 2006, p. 28-38. Available: http://ceas2009.cc/discovery_challenge_proceedings.pdf#page=32.
60. N. Grira, M. Crucianu, and N. Boujemaa, 'Unsupervised and semi-supervised clustering: A brief survey', A Review of Machine Learning Techniques for Processing Multimedia Content’, Report of the MUSCLE European Network of Excellence (FP6), 2004, pp. 1-12. Available: http://cedric.cnam.fr/~crucianm/src/BriefSurveyClustering.pdf.

194-198

41.

Auths:

Syeda Erfana Zohora, A.M.Khan, A.K.Srivastava, Nisar Hundewale

Paper Title:

Electronic Noses Application to Food Analysis Using Metal Oxide Sensors: A Review

Abstract: Electronic noses employs different types of electronic gas sensors that have partial specificity and an appropriate pattern recognition techniques capable of recognizing simple and complex odors. This paper focuses on use of electronic noses that use metal oxide gas sensors. In this paper,we present the quality assessment applications to food and beverages, that includes determination of freshness and identification of spoilage, polluted, contaminated , unhygienic or adulteration in the food. The applications of electronic noses to a wide collection of food and beverages are considered, that consists of fruits, milk and dairy products, fresh vegetables, eggs, meat, fish, grains, alcoholic drinks and non-alcoholic drinks.

Keywords: Electronic nose, E-nose, Food analysis, Metal oxide sensors.

References:
1. P. Ciosek, Z. Brzozka, and W. Wroblewski, “Classification of beverages using a reduced sensor array,” Sens. Actuators B, vol. 103, pp.76–83, 2004.
2. J. Lozano, J. P. Santos, M. Aleixandre, I. Sayago, J. Gutierrez, and M. C. Horrillo, “Identification of typical wine aromas by means of an electronic nose,” IEEE Sensors J., vol. 6, pp. 173–178, Feb. 2006.
3. M. Aleixandre, J. Lozano, J. Guiterrez, I. Sayago, M. J. Fernandez, and M. C. Horrillo, “Portable e-nose to classify different kinds of wine,” Sens. Actuators B, vol. 131, pp. 71–76, 2008.
4. M. Pardo, G. Niederjaufnes, G. Benussi, E. Comini, G. Faglia, G. Sberveglieri, M. Holmberg, and I. Lundstorm, “Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose,” Sens. Actuators B, vol. 69, pp. 397–403, 2000.
5. K. Brudzewska, S. Osowiki, and T. Markiewicz, “Classification of milk by means of an electronic nose and SVM neural network,” Sens. Actuators B, vol. 98, pp. 291–298, 2004.
6. W. M. Sears and K. Colbow, “Selective thermally cycled gas sensing using fast Fourier transform techniques,” Sens. Actuators B, vol. 2, pp.283–289, 1990.
7. J. W. Gardner,H. V. Shurmer, and T. T. Tan, “Application of electronic nose to the discrimination of coffees,” Sens. Actuators B, vol. 6, pp.71–75, 1992.
8. M. S. Nayak, “Transformed cluster analysis: An approach to the identification of gases/odors using integrated gas sensor array,” Sens. Actuators B, vol. 12, pp. 103–110, 1993.
9. K. Srivastava, K. K. Shukla, and S. K. Srivastava, “Exploring neurogenetic processing of electronic nose data,” Microelectronics J., vol.29, pp. 921–931, 1998.
10. F. C. Chen and M. H. Lin, “On the learning and convergence of the radial basis networks,” in Proc. IEEE Conf. Neural Networks, San Fransisco, CA, Mar. 28, 1993, pp. 983–988.
11. K. K. Shukla, R. R. Das, and R. Dwivedi, “Adaptive resonance neural classifier for identification of gas/odors using an integrated gas sensor array,” Sens. Actuators B, vol. 50, pp. 194–203, 1998.
12. S. Marco, A. Ortego, A. Pardo, and J. Samitier, “Gas identification with tin oxide sensor array and self-organizing map: Adaptive correction of sensor drifts,” IEEE Trans. Instrum. Measurment, vol. 47, pp. 316–320, 1998.
13. R. R. Das, K. K. Shukla, R. Dwivedi, and A. R. Srivastava, “Discrimination of individual gas/odor using responses of integrated thick film tin oxide sensor and fuzzy-neuro concept,” Microelectronics J., vol. 30, pp. 793–800, 1999.
14. Syeda Erfana Zohora; Dr.A.M.Khan; Dr.Nisar Hundewale; Electronic Noses Principles And Its Applications;International Journal of Imaging Science and Engineering; Volume 6 No 1 Apr 2012.
15. Gardner, J.; Bartlett, P.N. Electronic Nose. Principles and Applications; Oxford University,Press: Oxford, UK, 1999.
16. Syeda Erfana Zohora; A.M.Khan; Nisar Hundewale; Chemical Sensors Employed In Electronic Noses: A Review; Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY-2012); July 13-15,2012 Chennai; India-Volume 3; Springer-Verlag Berlin Heidelberg (2012),page 177-184; Accepted & Published.
17. Fox, P.F. Advanced Dairy Chemistry; Chapman & Hall: London, UK/New York, NY, USA,1992.
18. Yu, H.C.; Wang, J.; Xu, Y. Identification of Adulterated Milk Using Electronic Nose. Sens.Mater. 2007, 19, 275-285.
19. Benedetti, S.; Bonomi, F.; Iametti, S.; Mannino, S.; Cosio, M.S. Detection of aflatoxin M1 in ewe milk by using an electronic nose. In Proceedings of the 2nd Central European Meeting 5th Croatian Congress of Food Technologists, Biotechnologists and Nutritionists, Opatija, Croatia,October 17–20, 2004; pp. 101-105
20. Ampuero, S.; Bosset, J.O. The Electronic nose applied to dairy products: a review. Sens.Actuat.B 2003,94,1-12.
21. Mulville, T. UHT the nose knows. Food Manufact. March 2000, 27-28.
22. Mariaca, R.; Bosset, J.O. Instrumental analysis of volatile (flavour) compounds in milk and dairy products. Lait 1997, 77, 13-40.
23. Capone, S.; Epifani, M.; Quaranta, F.; Siciliano, P.; Taurino, A.; Vasanelli, L. Monitoring of rancidity of milk by means of an electronic nose and a dynamic PCA analysis. Sens. Actuat. B 2001, 78, 174-179.
24. Capone, S.; Siciliano, P.; Quaranta, F.; Rella, R.; Epifani, M.; Vasanelli, L. Analysis of vapours and foods by means of an electronic nose based on a sol-gel metal oxide sensors array. Sens. Actuat. B 2000, 69, 230-235.
25. Labreche, S.; Bazzo, S.; Cade, S.; Chanie, E. Shelf life determination by electronic nose:application to milk. Sens. Actuat. B 2005, 106, 199-206.
26. Dainty, R.H.; Edwards, R.A.; Hibbard, C.M. Time course of volatile compound formation during refrigerated storage of naturally contaminated beef in air. J. Appl. Bacteriol. 1985, 59, 303-309.
27. Mayr, D.; Hartungen, E.; Mark, T.; Margesin, R.; Schinner, F. Determination of the spoilage status of meat by aroma detection using proton-transfer-reaction mass-spectrometry. In Proceedings of the 10th Weurman Flavour Research Symposium, Beaune, France, June 25−28, 2002; pp. 757-760.
28. Lindinger, W.; Hansel, A.; Jordan, A. On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MM)—medical applications, food control and environmental research. Int. J. Mass Spectrom. 1998, 173, 191-241.
29. Winquist, F.; Hornsten, E.G.; Sundgren, H.; Lundstrom, I. Performance of an electronic nose for quality estimation of ground meat. Meas. Sci. Technol. 1993, 4, 1493-1500.
30. Vilaseca, M.; Coronas, J.; Cirera, A.; Cornet, A.; Morante, J.R.; Santamaria, J. Gas detection With SnO2 sensors modified by zeolite films. Sens. Actuat. B 2007, 124, 99-110.
31. Balasubramanian, S.; Panigrahi, S.; Logue, C.M.; Doetkott, C.; Marchello, M.; Sherwood, J.S. Independent component analysis-processed electronic nose data for predicting Salmonella Typhimurium populations in contaminated beef. Food Control 2008, 19, 236-246.
32. Vernat-Rossi, V.; Garcia, C.; Talon, R.; Denoyer, C.; Berdague, J.L. Rapid discrimination of meat products and bacterial strains using semiconductor gas sensors. Sens. Actuat. B 1996, 37, 43-48.
33. Bene, A.; Hayman, A.; Reynard, E.; Luisier, J.L.; Villettaz, J.C. A new method for the rapid determination of volatile substances: the SPME-direct
method—Part II. Determination of the freshness of fish. Sens. Actuat. B 2001, 72, 204-207.
34. Olafsdottir, G.; Chanie, E.; Westad, F.; Jonsdottir, R.; Thalmann, C.R.; Bazzo, S.; Labreche, S.;Marcq, P.; Lundby, F.; Haugen, J.E. Prediction of microbial and sensory quality of cold smoked atlantic salmon (Salmo Salar) by electronic nose. J. Food Sci. 2005, 70, S563-S574.
35. Haugen, J.E.; Chanie, E.; Westad, F.; Jonsdottir, R.; Bazzo, S.; Labreche, S.; Marcq, P.; Lundby, F.; Olafsdottir, G. Rapid control of smoked Atlantic salmon (Salmo salar) quality by electronic nose: Correlation with classical evaluation methods. Sens. Actuat. B 2006, 116, 72-77.
36. El Barbri, N.; Amari, A.; Vinaixa, M.; Bouchikhi, B.; Correig, X.; Llobet, E. Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage.Sens.Actuat.B 2007,128,235-244.
37. El Barbri, N.; Llobet, E.; El Bari, N.; Correig, X.; Bouchikhi, B. Application of a portable electronic nose system to assess the freshness of moroccan sardines. Mat. Sci. Eng. C-Bio S. 2008, 28, 666-670.
38. Wang, Y.W.; Wang, J.; Zhou, B.; Lu, Q.J. Monitoring storage time and quality attribute of egg based on electronic nose. Anal. Chim. Acta 2009, 650, 183-188.
39. Dutta, R.; Hines, E.L.; Gardner, J.W.; Udrea, D.D.; Boilot, P. Non-destructive egg freshness determination: an electronic nose based approach. Meas. Sci. Technol. 2003, 14, 190-198.
40. Brown, M.L.; Holbrook, D.M.; Hoerning, E.F.; Legendre, M.G.; Stangelo, A.J. Volatile indicators of deterioration in liquid egg products. Poult. Sci. 1986, 65, 1925-1933.
41. Suman, M.; Riani, G.; Dalcanale, E. MOS-based artificial olfactory system for the assessment of egg products freshness. Sens. Actuat. B 2007, 125, 40-47.
42. Gomez, A.H.; Hu, G.X.; Wang, J.; Pereira, A.G. Evaluation of tomato maturity by electronic nose. Comput. Electron. Agri. 2006, 54, 44-52.
43. Concina, I.; Falasconi, M.; Gobbi, E.; Bianchi, F.; Musci, M.; Mattarozzi, M.; Pardo, M.;Mangia, A.; Careri, M.; Sberveglieri, G. Early detection of microbial contamination in processed tomatoes by electronic nose. Food Control 2009, 20, 873-880.
44. Simon, J.E.; Hetzroni, A.; Bordelon, B.; Miles, G.E.; Charles, D.J. Electronic sensing of aromatic volatiles for quality sorting of blueberries. J. Food Sci. 1996, 61, 967-970.
45. Supriyadi; Shimazu, K.; Susuki, M.; Yoshida, K.; Muto, T.; Fujita, A.; Tomita, N.; Watanabe, N.Maturity discrimination of snake fruit (Salacca edulis Reinw.) cv. Pondoh based on volatiles analysis using an electronic nose device equipped with a sensor array and fingerprint mass spectrometry. Flavour Frag. J. 2004, 19, 44-50.
46. Gomez, A.H.; Wang, J.; Hu, G.X.; Pereira, A.G. Discrimination of storage shelf-life for mandarin by electronic nose technique. Lwt-Food Sci. Technol. 2007, 40, 681-689.
47. Gomez, A.H.; Wang, J.; Pereira, A.G. Mandarin ripeness monitoring and quality attribute evaluation using an electronic nose technique. Trans. ASABE 2007, 50, 2137-2142.
48. Steinmetz, V.; Crochon, M.; Talou, T.; Bourrounet, B. Sensor fusion for fruit quality assessment:application to melons. In Proceedings of International Conference on Harvest and Postharvest Technologies for Fresh Fruits and Vegetables, Guanajuato, Gto, Mexico, February 20–24, 1995;pp. 488-496.
49. Steinmetz, V.; Sevila, F.; Bellon-Maurel, V. A Methodology for sensor fusion design:Application to fruit quality assessment. J. Agr. Eng. Res. 1999, 74, 21-31.
50. Berna, A.Z.; Trowell, T.; Clifford D.; Stone, G.; Lovell, D. Fast aroma analysis of Cabernet Sauvignon and Riesling grapes using an electronic nose. Am. J. Enol. Vitic. 2007, 58, 416A-417A.
51. Marti, M.; Boque, R.; Busto, O.; Guasch, J. Electronic noses in the quality control of alcoholic beverages. Trends Anal. Chem. 2005, 24, 57-66.
52. Herbele, I.; Liebminger, A.; Weimar, U.; Gopel, W. Optimised sensor arrays with chromatographic preseparation: characterisation of alcoholic beverages. Sens. Actuat. B 2000,68, 53-57.
53. Garcia, M.; Aleixandre, M.; Gutierrez, J.; Horrillo, M. Electronic nose for wine discrimination.Sens. Actuat. B 2006, 113, 911-916.
54. Aishima, T. Discrimination of liquor aromas by pattern-recognition analysis of responses from a gas sensor array. Anal. Chim. Acta 1991, 243, 293-300.
55. Schafer, T.; Serrano-Santos, M.; Rocchi, S.; Fuoco, R. Pervaporation membrane separation process for enhancing the selectivity of an artificial olfactory system (“electronic nose”). Anal.Bioanal. Chem. 2006, 384, 860-866.
56. Pinheiro, C.; Rodrigues, C.; Schafer, T.; Crespo, J. Monitoring the aroma production during wine-must fermentation with an electronic nose. Biotechnol. Bioeng. 2002, 77, 632-640.
57. Guadarrama, A.; Fernandez, J.; Iniguez, M.; Souto, J.; Saja, J. Array of conducting polymer sensors for the characterisation of wines. Anal. Chim. Acta 2000, 411, 193-200.
58. McKellar, R.; Young, J.; Johnston, A.; Knight, K.; Lu, X.; Buttenham, S. Use of the electronic nose and gas chromatography-mass spectrometry to determine the optimum time for aging beer.Master Brewers Assoc. Amer. 2002, 39, 99-105.
59. McKellar, R.; Rupasinghe, H.; Lu, X.; Knight, K. The electronic nose as a tool for the classification of fruit and grape wines from different Ontario wines. J. Sci. Food. Agric. 2005,85, 2391-2396.
60. Berna, A.Z.; Trowell, S.; Cynkar, W.; Cozzolino, D. Comparison of metal oxide-based electronic nose and mass spectrometry-based electronic nose for the prediction of red wine spoilage. J.Agric. Food Chem. 2008, 56, 3238-3244.
61. Berna, A.Z.; Trowell, S.; Clifford, D.; Cynkar, W.; Cozzolino, D. Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose.Anal. Chim. Acta 2009, 648, 146-152.
62. Pinho, O.; Ferreira, I.; Santos, L. Method optimization by solid-phase microextraction in combination with gas chromatography with mass spectrometry for analysis of beer volatile fraction. J. Chromatogr. A 2006, 1121, 145-153.
63. Pearce, T.C.; Gardner, J.W.; Friel, S.; Bartlett, P.N.; Blair, N. Electronic nose for monitoring the flavor of beers. Analyst 1993, 118, 371-377.
64. Sakuma, S.; Amano, H.; Ohkochi, M. Identification of off-flavor compounds in beer. J. Am. Soc.Brew. Chem. 2000, 58, 26-29.
65. Penza, M.; Cassano, G. Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks. Food Chem. 2004,
86, 283-296.
66. Santos, J.; Arroyo, T.; Aleixandre, M.; Lozano, J.; Sayago, I.; Garcia, M.; Fernandez, M.; Ares, L.; Gutierrez, J.; Cabellos, J.; Gil, M.; Horrillo, M. A comparative study of sensor array and GC-MS: application to Madrid wines characterisation. Sens. Actuat. B 2004, 102, 299-307.
67. Buratti, S.; Benedetti, S.; Scampicchio, M.; Pangerod, E. Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal.Chim. Acta 2004, 525, 133-139.
68. Di Natale, C.; Davide, F.A.M.; D’Amico, A.; Nelli, P. An electronic nose for the recogition of the vineyard of a red wine.Sens.Actuat. B 1996,33,83-88.
69. Di Natale, C.; D’Amico, A. The electronic nose: a new instrument for wine analysis. Ital. Food Bever. Tech. 1998, 14, 17-19.
70. Villanueva, S.; Guadarrama, A.; Rodriguez-Mendez, M.L.; De Saja, J.A. Use of an array of metal oxide sensors coupled with solid phase microextraction for characterisation of wines study of the role of the carrier gas. Sens. Actuat. B 2008, 132, 125-133.
71. Ragazzo-Sanchez, J.A.; Chalier, P.; Chevalier, D.; Ghommidh, C. Electronic nose discrimination of aroma compounds in alcoholised solutions. Sens. Actuat. B 2006, 114, 665-673.
72. Bhuyan, M.; Borah, S. Use of electronic nose in tea industry. In Proceedings of International Conference on Energy, Automation and Information Technology, Kharagpur, India, December 2001; pp. 848-853.
73. Dutta, R.; Hines, E.L.; Gardner, J.W.; Kashwan, K.R.; Bhuyan, A. Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach. Sens. Actuat. B 2003, 94, 228-237.
74. Yu, H.; Wang, J.; Xiao, H.; Liu, M. Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals. Sens. Actuat. B 2009, 140, 378-382.
75. Gardner, J.W.; Shurmer, H.V.; Tan, T.T. Application of an electro-nic nose to the discrimination of coffees. Sens.Actuat. B 1992, 6, 71-75.
76. Aishima, T. Aroma discrimination by pattern-recognition analysis of responses from semiconductor gas sensor array. J. Agric. Food Chem. 1991, 39, 752-756.
77. Pardo, M.; Niederjaufner, G.; Benussi, G.; Comini, E.; Faglia, G.; Sberveglieri, G.; Holmberg,M.; Lundstrom, I. Data preprocessing enhances the classification of different brands of espresso coffee with an electronic nose. Sens. Actuat. B 2000, 69, 397-403.
78. Pardo, M.; Sberveglieri, G. Coffee analysis with an electronic nose. IEEE T. Instrum. Meas.2002, 51, 1334-1339.

199-205

42.

Auths:

P Srinivas, K Vijaya Lakshmi, V Naveen Kumar

Paper Title:

Design of GA based PID Controller for three tank system with various Performance Indices

Abstract: Proportional–integral-derivative (PID) controllers tuning is a challenging aspect for researchers and process operators. This paper proposes PID controller tuning of a three tank level process using genetic algorithm. Genetic algorithms are based on the principle of genetics and evolution and stand up as a powerful tool for solving search and optimization problems. Selection, crossover and mutation are three basic genetic operators for functioning of the algorithm. Optimization of PID parameters is evaluated using genetic algorithm with various performance indices i.e. ISE, ITAE, IAE and IME. Also the responses of three tank level process using genetic PID controller with various performance indices are analyzed and compared. Analysis is performed through computer simulation using Matlab/Simulink toolbox. The comparative study shows that the application of genetic algorithm based PID controller with ISE index gives the best performance compared to other indices for three tank level process.

Keywords: Genetic algorithm, PID controller, Performance indices, Three tank process.

References:
1. Joseph J. DiStefano, Allen R. Stubberud, Ivan J. Williams,Feedback and control systems, McGraw-Hill Professional,1995- Schaum's Series.
2. David W. Spitzer, ‘Advanced Regulatory Control Applications and Techniques’, Momentum Press, 2009.
3. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Co., Inc., 1989.
4. S.N.Sivanandam, S.N.Deepa, Introduction to Genetic Algorithms, Springer Publications Co., 2010.
5. Gotshall,S. and Rylander, B., Optimal Population Size And The Genetic Algorithm.”, Proc On Genetic And Evolutionary Computation Conference,2000.
6. K. Krishnakumar and D. E. Goldberg, Control System Optimization Using Genetic Algorithms”, Journal of Guidance, Control and Dynamics, Vol. 15, No. 3, pp. 735-740, 1992.
7. Chris Houck, Jeff Joines, and Mike Kay, "A Genetic Algorithm for FunctionOptimization: A Matlab Iplementation”.
8. Chipperfield, A. J., Fleming, P. J., Pohlheim, H. and Fonseca, C. M., A Genetic Algorithm Toolbox for MATLAB , Proc. International Conference on Systems Engineering, Coventry, UK, 6-8 September, 1994.
9. Ayman A. Aly, PID Parameters Optimization Using Genetic Algorithm Technique for Electrohydraulic Servo Control System, Intelligent Control and Automation, 2011, 2,69-76.
10. Y. J. CAO and Q. H. WU, Bristol, Teaching Genetic Algorithm Using Matlab, Int. J.Elect. Enging. Educ., Vol. 36, pp. 139–153. Manchester U.P., 1999. NCSU-IE TR 95-09, 1995.

206-209

43.

Auths:

Mohannad J Mnati

Paper Title:

Design and Performance Antenna Diversity for MIMO WiMAX Mobile Terminals

Abstract: New evolutions in wireless communications have shown using multiple antenna elements at both transmitter and the receiver, it is possible to substantially increase the capacity in a wireless communication system without increasing the transmission power and bandwidth. This system with multiple antenna elements at both link-ends is termed the MIMO (Multiple-Input Multiple-Output) system. In spite of considerable research being done on MIMO systems; the design of realistic diversity antennas on mobile terminals for MIMO systems remains a challenging issue. The main challenge in designing two or more antennas on a small mobile terminal is to achieve a high isolation between the antennas. It is very difficult to achieve a high isolation with the existing handset antennas. This article presents the performance analysis of the multiple-input-multiple-output Diversity antenna fornext generation WiMAX mobile handset applications Planar Inverted-F Antenna (PIFA). Antenna structures considering the practical ground size of a mobile handset proposed to estimate the proposed structures, mutual coupling and envelope correlations were considered. Also the diversity gain and correlation were measured from simulation result. The relationships between all parameters were analyzed based on the measured results.

Keywords: PIFA, Mobile, Antenna, WiMAX, MIMO, Diversity.

References:
1. D. Chizhik, J. Ling, P.W. Wolniansky, R.A. Valensuela, N. Costa and K.Huber, “Multiple-input multiple-output measurements and modeling in Manhattan,” IEEE Journal on Selected Areas on Communications, vol. 21, no. 3, pp. 321-331, April 2003.
2. D. Chizhik, J. Ling, D. Samardzija and R.A. Valenzuela, “Spatial and polarization characterization of MIMO channels in rural environment,” IEEE 61st Vehicular Technology Conference 2005, vol. 1, pp. 161-164, 2005.
3. Adjoudani, E.C. Beck, A.P. Burg, G.m. Djuknic, T.G. Gvoth, D. Haessig, S. Manji, M.A. Milbrodt, M. Rupp, D. Samardzija, A.B. Siegel, T. Sizer, C. Tran, S. Walker, S.A. Wilkus and W. Wolniansky, “Prototype experience for MIMO BLAST over third-generation wireless system,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 3, pp. 440-451, April 2003.
4. D. Samardzija, A. Lozano and C. Papadias, “Experimental validation of MIMO multiuser detection for UMTS high-speed downlink packet access,” IEEE Global Telecommunications Conference, vol. 6, pp. 3840-3844, 2004.
5. Lucent press release, URL: http://www.lucent.com/press/ 1002/021016.bla.html.
6. Ofcom research report, “Antenna arrays technology and MIMO systems”contract number: AY4476B, 2004.
7. Ofcom research report, “Antenna designs for MIMO systems,” Contract number: AY4476A, 2004.
8. IST-IMETRA, URL: http://www.ist-imetra.org/
9. Airgo, URL: http://airgo.wdwconsulting.net/mymoin last update: 2013.
10. Netgear, URL: http://www.netgear.com/.
11. Linksys, URL: http://www.linksys.com/.
12. Belkin, URL: http://www.belkin.com/.
13. Ruckus Wireless, URL: http://www.ruckuswireless.com/.
14. D. Tse and P. Viswanath, Eds., Fundamentals of Wireless Communication. Cambridge University Press, 2005.
15. T. S. Rappaport, Ed., Wireless communications: principles and practice. Prentice Hall, 2002.
16. J. Proakis, Ed., Digital Communications. McGraw-Hill 4 edition, 2000
17. ] S. SEKINE, H. SHOKI, and H. MORISHITA, Antennas for wireless terminals IEICE transactions on 2003,".
18. A multi-band and multi-functional small handset (t68i) produced by sony ericsoon in 1999," in http://www.sonyericsson.com/, Sony Ericsson, 1999.
19. J. D. Kraus and R. J. Marhefka, Eds., Antennas. McGraw-Hill Science, 3rd Edition, 2001.
20. Mobile WIMAX - Part I: A Technical Overview and Performance Evaluation, WIMAX Forum 2009, http://www.wimaxforum.org
21. K. Hirasawa and M. Haneishi,Measurement of Small and Low-Profile Antennas. Artech House, 1992.
22. Blanch S., Romeu J., Corbella I., Exact representation of antenna system diversity performance from input parameter description, Electronic Letters, Vol. 39, No. 9,May 2003, pp.705-707.
23. Z. Ying and D. Zhang, Study of the mutual coupling, correlations and efficiency of two pifa antennas on a small ground plane," in IEEE Antennas and Propagation Society International Symposium, vol. 3B, July 2005.
24. C. C. Chiau, Study of the diversity antenna array for the mimo wireless communication systems," in a thesis of Queen Mary University of London for the degreeof Doctor of Philosophy, 2006.
25. Braun C., Engblom G., Beckman C., Evaluation of Antenna Diversity Performance for Mobile Handsets Using 3-D Measurement Data, IEEE Trans. On Antennas and Propagation, Vol. 47, No.11, November 1999, pp.1736-1738.

210-215

44.

Auths:

N. Venkatesan, N. Chandru

Paper Title:

Student's Performance Measuring using Assistant Algorithm

Abstract: The main theme of school education management is always willing to impart quality education to its students. In this paper we focused higher level to apply Data Mining techniques for implementing and give a quality in technical education. Many ways to achieve to the highest level of quality in technical education as well as increase the student's academic performance and predict that performed and underperformed of the student's for providing training and placement. In our work the data set can be prepared from student's academic's (technical training or higher education) like a student's roll no., student's name, student’s date of birth, student's10th, student's 12th, academic percentage upto 7th semester student's database have to take. In this paper we propose to create a data set for five departments , we have taken (CSE, IT, ECE, EEE, MECH) each department 10 students were taken in preparing the data set , after preprocessing the data set final data can be obtained for training and placement from performing and underperformed student's from each department . In Educational Data Mining knowledge is hidden we can retrieve the knowledge through data mining techniques. By this process we extract knowledge that measure student's performance at the end of the semester examination. It helps earlier in identifying performed and underperformed student's who needs a special attention in academic wise and based on that we give training and placement for the student's. Classification method , decision tree , ASSISTANT algorithm were used In future this study will be assisted to develop new concepts of data mining techniques in technical education

Keywords: Data Mining, discover knowledge, Technical Education, Knowledge Discovery in databases

References:
1. Heikki, Mannila, Data mining: machine learning, statistics, and databases, IEEE, 1996.
2. J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000
3. Alaa El-Halees, “Mining students' data to analyze e-Learning behavior: A Case Study”, 2009..
4. Shaeela Ayesha, Tasleem Mustafa, Ahsaan Raza star, M Inayat Khan-“Data mining Model for higher education” ISSN 1450-216X Vol.43 No.1 (2010) pp24-29
5. Thearling, K., “An Introduction to Data Mining” ,http://thearling.com/text/dmwhite/dm
6. Westphal, C., Blaxton, T., 1998. Data Mining Solutions, John Wiley.
7. www.educationaldatamining.org
8. U . K. Pandey, and S. Pal, “Data Mining: A prediction of performer or underperformer using classification”, (IJCSIT) International Journal of Computer Science and Information Technology, Vol. 2(2), pp.686-690, ISSN:0975-9646, 2011.
9. S. T. Hijazi, and R. S. M. M. Naqvi, “Factors affecting a student’s performance: A Case of Private Colleges”, Bangladesh e-Journal of Sociology, Vol. 3, No. 1, 2006.
10. Z. N. Khan, “Scholastic achievement of higher secondary students in science stream”,Journal of Social Sciences, Vol. 1, No. 2, pp. 84-87, 2005.
11. Galit.et.al, “Examining online learning processes based on log files analysis: a case study”. Research, Reflection and Innovations in Integrating ICT in Education 2007.

216-222

45.

Auths:

Mohamed Rafi, Hédi Khammari, R.S.D Wahidabanu, Yasmeen Taj

Paper Title:

A Model Based Approach for Gait Recognition System

Abstract: In this paper, we propose a model based approach for gait recognition using the mathematical theory of geometry and image processing techniques. In such approach, feature matrices used for gait recognition are constructed using segmentation, Hough transform and corner detection techniques. Indeed, it is possible to recognize a subject by analyzing the gait parameters extracted from his footsteps taken in different frames. In the preprocessing stage, the picture frames taken from video sequences are inputted to Canny Edge detection algorithm in order to detect the image edges and to reduce the noise by means of Gaussian filtering. The Hough transform is then applied to isolate the features of the preprocessing output and to get a gait model. The latter is used to extract the gait parameters, and the Harris Corner Detection technique is used to detect the corners and to generate the feature points. The gait parameters are measured by means of feature points and then stored in a gait database. Using a gait recognition interface the random subjects parameters are compared against a template set in the available database for recognition. In the proposed method, we have considered a database including ten subjects and a five parameters based gait recognition system. It is worth noting to remark that when the camera is placed at 90 and 270 degrees towards the subject, all the recognition parameters are clearly visible, measurable and lead to have more than 80% accuracy in recognition results.

Keywords: Biometric, Gait recognition, Canny Edge Detection, Hough Transform, Harris Corner Detection

References:
1. Mridul Ghosh, Debotosh Bhattacharjee, “Human Identification by Gait Using Corner Points”, I.J. Image, Graphics and Signal Processing, 2012, 2, pp 30-36
2. Imed Bouchrika and Mark S. Nixon, “Model-Based Feature Extraction for Gait Analysis and Recognition” AGagalowicz and W. Philips (Eds.):
3. MIRAGE 2007, LNCS 4418, Springer-Verlag Berlin Heidelberg 2007, pp. 150–160, 2007.
4. L. Jiwen, E. Zhang, ”Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion”, School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, 25 July 2007.
5. Aristotle (350BC), “On the Gait of Animals”, Translated by A. S. L. Farquharson, 2007,http://etext.library.adelaide.edu.au/a/aristotle/gait/.
6. S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. Bowyer, “The humanID gait challenge problem: Data sets,performance and analysis”, IEEE Trans.Pattern Anal. Machine Intelligence., vol. 27, no. 2,pp. 162–177, Feb. 2005.
7. James, H. Acquah, M. S. Nixon, J. N. Carter, ”Automatic gait recognition by symmetry analysis”, Pattern Recognition letters , Spronger, 2003, pp 2175-2183Mark ruane, “Gait Recognition” Final Report, Imperial College, pp 1-75
8. X. Han, ”Gait Recognition Considering Walking Direction”, University of Rochester, USA, August 20, 2010.
9. L. Wang, T. Tan, H. Ning, and W. Hu, “Silhouette Analysis-Based Gait Recognition for Human Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, December 2003,pp 1505-1518
10. Ai-Hua Wang, JiI-Wei Liu, “A gait recognition method based on positioning human body joints”, IEEE Wavelet Analysis and Pattern Recognition, 2007, Volume: 3, pages 1067 - 1071
11. Yanmei Chai Jinchang Ren, Rongchun Zhao and Jingping Jia, “Automatic Gait Recognition using Dynamic Variance Features”, Automatic Face & Gesture recognition, IEEE 2006, Pages 475-480
12. M. Rafi, M. E. Hamid, M. Samiullah, and R.S.D Wahidabanu “A Parametric Approach to Gait Signature Extraction for Human Motion Identification”, International journal of Image Processing,CSC Press, vol. 5, issue 2, 2011, pp 1-14
13. J. Canny, “A computational approach to edge detection,” IEEE Trans.Pattern Analysis and Machine Intelligence, vol. 8, 1986, pp. 679–714
14. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the 4th Alvey Vision Conference, 1988, pp. 147–151.
15. Mohamed Rafi, Ekram Hamid, Wahidabanu, “Pedestrian Gait Parameter Measurement Using a Cascade of Edge and Corner Detection Technique”, Journal of Computer technology & Applications, STM journals, Volume 2, Issue 2, Nov 2011, pages 37-48

223-228

46.

Auths:

Nihar M. Ranjan, Rahul Pardeshi, Piyush Bhattad, Pavan Shah, Nirav Shah

Paper Title:

Helping Hands: Enabling the Disabled

Abstract: A major community in the world population is of physically handicapped and blind people. These people cannot access the computers due to their disabilities. There is a need of some technology which allows these people to get access of the mouse and keyboard without using hands and eyes. This paper gives us the brief introduction of a technology which is intended to help handicapped in getting access of the basic technologies. This technology uses the head mouse technology by which the real time head movements of the computer user is translated into a directly proportional cursor movement of the computer mouse. This technology also provides an interface for the user to handle the click event of the computer. A handicapped user can access the mouse of the computer without using hands. The other module of this system is the speech recognition technology which allows the computer user to give commands to the system by using the keywords specified in the natural language. A blind user can give speech commands to the computer allowing him to access different applications of the computer. This system also provides a screen reader to the user which can read different documents, internet files, emails of the user.

Keywords: A handicapped user can access the mouse of the computer without using hands.

References:
1. Head Pose Estimation for Driver Assistance Systems:
2. A Robust Algorithm and Experimental Evaluation Erik Murphy-Chutorian, Anup Doshi, and Mohan Manubhai Trivedi Computer Vision and Robotics Research Laboratory University of California, San Diego
3. HeadMouse: Robotic Research Team University of Lleida.
4. Gyro-Mouse for the Disabled: ‘Click’ and ‘Position’ Control of the Mouse Cursor Gwang-Moon Eom, Kyeong-Seop Kim, Chul-Seung Kim, James Lee, Soon-Cheol Chun Bongsoo Lee, Hiroki Higa, Noria Furuse, Ryoko Futami and Takashi Watanabe

229-230

47.

Auths:

Amel SERRAT, Mohamed BENYETTOU

Paper Title:

GA-SVM and MLP-BBO to estimate Robot Manipulator Joint Angles

Abstract: The kinematic of serial manipulators comprises the study of the relations between joint variables and Cartesian variables. We distinguish two problems, commonly referred to as the direct and inverse kinematic problems. The former reduces matrix multiplications, and poses no major problem. The inverse kinematic problems, however, is more challenging, for it involves intensive variable-elimination and nonlinear-equation solving. In this work, we have used Support Vector Machine with Genetic Algorithm and and Multi Layer Perceptron (MLP) with Biogeography-Based Optimization(BBO) to solve the inverse problem on a manipulator arm, to determine its various articulations. The results of simulation are presented to show the validity of approaches suggested above.

Keywords: Support Vector Machine, Genetic Algorithm, BBO algorithm, MLP, inverse kinematic, minimally invasive surgery.

References:
1. R. Kolodny, L.Guibas , M. Levitt , P. Koehl.:Inverse kinematics in biology: The protein loop closure problem. Int. J. Robot. Res., vol. 24, no. 2-3, 2005, pp. 151–163.
2. H.Van den Bedem , I.Lotan,J. Latombe , A. M. Deacon: Realspace protein-model completion: an inverse-kinematics approach. Acta Crystallographica, vol. D61, 2005, pp. 2–13.
3. J. M.Porta, L.Ros, F. Thomas.: Inverse Kinematics By Distance Matrix Completion. Proceedings of CK2005, 12th International Workshop on Computational Kinematics Cassino May 4-6, 2005.
4. C. Hua, C. Wei-shan, T. Xie:Wavelet network solution for the inverse kinematics problem in robotic manipulator. Journal of Zhejiang University SCIENCE A, 2006.
5. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1476144/ accessed 17/10/2013.
6. M.Ghodousssi M., Butner S., Wang Y.: Robotic surgery – the transatlantic case. In Proceedings of the 2002 IEEE International Conference on Robotics and Automation, 2002.
7. Preumont : Théorie Générale des Systèmes Articulés. 2nd Edition , UBR, 2001.
8. P. Gupta and K. Shinde.:Genetic Algorithm Technique Used to Detect Intrusion Detection, Proc. of the 1st International Conference (ACITY 2011),July 2011, pp. 122-131.
9. V.Vapnik .: The Nature of Statistical Learning Theory , Springer, New York,1995.
10. K. P. Bennett. and O.L. Mangasarian.:«Robust linear programming discrimination of two linearly inseparable sets». Optimization Methods and Software,1992, pp 1,23-34.
11. A.J. Smola . and B. Scholkopf .:A Tutorial on Support Vectors regression». NeuroColt2,1998.
12. McCormick G. P.: Nonlinear Programming: Theory, Algorithms, and Applications. John Wiley and Sons, New York,1983.
13. L. Miclet L.: Apprentissage Artificiel : Méthodes et Algorithmes. Eyrolles, Novembre, 2002.
14. H.W. Kuhn and A. W. Tucker,: Nonlinear programming. In Proc. 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, Berkeley, University of California Press. 1951, pp 481-492
15. N.Leonard de Castro, F. J. Zuben.:Learning and Optimization Using the Clonal Selection Principle, IEEE Transaction on Evolutionary Computation, vol. 6. no. 3, 2002.
16. D. Simon;: Biogeography-based optimization. IEEE Trans. on Evolutionary Computation. 12, 6,2008, pp. 702–713.
17. Wallace.: The Geographical Distribution of Animal. Adament Media Corporation, 2006 (first published in 1876).
18. Darwin . : The origin of species. Gramercy 1995 (first published in 1859).
19. V. Panchal, P.Singh, N. Kaur , H. Kundra.:Biogeography based satellite image classification. International Journal of Computer Science and Information Security, vol. 6, no. 2, 2009, pp. 269-274.

231-234

48.

Auths:

B. Achiammal1, R.Kayalvizhi2

Paper Title:

Optimal Tuning of PI Controller using Bacterial Foraging Algorithms for Power Electronic Converter

Abstract: DC-DC converters are widely used in application such as computer peripheral power supplies, car auxiliary power supplies and medical equipments. Positive output element Luo converter performs the conversion from positive source voltage to positive load voltage. Due to the time- varying and switching nature of the power electronic converters, their dynamic behaviour is highly non-linear. Conventional controllers are incapable of providing good dynamic performance and hence optimized techniques have been developed to tune the PI parameter. In this work, Bacterial Foraging (BF) algorithm and Modified Bacterial Foraging (MBF) algorithm are developed for PI optimization. Simulation results show that the performances of BF-PI and MBF-PI controllers are better than those obtained by the classical ZN-PI controller.

Keywords: PID controller, DC-DC converter, positive elementary Luo converter, Bacterial Foraging Algorithm, Modified Bacterial Foraging Algorithm

References:
1. Luo ,F.L.: “Positive output Luo-converter:voltage lift Technique”,IEE-EPAprocessdings,146(4),July 1999,pp.415-432.
2. E.Mezura-Montes and B.Hernandez-Ocana, “Modified bacterial foraging optimization for engineering design”, in proceedings of the Artifiical Neural Netowrks in Engineering Conference (ANNIE 2009), ser. Intelligent Engineering Systems Through Artificial Neural Networks, C.H.Dagli and et al., Eds., Vol.19. St.Louis, MO, USA:ASME Press, November 2009, pp.357-364.
3. Janardan Nanda, S. Mishra, and Lalit Chandra Saikia, “Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control”, IEEE Transactions on Power systems, Vol.24, No 2, May 2009.
4. J.F.HUANG, F.B.DONG,"Modelling and Control on Isolated DC-DC Converter", Power Electronics, voI.44, pp.87-89, April 2010.
5. M.Namnabat, M.Bayati Poodeh, S.Eshtehardiha “Comparison the Control Methods in Improvement the Performance of the DC-DC Converter,” International Conference on Power Electronics 2007, (ICPE’07), pp.246-251, 2007.
6. Martin Plesnik., “Use of the State space averaging technique in fast steady state simulation algorithms for switching power converters” ieeexplore.ieee.org CCECE’2006.
7. J.YOU, S.B.KANG , "Generalized State Space Averaging based PWM Rectifier Modeling", Electrical Measurement &Instrumentation vo1.46, pp.67-70, October 2009.
8. Visioli A. “Optimal Tuning of PID Controllers for Integral and Unstable Processes”, IEEE proceedings-Control Theory & Application,2001 ,148(2):pp.180—184.
9. R.Suresh Kumar and J.Suganthi, “Improving the Boost Converter PID Controller performance using particle Swarm Optimization”, European Journal of Scientific Research, Vol.85 No.3 September, 2012, pp.327-335.
10. Kevin M.Passino, “Biomimicry of Bacterial foraging for Distributed Operation and Control”, IEEE control systems Magazine, June 2002.

235-239

49.

Auths:

M.L.Dongare

Paper Title:

Suspended Solid Measurement System with Two Frequency Four Beam Technique for Distillery Spent Wash

Abstract: An attempt has been made to measure suspended particles in the effluent from Sugar factories and distilleries. Waste water generation and its subsequent treatment is major problem for distillery industry and society as well. The effluent is generated in large amount and therefore prior to treatment, the effluent need to be monitored so as to permit their discharge into the local water sources or in a land. The comparison of conventional method used in distillery for the measurement of suspended solid and outcome of experimental procedure used is discussed in this paper. Basically, the incident light source, detection angle of detector, and ratioing are the important aspects which affects on optimization, performance and consistency of the measurement system. These aspects are concentrated mainly for precise measurement. Intensity of light scattered by the sample under defined condition with the intensity of light transmitted by the standard reference suspension under the same condition is studied. The conventional suspended solid measurement requires 4-5 hours where this system may take 4-5 minute. This technique will offer the potential for reliable for distillery industrial process measurement, and to follow the standard environmental norms prescribed by regulatory authority before depriving from distillery column, which would otherwise require for the pretreatment and controlling in the said industry.

Keywords: Spent Wash, Scattering, Modulated light, four beam , band pass and absorption.

References:
1. Dr Y.S.Nerkar,VSI,Manjari(BK) Pune,)International level short term training programme for Distillery personal from Colombia south America412307.
2. Uppal J2004 water utilization and effluent treatment in the Indian alcohol industry –an overview(Indo EU workshop on promiting Efficient water use in agro-based industries, New Delhi15-16Jan2004.
3. Questionnaire survey of Indian distilleries conducted by TERI(The Energy and Resources institute)and AIDA(all India Distillery association
4. Manohar Rao,P.J.Energy conservation and alternative sources of energy .pp561 to 563.
5. Hobart H.Willard,Lynne.L.Merritt Jr,John.A.Dean,Frank.A.Settle Jury Instrumental Methods of analysis., CBS publishers and distributors ,Delhi. Sixth editionPP90 to 94. ,(1986),
6. M.Sadar,Turbidimeter Instrumentation Comparision:Low level sample Measurements,Hach company Techenical information series, Loveland ,Colorado. 1999.
7. N.C.Varma,System of Technical control for cane sugar factories in India, Publish by The sugar Technologist Assosiation Of India Kanpur India (1988) pp88,89.
8. Arnold .E. Greenberg, Lenore. S. Clesceri, American Publisc Health association (APHA), Standrad Methods For Examination of Water &Waste water American Waterwork association (AWWA), Water Environment Federation (WEF).physical and aggregate properties),approved by standard methode comittee.(1988),2130A to 2540G. (2000) Allan Whitaker, Stephen.J.Hall Principles of fermentation Technology Peter F.Stanbury (U.K), University of Hertfordshire.pp313-326.
9. K.Jacques,T.P.Iyons,Dr.Kelsa The Alcohol Text book 3rd Edition A reference for beverage fuel & Industrial alcohol Industries, ll310-311.Ramakant Gaikwad(1993) OpAmps and linear Integrated circuits 3rd edition PHI

240-243