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Volume-1 Issue-4: Published on March 15, 2013
Volume-1 Issue-4: Published on March 15, 2013

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Volume-1 Issue-4, March 2013, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



S. Angeline Julia, N. Snehalatha, Paul Rodrigues

Paper Title:

Comparison of Software Architecture Styles Using Quality Attributes

Abstract: Every software system has an architecture because every system can be shown to be composed of components and relations among them. The Software architecture of a program is the structure of the system, which comprise software elements, the externally visible properties of those elements, and the relationships among them. Architecture styles plays a dominant role in solving complex systems. Here we analyzed these architecture styles using the quality attributes and the survey is given. From our analysis we conclude that software architecture which is flexible is very important in developing complex distributed applications.

Software Architecture, Styles, Quality Attributes.


1.        David Garlan and Mary Shaw,” Software Architecture perspectives on an emerging discipline” Prentice Hall of India private limited.
2.        David Garlan and Mary Shaw,” An Introduction to Software Architecture”.

3.        Len Bass, Paul Clements, Rick Kazman,” Software Architecture in practice” Addison-Wesley Longman, Inc.

4.        Emad Majidi,Mahdieh Alemi, Hassan Rashidi, “Software Architecture: A Survey and Classification”, 2010 Second International Conference on Communication Software and Networks,2010 IEEE.

5.        Francisca Losavio, Nicole Levy “Quality characteristics for Software Architecture”, Journal of Object Technology, 2003.

6.        Mikael Svahnberg, Claes Wohlin “ A Comparative Study of Quantitative and Qualitative Views of Software Architectures” Proceedings EASE : Emprical Assessment and Evaluation in Software Engineering,Keele,UK,2003.

7.        Roy Thomas Fielding “Architectural Styles and the Design of Network-based Software Architectures” 2000.


9. architecture








A. Brillia, D. Jagadiswary, R. Muthu Venkata Krishnan

Paper Title:

Implementing Cryptographic Techniques in Message Passing Interface Systems

Abstract: In the concept of Message Passing Interface (MPI) chatting and file transmission the decryption part will be done automatically. Here three types of keys are used; they are public, private and secret key. Keys are displayed to the destination only if they accept the request or else displaying of key is not possible in the destination side and also it won’t give or establish the Connection. In largely spread clusters, computing nodes are naturally deployed in a variety of computing sites. The Information processed in a spread cluster is communal among a group of distributed processes or client by high-quality of messages passing protocols (e.g. message passing interface - MPI) running on the Internet. Because of the open available nature of the Internet, data encryption for these large-scale distributed clusters becomes a non-trivial and challenging problem. We improved the security of the MPI protocol by encrypting and decrypting messages sent and received among computing nodes. We are listening carefully on MPI rather than more protocols because MPI is one of the most accepted communication protocols for cluster computing environments. From among a multiple of MPI implementations, we selected MPICH2 developed by the Argonne National Laboratory. Design goal of MPICH2 - a commonly use MPI implementation - is to join portability with high presentation. we gives a security enhanced MPI-library with the standard MPI interface, data communications of a conservative MPI program can be secured without converting the program into the corresponding secure report. We included encryption algorithms into the MPICH2 library so that data in secret of MPI applications could be readily preserved without require modifying the source codes of the MPI applications. This system use Sandia Micro Benchmark and Intel MPI Benchmarks to evaluate and compared the performance of original MPICH2 and Enhanced Security MPICH2. According to the performance estimation, ES-MPICH2 provides protected Message Passing Interface by give up sensible system performance.

Secret key, Encryption, MPI, Parallel Computing, Cryptosystem.


1.        Darrel Hankerson, Julio Lopez Hernandez, Alfred Menezes, Software Implementation of Elliptic Curve Cryptography over Binary Fields, 2000.
2.        M. Brown, D. Hankerson, J. Lopez, A. Menezes, Software Implementation of the NIST Elliptic Curves Over Prime Fields, 2001.

3.        Certicom, Standards for Efficient Cryptography, SEC 1: Elliptic Curve Cryptography, Version 1.0, September 2000

4.        Certicom, Standards for Efficient Cryptography, SEC 2: Recommended Elliptic Curve Domain Parameters, Version 1.0, September 2000.

5.        Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, Handbook of Applied Cryptography, CRC Press, 1996

6.        D.E. Denning, “Secure Personal Computing in an Insecure Network,” Comm. ACM, vol. 22, no. 8, pp. 476-482, 1979.

7.        J.J. Dongarra, S.W. Otto, M. Snir, and D. Walker, “An

8.        Introduction to the Mpi Standard,” technical report, Knoxville, TN, 1995.

9.        W. Ehrsam, S. Matyas, C. Meyer, and W. Tuchman, “A Cryptographic Key Management Scheme for Implementing the Data Encryption Standard,” IBM Systems J., vol. 17, no. 2, pp. 106- 125, 1978.

10.     I.F. Blake, G. Seroussi, and N.P. Smart, Elliptic Curves in Cryptography. Cambridge Univ. Press, 1999.

11.     ] I. Foster, N.T. Karonis, C. Kesselman, G. Koenig, and S. Tuecke, “A Secure Communications Infrastructure for High-Performance Distributed Computing,” Proc. IEEE Sixth Symp. High Performance Distributed Computing, pp. 125-136, 1996

12.     A. Geist, W. Gropp, S. Huss-Lederman, A. Lumsdaine, E.L. Lusk, W. Saphir, T. Skjellum, and M. Snir, “Mpi-2: Extending the Message-Passing Interface,” Proc. Second Int’l Euro-Par Conf. Parallel Processing (Euro-Par ’96), pp. 128-135, 1996.

13.     R. Grabner, F. Mietke, and W. Rehm, “Implementing an mpich-2 Channel Device over Vapi on Infiniband,” Proc. 18th Int’l Parallel and Distributed Processing Symp., p. 184, Apr. 2004.

14.     W. Gropp, E. Lusk, N. Doss, and A. Skjellum, “A High- Performance, Portable Implementation of the Mpi Message Passing Interface Standard,” Parallel Computing, vol. 22, no. 6, pp. 789-828, 1996.

15.     P. Hamalainen, M. Hannikainen, T. Hamalainen, and J. Saarinen, “Configurable Hardware Implementation of Triple-des Encryption Algorithm for Wireless Local Area Network,” Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing (ICASSP ’01), pp. 1221-1224, 2001.

16.     G.A. Koenig, X. Meng, A.J. Lee, M. Treaster, N. Kiyanclar, and W. Yurcik, “Cluster Security with Nvisioncc: Process Monitoring by Leveraging Emergent Properties,” Proc. IEEE Int’l Symp. Cluster Computing and Grid (CCGrid ’05), 2005.

17.     M. Lee and E.J. Kim, “A Comprehensive Framework for Enhancing Security in Infiniband Architecture,” IEEE Trans. Parallel Distributed Systems, vol. 18, no. 10, pp. 1393-1406, Oct. 2007.

18.     J. Liu, W. Jiang, P. Wyckoff, D.K. Panda, D. Ashton, D. Buntinas, W. Gropp, and B. Toonen, “Design and Implementation of Mpich2 over Infiniband with rdma Support,” Proc. 18th Int

19.     Greg Burns, Raja Daoud, and James Vaigl. LAM: An open cluster environment for MPI. In John W. Ross, editor, Proceedings of Supercomputing Symposium ’94, pages 379–386. University of Toronto, 1994.




Riya Mary Thomas

Paper Title:

Survey of Bacterial Foraging Optimization Algorithm

Abstract: Bacterial Foraging Optimization Technique is used in optimization for grid computing as they get their inspirations from evolutionary idea of natural evolution. It has been broadly accepted as a global optimization algorithm of current interest for distributed optimization and control. This algorithm is inspired by the social foraging behavior of Escherichia coli. Bacterial Foraging Optimization (BFO) algorithm is a novel evolutionary computation algorithm. It is proposed based on the foraging behavior of the Escherichia coli (E. coli) bacteria living in the human intestine. The BFO algorithm is a biologically inspired computing technique. This paper presents a broad overview on the formalization of works contributed by Bacterial Foraging Optimization Algorithm to the field of grid scheduling.

Bacterial Foraging Optimization Algorithm, Grid Computing, Chemotaxis.


1.        Ajith Abraham, Hongbo Liu, Crina Grosan, Fatos Xhafa, Nature   Inspired   Meta-heuristics for Grid Single and Multi-objective Optimization Approaches, Centre for Quantifiable Quality of Service in Communication Systems.
2.        R. Buyya, Economic-based distributed resource management and scheduling for grid computing, Ph.D. Thesis,   Monash University, Australia, 2002.

3.        Dong Hwa Kim,  Ajith Abraham,  Jae Hoon Cho A hybrid genetic algorithm and bacterial foraging approach for global optimization, Information Sciences 177 (2007) 3918–3937

4.        Samaneh Zareh, Hamid Haj Seyedjavadi, Hossein Erfani, Grid Scheduling using Cooperative BFO Algorithm, American Journal of Scientific Research ISSN 1450-223X Issue 62(2012), pp.78-87.

5.        R. Vijay, Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012.

6.        Swagatam Das, Arijit Biswas, Sambarta Dasgupta, and Ajith Abraham, Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications.

7.        Sastry. V.R.S. Gollapudi, Dr. Shyam.S.Pattnaik1,Dr.O.P.Bajapai,Smt. Swapna Devi, K.M.Bakwad and Patra.K Pradyumna, Intelligent Bacterial Foraging Optimization Technique to Calculate Resonant Frequency of RMA, International Journal of Microwave and Optical Technology, Vol.4, No.2, March 2009.

8.        W. J. Tang, Q. H. Wu, Senior Member, IEEE, and J. R. Saunders ,Bacterial Foraging Algorithm For Dynamic Environments, IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006.




D. Aravind, K. Soujanya, T. Naveen Kumar

Paper Title:

Ann Based SVC Switching At Distribution Level for Minimal Injected Harmonics

Abstract: Electrical distribution system suffers from various problems like reactive power burden, unbalanced loading, voltage regulation and harmonic distortion. Though DSTATCOMS are ideal solutions for such systems, they are not popular because of the cost and complexity of control involved. Phase wise balanced reactive power compensations are required for fast changing loads needing dynamic power factor correcting devices leading to terminal voltage stabilization. Static Var Compensators (SVCs) remain ideal choice for such loads in practice due to low cost and simple control strategy. These SVCs, while correcting power factor, inject harmonics into the lines causing serious concerns about quality of the distribution line supplies at PCC. This paper proposes to minimize the harmonics injected into the distribution systems by the operation of TSC-TCR type SVC used in conjunction with fast changing loads at LV distribution level. Fuzzy logic system and ANN is used to solve this nonlinear problem, giving optimum triggering delay angles used to trigger switches in TCR. The scheme with Artificial Neural Network (ANN) is attractive and can be used at distribution level where load harmonics are within limits.

ANN, Fuzzy logic control, Harmonic distortion, Reactive power, Static Var Compensators.


1.        George J. Wakileh, Power system harmonics, fundamentals, analysis and filter design, New York, Springer-Verlog Berlin Heidelberg, 2001, pp 81-103.
2.        IEEE recommended practices & requirements for harmonic control in electrical power systems, IEEE 519 standard, 1993.

3.        Arindam Ghosh and Gerard Ledwich, Power quality enhancement using custom power devices, London, Kluwer Academic Publishers, 2002, pp 55-111.

4.        B. Singh, K. A. Haddad and Ambrish Chandra, "A review of active filters for power quality improvement", IEEE trans. Industrial Electronics, Vol. 46, No.5, Oct.99.

5.        A.Elnady and Magdy M.A.Salama,"Unified approach for mitigating voltage sag and voltage flicker using the DSTATCOM", IEEE trans. Power Delivery, Vol.20, No.2, April 2005.

6.        D. Thukaram., A. Lomi and S Chirarttananon, "Minimization of harmonics under three phase unbalanced operation of static VAR compensators", Proceedings of the 12th International Conference on Power Quality, Chicago, U.S.A., 1999.

7.        Athula Rajapakse, Anawat Puangpairoj, Surapong Chirarattananon, and D.Thukaram, "Harmonic Minimizing Neural Network SVC Controller for Compensating Unbalanced Fluctuating loads", 10th International Conference on Harmonics & quality of power 2002, vol.2, pp 403-408, Oct. 2002,..

8.        Gaber El-Saady, "Adaptive Static VAR controller for simultaneous elimination of voltage flickers and phase current imbalances due to arc furnaces loads", Electric Power Systems Research, vol.58, ppl33-140, 2001

9.        D.B. Kulkarni and G.R. Udupi, "Harmonic minimizing fuzzy logic controller for SVC used for fluctuating loads", Proceedings of National Power Systems conference, I I T, Roorkee, India, December 2006.

10.     D.B. Kulkarni and G.R. Udupi "SVC operation for optimal demand distortion at LV feeders", proceedings of International conference on Power Systems (ICPS 2007), CPRI, Bangalore, India, December 2007.

11.     D. Driankov, H. Hellendoorn and M. Reinfrank, An introduction to fuzzy control, New Delhi, Narosa Publishing House, 2001.

12.     S. Rajasekaran and G.A.Vijaylakshmi Pai, Neural networks, fuzzy logic and genetic algorithms, New Delhi, P.H.I., 2003, pp 11-85.




S. M. Sorte, S. M. Sheikh

Paper Title:

Stress Analysis And Design Optimization Of Crankpin

Abstract: The stress analysis and design optimization of a single cylinder crankpin of TVS Scooty Pep crankshaft assembly are discussed using stress analysis in this paper. Three-dimension models of crankshaft and crankpin forces were created using Pro/ENGINEER software and software ANSYS was used to analyze the stress status on the crankpin. The maximum deformation, maximum stress point and dangerous areas are found by the stress analysis. The relationship between the crank rotation and load acting on crank pin would provide a valuable theoretical foundation for the optimization and improvement of crankpin and engine design. [2]

Stress analysis; crankshaft; crankpin.


1.        “A Generalized Methodology for the Analysis and Simulation of Four Stroke Diesel Engine Crankshaft”   Vol.1,Issue.X/April 2012
2.        Jian Meng “Finite Element Analysis of 4-Cylinder Diesel Crankshaft” Published Online August 2011 in MECS

3.        Mohammad Reza Asadi “Analysis of Stress in the Nissan Z-24 Moulding Crankshaft “Australian Journal of Basic and Applied Sciences, 5(12): 798-803, 2011

4.        H. D. Desai “Computer Aided Kinematic and Dynamic Analysis of a Horizontal Slider Crank Mechanism Used for Single-Cylinder Four Stroke Internal Combustion” , Paper published 2009 –

5.        M.A.Alfares”Failure analysis of a vehicle engine crankshaft”, Paper published 2007 in ASM international –

6.        Farzin H. Montazersadgh and Ali Fatemi “Dynamic Load and Stress Analysis of a Crankshaft” Published 2007 SAE International

7.        M. A. Alfares Æ A. H. Falah Æ A. H. Elkholy “Failure Analysis of a Vehicle Engine Crankshaft” Submitted: 24 June 2006 / in revised form: 1 December 2006 / Published online: 17 March 2007 ASM International 2007

8.        Anthony P. Sime, B.Eng “Stress analysis of overlapped crankshafts”  Thesis submitted to the University of Nottingham For the degree of Doctor of Philosophy October1998

9.        EstimationV. Prakash and K. Aprameyan”An FEM Based Approach to Crankshaft Dynamics and Life” Bharat Earth Movers Ltd... SAEInternational Congress and Exposition Detroit, MichiganFebruary 23-26, 1998

10.     ” FEM based approach to  Crankshaft dynamics and life estimation “Paper published 1998 SAE  The variation of life of crankshafts due to changes in their material properties.

11.     R.S.Khurmi, J.K.Gupta” Text book of Machine Design” page no.1125 -1200.




Gajendra Singh Chandel, Ankesh Bhargava

Paper Title:

Identification of People by Iris Recognition

Abstract: Biometrics is a best medium of identification .iris recognition used to recognize people by iris. I present the new method of iris recognition “iris recognition by neural network”. In this method first we collect the iris images and using image processing after this calculate the length of iris from left to right and top to bottom. Finally we use neural network for training and testing purpose .We have selected training algorithm and setting different parameter for training. CASIA iris database used in this work. Many types training and testing we get different results. We get best accuracy is 97.5%.

Biometrics, iris recognition, neural network, Feature extraction.


1.        Zhifang wang, Qi Han, Xiamu niu and Christoph Busch.” A Novel templet Protection Algorithm for Iris Recognition”. 978-0-7695-3382-7/08 2008 IEEE
2.        Khin Sint Sint kyaw “iris recognition system using statistical features for Biometric identification”2009 international conference on electronic computer technology.978-0-7695-3559-3/09 2009 IEEE.

3.        Farrukh Sayeed, M Hanmandlu, A.Q.Ansari and Shantaram Vasikarla “Iris recognition using segmental Euclidean Distances” 2011 eight International Conference on Information Technology: new Generations.

4.        Zhonghua Lin “A Novel iris recognition Method based on the natural – open eyes” 978-1-4244-5900-1/10 IEEE.

5.        C.tisse, L.Martin, L.Torres, And M.robert, “person identification technique using iris recognition”, in proc.Vision interface, pp.294-299, 2002.

6.        A.k.jain,p.Flynn,A.Ross , “Handbook of biometrics”, Springer, ISBN-13:978-0-387-71040-2,2008

7.        J.daugman, “How iris recognition works”, IEEE Trans, Circuits and Systems for Video Technology, vol 14, issue 1, pp.21-30.jan.2004.

8.        L.Ma, Y.Wang.T.Tan. “iris recognition using circular symmetric filters.” National Laboratory of pattern recognition, institute of Automation, Chinese Academy of sciences, 2002

9.        L.Flom, A.Safir, “Iris recognition system, U.S.” patent 4641349, patent and trademark office, Washington, DC, 1987.

10.     Mohammed A.M.Abdullah ,F.H.A. Al-Dulaimi,Waleed Al-Nuaimy ,Ali Al-Ataby “smart card with iris recognition for high security access environment” 9781-4244-7000-6/11 2011 IEEE

11.     Feed forward network, backprapogation, Neural Network Toolbox in MATLAB.

12.     Zhou Jianhong, Parvinder S. Sandhu, Seema Rani, “A neural based approach for modeling of severity of defects in function baesd software system”, International Conference on Electronics and Information Engineering (ICEIE 2010)




Jijy George, Sandhya N, Suja George

Paper Title:

Search On Web- From Classical Web to Semantic Web

Abstract: The WWW is a vast information resource with enormous potential. The retrieval of relevant information from the web is a major issue because it is difficult for the machines to process and integrate the information.  Internet is growing very fast as pages are added in a very fast pace. Searching on the web for a specific topic results in hundreds of pages and it is up to the user to extract the useful information from the result set. This paper presents insight on how current search engine works and also the potential gain of using current search engines. This paper further gives an overview of the challenges surrounding current search techniques and looks at the need of an intelligent information retrieval system on web. This paper also reviews the foundations required to make the search engine an intelligent one and also gives an insight on concepts like metadata, RDF, URI, XML, triples and ontologies.

Semantic web, RDF, XML, Triples, Ontologies.


1. tp=&arnumber=5655402&
2.        Search Engine Tutorial. Berkley (CA) University Library

3. Internet/2003/HowWebSearchEnginesWork.asp

4. liddy.htm

5. default.asp? newsID=2285


7.        Research Buzz Web Site.


9.        “Using Ontologies in the Semantic Web:A Survey” Li Ding, Pranam   Kolari, Zhongli Ding,  Sasikanth Avancha, Tim Finin, AnupamJoshi

10.     C.Anantaram,“Semantic-WebTechnology-the next generation    internet,”CSI Sept. 2006

11.     Sheila A. Mcllraith, Tran Cao Son and Honglei Zeng ,“ Semantic Web Services,” IEEE Intelligent Systems 2001,

12.     C.Anantaram,“Semantic-WebTechnology- the next generation internet,”CSI Sept. 2006

13.     Li Ding, Tim Finin, Anupam Joshi, Yun Peng, Rong Pan, and Pavan    Reddivari, Search on the Semantic Web, IEEE Computer,10(38):62–69, 2005

14. Semantic_Web

15. story/31199.html




A. Duraisamy, K. Somasundaram, M. Sathiyamoorthy

Paper Title:

Protection of Data from Cipher-Text Only Attack Using Key Based Interval Splitting

Abstract: Modifications of Arithmetic Coding (AC) is to improve the security in two methods are: RAC (Randomized Arithmetic Coding) and KSAC (AC with Key-based interval splitting). For the security, encryption uses AC that is based on the inability of the opponent to distinguish between the encryption of one plaintext from the encryption of another. Chosen plaintext attacks are insecure in RAC, because same key is used to encrypt different messages even random key is used for compress every messages. The new encryption scheme is used for improve security in RAC that is the encryption is performed by a bit wise X-OR of the compressed output with the pseudorandom bit sequence for chosen plaintext attacks. Then encryption scheme is used for improve security in KSAC that is the encryption is performed by a bit wise X-OR of the compressed output with the pseudorandom bit sequence for chosen plaintext attacks.

AC, RAC, KSAC, Plaintext, Ciphertext, Plaintext attacks, AES.


1.        Kim, H. Wen, J. and Villasenor, J.D. (2007) “Secure arithmetic coding”,in IEEE Transaction Signal Process,volume.55, no.5, pp.2263–2272.
2.        Jiangtao(Gene)Wen,Irvine,S.A.andRinsma-Melchert (1995) “On the in security of arithmetic coding”, in Computer Security, volume14, pp.167–180.
3.        Langdon, G. and Rissanen, J. (1981) “Compression of black-white images with arithmetic coding”,IEEE Trans. Commun., vol. COM-29, no. 6, pp.858–867.
4.        Wen, J. Kim,H. and Villasenor, J.D. (2006) “Binary arithmetic coding with key-based interval splitting”, in IEEE Signal Processing Lett,volume13, no.2, pp. 69–72.
5.        N. K. Ratha, J. H. Connell, and R. M. Bolle, “Enhancing security and privacy in biometrics-based authentication systems,” IBM Syst. J., vol.40, no. 3, pp. 614–634, Mar. 2001.
6.        Goldwasser, S. and Bellare, M. (1996-2008) “Lecture Notes on Cryptography, Lecture Notes for a Summer Course on Cryptography”, Cambridge, MA:MIT.
7.        MacKay,D.J.C. (2003) “Information Theory”, Inference, and Learning Algorithms Cambridge, U.K.: Cambridge Univ. Press.
8.        Moo, P.W. and Wu, X. (1999) “Resynchronization properties of arithmetic coding”, in Proc. Data Compression Conf., Snowbird, UT, p. 540.
9.        Lookabaough, T. and Sicker, D.C. (2004)  “Selective encryption   for consumer applications”, IEEE Commun. Mag., vol. 42, no. 5, pp. 124–129.
10.     Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srinathan, and C. V. Jawahar, “Blind Authentication: A Secure Crypto-Biometric Verification Protocol”, IEEE Transaction on Biometric, Vol. 5, No. 2, June. 2009.
11.     A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1,pp. 4–20, Jan. 2004.
12.     Katz, J. and Lindell, Y. (2008) ‘Introduction to Modern Cryptography’, London, U.K.: Chapman & Hall/CRC.
13.     R. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Commun. ACM, vol. 21, no.2, pp. 120–126, 1978.
14.     Taher  ElGamal ”A Public Key Cryptosystem and a Signature Scheme Based on  Discrete Logarithms” IEEE transactions on information theory, VOL. IT-31, NO. 4, JULY 1985.
15.     C. Shi, S.-Y.Wang, and B. Bhargava, “MPEG video encryption in realtime  using secret key cryptography,” in Proc. 1999 Int. Conf. Parallel and Distributed Processing Techniques and Applications (PDPTA’99), Las Vegas, NV, Jun./Jul. 28–1, 1999.
16.     H. Cheng and X. Li, “Partial encryption of compressed images and video,” IEEE Trans. On Signal Process., vol. 48, no. 8, pp. 2439–2451, Aug. 2000.
17.     M. Van Droogenbroeck  and R. Benedett, “Techniques for a selective encryption of uncompressed and compressed images,” in Proc. Advanced Concepts for Intelligent Vision Systems (ACIVS) 2002, Ghent, Belgium, Sep. 9–11, 2002.
18.     A. Pommer and A. Uhl, “Selective encryption of wavelet-packet encoded  image data,” Multimedia Syst. J., vol. 9, no. 3, pp. 279–287, 2003.
19.     W. Zheng and S. Lei, “Efficient frequency domain selective scrambling of digital video,” IEEE Trans. Multimedia, vol. 5, pp. 118–129, 2003.
20.     D. Kundur and K. Karthik, “Video fingerprinting and encryption principles for digital rights management,” Proc. IEEE, vol. 92, no. 6, pp. 918–932, Jun. 2004.
21.     T. Lookabaough and D. C. Sicker, “Selective encryption for consumer  applications,” IEEE Commun. Mag., vol. 42, no. 5, pp. 124–129, May 2004.
22.     D. S. Taubman and M. W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards and Practice. Norwell, MA: Kluwer Academic, 2002.





S. Vamshi Kumar, P. Raghuvedran, K. Sri Vidya Savithri

Paper Title:

Modeling and Analysis of a Dynamic VAR Compensator for Wind Energy Conversion System

Abstract: In wind energy conversion system voltage control and reactive power compensation is the main problem. This paper presents vector oriented control of three-phase voltage source pulse width modulation (PWM) inverter which aims to control of active and reactive power injected into the grid. A wind driven Induction Generator in Self excited mode feed power to load through a ac-dc-ac converter. The modulation index of the inverter is adjusted using vector oriented control to enhance the active power export and reduce the reactive power requirement. The scheme is modeled in Matlab/Simulink and simulation is carried out to study the performance at varying wind speed.

Self-excited induction generator (SEIG), voltage source inverter (VSI), wind energy conversion system (WECS).


1.        Mahmoud M. Neam, Mohamed A. Ghazy” A Novel Method of Evaluating Performance Characteristics of a Self-Excited Induction Generator” ,IEEE Transactions on Energy Conversion 2009 , Volume: 24,No.2,pp 358-365.
2.        Tudorache, T, Melcescu, L, Paturca, S.V.” Finite Element Analysis of Self-Excited Induction Generator for Isolated Small Power Wind Turbines “, International Conference on Clean Electrical Power, 2007,pp 656-661.

3.        Szabo, L.; Biro, K.A.; Nicula, C.; Jurca, F.” Simulation of Wind Turbine Driven Autonomous Squirrel Cage Induction Generators “, 11th International Conference on Intelligent Engineering Systems, 2007,pp 213-218.

4.        Hazra, S.; Sensarma, P.S.,” Self-excitation and control of an induction generator in a stand-alone wind energy conversion system “,IET  Transactions on Renewable Power Generation, Volume: 4,No.4,pp 383-393.

5.        Sharaf, A.M.; Aljankawey, A.; Altas, I.H,” A Novel Voltage Stabilization Control Scheme for Stand-alone Wind Energy Conversion Systems “,International Conference on  Clean Electrical Power,pp 514-519.

6.        Neam, M.M.; El-Sousy, F.; Ghazy, M.A.; Abo-Adma, M.A,” DC-cus voltage control of three-phase ac/dc PWM converters for renewable energy applications “,IEEE International Conference on Electric Machines and Drives ,pp 1682-1691.

7.        Housheng Zhang; Yanlei Zhao,” Vector Decoupling Controlled PWM Rcetifier for Wind Power Grid-Connected Inverter “,International Conference on Energy and Environment Technology, Volume: 2,pp 373-376.

8.        Yang Ye; Kazerani, M.; Quintana, V.H,” Modeling, control and implementation of three-phase PWM converters “,IEEE Transactions on Power Electronics ,Volume: 18 ,No.3,pp 857-864,2003.

9.        Lopes, L.A.C.; Almeida, R.G,” Wind-driven self-excited induction generator with voltage and frequency regulated by a reduced-rating voltage source inverter “,IEEE Transactions on Energy Conversion, Volume: 21,No.2,pp 297-304,2006.




T. Mekala, N. Madhu Suganya

Paper Title:

Secure Transaction Using Dynamic Session Key

Abstract: Cryptography is a concept to protect data during transmission over wireless network. Cryptography is used in information security to protect information from unauthorized or accidental disclosure while the information is in transmitting (either electrically or physically) and while information is in storage. The information could be accessed by the unauthorized user for malicious purpose. Therefore, it is necessary to apply effective encryption/decryption methods to enhance data security. The existing system limits only the total number of users from the unknown remote host to as low as the known remote host. It uses the white list values for tracking legitimate users. But the cookie value expires after certain time period. So the attackers may use different browsers or may try on another machine or may retry after certain time. If any malicious attacks occurred the authenticated user does not know about that. The proposed system uses two algorithms known us Bio-Metric Encryption Algorithm (BEA), Minutiae Extraction Algorithm (MEA). It uses Multi Bio-metric features for authentication purpose. And also this system dynamically generates a new Session Key for each transaction. So the proposed system will protect Data Confidentiality, Data Integrity, Authentication, Availability, Access control of information over the network.

Biometric Encryption Algorithm, Finger print, Minutiae Extraction Algorithm, Session key, Biometrics.


1.        Mansour Alsaleh, Mohammad Mannan, and P.C. van Oorschot, Member, IEEE “Revisiting Defenses Against Large-Scale Online Password guessing Attacks” IEEE transactions on dependable and secure computing, vol. 9, no. 1, january/february 2012.
2.        S.M. Bellovin, “A Technique for Counting Natted Hosts,” Proc. ACM SIGCOMM Workshop Internet Measurement, pp. 267-272, 2002.

3.        Y. He and Z. Han, “User Authentication with Provable Security against Online Dictionary Attacks,” J. Networks, vol. 4, no. 3, pp. 200-207, May 2009.

4.        DHOLE S.A, PATIL V.H “Minutiae based Fingerprint Identification” Journal of signal and Image Processing ISSN: 0976-8882 & E-ISSN: 0976-8890, Volume 3, Issue 3, 2012, pp. -122-125.

5.        W. Y. Leng and S. M. Shamsuddin “Fingerprint Identification using Discretization Technique” International Journal of Computer and Communication Engineering 6 2012.

6.        Colin Soutar, Danny Roberge, Alex Stoianov, Rene Gilroy and B.V.K. Vijaya Kumar “Biometric Encryption”.

7.        D. Ramsbrock, R. Berthier, and M. Cukier, “Profiling Attacker Behavior following SSH Compromises,” Proc. 37th Ann. IEEE/IFIP Int’l Conf. Dependable Systems and Networks (DSN ’07), pp. 119-124, June 2007.

8., “Important Information: Distributed SSH Brute Force Attacks,” SANS Internet Storm Center Handler’s Diary, http://, June 2010.

9.        “The Top Cyber Security Risks,”,, Sept. 2009.

10.     C. Stoll, The Cuckoo’s Egg: Tracking a Spy through the Maze of Computer Espionage, Doubleday, 1989.

11.     Nimithachama, Dept. of Electrical & Computer Engineering Clemson University, “Fingerprint image enhancement and minutiae extraction”.

12.     Dr.R.Seshadri,T.RaghuTrivedi, “Efficient Cryptographic Key Generation using Biometrics” Int. J. Comp. Tech. Appl., Vol 2 (1), 183-187.

13.     Sunil V. K. Gaddam, and Manohar Lal “Efficient Cancellable Biometric Key Generation Scheme for Cryptography”, International Journal of Network Security, Vol.11, No.2, PP.6169, Sept. 2010.

14.     A. K. Jain, L. Hong, S. Pantanki and R. Bolle, “An Identity Authentication System Using Fingerprints”, Proc of the IEEE, vol, 85, no.9,1365-1388, 1997.

15.     Tanmay Bhattacharya, Sirshendu Hore, Ayan Mukherjee and S. R. Bhadra Chaudhuri, “A Novel data encryption technique by genetic crossover of robust biometric key and session based password”, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.2, March 2011.




N. Bharath Choudary, D. Ramakrishna Sharma, P.  Ramesh Chandra

Paper Title:

Dynamic Stability Improvement for Non- Conventional Energy Resources by Using STATCOM Control Scheme

Abstract: This paper presents a control scheme based on a static synchronous compensator (STATCOM) to achieve both voltage control and damping enhancement of a grid-connected integrated 80-MW offshore wind farm (OWF) and 40-MW ma- rine-current farm (MCF). The performance of the studied OWF is simulated by an equivalent doubly-fed induction generator (DFIG) driven by an equivalent wind turbine (WT) while an equivalent squirrel-cage rotor induction generator (SCIG) driven by an equivalent marine-current turbine (MCT) is employed to simulate the characteristics of the MCF. A damping controller of the STATCOM is designed by using modal control theory to contribute effective damping characteristics to the studied system under different operating conditions. A frequency-domain approach based on a linearized system model using  Eigen value techniques  and  a  time-domain  scheme  based  on  a  nonlinear system model subject to various disturbances are both employed to simulate the  effectiveness of the proposed control scheme. It can be concluded from the simulated results that the proposed STATCOM joined with the designed damping controller is very effective to stabilize the studied system under disturbance conditions. The voltage fluctuations of the AC bus subject to the active-power variations of the studied system can also be effectively controlled by the proposed control scheme.

Dynamic stability, marine-current farm, offshore wind farm, static synchronous compensator.


1.        S. E. B. Elghali, R. Balme, K. L. Saux, M. E. H. Benbouzid, J. F. Charpentier, and F. Hauville, “A simulation model for the evaluation of the electrical power potential harnessed by a marine current turbine,” IEEE J. Ocean. Eng., vol. 32, no. 4, pp. 786–797, Oct. 2007.
2.        W. M. J. Batten, A. S. Bahaj, A. F. Molland, and J. R. Chaplin, “Hydrodynamics of marine current turbines,” Renewab. Energy, vol. 31, no. 2, pp. 249–256, Feb. 2006.

3.        L. Myers and A. S. Bahaj, “Simulated electrical power potential harnessed by marine current turbine arrays in the alderney race,” Renewab.Energy, vol. 30, no. 11, pp. 1713–1731, Sep. 2005.

4.        H. Chong, A. Q. Huang, M. E. Baran, S. Bhattacharya, W. Litzenberger, L. Anderson, A. L. Johnson, and A. A. Edris, “STATCOM impact study on the integration of a large wind farm into a weak loop power system,” IEEE Trans. Energy Convers., vol. 23, no. 1, pp. 226–233, Mar. 2008.

5.        H. Gaztanaga, I. Etxeberria-Otadui, D. Ocnasu, and S. Bacha, “Real-time analysis of the transient response improvement of fixed-speed wind farms by using a reduced-scale STATCOM prototype,”IEEE Trans. Power Syst., vol. 22, no. 2, pp. 658–666, May 2007.

6.        K. R. Padiyar and N. Prabhu, “Design and performance evaluation of subsynchronous damping controller with STATCOM,” IEEE Trans. Power Del., vol. 21, no. 3, pp. 1398–1405, Jul. 2006.

7.        W. L. Chen and Y. Y. Hsu, “Controller design for an induction generator driven by a variable-speed wind turbine,” IEEE Trans. Energy Convers., vol. 21, no. 3, pp. 635–625, Sep. 2006.

8.        A. Jain, K. Joshi, A. Behal, and N. Mohan, “Voltage regulation with STATCOMs: Modeling, control and results,” IEEE Trans. Power Del., vol. 21, no. 2, pp. 726–735, Apr. 2006.

9.        B. Blˇazˇic and I. Papic, “Improved D-STATCOM control for operation with unbalanced currents and voltages,” IEEE Trans. Power Del., vol. 21, no. 1, pp. 225–233, Jan. 2006.

10.     A. H. Norouzi and A. M. Sharaf, “Two control schemes to enhance the dynamic performance of the STATCOM and SSSC,” IEEE Trans. Power Del., vol. 20, no. 1, pp. 435–442, Jan. 2005.

11.     K. V. Patil, J. Senthil, J. Jiang, and R. M. Mathur, “Application of STATCOM for damping torsinal oscillations in series compensated AC system,” IEEE Trans. Energy Convers., vol. 13, no. 3, pp. 237–243,

12.     N. Mithulananthan, C. A. Canizares, J. Reeve, and G. J. Rogers, “Comparison of PSS, SVC, and STATCOM controllers for damping power system oscillations,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 786–792, May 2003.

13.     P. Rao, M. L. Crow, and Z. Yang, “STATCOM control for power system voltage control applications,” IEEE Trans. Power Del., vol. 15, no. 4, pp. 1311–1317, Oct. 2000.

14.     Y. Ye, M. Kazerani, and V. H. Quintana, “Current-source converter based STATCOM: Modeling and control,” IEEE Trans. Power Del., vol. 20, no. 2, pp. 795–800, Apr. 2005.

15.     Z. Yang, C. Shen, L. Zhang, M. L. Crow, and S. Atcitty, “Integration of a STATCOM and battery energy storage,” IEEE Trans. Power Syst., vol. 16, no. 2, pp. 254–260, May 2001.

16.     R. Griinbanm, P. Halvarsson, D. Larsson, and P. R. Jones, “Conditioning of power grids serving offshore wind farms based on asynchronous generator,” in Proc. Conf. Power Electronics, Machine and Drives, Mar./Apr. 2004, vol. 1, pp. 34–39.

17.     P. M. Anderson and A. Bose, “Stability simulation of wind turbine system,” IEEE Trans. Power App. Syst., vol. PAS-102, pp. 3791–3795, Dec. 1983.




Atharva Girish Puranik, Abhijit Gohokar, Ravi Batheja, Nirman Rathod, Ojasvini Bali

Paper Title:

Design of Electricity & Energy Review Dashboard Using Business Intelligence and Data Warehouse

Abstract: The advances in the computer and electronics industry along with the widespread of on-the-move internet has lead an enormous of data being generated on daily basis. Such large data stored in the datacenters is critical for businesses to analyze and plan future business strategies. Business Intelligence is thus used to transform the large raw data into meaningful and useful information. In this work the concept of Business Intelligence in combination with Data warehousing is applied to design an Electricity & Energy Review Dashboard by taking a scenario which involves the use of large raw data for electricity and energy production and consumption in US for last few years.   

Unstructured data, Business Intelligence (BI), Competitive intelligence, Data Warehouse, MicroStrategy.


1.        H P Luhn (1958). "A Business Intelligence System" ( pdf). IBM Journal 2(4):314. doi:10.1147/rd.24.0314.
2.        D. J. Power (10 March 2007). "A Brief History of Decision Support Systems, version 4.0" (

3.        Boris Evelson, “Topioc Overview: Business Intelligence”, Report for business process professionals, November 2008.

4.        Lida Xu, Li Zend, Zongzhi Shi, Qing He, Maoguang Wang. (2007) “Research on business intelligence in enterprise computing environment”, Systems, Man and Cybernetics, 2007, ISIC. IEEE International Conference, 3270-3275.

5.        Inmon W.H., “Building the Data Warehouse”, Second Edition, J.Wiley and Sons, New York, 1996

6.        M. Nelson, "What are the key components of a key performance indicator", 2010, from Ibis Associates:


8.        Microstrategy Blogs sites.(




Babita Gupta, S. V. Phanidhar, B. Venkatesh

Paper Title:

Development of VSI Based STATCOM for Voltage Improvement & Reactive Power Compensation

Abstract: Voltage fluctuations caused by rapid industrial load changes have been a major concern for both power companies and customers in the area of power quality. The fast response of the Static Compensator (STATCOM) makes it an efficient solution for improving power quality in distribution systems.This paper describes a model for a PWM-based STATCOM used in a distribution system for mitigation of voltage fluctuations produced by an Electric Arc Furnace (EAF).The analyzed system is modeled using MATLAB/Simulink Power system Blockset (PSB), including a complete STATCOM model with its power circuits and its control system. The complete model is validated by field test.Static and dynamic performance of STATCOM is evaluated and voltage fluctuation mitigation studies are performed and discussed. The voltage fluctuation mitigation is obtained by measurements and according to international standards.

Arc Furnaces. Flicker. Harmonics. Power Quality. STATCOM.


1.        Reactive Power Control In Electric Systems. Timothy J. E. Miller. JohnWiley & Sons, Inc. 1982.ISBN 0-471-86933-3.
2.        Static Synchronous Compensator (STATCOM) for Arc Furnace andFlicker Compensation. WG B4. 19. CIGRE Publication. December 2003.

3.        Understanding FACTS: Concepts and Technology of Flexible ACTransmission Systems. Narain G. Hingorani, Laszlo Gyugyi. Wiley-IEEEPress 1999. ISBN-0780334558

4.        Thyristor-Based FACTS Controllers for Electrical Transmission Systems.R. Mohan Mathur, Rajiv K. Varma. IEEE Press and Wiley & Sons, Inc.2002.ISBN 0-471-20643-1.Papers from Conference Proceedings (Published):

5.        “Modeling and Simulation of a Distribution STATCOM using Simulink´sPower System Blockset”. P. Giroux, G. Sybille, H. L. Huy. Pp. 990-994.0-7803-7108-9/01.

6.        “Voltage Flicker Mitigation Using PWM-Based Distribution STATCOM”. J. Sun, D. Czarkowski and Z. Zabar. Pp 616-621. 0-7803-7519-X/02.

7.        “Power Quality Measurements and Operating Characteristics of ElectricArc Furnaces”. F. Issouribehere, P. E. Issouribehere and G. Barbera. 2005PES General
Meeting.0-7803-9156-X/05. Standards:

8.        IEC 61000-4-15. Electromagnetic Compatibility (EMC). Part 4: Testingand measurement techniques. Section 15: Flickermeter. Functional anddesign specifications.

9.        IEC 61000-3-5. Electromagnetic Compatibility (EMC). Part 3: Limits.Section 5: Limitation of voltage fluctuations and flicker in low-voltagepower supply systems for equipment with rated current > than 16 A.

10.     Anexo a la Resolución ENRE 99/97. Base Metodológicapara el Controlde la Emisión de Perturbaciones. Etapa 2.




Avisha Sharma, Sanyam Anand

Paper Title:

An Efficient Technique of De-Noising Medical Images Using Neural Network and Fuzzy -A Review

Abstract: Medical imaging technology is becoming an important component of large number of applications such as diagnosis, research, and treatment. Medical images like X-Ray, CT, MRI, PET and SPECT have minute information about heart brain and nerves. These images need to be accurate and free from noise. Noise reduction plays an important role in medical imaging. There are various methods of noise removal such as filters, wavelets and thresholding based on wavelets. Although these methods produced good results but still have some limitations. Considering and analyzing the limitations of the previous methods our research presents neural networks and fuzzy as an efficient and robust tool for noise reduction. In our research we use BPNN as the learning algorithm which follows the supervised learning and fuzzy. The proposed research use both mean and median statistical functions for calculating the output pixels of training patterns of the neural network and fuzzy provide promising results in terms of PSNR and MSE. The work focuses on study and performance evaluation of these categories using MATLAB 7.14.

Neural Network, Image De-noising, BPNN, PSNR, Fuzzy Logic.


1.        Abuzoum Mohamed Saleh “Efficient analysis of medical image de-noising for MRI and Ultrasound Images”, (2012). 
2.        Akutagawa Mastake, ChanYongjia, Katayama Masato, Yohsuke Kinouchi, Qinyu Zhang,“Additive and multiplicative noise reduction by back propagation neural network”, Proceedings of the 29th Annual International Conference of the IEEE EMBS Internationale, Lyon, France August 23-26, 2007 IEEE(2007).

3.        Al-Sobou Yazeed A. (2012) “Artificial neural networks as an image de-noising tool” World Appl. Sci. J., 17 (2): 218-227, 2012

4.        Dr. T.Santhanam, S.Radhika, “Applications of neural networks for noise and filter classification to enhance the image quality”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011  (IJCAI 2011).

5.        E.Salari, S. Zhang ,“Image de-noising using neural network based non-linear filter in wavelet domain”, 0-7803-8874-7/05/IEEE(2005)

6.        F.Marvasti, N.sadati, S.M.E Sahraeia, “Wavelet image De-noising based on neural network and cycle spinning” 1424407281/07/IEEE (2007).

7.        Gupta Manoj, KumarPapendra, KumarSuresh (IJCA-2010) “Performance comparison of median and the weiner filter in image de-noising.”

8.        KaurJappreet, KaurManpreet, KaurManpreet, KaurPoonamdeep “Comparative analysis of image de-noising techniques.” (IJETAE2012)

9.        Leavline E.Jebamalar Sutha S, Singh D.Asir Anton Gnana (IJCA-2011) “Wavelet domain shrinkage methods for noise removal in mages.”

10.     Mr. S. Hyder Ali, Dr.(Mrs.) R. Sukanesh, Ms. K. Padma Priya “ Medical image de-noising using neural networks”.

11.     Rehman Amjad, Sulong Ghazali, Saba Tanzila “An intelligent approach to image denoising”, (JATIT 2005-2010).

12.     Sontakke Trimbak R, Rai RajeshKumar, “Implementation of image de-noising using thresholding techniques”, IJCTEE.

13.     Toshihiro Nishimura, Masakuni Oshiro, “US Image Improvement Using Fuzzy NeuralNetwork with Epanechnikov Kernel”, 978-1-4244-4649-0/09/ ©2009 IEEE




Shabnam, Sumit Kumar Yadav

Paper Title:

Enhanced Coherency Technique for XML Keyword Search-A Review

Abstract: Keyword search techniques which use advantages of XML structure make it simpler for ordinary users to query XML databases, but latest approaches to processing these queries depend on heuristics that are ultimately ad hoc. These approaches often retrieve not correct answers, overlook appropriate answers, and cannot rank answers properly. To remove these problems for data-centric XML, we propose enhanced coherency ranking, a domain and database design-independent ranking method for XML keyword queries that is based on an extension of the concept of mutual information. Keyword search is widely recognized as a best way to retrieve information from XML data. In order to specifically meet users search requirements, we proof how to effectively return the targets that users intend to search for. We mold XML document as a set of interconnected object-trees, where each object contains a sub tree to represent a concept in the real world. The work focuses on study and performance evaluation of these categories using MATLAB 7.14.

XML, DATABASE, DATA MINING, Enhanced Coherency.


1.        A. Schmidt, M. L. Kersten, and M. Windhouwer, “Querying xml documents made easy: Nearest concept queries.” in ICDE, 2001, pp. 321–329.
2.        H. He, H. Wang, J. Yang, and P. S. Yu, “Blinks: ranked keyword searches on graphs,” in SIGMOD Conference , 2007, pp. 305–316.
3.        L. Guo, F. Shao, C. Botev, and J. Shanmugasundaram, “XRANK:ranked keyword search over XML documents,” in SIGMOD , 2003.
4.        S. Cohen, J. Mamou, Y. Kanza, and Y. Sagiv, “XSEarch: A semantic search engine for XML,” in Proc. of VLDB Conference , 2003, pp. 45–56.

5.        S.Agrawal, S.Chaudhuri, and G.Das. DBXplorer: A system for keyword-based search over relational databases. In ICDE, page 5, Washington, DC, USA, 2002. IEEE Computer Society.

6.        Y. Xu and Y. Papakonstantinou, “Efficient keyword search for smallest LCAs in XML databases,” inSIGMOD , 2005, pp. 537–538.

7.        A. Baid, I. Rae, J. Li, A. Doan, and J. F. Naughton. Toward scalable keyword search over relational data. PVLDB, 3(1):140–149, 2010.

8.        A. Balmin, V. Hristidis, and Y. Papakonstantinou. ObjectRank: authority-based keyword search in databases. In VLDB, pages 564–575. VLDB Endowment, 2004.

9.        G. Bhalotia, A. Hulgeri, C. Nakhe, and Chakrabarti. Keyword searching and browsing in databases using BANKS. In ICDE, page 431, Washington, DC, USA, 2002. IEEE Computer Society.

10.     S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107–117, 1998.

11.     B. Ding, J. X. Yu, S. Wang, L. Qin, X. Zhang, and X. Lin. Finding top-k min-cost connected trees in databases. In ICDE, pages 836–845, 2007.

12.     V. Hristidis, L. Gravano, and Y. Papakonstantinou. Efficient IR-style keyword search over relational databases. In VLDB, pages 850–861. VLDB Endowment, 2003.

13.     V. Hristidis, H. Hwang, and Y. Papakonstantinou. Authority-based keyword search in databases. ACM Trans. Database Syst., 33(1), 2008.

14.     V. Hristidis and Y. Papakonstantinou. DISCOVER: keyword search in relational databases. In VLDB, pages 670–681. VLDB Endowment, 2002.

15.     V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar. Bidirectional expansion for keyword search on graph databases. In VLDB, pages 505–516. VLDB Endowment, 2005.




Sunil V. Desale, S. V. Deodhar

Paper Title:

Lean-Management approach to Construction Engineering & Management

Abstract: This paper was written to fill a vacuum of research on the fundamental principles of site construction management. Efficient material management is essential in managing a productive and cost efficient site. In his working career,  the author has been observing inefficient labour productivity practices resulting from poor site material management and this paper attempts to rectify those using techniques such as zoning which have been    construed with deductive logic and a heuristic approach. These zones being outside storage, staging areas, and inside storage. Each has a unique function in relation to site material management. Using these areas as the basis of the study, heuristic principles are deduced and illustrated with a case study project accompanied by numerous photographs. 

Keywords: Construction material, lean management, cost control.


1.        Riley, D. R. and Sanvido. V. E (1995) ”patterns of construction-space used in multistory building.” J. constr. Eng. Manage. 121(4),464-473
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3.        Thomas H.R. and Sanvido, V.Eand sanders S.R. (1989).”Impact of material management on productivity—A case study”J.constr.Eng.Manage.115 (3),370-384.

4.        Thomas H.R. Horman M.D.,de Souza U,and Zaviki,I (2002a)”Bench marking of labour –intensive

5.        Construction activity: lean construction fundamental.

6.        Principle of work force management.”Publication 276, international council of research and innovation in building and construction Rotterdam. The Netherlands

7.        Thomas H.R. Horman M.D.,de Souza U,and Zaviki,I (2002b) “Reducing variability to improve performance as a lean construction principle”  J.constr.Eng.Manage.128(2),144-154

8.        Thomas H.R and Simit G.R.(1992)”loss of  labour productivity : the weight expert opinion .”PTI Rep. No.9019,penn state university park, a.

9.        Thomas H.R., Riley D.R. and John Messener (2005) “fundamental principle of site material Management” ASCE Vol. 131(7) July 2005, Page No. 80-8-81