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Volume-3 Issue-2: Published on May 05, 2013
Volume-3 Issue-2: Published on May 05, 2013

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Volume-3 Issue-2, May  2013, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

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Supiah Shamsudin, AzmiAb Rahman, Zaiton Haron, Anieziatun Aqmal Puteh Ahmad

Paper Title:

Detention Pond Phosphorus Loadings Uncertainty Using Fuzzy Logic 

Abstract: This study vitalized the uncertainty and fuzzy rules consideration in the estimation of phosphorus loadings and eutrophication status of the hydrologic system namely detention pond using Fuzzy Logic (MATLAB). These methods were chosen to cater for the uncertainty of loading factors such as sediment and phosphorus inflow, inflowing discharge and pond storage volume. The average of phosphorus concentrations obtained from site investigation was 0.178 mg/L, hydraulic residence time was 1.77 year and the average annual hydraulic loadings was 694.70 m/yr, obtained based on the 12 years period (2000-2012). The results showed that the maximum and minimum of phosphorus loadings was 2.00 x 10-3 ton/year and 5.00 x 10-3 ton/year.  Phosphorus loadings obtained from MATLAB fuzzy logic was 3.9 x 10-3 ton/year. The eutrophication status of the detention pond was investigated using Fuzzy Logic Approach, incorporating various fuzzy rules (MATLAB). This evaluation required the twinning usage of Vollenweider P-Loadings diagram. Generally, eutrophication status in the detention pond at KolamTadahan UTM was still considered Oligotrophic stage. However precautions need to be established as the pond are alarmingly approaching the Eutrophic Status.

Detention Pond, Phosphorus loadings, Eutrophication, MATLAB Fuzzy Logic, Uncertainty.


1.             Bae, D. H., Jeong, D. M. and Kim, G. (2007). Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrology Science Journal. 52(1), pp. 99–113.
2.             Chang, F. J. and Chen, Y. C. (2001). A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology. 245, pp. 153–164.

3.             Cigizoglu, H. K. (2004). Estimation and forecasting of daily suspended sediment data by multi layerperceptrons. Advance Water Resource. 27, pp. 185–195.

4.             Davis, A. P. and McCuen, R. H. (2005). Stormwater Management of Smart Growth. United State of America, Springer Science Business Media.

5.             Dong, W. and Shah, H. C. (1987). “Vertex Method for Computing Functions of Fuzzy Variables”. Fuzzy Sets and Systems. Vol. 24: pp. 65-78

6.             Giustolisi, O. and Laucelli, D. (2005). Improving generalization of artificial neural networks in rainfall–runoff modeling. Hydrol. Science Journal. 50(3), pp. 439–457.

7.             Jobgen, A.M., Palm, A. and Melkonian, M. (2004). Phosphorus Removal from Eutrophic Lakes using Periphyton on Submerged Artificial Substrata.  Hydrobiologia. Kluwer Academic Publishers. 528: pp. 123–142.

8.             Kisi, O. (2004a). River flow modeling using artificial neural networks. ASCE, Journal Hydrology Engineering. 9(1), pp. 60–63.

9.             Kisi, O. (2004c). Daily suspended sediment modelling using a fuzzy differential evolution approach. Hydrology Science Journal. 49(1), pp. 183–197.

10.          Lohani, A. K., Goel, N. K. and Bhatia, K. K. S. (2007). Deriving stage–discharge–sediment concentration relationships using fuzzy logic, Hydroogy. Scencei Journal. 52(4), pp. 793–807.

11.          Salas, J. D., Member, ASCE and Hyun-Suk Shin 1999. Uncertainty analysis of reservoir sedimentation. Journal of Hydraulic Engineering, 125 (4), pp. 339-350.

12.          Schnoor, J. L. (1996), Environmental Engineering: fate and transport of pollutants in water, air, and soil, New York: John Wiley & Sons, Inc.

13.          Shamseldin, A.Y. (1997). Application of a neural network technique to rainfall–runoff modeling. Journal Hydrology. 199, pp 272-294

14.          SupiahShamsudin and Noor BaharimHashim (2006). Putrajaya River System Ranking Using Fuzzy Composite Programming.  Malaysian Journal of Civil Engineering, 18(1) : 28-36

15.          SupiahShamsudin, Lat Da A/P Ai Nam.AzmiAbRahman.  “Optimum Water  Level Evaluation using Multi Criteria Decision Making Approach at Layang Reservoir”. International Conference on Integrated Water Management (IWM 2011). Organized by Environmental Technology Centre, Murdoch University, Perth. Western Australia. 2-5 February 2011

16.          Rodriguez, R., Amrhein, C. and Anderson, M. A. (2008). Reducing Dissolved Phosphorus Loading to the Salton Sea with Aluminum Sulfate. Hydrobiologia. Springer Science+Business Media B.V. 604:37–44.

17.          Ross, T. J. (2010). Fuzzy Logic with Engineering Applications. (3rd ed.). United Kingdom: John Wiley & Sons Ltd. 

18.          Yang, X., WU, X., Hao, H. and HE Z. (2008) Mechanisms and assessment of water eutrophication.  Journal of Zhejiang University SCIENCE B. 9(3), 197-209

19.          Yen, B. C., Cheng, S. T and Melching, C. S. (1986). First Order Reliability Analysis, Stochastic and Risk Analysis in Hydraulic Engineering, B. C. Yen, ed., Water Resources Publications, Littleton, Colo; 1-36




A. Banerjee, M. Mitra

Paper Title:

Analysis of Ka Band DDR Impatt Diode Based On Different Solidstate Materials

Abstract:    IMPATT diode is a   junction diode reversed bias to breakdown and can generate microwave power when embedded in a resonant cavity. From the date of its inception it is increasingly proving its worth as a prime solid state source for microwave and mm-wave frequency. The available structures of IMPATT are SDR, DDR, DAR, lo-high-lo, etc which shows gradually better efficiency and power output for different materials like Wz-GAN, InP, GaAs, Si, Ge. A detailed study in terms of the following parameters like (i) Electric field profile [E(x)] (ii) Normalized current density profile [P(x)] (iii) Doping Profile  (iv)Susceptance Vs Conductance characteristics (v) RF power output (vi) Negative resistivity profile [R(x)] (vii) Quality factor profile [Q(x)] of the diodes through simulation scheme. It is being observed that the wide band gap semiconductors are with higher efficiency (12.09 %) compare to normal Si, Ge at Ka-band and because of the relatively high breakdown voltage also power output is highest as 14.3142 W for InP compare to other material.

   Ka-band IMPATT, IMPATT with wide band gap materials, DDR IMPATT, Small signal Analysis of Ka band IMPATT


1.             H.K Gummel and J.L Blue, IEEE Transaction. Electron Devices, Volume: 14, PP: 569, 1967.
2.             S.M Sze and R.M Ryder, Proceedings of the IEEE, Volume: 59, PP: 1140,1971.

3.             M.Mitra, M.Das, S.Kar, and S.K.Roy: “A Study of the Electrical Series Resistance of Silicon

4.             IMPATT Diodes” IEEE transactions on Electron Devices, Volume: 40, No. 10, PP: 1890-1893, 1993.

5.             M.Mukherjee and N.Mazumder: “Optically Illuminated 4H-SiC Terahertz IMPATT Device” Egypt. J.Solids, Volume: 30, No.1, PP: 87-101, 2007.

6.             S.Banerjee, J.P.Banerjee : “Study on optical modulation of III-V GaN and InP based DDR IMPATT diode at sub-millimeter wave frequency” International Journal of Engineering Science and Technology, Volume:2, No. 7, PP: 2790-2801,2010.

7.             S.Banerjee, P.Chakrabarti, R.Baidya: “Effect of Bump width on the efficiency of high-low 4H-SiC IMPATT at Ka-band window frequency” International Journal of Engineering Science and Technology Volume: 2, No.10, PP: 5657-5662, 2010.

8.             M.Mukherjee, S.Banerjee and J.P.Banerjee: “Dynamic Characteristics of III-V and IV-IV Semiconductor Based Transit Time Devices in the Terahertz Regime: A Comparative Analysis” Terahertz Science and Technology, Volume: 3, No. 3, PP: 97-109, 2010.

9.             S.Banerjee, M.Mukherjee and J.P.Banerjee: “Bias current optimization of Wurtzite-GaN DDR IMPATT diode for high power operation at THz frequencies” International Journal of Advanced Science and Technology, Volume: 16, PP: 11-20, 2010.

10.          M.Mitra:”Microwave Engineering” Dhanpat Rai & Co., New Delhi, (3rd Ed), 2011.

11.          A.Acharyya, J.P.Banerjee: “Design and Optimization of pulsed mode silicon based DDR IMPATT diode operation at 0.3 THz” International Journal of Engineering Science and Technology, Volume: 3, No: 1, PP: 332-340, 2011.

12.          A.Acharyya, M.Mukherjee and J.P.Banerjee: “Noise in Millimeter-wave Mixed Tunneling Avalanche Transit Time Diodes” Arch. Appl. Sci. Res., Volume: 3, No.1, PP: 250-266, 2011.

13.          S.Banerjee : “Dynamic Characteristic of IMPATT diode based on wide band gap and narrow  band gap semiconductors at W-Band” International Journal of Engineering Science and Technology, Volume: 3, No. 3,PP: 2143-2153,2011.

14.          B. Chakrabarti, D. Ghosh, M. Mitra:” High Frequency Performance of GaN Based IMPATT Diodes” International Journal of Engineering Science and Technology, Volume: 3, No.8, PP: 6153-6159, 2011.

15.          S.Banerjee, P.Chakrabarti, R.Baidya: “Bias Current Optimization Studies on Avalanche Transit Time Diode Based on Wurtzite and Zinc-Blende Phase of GaN at Terahertz Frequency” International Journal of Advanced Science and Technology, Volume: 28, PP: 35-44, 2011.




Anuja Jadhav, Nikhil Borawake, Pradnya Shinde, Vishal Bharate

Paper Title:

Novel T-Shaped Planar Dual Band Antenna with Slotted Ground for ISM/WLAN Operations

Abstract:     In this paper, we propose a novel T-shaped planar dual band antenna in that two inverted L-shapes are used as additional resonators to produce the lower and upper resonant modes with high return loss. As a result, a dual band antenna for covering 2.4GHz and 5.2GHz ISM/WLAN bands is implemented. The dimension of the antenna is 30x42x1.6 mm3 and provides an impedance bandwidth of 563.6MHz and 1743.6MHz at lower and upper frequency band respectively. The antenna system shows good radiation patterns as well as good total gains of 9.76dB and 4.28 dB. Detail design criteria with respect to geometrical parameter variation are given. The proposed antenna with relatively low profile is very suitable for multiband mobile communication systems.

    dual band antenna, impedance bandwidth, radiation pattern, resonant mode, total gain.


1.             Yen-Liang Kuo and Kin-Lu Wong, “Printed Double-T Monopole Antenna for 2.4/5.2 GHz Dual-Band WLAN Operations”, IEEE Transaction on Antennas and Propagation, vol.51, No.9, September 2003.
2.             Md. Selim Hossain, A. N. M. Enamul Kabir, Debabrata Kumar Karmokar, “Wire Type Multiband Strip Antenna for WiMAX /WLAN Operations”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-5, November 2011.

3.             Cheng-Chi Yu, Jiin-Hwa Yang, Chang-Chih Chen, and Wen-Chao Hsieh, “A Compact Printed Multi-band Antenna for Laptop Applications ”.

4.             J.-H. Lu and Y.-H. Li, “Planar Multi-Band T-Shaped Monopole Antenna with A Pair of Mirrored L-Shaped Strips for WLAN/WIMAX Operation.”,  Progress In Electromagnetics Research C, Vol. 21, 33{44, 2011}.

5.             Sheng-Bing Chen, Yong-Chang Jiao, Wei Wang, and Fu-Shun Zhang, “Modified T-Shaped Planar Monopole Antennas for Multiband Operation”, IEEE transaction on microwave theory

6.             Akhilesh Kumar, Charu Tyagi, Rekha Rani , Ritika Tripathi, “ A Compact Planar Multiband Antenna For WLAN: Design, Simulation and Results”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 1, Issue 2, December 2011.

7.             Neha Ahuja, Rajesh Khanna, Jaswinder Kaur, “ Dual Band Defected Ground Microstrip Patch Antenna for  WLAN/WiMAX and satellite Application”, International Journal of Computer Application (0975-8887) volume 48 No.22, June 2012.

8.             D. M. Pozer, “Microwave Engineering”, Wiley Publication, Third Edition, pages 163-167.

9.             C.A. Balanis “Antenna Theory- Analysis and Design”, Wiley Publication, Third Edition, pages722-779.




Prashant Pant, Sanjeev Thakur

Paper Title:

Data Migration Across The Clouds

Abstract:      Having an effective and efficient strategy for optimizing long distance data migration is essential for every data center. With the ever increasing demands for the IT needs of businesses it is also important for data centers to deliver data migration cost effectively especially when faced with the demands from remote office back up, outsourcing, data center movers and cloud computing.Data management and migration are important research challenges of novel Cloud environments. While moving data among different geographical domains, it is important to lower the transmission cost for performance purposes. Efficient scheduling methods allow us to manage data transmissions with lower number of steps and shorter transmission time. In previous research efforts, several methods have been proposed in literature in order to manage data and minimize transmission cost for the case of Single Cluster environments.This paper Explores the issues and method of Data Migration across the Clouds

     Cloud Computing, Data Migration, Security Issue, Cloud Architecture.


1.                F. A. Alvi1, B.S Choudary,N. Jaferry, E.Pathan.- A review on cloud computing security issues & challenges.
2.                B.Meena,Krishnaveer ,Abhishek Challa- Cloud Computing Security Issues with Possible Solutions.

3.                Dikaiakos, M.D; Katsaros, D.; Mehra, P.; Pallis, G.; Vakali, A.; (2010), “Cloud Computing Distributed Internet Computing for IT and Scientific Research”.Vol.13 ,pp 10, Sept.-Oct. 2009.

4.                Shuai Z; Shufen Z; Xuebin C; Xiuzhen H; (2010), “Cloud Computing Research and Development Trend”, 2nd International conference on Future Networks, 2010. ICFN ' 10. pp 23, 22-24 Jan 2010.

5.                Chang, L, Ti ; Chin L; Chang, A.Y.; Chun J, C;(2010), “ Information security issue of enterprises adopting the application of cloud computing”, IEEE 2010 Sixth International Conference on Networked Computing and Advanced Information Management (NCM),pp 645, 16-18 Aug. 2010.

6.                R. Maggiani; (2009), "Cloud computing is changing how we communicate," 2009 IEEE International Professional Communication Conference, IPCC 2009,Waikiki, HI, United states ,pp 1, 19-22 July.

7.                Geng L; David F; Jinzy Z; Glenn D; (2009), “Cloud computing: IT as Service, “IEEE computer society IT Professional”, Vol. 11, pp.10-13, March-April 2009.

8.                Basit Ali; (2009), “Ufone Launches Uconnect”, published in TelecomPK.Net,12 August 2009.

9.                Muzzammil Sheikh; (2011), “PTCL Launched EVO USB become Wi-Fi Hotspot”, The Frontier Star (Northwest Frontier Province, Jan 26 2011 Issue.

10.             Grobauer, B.; Walloschek, T.; Stocker,E.;(2011), “Understanding Cloud Computing Vulnerabilities”,5487489 searchabstrSecurity & Privacy, IEEE, Vol 9, pp 50.

11.             Gansen Z; Chunming R; Jin L; Feng Z; Yong T; (2010),,“Trusted Data Sharing over Untrusted Cloud Storage Providers”,2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), pp 97, Nov. 30 2010-Dec. 3 2010.

12.             Pearson, S.; (2009), “Taking account of privacy when designing cloud computing services”,5071532 searchabstract CLOUD '09. ICSE Workshop on Software Engineering Challenges of Cloud Computing, 2009. pp 44, 23-23 May 2009.

13.             Kresimir P; Zeljko H; (2010), “Cloud computing security issues and challenges”, MIPRO 2010, May 24-28, 2010, Opatija, Croatia.

14.             Minqi Z; Rong Z; Wei X; Weining Q; Aoying Z; (2010),“Security and Privacy in Cloud Computing: A Survey”, Sixth international conference on Semantics Knowledge and Grid (SKG), pp 105, 1-3 Nov. 2010.

15.             Popovic K; Hocenski Z; (2010), “Cloud computing security issues and challenge”, 5533317searchabstractMIPRO, 2010 Proceedings of the 33rd International Convention , pp 344,24-28 May 2010.

16.             Jensen, M.; Schwenk, J.; Gruschka, N.; Iacono, L.L.; (2010), “On Technical Security Issues in Cloud Computing”, IEEE International Conference on Cloud Computing, 2009. CLOUD '09, pp 109, 21-25 Sept. 2009. 5708519 searchabstract

17.             Jianfeng Y; Zhibin C; (2010), “Cloud Computing Research and Security Issues”, IEEE 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), pp1, 10-12 Dec 2010.

18.             Jansen, W.A.; (2010), “ Cloud Hooks: Security and Privacy Issues in Cloud Computing5719001 IEEE 2011 44th Hawaii International Conference on System Sciences (HICSS), pp1, 4-7 Jan. 2011.

19.             Tian L.Q; NI Y,LING; (2010) , “Evolution of user Behavior Trust in Cloud Computing”, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010),Vol. 7,pp V7-567, 22-24 Oct. 2010.

20.             Mathur, P; Nishchal, N.; (2010), “Cloud Computing: New challenge to the entire computer industry”, 2010 1st International Conference on Parallel, Distributed and Grid Computing (PDGC - 2010), pp 223.

21.             Yuefa D; Wu B; Yaqiang G; Zhang Q; Tang C; (2009), “ Data Security Model for Cloud Computing”, Proceedings of the 2009 International Workshop on Information security and Applications (IWISA 2009)

22.             Dean and S. Ghemawat; (2010), “MapRduce: Simplified data processing large clusters”, communication of the ACM, Vol.51, pages 107-113.

23.             Xue J; Zhang J.J; (2010),"A Brief Survey on the Security Model of Cloud Computing",2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

24.             Migrating to the Cloud By Tom Laszewski  P Nauduri.




V.V.R. Seshagiri Rao, T. Kishen Kumar Reddy M.V.S. Murali Krishna, P.V. Krishna Murthy

Paper Title:

Comparative Studies on Exhaust Emissions from a High Grade Low Heat Rejection Diesel Engine with Carbureted Alcohol and Crude Jatropha Oil

Abstract:       Investigations were carried out to study the exhaust emissions from high grade low heat rejection (LHR) diesel engine consisting of air gap insulated piston with 3-mm air gap with superni (an alloy of nickel) crown, air gap insulated liner with superni insert and ceramic coated cylinder head with normal temperature condition of crude jatropha oil and carbureted alcohol (ethanol / methanol) with varied injection timing and injection pressure and compared with methanol operation over ethanol operation and also with pure diesel operation on conventional engine (CE). Exhaust emissions of smoke and oxides of nitrogen (NOx) were recorded by AVL Smoke meter and Netel Chromatograph NOx analyzer respectively at different values of brake mean effective pressure (BMEP). Aldehydes were measured by dinitrophenyl hydrazine (DNPH) method at peak load operation of the engine. Smoke levels and NOx levels decreased by 47% 12% respectively with LHR engine at 27obTDC and at an injection pressure of 190 bar with methanol operation in comparison with pure diesel operation on CE.

Crude Vegetable Oil, Methanol, Ethanol, CE, LHR engine, Emissions and Combustion characteristics.


1.             Use of raw vegetable oil or animal fats in diesel engines. March,  “Engine Manufacturer’s Association”, Chicago, 2006.
2.             Cummins, C. Lyle, Jr. Diesel's Engine, Volume 1: From Conception To 1918. Wilsonville, OR, USA: Carnot Press, 1993.

3.             Forson, F.K., Oduro, E.K. and Hammond-Donkoh, E. “Performance of jatropha oil blends in a diesel engine”, Renewable Energy, vol.29, 2004, Pp.1135–1145.

4.             Mahanta, P., Mishra, S.C. and Kushwash, Y.S. “An experimental study of pongamia pinnata oil as a diesel substitute fuel”, Proceedings IMechE., Journal of Power and Energy, ISSN: 2041-2947, 220, Part-A, 2006, pp.803-808.   

5.             Agarwal, D., Agarwal, A.K. “Performance and emissions characteristics of jatropha oil (preheated and blends) in a direct injection compression ignition engine”, Int. J. Applied Thermal Engineering, vol.27, 2007, pp. 2314-23.

6.             Hanbey Hazar and Huseyin Aydin. “Performance and emission evaluation of a CI engine fueled with preheated raw rapeseed oil (RRO)-diesel blends”, Applied Energy,   87, 2010, pp.786-790.

7.             Misra, R.D., Murthy, M.S., “Straight vegetable oils usage in a compression ignition engine—A review”, Renewable and Sustainable Energy Reviews, vol.14, 2010, pp.3005–3013.

8.             Jiwak Suryawanshi, “Performance and emission characteristics of  CI  engine fueled by coconut oil methyl ester”,    2006, SAE Paper No.               2006-32-0077.

9.             Murugesan, A., Umarani, C., Subramanian,R., Nedunchezhian, N. “Bio-diesel as an alternate fuel for diesel engines”, Renewable and Sustainable Energy Reviews, vol.13(3), 2009, pp.653-662. 

10.          Jindal, S., Nandwana, B.P., Rathore, N.S., Vashistha,V. “Experimental investigation of the effect of compression ratio and injection pressure in       a direct injection diesel engine running on Jatropha methyl ester”, Applied Thermal Engineering, vol. 30, 2010, pp.442–448.

11.          Jaichandar, S. and Annamalai, K. “The status of biodiesel as an alternative fuel for diesel engine- An Overview”,  Journal of Sustainable Energy & Environment, vol.2, 2011, pp.71-75 

12.          Rasim, B. “Performance and emission study of waste anchovy fish biodiesel in a diesel engine”, Fuel Processing Technology, vol.92, 2011, pp.1187-1194.

13.          Gupta, C.P. “Use of alcohols in diesel engine”, Transactions of Journal of Indian   Institute of Engineers, 1983, pp.199-212.

14.          Samaga, B.S., Suresh Kumar, Y. and Venukumar, S. “A dual fuel stratified charge combustion system”, Proc. of VIII National Conference on I.C. Engines and Combustion, 1983, F30-F37, Trivandrum.

15.          Haragopala Rao, B. Partial substitution of alcohols for diesel fuel, “Proceedings of Workshop on Perspective of Alcohol Fuel Utilization in I.C. Engine”, Indian Institute of Petroleum, Dehradun, 1984, pp.  33-38.

16.          Venkanan, B.K., Gangavathi, P.B. and Swati, B.W. “Alcohol fumigation close to the intake valve at increased injection pressure and with ignition improver enhances the performance and reduces emissions in a D.I. engine”, Proc. of the XV National Conference on I.C. Engines and Combustion, Chennai, 1997, pp.144-150.

17.          Parlak, A., Yasar, H., ldogan O. “The effect of thermal barrier coating on a turbocharged Diesel engine performance and exergy potential of the exhaust gas”, Energy Conversion and Management, vol. 46(3), 2005, pp.489–499.

18.          Ekrem, B., Tahsin, E., Muhammet, C. “Effects of thermal barrier coating on gas emissions and performance of a LHR engine with different            injection timings and valve adjustments”,  Journal of Energy Conversion and Management, vol.47, 2006, pp.1298-1310.

19.          Ciniviz, M., Hasimoglu, C., Sahin, F., Salman, M. S. “Impact of thermal barrier coating application on the performance and emissions of a              turbocharged diesel engine”, Proceedings of The Institution of Mechanical Engineers Part D-Journal Of Automobile Engineering, vol.222 (D12), 2008, pp.2447–2455.

20.          Hanbey Hazar. “Effects of bio-diesel on a low heat loss diesel engine”, Renewable Energy, vol. 34, 2009, pp.1533–1537.

21.          Modi, A.J., Gosai, D.C. “Experimental study on thermal barrier coated diesel engine performance with blends of diesel and palm bio-diesel”,          SAE International Journal of Fuels and Lubricants, vol.3 (2), 2010, pp.246-259.

22.          Rajendra Prasath, B., P. Tamilporai, P., Mohd.Shabir, F. “Analysis of combustion, performance and emission characteristics of low heat rejection engine using biodiesel”, International Journal of Thermal Science, vol.49, 2010, pp.2483-2490.

23.          MohamedMusthafa, M., Sivapirakasam, S.P. and Udayakumar.M” Comparative studies on fly ash coated low heat rejection diesel engine on  performance and emission characteristics fueled by rice bran and pongamia methyl ester and their blend with diesel”, Energy, vol.36(5), 2010,pp.2343-2351.

24.          Parker, D.A. and Dennison, G.M. “The development of an air gap insulated piston”, SAE Paper No.  870652, 1987.

25.          Rama Mohan, K., Vara Prasad, C.M., Murali Krishna, M.V.S. “Performance of a low heat rejection diesel engine with air gap insulated piston”,  ASME Journal of Engineering for Gas Turbines and Power, vol.121 (3), 1999, pp. 530-540.

26.          Ratna Reddy, T., Murali Krishna, M.V.S., Kesava Reddy, Ch., and Murthy, P.V.K., “Performance evaluation of a medium grade low heat rejection diesel engine with mohr oil”, International Journal of  




Nadia Adnan Shiltagh, Lana Dalawr Jalal

Paper Title:

Path Planning of Intelligent Mobile Robot Using Modified Genetic Algorithm

Abstract:        One aspect of interest in robotics is planning the optimal path for a mobile robot. The objective of path planning is to determine the shortest feasible path with the minimum time required for mobile robots to move from a starting position to a target position. In this study, Modified Genetic Algorithm (MGA) is developed for a global path planning, and the application of MGA to the problem of mobile robot navigation is investigated under an assumption that an environment model has been established already. The proposed algorithm read the map of the working environment which expressed by grid model and then creates an optimal or near optimal collision free path. The MGA algorithm was simulated using MATLAB R2012a. Adaptive population size without selection and mutation operators are used in the proposed algorithm. The simulation results demonstrate that this algorithm has a great potential to solve the path planning with satisfactory results in terms of minimizing distance and execution time.

  global path planning, intelligent mobile robot, modified genetic algorithm, optimal path.


1.                 S. Kolski, “Mobile Robots Perception and Navigation”, Advanced Robotic Systems International and pro literaturVerlag, Germany, 2007.
2.                 G.Yogita, G. Kusum, Artificial Intelligence in Robot Path Planning, International Journal of Soft Computing and Engineering (IJSCE), , 2012, 2(2): 471-474.

3.                 K. M. Han, “Collision free path planning algorithms for robot navigation problem”, Master Thesis, University of Missouri- Columbia, 2007.

4.                 P. K. Mohanty, D. R. Parhi, “Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: a Review”, Journal of Advance Mechanical Engineering, 2013, 1: 24-39

5.                 Al-Taharwa, A. Sheta, M. Al-Weshah, “A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment”, Journal of Computer Science, 2008, 4 (4): 341-344.

6.                 Tuncer, M. Yıldırım, K. Erkan, “A Motion Planning System for Mobile Robots’, Advances in Electrical and Computer Engineering, 2012, 12(1): 57-62.

7.                 W. Yan-Ping, W. Bing, “Robot Path Planning Based on Modified Genetic Algorithm”, 2nd International Conference on Future Computer and Communication, 2010 3:725-728.

8.                 Konar, “Artificial Intelligence and Soft Computing Behavioral and Cognitive Modeling of the Human Brain”, CRC Press LLC, 2000.

9.                 S. Dutta, “Obstacle Avoidance of Mobile Robot using PSO-based Neuro Fuzzy Technique”, International Journal of Computer Science and Engineering, 2010, 2(2): 301-304.

10.              P. Li, X., Huang, M. Wang, “A New Hybrid Method for Mobile Robot Dynamic Local Path Planning in Unknown Environment”, Journal of Computers, North America, 2010, 5(5): 773-781.

11.              M. Saska, M., Macas, L., Preucil, L. Lhotska, “Robot Path Planning using Particle Swarm Optimization of Ferguson Splines”, In Proceedings 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2006), Prague, Czech Republic, 2006, pp. 833-839.

12.              P. Raja, S., Pugazhenthi, “Path Planning for Mobile Robots in Dynamic Environments using Particle Swarm Optimization”, International Conference on Advances in Recent Technologies in Communication and Computing, 2009, pp. 401-405.

13.              N. Sariff, N. Buniyamin, “An overview of autonomous mobile robot path planning algorithms”, 4th Student Conference on Research and Development, Scored
2006, pp. 183-188.

14.              S. C. Yun, V. Ganapathy, L. O. Chong, “Improved genetic algorithms based optimum path planning for mobile robot”, Proceedings of ICARCV, 2010, pp. 1565-1570. 

15.              P. Raja, S. Pugazhenthi, “Optimal path planning of mobile robots: A review”, International Journal of Physical Sciences, 2012, 7(9): pp. 1314 - 1320.

16.              P. K. Mohanty, D. R. Parhi, “Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: a Review”, Journal of Advance Mechanical Engineering, 2013,1: 24-39.

17.              W. Yan-Ping, W. Bing, “Robot Path Planning Based on Modified Genetic Algorithm”, 2nd International Conference on Future Computer and Communication, 2010, 3: 725-728.




Hassan Nosrati Nahook, Mahdi Eftekhari

Paper Title:

A Feature Selection Method Based on ∩ - Fuzzy Similarity Measures Using Multi Objective Genetic Algorithm

Abstract:         Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm.  Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on ∩ - fuzzy similarity measures by multi objective genetic algorithm (FSFSM – MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it.

   Feature Selection, Fuzzy Similarity Measures, Multi Objective Genetic.


1.             M. Dash and H. Liu, “Feature Selection for Classification,” (1997):  Intelligent Data Analysis, 1(1 – 4): 131 – 156.
2.             Kaufman L, Rousseeuw PJ. , (1990):   Analysis New York: Wiley.

3.             Zadeh LA. Fuzzy sets. (1965): Information Control 8:338–356.

4.             Zwick R, Carlstein E, Budescu DV. Measures of similarity among fuzzy concepts: A comparative analysis. (1987): Int J Approximate Reason 1:221–242.

5.             Pappis CP, Karacapilidis NI. A comparative assessment of measures of similarity of fuzzy values. (1993): Fuzzy Sets and Systems 56:171–174.

6.             Wang WJ. New similarity measures on fuzzy sets and on elements. (1997): Fuzzy Sets and Systems 85:305–309.

7.             Liu X. Entropy, distance measure and similarity measure of fuzzy sets and their relations.  (1992): Fuzzy Sets and Systems 52:305–318.

8.             Fan J, Xie W. Some notes on similarity measure and proximity measure. Fuzzy Sets Syst 1999; 101:403–412.

9.             Turksen IB, Zhong Z. An approximate analogical reasoning approach based on similarity measures. (1988): IEEE Trans Syst Man Cybern 18:1049–1056.

10.          Buckley JJ, Hayashi Y. Fuzzy input–output controllers are universal approximates. (1993):  Fuzzy Sets and Systems 58:273–278.

11.          Kalle Saastamoinen (2008): many valued algebraic structure as measures of comparison. PhD thesis, Lappenranta University of Technology.

12.          Gottwald Siegfried (1993): Fuzzy sets and fuzzy logic. Arti_cial intelligence, ISNB 3-528-05311-9.

13.          Bustince, H. Indicator of inclusion grade for interval valued fuzzy sets: Application to approximate reasoning based on interval-valued fuzzy sets, (2000): Int. J. Approx. Reasoning, 23: 137-209.

14.          De Baets, Bernard, and Hans De Meyer., “The Frank T – norm Family in Fuzzy Similarity Measurement.”, (2001): In EUSFLAT Conference, 2nd, Proceedings,249–252.

15.          Jones DF, Mirrazavi SK, Tamiz M. Multiobjective meta-heuristics: an   overview of the current state-of-the-art. (2002):Eur J Oper Res;137(1):1–9.

16.          Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. (2000): Evol Comput; 8(2):173–95.

17.          Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. (1999): IEEE Trans Evol Comput; 3(4):257–71.

18.          Coello CAC. An updated survey of GA-based multiobjective optimization techniques. (2000): ACM Comput Surv; 32(2):109–43.

19.          Jensen MT. Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. (2003): IEEE Trans Evol Comput; 7(5):503–15.

20.          Xiujuan L, Zhongke S. Overview of multi-objective optimization methods. (2004): J Syst Eng Electron;15(2):142–6.

21.          Huawen Liu, JiguiSun, LeiLiu, HuijieZhang, Feature selection with dynamic mutual information. (2009): Pattern Recognition 42: 1330 – 1339.

22.          Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J, (2007): UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. 




Sandya H. B., Hemanth Kumar P. , Himanshi Bhudiraja, Susham K. Rao

Paper Title:

Fuzzy Rule Based Feature Extraction and Classification of Time Series Signal

Abstract: Time series signal is a continuous signal which varies continuously with respect to time. These signals involve a great deal of useful information, the information content in these signals can be used for Feature Extraction and Classification. The purpose of Feature Extraction is to reduce the dimension of feature space and achieving better performances. The Features are extracted based on the mathematical calculations like Average, Maximum, Minimum, Standard Deviation and Variance. The Classification of extracted features is carried out by Fuzzy Rule based Selection System. Fuzzy Systems (FS) are evaluated for accuracy, multiplexity, flexibility and transparency for simple and complex systems. In this paper mamdani based Fuzzy System is used to achieve accurate results. Based on feature extracted data the Fuzzy System generates a fuzzy score and the Classifier Algorithm classify the feature extracted signals as Good, Bad and Best signals.

    Fuzzy, Feature Extraction, Classification, Time series signal.


1.           Ayan Banerjee, Kanad Basu and Aruna Chakraborty, “Prediction of EEG Signal by Digital Filtering”, University of  Florida, branch of Computer & Information Science and engineering.
2.           Ramasubramanian V.,Time series analysis. I.A.S.R.I., Library Avenue, New   Delhi-110 012

3.           Nandish.M, Stafford Michahial, Hemanth Kumar P, Faizan Ahmed, “Feature Extraction and Classification of EEG Signal Using Neural Network Based Techniques”, ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of  Engineering and Innovative Technology (IJEIT) Volume 2, Issue 4, October 2012

4.           Regina Kaiser and Agustín Maravall, Notes on Time Series Analysis Arima Models and Signal Extraction.

5.           Arshdeep Kaur, Amrit Kaur, “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012

6.           HU Jian-feng, “Multifeature analysis in motor imagery EEG classification”, Proc. IEEE, 2010 Third International Symposium on Electronic Commerce and Security, pp.114-117, 2010.

7.           Abdulhamit Subasia, M. Kemal Kiymika, Ahmet Alkana, Etem Koklukayab,” Neural Network Classification of EEG  signals by using AR with MLE preprocessing for epileptic seizure detection”, Mathematical and Computational Applications, Vol. 10, No. 1, pp. 57-70, 2005.

8.           Boyu Wang, Chi Man Wong, Feng Wan, Peng Un Mak, Pui In Mak, and Mang I Vai,” Comparison of Different methods for EEG signal classification”, IEEE Trans. Neural Syst. Rehabil. Eng., vol.11, pp. 141-144, June 2003.

9.           D. Garrett, D. A. Peterson, C. W. Anderson, and M. H. Thaut, “Comparison of  linear, nonlinear, and feature selection methods  for  EEG signal classification”,  IEEE Trans. Neural Syst. Rehabil. Eng., vol.11, pp.   141-144, June 2003.

10.        Deepak R. Keshwani, David D. Jones, George E. Meyer, Rhonda M. Brand, “Rule based Mamdani type fuzzy modelling of skin permeability”, Published in Applied Soft Computing 8 (2008), pp. 285–294; doi: 10.1016/j.asoc.2007.01.007

11.        J.R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M.  Vaughan, “Brain-computer interface for communication and control,” Clin. Neurophsiol., vol. 133, pp. 767-791, June 2002.

12.        Jang, J.-S. R., “ANFIS: Adaptive-Network-based Fuzzy Inference  Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, May 1993.

13.        J. Yen and R. Langari, Fuzzy Logic.  Pearson Education, 2004.

14.        K.P. Mohandas and S. Karimulla, “Fuzzy and Neuro-fuzzy modeling and control of non linear systems”, Second International Conference on Electrical and Electronics, 2001.

15.        T. J. Ross, Fuzzy Logic with Engineering Applications. John Wiley and sons, 2010. 




Vimlesh Ramesh Bhat, Ashu Vashishtha, Naina Goel, Laxmi R. Sisode

Paper Title:

Real Time GPS Tracking System for Transport Operations

Abstract:  It is a Dynamic GPS based auto-fare calculator made for India. This application helps in bringing fairness to Indian Auto rickshaw industry. This application enables the user to be in more control of his travel, check where exactly he is being driven to and checking the fare and the distance. It acts as a guard against faulty meters and is an application so simple which anyone can use easily. The Global Positioning System (GPS) is a satellite-based navigation system made up of a network of 24 satellites placed into orbit. GPS works in any weather condition, anywhere in the world, 24 hours a day. Due to rapid advancement in the mobile computing field, the use of Mobile application is dramatically increasing. Thus, this project deal with  finding the optimal paths and calculating the fare of Rented Vehicle by using Android SDK (Software Development Kit) and GPS (Global Positioning System) system.

     Dynamic GPS, Global Positioning system, Computing, Android SDK (Software Development Kit)


1.             Future Mobile CRM in the automotive and tourist area (IEEE-DEST 2001) (SICE Annual Conference 2011Sept. 17-20, 2011, Kagawa University, Japan).
2.             Project Report - GPS Tracker Pascal Bragger Msc Course- Ubiquitous Computing University of Fribourg, Switzerland March 9, 2006.

3.             Master Thesis Mathematical Modeling and Simulation Thesis no: 2008 – 6 May 2008.

4.             M. Azam, K. Pattipati, J. Allanach, S. Poll, and A. Patterson-Hine, “In-flight fault detection and isolation in aircraft flight control systems,” presented at the IEEE Aerospace Conf., Big Sky, MT, March. 2005.

5.             Hertel, Sascha: Mobile Client fürzukünftige Mobile Anwendungen; Thesis SS 2006.

6.             Non –Intrusive GPS Tracking method (Gdanak University of  Technology, Multimedia systems Department , Poland 2005.

7.             Differential GPS reference Station Algorithm- Design And Analysis by Jay Farrel and Tony Givargis, Vol. 8, NO. 3, May 2000.

8.             GPS Techical Specifications “Garmin International, Inc. , June 2005, Olathe, USA.

9.             M.Sayre,”Development of  a Block Processing carrier to Noise ratio Estimates for the global Positioning System,”M.S. thesis, ohio University,USA 2003.

10.          Mathematical Modelling of the GPS Tracking Signals Master Thesis Mathematical Modelling and Simulation ,Thesis no :2008-6 May 2008




Mustapha Ben Saidi, Abderrahim Marzouk

Paper Title:

Multi-Trust_OrBAC: Access Control Model for Multi-Organizational Critical Systems Migrated To the Cloud

Abstract:  Security of information systems is a problem chronic, the arrival of cloud computing as a new computing model, feeds the difficulty of implementing effective solutions. Thus more research is currently focused on data security in the cloud, and especially the issue of confidentiality. In this paper we propose a new protocol access control for complex, heterogeneous, interoperable, and distributed systems in the context of Cloud Computing : « Multi-TrustOrBAC » (Multi-Organization - Trust Based Access Control). This protocol allows a TTP «Trust Tierd Party [10] » to force users belonging to several organizations to cooperate to meet the security policies defined independently by them. The aim is to offer to organizations working together and having decided to migrate to the cloud, a means of real-time monitoring of their safety. Our solution is based on both the concept of trust assigned to users and to the definition of an order on the set of security policies. The logical formalism is used to specify and describe the rules of the security policies of different organizations.

  Policy security, interoperable system, heterogeneous and distributed systems, actions weighted, access control.


1.              Mustapha Ben Saidi – Abderrahim Marzouk Journal : International Journal of Soft Computing & Engineering ISSN: 22312307 Year: 2012 Volume: 2 Issue: 5 Pages: 134-138 Provider: DOAJ Publisher: International Journal of Soft Computing & Engineering
2.              Mustapha Ben Saidi - Anas Abou Elkalam - Abderrahim Marzouk Journal: International Journal of Soft Computing & Engineering ISSN: 22312307 Year: 2012 Volume: 2 Issue: 4 Pages: 122-130 Provider: DOAJ Publisher: International Journal of Soft Computing & Engineering

3.              Anas Abou Elkalam Yves Deswarte Multi-OrBAC :un modèle de contrôle d’accès pour les systèmes multi-organisationnels Anas Abou El Kalam LIFO-ENSI de Bourges ;LAAS - CNRS ;

4.              Abou El Kalam, R. El Baida, P. Balbiani, S. Benferhat, F. Cuppens, Y. Deswarte, A. Miège, C. Saurel et G. Trouessin.

5.              Organization Based Access Control. IEEE 4th International Workshop on Policies for Distributed Systems and Networks (Policy 2003), Lake Come, Italy, June 4-6, 2003.

6.              Ouvrage : « Cloud Computing, une rupture décisive pour l’informatique d’entreprise ». Guillaume Plouin  2ème  édition- 2011.

7.              Anas Abou ElKalam, Philippe BalbianiEmerging :Policy Language for Modelling Recommendations - Challenges for Security, Privacy and Trust IFIP Advances in Information and Communication Technology Volume 297, 2009, pp 176-189 

8.              Effective Ways of Secure, Private and Trusted Cloud Computing Article Authors: Kumar Pardeep --- Sehgal VivekKumar --- Chauhan Durg Singh --- Gupta P. K.  Diwakar ManojYear: 2011 Provider: arXiv

9.              Dimitrios Zissis and Dimitrios Lekkas Addressing cloud computing security issues Journal : Future Generation Computer Systems Volume 28, Issue 3, March 2012, Pages 583–592

10.           P.G. Dorey, A. Leite Commentary: Cloud computing a security problem or solution? University of London, UK KPMG LLP, UK.

11.           Renaud Francou Daniel Kaplan : Nouvelles Approches De La Confiance Numérique, Conclusions de l'expédition, Février 2011 confiance numérique, Une "expédition" commune de la Fing et la Fondation Télécom, 




Daulat Singh, Rakesh Shrivastava, Dinesh Varshney

Paper Title:

Effective Admission Procedure and Quality Teaching For Programmes Offered In Distance Learning Mode Using Edusat Satellite Based Education

Abstract:   Higher education plays a leadership role in education. The present study is designed to measure the effectiveness of the process of admission and quality of teaching in university that offers courses through distance learning mode using Edusat satellite based education. It is a descriptive study and the sample of the study consisted of students as respondents. For the same a total of one hundred students participated in the study. A questionnaire consisting of nine items were used to collect data from the respondents. The analyses included the reflections of the respondents on the process of admission, choice of course, course contents, teaching pedagogy and satisfaction related to the course content. The analysis is conducted with the help of non-parametric tests and chi-square method.

   Higher education, process of admission and quality of teaching.


1.             F. Mustard, (1998). The Nurturing of creativity: The role of higher education. Oxford University Press
2.             R. Dearing, (1997). Higher education in the learning society. Committee of enquiry into higher education. London

3.             Bhaskaranarayana, B. S. Bhatia, K. Bandyopadhyay and P. K. Jain (2007)- Applications of space communication, Current Science, Vol. 93, No. 12, 25 December, 2007

4.             I. Fabiyi, and S. A. Oladipo, (2008) Resource and Policy as Determinants of Access to University Education in Nigeria. Journal of the World Universities Forum. 1(4), 25-34.

5.             H. Ali, and B. Akubue, (1998) Nigeria Primary Schools Compliance with National Policy on Education: An Evaluation of Continuous assessment Practices. Evaluation Review, 12 (6), 625-637.

6.             P. Altbach, (2006), The Dilemmas of Ranking, Boston College Center for International Higher Education, International Higher Education, Vol. 42.

7.             World Bank. (2000), Report on Higher Education in the Developing Countries: Peril and Promise. Oxford University Press

8.             S. K. Pandey, (1999) "Handbook of Satellite communications" Authors Press, Delhi; D. C. Agrawal,  (2004) "Digital Satellite Communication" Khanna Publication, Delhi.  




Priti Jadhao, Lalit Dole

Paper Title:

Survey on Authentication Password Techniques

Abstract:    Authentication is process of determining whether someone or something is ,in fact who or what to be declared. For authentication mostly textual passwords are used. Passwords are the most commonly used method for identifying users in computer and communication systems. Typically, passwords are strings of letters and digits, i.e., they are alpha-numeric. Such passwords have the disadvantage of being hard to remember. Graphical passwords, which consist of some actions that the user performs on an image. Such passwords are easier to remember, but are vulnerable to shoulder surfing (which consists of simply watching a user login). We present a few graphical password schemes that offer resistance to shoulder surfing.

    Graphical Password , Session Password, Textual Password.


1.           GAJBHIYE S.K.1* AND ULHE P.2 -Authentication Schemes for Session Passwords Using color and gray-scale images(2012).
2.           Bin Hu, Qi Xie, Yang Li- Automatic verification of password based authentication protocols using smart card (2011).

3.           1S.Balaji, 2Lakshmi.A, 3V.Revanth, 4M.Saragini, 5 V.Venkateswara Reddy-Authentication Techniques for Engendering Session Passwords With Colors and  Text. (2012).

4.           L.Sobrado and J.C. Birget, “Graphical Passwords”, The Rutgers Schloar, An Electronic Bulletin for Undergraduate Research, vol 4, 2002,

5.           Hai tao, “Pass-Go, a New Graphical Password Scheme”, Master Thesis, University of Ottawa Canada, June 2006.

6.           E. Blonder. Graphical passwords. United States Patent5559961, 1996.

7.           W. Jansen, "Authenticating Users on Handheld Devices “in Proceedings of Canadian Information Technology Security Symposium, 2003.




Firas Ajil Jassim, Fawzi H. Altaani

Paper Title:

Hybridization of Otsu Method and Median Filter for Color Image Segmentation

Abstract:     In this article a novel algorithm for color image segmentation has been developed. The proposed algorithm based on combining two existing methods in such a novel way to obtain a significant method to partition the color image into significant regions. On the first phase, the traditional Otsu method for gray channel image segmentation were applied for each of the R,G, and B channels separately to determine the suitable automatic threshold for each channel. After that, the new modified channels are integrated again to formulate a new color image. The resulted image suffers from some kind of distortion. To get rid of this distortion, the second phase is arise which is the median filter to smooth the image and increase the segmented regions. This process looks very significant by the ocular eye. Experimental results were presented on a variety of test images to support the proposed algorithm.

     Color image segmentation, Median filter, Otsu method, Thresholding.


1.             A. Gulhane, A. S. Alvi, “Noise Reduction of an Image by using Function Approximation Techniques”, International Journal of Soft Computing and Engineering (IJSCE), vol.2, no.1, March 2012, pp. 60-62.
2.             Computer Vision CITS4240 School of Computer Science & Software Engineering, The University of Western Australia.

3.             D.-Y. Huang, T.-W. Lin and W.-C. Hu, ”Automatic Multilevel Thresholding Based On Two-Stage Otsu's Method With Cluster Determination By Valley Estimation”, International Journal of Innovative Computing, Information and Control, ICIC International, vol.7, no.10, 2011, pp. 5631-5644.

4.             H. D. Cheng, X. H. Jiang, Y. Sun, and J. L. Wang. Color image segmentation: advances and prospects. Pattern Recognition, vol.34, 2001, pp. 2259–2281.

5.             H. Gomez-Moreno, S. Maldonado-Bascon, F. Lopez-Ferreras, M. Utrillamanso And P. Gil-Jimenez, “A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines”, Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods (IWANN 03), Menorca, Spain, 2003, pp. 536-543.

6.             J. M. C. Geoffrine and N. Kumarasabapathy, “Study And Analysis Of Impulse Noise Reduction Filters”, Signal & Image Processing : An International Journal (SIPIJ), Vol.2, No.1, March 2011

7.             J. V. Llahi, “Color Constancy and Image Segmentation Techniques for Applications to Mobile Robotics”, Universitat Politècnica de Catalunya, Doctoral thesis, 2005.

8.             K. Pulli and M. Pietikainen, “Range image segmentation based on decomposition of surface normals”, In Proceeding of the 8th Scandinavian Conference on Image Analysis, vol. 2, May 1993, pp. 893-899.

9.             L. Busin, N. Vandenbroucke, and L. Macaire, “Color spaces and image segmentation”, Advances in Imaging and Electron Physics, vol. 151, 2008, pp. 65-168.

10.          M. Fang, G. Yue, and Q. Yu, “The Study on An Application of Otsu Method in Canny Operator”, Proceedings of the 2009 International Symposium on Information Processing (ISIP’09) Huangshan, P. R. China, August 21-23, 2009, pp. 109-112

11.          N. Ikonomakis, K. N. Plataniotis, A. N. Venetsanopoulos, “Color Image Segmentation for Multimedia Applications”, Journal of Intelligent and Robotic Systems, vol.28, 2000, pp. 5–20.

12.          N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Transactions on System Man Cybernetics, vol. SMC-9, no.1, 1979, pp. 62-66.

13.          R. C. Gonzalez and R. E. Woods. Digital Image Processing, Prentice Hall, New Jersey 07458, second edition, 2001.

14.          W. K. Pratt, Digital Image Processing, 4th edition, WILEY, 2007. pp. 579.

15.          W. Skarbek and A. Koschan, Colour image segmentation: a survey, Leiter der Fachbibliothek Informatik, Sekretariat FR 5-4, 1994.

16.          W. X.-ke and Z. Z.-qiang, “An Application of Multi-Thresholding Ostu Algorithm on Chromatic Image”, Computer Application, vol.26, 2006, pp.14-15.




B. Chakrabarti, D. Ghosh, M. Mitra

Paper Title:

Effects of Photo Illumination on Diamond Based DDR IMPATT Diode Operating at MM-wave Frequency Band

Abstract:      The effect of photo illumination on the d.c and small signal performance of diamond based IMPATT diode operating at W-band is investigated using a modified double iterative simulation method. Under optical illumination additional photo generated carriers are produced in the device which modulates the admittance and negative resistance properties of the diode. It is found that the operating frequency shift upward accompanied by degradation of negative conductance, negative resistance, quality factor and output power density level under photo illumination. Decrement in the values of negative conductivity by 19.2 % and in total negative resistance by 21 % has been observed when the diode is exposed to photo illumination. It is also established that the d.c properties of the diode become inferior as the intensity of optical illumination increases.

      Diamond IMPATT, Photo illumination, W-band, Negative Conductivity, Resistivity.


1.                 V.V.Buniatyan and V.M.Aroutiounian, “Wide gap semiconductor Microwave devices”, J.phys.D: Appl.phys. vol.40, no.20, pp.6355-6385.
2.                 I.Mehdi, G.I.Haddad, and R.K Mains, “Microwave and millimeter wave power generation in Silicon carbide Avalanche devices”, J. Appl. Phys., 64(3), August 1988.

3.                 M.Mukherjee, N. mazumder, S.K.Roy, and K.Goswami, “GaN IMPATT diode: a photo-sensitive high power terahertz source”, Semicond. Sci. Technol. (UK), vol.22, pp.1258-67,(2007).

4.                 M.Mukherjee, N. mazumder,andS.K.Roy, “Photosensitivity analysis of Gallium nitride and Silicon carbide terahertz IMPATT oscillators: comparison of theoretical reliability and study on experimental feasibility”, Semicond. Sci. Technol. (UK), vol.22, pp.1258-67, (2007).

5.                 S.R.Pattanaik, G.N.Dash, and J.K.Mishra, “Prospects of 6H-SiC for operation as an IMPATT diode at 140GHz”, Semicond. Sci. Technol., vol.20, pp.299-304, (2005).

6.                 B.Chakrabarti, D.Ghosh, M.Mitra ‘’ High Frequency Performance of GaN Based IMPATT Diodes“, I.J.E.S.T., vol.3 No.8, August 2011.

7.                 D. Ghosh, B. Chakrabarti, M. Mitra “A Detailed Computer Analysis Of SiC And GaN Based IMPATT Diodes Operating at Ka, V And W Band”,  I.J.S.E.R., vol. 3 issue 2, 2012

8.                 N.Fujimori, T.Imai, and A.Doi, “characterization of conducting Diamond films”, Vaccum, vol.36, Issuses 1-3, pp.99-102, 1986.

9.                 K.Okano, H. Naruki, Y.Akiba, T.Kurosu, M. Lida, and y. Hirose, “Synthesis of Diamond thin films having semicoductive properties”, Jpa.J.Appl.phys. 27, L173-5, 1988.

10.              P.W. May, “Diamond thin films: a 21st-century material”, Phil. Trans. R. Soc. Lond. , vol. 358, no.1766, pp. 473–495, (2000).

11.              D.S. Hwang, T. Saito, and  N. Fujimori,  “New etching process for device fabrication using diamond”, Diamond and Related Materials, vol.13,  pp.2207–2210 ,2004.

12.              V. Dmitriev, S.Rendakova, N.Kuznetsov, N.Savkina, A.Andreev, M.Rastegeava, M.Mynbaeva, and A. Morozov, “Large area silicon carbide devices fabricated on SiC wafers with reduced micropipe density”, Materials science and Engineering, B 61-62, 446-449 (1999).

13.              R.J.Trew, J.B.Yan, P.M.Mock, “The potential of Diamond and SiC electronic devices for microwave and millimeter wave power applications”, Proc. IEEE, vol.79, No.5, May 1991.

14.              T.Wu,” Diamond Schottky Contact Transit-time Diode for Terahertz Power Generation”, Int. J. Infrared Milli. Waves, vol. 29, pp.634–640, 2008.

15.              H.P.Vyas, R.J.Gutmann, and J.M.Borrego, ”Effect of hole versus electron photocurrent on microwave-optical interactions in impatt oscillator”, IEEE trans. Elect. Devices, vol.26, no.3, pp.232-234, 1979.

16.              H.K.Gummel and J.L.Blue “Small Signal Theory of Avalanche Noise in IMPATT diodes”, 1967 IEEE Trans.Eletron Devices, vol.14, 569.

17.              S.K.Roy, J.P.Banerjee, and S.P.Pati ,” Numerical Analysis of semiconductor devices”, (NASACODE IV) Dublin: Boole, 1985,P. 494.

18.              M. Mukherjee, S.K. Roy, “Wide band gap III-IV nitride based avalanche transit time diode in terahertz regime: studies on the effects on punch through on
high frequency characteristics and series resistance of the device “, Current Appl. Phys., vol.10, pp.646-651.

19.              H.Eisele and G.I.Haddad, “Active microwave Devices in Microwave Semiconductor Device Physics ed S M Sze (New York)”, p.343, 1997.

20.              T.Watanabe, T.Teraji, T.Ito, Y.Kamakura, and K.Taniguchi, “Monte Carlo simulations of electron transport properties of diamond in high electric fields using full band structure”, J. Appl. Phys., vol.95, No.9, May 2004.

21.              Electronic Archive: New Semiconductor Materials, Characteristics and properties [online]. Available:




B. Ashok, A.Rajendran

Paper Title:

Selective Harmonic Elimination of Multilevel Inverter Using SHEPWM Technique

Abstract:       The emergence of multilevel inverters has been in increase since the last decade. These new types of converters are suitable for high voltage and high power application due to their ability to synthesize waveforms with better harmonic spectrum. Numerous topologies have been introduced and widely studied for utility and drive applications. Amongst these topologies, the multilevel cascaded inverter was introduced in Static VAR compensation and drive systems. This project presents a new technique for getting an effective multilevel SHEPWM control techniques is used to reduce odd harmonics. Selective harmonic elimination Technique in Seven Level Multilevel inverter with SRM is used in MATLAB Simulink environment is used to simulate the results.

       MATLAB Simulink, PWM control techniques, Multilevel Inverter.


1.             EbrahimBabaei, 2008, “A Cascade Multilevel Converter Topology With Reduced Number of Switches” IEEE Transactions on power electronics,Vol. 23, No.6.
2.             J. Rodríguez, J. S. Lai, and F. Z. Peng, “Multilevel inverters: A survey of topologies, controls, and applications,” IEEE Transaction on Industrial electronics, vol. 49, no. 4, pp. 724–738, Aug. 2002

3.             Rashid, M.H, 2004. “Power Electronics:Circuits, devices and applications. Third Edition, Prentice Hall.

4.             Mohan N, Undeland T. M, and Robbins W.P. 2003, Power Electronics: converters, applications and design”, Third Edition.John Wiley and sons.

5.             Selective Harmonics Elimination of PWM cascaded multilevel inverter ANIKET ANAND1, K.P.SINGH2Department of Electrical EngineeringMadan Mohan Malaviya Engineering College Gorakhpur-273010, India

6.             Selective harmonics elimination pwm with self-balancing dc-link capacitors in five-level inverter K. Imarazene1, H. Chekireb2 And E.M. Berkouk2

7.             new approach to solving the harmonic elimination equations for a multilevel converter”, in Proc. IEEE Industry Applications Soc. Annu. Meeting, Salt Lake City, UT, pp. 640-645, Oct.12-16, 2003.

8.             BurakOzpineci, Leon M. Tolbert, John N. Chiasson, “Harmonic Optimization of Multilevel Converters Using Genetic Algorithms”, IEEE Power Electronics Letters, vol. 3, no. 3, pp.92-95, September 2005.

9.             C. Woodford and C. Phillips, “Numerical Methods with Worked Examples”, CHAPMAN & HALL, pp. 45-57, First edition 1997.

10.          S. R. Bowes and S. Grewal, “Simplified harmonic elimination PWM control strategy,” Proc. Inst. Elect. Eng.—Electron. Lett., vol. 34, no. 4, pp. 325–326, Feb. 19, 1998.

11.          P. N. Enjeti, P. D. Ziogas, and J. F. Lindsay, “Programmed PWM techniques to eliminate harmonics: A critical evaluation,” IEEE Trans. Ind.Applicat., vol. 26, pp. 302–316, Mar./Apr. 1990.

12.          H. S. Patel and R. G. Hoft, “Generalized technique of harmonic elimination and voltage control in thyristor inverters—Part I: harmonic elimination,” IEEE Trans. Ind. Applicat., vol. IA-9, pp. 310–317, May/June 1973.




S.kanagavalli, A.Rajendran

Paper Title:

Sensorless Rotor Position Estimation of Switched Reluctance Motor Drive Using Computational Intelligence Techniques

Abstract: This paper deals with an accurate method to detect the rotor position, which is used for high performance operation of Switched Reluctance Motor (SRM). Earlier, a several type of position sensors were used to detect the rotor position but this has many disadvantages like additional cost, electrical connections, mechanical alignment problems, and unreliability. To overcome these disadvantages several sensor less schemes were proposed for the SR Motor in the recent years, there by facilitating the elimination of the rotor position sensor. Here, the sensor fewer schemes is proposed based on fuzzy technique and also using adaptive Neuro fuzzy inference system (ANFIS) which it overcomes the disadvantages of sensor scheme and also it does not require any mathematical models and large lookup tables to predict the position angle. Then position estimation based on fuzzy and ANFIS are compared. In this paper, the rotor position or angle is estimated by using the relationship between flux linkage and phase current based on fuzzy rule base. ANFIS-based model reference system is continuously tuned by using Back Propagation method with actual value of SRM. The simulation results for novel sensorless schemes is described and developed in MATLAB and shown the effectiveness of this sensor less Scheme.

 ANFIS, SRM, Sensorless Rotor Position Scheme, Fuzzy Logic Estimator.


1.              Buju G. S., Menis Roberto and Valla Maria. J, ―Variable Structure Control of an SRM Drives‖, IEEE Trans. on Ind.Electron., Vol. 1, Feb 1993, pp 56-63.
2.              R. Krishnan, ―Switched Reluctance Motor Drives: Modelling, Simulation, Analysis, Design, and Applications‖, CRC Press,2001.

3.              Speed control of SR motor by self-tuning fuzzy PI controller with artificial neural network ERCUMENT KARAKAS1, and SONER VARDARBASI

4.              Application Of Fuzzy Logic In Control Of Switched Reluctance Motor P. Brandstetter, R. Hrbac, M. Polak,

5.              L.StepanecF.Soares, P.J.CostaBranco, ―Simulation of a 6/4 Switched Reluctance Motor Based on Matlab/Simulink Environment,‖IEEE Trans. on Aerospace and Electronic System, vol. 37, no. 3, pp. 989-1009, July 2001.

6.              G. Baoming and Z. Nan, ―DSP- based Discrete-Time Reaching Law Control of Switched Reluctance Motor‖, IEEE Internationalconference IPEMC, 2006, pp. 1-5.

7.              G. Gallegos-Lopez, P. C. Kjaer, and T. J. E. Miller, ―High-grade position estimation for SRM drives using flux linkage/current correction model,‖IEEE Trans. Ind.
Appl., vol. 35, no. 4, pp. 859–869, Jul./Aug. 1999.

8.              Ramasamy G., Rajandran R.V., Sahoo N.C., ―Modeling of Switched Reluctance Motor drive System using Matlab/Simulink for Performance Analysis of Current Controllers‖, IEEE PEDS,2005, pp. 892-897.

9.              MehrdadEshani, Iqbal Husain, SailendraMahajan and K. R. Ramani, New Modulation Encoding Techniques for Indirect Position Sensing in Switched Reluctance Motors‖, IEEE Trans.Industry Appl., Vol. 30, No. 1, January/February 1994, pp. 85-91.

10.           MehrdadEshani, Iqbal Husain and Ashok B. Kulkarni, ―Elimination of Discrete Position Sensor and Current Sensor in Switched Reluctance Motor Drives‖, IEEE Trans. Industry Appl.,Vol. 28, No. 1, January/February 1992, pp. 128-135.

11.           HongweiGao, Salmasi, F.R., Ehsani, M., ―Inductance model based sensorless control of the switched reluctance motor drive at low speed,” IEEE Trans. On Power Electron.,Vol. 19, Issue 6, pp. 1568-1573, Nov. 2004.

12.           Gilberto C. D. Sousa, B. K. Bose,” A Fuzzy Set Theory Based Control of a Phase Controlled Converter DC Machine Drive”, Trans. on Industry Appl., Vol.30, No.1, Jan.1994.




K. L. Neela, P. Mercy Nesa Rani, T. Rajesh

Paper Title:

A Simulation Model for Corner Detection in Fruits Foveated Images

Abstract:   Corner detection is a challenging and important research area in computer vision and object recognition systems. However, they have some problems such as sensitive to noise, poor localization. The corner detector -Feature Accelerated Segment Test (FAST) which will be a good locator of corners in foveated images similar to Human Visual Fixations. The feature detector considers pixels in a circular region. This technique creates uniformity over the image area considering the brightness and darkness for estimation that constitutes as corner. The resulting detector will detect very stable features in foveated images. This paper deals with foveation filtering and corner detection to establish foveal location in natural images. The proposed approach is implemented with the help of VC++ language and will provide fine location for all real world applications.

 Foveation Filtering, Corner Detection, Foveated images, FAST algorithm, Fruit Images


1.              B.K.Chauhan , L. Mitra, and H.K. dabas . Facts and Figures: Indian    fruits and Vegetables. National Horticulre Board, Gurgaon, India.1997.
2.              S. Kiranyaz, H. Liu, M. Ferreira and   M. Gabbouj ,” An Efficientapproach for boundary based corner detection by maximizing               bending ratio and Curvature”, Proceedings of IWSSIP ,2006, pp. 475-378.

3.              Q. Ji, and R. Haralick, “ Corner detection with covariance propagation” in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 362–367.

4.              H. P. Moravec ,” Visual Mapping by a Robot Rover”      International Joint Conference on  Artificial Intelligence, pp. 598-600, 1979.

5.              C .Harris, and M. Stephens, “A combined    corner and edge detector”, in 4th Alvey Vision Conference Proceedings,1988, pp. 147–151.

6.              J.A. Noble ,”Finding corners”, Image and Vision Computing, vol. 6, no.2,1988 pp.121–128.

7.              F. Mokhtarian,  and R. Suomela, “Curvature Scale Space Based Image Corner Detection”, Proceedings of European Signal Processing       Conference, Island of Rhodes, Greece,1998, pp. 2549-2552.

8.              S.M. Smith, and J.M. Brady, “SUSAN—a new approach to low levelimage processing,”  International Journal of Computer Vision., vol.  23, no. 1,1977,  pp. 45–78.

9.              T. L. Arnow,  and A. C. Bovik, “ Finding     corners in images by foveated search”,. IEEE         Transactions on Image Processing, Volume:16 ,2007,  Page(s): 813 – 823.

10.           E. Rosten,  and T. Drummond, “Machine learning for high  speed corner detection,” in 9th Euproean Conference       on Computer Vision, vol. 1, 2006, pp. 430–443.




U.Dinesh, Aravind Ram.S, M.Hariharan, K.Hariharan

Paper Title:

A Novel Rom-less Direct Digital Frequency Synthesizer based on Euler Infinite Series

Abstract:    The traditional DDFS based on a look up table needs a large sized ROM and it is more complex. This paper deals with a novel ROM-less architecture based on the approximation of Euler’s Infinite series. It has advantages of low complexity, low computational delay, and high spectral purity. The proposed DDFS has a high SFDR (spurious free dynamic range) when compared with [1] and the value is as good as 72.3dBc.

    Direct Digital Frequency Synthesis, Euler Infinite Series, low complexity, spectral.


1.             M. Kesoulis, D. Soudris, C. Koukourlis and A.Thanailakis, 'Systematic methodology for designing low power direct digital frequency synthesizers', lET Circuits Devices Syst., 4, p.293 (2007).
2.             J.Tierney, C.M.Radre and B.Gold,’A Digital Frequency Synthesizer’, IEEE Transactions on Audio and Electroacoustics, March 1971.

3.             Avanindra Madisetti, Alan Y Kwentus, Alan N Wilson, ’A 100-MHz, 16-b, Direct Digital Frequency Synthesizer with a 100-dBc Spurious-Free Dynamic Range’, IEEE Journal of solid-state circuits, vol 34, 8 August 1999, pp 1034-1043.

4.             D.Soudris, M.Kesoulis, C.Koukourlis, A.Thanailakis and S.Blionas, ’Alternative Direct Digital Frequency Synthesizer Architectures with Reduced Memory Size’,
Proceedings of The 2003 IEEE International Symposium on Circuits and Systems, vol 2,pp 73-76.

5.             J.Vankka, ’Methods of mapping from phase to sine amplitude in direct digital frequency  synthesis’, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol 44,no.2,pp.526-534,(1997)March.

6.             Kenneth A.Essenwanger, Victor S.Reinhardt, ’Sine Output DDSs A Survey Of The State Of The Art’, IEEE International Frequency Control Symposium(1998)

7.             Paul Trow’s Math Page, ’A Famous Infinite Series’, March 25,2013,

8.             Run Xing, Xiao-tong Zhang, Han Li, Qin Wang and Zhan-cai Li, ”An Area Optimized Direct Digital Frequency Synthesizer Based on Improved Hybrid CORDIC Algorithm”, IEEE Proceedings of International Workshop on Signal Design and its Applications   in communications,pp.243-246 (2007).

9.             Tze-Yun Sung,Lyu-Ting Ko and His-Chin Hsin, ”Low Power and High SFDR Direct Digital Frequency Synthesizer Based on Hybrid CORDIC Algortihm”, IEEE International Symposium on Circuits and Systems,pp.249-252 (2009).

10.          Shu-Chung Yi, Kun-Tse Lee, Jin-Jia Chen, Chien-Hung Lin, NCUE, Changhua, Taiwan 500,ROC,”A low power efficient direct digital frequency synthesizer based on new two-level lookup table”, IEEE CCECE/CCGEI, Ottawa (2006) May.

11.          Yu Xuefeng, Dai Foster, Shi Yin, Zhu Ronghua,”2 GHz 8-bit CMOS ROM-less direct digital frequency synthesizer”, ISCAS 2005 IEEE International Symposium on Circuits and Systems, Vol 5,pp.4397-4400.

12.          Heiskanen Antti, Mantyniemi Antti, Rahkonen Timo E,”A 30 MHz DDS Clock Generator with 8-bit,130ps delay generator and -50dBc spurious level”, Proceedings of the 27th European Solid State Circuits Conference,2001.

13.          “Qualcomm 2368 Dual Direct Synthesizer”, Synthesizer Products Data Book, 80-24127-1 A, 8/97.

14.          K.Hariharan, E.Benitta Hubert, K.V.O.Divyalakshmi,K.Shamalla and  V.Abhai Kumar, ”Coherent Sinusoid Generation using Novel DDFS Architecture”, Internation Journal of Smart Home,Vol.6,No.1,January 2012.




Abhijit Maidamwar, D.Marotakar, Manisha Khorgade, Swati Sorte

Paper Title:

Reduction of Complexity for Estimating the Open Loop Pitch of the CS-ACELP Codec

Abstract:     G.729 or Conjugate structure algebraic CELP is a audio voice codec that compresses speech signal based on model characteristics of human voice. This paper deals with the reduction of the computational complexity for estimating the open loop pitch of the CS-ACELP codec, described in ITU recommendation G.729. For reduction in computation of open loop pitch analysis using Matlab 7.4, the weighted delta-LSP function is used. This depth first tree search is also used in G.729 for reducing the search complexity with minimum effort. In experimental study of our paper we are showing the comparing graphical result of Open Loop Pitch in Matlab 7.4, we are trying to prove that our proposed method save the computational time for calculation of open loop pitch

     Open loop pitch analysis of G.729, Graphical result of open loop pitch, A-CELP, bit allocation of 8 kbps in G.729


1.                Salami et al: ‘Design and Description of CS-ACELP: A toll quality 8kb/s speech coder’, IEEE trans Speech Audio Process, 1996.
2.                ITU-T G.729: ‘Coding of speech at 8 kb/s using CS-ACELP’, 1996.

3.                Kataoka et al: ‘An 8 kb/s speech coder based on conjugate structured CELP’, IEEE int. conf. acoustic, speech, signal processing, 1993.

4.                kataoka et al: ‘LSP and gain quantization for proposed ITU-T 8 kb/s speech coding standard’, IEEE workshop on speech coding, 1995.

5.                Shaw Hwa Hwang: ‘ Comput tional improvement for G.729 standard’, 2003.

6.                B. Roach, “Session Initiation Protocol (SIP) -specific event notification,” RFC 3265, June 2002.

7.                Johnston, S. Donovan, R. Sparks, C. Cunningham, and K. Summers, “Session Initiation Protocol (SIP) Public Switched Telephone Network (PSTN) call flows,” RFC 3666, December 2003.

8.                R. Sparks, “The Session Initiation Protocol (SIP) refer method,” RFC 3515, April 2003.

9.                ITU-T Recommendation P.862, “Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs,” Feb. 2001.

10.             ITU-T Recommendation P.862 Amendment 1, “Source code for reference implementation and conformance tests,” March 2003.

11.             E. Conway, “Output-based method of applying PESQ to measure the perceptual quality of framed speech signals”, in IEEE Wireless Communications and Networking Conference, Vol. 4, pp. 2521-2526,

12.             J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.

13.             Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals
style),” IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301].

14.             M. Young, The Techincal Writers Handbook.  Mill Valley, CA: University Science, 1989.

15.             (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume (issue).               Available: http://www

16.             J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available:




Purnima Pandit

Paper Title:

Multi-objective Linear Programming Problems involving Fuzzy Parameters

Abstract:  In many Linear programming problems it becomes desirable to have multiple criterions being optimized under the similar stated constraints. Also in the real life model the data can rarely be determined exactly with certainty and precision. The experimental data or the experts’ estimation may lead us to the interval of real numbers as the estimates of the parameters involved in the optimization problems. Such parameters can be efficiently modeled as fuzzy number. The situation can be then represented as Multi-objective LPP with fuzzy parameters. We here propose the method to compute the solution for multi-objective fully fuzzy LPP involving parameters represented by triangular fuzzy numbers.

 fully fuzzy LPP,  fuzzy numbers,  multi - objective, triangular fuzzy numbers.


1.              Buckley, J.J., Fuzzy Probabilities: New Approach and Application. Springer (2005).
2.              Mangasarian, O.L., “Non-Linear Programming” Mc-Graw Hill Book Co. New York. (1969).

3.              Zadeh, L.A. , Fuzzy sets as a basic for theory of possibility, FSSI, 3-28,(1978) .

4.              Zimmermann, H.J., Using Fuzzy sets in operational research, EJOR 13, 201-216,(1983).

5.              Bellman R. E. ,Zadeh L.A., Decision Making in A Fuzzy Environment, Management Science, vol. 17, 1970, pp. 141-164.

6.              Zimmerman H. J., Fuzzy Programming and Linear Programming with Several Objective Functions, Fuzzy Sets and Systems, vol. 1, 1978, pp. 45-55.

7.              Tanaka H, Ichihashi H, Asai K, Formulation of fuzzy linear programming  problem based on comparison of fuzzy numbers, Control Cybernetics 3 (3): 185-194. (1991).

8.              Pandian P., Multi-objective Programming Approach for  Fuzzy Linear Programming Problems, Applied Mathematical Sciences, Vol. 7, no. 37, 1811 - 1817 , 2013.




Soumik Basak, Koustav Sarkar, Deepak Kumar, Sudarshan Chakravorty

Paper Title:

A Novel DFT Spreading Technique for Reduction of Peak- to-Average Power Ratio (PAPR) in OFDM Systems

Abstract:   The transmitted signal in an OFDM system can have high peak values due to the presence of many subcarriers. The high Peak-to-average Power Ratio (PAPR), as compared to a single carrier system in an OFDM system is detrimental for its proper operation. It decreases the signal-to- quantization noise ratio (SQNR) of the Analog-to-Digital and Digital-To-Analog converters. This degrades the efficiency of the power amplifiers in the transmitter. This paper aims to improve the PAPR in the uplink by DFT spreading so as to preserve the limited battery power in a mobile terminal. 

 DFT spreading, OFDM,Peak-to-AveragePower Ratio,SQNR.


1.                Litsyn.s(2007) Peak Power Control in Multicarrier Communications, Cambridge University Press.
2.                Han S.H. and Lee J.H.(2005)”An overview of peak-to-average power ratio reduction techniques for multicarrier transmission,” IEEE  Trans in Wireless Communication, 12(2),56-65.

3.                Jeon W.G., Chang K.H., and Cho Y.S. (1997) “An adaptive data predistorter for compensation of nonlinear distortion in OFDM system”, IEEE Trans in comm.., 45(10), 1167-1171.

4.                Galda, D and Rohling,H.(2002)”A low complexity transmitter structure for OFDM-FDMA uplink system”,IEEE VTC’02, Vol.4,pp 1737-1741.

5.                Myung, H.G., Lin,J, and goodman D.J. “peak-to-average power ratio of Single Carrierfdma  signals with pulse shaping”, PIMRC’06,ppl-5.




Tong Fu, Di Yin, Li Xiaoli, Chen Hui

Paper Title:

Simplified Model for Representing Dynamic Textures using Markov Model

Abstract:    Dynamic textures are sequences of images of moving scenes that exhibit certain stationary properties in time; these include sea-waves, smoke, foliage, whirlwind etc. In previous works [1,2], dynamic textures are usually modeled as linear models, and parameters of the model are identified in the sense of maximum likelihood or minimum prediction error variance. Once its parameters are learned, a model has predictive power and can be used for extrapolating synthetic sequences. In this work we study a particular type of dynamic textures that can be represented in the form of Markov Models. An aggregation algorithm can then be adopted to reduce its complexity. The resulting low-dimensional models can capture complex visual phenomena with low computation cost.

 Dynamic Texture, Markov Model, Aggregation, Reduced order model.


1.              LennartLjung “System Identification: Theory for the User”, Pearson Education,1998.
2.              Dmitry Chetverikov and Renaud Péteri, "A Brief Survey of Dynamic Texture Description and Recognition", Computer Recognition Systems Advances in Soft Computing Volume 30, 2005, pp. 17-26.

3.              Renaud Peteri and Dmitry Chetverikov, “Dynamic Texture Recognition Using Normal Flow and Texture Regularity”, In Proceedings of Iberian Conference on Pattern Recognition and Image Analysis, 2005, pp.223--230

4.              Sun Yu, Prashant G. Mehta, “Bode-Like Fundamental Performance Limitations in Control of Nonlinear Systems”. IEEE Transaction on Automatic Control, vol. 55, pp.1390-1405, 2010.

5.              Martin Szummer and Rosalind W. Picard, “Temporal Texture Modeling”, In IEEE International Conference on Image Processing, 1996, pp.823—826

6.              Franco Woolfe and Andrew Fitzgibbon, "Shift-Invariant dynamic texture recognition", Proceeding of ECCV 2006, Pages 549-562

7.              Sun Yu, Prashant G. Mehta, “Fundamental performance limitations with Kullback-Leibler control cost,” IEEE Conference on Decision & Control, pp.7063-7068, 2010.

8.              Gianfranco,"Dynamic Textures", International Journal of Computer Vision, 2001, pp.439-446

9.              Kun Deng, Y. Sun, P. Mehta and S. Meyn, “An information-theoretic framework to aggregate a Markov chain,” In Proceedings of American Control Conference, pp.731-736, 2009.

10.           Guoying Zhao and MattiPietikäinen," Dynamic Texture Recognition Using Volume Local Binary Patterns", Proceedings of ECCV 2006 Workshop on Dynamical Vision, 2006, pp.165-177.

11.           Sun Yu, Prashant G. Mehta, “The Kullback-Leibler Rate Pseudo-Metric for Comparing Dynamical Systems”. IEEE Transaction on Automatic Control, vol.55, pp.1585-1598, 2010.

12.           Guoying Zhao and Pietikainen, M., "Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions", IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 2007, pp.915-928

13.           Fujita and S. Nayar. “Recognition of Dynamic Textures using Impulse Responses of State Variables”, In Proceedings of the Third International Workshop on Texture Analysis and Synthesis, 2003

14.           Y. Sun and P. G. Mehta, “Fundamental performance limitations via entropy estimates with hidden Markov models,” IEEE Conference on Decision & Control, pp. 3982–3988, 2007.

15.           Ravichandran, A, Chaudhry, R.and Vidal, R. “Categorizing Dynamic Textures Using a Bag of Dynamical Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2),2013, pp.342 – 353

16.           Sun Yu, Prashant G. Mehta, “The Kullback-Leibler rate metric for comparing dynamical systems”, in Procs. of Conference on Decision & Control, pp.8363-8368, 2009.

17.           Ghanem, B.; Ahuja, N., "Phase Based Modelling of Dynamic Textures," Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pp.14-21, 2007

18.           Ravichandran, A.; Chaudhry, R.; Vidal, R., "View-invariant dynamic texture recognition using a bag of dynamical systems," IEEE Conference on Computer Vision and Pattern Recognition, pp.1651-1657, 2009

19.           Rached, Z.; Alajaji, F.; Campbell, L.L., "The Kullback-Leibler divergence rate between Markov sources," IEEE Transactions on Information Theory, vol.50, no.5, pp.917-921, 2004

20.           Mager DE, et. al., “Kullback-Leibler clustering of continuous wavelet transform measures of heart rate variability”, Biomed SciInstrum, 2004, pp.337-42.




Abdelmajid Hassan Mansour Emam

Paper Title:

Additional Authentication and Authorization using Registered Email-ID for Cloud Computing

Abstract:     Cloud computing is a new computing paradigm that changes the way of information technology is provided and used. But achieving acceptable level of information security issues are an important aspect and a key factor in the cloud. This paper firstly lists some of the different security issues of the cloud computing, and then proposes additional security mechanism of authenticating and authorizing users by using registered Email-ID in the cloud computing. To ensure that only authorized persons may use the resources in the role of identity and authorizations management.

   Cloud Computing, Authentication, Authorization, Identity management.


1.              Manjea Kim, Hoon Jeong, Euiin Choi,” Context-aware Platform for User Authentication in Cloud Database Computing”, International Conference on Future Information Technology and Management Science & Engineering Lecture Notes in Information Technology, Vol.14, pp.170-176, 2012.
2.              Keshou Wu, Lizhao Liu, Jian Liu, Weifeng Li, Gang Xie, Xiaona Tong and Yun Lin, “Researches on Grid Security Authentication Algorithm in Cloud Computing”, JOURNAL OF NETWORKS, VOL. 6, NO. 11, pp. 1639-1646, NOVEMBER 2011.

3.              Janita S. Patel, G.B.Jethava ,”Providing Authorization by Using Face Recognization for Private Cloud Computing”, International Journal of Engineering and Advanced Technology(IJEAT) ISSN: 2249-8958, Volume-2, Issue-2, pp. 231-234, December 2012.

4.              B.Meena, Krishnaveer Abhishek Challa, “Cloud Computing Security Issues with Possible Solutions”, International Journal of Computer Science And Technology (IJCST), ISSN:0976-8491 (Online) | ISSN : 2229-4333 (Print) Vol. 3, pp. 340-344, Issue 1, Jan. - March 2012.

5.              Himabindu Vallabhu, R V Satyanarayana, “Biometric Authentication as a Service on Cloud: Novel Solution”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-4, pp.163-165, September 2012.

6.              Maninder Singh , Sarbjeet Singh, “Design and Implementation of Multi-tier Authentication Scheme in Cloud”, International Journal of Computer Science Issues(IJCSI), ISSN (Online):1694-0814, Vol. 9, Issue 5, No 2, pp. 181-187, September 2012.

7.              Ajey Singh, Dr. Maneesh Shrivastava, “Overview of Attacks on Cloud Computing”, International Journal of Engineering and Innovative Technology (IJEIT), ISSN: 2277-3754, Volume 1, Issue 4, pp. 321-323, April 2012.

8.              Abdul Raouf Khan, “ACCESS CONTROL IN CLOUD COMPUTING ENVIRONMENT”, Asian Research Publishing Network (ARPN), Journal of Engineering and Applied Sciences, ISSN 1819-6608, VOL. 7, NO. 5, pp. 613-615, MAY 2012.

9.              Sanjeet Kumar Nayak,  Subasish Mohapatra, Banshidhar Majhi, “ An Improved Mutual Authentication Framework for Cloud Computing”, International Journal of Computer Applications (0975 – 8887) Volume 52– No.5, pp. 36-41, August 2012.

10.           B.Prasanalakshmi, A.Kannammal, “Secure Credential Federation for Hybrid Cloud Environment with SAML Enabled Multifactor Authentication using Biometrics”, International Journal of Computer Applications (0975 – 8887) Volume 53– No.18, pp. 13-19, September 2012.

11.           Pradnya B. Rane, Pallavi Kulkarni, Suchita Patil, Dr. B.B.Meshram, “Authentication and Authorization:Tool for Ecommerce Security”, IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498, Vol.2, No.1, pp. 150-157, 2012.

12.           Linda A. Cornwall, Jens Jensen, David P. Kelsey, A kos Frohner, Daniel Kouril, Franck Bonnassieux, Sophie Nicoud, Karoly Lorentey, Joni Hahkala, Mika Silander, Roberto Cecchini, Vincenzo Ciaschini, Luca dell’Agnello, Fabio Spataro, David O’Callaghan, Olle Mulmo, Gian Luca Volpato, David Groep, Martijn Steenbakkers and Andrew McNab, “Authentication and Authorization Mechanisms for Multi-Domain Grid Environments”, Journal of Grid Computing (2004) 2: pp. 301–311.

13.           Federal office for information security, “Security Recommendations for Cloud Computing Providers (Minimum information security requirements)”, White Paper, Section 114, Security Management and IT-Grundschutz, P.O. Box 20 03 63, 53133 Bonn.

14.           TRIANZ, “Authentication and Access Control- The Cornerstone of Information Security“, White Paper, Vinay Purohit, September 2007.

15.           Jeon SeungHwan, Yvette E. Gelogo and Byungjoo Park, “Next Generation Cloud Computing Issues and Solutions”, International Journal of Control and Automation Vol., No. 1, pp. 63-70, March, 2012.

16.           Jaejung Kim, Seng-phil Hong, “A Consolidated Authentication Model in Cloud Computing Environments”, International Journal of Multimedia and Ubiquitous Engineering Vol. 7, No. 3, pp. 151-160, July, 2012.

17.           Pardeep Kumar, Vivek Kumar Sehgal , Durg Singh Chauhan, P. K. Gupta and Manoj Diwakar, “Effective Ways of Secure, Private and Trusted Cloud Computing”, International Journal of Computer Science Issues(IJCSI), ISSN (Online): 1694-0814, Vol. 8, Issue 3, No. 2, pp. 412-421, May 2011.




D. Hari Hara Santosh, P. Venkatesh, P. Poornesh, L. Narayana Rao, N. Arun Kumar

Paper Title:

Tracking Multiple Moving Objects Using Gaussian Mixture Model

Abstract:      The advance of technology makes video acquisition devices better and less costly, thereby increasing the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more information about how objects and scenarios change over time. For object recognition, navigation systems and surveillance systems, object tracking is an indispensable first-step. The conventional approach to object tracking is based on the difference between the current image and the background image. The algorithms based on the difference image are useful in extracting the moving objects from the image and track them in consecutive frames. The proposed algorithm, consisting of three stages i.e. color extraction, foreground detection using Gaussian Mixture Model and object tracking using Blob Analysis. Initially color extraction is done to extract the required color from a particular picture frame, after color extraction the moving objects present in the foreground are detected using Gaussian Mixture Model and Blob Analysis is applied on consecutive frames of video sequence, so as to observe the motion of the object, hence the moving object in the video sequences will be tracked.

    gaussian mixture model, multiple object tracking blob analysis, background subtraction, foreground detection


1.             Takashi morimoto, Osama kiriyama, youmei harada,Object tracking in video images based on image segmentation and pattern matching IEEE conference proceedings, vol no 05, page no, 3215-3218, 2005
2.             Zehang, S., Bebis, G., Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)

3.             Bin, D., Yajun, F., Tao,W.: A vehicle detectionmethod via symmetry in multi-scale windows. In: 2nd IEEE Conference on Industrial Electronics and Applications, 2007 (ICIEA 2007), pp. 1827–1831 (2007)

4.             Nedevschi, S., Vatavu, A., Oniga, F., Meinecke, M.M.: Forward collision detection using a stereo vision system. In: 4th International Conference on Intelligent Computer Communication and Processing, 2008 (ICCP 2008), pp. 115–122 (2008)

5.             Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification using Gaussian Mixture Speaker Models. IEEE Transactions on Acoustics, Speech, and Signal Processing 3(1) (1995) 72–83

6.             Boeing, A., Braunl, T.: ImprovCV: Open component based automotive vision. In: Intelligent Vehicles Symposium, 2008 IEEE, pp. 297–302 (2008)

7.             Wei, L., XueZhi, W., Bobo, D., Huai, Y., Nan, W.: Rear vehicle detection and tracking for lane change assist. In: Intelligent Vehicles Symposium, 2007 IEEE, pp. 252–257 (2007)

8.             Junqiu wang [Member IEEE], yasushi yagi [Member IEEE],‖Integrating colour and shape texture features for adaptive real time object tracking‖, IEEE transactions on image processing, vol no 17,no 2, page no 235-240,2007 




Pranjali Raturi, Aarti Pandey

Paper Title:

Field Strength Predicting Outdoor Models

Abstract:       The main objective of this paper is a comprehensive review of outdoor propagation model in different geographical areas. A wide variety of radio propagation models for different wireless services that specifically address varying propagation environments and operating frequency bands are generally known. A large number of propagation prediction models have been developed for various terrains Irregularities, tunnels, urban streets and buildings, earth curvature, etc

  outdoor, propagation model, path loss


1.              J. A. Molisch, L. Greenstein, and M. Shafi, “Propagation issues for cognitive radio,” Proceedings of the IEEE, vol. 97, no. 5, pp. 787–804, May 2009.
2.              J. H. Tarng, W.-S. Liu, Y.-F. Huang, and J.-M. Huang, “A novel and efficient hybrid model of radio multipathfading channels in indoor environments,” IEEE transactions on Antennas and Propagation, vol. 51, no. 9, March 2003.

3.              C. Phillips, D. Sicker, and D. Grunwald, “Bounding the error of path loss models,” in IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), May 2011, pp. 71–82.

4.              Valcarce et al., “Applying FDTD to the coverage prediction of WiMAX femtocells,” EURASIP Journal on Wireless Communications and Networking, Mar. 2009. [Online]. Available:

5.              P. Ky¨osti et al., “WINNER II Channel Models,” WINER II Public Deliverable, Sep. 2007.

6.              “Predicting coverage and interference involving the indoor-outdoor interface,” Ofcom, Project SES-2005-08, Tech. Rep., Jan. 2007.

7.              H. Claussen, L. T. W. Ho, and L. G. Samuel, “An overview of the femtocell concept,” Bell Labs Technical Journal, vol. 13, no. 1, pp. 221–245, May 2008.

8.              Medeisis and A. Kajackas, “On the use of the universal Okumura-Hata propagation prediction model in rural areas,” in IEEE 51st Vehicular Technology Conference (VTC-Spring 2000), May 2000, pp. 450 – 453.

9.              R. Vaughan and J. B. Andersen, “Channels, propagation and antennas for mobile communications,” IEE press, 2003.

10.           M. Gordziel, “The distributed spectrum sensing GUI,” in Evaluation of Time-Synchronized Spatially Distributed Radio Spectrum Occupancy Measurements. Diploma thesis, Department of Wireless Networks RWTH Aachen University, December 2008, pp. 95–96.

11.           3GPP TS25.224 (Release 5), “(UTRAN) overall description,” September 2003.

12.           J. Nasreddine, L. Nuaymi, and X. Lagrange, “Adaptive power control algorithm with stabilization zone,” Annals of telecommunications, vol. 61, no. 9-10, Sept-Oct 2006.

13.           J. Zhang, G. De La Roche, A. Valcarce, D. L´opez-P´erez, E. Liu, and H. Song, Femtocells: Technologies and Deployment. Wiley, Jan. 2010.

14.           D. Lopez-Perez, A. Valcarce, G. De La Roche, and J. Zhang, “OFDMA Femtocells: A Roadmap on Interference Avoidance,” IEEE Communications Magazine, vol. 47, no. 9, pp. 41–48, Sep. 2009.

15.           V. Chandrasekhar and J. G. Andrews, “Uplink Capacity and Interference Avoidance for Two-Tier Femtocell Networks,” IEEE Transactions on Wireless Communications, February 2008.

16.           Alcatel-Lucent, picoChip Designs and Vodafone, “Simulation assumptions and parameters for FDD HENB RF requirements,” R4-092042, 3GPP TSG RAN WG4 (Radio) Meeting # 51, San Francisco, 2009.




S. Elangovan, K. Thanushkodi

Paper Title:

A Novel Direct Power Control of 3phase Induction Multi Motor Drive with AFF

Abstract:     Direct Power Control (DPC) of three phase PWM rectifiers without line voltage sensors and based on Virtual flux estimation is presented.  In this paper, the compensation of neighboring nonlinear power load is proposed.  The active filtering function enlarges the functionality of PWM rectifiers, which decreases the cost of additional installation of compensating equipment.  It gives a chance to fulfill both shunt active power filter and PWM rectifier tasks in a multimotor drive system by one advanced PWM rectifier.  The Direct Power Control Space Vector modulated (DPC-SVM) and new Synchronous Double Reference Frame Phase locked Loop (SDRF-PLL) approach, made the control system resistant to a majority of line voltage disturbances and also it allow a constant switching frequency of the converters, leading to least switching losses.  This assures proper operation of the system for abnormal and failure grid conditions.  Simulation results have proven excellent performance and verify the validity of the proposed system.

 Active filtering function, Direct Power Control, SDRF, Space vector modulation.


1.              T. Kataoka, Y. Fuse, D. Nakeajima, and S. Nishikata, “A three phase voltage type PWM rectifier with the function of an active power filter”, Power Electron. Variable speed drives, pp.386-391, Sep.2000.
2.              M. Malinowski, M.P. Kazmierkowski, “Simple direct power control of three phase PWM rectifier using space vector modulation(DPC-SVM)”, IEEE Trans. Industrial Electronics, Vol.51, No.2, Apr 2004,pp.447-454.

3.              H. Akagi, “New trends in active filters for power conditioning”, IEEE Trans. Industrial Application, Vol.32, No.6, pp.1312-1332, Nov./Dec. 1996.

4.              F.Z. Peng,”Application issues of active power filters”, IEEE Industrial Application magazine, Vol.4, no.5, pp.21-30, Sep/Oct.1998.

5.              F. Abrahamsen and A. David, “Adjustable speed drive with active filter capability for harmonic current compensations” in Proc.IEEE PESC’95,1995, pp.1137-1143.

6.              M. Malinowski, M.P. Kazmierkowski, S. Hansen, F. Blaabjerg and G.D. Marques,”Virtual flux based direct power control of three phase PWM rectifiers”, IEEE Trans. Ind. Applications, Vol.37, no.4, pp,1019-1027, Jul/Aug.2001.

7.              M. Malinowski, G. Marques, M. Chichowlas, and M.P. Kazmierkowski, “New direct power control of three phase PWM boost rectifiers under distorted and imbalanced line voltage conditions”, In proc. IEEE ISIE’03. Rio de Jeneiro, Brazil, 2003, CD ROM.

8.              Ghosh and A. Joshi, “a new algorithm for the generation of reference voltages of a DVR using the method of instantaneous symmetrical components”, IEEE power Eng. Rev., Vol2, no.1, pp. 63-65. Jan 2002.

9.              S. Bhattacharya and D. Divan, “Active filter solutions for utility interface of industrial loads”, In proc. Int. Conf. Drives and Energy Systems for Industrial growth, vol.2, Jan 8-11, 1996, pp.1078-1084.

10.           J.A. Oliver, R. Lawrence and B.B. Banerjeee, “How to specify power quality – tolerant process equipment”, IEEE Ind. Appl. Mag., vol.8, no.5, pp.21-30, Sep/Oct.2002.

11.           M.P. Kazmierkowski, R. Krishnan and F. Blaabjerg, “control in power electronics. New York; Academic 2002.

12.           P. Rodriguesz, J, Bergas, and L. Sainz , “New PLL approach considering unbalanced line voltage condition” in Proc. IEEE Int. conf. Power and Energy systems, June 2002, pp.329-334.

13.           M. Cichowlas, M. Malinowski, M.P. Kazmierkowski, and F. Blaabejerg, “Direct power control for three phase PWM rectifier with active filtering function”,  in Proc. IEEE APEC’03, Miami beach, FL, Feb.9-13,2003, CD ROM.

14.           S. Chung, “A phase tracking system for three phase utility interface inverters”, IEEE Trans. Power Electronics, Vol15, no.3, pp.431-438.  May 2000. 




Dasari Sowmya, Vemu Samson Deva Kumar, Ch.V.Phani Krishna

Paper Title:

Crew Scheduling Management System

Abstract:      Indian Railways is the world's ninth largest commercial or utility employer, by number of employees, with over 1.4 million employees. Indian Railways (reporting mark IR) is an Indian state-owned enterprise, owned and operated by the government of India through the Ministry of Railways. Our Railways were first introduced to India in 1853 from Bombay to Thane. Indian Railways is the golden era to our nation. RUNNING ROOM is a railway project which aims “To allocate the beds for the loco Pilots (LP’s), Assistant Loco Pilots (ALP’s) and Guards in the running room of all divisions. It is also used to display the allocated beds of different sections through online”, is using J2EE technology. The project maintains the registers of the LP’s, ALP’s, Guards those who are traveling between different sections and to know the timings of Loco Pilots. In this the Loco-Pilot and Assistant-Pilots will take rest after traveling to a particular distance for a specified time. The LP/ALP must register into the book by giving the details like his name, incoming train no, outgoing date, outgoing train no, room which is allocated, room departure time and also along with his designation and head quarters. It is also called bed occupation register. In this project if the bed is filled it will be indicated with a particular color. By this we can get the remaining number of beds so that we can allocate for the other pilots all this information must be provided through online. When a pilot register in the login form and enter his details like username, password he will redirect to the bed occupation register. While entering to the designation and some other details the particular room number and the bed number will be allocated to the LP/ALP. The process will be easy, comparing to the present situation, which is done as paper work.

  Running Room, Loco-pilot, Assistant Loco-pilot, Bed allocation.


1.             Core Java™ 2 Volume I  Fundamentals 7th Edition-Cay S. Hortsman
2.             Pearson Education  Sun Microsystems Gary Cornell  

3.             Core Java™ 2 Volume II  Advanced -Cay S. Hortsman              

4.             Pearson Education  Sun Microsystems Gary Cornell  

5.             Head First Servlets & JSP- Eric Freeman     

6.             O'Reilly  SPD Elisabeth Freeman 

7.             The Book of JavaScript 2nd Edition-Thau  

8.             Effective Java  Programming Language Guide-Joshua Bloch       

9.             Pearson Education  Sun Microsystems                       

10.          Java Database Best Practices - George Reese               

11.          O'Reilly SPD 




Santosh Kumar R, Narasimham Ch, Pallam Setty S

Paper Title:

Small Secret Exponent Attack on Multiprime RSA

Abstract: Lattice reduction is a powerful algorithm for cryptanalyzing public key cryptosystems, especially RSA. There exist several attacks on RSA by using the lattice reduction techniques. In this paper, we attack on the version of RSA, called Multiprime RSA, by using the lattice reduction techniques.  

 Lattice reduction, Multiprime RSA, Unravelled linearization.


1.              H. Cohen, A Course in computational Algebraic NumberTheory. Springer-Verlag, second edition, 1995
2.              A.Lenstra, H.Lenstra, L.Lovasz ,” Factoring Polynomials with Rational Coeffiecients”, Mathematiche Annalen 261, pp.515-534, 1982

3.              Boneh, D., Durfee, G.: Cryptanalysis of RSA with Private Key d Less than N0:292. Advances in Cryptology - Proceedings of Eurocrypt '99, Lecture Notes in Computer Science 1952 (1999) 1-11.

4.              De Weger, B.: Cryptanalysis of RSA with small prime difference, Applicable Algebra in Engineering,Communication and Computing, Vol 13(1), 17-28, 2002

5.              Boneh, D., Durfee, G.: Cryptanalysis of RSA with Private Key d Less than N^292. IEEE Transactions on Information Theory 46:4 (2000),1339-1349

6.              Boneh, D.: Twenty Years of Attacks on the RSA Cryptosystem. Notices of the American Mathematical Society 46:2 (1999) 203-213.

7.              Boneh, D. and Durfee, G. and Howgrave-Graham, N.: Factoring N =prq for Large r. Advances in Cryptology, Proceedings of CRYPTO 1999, Lecture Notes in Computer Science 1666 (1999) 326-337

8.              Coppersmith, D.: Small Solutions to Polynomial Equations, and Low Exponent RSA Vulnerabilities. Journal of Cryptology 10:4 (1997) 233-260.

9.              Durfee, G., Nguyen, P. Q.: Cryptanalysis of RSA Schemes with Short secret Exponent from Asiacrypt '99. Advances in Cryptology – Proceedings of Asiacrypt '00, Lecture Notes in Computer Science 1976 (2000).

10.           M.J.Hinek. Small private exponent partial key-exposure attacks on multiprime RSA. CACR Technical Report CACR 2005-16, Centre for Applied Cryptographic Research, University of Waterloo, 2005

11.           M.J.Hinek, M.K.Low, and E.Teske. On some attacks on multiprime RSA. In K.Nyberg and H.M.Heys, editors, Selected areas in Cryptography, volume 2595 of Lecture Notes in Computer Science, pages 385-404. Springer, 2002.

12.           M.Ciet, F. Koeune, F. Laguillaumie, Jean-Jacues Quisuater. Short private exponent attacks on fast variants of RSA. UCL Technical report CG-2003\2004.

13.           Hermann, M., May, A.,: Maximizing Small Root Bounds by Linearization and Applications to Small Secret Exponent RSA, In Practice and Theory in Public Key Cryptography (PKC 2010), Lecture Notes in Computer Science 6056, Berlin: Springer-Verlag 2010, pp.53-69.

14.           R. Santosh kumar, C. Narasimham, S. Pallam setty, “Lattice based tools for cryptanalysis in various applications”, springer-LNICST, 84:530-537, 2012.

15.           R.Santosh kumar, C.Narasimham, S.Pallam setty,” Lattice bases attacks on short secret exponent RSA: A Survey”,  International Journal of Computer Applications (0975 – 8887) Volume 49– No.19, July 2012.

16.           Victor Shoup. NTL: A library for doing number theory. Website:

17.           Wiener, M.: Cryptanalysis of short RSA secret exponents, IEEE Transactions on Information Theory 36, 553-558 (1990).

18.           M.Ernst, E. Jochemsz, A.May, Weger,” Partial Key Exposure Attacks on RSA up to Full Size Exponents”, Advanced in Cryptology-EUROCRYPT’05, Springer-Verlag pp 1-11, 2000

19.           R.Santosh Kumar, C.Narasimham, S.Pallam Setty,” Cryptanalysis of RSA with Small Prime Difference using Unravelled Linearization,  International Journal of Computer Applications (0975 – 8887) Volume 61– No.3, January 2013.




Ramesh Chandra Chourasia, Mukesh Kumar

Paper Title:

Speed Control of S.E.D.C. Motor by Using Pi and Fuzzy Logic Controller

Abstract:  In this paper we proposed PI controller & fuzzy controller design for reducing the sensitivity of the effect of load variations dynamic load changes for the response of the output speed of the system S.E.DC motor which can cause malfunctions in the electronic circuits or the complete failure of the control system. The paper describes the implementations of a PI controller and fuzzy controller which can orate successfully in hostile environments such as an orbiting space vehicle.

    SCDC Motor, PI controller & fuzzy controller


1.                 P. V. Vas, and W. Druy, "Electrical Machine and Drives: Present and Future," 8th Mediterranean Electro technical Conference, Vol. 1, pp. 404-408,1996.
2.                 N. Govind, and A. R. Hasan, "Real Time Fuzzy Logic Speed Control Using Conventional, Assembly and Simulation Methods for Industrial DC Motors," IEEE /IAS International Conference on Industrial Automation and Control, pp. 203-208, 1995.

3.                 Pagni, R. Poluzzi,G. Rizzotto, and M. Presti, "Automatic Synthesis ,Analysis and Implementation of Fuzzy Controller," IEEE international Conference on Fuzzy Systems , Vol. 1, pp.105-110, 1993.

4.                 H. A Malki, and D. Feigenspan, "DC Motor Control Using Fuzzy Proportional- Derivative Technique," Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA joint Technology Workshop on Neural Networks and Fuzzy Logic {NAFIPS/IFIS/NASA’94), pp. 373-374, 1994.

5.                 EL Zawawi, and W. A. Maher, "A Fuzzy Compensated Microcomputer Based DC Drive," IECON ’99, Vol. 1, pp .412- 417,1999.

6.                 H. Niasar, H. Moghbelli, and A. Vahedi; "Speed Control of a Brushless DC Motor Drive Via Adaptive Neuro-Fuzzy Controller Based on Emotional Learning Algorithm," Proceedings of the Eighth International Conference on Electrical Machines and Systems, 2005. ICEMS 2005. Vol. 1, pp. 230-234, 27-29 Sept. 2005.

7.                 N. S. Kumar, V. Sadasivam, and K. Prema; "Design And Simulation of FuzzyControllerFor Closed Loop Control of Chopper Fed Embedded DC Drives," International Conference on Power System Technology 2004, Vol. 1, pp. 613-617, 21- 24 Nov. 2004.

8.                 Xia Changliang, Guo PeiJian, Shi Tingna, and wang Mingehao; "Speed Control of Brushless DC Motor Using Genetic Algorithm Based Fuzzy Controller," International Conference on Intelligent Mechatronics and Automation, pp. 460- 464, Aug. 26-31, 2004.

9.                 W. G. da Silva, P. P. Acarnley, and J. W. Finch; "On-Line Optimisation of A Fuzzy Drive Controller Using Genetic Algorithm," IEEE International Symposium on Industrial Electronics, Vol. 2, pp.144 – 1446, 4-7 May 2004.

10.              F. Cupertino, V. Giordano, D. Naso, B. Turchiano, and L. Salvatore; "On-Line Genetic Design of Fuzzy Controllers For DC Drives With Variable Load," Electronics Letters Vol. 39, No. 5, pp. 479- 480, 6 March 2003.

11.              Rubaai, D. Ricketts, and M. D. Kankam; "Experimental Verification of a Hybrid Fuzzy Control Strategy For A High-Performance Brushless DC Drive System," IEEE Transactions on Industry Applications, Vol. 37, No. 2, pp. 503-512, March-April 2001.

12.              H. L. Tan, N. A. Rahim, and W. P. Hew; "A Simplified Fuzzy Logic Controller For DC Series Motor With Improve Performance," The 10th IEEE International Conference on Fuzzy Systems, Vol. 3, pp. 1523-1526, 2-5 Dec. 2001.

13.              N. B. Almutairi, and M. Chow; "A Modified PI Control Action With A Robust Adaptive Fuzzy Controller Applied to DC Motor," International Conference on Neural Networks (IJCNN '01), Vol. 1, pp. 503-508, 15-19 July 2001.

14.              S. Ushakumari, R. Sankaran, and P. S. C. Nair; "Adaptive Neuro-Fuzzy Controller For Improved Performance of A Permanent Magnet Brushless DC Motor," The 10th IEEE  International Conference on Fuzzy Systems, Vol. 1, pp. 493-496, 2- 5 Dec. 2001.

15.              F. Cupertino, A. Lattanzi, and L. Salvatore; "A New Fuzzy Logic-Based Controller Design Method For DC and AC Impressed-Voltage Drives," IEEE Transactions on Power Electronics, Vol. 15, No. 6, pp. 974-982, Nov 2000.

16.              G. Uma, and C. Chellamuthu; "Design and Implementation of Fuzzy Logic Control Speed Control System For A Converter Fed DC Drive Using 8097 Micro Controller," Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE 2000), Vol. 2, pp. 735-740, 4-8 Dec. 2000.

17.              M. S. A. Moteleb, and A. A. Ibraheem; "Robust Fuzzy Sliding Mode Controller Design For DC Motor With Experimental Implementation," Proceedings of the 39th Annual Conference (SICE 2000), pp. 159- 164, 26-28 July 2000.




Rakesh Kumar, Sanjay Tyagi, Manju Sharma

Paper Title:

Memetic Algorithm: Hybridization of Hill Climbing with Selection Operator

Abstract:   Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Premature Convergence and genetic drift are the inherent characteristics of genetic algorithms that make them incapable of finding global optimal solution. A memetic algorithm is an extension of genetic algorithm that incorporates the local search techniques within genetic operations so as to prevent the premature convergence and improve performance in case of NP-hard problems. This paper proposes a new memetic algorithm where hill climbing local search is applied to each individual selected after selection operation. The experiments have been conducted using four different benchmark functions and implementation is carried out using MATLAB. The function’s result shows that the proposed memetic algorithm performs better than the genetic algorithm in terms of producing more optimal results and maintains balance between exploitation and exploration within the search space.

     benchmark functions, hybrid genetic algorithms, hill climbing, memetic algorithms.


1.              D. E. Goldberg, Genetic algorithm in search and optimization and machine learning, Addison Wesley Longman, Inc., ISBN 0-201-15767-5, 1989.
2.              D. Fogel, Evolutionary computation, IEEE Press, 1995.

3.              J. Holland, Adaptation in natural and artificial systems, University of Michigam Press, Ann Arbor, 1975.

4.              W. E. Hart, Adaptive global optimization with local search, Doctoral diss., San Diego, University of California, 1994.

5.              D. E. Goldberg and P. Segrest, “Finite Markov chain analysis of genetic algorithms”, Proceedings of 2nd International Conf. on Genetic Algorithms, Lawrence Erlbaum Associates, 1987, pp 1-8.

6.              L .Booker, Improving search in genetic algorithm, genetic algorithm and simulated annealing, Pitman, vol 5, 1987, pp 61-73.

7.              Rakesh kumar and Jyotishree, “Novel knowledge based tabu crossover in genetic algorithms”, International Journal of Advanced research in Computer science
and software Engineering, vol 2, No. 8, Aug 2012, pp 78-82.

8.              H. A. Sansi, A. Zubair and R. O. Oladele, “Comparative assessment of genetic and memetic algorithms”, Journal of Emerging Trends in Computing and Information Science, vol 2, No. 10, Oct 2011, pp 498-508.

9.              Poonam Garg, “ A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm”, International Journal of Network Security and its Applications,vol  1, No. 1, April 2009, pp 34-42.

10.           Antariksha Bhaduri, “A mobile robot path planning using genetic artificial immune network algorithm”, Proceedings of World Congress on Nature and biologically Inspired Computing, NaBIC, IEEE, 2009, pp 1536-1539.

11.           E. K. Burke and A. J. Smith, “A memetic algorithm for the maintenance scheduling problem”, Proceedings of International Conf on Neural Information Processing and Intelligent Information,  Springer 2010, pp 469-473.

12.           Malin Bjornsdotter and Johan Wessberg, “A memetic algorithm for selection of 3D clustered featured with applications in neuroscience”, Proceedings of International Conference on Pattern Recognition, IEEE, 2010, pp 1076-1079.

13.           P. Mascato and P. C. Cotta, “A gentle introduction to memetic algorithms”, Handbook of Metaheuristics, 2003, pp 105-144.

14.           R. Dawkins, The selfish gene , Oxford University Press, Oxford, 1976.

15.           K. Ku, M. Mak, “Empherical analysis of the factors that affect the Baldwin effect: Parallel problem solving from nature”, Proceedings of  5th International Conference on lecture notes in computer science, Berlin, Springer,  Heidelberg, 1998, pp 481-90.

16.           G. M. Morris, D. S. Goodsell, R. S. Halleday, W. E. Hartand and R. K. Belew, “Automated docking using a Lamarckian genetic algorithm and an empherical bending free energy function”, Journal of Computational Chemistry, vol 19, 1998, pp 1639-62.

17.           J. G. Digalakis and K. G. Margaritis, “An experimental study of Benchmarking Functions for genetic algorithms”, International Journal of Computer Mathematics,  vol 79, No. 4, 2002,pp 403-416.

18.           Marcin Molgo and Czeslaw Smatnicki, Test functions for optimization needs, kwietnia, vol 3, 2005.





Paper Title:

Robust code based Fault Tolerant Architecture using OFB mode for Onboard EO satellites

Abstract:    The demand to protect the sensitive and valuable data transmitted from satellites to ground has increased and hence the need to use security algorithm on board in Earth Observation satellites also increased. The security algorithms like Advanced Encryption Standard by NIST (National Institute of Standards and Technology), is popular in the aerospace industry including satellites. The analysis of the effects of single even upsets (SEUs) on imaging data during on-board encryption is detailed. To avoid data corruption due to SEUs, fault-tolerant model of OFB mode encryption based on robust error detection and corrections codes is proposed. The satellite imaging data is encrypted using OFB mode encryption is done using Matlab. Then its encrypted output image is converted into gray codes is also done using Matlab. The gray codes with injected faults is given as an input to the proposed Robust error detection and correction code model which is designed using VHDL, from which single bit upset and multiple bit upsets are detected and corrected. The implementation of proposed model is done using Field programmable gate array (FPGA). Hence power and throughput of fault tolerant model are increased.

      OFB mode encryption, Error detection and correction codes, Robust codes, SEUs.


1.              Praveen.H.L , H.S Jayaramu, M.Z.Kurian,”Satellite Image Encryption Using AES” International Journal of Computer Science and Electrical Engineering (IJCSEE) ISSN No. 2315-4209, Vol-1, Iss-2, 2012
2.              Praveen.H.L, H.S Jayaramu & M.Z.Kurian,”Single Event Upset Correction for Satellite Images by using AES” International Conference on Electronics and Communication Engineering, 20th May 2012, Bangalore,ISBN: 978-93-81693-29-2
3.              Z.Wang and M.G.Karpovsky(June 2012)”Reliable and Secure Memories Based on Algebraic  Manipulation Correction Codes”, Proc Int Symp. on On-line Testing.

4.              Z.Wang and M.G.Karpovsky,(2012)“New Error Detecting Codes for design of Hardware Resistant to Strong Fault Injection Attacks”, Proc. Int. Conference on Security and management, SAM.

5.              T.Mangaiyarkarasi and B.Nandhini (March 2012)”Fault and tolerant method using AES for images”,International Journal of Communications and  Engineering,Vol.05,No.5,Issue:01,

6.              K.D.Akdemir, Z. Wang, M. G. Karpovsky, and B. Sunar, (2011) “Design of Cryptographic Devices Resilient to Fault Injection Attacks Using Nonlinear Robust Codes”,  Fault Analysis in Cryptography, M.  Joye Editor.

7.              Z. Wang, M. G. Karpovsky, K. Kulikowski, "Design of Memories with Concurrent Error Detection and Correction by  Non-Linear SEC-DED Codes", Journal of Electronic Testing, vol. 26, Oct 2010.

8.              Z. Wang, M. G. Karpovsky, K. Kulikowski,(July 2009),"Replacing Linear Hamming Codes by  Robust Nonlinear Codes Results in Reliability Improvement for Memories", Proc. Int. Symp. Dependable Computing.

9.              Roohi Banu and Tanya Vladimirova (January 2009) “Fault-Tolerant Encryption for Space Applications”,IEEE transactions on Aerospace and Electronic Systems     Vol. 45, No.1,(266-279).

10.           Konrad.J, Kulikowski, Mark.G.Karpovsky, and Alexander Taubin(2007) “Robust codes and robust, fault tolerant architectures of the advanced encryption   standard”,ELSEVIER, Journal of System Architecture, Vol.53,(139-149).

11.           M.G.Karpovsky,K.Kulikowski,Z,Wang,”Robust Error Detection in Communication and Computation Channels”,(2007) Keynote paper, Int. Workshop on Spectral Techniques.

12.           Vladimirova.T,  and  Banu.R(Sept. 2005),”On-board security services in small satellites”.In Proceedings of the 8th Military and Aerospace Applications of Programmable Logic Devices and Technologies International Conference (MAPLD’2005), F-184, NASA, Washington, D.C.

13.           Mark Karpovsky, Konrad J. Kulikowski, Alexander Taubin(2004)”Differential Fault Analysis Attack resistant Architectures  for  the Advanced Encryption Standard” ,In: Ser. Proc. IFIP world computing congress,Cardis,pp (177-193)

14.           M.G.Karpovsky and A. Taubin,(2004)"A New Class of Nonlinear Systematic Error Detecting Codes",  IEEE Trans Info Theory, Vol 50, No.8, pp.1818-1820

15.           Karpovsky, M.G., K. Kulikowski, and A. Taubin,(July, 2004) "Robust Protection Against Fault-Injection Attacks on Smart Cards Implementing the Advanced Encryption Standard", Proc. Int. Conference on Dependable Systems and Networks (DNS 2004).

16.           Bertoni.G,  Breveglieri.L, Koren.I, Maistri.P, and Piuri.V. (Apr. 2003) “Error analysisand detection procedures for a hardware implementation of the AES”,IEEE  Transactions on Computers, Vol.52, 4, 493-505.

17.           Behrouz A.Forouzon and Debdeep Mukhopadhyay,2nd Edition, Crytography and      Network Security, Tata Mcgraw Hill Education Pvt. Limited.

18.           William Stallings,5th Edition,Cryptography and Network Security, Principles and       Practice, Pearson.

19.           Charlie Kaufman,Radia Perlman,and Mike Speciner,2nd Edition,Network Security,  Private Communication in a Public World, Pearson Education.

20.           Input image from Google Search engine




Ankit Rastogi, Pratibha Tiwari

Paper Title:

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control Using Particle  Swarm Optimization

Abstract:     PID controller  is the most widely used controller in industry for control applications due to its simple structure and easy parameter adjusting.But increase in complexity of control systems has introduced many modified PID controllers.The recent advancement in fractional order calculus has introduced fractional order PID controller and it has recieved a great attention for researchers.Fractional order PID (FOPID) controller is an advancement of conventional PID controller in which the derivative and integral order are fractional rather than integer.Apart from the usual tuning parameters of PID, it has two more parameters λ (integer order) and μ (derivative order) which are in fractions.This increases the flexiblity and robustness of the system and gives a better performance than classical PID controller. In this research paper, FOPID has been applied to DC motor for speed control and optimal values of λ and μ has been obtained using particle swarm optimization technique.

Keywords:       DC motor, Fractional order PID controller, PID controller, Particle swarm optimization


1.              Jun-Yi Cao and Bing-Gang Cao, “Design of fractional order controller based on particle swarm optimization,” Int. J. Control, Automation and Systems, vol. 4, no. 6, 2006, pp. 775-781.
2.              Dingyu Xue, Chunna Zhao, YangQuan Chen, “Fractional order PID control of a DC motor with elastic shaft: A case study,” Proc. 2006 American control conference, 2006, pp. 3182-3187.

3.              Majid Zamani, Masoud Karimi-Ghartemani, Naseer Sadati, “FOPID controller design for robust performance using particle swarm optimization,” Fractional Calculus and Applied Analysis, vol. 10, no. 2, 2007, pp. 169-187.

4.              Deepyaman Maiti, Sagnik Biswas and Amit Konar, “Design of a fractional order PID controller using particle swarm optimization technique,” Proc. ReTIS’08,

5.              Andrezej Dzielinski and Dominik Sierociuk, “Simulation and experimental tools for fractional order control education,” Proc. IFAC’08, 2008.

6.              Hyo-Sung Ahn, Varsha Bhambhani and YangQuan Chen, “Fractionalorder integral and derivative controller design for temperature profile control,” in Proc. 2008 CCDC-2008, pp. 4767-4771.

7.              Concepcion A. Monje, Blas M. Vinagre, Vicente Feliu, YangQuan Chen, “Tuning and auto-tuning of fractional order controllers for industry applications,” Control Engineering Practice, 16, 2008, pp. 792 – 812.

8.              Ivo Petras, “Fractional-order feedback control of a DC motor,” J. Elect. Engg., vol. 60, no. 3, 2009, pp. 117-128.

9.              Arijit Biswas, Swagatam Das, Ajith Abraham, Sambarta Dasgupta, “Design of fractional order PIλDμ controller with improved differential evolution,” Engineering Applications of Artificial Intelligence, 22, 2009,   pp. 343-350.

10.           Venu Kishore Kadiyala, Ravi Kumar Jathoth, Sake Pothalaiah, “Design and implementation of fractional order PID controller for aerofin control system,” Proc. 2009 World Congress NaBIC, 2009, pp. 696-701.

11.           Ammar A Aldair, and Weiji J Wang, “Design of fractional order controller based on evolutionary algorithm for a full vehicle nonlinear active suspension system,” Int. J. Contr Automation, vol. 3, no. 4, 2010, pp. 33-46.

12.           Vishal Mehra, Smriti Srivastava, and Pragya Varshney, “Fractional-Order PID Controller Design for Speed Control of DC Motor,” Third International Conference on Emerging Trends in Engineering and Technology, IEEE Trans.,2010.

13.           Radek Matusu, “Application of fractional order calculus to control theory,” International Journal of Mathematical Models and Methods in Applied Sciences, issue 7, vol 5, 2011, pp. 1162 – 1169.

14.           Subhransu Padhee, Abhinav Gautam, Yaduvir Singh, and Gagandeep  Kaur , “A Novel Evolutionary Tuning Method for Fractional Order PID Controller “,International Journal of Soft Computing and Engineering (IJSCE), Vol.1, Issue-3, July 2011.

15.           K. Sundaravadivu and K. Saravanan, “Design of Fractional Order PID Controller for Liquid Level Control of Spherical Tank,” European Journal of Scientific Research,Vol.84 No.3,2012, pp.345-353.

16.           Mehdi Ghazavi Dozein, Amin Gholami, Mohsen Kalantar, “Speed Control of DC Motor Using Different Optimization Techniques Based PID Controller,” J. Basic. Appl. Sci. Res., 2(7), 2012.

17.           Karanjkar D. S.,Chatterji S.,Venkateswaran P.R.,“Trends in Fractional Order Controllers,” International Journal of Emerging Technology and Advanced Engineering,Vol.2, Issue 3, March 2012.

18.           Rinku Singhal, Subhransu Padhee, Gagandeep Kaur, “Design of Fractional Order PID Controller for Speed Control of DC Motor”, International Journal of Scientific and Research Publications, Vol 2, Issue 6, June 2012.

19.           Shivaji Karad, Dr. S. Chatterji, Prasheel Suryawanshi, “Performance Analysis of Fractional Order PID Controller with the Conventional PID Controller for Bioreactor Control,” International Journal of Scientific & Engineering Research,Vol.3, Issue 6, June-2012.

20.           Mohammad Reza Dastranj, Mojtdaba Rouhani, and Ahmad Hajipoor, “Design of Optimal Fractional Order PID Controller Using PSO Algorithm,” International Journal of Computer Theory and Engineering, Vol. 4, No. 3, June 2012.




Shruti S Jamsandekar, R.R Mudholkar

Paper Title:

Performance Evaluation by Fuzzy Inference Technique

Abstract: The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. The ability to monitor the progress of student’s academic performance is a critical issue to the academic community of higher learning. The present  work intends to approach this problem by taking the advantage of   fuzzy inference technique in order to  classify  student  scores data according to the level of their performance In this  proposed approach we have performed  fuzzification of  the input data( students marks) by creating fuzzy inference system(FIS) subject wise, next each FIS output is passed to next level  FIS with two  inputs, outputs of the final FIS are performance value calculated based on all subject marks with/without lab marks. In the  proposed approached a combination of two membership function is carried out (trapezoidal and triangular).The experimental results are compared with traditional evaluation method, it helps in  identifying students lying at overlapping section of two class distribution   the results also could help  educators to monitor the progress and provide timely guidance to students to achieve better performance score.

   Performance Evaluation, Academic Institute, Fuzzy Classification, Fuzzy Inference


1.              Khairul A. Rasmani and QiangShen, “Data-Driven Fuzzy Rule Generation and its Application   for Student Academic Performance Evaluation”, Journal on Applied Intelligence, vol.  No.25,pp.305-319, 2006.
2.              Ramjeet Singh Yadav et al. “Modeling Academic Performance Evaluation Using Soft Computing   Techniques: A Fuzzy Logic Approach”  International Journal on Computer Science and Engineering (IJCSE) Vol. 3 No. 2 Feb 2011

3.              J. Ma and D. Zhou, "Fuzzy set approach to the assessment of student-centered   learning," IEEE Transactions on Education, vol. 43, no. 2, pp. 237-241, 2000.

4.              S. M. Bai and S. M. Chen, "Evaluating students' learning achievement using fuzzy membership functions and fuzzy rules," IEEE Expert Systems with Applications, vol. 34, no. 1, pp. 399-410, 2008.

5.              T. T. Chiang and C. M. Lin," Application of fuzzy theory to teaching assessment," Proceedings of the 1994 Second National Conference on Fuzzy Theory and Applications, Taipei, Taiwan, Republic of China, pp.92-97, 1994.

6.              S. M. Chen and C. H. Lee, "New methods for students' evaluating using fuzzy sets," ELSEVIER  Fuzzy Sets and Systems, vol. 104, no. 2, pp. 209-218, 1999.

7.              D. F. Chang and C. M. Sun, "Fuzzy assessment of learning performance of junior high school students," Proceedings of the 1993 First National Symposium on Fuzzy Theory and Applications, Hsinchu, Taiwan, Republic of China, pp. 10-15, 1993.

8.              L.A Zadeh “Fuzzy sets”, International Journal of Information and control, vol. 8, pg. 338-353, 1965

9.              Prosser, M., &Trigwell, K.. “Student evaluations of teaching and courses:Student learning approaches and outcomes as crtieria of validity”. ContemporaryEducational Psychology, 16, 293-301, 1991.

10.           Sambell, K., McDowell, L. & Brown, S. ‘But is it fair?’: an exploratory study of student perceptions of the consequential validity of assessment, Studies in Educational Evaluation,23(4), 349–371, 1997.

11.           Gagne, R. M., Wager, W. W., Golas, K. C., & Keller, J. M. “Evaluating Instruction. In Principles of Instructional Design”, Chapter 16 (pp. 346-375), (2005).

12.           Olufunke O. Oladipupo1 ,Olanrewaju. J. Oyelade2 and Dada. O. Aborisade3. “Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance “IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 3, November 2012

13.           Grade point Averageavalable :

14.           Fuzzy Inference System Chapter 4 available:

15.           John Yen,RezaLangari “Fuzzy Logic –Intelligence, control and Information”, LPE Pearson.




Hanamane M. D., Attar K. D., Mudholkar R. R.

Paper Title:

Embedded Fuzzy Module for Sugar Industrial Boiler Parameter Control

Abstract:  In sugar industry, past the sugar was main product and bagasse was considered as west and its disposal was the problem. The present paper highlights the design and development of Embedded Fuzzy Module for energy efficiency improvement of bagasse boiler for a sugar factory intended for cogeneration system. The multipurpose boiler considered parameters are Water flow, Steam flow, Amount of fuel and Air flow. The Fuzzy Logic Inference is to find out the desirable amount of fuel (bagasse) and Air flow for targeted steam flow. In this paper Embedded Fuzzy Logic Module for improving the steam generation performance as well as saving fuel of boiler in the sugar industry.

    Boiler parameters, Control system, Fuzzy Logic, Sugar industry etc.


1.           B. Hemalatha, Dr. A. Vimala and Juliet N. Natarajan “Boiler Level Control Using Labview” International Journal of Computer Applications (0975 - 8887) 2010, PP:85-88.
2.           N. Magasiner “Automatic Control Of Boiler Plant In The Cane Sugar Industry” Proceeditigs of The Soutlz Africat Sugar Technologists'  Associntiorz - April 1968, PP:91-101.

3.           Yonghong Huang, Nianping Li, YangchunShil and YixunYil,“Genetic Adaptive Control for Drum Level of a Power Plant Boiler”,  IEEE Computational Eng. Syst. Applicat. (IMACS), vol.2, pp. 1965-1968, Oct 2006.

4.           Sebastian George and D. N. Kyatanavar “Applications of Fuzzy Logic in Sugar Industries:A Review” International Journal of Engineering and Innovative Technology (IJEIT) ISO 9001:2008 Certified, ISSN: 2277-3754, June 2012,pp226-231.

5.           Essam Natsheh and Khalid A. Buragga “Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors” International Journal of Computer Science IJCSI Issues, ISSN (Online): 1694-0814,  September 2010, pp: 128-134.

6.           Daniel Ramot, Menahem Friedman, Gideon Langholz, and Abraham Kandel “Complex Fuzzy Logic” IEEE Transactions On Fuzzy System August 2003, Pp:450-461.

7.           Sangamesh Y G, Suchitra G and Jangamshetti S H “Performance Assessment of 2500 TCD Cogeneration Plant” International Journal of Scientific & Engineering Research, ISSN 2229-5518,  May-2012. pp: 1-6.

8.           Samsher Kadir Sheikh and Manik Hapse “Unit Costs Estimation In Sugar Plant Using Multiple Regression Least Squares Method” International Journal of Advances in Engineering & Technology, ISSN: 2231-1963, Sept 2011, pp: 299-306.

9.           F. Ilter Turkdogan-Aydınol, and  Kaan Yetilmezsoy “A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater” Journal of Hazardous Materials182 (2010) pp: 460–471.

10.        B. Hemalatha, Dr. A. Vimala Juliet and N. Natarajan “Boiler Level Control Using Labview” International Journal of Computer Applications (0975 - 8887), 2010, pp: 85-88.

11.        V. G. Vijaya and A. Kumaraswamy “Design and Implementation of pH Control System for Boiler Feedwater using Industrial Automation Techniques” International Journal of Scientific & Engineering Research ISSN 2229-5518, January-2013, pp: 1-4.

12.        F. Fattahi, N. Rahmanov and A. Hashimov “Modeling And Control Of Distributed Generation Systems Including Wind And Gas Turbine On Azarbaijan Electric Network” International Journal on Technical and Physical Problems of Engineering (IJTPE) Transaction on Power Engineering ISSN 2077-3528,  December 2009, pp: 38-47

13.        Stef Smith and Alessandra Orsoni “Alternative Power Technologies: A Decision Model For A Sugar Refinery” application note on Kingston University, Faculty of Business and Law UK.

14.        G. E. Angus “Conditioning Boiler Feedwater For The Sugar Mill” Proceedings of The South African Sugar Technologists' Association March 1966, pp: 79-88.

15.        Orosun Rapheal and Adamu SunusiSani “Modeling and Controller Design of an Industrial Oil-Fired Boiler Plant” International Journal of Advances in Engineering & Technology, ISSN: 2231-1963, March 2012, pp: 334-341.

16.        Joan M. Ogden, Simone Hochgreb and Michael Hylton “Steam economy and cogeneration in cane sugar factories” INT. SUGAR JNL., 1990. Pp: 131-140.




R.Ramesh, S.Balamurugan, P.Venkatesh

Paper Title:

Real Time Servo Motor Control of Single Rotary Inverted Pendulum Using Dspace

Abstract:   The objective of the paper is to carry out real time experiment using state of art hardware dSPACE DS1104 R&D controller board in a laboratory education point of view. The Quanser servo plant module and dSPACE software with the DS1104 R&D controller board are used in the experiment to derive state space equation for the inverted pendulum (ROTPEN-E). The linear and nonlinear analysis of the plant gives both angles (θ and α) control variations. The LQR controller is stabilizing pendulum upright.

Quanser servo plant with Rotary inverted pendulum (SRV02), dSPACE R&D controller board (DS1104), State space equations; LQR control.


1.             Quanser . “SRV02 MODELING USING QUARC”- User manual. 
2.             Quanser. “SRV02-ET ROTARY INVERTED PENDULUM”-  User manual. 

3.             Muskinja, N. and Tovornik and B,  ” Swinging up and stabilization of a real inverted pendulum”,  IEEE Transactions  of Innovative Information and Control Vol.3, no 6(B),pp. 631–639. December 2007.

4.             Park M.S. and Chwa D, “Swing-up and stabilization control of inverted-pendulum systems via coupled sliding-mode control method”, IEEE Transactions on Industrial Electronics, vol. 56, no. 9, September 2009.

5.             Priya. J, Balamurugan. S and Venkatesh. P,  “Real  Time  Control  of  Quanser Module  Using  dSPACE  DS-1104”  Dept.  of Electrical  Engineering,  Thiagarajar college engineering, Madurai. International Conference on Computing and Control Engineering, April 12, 2012.

6.             Shailaja Kurode, Asif chalanga and Bandyopandhyay, “Swing-Up and Stabilization of Rotary Inverted Pendulum using Sliding Modes”, IFAC world congress 2011 – Italy September 2, 2011.

7.             Sukontanakarn V and Parnichkun M, ” Real-time optimal control for rotary inverted pendulum”,  American Journal of  Applied Sciences 6, 1106–1115, 2009.

8.             Astrom .K.J. and Furuta, K, ” Swing up a pendulum by energy control” , Automatica, vol. 36, no. 2, pp. 287–295, February  2000




Anil Kumar, Sanjay Kumar Bagri

Paper Title:

Improving the Productivity of Lever Combination Switch using Continuous Improvement Process

Abstract:    Most of the companies always report poor quality of particular product during its operation which results in increasing cost, and customer complaints. The purpose of this study is to help Company Mindarika to improve the product quality and to increase productivity by using Continuous Process Improvement and the Quality Control Techniques. Methods and procedures of this study include a review of literature relevant to Continuous Improvement, Quality Control Techniques, Root cause Analysis. After the causes of defects are identified, solutions and procedures are recommended to the Company to eliminate defects in the assembly process of Lever Combination Switch so that the productivity can be improved.

  Lever Combination Switch; Continuous Process Improvement; Quality Control Tools; Noise in Switch. Greasing of ratchet.


1.             Dale H. Besterfield “Total Quality Management”, Prentice Hall, 2006.
2.             Narongsawas Chongwatpol “Implementing Continuous Process improvement methods in a Mid Size Plastic Company” May, 2006, pp 1-60.


4.             Your MBA: The Business Study Reference Site.

5.             Nadia Bhuiyan and Amit Baghel, “An overview of continuous improvement: from the past to the present”, pp 761-771.

6.             Vito Romaniello, Paolo Renna and Vincenzo Cinque, A Continuous Improvement and Monitoring Performance  System: Monitor - Analysis -Action – Review (MAAR) Charts”, IBIMA Publishing Vol. 2011 (2011), Article ID 917557, 15 pages.

7.             Robert V. Hogg and Mary C. Hogg, “Continuous Quality Improvement in Higher Education”, International Statistical Review (1995), pp 35-48.

8.             He Zhen Qi Ershi Liu Zixian, “A Study on continuous quality improvement strategies and methods”,

9.             Marta KUČEROVÁ, Jaromíra VAŇOVÁ, Helena FIDLEROVÁ, “Important aspects of continuous quality improvement in Slovak Enterprises”, Research papers, Faculty of Materials Science and Technology in Trnava Slovak University of Technology in Bratislava 2009 ,pp 27-32.

10.          T. Karkoszka, J. Honorowicz, “Kaizen philosophy a manner of continuous improvement of processes and products”, Journal of Achievements in Materials and Manufacturing Engineering 35/2 (2009), pp 197-203.  




Navneet Kaur, Ashima Singh

Paper Title:

A Complexity Metric for Black Box Components

Abstract:     The Component Based Software Development (CBSD) approach is becoming the trend for software development which is based on developing the software from existing components instead of developing software from scratch everytime. Measuring software complexity is an important aspect during software development. Because software complexity is  an important determinant of software development effort, testing effort , cost, maintainability etc.  Researchers have proposed a wide range of complexity metrics for software systems . But the traditional software product and process metrics are neither suitable nor sufficient in measuring the Component  and Component Based Software (CBS) complexity. So CBSD  provides one of the central problems in measuring component and CBS complexity. Measuring component complexity plays an important role in determining the complexity of CBS system. Because component complexity affects the complexity of whole CBS . Component complexity affects  integration and testing effort, cost, maintainability of CBS system . But now a days black box components are being used during CBSD and most of the time source code is not available which creates difficulty in measuring component complexity. In this paper a metric has been proposed for determining the black box component complexity. The proposed metric measures component complexity on the basis of component interface specification and use the concept of assigned weights

   Black Box Component,  CBSD, CBS system , component complexity, complexity metrics, traditional software product and process metrics.


1.          Sandeep Khimta, Parvinder S. Sandhu, and Amanpreet Singh Brar, “A Complexity Measure for JavaBean based Software Components”, World Academy of Science, Engineering and Technology,2008 .
2.          Luiz Fernando Capretz and Miriam A. M. Capretz, “Component-Based Software   Development,” The 27th Annual Conference of the IEEE Industrial Electronics Society,2001 .

3.          Ben Whittle and Mark Ratcliffe, “Software Component Interface Description for Reuse,” Software Engineering Journal, November 1993.

4.          Usha Kumari and Shuchita Upadhyaya, “An Interface Complexity Measure for Component-based Software Systems,” International Journal of Computer Applications , Volume 36– No.1, December 2011.

5.          Chidamber, S. R., Kemerer and C.F, “A Metrics Suite  for Object Oriented Design,” IEEE Transactions on Software Engineering, 1994,pp. 476-49.

6.          Sedigh Ali, S Gafoor, A. Paul and Raymond A., “Software Engineering Metrics for COTS-based Systems,” IEEE Computer, May 2001, pp 44-50.

7.          D. Kafura and S. Henry, “Software Quality Metrics Based on Interconnectivity,” Journal of Systems and Software, June 1981, pp 121-131.

8.          Seyyed Mohsen Jamali, “Object Oriented Metrics,” Department of Computer Engineering ,Sharif University of Technology, January 2006.

9.          V. L. Narasimhan and  B. Hendradjaya, “A New Suite of Metrics for the Integration of Software Components,” University of Newcastle , Australia.

10.       Nasib S. Gill and P. S. Grover, “Few important considerations for deriving interface complexity metric for component-based systems,” ACM SIGSOFT Software Engineering Notes, March 2004, Volume 29 Issue .

11.       H. Li, “Object-oriented metrics that predict maintainability,” Journal of Systems and Software ,Volume 23 Issue 2, 1993, pp: 111-122 .

12.       Dr. P. K. Suri and Neeraj Garg,“   Software Reuse Metrics: Measuring Component Independence and its applicability in Software Reuse,” International Journal of Computer Science and Network Security, VOL.9 No.5, May 2009.  




Niti Desai, Amit Ganatra

Paper Title:

Comprehensive Study of Weighted Sequential Pattern Mining

Abstract:      Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. In the real world, there are several applications in which specific sequences are more important than other sequences. Traditional Sequential pattern approaches are suffering from two disadvantages: Firstly, all the items and sequences are treated uniformly. Second, conventional algorithms are generating large number of patterns for lower support. In addition, the unimportant patterns with low weights can be detected. This paper addresses problem of traditional framework and various framework of weighted sequential pattern. Paper also discuses how algorithm mines sequential pattern which reduces the search space and new pruning technique prune the unimportant pattern and pick only those patterns which leads to important and emerging pattern. Later section of paper discuses results of simulation study and how researcher can lead current research.  

    Weighted Sequential Pattern Mining, Weighted Association Mining Framework, Weighted sequential pattern Mining Framework


1.              R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 1994 Int’l Conf. Very Large Data Bases (VLDB ’94), pp. 487-499, Sept. 1994.
2.              Agrawal R. And Srikant R. ‘Mining Sequential Patterns.’, In Proc. of the 11th Int'l Conference on Data Engineering, Taipei, Taiwan, March 1995

3.              AYRES, J., FLANNICK, J., GEHRKE, J., AND YIU, T., ‘Sequential pattern mining using a bitmap representation’, In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-2002.

4.              D. Chiu, Y. Wu, A.L. Chen : An efficient algorithm for mining frequent sequences by a new strategy without support counting, in: Proc. the Twentieth International Conference on Data Engineering, March/April 2004, 2004, pp. 375–386.

5.              M. Garofalakis, R. Rastogi, and K. Shim, ‘SPIRIT: Sequential pattern mining with regular expression constraints’, VLDB'99, 1999.

6.              Han J., Dong G., Mortazavi-Asl B., Chen Q., Dayal U., Hsu M.-C.,’ Freespan: Frequent pattern-projected sequential pattern mining’, Proceedings 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD’00), 2000, pp. 355-359.

7.              J. Han, J. Pei, and Y. Yin, ‘Mining Frequent Patterns without Candidate Generation’, Proc. 2000 ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD ’00), pp. 1-12, May 2000.

8.              Joong Hyuk Chang a, Nam Hun Park : Comparative analysis of sequence weighting approaches for mining time-interval weighted sequential patterns Expert Systems with Applications Science Direct

9.              Keith C. C. Chan and Wai-Ho Au, Mining Fuzzy Association Rules, In Proceeding of the 6“ lnfemutionul Conference on ltnformution and Knciwledge Munugemetit, Pages 209-2 15, 1997

10.           J. Pei, J. Han, B. Mortazavi-Asi, H. Pino, ‘PrefixSpan: Mining Sequential Patterns Efficiently by Prefix- Projected Pattern Growth’, ICDE'01, 2001

11.           Srikant R. and Agrawal R.,’Mining sequential patterns: Generalizations and performance improvements’, Proceedings of the 5th International Conference Extending Database Technology, 1996, 1057, 3-17.

12.           Ke Sun and Fengshan Bai : Mining Weighted Association Rule without Preassigned Weights IEEE Transactions on Knowledge and Data engineering Vol. 20, No. 4, April 2008, pp. 489-495

13.           M. Sulaiman Khan, Maybin Muyeba, Frans Coenen : Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework

14.           S.P.Syed Ibrahim and K.R.Chandran: COMPACT WEIGHTED CLASS ASSOCIATION RULE MINING USING INFORMATION GAIN , International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.6, November 2011 DOI : 10.5121/ijdkp.2011.1601 1

15.           F. Tao: Weighted association rule mining using weighted support and significant framework, in: Proc. the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2003, 2003, pp. 661–666.

16.           Unil Yun: A new framework for detecting weighted sequential patterns in large sequence databases, Science direct Knowledge-Based Systems 21 (2008) 110–122

17.           W. Wang, J. Yang, and P.S. Yu, “Efficient Mining of Weighted Association Rules (WAR),” Proc. ACM SIGKDD ’00, pp. 270-274, 2000.

18.           Yihua Zhong,Yuxin Liao Mining Effective and Weighted Association Rules Based on Dual Confidence Fourth International Conference on Computational and Information Sciences Research 2012

19.           M. Zaki, ‘SPADE: An Efficient Algorithm for Mining Frequent Sequences’, Machine Learning, vol. 40, pp. 31-60, 2001.




Mandeep Singh, Kanwalvir Singh Dhindsa

Paper Title:

Securing RJSON data between Middleware and Smart phones through Java Script based Cryptographic Agorithms

Abstract:       Smartphone has become the most typical and popular mobile device in recent years. It combines the functionality of mobile phone and PDA. Besides, it provides many computers’ functionality, Middleware as processing, communication, data storage and etc. It also provides many computers’ service, such as web browser, portable media player, video call, GPS, Wi-Fi and etc. This paper provides an effective mechanism for securing the communication between the RJSON data from the middleware and back using various secure cryptographic algorithms implemented in JavaScript. Security plays a vital role in today’s mobile world. There are security issues like sniffing of data while accessing information through open channel. Cryptographic techniques play an important role in protecting communication links and data, since access to data can be limited to those who hold the proper key. This paper discusses different cryptographic techniques available in lightweight RJSON format to securely transfer information in a network by an android smartphone. A native android application Sadelok Newspaper is used to securely send data using AES, 3Des and Blowfish and compare them. The paper describes and compares the JavaScript based cryptographic techniques on the JavaScript Object Notation, for faster and efficient encryption of data that is suitable for use in smartphones.

    3DES, Blowfish, JSCrypt, Smartphone Security, RJSON, Android, Smartphones.


1.             Uribarren, J. Parra, J.P. Uribe, M. Zamalloa, and K. Makibar, “Middleware for Distributed Services and Mobile Applications,” InterSense '06: Proceedings of the first international conference on Integrated internet ad hoc and sensor networks, New York, NY, USA: ACM, 2006.
2.             William Stalling, “Cryptography and Network Security Principles and Practice 5th Edition”, Pearson. 30th International Conference on, 2008, pp. 31–40.

3.             Hardjono, “Security in Wireless LANs and MANs”, Architect House Publishers, 2005.

4.             E. Oliver, “A survey of platforms for mobile networks research,” SIGMOBILE Mob. Comput. Commun. Rev., vol. 12, 2008, pp. 56–63.

5.             SimarPreet Singh, and Raman Maini (2011), “Comparison of Data Encryption Algorithms”, International Journal of Computer Science and Communication Vol. 2,
No. 1, January-June 2011, pp. 125-127.

6.             Mingyan Wang, Yanwen Que (2009),“The Design and Implementation of Passwords Management System Based on Blowfish Cryptographic Algorithm”,

7.             Challa Narasimham , Jayaram Pradhan (2008), “Evaluation of Performance Characteristics of Cryptosystem Using Text Files”, Journal of Theoretical and Applied
Information Technology, pp 254-259.

8.             Tingyuan Nie, Chuanwang Songa and Xulong Zhi (2010), “Performance Evaluation of DES and Blowfish Algorithms”, Proceedings of 2010 IEEE International Conference on Biomedical Engineering and Computer Science (ICBECS- 2010), 23-25 Apr 2010. pp 1-4.

9.             H.E. Bal, J.G. Steiner, and A.S. Tanenbaum, “Programming languages for distributed computing systems,” ACM Comput. Surv., vol. 21, 1989, pp. 261–322.

10.          A.Rathika, Parvathy Nair and Parvathy Nair (2011), “A High Throughput Algorithm for Data Encryption” International Journal of Computer Applications (0975 – 8887) Volume 13, No.5, January 2011 pp 13-16.

11.          M.Umaparvathi, Dr.Dharmishtan and K Varughese (2010), “Evaluation of Symmetric Encryption Algorithms for MANETs”, Proceedings of 2010 IEEE International conference on Computational Intelligence and Computing Research, 28-29 Dec. 2010, pp 1-3.

12.          Allam Mousa and Ahmad Hamad (2006), “Evaluation of the 3DES Algorithm for Data Encryption”, International Journal of Computer Science & Applications Vol. 3, No.2, June 2006, pp 44-56.

13.          Diaa Salama Abdul. Elminaam, Hatem Mohamed Abdul Kader and Mohie Mohamed Hadhoud (2008), “Performance Evaluation of Symmetric Encryption Algorithms”, IJCSNS, VOL.8 No.12, December 2008, pp 280-286.

14.          Allam Mousa and Ahmad Hamad (2006), “Evaluation of the RC4 Algorithm for Data Encryption”, International Journal of Computer Science & Applications Vol. 3, No.2, June 2006, pp 44-56.

15.          Lavanya P and M Rajashekhara Babu (2011), “Performance Analysis of Montgomery Multiplication Algorithm for Multi-core Systems Using Concurrent International Journal of Computer Applications (0975 – 8887) Volume 44– No11, April 2012.

16.          Y. Kumar, R. Munjal and H. Sharma, Comparison of Symmetric and Asymmetric Cryptography with existing vulnerabilities and counter measures, International Journal of Computer Science and Management Studies, 11( 03), Oct 2011.




Naveen Choudhary

Paper Title:

Turn Prohibition Routing Investigation for Irregular, 2D-Mesh and 3D-Mesh Based Network on Chip

Abstract:        Network on Chip (NoC) has established itself as an alternative to the on chip bus to meet the increasing requirements of complex communication needs of system on Chip (SoC). A popular choice of topology for generic Network on Chip has been 2D Meshes. Similarly for application specific Network on Chip irregular topologies customized to application needs is preferred. However as the feature size continue to shrink and integration densities continue to increase, the interconnect delay is emerging as the critical bottleneck for the performance of 2D NoC.  The advances in technology such as over the cell routing and Through-Silicon-Vias (TSV) has made possible performance conscious and scalable Network on Chip with more than 2 dimension. As was the case with 2D Mesh NoC, the 3D Mesh NoC is proving to be a preferred choice for the NoC designers due to its simple and scalable design.The communication over the Network on Chip is required to be deadlock and livelock free. Turn prohibition based routing function are a popular choice for NoC communication as it provides deadlock free communication over the NoC without the requirement of additional physical or virtual channels. Moreover turn prohibition based routing is capable of providing deadlock free, livelock free, minimal or nonminimal and maximally adaptive communication over NoCs. Turn prohibition routing is based on analyzing the directions in which packets can turn in the network and the cycles that the turns can form. Prohibiting just enough turns to break all the resource dependence cycles in the network can help researchers design an effective and efficient deadlock and livelock free routing functions for the NoCs. This paper presents an investigation of the various popular turn prohibition based routing algorithms presented in the NoC research literature for 2D mesh, 3D mesh and irregular topology based on chip networks.  

     Turn Model, Routing, Network-on-Chip, Livelock, Deadlock.


1.              L. Benini and G. De Micheli, “Networks on Chips: A New SoC Paradigm” in IEEE Computer, pp. 70-78, Jan. 2002.
2.              W. J. Dally and B. Towles, “Route packets, not wires: on-chip interconnection networks,” in Proceedings of the 38th conference on Design automation, June 2001, pp. 684–689.

3.              G. Philip, B. Christopher, and P. Ramm, Handbook of 3D Integration: Technology and Applications of 3D Integrated Circuits, Wiley-VCH, 2008.

4.              P. Morrow, M. Kobrinsky, S. Ramanathan, C.-M. Park, M. Harmes, V. Ramachandrarao, H. Park, G. Kloster, S. List, and S. Kim, “Wafer-Level 3D Interconnects Via Cu Bonding”, in Proceedings of  the 21st Advanced Metallization Conference, Oct. 2004.

5.              J. Duato, S. Yalamanchili, L. Ni, Interconnection Networks : An Engineering Approach, Elsevier, 2003.

6.              C. J. Glass, L. M. Ni, “The Turn Model for Adaptive Routing”, In the Journal on ACM, Vol. 41, No. 5, pp. 874-902, Sept. 1994.

7.              J. Wu, L. Sheng, “Deadlock-Free Routing in Irregular Networks Using Prefix Routing”,  DIMACS  Tech. Rep. 99-19, Apr. 1999.

8.              e. a. M. D. Schroeder, “Autonet: A High-Speed Self-Configuring Local Area Network Using Point-to-Point Links”, In Journal on Selected Areas in Communications, vol. 9, Oct. 1991.

9.              Jouraku, A. Funahashi, H. Amano, M. Koibuchi, “L-turn routing: An Adaptive Routing in Irregular Networks”, In the International Conference on Parallel Processing, pp. 374-383, Sep. 2001.

10.           Y.M. Sun, C.H. Yang, Y.C Chung, T.Y. Hang, “An Efficient Deadlock-Free Tree-Based Routing   Algorithm for Irregular Wormhole-Routed Networks Based on Turn Model”, In International Conference on Parallel Processing, vol. 1, pp. 343-352, Aug. 2004.

11.           C. J. Glass, L. M. Ni, “Adaptive Routing in Mesh Connected Networks”, in proceedings of 12th international conference on Distributed Computing Systems, pp. 12-19, 1992.




S. Gayathri, R. Meenakumari

Paper Title:

Hybrid State Estimation Approach for the Optimal Placement of Phasor Measurement Units

Abstract:   Power systems are rapidly becoming populated by Phasor Meaurement Units (PMU).  Compared to conventional one(SCADA), PMU has synchrophasor technology and it measures the dynamic behaviour of the system.  Real time monitoring operations are done through PMU in the smart grid environment.  Finding a suitable location for the placement of PMU is an optimization problem which could be solved by various Optimization  technique. PMUs actually measure the system state instead of indirectly estimating it, the idea to improve the quality of state estimate is  that inclusion of this type of data in a state estimator. For analysis, operation and planning of power system state estimation and load flow analysis is most important.   A hybrid state estimation technique (fixing a PMU in the conventional load flow analysis)  is applied for the test case system and the  results are validated. The true value is obtained by load flow analysis and the estimated value is obtained by weighted least squares (WLS) state estimation technique. From the simulated results it is observed that the residue will be less if PMU data’s are included. 

 Newton Raphson Method, PMU, State Estimation, Load Flow, Weighted Least Squares.


1.              R.Sodhi, S.C.Srivastava and S.N.Singh, "Multi-criteria decision-making approach for multistage optimal placement of phasor measurement units”, Generation, Transmission & Distribution, IET, vol. 5, no. 2, pp. 18 –190, July 2010.
2.              Mehdi Davoudi, “Sensitivity Analysis of Power System State   Estimation Regarding to Network Parameter Uncertainties”,2012.

3.              N.H. Abbasy and H.M. Ismail, “A Unified Approach for the Optimal PMU Location for Power System State Estimation”, IEEE Trans. on power syst., vol. 24, no.2,
pp. 806 – 813, May 2009.

4.              Gou, “Generalized Integer Linear Programming Formulation for Optimal PMU Placement”, IEEE Trans. on power syst., vol. 23, no. 3, pp. 1099–1104, Aug. 2008.

5.              Dua, S. Dambhare, R.K.Gajbhiye and S.A. Soman, “Optimal Multistage Scheduling of PMU  Placement: An ILP Approach”, IEEE Trans. on power Del., vol.23, no. 4, pp. 1812–1820, Oct. 2008.

6.              Rakpenthai, S. Premrudeepreechacharn, S. Uatrongjit and N.R. Watson, “An Optimal PMU Placement Method Against Measurement Loss and Branch Outage”, IEEE Trans. on power del., vol. 22, no. 1,  pp. 101–107, Jan. 2007 .

7.              Azizi et al, “Optimal PMU Placement by an Equivalent Linear Formulation for Exhaustive Search”, IEEE Trans. on Smart Grid, vol. 3,  no. 1,pp. 174 –182, Mar. 2012.

8.              Saikat Chakrabarti,, Elias Kyriakides ,  “Optimal Placement of Phasor Measurement Units for Power System Observability “, IEEE Trans. on power sys., vol. 23,no. 3, pp. 1433–1440, Aug. 2008.

9.              Aminifar et al., “Optimal Placement of Phasor Measurement Units Using Immunity Genetic Algorithm”, IEEE Trans. on power Del., vol. 24, no. 3, pp. 1014–1020, July 2009.

10.           Liu et al., “Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids”, IEEE Trans. on smart grid, vol. 3, no. 2,  pp. 915–924, June 2012.

11.           Bei Gou and Ali Abur, “A Direct Numerical Method for Observability Analysis”, IEEE Trans. on power syst., vol. 15, no. 2, pp. 625 – 630, May 2000.

12.           Saikat Chakrabarti and Elias Kyriakides, “PMU Measurement Uncertainty Considerations in WLS State Estimation”, IEEE Trans. on power syst., vol. 24, no. 2, pp. 1062–1071, May 2009.

13.           Valverde et al., “A Constrained Formulation for Hybrid State Estimation”, IEEE Trans. on power syst., vol. 26, no. 3, pp. 1102–1109, Aug. 2011.

14.           Sebastian Anghelescu, Gianfranco Chicco,  “Optimal observability of pmu's using analytical hierarchy process (ahp) method”, U.P.B. Sci. Bull., Series C, vol. 73, no. 4, pp. 258–266, 2011.

15.           K.Jamuna and K.S.Swarup, “Critical Measurement Set with PMU for Hybrid State Estimation”, 16th national power systems conference, 15th-17th Dec. 2010, pp. 254–259.

16.           Zhou et al., “An Alternative for Including Phasor Measurements in State Estimators”, IEEE Trans. on power syst., vol. 21, no. 4, pp. 1930–1937, Nov. 2006.




Vandna Kamboj, Amrit Kaur

Paper Title:

Comparison of Constant SUGENO-Type and MAMDANI-Type Fuzzy Inference System for Load Sensor

Abstract:    Load sensor is developed using mamdani fuzzy inference system and sugeno fuzzy inference system. It is two input and one output sensor. Both mamdani-type fuzzy inference system and sugeno-type fuzzy inference system are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between these two fuzzy inference system and their simulated results are compared.

 Fiber Bragg Grating sensor, fuzzy inference system (FIS), fuzzy logic, mamdani, sugeno, windmill blades.


1.             J.Yen and R.Langari, “Fuzzy Logic,” Pearson Education, 2004.
2.             K.P. Mohandas and S. Karimulla, “Fuzzy and Neuro-fuzzy modeling and control of non linear systems”, Second  International Conference on Electrical and Electronics, 2001.

3.             Adedeji B.Badiru and John Y. Cheung, “ Fuzzy Engineering Expert System with Neural Network Applications,” John Wiley and Sons Inc., 2002.

4.             David Naso, Biagio Turchiano, “A Fuzzy-Logic Based Optical Sensor for Online Weld Defect-Detection”, IEEE Trans. on industrial informatics, vol.1, no.4, November, 2005.

5.             T.J.Ross, “Fuzzy Logic with Engineering Applications,” John Wiley and sons, 2010.                                        

6.             Arshdeep Kaur, Amrit Kaur, “Comparison of Mamdani Fuzzy Model and Neuro Fuzzy Model for Conditioning     System,” International Journal of Computer Science and Information Technologies, vol. 3 (2), 3593-3596, 2012.   

7.             Gaizka Durana, Marlene Kirchhof, Michael Luber, Idurre Sáez de Ocáriz, Hans Poisel, Joseba Zubia, and Carmen Vázquez, “Use of a Novel Fiber Optical Strain Sensor for Monitoring the Vertical Deflection of an Aircraft Flap,” IEEE sensors journal, vol. 9, no. 10, October, 2009.

8.             Xiao-fu LI, Hai-hu YU, HUANG Hua, Dong-sheng ZHANG ,” Process Monitoring and Damage Detection in Composites Using FBG Sensors,” Photonics and Optoelectronics (SOPO), Symposium on,16-18 May, 2011.

9.             Sang-Woo Kim, Eun-Ho Kim, Mi-Sun Rim, Pratik Shrestha and In Lee, “Structural Performance Tests of Down Scaled Composite Wind Turbine Blade using Embedded Fiber Bragg Grating Sensors,” Int’l J. of Aeronautical & Space Sci. 12(4), 346–353  (2011).

10.          Haman, N. D. Geogranas, “Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Evaluating the Quality of Experienceof Hapto-Audio-Visual Applications”, IEEE International Workshop on Haptic Audio Visual Environments and their Applications, 2008.

11.          Arshdeep Kaur, Amrit Kaur, “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System,” International Journal of Soft Computing and Engineering (IJSCE), vol.2, issue-2, May 2012.




Srishtee Chaudhary, Rajesh Mehra

Paper Title:

FPGA Based Adaptive Filter Design Using Least PTH-Norm Technique

Abstract:     Adaptive filters are considered nonlinear systems; therefore their behavior analysis is more complicated than for fixed filters. As adaptive filters are self-designing filters, their design can be considered less involved than in the case of digital filters with fixed coefficients. This paper presents simulation of Low Pass FIR Adaptive filter using least mean square (LMS) algorithm and least Pth norm algorithm. LMS algorithm is a type of adaptive filter known as stochastic gradient-based algorithms as it utilizes the gradient vector of the filter tap weights to converge on the optimal wiener solution whereas Least Pth does not need to adapt the weighting function involved and no constraints are imposed during the course of optimization. In this paper FPGA implementation of a low pass FIR filter is done using least Pth-norm technique. The performance of both approaches is compared.

 Adaptive filters, FIR , Least Pth norm, LMS, Matlab, FPGA.


1.              S. Haykin, "Adaptive Filter Theory ", Prentice Hall, 4th edition,  pp.  3-30,2002.
2.              Ali H. Sayed, “Fundamentals of Adaptive Filtering”, John Wiley &  Sons, pp. 170-245, 281-325, 2003.

3.              J. R. Treichler, C. R. Johnson, Jr., and M. G. Larimore, “Theory and Design of Adaptive Filters, “JohnWiley & Sons, pp.1-60, 1987.

4.              Rafaely, B.  Elliot, “A computationally efficient frequency-domain LMS algorithm with constraints on the adaptive filter”, IEEE Transactions on Signal Processing, Vol.48, Issue 6, pp. 1649 -1655, 2002.

5.              Guo Yecai , He Longqing , “ Design and Implementation of Adaptive Equalizer Based on FPGA” , 8th International Conference on Electronic Measurement and Instruments, pp. 790-794, 2007.

6.              Chanpreet Kaur , Rajesh Mehra , “ An FPGA Implementation Efficient Equalizer for ISI Removal in Wireless Applications “, IEEE Conference on  Emerging Trends in Robotics and Communication Technologies, pp. 96-99, Dec 2010.

7.              Banovic. K, Khalid, M.A.S, Abdel –Raheem, “FPGA Implementation of a configurable complex blind Adaptive Equalizer, “Signal Processing and Information Technology, IEEE International Symposium, pp. 150-153, 2007.

8.              Sudhanshu Baghel and Rafiahamed Shaik "FPGA Implementation of Fast Block LMS Adaptive Filter using Distributed Arithmetic for High Throughput" International Conference on Communication and Signal Processing ( ICCSP ) pp. 443 – 447, March 2011 .

9.              Sipneel Kaur, Ranjit Kaur “Least Square Linear Phase Non-recursive Filter Design”  IJEST Vol. 3 ,  No. 7, pp. 5845-5850, July 2011.

10.           Wu-Sheng Lu, Takaos Hinamoto” Minimax Design of Non-linear phase FIR Filters: A Least Pth approach”, IEEE Conference Vol. 1 , pp 409-412, 2002.

11.           Shpak, D.  Syscor Res.  “Design of mixed-norm FIR filters using an unconstrained least-pth algorithm” Communication Computers and signal processing, (PACRIM), IEEE Pacific Rim Conference, Vol. 1, pp. 253 – 255, Aug 2003.

12.           Z.  Ramandan, A. Poularikas, “Performance analysis of a new variable step-size LMS algorithm with error nonlinearities”, Procedings IEEE, pp.384 – 388, 2004.

13.           H. C. Shin, A. Sayed, W. J. Song, “Variable step-size NLMS and affine projection algorithms”, IEEE Signal Processing Lett., Vol. 11, No. 2,  pp.132 – 135, Feb 2004.

14.           Hadi Sadoghi Yazdi,” Adaptive Data Reusing Normalized Least Mean Square Algorithm Based on Control of Error”, Iranian Conference on Electrical Engineering, (ICEE), 2006.

15.           Amrita and Rajesh Mehra ” Embedded Design of an efficient Noise Cancellor for Digital Receivers ” International Journal of Engineering Science and Technology (IJEST) ,Vol. 3 No. 2 , Feb 2011, pp.1252 – 1257.

16.           Rafid Ahmed Khalil "Adaptive Filter Application in Echo Cancellation System and Implementation using FPGA" Vol. 16 No. 5, Dec 2008 pp. 20-32.

17.           Farzad Nekouei, Neda Zargar Talebi, Yousef S. Kavian, Ali Mahanii," FPGA Implementation of LMS Self Correcting Adaptive Filter (SCAF) and Hardware Analysis", 8th IEEE, IET International Symposium on Communication Systems, Networks and Digital system Processing (CNSDSP), 2012 ,  pp. 1-5.




Abhishek Kumar, J.E. Nalavade, Vinay Yeola, Vishal Vivek, Yatharth Srivastava

Paper Title:

An Adaptive Learning System Based on Ant Colony Algorithm

Abstract:      One of the most important emerging requirements of the learning is adaptation to learner’s needs. Adaptive learning will permit improvements in the current scenario. It suggests courses adapted to results, behaviors, preferences, tastes of learners. In the present paper, we have proposed an approach based on the Ants colonies' optimization algorithm. This helps to recommend a learning course. It adapts to fit in the best manner into learner's profiles. The approach is helpful in improving both the learning achievement and learning efficiency of individual Learners. Learners with different attributes may locate learning objects (LO) which have a higher probability of being suitable. A web-based learning approach was created for learners to find the learning objects more effectively. We propose an attribute based ant colony system to help learners find an adaptive LO more effectively.

 adaptive learning, ant colony, learning object, learning style, learner


1.             D. A. Kolb, “Learning style inventory technical“. Boston McBer &Co.    
2.             Yao Jung Yang a,b,*, Chuni Wua,”An attribute-based ant colony system for adaptive learning object recommendation”(20081)

3.             Ainhoa Alvarez, Isabel Fernandez-Castro, Maite Urretavizcaya. “Adaptive Learning Based on Variable Student and domain Models in Magadi”. Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT’04)

4.             T. Wang, “Using a style-based ant colony system for adaptive learning“, Expert Systems with Applications 34, pp. 2449–2464.(2008)

5.             Ruiz et al.” Learning Objects and Methods of Adaptive Hypermedia”(2009)

6.             Yaghmaie et al." Multi –agent systems with SCORM and WEB ontology”(2007)

7.             Chih-Ming Chen, Chi-Jui Peng.” Personalized E-learning System based on Ontology-based Concept Map Generation Scheme.” Seventh IEEE International Conference on Advanced Learning Technologies (ICALT  2007)

8.             Pushpa M  ," Matching  learner attributes and learning object    attributes”,(2006)




A. Swetha, Y. Madhavi Latha

Paper Title:

Network Security-Proposals

Abstract:       The explosion of the public Internet and e-commerce, private computers, and computer networks, if not adequately secured, are increasingly vulnerable to damaging attacks. Hackers, viruses, vindictive employees and even human error all represent clear and present dangers to networks. Loss of irreplaceable data is a very real threat for any business owner whose network connects to the outside world. Remote access for employees and connection to the Internet may improve communication in ways you’ve hardly imagined. Access to the Internet can open the world to communicating with customers and vendors, and is an immense source of information. But these same opportunities open a local area network (LAN) to the possibility of attack by thieves and vandals.



1.              Cryptography and Network Security by William Stallings-2nd Edition
2.              Network Security Fundamentals by Gert Delaet, Gert Svhauwers

3.              Cryptography and Network Security by Kahate

4.              Cryptography and Network Security by P S Gill.

5.              Cryptography and Network security by William Stallings-5th edition




Monika, Amrit Kaur

Paper Title:

Comparison of Fuzzy Logic and NEURO Fuzzy Algorithms for Load Sensor

Abstract:  Load sensor is developed using fuzzy logic as well as neuro-fuzzy method. It is two inputs and one output sensor. Both fuzzy logic and neuro-fuzzy algorithms are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between the results of fuzzy logic and neuro-fuzzy algorithms and provides the better algorithm for load sensor.

 fuzzy logic, load sensor, neuro-fuzzy, optical fiber, rule base.


1.              Alfredo Petrosino and Antonino Staiano, “A Neuro fuzzy Approach for Sensor Network Data cleaning” Italian Workshop on Neural Networks (WIRN), pp. 140-147, 2007.
2.              Gaizka Durana, Marlene Kirchhof, Michael Luber, Idurre Sáez de Ocáriz, Hans Poisel, Joseba Zubia, and Carmen Vázquez, “Use of a Novel Fiber Optical Strain Sensor for Monitoring the Vertical Deflection of an Aircraft Flap,”IEEE SENSORS JOURNAL, VOL. 9, NO. 10,      OCTOBER 2009.

3.              Byoungho Lee, “Review of the present status of optical fiber sensors” Science Direct Journal on Optical Fiber Technology, Vol 9, pp. 57-79, April 2003

4.              SrismritaBasu,“Realization of Fuzzy Logic Temperature Controller”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 6, June 2012.

5.              M. S. Islam, M. S. Z. Sarker, K.A.A. Rafi and M. Othman,“Development of a fuzzy logic controller algorithm for air conditioning system”, ICSE Proceedings, 2006

6.              Gaurav ,amrit kaur,“ Comparison between Conventional PID and Fuzzy Logic Controller for Liquid Flow Control”, International Journal of Innovative Technology and  and Exploring Engineering (IJITEE), Volume-1, Issue-1, June 2012.

7.              Arshdeep Kaur, Amrit Kaur, “Comparison of Mamdani Fuzzy Model and Neuro Fuzzy Model for Conditioning     System,” International Journal of Computer Science and Information Technologies, vol. 3 (2), 3593-3596, 2012.

8.              K.P. Mohandas and S. Karimulla, “Fuzzy and Neuro-fuzzy modeling and control of non linear systems”, Second  International Conference on Electrical and Electronics, 2001




Nikita Gupta, Swapna Devi

Paper Title:

Improved EEG Source Localization for an Isotropic Multi - Spherical Head Model

Abstract:   Human head comprises multiple layers and tissues. The aim of this work is to investigate the effect of conductivity variation, due to presence of Gray matter and White matter in Brain, on Source Localization in Electroencephalography (EEG). Particle Swarm Optimization (PSO) Algorithm, a global optimization algorithm, has been used for finding Inverse Solution of EEG. It has been found that a five-spherical head model comprising, Scalp, Skull, CSF, Gray Matter and White Matter give better performance in source localization than a four spherical head model comprising, Scalp, Skull, CSF and Brain.

      EEG, Head Models, PSO, Source Localization.


1.          He. B., Neural Engineering. Kluwer  Academic publishers, Norwell, (2005).
2.          P. N. Sen, P. J. Basser, “A Model for Diffusion in White Matter in Brain,” Biophysical Journal, vol. 89, pp. 2927-2938, 2005.

3.          T. Liu, G.Young, L. Huang, N. Chen, S. TC Wong, “76-space Analysis of Grey Matter Diffusivity: Methods and Application,” NeuroImage, vol. 31, Issue 1, pp. 51-65, 2006.

4.          Hallez, H., Vanrumste  B., Hese P., Delputte S., Lamhieu I., “Dipole estimation errors due to differences in modeling anisotropic conductivities in realistic head models for EEG source analysis,” Journal of Phys. Med. and Bio., vol. 53, pp. 1877-1894, 2008.

5.          J. O. Ollikainen, M. Vaukonen, P. Karjalainen, J.P. Kaipio., “Effects of local skull inhomogeneities on EEG source estimation,” Med. Eng. and Phy., vol. 21, pp. 143-154, 1999.

6.          J. Haueisen, “The influence of forward model conductivities on EEG/MEG source reconstruction,” IEEE Proceedings of Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaginf, pp. 18-19, 2007.

7.          J. C. de Munck, M. J. Peters, “A fast method to compute the potential in the multisphere model,” IEEE Trans. Biomed. Eng., vol. 40, pp. 1166–1174, 1993.

8.          Y.K. Alp, O. Arikan, S. Karakas, “Dipole Source Reconstruction of Brain Signals by Using Particle Swarm Optimization,” IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Apr. 2009.

9.          E. Franck, “Electric potential produced by two point current sources in a homogenous conducting sphere,” J. Appl. Phys., Vol. 23, No. 11, pp.1225-1228, 1952.

10.       D.B. Geselowitz, “On bioelectric potentials in an inhomogeneous volume conductor,” Biophys., Vol. 7, pp.1-11, 1967.

11.       R.S. Hosck, A. Sances, Jr. R. W. Jodat. S. J. Larson, “The contributions of intracerebral currents to the EEG and evoked potentials,” IEEE Trans. Biomed. Eng., Vol. 25, pp.405-413, 1978.

12.       M.A. Huerta, G. Gonzalez, “The surface potentials produced by electric sources in stratified spherical and spheroidal volume conductors,” Int. J. Electronics, Vol.54, pp.657-671, 1983.

13.       J. C. de Munck, “The potential distribution in a layered anisotropic spheroidal volume conductor,” J. Appl. Phys., vol. 64, pp. 464–470, 1988.

14.       Md R. Bashar, Y. Li, P. Wen, “Effects of White Matter on EEG of Multi-layered Spherical Head Models”, IEEE Conf. on Elec. & Comp. Eng., Dec. 2008.

15.       C. H. Wolters, “Influence of Tissue Conductivity Inhomogeneity and Anisotropy on EEG/MEG based Source Localization in the Human Brain,” PhD dissertation, University of Leipzig, France, 2003.

16.       S. Mingui, “An Efficient Algorithm for Computing Multishell Spherical Volume Conductor Models in EEG Dipole Source Localization,” IEEE Trans. on Biomed. Engg., Vol. 44, No. 12, Dec. 1997.

17.       Z. Zhang, “A Fast Method to Compute Surface Potentials Generated Dipoles within Multilayer Anisotropic Spheres,” Phys. Med. Bio., Vol. 40, pp. 335-349, 1995.




Shaheen Taj S.A, Prathibha Kiran, Elavarasi

Paper Title:

A Novel Method for Patient Centric Secure and Scalable sharing of PHR in Cloud Computing using Encryption

Abstract:    PHRs grant patients access to a wide range of health information sources, best medical practices and health knowledge. In patient centric secure sharing, patients will create, manage and control their personal health data from one place using the web. Prior to storing the records in cloud server, they are encrypted using encryption algorithm which ensures the patient’s full control over their PHR. In addition to PHR (Medical history, current exams), personal files, insurance details and sensitive information can also be stored and shared. Patients only decide which set of users can access which set of files. All the files stored in clouds which are semi-trusted servers, are in the encrypted form and are confidential to other users. We make use of Attribute Based encryption (ABE) to encrypt the files. In this scheme, users are categorized into personal and professional domains which greatly reduce the key management complexity. There is a structured way to access the files for personal and professional purposes. Patients are able to dynamically modify the access policy and attributes.

  Attribute Based Encryption, Cipher, DES, Feistel.


1.              M.  Li, S. Yu, K.  Ren, and   W.  Lou,  “Securing personal health  records in cloud  computing: Patient-centric and  fine-grained data  access  control  in multi-owner settings,”  in  SecureComm’10, Sept. 2010, pp. 89–106.
2.              H. Lo¨ hr, A.-R. Sadeghi,  and M. Winandy, “Securing the  e-health cloud,” in Proceedings of the 1st ACM International Health Informatics Symposium,  ser. IHI ’10, 2010, pp.  220– 229.

3.              M. Li, S. Yu, N. Cao, and  W. Lou, “Authorized private keyword search  over  encrypted Personal health records in cloud computing,”  in ICDCS ’11, Jun. 2011.

4.              “The health insurance portability and accountability act.”[Online]. Available:

5.      Overview.asp

6.              “Google,   microsoft   say hipaa   stimulus rule doesn’t apply to them,”                


8.              “At  risk  of exposure  in  the push for electronic medical records, concern is growing  about how well privacy can be safeguarded,” 2006. [Online]. Available:          


10.           K. D.  Mandl,   P.  Szolovits,  and  I. S. Kohane,  “Public  standards and   patients’ control: how   to  keep  electronic   medical records accessible  but private,” BMJ, vol. 322, no. 7281, p. 283, Feb. 2001.

11.           J. Benaloh,  M.  Chase,  E. Horvitz, and  K. Lauter,  “Patient con- trolled  encryption: ensuring privacy of electronic  medical record- s,” in CCSW ’09, 2009, pp. 103–114.

12.           S. Yu, C. Wang,  K. Ren, and  W. Lou, “Achieving secure,  scalable, and fine-grained data  access control in cloud  computing,” in IEEE INFOCOM’10,  2010.

13.           C.  Dong,   G.  Russello,   and   N.  Dulay,   “Shared  and  searchable encrypted  data   for   untrusted  servers,”  in  Journal of Computer Security, 2010.

14.           V. Goyal, O. Pandey, A. Sahai and B. Waters, “ Attribute-based encryption for a fine           grained access control of encrypted data’’ in CCS 06, 2006, pp. 89-98.

15.           M. Li, W. Lou and K. Ren, “Data security and  Privacy in wireless body area network,”    IEEE Wireless Communication Magazine, Feb,

16.           Boldyreva, V. Goyal, and V. Kumar, “Identity-based encryption with efficient revocation,” in ACM CCS, ser. CCS ’08, 2008, pp. 417–426.

17.           L. Ibraimi, M. Petkovic, S. Nikova, P. Hartel, W. Jonker,“Ciphertext-policy attribute- bas- d threshold decryption with flexible delegation and revocation of user attributes,”  2009.

18.           S. Yu, C. Wang, K. Ren, and W. Lou, “Attribute based data sharing with attribute revocation,” in




Naveen Choudhary, Chand Mal Samota

Paper Title:

A Survey of Logic Based Distributed Routing for On-Chip Interconnection Networks

Abstract:     The availability of increased number of resources on a single silicon chip is enforcing the designers to come up with mechanisms for efficient and effective management of these resources on a chip. Moreover defective components, chip virtualization and power-aware techniques may lead to irregular on chip interconnection topology making efficient routing a non trivial challenge. Nearly, all routing algorithms and topologies support switches that make use of routing tables for efficient routing. However memories do not scale well in terms of area and power consumption for the routing tables, thus not practical for scalable on chip networks.Logic based distributed routing (LBDR) is recently proposed as an alternative solution to the table based distributed routing which can drastically reduce the memory requirement even while being as efficient as table based distributed routing. LBDR is a simple methodology of routing that enables the removal of the routing tables at every switch and uses only a small set of bits per switch to enable efficient routing. This paper surveys different variations of efficient Logic-based distributed routing (LBDR) proposed in the NoC research literature for regular and irregular on chip interconnection topologies.

 Networks-on-chip, Routing, LBDR, LBDRe, uLBDR, Fault-tolerance.


1.                 G International Technical Roadmap for Semiconductors. Available at, 2004.
2.                 Jose Flich and Jose Duato, “Logic based distributed routing for NOCs”, IEEE computer architecture letters, vol. 7, no. 1, January-June 2008.

3.                 S. Rodrigo, S. Medardoni, J. Flich, D. Bertozzi, and J. Duato, “Efficient implementation of distributed routing algorithms for NoCs”, IET Comput. Digital Tech., vol. 3, no. 5, pp. 460–475, 2009.

4.                 Mejia, J. Flich, and J. Duato,“On the Potentials of Segment-Based Routing for NoCs”, 37th International Conference on Digital Object Identifier: 10.1109/ICPP.2008.56  Publication Year: 2008.

5.                 L.K. Arora, Rajkumar,“Optimizing up/down routing by minimal path“, International Journal of Computer Applications (0975 – 8887) vol. 5– No.1, August 2010.

6.                 J. Van Leeuwen and R.B. Tan,“Interval routing”, in The Computer Journal, 30(4):298-307.

7.                 M.E Gomez. P. Lopez. J. Duato, “A Memory Effective Routing Strategy for Regular Interconnection Networks”, in International Parallel and Distributed Processing Symposium (IPDPS), 2005.

8.                 S. Borkar, R. Cohn, G. Cox, S. Gleason, T. Gross, H. T. Kung, M. Lam, B.Moore, C.Peterson, J. Pieper, L. Rankin, P. S. Tseng , J. Sutton , J. Urbanski , J. Webb, “iWarp: An Integrated Solution to High-Speed Parallel Computing”,in Supercomputing Conference, 1988.

9.                 S. Rodrigo,C. Hern´andez, J. Flich, F. Silla, J. Duato, S. Medardoni, D. Bertozzi, A. Mej´ia, and D. Dai, “Yield-oriented evaluation methodology of network-on-chip routing implementations,” in 11th Int. Conf. SoC, 2009.

10.              S. Rodrigo, J. Flich, A. Roca, S. Medardoni, D. Bertozzi, J. Camach1, F. Silla  and J. Duato, “Addressing Manufacturing Challenges with Cost-Efficient Fault Tolerant Routing”, Fourth ACM/IEEE International Symposium on Networks-on-Chip (NOCS), 2010.

11.              J. Cano, J. Flich, J. Duato, M. Coppola, R. Locatelli, “Efficient routing implementation in complexsystems-on-chip”, Fifth ACM/IEEE International Symposium on
Networks-on-Chip (NOCS), 2011.

12.              S. Rodrigo, J. Flich, A. Roca, S. Medardoni, D. Bertozzi, J. Camacho,   F. Silla, and J. Duato, “Cost-Efficient On-Chip Routing Implementations for CMP and MPSoC Systems”, IEEE transactions on computer-aided design of integrated circuits and systems, vol. 30, no. 4, april 2011.

13.              S. Rodrigo, J. Flich, J. Duato, and M. Hummel, “Efficient unicast and multicast support  for cmps”, in Microarchitecture, MICRO-41,st IEEE/ACM International Symposium on, 2008.

14.              J. Flich, A. Mejıa, P. Lopez and J. Duato, “Region-Based Routing: An Efficient Routing Mechanism toTackle Unreliable Hardware in Networks on Chip”, in First International Symposium on Networks on Chip(ISNOC), 2007.

15.              M. Palesi, S. Kumar, R. Holsmark, “A Method for Router Table   Compression for Application Specific Routing inMesh Topology NoC Architecture”, in International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS),2006.

16.              Sanusi and M. Bayoumi, “Smart-flooding: A novel scheme for faulttolerant NoCs”, in  IEEE SOCC, Sep. 2009.

17.              M. Pirretti, G. Link, R. Brooks, N. Vijaykrishnan, M. Kandemir, andM. Irwin,“Fault tolerant algorithms for network-on-chip interconnect,”in  IEEE Comput. Soc. Annu. Symp. VLSI, Feb. 2004, pp. 46–51.

18.              P. Bogdan, T. Dumitras, and R. Marculescu, “Stochastic communication:A new paradigm for fault-tolerant networks-on-chip,” VLSI Des., vol.2007, no. 95348, p.17, 2007.

19.              W. Song, D. Edwards, J. Nunez-Yanez, and S. Dasgupta, “Adaptive stochastic routing in fault-tolerant on-chip networks,” in  NoCs, May 2009, pp. 32–37.

20.              V. Puente, J. A. Gregorio, F. Vallejo, and R. Beivide, “Immunet: A cheap and robust fault-tolerant packet routing mechanism,” ACM SIGARCH Computer Architecture News, vol. 32, no. 2, Mar. 2004,pp. 198–209.

21.              J.Cano, J.Flich,J. Duato,M.Coppola,R.Locatelli,Tableless Distributed Routing in heterogeous  MPSoC system,En Actas de las XXII Jornadas de Paralelismo,pp. 675-680 ISBN:  978-84-694-1791-1, La Laguna (Tenerife) (Spain), September 2011.




Bidyut Das, Subhajit Pal, Suman Kr. Mondal, Dipankar Dalui, Saikat Kumar Shome

Paper Title:

Automatic Keyword Extraction From Any Text Document Using N-gram Rigid Collocation

Abstract:      An unsupervised method for extracting keywords from a single document is proposed in this paper. A fuzzy set theoretic approach, fuzzy n-gram indexing, is used to extract n-gram keywords. It is noticed that n-gram keyword renders a better result as compared to mono-gram keyword, but for some documents the most relevant keyword is mono-gram. This paper focuses on a keyword extraction approach which neither requires a dictionary or thesaurus nor does it depend on the size of text document. The algorithm is efficient enough to dynamically determine the mono-gram, bi-gram as well as n-grams keywords for different documents.

 n-gram collocation, fuzzy set; information retrieval, natural language processing.


1.                 M. Andrade and A. Valencia, Automatic extraction of keywords from scientific text: application to the knowledge domain of protein families, Bioinformatics, Vol. 14(7),1998, pages 600-607
2.                 S. Jones and G. W. Paynter, ”Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications,” Journal of the American Society for Information Science and Technology, vol. 53, no. 8, pp. 653-677, 2002.

3.                 K. Coursey, R. Mihalcea, and W. Moen, ”Automatic keyword extraction for learning object repositories,” in Proc. Conf. Amer. Soc. Inf. Sci. Technol., 2008.

4.                 L. Plas, V. Pallotta, M. Rajman, and H. Ghorbel. 2004. Automatic keyword extraction from spoken text. A comparison of two lexical resources: the EDR and WordNet. In Proceedings of the LREC.

5.                 Y. Matsuo and M. Ishizuka, ”Keyword extraction from a single document using word co-occurrence statistical information,” International Journal on Artificial Intelligence Tools, vol. 13, no. 1, pp. 157-169, 2004.

6.                 Y. HaCohen-Kerner, ”Automatic extraction of keywords from abstracts,” in Proc. 7th Int. Conf. Knowledge-Based Intell. Inf. Eng. Syst., 2003, vol. 2773, pp. 843-849.

7.                 Hulth. 2003. Improved automatic keyword extraction given more linguistic knowledge. In Proceedings of EMNLP, pages 216-223.

8.                 A Hulth, 2004, Combining machine learning and natural language processing for automatic keyword extraction. Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences (together with KTH).

9.                 H. P. Luhn, A statistical approach to mechanized encoding and searching of literary information, IBM Journal of Research and Development, Vol. 1(4), 1957, Pages 309-317

10.              Y. Choueka, Looking for Needles in a Haystack or Locating Interesting Collocational Expressions in Large Textual Databases, In Proceedings of RIAO’1988,
1988, pages 609-624.

11.              Goldstein, Ira. Collocations in Machine Translations [Internet]. Version 4. Knol. 2008 Jul 27.

12.              Church, Kenneth W. and Hanks, Patrick, Word association norms, mutual information and lexicography, Computational Linguistics, 1990, Vol. 16(1), pages 22-29

13.              R.E. Bellman, L. A. Zadeh, Decision making in fuzzy environment, Management Science, Vol. 17(4), 1970, pages 141-164

14.              J. Makhoul, F. Kubala, R. Schwartz and R. Weischedel, Performance Measures For Information Extraction, In Proceedings of DARPA Broadcast News Workshop, 1999, pages 249-252

15.              Y. HaCohen-Kerner, Z. Gross, and A. Masa, Automatic extraction and learning of keyphrases from scientific articles, Comput. Linguist. Intell. Text Process, pages 657-669, 2005.

16.              Y. HaCohen-Kerner, I. Stern, D. Korkus, and E. Fredj, ”Automatic machine learning of keyphrase extraction from short html documents written in Hebrew,” Cybern. Syst., 2007, Vol. 38(1), pages 1-21

17.              F. Liu, F. Liu, and Y. Liu, Automatic keyword extraction for the meeting corpus using supervised approach and bigram expansion, In Proc. IEEE Workshop Spoken Lang. Technol., 2008, pages 181-184

18.              Q. G. Zhang, D. J. Xue, Z. H. Zhang, J. Y. Zhang, Automatic Keyword Extraction from Massive Data Sets Based on Feature Combination, Journal of the China Society for Scientific and Technical Information, 2006, Vol. 25(5),  pages 587-593




Shah Murtaza Rashid Al Masud

Paper Title:

Study and Analysis of Scientific Scopes, Issues and Challenges towards Developing a Righteous Wireless Body Area Network

Abstract: The escalating applies of wireless networks and the constant tininess of electrical devices have empowered the development of Wireless Body Area Network (WBAN). In this network various sensors are attached on clothing or on the body or even implanted under the skin. This network enables medical doctor to distantly monitor essential signs and organs of patients and provide real time opinions for medical diagnosis. The numerous new, realistic and ground-breaking applications of WBAN facilitate to advance health care and the quality of life. By means of a WBAN, the patient experiences a superior and greater physical mobility and is no longer constrained to reside in the hospital. The amalgamation of low-power, miniaturized, lightweight sensors nodes lead to the development of a proactive and unobtrusive Wireless Body Area Network (WBAN). A WBAN presents a long term health monitoring of a patient devoid of any restriction on his/her normal daily life activities. It is the easiest and fastest way to monitor patient’s health status effectively. Although WBAN is the efficient way to diagnose patients existing condition but the challenges related to developing an effective WBAN is not studied and analyzed significantly. The effectiveness of the WBAN strongly depends on controlling the energy consumption of sensor nodes. To achieve energy efficiency, low duty cycle MAC protocols are used. In this paper, we discuss about the basic idea and key components of WBAN, basic difference between wireless sensor networks (WSN) and WBAN, technical challenges, and its importance, quality of service (QoS) and security, analysis of MAC features, various applications, different sensors; physiological signals, their frequency; different data rate, latency of WBANs, issues related to energy or power efficiency, and existing WBAN technologies.Finally, the open research issues and challenges are also pointed out.

   WBAN, WSN, MAC, QoS, Energy efficiency


1.             D. Cypher, N. Chevrollier, N. Montavont, and N. Golmie, \Prevailing over wires in healthcare environments: benefits and challenges," IEEE Communications Magazine, vol. 44, no. 4, pp. 56{63, Apr. 2006.
2.             R. S. H. Istepanian, E. Jovanov, and Y. T. Zhang, \Guest editorial introduction to the special section on m-health: Beyond seamless mobility and global wireless health-care connectivity," Information Technology in Biomedicine, IEEETransactions on, vol. 8, no. 4, pp. 405{414, Dec. 2004.

3.             K. Van Dam, S. Pitchers, and M. Barnard, \Body area networks: Towards a wearable future," in Proceedings ofWWRF kick o_ meeting, Munich, Germany, 6-7 March 2001.

4.             R. Schmidt, T. Norgall, J. Morsdorf, J. Bernhard, and T. von der G"un, \Body area network ban{a key infrastructure element for patient centered medical applications." Biomedizinische Technik. Biomedical engineering, vol. 47, no. 1, pp. 365{368, 2002.

5.             B. Gyselinckx, C. Van Hoof, J. Ryckaert, R. F. Yazicioglu, P. Fiorini, and V. Leonov, \Human++: autonomous wireless sensors for body area networks," in Custom IntegratedCircuits Conference, 2005. Proceedings of the IEEE 2005, Sep. 2005, pp. 13-19.

6.             C. Otto, A. Milenkovic, C. Sanders, and E. Jovanov, \System architecture of a wireless body area sensor network for ubiquitous health monitoring," Journal of Mobile Multimedia, vol. 1, no. 4, pp. 307{326, 2006.

7.             B. Lo and G.-Z. Yang, \Body sensor networks: Infrastructure for life science sensing research," in Life ScienceSystems and Applications Workshop, 2006. IEEE/NLM, Bethesda, MD,, Jul. 2006, pp. 1-2. 8.

8.             D. Jurik and A. C. Weaver, \Remote medical monitoring," Computer, vol. 41, no. 4, pp. 96{99, 2008.

9.             S. Park and S. Jayaraman, \Enhancing the quality of life through wearable technology," IEEE Engineering in Medi- cine and Biology Magazine, vol. 22, no. 3, pp. 41{48, May/Jun. 2003.

10.          IEEE 802.15 WPAN Task Group 6 Body Area Networks. [Online]. Available:

11.          World Health Organization [online]

12.          International Diabetes Federation (IDF) [Online]

13.          WHO, Global atlas on cardiovascular disease prevention and control

14.          “Facts & statistics,” Spinal Injuries Association, 2011. [Online]. Available:

15.          H. Zhou and H. Hu, “Human motion tracking for rehabilitation—Asurvey,” Biomedical Signal Processing and Control, vol. 3, no. 1,pp. 1-18, Jan. 2008

16.          Jemal, F. Bray, and J. Ferlay, “Global Cancer Statistics,” American Cancer Society, Inc, vol. 61, no. 2, pp. 69-90, 2011.

17.          “Diabetes Statistics,” Association, American Diabetes, 2011.[Online]. Available:

18.          J. Zhao, S. Carolina, O. Mems, I. Diabetes, U. States, and U. States, “A MEMS Viscometric Glucose Monitoring Device,” in 13th International Conference on Solid-State Sensors, Actuator sand Microsystems, 2005, pp. 1816-1819.

19.          “Asthma Statistics,” American Academy of Allergy Asthma &Immunology, 2011. [Online]. Available:

20.          L. J. Akinbami, J. E. Moorman, and X. Liu, “Asthma prevalence ,health care use, and mortality: United States, 2005-2009.,”National health statistics reports, no. 32, pp. 1-14, Jan. 2011

21.          Martin Brandl, Julius Grabner, Karlheinz Kellner, Franz Seifert,Johann Nicolics, Sabina Grabner, and Gerald Grabner,”A Low-Cost Wireless Sensor System and Its Application in Dental Retainers”, IEEE SENSORS JOURNAL, VOL. 9, NO. 3, MARCH2009

22.          Stroke,” U.S News Health, 2011. [Online]. Available:

23.          “Visual impairment and blindness,” World Health Organization,2011. Available:

24.          “Statistics on Parkinson’s,” Parkinson’s disease Foundation, 2011. [Online]. Available:

25.          “Neurology and public health,” World Health Organization, 2011.[Online]Available:

26.          S. Patel et al., “Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors.,” IEEE transactions on information technology in biomedicine, vol. 13, no. 6, pp. 864-73,Nov. 2009.

27.          Huasong Cao and Victor Leung, Cupid Chow and Henry Chan “Enabling Technologies for Wireless Body Area Networks: A Survey and Outlook”, IEEE Communications Magazine , December 2009

28.          M. Arif Siddiqui, Shah Murtaza Rashid Al-Masud, " Towards Design of Novel Low Power MAC Protocol for Wireless Body Area Networks", International Journal of Computer Information Systems, Vol. 4, No. 4, 2012

29.          Deena M. Barakah, Muhammad Ammad uddin, " A Survey of Challenges and Applications of Wireless Body Area Network (WBAN) and Role of A Virtual Doctor Server in Existing Architecture", 2012 Third International Conference on Intelligent Systems Modeling and Simulation

30.          W. Ye, J. Heidemann, and D. Estrin, “An energy-efficient MACprotocol for wireless sensor networks”, In Proceedings of theIEEE Infocom, New York, USA, pp. 1567-1576, Jun. 2002.

31.          T. Van Dam and K. Langendoen, “An adaptive energy-efficientMAC protocol for wireless sensor networks”, In ACMConference on Embedded Networked Sensor Systems (Sensys),Los Angeles, USA, pp. 171-180, Nov. 2003.

32.          J. Polastre, J. Hill, and D. Culler, “Versatile low power media access for wireless sensor networks”, In ACM Conference onEmbedded Networked Sensor Systems (Sensys), Baltimore,Maryland, USA, pp. 95-107, Nov. 2004.

33.          El-Hoiydi, J.D. Decotignie, and J. Hernandez, “Low powerMAC protocols for infrastructure wireless sensor networks”, InProc of the fifth European Wireless Conference (EW’04),Barcelona, Spain, pp. 563-569, Feb. 2004.

34.          G. Pei and C. Chien, “Low power TDMA in large wirelesssensor networks”, IEEE Military Communications Conference(MILCOM), pp. 347-351, Oct. 2001.

35.          W.B.Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wirelessmicrosensor networks”, IEEE Transactions on WirelessCommunications. vol. 1, no. 4, pp. 660-670, Oct 2002.

36.          V. Rajendran, J.J. Garcia-Luna-Aveces, and K. Obraczka,“Energy-efficient, application-aware medium access for sensornetworks”, In Proceedings of 2nd IEEE Conference on MobileAdhoc and Sensor Systems Conference, Washington, DC, USA,Dec. 2005.

37.          O. Younis and S. Fahmy, “HEED: A hybrid, energy-efficient,distributed clustering approach for adhoc sensor networks”,IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 366-379, Oct. 2004.

38.          S.Ullah, R. Islam, A. Nessa, Y. Zhong, and K.S. Kwak,“Performance analysis of preamble based TDMA protocol forwireless body area network”, Journal of CommunicationSoftware and Systems, vol. 4, no. 3, pp. 222-226, Sept. 2008.

39.          H.M. Li and J.D. Tan. “Heartbeat driven MAC for body sensornetworks”, In Proc of the 1stACM SIGMOBILE internationalworkshop on systems and networking support for healthcareand assisted living environments, San Juan, Puerto Rico, pp. 25-30, Jun. 2007.

40.          C. Li, H.B. Li, and R. Kohno, “Reservation-based dynamic TDMA protocol for medical body area networks”, IEICETransactions on Communications, E92.B (2), pp. 387-395,2009.

41.          B. Otal, L. Alonso, and C. Verikoukis, “Highly reliable energysaving MAC for wireless body sensor networks in healthcaresystems”, IEEE Journal on Selected Areas in Communications,vol. 27, no. 4, pp. 553-565, 2009.

42.          H. Su and X. Shang. “Battery-dynamics driven TDMA MAC protocols for wireless body area monitoring networks inhealthcare applications”, IEEE Journal on Selected Areas inCommunications, vol. 27, no. 4, pp. 424-434, 2009.

43.          C. Li, L. Wang, J. Li, B. Zhen, H.B. Li, and R. Kohno,“Scalable and robust medium access control protocol in wirelessbody area networks”, IEEE 20th International Symposium onPersonal, Indoor and Mobile Radio Communications, pp.2127-2131, 2009.

44.          B. Zhen, H.-bang Li, and R. Kohno, “Networking issues in medical implant communications,” International Journal of Multimedia and Ubiquitous Engineering, vol. 4, no. 1, 2009.

45.          M. Ameen, A. Nessa, and K. S. Kwak, “QoS Issues with Focus on Wireless Body Area Networks,” in 2008 Third International Conference on Convergence and Hybrid Information Technology, 2008, pp. 801-807.

46.          C. Li, H.-B. Li, and R. Kohno, “Performance Evaluation of IEEE 802.15.4 for Wireless Body Area Network (WBAN),” in 2009 IEEE International Conference




Rakesh Kumar, Girdhar Gopal, Rajesh Kumar

Paper Title:

Novel Crossover Operator for Genetic Algorithm for Permutation Problems

Abstract:  Simple Symmetric Traveling Salesman Problem (TSP) has a combinational nature. When there are 25 or more cities to visit, brute force search is not feasible. Instead, heuristic & probabilistic search methods are more reasonable for obtaining optimal solutions. In this paper, Genetic algorithm and crossover are researched and a novel crossover operator has been introduced by combining two existing crossover methods named PMX and OX crossover. The proposed operator is tested on 4 different inputs from TSPLIB provided by Heidelberg University and the result are compared  with Partial Matched Crossover(PMX), Order Crossover(OX) and cyclic crossover(CX) and is found that proposed crossover has outperformed  the rest in all the problems..

    Crossover, Genetic Algorithm, Traveling Salesman Problem (TSP).


1.             Goldberg D.E. “Genetic algorithms in search, optimization, and machine learning”, Addison Wesley Longman, Inc., ISBN 0-201-15767-5, 1989.
2.             Goldberg D.E. and Lingle R. “Alleles, loci and the travelling salesman problem,” Proceedings of an International Conference on Genetic Algorithms, Morgan Kauffman, 1985, pp 10-19.

3.             Sivanandam S.N. and Deepa S. N. “Introduction to Genetic Algorithms”, Springer, ISBN 9783540731894, 2007. 

4.             Eiben A.E. and Smith J.E. “Introduction to Evolutionary Computing”, Springer, Heilderberg, Germany, 2003.

5.             Davis L, “Handbook of Genetic Algorithms”, New York, Van Nostrand Reinhold, 1991.

6.             Syswerda G., “Schedule optimization using genetic Algorithms”, in Davis L. Ed. Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 2008, pp.-332-349.

7.             Oliver I.M., Smith D.J. and Holland J.H. “A study of permutation crossover operators on the travelling salesman problem”, In Proceedings of the Third International Conference on Genetic Algorithms, London, Lawrence Eribaum Associates, 1987.

8.             Kumar Rakesh, Jyotishree, 2012, “Novel Knowledge Based Tabu Crossover In Genetic Algorithms”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 8, August 2012 ISSN: 2277 128X.

9.             Grefenstette, J. J., “Incorporating Problem Specific Knowledge into Genetic Algorithms”. In L. Davis, ed., Genetic Algorithms and Simulated Annealing, 42-60, Los Altos, CA,Morgan Kaufmann, 1987.

10.          Whitley Darrell, “Functions as Permutations: Regarding No Free Lunch”, Walsh Analysis and Summary Statistics. PPSN, 2000, 169-178

11.          Muhlenbein H. Gorges-Schleuter M. Kramer O. “Evolution algorithms in Combinatorial Optimization”, Parallel Computing 7, 1988, pp. 65-85

12.          Muhlenbein H. and Kinderman J. “the Dynamics of Evolution and learning – Towards Genetic Neural Networks”, in J. Pfeiffer (Ed.), Connectionism in Perspectives, 1989.

13.          Larranaga P., Kuijpers C.M.H., Poza M., y Murga R.H., “Decomposing Baysian Networks: Triangulation of the Moral Graph with genetic Algorithms” Statistics and Computing, 1996.




Swati Dhull, Deepender Dhull, Swati Juneja

Paper Title:

Implementing Security Consideration in Dynamic Source Routing

Abstract:   Security has become one of the major issues for data communication over wired and wireless networks. To enhance the security of data transmission, existing system works on the cryptography based algorithms such as SSL, IPSec. Although IPSec and SSL accounts for great level of security, they introduce overheads. A mass of control messages exchanging also needed in order to adopt multiple path deliveries from source to destination. Different from the past work on the designs of cryptography algorithms and system infrastructures, we will propose a dynamic routing algorithm that could randomize delivery paths for data transmission. The algorithm is easy to implement and compatible with popular routing protocols, such as the Routing Information Protocol in wired networks and Destination-Sequenced Distance Vector protocol in wireless networks, without introducing extra control messages. An analytic study on the proposed algorithm is presented, and a series of simulation experiments are conducted to verify the analytic results and to show the capability of the proposed algorithm. 



1.             G. Apostolopoulos, V. Peris, P. Pradhan, and D. Saha, “Securing Electronic Commerce: Reducing the SSL Overhead,” IEEE Network, 2000.
2.             S. Bohacek, J.P. Hespanha, K. Obraczka, J. Lee, and C. Lim, “Enhancing Security via Stochastic Routing,” Proc. 11th Int’l Conf. Computer Comm. and Networks (ICCCN), 2002.

3.             Gojmerac, T. Ziegler, F. Ricciato, and P. Reichl, “Adaptive Multipath Routing for Dynamic Traffic Engineering,” Proc. IEEE Global Telecommunications Conf. (GLOBECOM), 2003.

4.             C. Hopps, “Analysis of an Equal-Cost Multi-Path Algorithm”, Request for comments (RFC 2992), Nov. 2000.

5.             S.-H. Liu, Y.-F. Lu, C.-F. Kuo, A.-C. Pang, and T.-W. Kuo, “The Performance Evaluation of a Dynamic Configuration Method over IPSEC,” Proc. 24th IEEE Real-Time Systems Symp.: Works in Progress Session (RTSS WIP), 2003.

6.             W. Lou and Y. Fang, “A Multipath Routing Approach for Secure Data Delivery,” Proc. IEEE Military Comm. Conf. (MilCom), 2001. 38

7.             W. Lou, W. Liu, and Y. Fang, “SPREAD: Improving Network Security by Multipath Routing,” Proc. IEEE Military Comm. Conf. (MilCom), 2003.

8.             C. Perkins and P. Bhagwat, “Highly Dynamic Destination- Sequenced Distance-Vector Routing (DSDV) for Mobile Computers,” Proc. ACM SIGCOMM ’94, pp. 234-244, 1994.

9.             J. Yang and S. Papavassiliou, “Improving Network Security by Multipath Traffic Dispersion,” Proc. IEEE Military Comm. Conf. (MilCom), 2001.

10.          C. Kaufman, R. Perlman, and M. Speciner, “Network Security”—PRIVATE Communication in a PUBLIC World, second ed. Prentice Hall PTR, 2002.

11.          Shou-heng Liu ,  Yung-feng Lu ,  Chin-fu Kuo ,  Ai-chun Pang , Tei-wei Kuo, “The Performance Evaluation of a Dynamic Configuration Method over IPSEC”.

12.          M. Faloutsos, P. Faloutsos, and C. Faloutsos, “On Power-Law Relationships of the Internet Topology,” Proc. ACM SIGCOMM '99, pp. 251-262, 1999.

13.          ER. Yashpaul Singh, and A. Swarup,  “Analysis of Equal cost Adaptive Routing Algorithms using Connection-Oriented and connectionless protocol.

14.          Andrew Brzezinski, Student Member, IEEE, and Eytan Modiano, Senior Member, IEEE,“Dynamic Reconfiguration and Routing Algorithms for IP-Over-WDM Networks With  Stochastic Traffic”.

15.          Sinem Coleri Ergen and Pravin Varaiya,  “Energy Efficient Routing with Delay Guarantee for Sensor Networks”.

16.          LAURA MARIE FEENEY, “An Energy Consumption Model for Performance Analysis of Routing Protocols for Mobile Ad Hoc Networks”.

17.          J.F. Kurose and K.W. Ross, “Computer Networking—A Top-Down Approach Featuring the Internet” Addison Wesley, 2003.

18.          V.I. Levenshtein, “Binary Codes Capable of Correcting Deletions, Insertions, and Reversals” , Soviet Physics Doklady




Shalini Singh, Ejaz Aslam Lodhi

Paper Title:

Study of Variation in TSP using Genetic Algorithm and Its Operator Comparison

Abstract:    The Purpose of this Paper is to give near optimal solution in terms of quality and computation time. By implementing Genetic Optimization Technique, the effectiveness of the path has been evaluated in terms of fitness function with the parameter such as tour length. In this research work, we see different variation in traveling salesmen problem using Genetic Algorithm Technique. Considering the Limitation of Nearest Neighbor we find that the number of iteration and resulting time complexity can be minimized by using Genetic approach. We also compare the operator of pursued approach which give the best result for finding the shortest path in a shortest time for moving toward the goal. Thus the optimal distance with the tour length is obtained in a more effective way.

 TSP, Fitness Function, Genetic Algorithm, Nearest Neighbour, GA operators.


1.             An introduction to Genetic Algorithms. mit press edited by Melanie Mitchell
2.             http://www.darwins-theory-of-evolution .com

3.             Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine        Learning.  Reading, MA: Addison-Wesley.

4.             A Genetic Algorithm Tutorial: Darrel Whitley, Computer Science Department, Colorado State University, USA.

5.             Gerard Reinelt. The Traveling Salesman: Computational Solutions for TSP Applications. Springer-Verlag, 1994.

6.             Potvin, J.V. (1996). Genetic Algorithm for Travelling Salesmen Problem, Annals of operational research , vol.63, pp.339-370.

7.             Wei, J.D & Lee, D.T. (2004). Anew approach to genetic algorithm  using Genetic Algorithm with priority encoding,Proc. 2004 IEEE Congress on evolutionay computation portland, pp.1457-1464





Harish Kumar Pal, Anand Kumar Singh

Paper Title:

PAPR Reduction Technique Using Advanced Peak Windowing Method of OFDM System

Abstract:     PAPR reduction techniques, peak windowing found its way into practical implementation without side information while maintaining a good spectral characteristic compared with the clipping method. In a real system, however, when successive peaks emerge less than a half of the window size, windows will unfortunately overlap. As a result, the signal peaks are suppressed much more than the required threshold and degrade the BER performance. we propose an advanced peak windowing method. The proposed method overcomes the drawback of the conventional one while maintaining almost the same spectral mask and providing more efficient BER performance

 PAPR, BER, OFDM, Windowing Technique


1.                 Chini, Y. Wu, M. El-Tanny,  and  S. Mahmoud, “Hardware non linearity’s in digital TV broadcasting using OFDM modulation”, IEEE Trans. on Broadcasting, vol. 44, pp. 12–21, 1998.
2.                 E. Costa , M. Midrio, and S. Pupolin, “Impact of amplifier nonlinearities on OFDM transmission system performance,” IEEE Comm. Letters, vol.3, pp. 37–39, Feb. 1999.

3.                 M. Park, H. Jun, J. Cho, N. Cho, D. Hong, and C. Kang, “PAPR reduction in OFDM transmission using Hadamard transform,” in Proc.EEE International Conference on Communications, vol. 1, pp. 430–433, June 2000.

4.                 S. H.Muller and J. B. Huber, “A comparison of peak power reduction schemes for OFDM,” in Proc. IEEE Global Telecommunications Conference, vol. 1, pp. 1–5, Nov. 1997.

5.                 S. Han and J. LEE, “An overview of peak-to-average power ratio reduction techniques for multicarrier transmission,” IEEE Trans.Wireless Communication., vol. 12, pp. 5665, 2005.




K. Asokan, R. Ashok Kumar

Paper Title:

Modeling of bidding Strategies for Power Suppliers and Large Consumers in Electricity Market with Risk Analysis

Abstract:      In the competitive electricity market, Generation companies and large consumers are participating in bidding methodologies for their own benefits. In oligopoly market structure, GENCOs tries to maximize their profit and minimize the risk factor. So it is very essential and important for the GENCOs to formulate optimal bidding strategies with risk terminology before entering into the electricity market to achieve a maximum profit , since the market clearing price (MRP) are variable in nature.In this paper an optimal bidding strategy associated with risk management is devised as a multi objective stochastic optimization problem and solved by Quantum inspired PSO. The impact of risk on the GENCOs is analyzed by introducing the factor λ.  The proposed Quantum inspired PSO effectively maximize the GENCOs profit and benefit of large consumers. A numerical example with six suppliers and two large consumers is considered to illustrate the essential features of the proposed method and test results are tabulated. The simulation result shows that these approaches effectively maximize the Profit and Benefit of Power suppliers and Large Consumers, converge much faster and more reliable when compared with existing methods.

 Electricity market, Optimal bidding, Profit maximization, Risk analysis, Quantum inspired PSO.


1.              Mohammad Shahidehpour, H.Yamin, and Zuyili, “Market Operations in Electric Power Systems: Forecasting, Scheduling and Risk Management”. Wiley, New York, 2002.
2.              Guan X, Ho Y, Lai F,”An ordinal optimization based  bidding strategy for electric power suppliers in the daily energy market”,      IEEE Transactions on power systems, 2001:16(4):788-97.

3.              Li, C A., Svoboda, A.J., Guan, X. and Singh, H. “Revenue adequate bidding strategies in competitive Electricity markets”. IEEE Transactions on Power Systems, 14(2), 492–497. 1999.

4.              David A.K. and Fushuan W. “ Optimal bidding strategies for competitive generators and large consumers ” Electrical Power and               Energy Systems Vol. 23, No.1, pp. 37-43, 2001.

5.              Gountis, V.P. and Bakirtzis, A.G. “Bidding strategies for electricity    producers in a competitive electricity marketplace”. IEEE Transactions on Power Systems, 19(1), 356–365, 2004.

6.              Niu, H., Baldick, R. and Zhu, G. “Supply function equilibrium bidding strategies with fixed forward contracts”. IEEE Transactions on Power Systems, 20(4), 1859–1867, 2005.

7.              He,Y. and Song Y.H. “Integrated bidding strategies by optimal response to probabilistic locational marginal prices”. IEE Proceedings C: Generation, Transmission and Distribution, 149(6), 633–639, 2002.

8.              Mas-Colell, A., Whinston, M.D. and Green, J.R.     “Microeconomic Theory”, Oxford University Press, New York, 1995.

9.              De la Torre S, Arroyo JM,Conejo AJ, Contreras J. “Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach.” IEEE Transactions on Power Systems,17(4):1037-42. 2002

10.           Wei,t., & Dianou, L. “Chaotic optimization for economic dispatch of power systems”. Proceedings of CSEE, 20(10), 36-40, 2000.

11.           Yunhe, H., Lijuan, L., & Yaowu, W. “Enhanced particle swarm                                         optimization algorithm and its application on economic dispatch of power systems”. Proceedings of CSEE, 24(7), 95-100,  2004.

12.           Eberhart, R.C., Shi, Y. and Kennedy, J. “Swarm Intelligence. The Morgan Kaufmann Series in artificial Intelligence”. USA 2001.

13.           Conejo AJ, Nogales FJ, Arroyo JM. “Price-taker bidding strategy under price uncertainty”. IEEE Transactions on Power system:17(4):1081-88. 2002

14.           Federico G, Rahman D, “Bidding in an electricity pay-as-bid auction”, Journal of Regulatory Economics;24(2):175-211. 2003.

15.           Yamin H, Shahidehpour SM. “Unit commitment using a hybrid model between Lagrangian relaxation and Genetic algorithm in competitive electricity market”, Electric Power Systems Research, 68(2):83-92. 2003.

16.           Ernan Ni and Peter B. Luh, Fellow. ”Optimal Integrated Generation Bidding and Scheduling with Risk Management under a Deregulated Daily Power Market”, IEEE Trans Power Systems ,19(1):600-9. 2004.

17.           M. Shahidehpour, H.Yamin, S. AI-Agtash, ”Security    Constrained Optimal Generation Scheduling for GENCOs”, IEEE Transactions on power systems, vol. 19,no.3,pp. 1365-1372, August 2004.

18.           Ma X, Wen F, Ni Y, Liu J. “Towards the development of risk-constrained optimal bidding strategies for generation companies in electricity markets”, Electric Power Systems Research, 73(3):305-12. 2005

19.           Rahimiyan M, Rajabimashhadi H, “Risk analysis of bidding strategies in an electricity pay as bid auction”, Energy Conversion and Management, 48(1):131-7. 2007

20.           Zhang N, “Generators bidding behaviors in the NYISO day-ahead           wholesale Electricity market”, Energy Economics;31(6):897-913, 2009.

21.           Zhang, Zhisheng, “Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system”, Expert system with application, Vol. 37,PP.1800 -1803, 2010..

22.           Chegfu Sun, Songfeng Lu, “Short-term combined economic hydrothermal scheduling using improved quantum-behaved particle swarm optimization”, Expert system with applications, Vol. 37, PP. 4232-4241, 2010.




Vandana Choudhary Rajesh Mehra

Paper Title:

2- Bit Comparator Using Different Logic Style of Full Adder

Abstract:       In this paper a new design of comparator is described with the help of Full adder which are the basic building block of ALU and ALU is a basic functioning unit of the microprocessors and DSP. In the world of technology it has become essential to develop various new design methodologies to reduce the power and area consumption. In this paper comparator are developed using various design of full adder. This will reduce the power of the comparator design. The proposed comparator has been designed using DSCH 3.1 and Microwind 3.1 at 120 nm technologies. The developed comparator with show an improvement of 25.14% in power.

 Full adder, nmos, pmos, cmos, speed, low power, less transistor count, efficiency.


1.              H. Traff, “Noval approach to high speed CMOS current Comparator,” Electron. Letter, vol. 28, no. 3, pp. 310- 312, Jan.1992.
2.              A.T K. Tang and C. Toumazou, “High performance CMOS current comparator,” Electron. Letter, vol. 30, pp. 5-6, 1994.

3.              L. Ravezzi, D. Stoppa and G. F. Dalla Betta, “Simple High speed CMOS current comparator,” Electron. Letter, vol.33, pp.1829-1830, 1997.

4.              C. B. Kushwah, D. Soni and R. S. Gamad, “New design of CMOS Current comparator,” Second International Conference on Emerging Trends in Engineering and Technology, ICETET, pp.125-129, June, 2009.

5.              Current Comparator Design,” Electron. Letter, vol. 44, no.3,pp.171-172, Jan. 2008.

6.              Lu Chen, Bingxue Shi and Chun Lu, “A Robust High-Speed and Low-power CMOS Current Comparator Circuit,” IEEE Asia-Pacific Conf. On Circuits and Systems, pp. 174-177, 2000.

7.              S. Rahul, F. L. Richard and M. Carver, “A Low Power Wide Dynamic-Range Analog VLSI Cochlea,” Analog Integral Circuits Signal Process, vol. 16, pp. 245– 274, 1998.

8.              P. F. Ruedi, P. Heim, F. Kaese, E. Grenet, F. Heitger, P. Y. Burgi,S. Gyger and P. Nussbaum, “A 128X128 pixel 120 dB dynamic range vision-sensor chip for image contrast and orientation extraction, ” IEEE Journal Solid-State Circuits, vol. 38,pp. 2325-2333, 2003.

9.              Niels van Bakel, Jo van den Brand, “Design of a comparator in a 0.25μm CMOS technology”.

10.           Sung-Mo Kang and Yusuf Leblebici, “CMOS Digital Integrated Circuits Analysis and Design”, Tata McGraw-Hill third edition.

11.           Jan M. Rabaey, Anantha Chandrakasan and Borivoje Nikolic, “Digital Integrated Circuit”, Pearson Education Electronics and VLSI series, second edition.]




P. R. Devale, Shrikala M. Deshmukh, Anil B. Pawar

Paper Title:

Persuasive Cued Click Points with Click Draw Based Graphical Password Scheme

Abstract:        Now a days, graphical password is used as an alternative to text-based passwords, biometric and tokens. We use Graphical passwords because peoples can remember images better than the text. The Graphical passwords are divided into three categories: click-based graphical password, choice-based graphical password and draw-based graphical password. In this paper, we combine the features of these three methods. Our proposed system is mainly the combination of Persuasive Cued Click Points and click-draw based graphical password scheme (CD-GPS). In this, users first choose an ordered sequence of 5 images and then select single image to click-draw their secrets. On remaining 4 images we select click points using features of PCCP (viewport and shuffle button). At the time of login images appear as per the decided sequence. For login user should click on the images for which we used features of PCCP for password creation and user should draw a secret on the previously selected image. By adding feature of secret drawing to PCCP , attackers fails to know that there is use of secret drawing  technique on a image in between these images, unfortunately if they knows about secret drawing, they don’t get exact idea that on which image secret has to done .Our proposed system provides higher security than other techniques.

 Authentication, Graphical Password, images, security.


1.              Sonia Caisson, Member, IEEE, Elizabeth Stobert, Student Member, IEEE, Alain Forget,Robert Biddle, Member, IEEE, and Paul C. van Oorschot, Member, IEEE  “Persuasive Cued Click Points: Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 9, NO. 2, MARCH/APRIL 2012
2.              Sonia Chiasson1,2, P.C. van Oorschot1, and Robert Biddle2 “Graphical Password Authentication Using Cued Click Points” 1 School of Computer Science, Carleton University, Ottawa, Canada  2 Human-Oriented Technology Lab, Carleton University, Ottawa, Canada(chiasson,paulv), robert

3.              Yuxin Meng “Designing Click-Draw Based Graphical Password

4.              Scheme for Better Authentication” 2012 IEEE Seventh International Conference on Networking, Architecture, and Storage

5.              Karen Renauda Department of Computing Science, University Of “Quantifying the Quality of Web Authentication Mechanisms A Usability Perspective”Journal of Web Engineering, Vol. 0, No. 0 (2003) 000–000_c Rinton Press.

6.              Nelson, D.L., U.S. Reed, and J.R. Walling. Picture Superiority Effect. Journal of Experimental Psychology: Human Learning and Memory 3, 485-497, 1977.

7.              S. Wiedenbeck, J. Waters, J.C. Birget, A. Brodskiy, and N. Memon. “PassPoints: Design and longitudinal evaluation of a graphical password system”. International Journal of Human Computer Studies, 2005.

8.              Jermyn, A. Mayer, F. Monrose, M. Reiter, and A. Rubin. The design and analysis of graphical passwords. Proceedings of the Eighth USENIX Security Symposium, pages 1–14, 1999.

9.              S. Wiedenbeck, J. Waters, J. Birget, A. Brodskiy, and N. Memon, “Authentication Using Graphical Passwords: Effects of Toler-ance and Image Choice,” Proc. First Symp. Usable Privacy and Se-curity (SOUPS), July 2005.

10.           Dirik, N. Menon, and J. Birget, “Modeling User Choice in the Passpoints Graphical Password Scheme,” Proc. Third ACM Symp. Usable Privacy and Security (SOUPS), July 2007.

11.           Ms. Uma D.Yadav and Mr. P. S. Mohod,“Enhancement of Knowledge Based Authentication Mechanism using Graphical Password via Persuasion”  JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 17, ISSUE 2, FEBRUARY 2013

12.           K. Golofit, “Click Passwords under Investigation,” Proc. 12th European Symp. Research in Computer Security (ESORICS), Sept. 
13.           2007.
14.           Dirik, N. Menon, and J. Birget, “Modeling User Choice in the

15.           Passpoints Graphical Password Scheme,” Proc. Third ACM Symp.

16.           Usable Privacy and Security (SOUPS), July 2007.

17.           S. Chiasson, A. Forget, R. Biddle, and P.C. van Oorschot, “User

18.           Interface Design Affects Security: Patterns in Click-Based Graphical Passwords,” Int’l J. Information Security, vol. 8, no. 6, pp. 387-398, 2009.

19.           Salehi-Abari, J. Thorpe, and P. van Oorschot, “On Purely Automated Attacks and Click-Based Graphical Passwords,” Proc.

20.           Ann. Computer Security Applications Conf. (ACSAC), 2008.

21.           S. Chiasson, P. van Oorschot, and R. Biddle, “Graphical Password Authentication Using Cued Click Points,” Proc. European Symp. Research in Computer Security (ESORICS), pp. 359-374, Sept. 2007.

22.           PD Photo, PD Photo Website,, Feb. 2007.

23.           J. Yan, A. Blackwell, R. Anderson, and A. Grant, “The Memorability and Security of Passwords,” Security and Usability:

24.           Designing Secure Systems That People Can Use, L. Cranor and S. Garfinkel, eds., ch. 7, pp. 129-142, O’Reilly Media, 2005.




Manpreet Singh, Loveleen Kaur

Paper Title:

New Energy Efficient Approach for Underwater Acoustic Networks

Abstract:    The Underwater acoustic network is the type wireless sensor network. The sensor network is deployed for sensing the environment conditions. Wireless sensor network is deployed on the far places like forests, deserts, underwater etc. The battery of the sensor node is limited, it is difficult to recharge or replace the battery of the sensor node. The underwater acoustic network is deployed inside the sea. In such type of environment, network interference is very high. In this paper, new technique is been proposed for reducing the power consumption of the sensor nodes and too enhance the network throughput.  

Underwater Acoustic Networks, under water Acoustic communications, Energy Efficiency, Robust, scalable, Cross layer Design


1.                C. Zhang, M. C. Zhou, and M. Yu, “Ad hoc Network Routing and Security: A Review,” International Journal of Communication Systems, Vol. 20, pp. 909-925, Aug. 2007.
2.                F. Schill, U.R. Zimmer, and J. Trumpf, “Visible Spectrum Optical Communication and Distance Sensing for Underwater Applications”, In Proc. Australasian Conf. Robotics and Automation, Canberra, Australia, Dec., 2004.

3.                International Journal of Scientific & Engineering Research Volume 2, Issue 7, July-2011 1ISSN 2229-5518

4.                F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges”, Ad Hoc Networks (Elsevier), vol. 3, no. 3, pp. 257-279, March 2005.

5.                K. Akkaya, M. Younis, A survey on routing protocols for wireless sensor networks, Ad Hoc Networks 3 (3) (2005) 325–349, in this issue.

6.                L. Brekhovskikh, Y. Lysanov, Fundamentals of Oceans Acoustics, Springer, New York, 2007.

7.                M. Abolhasan, T. Wysocki, E. Dutkiewicz, A review of routing protocols for mobile ad hoc networks, Ad Hoc Networks 2 (1) (2004) 1–22.

8.                P. Bose, P. Morin, I. Stojmenovic, J. Urrutia, Routing with guaranteed delivery in ad hoc wireless networks, ACM Wireless Networks 7 (6) (2006) 609–616.

9.                O.B. Akan, I.F. Akyildiz, Event-to-sink reliable transport in wireless sensor networks, IEEE/ACM Transactions on Networking, in press.

10.             Underwater Acoustic Networks Ethem M. Sozer, Milica Stojanovic, and John G. Proakis, Life Fellow, IEEE




Ashish Kumar Kendhe, Himani Agrawal

Paper Title:

A Survey Report on Various Cryptanalysis Techniques

Abstract:     This paper mainly focuses on various types of attacks on symmetric cipher & asymmetric cipher .In this paper  we tried to describe the existing cryptanalytic attacks on various ciphers and countermeasures to these attacks have been suggested on the basis of information available to attacker ,computational time requirements and memory requirements etc . In order to develop a new secure cipher, it is very necessary that these attacks should be taken into consideration during development and countermeasures of these attacks should be applied in the design, so that the new design is not vulnerable to these attacks. It will also facilitate the security analysis of the existing ciphers and provide an opportunity to understand the requirements for developing a secure and efficient cipher design. This paper surveys about various  cryptanalysis techniques for  image encryption schemes ,public key cryptosystems  ,various encryption standards such as AES ,DES,RSA  etc and then tries to suggest some points to improve the level of security .

 cipher, cryptanalysis, cryptanalyst, cryptography.


1.                 William Stalling “Network Security Essentials (Applications and Standards)”,  Pearson Education, 2004.
2.                 Atul Kahate (2009) ,“ Cryptography and Network Security”, 2nd edition, McGraw-Hill.

3.                 Stallings (1999), “Cryptography and Network Security”, 2nd edition, Prentice Hall.

4.                 William Stallings (2003), “Cryptography and Network Security”, 3rd edition, Pearson Education.

5.                 Hung-Min Sun, “Cryptanalysis of public key cryptosystem using generalized inverse of   matrices ”, IEEE communication letters, vol. 5, no.2, 2001.

6.                 J-J. Quisquater and D.Samyde ,“Side Channel Cryptanalysis ”,SEC publication  2002.

7.                 Chou-Chen Yang,Hung-Wen Yang and Ren-Chiun Wang,“Cryptanalysis of Security Enhancement For The Timestamp-Based Password Authentication Scheme Using Smart Cards”, IEEE Transactions on Consumer Electronics, vol 50,no.2,2004.

8.                 Chengqing Li ,“Cryptanalysis of Some Multimedia Encryption Schemes”,IEEE transactions on multimedia ,vol.10,no.3,2008.

9.                 Shujun Li ,Gonzalo Alwarez,guanrong chen,and xuanqin mou, “Breaking A Chaos-Noise-Based Secure Communication Scheme” ,2005.

10.              Liam Keliher ,“Refined Analysis of Bounds Related to Linear and Differential Cryptanalysis for the AES”published in  International Association for Cryptologic Research, 2005.

11.              Raphel C.W Phan and m umar siddiqui ,“A Framework for Describing Block Cipher cryptanalysis ” , IEEE transactions on computers ,vol.55,no.11,2006.

12.              Nalini N and G Raghevendra Rao,“Cryptanalysis of simplified data encryption standard via optimization heuristics”, IJCSNS, vol.6,no.1b,2006.

13.              Tianjie Cao and Dongdai Lin “Cryptanalysis of Two Password Authenticated Key Exchange Protocols Based on RSA”,IEEE communication letters,vol.10,no.8.2006.

14.              Floriane Anstett, Gilles Millerioux, and Gérard Bloch, “Chaotic Cryptosystems: Cryptanalysis and Identifiability”,IEEE Transactions on circuits and systems-1,regular papers ,vol.53,no.12,2006.

15.              Willi Geiselmann  and Rainer Steinwandt, “Cryptanalysis of a Hash Function Proposed at ICISC 2006” ICISC ,2007.

16.              Yukiyasu Tsunoo,Teruo saito,Hiroyasu kubo and Tomayasu suzaki, “Cryptanalysis Of Mir-1: A T-Function-Based Stream Cipher”, IEEE, 2007.

17.              S. Davod. Mansoori and H. Khaleghei Bizaki, “On the vulnerability of Simplified AES Algorithm Against Linear Cryptanalysis”, IJCSNS International Journal of Computer Science and Network Security, vol.7,no.7, 2007.

18.              Wentao Zhang, “New Results on Impossible Differential Cryptanalysis of Reduced AES”, SPRINGER , 2007.

19.              Haina Zhang and Xiaoyun Wang,“Differential Cryptanalysis of T-Function Based Stream Cipher TSC-4”,SPRINGER,2007.

20.              Jiqiang Lu, “Cryptanalysis of Reduced Versions of the HIGHT Block Cipher from CHES 2006”, SPRINGER ,2007.

21.              Chengqing Li ,Dan Zhang , and Guanrong Chen ,“Cryptanalysis of an image encryption scheme based on the Hill cipher” Preprint submitted to Zhejiang University SCIENCE,2008.

22.              David Arroyo,Chengqing Li,Shujun Li and Gonzalo Alvarez, “Cryptanalysis Of A Computer          Cryptography Scheme Based On A Filter Bank”, ELSIVER
publication, 2008.

23.              Nikhil Balaji ,“Cryptanalysis Of A  Image Encryption Algorithm”, ELSIVER publication, 2008.

24.              David Arroyo,Chengqing Li, Shujun Li, Gonzalo Alvarez, and Wolfgang A. Halang “Cryptanalysis Of An Image Encryption Scheme Based On A New Total Shuffling Algorithm”, ELSIVER publication ,2008.

25.              Enes Pasalic ,“Probabilistic Versus Deterministic Algebraic Cryptanalysis—A Performance Comparison”, IEEE Transactions on information theory ,vol.55,no.11,2009.

26.              Chengqing Li, David Arroyo2 and Kwok-Tung Lo, “Breaking A Chaotic Cryptographic Scheme Based On Composition Maps”, ELSIVER publication 2009.

27.              Huaqun Wang, Futai Zhang, and Yanfei Sun, “Cryptanalysis of a Generalized Ring  Signature Scheme” IEEE Transactions on dependable and secure computing ,vol.6,no.2,2009.

28.              Vimalathithan and  Dr.M.L.Valarmathi,“Cryptanalysis of simplified –DES Using Genetic algorithm”, International Journal of Recent Trends in Engineering,vol.2,no.4,2009.

29.              Hadi Soleimany , Alireza Sharifi, Behnam Bahrak and  Mohammadreza Aref, “ Cryptanalysis of 7-Round AES-128”,7th Iranian community conference,2009.

30.              Alex Biryukov and Dmitry Khovratovich,“ Related-key Cryptanalysis of the Full AES-192 and AES-256”, SPRINGER publication,2009.

31.              Florian Mendel,Thomas Peyrin,Christian Rechberger, and Martin Schlaffer, “Improved Cryptanalysis of the Reduced Grostl Compression Function, ECHO Permutation and AES Block Cipher”, SPRINGER publication ,2009.

32.              Chengqing Li,Shujun Li,and Kwok-Tung Lo. “Breaking A Modified Substitution-Diffusion Image Cipher Based On Chaotic Standard And Logistic Maps”, Preprint submitted to Communications in Nonlinear Science and Numerical Simulation, 2009.

33.              Shuhua Wu, “Cryptanalysis And Enhancements Of Three-Party Authenticated Key Exchange Protocol Using ECC” SPRINGER publication ,2011.

34.              Vimalathithan. R and M.L.Valarmathi,“Cryptanalysis of DES Using Computational Intelligence ”,WSEAS Transactions on computers,Issue.7,vol.10,2011.

35.              Huaqun Wang and Yuqing Zhang,“Cryptanalysis of an Efficient Threshold Self-Healing Key Distribution Scheme”, IEEE Transactions on wireless communications ,vol.10,no.1,2011.

36.              Youhua Shi,Nozomu Togawa,Masao Yanagisawa and Tatsuo Ohtsuki,“Robust Secure Scan Design Against Scan-Based Differential Cryptanalysis”,IEEE vol.10,2012.

37.              Kyung-Ah Shim, “Cryptanalysis Of Two Identity-Based Authenticated Key Agreement Protocols”, IEEE communication letters ,vol.16,no.4,2012.

38.              Rajashekharappa and Dr,K M S Soyjaudah,“ Heuristic Search Procedures for Cryptanalysis and Development of enhanced Cryptographic Techniques”, IJMER vol.2,Issue.3,2012.

39.              Amish Kumar ,“Security Enhancement By Using Double S-Box”,2012

40.              Sachin upadhyay,yashpal singh and Amit kumar jain,“ An analysis of the Attack on RSA Cryptosystem Through Formal Methods”, IJSCE,vol.2,Issue.2,2012.

41.              Henri Gilbert and Thomas Peyrin,“Super-Sbox Cryptanalysis: Improved Attacks For AES-Like Permutations”,IJRCSSE,2012.

42.              Vinod Saroha ,Suman Mor and Jyoti Mallik, “A review of various techniques of cryptanalysis”, IJRCSE ,vol.2,Issue.10,2012.

43.              Shujun Li, Chengqing Li, Guanrong Chen and Kwok-Tung Lo, “Cryptanalysis Of RCES/RSES Image Encryption Scheme” ,IJSCE, 2012.

44.              Shujun Li, Chengqing Li, Kowk-Tung Lo, “ Cryptanalysis of an Image Scrambling Scheme without Bandwidth Expansion” IEEE ,2012.

45.              Christopher Swenson,“ Modern Cryptanalysis Techniques For Advanced Code Breaking”, wiley publication  Inc.2008.




Dhanalakshmi Manikyam, Ganesh Kumar Ganjakunta

Paper Title:

A Novel High ICMR and High Frequency Response of an Inverting Summing OpAmp

Abstract:  In this paper, we propose a novel high ICMR (input common mode range) and high frequency response of an inverting summing pomp  (operational amplifier). While selecting an operational amplifier for circuit design the most critical parameters must be consider. Some of the parameters are supply voltage, gain-bandwidth product, input noise voltage, slew rate, PSRR (power supply rejection ratio) and CMRR (common mode rejection ratio). The most important parameter is input common mode range,  if we violate this parameter leads an undistorted waveform at the output stage. This leads an impact on the frequency response. Perhaps too much capacitance on the output stage causing clipping or oscillations on the output waveform. This paper work estimates the increasing the ICM range value by cascading the output stage combined with unusual implementation of differential amplifier to get better frequency response than conventional inverting summing operational amplifier.

   amplifier, CMRR, Gain, ICMR,  OpAmp


1.             Manjula sandhu ,Manjula Bala  IEEE  computer society “Design of Low Voltage Low Power Operational Amplifier”.2012 Second International Conference on Advanced Computing & Communication Technologies .p.p 368-373 .
2.             Johan H. Huijsing, Senior Member, Ron Hogervarst and Klass-Jan de Langen “Low Power Low Voltage VLSI OPAMP cells” IEEE Transaction on circuits and sytems, vol-42, No-11, p.p 841-852, Nov 95.          H. Poor, An Introduction to Signal Detection and Estimation.   New York: Springer-Verlag, 1985, ch. 4.

3.             Benjamin J. Blalock, Phillip E. Allen, Fellow, IEEE, and Gabriel A. Rincon-Mora,  “Designing 1-V Op Amp Using  Standard Digital CMOS Technology),” IEEE Transaction On Circuits And Systems-Ii: Analog And Digital Signal Processing, Vol. 45,No.7,July 1998.

4.             Eric Vittoz and Jean Fellrath “CMOS Analog Integrated Circuits Based on Weak Inversion Operation” IEEE JSSC, vol, SC-12, No-3, p.p. 224-231, June 1997.

5.             M. J. Hewitt, J. L. Vampola, S. H. Black, and C. J. Nielsen, “Infrared readout electronics: A historical perspective,” in Proc. SPIE Infrared Readout Electronics II, vol. 2226, Apr. 1994, pp. 108–120.

6.             Haiuk Kulah and Tayfun akin “A Current Mirroring Integration Based Readout circuits for high performance infrared circuits for high performance infrared FPA application”.IEEE transaction on circuits And systems-II Analog and digital signal processing,vol-50.

7.             P. Chandrakasan, S. Sheng, and R. W. Brodersen, “Low-power CMOS digital design,” IEEE J. Solid-state Circuits, vol. 27, pp.473-484,Apr. 1992.

8.             P. E. Allen and D. R. Holberg, CMOS Analog Circuit Design. New York: Holt, Rinehart and Winston, 1987.

9.             K. Laker and  W. sansen, Design of Analog Integrated circuits and systems. New York: McGraw-Hill, 1994,pp.20-22.

10.          J. P. Uyemura, Fundamentals of  MOS Digital Integrated circuits. Reading, MA: Addison-Wesely, 1998.

11.          Ramesh Harjani, Member, IEEE, Randy Heibeke, Member,  IEEE, and Feng Wang, Member, IEEE “ An Integrated Low-Voltage Class AB CMOS OTA” IEEE journal of solid-state circuits, vol.34,  no.2, February 1999.
12.          J. Huijsing, Analog circuit Design. Norwell, MA: Kluwer Academic, 1993.




Sharon D. Ronald, A. Sheela, S. Josephin Mary

Paper Title:

Three Phase to Three Phase Direct Matrix Converter using SPWM Technique

Abstract:   The principle of three phase SPWM AC-AC matrix converter using 9 bidirectional switching devices is explained. IGBT-power diode combination is used is main power switching device. Constant voltage and frequency sinusoidal supply voltage can be converted to variable voltage and frequency voltage using this converter. The working is described based on the working three phase to single phase matrix converter. MATLAB/ Simulink software is used for the simulation. The operation is analyzed for various modulation indexes and input voltages. The results are compared and the optimum condition for favorable operation is obtained.

    sinusoidal pulse width modulation, ac to ac converter, matrix converter.


1.              Huber et al, "Space vector modulated three phase to three phase  matrix converter with input power factor correction", IEEE Trans. IA, 1995 ,11(3),pp. 1234-1246.
2.              Alesina, M. Venturini, “Analysis and Design of Optimum-Amplitude Nine-Switch Direct AC-AC Converters”, IEEE Transactions on Power Electronics, Vol. 4, no. 1, pp.101-112, January 1989.

3.              Casadei, G. Grandi, G. Serra, A. Tani, “Space vector control of matrix converters with unity input power factor and sinusoidal input/output waveforms,” Proceedings of IEEEPE' 93, Vol. 7, pp. 170-175, 1993.

4.              L. Gyugyi and B. R. Pelly, Static Pow'f!r 'F: equency  Changers: Theory, Performance, and App1icc,ti n, John Wiley & Sons, 1976.E H. Miller, “A note on reflector arrays (Periodical style—Accepted for publication),” IEEE Trans. Antennas Propagat., to be published.

5.              B.R. Pelly, "Thyristor Phase-Controlled Cbnte Cycloconverters", New York, Wiley, 1971.

6.              Alesina and M. Venturini, "Solid State Power Conversion: A Fourier Anallysis Approach to Generalize Transfer Synthesis", IEEE Transactions oh ¢iruit and Systems, vol.CAS-28, No.4, pp.319-330., April 1981.

7.              P.D. Ziogas, S.1. Khan, and M.H. Rashid, ',some Improve Forced Commutated Cycloconverter Structure " IEEE Trans. Industry Application, voI.IA-21, pp.124 2-1253, Sept./Oct. 1985.

8.              Yang Xi-Jun, Lei Huai-ang, Cao Yi-Iong, GPng ou-min, "Realization of Matrix Electric Power conversion with Practicality", PCC-Osaka 2002, pp.1182-11 7.

9.              P. D. Ziogas, S. I. Khan, and M. H. Rashid, “Analysis and design of Forced Commutated Cycloconverter structures with Improved Transfer Characteristics",. IEEE Trans. Industry Application, vol. IE-33, NO.3, August 1986

10.           P. C. Loh, R. Rong, F. Blaabjerg, and P. Wang "Digital carrier modulation and sampling issues ,of matrix converters", IEEE Trans. Power Electron., vol 4, no. 7, pp. 1690-1700, July 2009.

11.           Y. D. Yoon, and S. K. Sui, "carrier-based modulation technique for matrix converter", IEEE Tran. Power Electron., vol. 21, no. 6, pp. 1691-1703, NoV. 2 6.

12.           Sato, J. Itoh, H. Ohguchi, A. Odaka, andl.H.Mine, "An improvement  method of matrix converter, drives under  input voltage disturbances", IEEE Trans. Power electron., vol. 22, no. 1, pp. 132-138, January 2007.

13.           C. Liu, B. Wu, N. R. Zargari, D. Xu, ant J.Wang, "A novel three-phase three-leg ac/ac converter using nine IGBTs", IEEE Trans. Power Electron., voL 2 , o. 5, pp. 1151-1160, May 2009.

14.           R. Vargas, U. Ammann, and J. Rodrigudz, predictive approach to increase efficiency and reduces switching losses on matrix converters", IEEE trans, Power Electron., vol. 24, no. 4, pp. 894-902, April 0 9.

15.           J. I. Itoh, and K. I. Nagayoshi, "A new bidirectional switch with regenerative snubber to realize. A simple series connection for matrix converters", IEEE Tran. Power Electron., vol. 24, no. 3, pp. 822-829, March2009 .




Akhil Gupta, Randhir Singh, Parveen Lehana

Paper Title:

Effect of Microwaves Treated Brassica Seeds on IR Irradiated Spectrum

Abstract:    Microwaves are non-ionizing electromagnetic radiation. With the growth of technology and increase in demand of cellular services day by day; the presence of microwaves in environment is also increasing. Mostly   these services are operated at 945 MHz. The wavelength of microwaves spans from one meter to one millimeter, covering the spectrum from 300 MHz to 300 GHz. These frequencies may affect the quality of the microwaves treated plant seeds and may reduce their fertility. This paper presents the results of some investigations carried out using microwaves treated Brassica seeds on IR (Infra-Red) irradiation. The spectrum of both microwaves treated and untreated seeds was studied under visible and IR radiation. It was observed that increasing the duration or power of the microwaves significantly affected the irradiated spectrum. Increasing the microwaves exposure decreases the IR absorption coefficient of the seeds. Further, microwaves reduce the fluid content of the seeds, which may affect the fertility of the seeds. The results may be useful for the development of an automatic quality assessment system for seeds.

 Microwaves, IR, Brassica Juncea. 


1.             Gupta, M. Wong, E., Microwave and Metal. Asia:  John Wiley & Sons 2009, pp. 65-74.
2.             K. S. Nageswari, “Biological Effects of Microwaves and Mobile Telephony,” in Proc. the International Conference on Non-Ionizing Radiation , 2003, pp. 1-11.

3.             Marjanovic,I. Pavicic, and I. Trosic, “Biological indicators in response to radiofrequency/microwave exposure,” Arh Hig Rada Toksikol; vol. 63, pp. 407- 416, 2012.

4.             J. Coates, “Interpretation of Infrared Spectra,” Newtown, USA. John Wiley & Sons Ltd, Chichester, 2000,  pp. 10815 – 10837.

5.             http://www.steves ing- repair/ how-an-infrared-thermal- imaging-camera-works.html



8.             J. C. Lin, “Evaluating Scientific Literature on Biological Effects of Microwave Radiation,” IEEE Microwave Magazine, Vol. 5, 2004, pp. 34-37.

9.             E. Ungureanu, C. L. Maniu, S. Vântu, and I. Cretescu, “Consideration on the Peroxidase Activity during Hippophae Rhamnoides Seeds Germination Ex-posed to Radiofrequency Electromagnetic Field Influ-ence,”Analele stiintifice ale Universitattii, Alexandru Ioan Cuza, Sectiunea Genetica si Biologie Moleculara, TOM X,  pp. 29-34, 2009

10.          F. T. Ulaby, Fundamentals of Applied Electromagnetics, Medaia Edition, 2004.

11.          J. Grover, S. Yadav and V. Vats, “Hypoglycemic and antihyperglycemic effect of Brassicajuncea diet and their effect on hepatic glycogen content and the key enzymes of carbohydrate metabolism,” Mol Cell Biochem, pp. 101, 2002.

12.          S. Yadav, V. Vats, A. Ammini, and J. Grove, “Brassica Juncea (Rai) significantly prevented the development of insulin resistance in rats fed fructose-enriched diet,” J.  Ethnopharmacol, pp. 113-116, 2004.

13.          M. S. Alam, G. Kaur, Z.  Jabbar, K. Javed, and M.  Athar, “Eruca sativa seeds possess antioxidant activity and exert a protective effect on mercuric chloride induced renal toxicity,” Food Chem Toxicol, vol. 45, pp. 910 – 920, 2007.






Ravijeet Singh Chauhan

Paper Title:

Predicting the Value of a Target Attribute Using Data Mining

Abstract:     In this paper, the short coming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently..

 Classification, Decision tree, ID3, Prediction, Clustering.


1.              Singh Vijendra. Efficient Clustering For High Dimensional Data:  Subspace Based Clustering and Density Based Clustering, Information Technology Journal; 2011, 10(6), pp. 1092-1105.
2.              D Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.“Classification and Regression Trees”. Wadsworth International Group. Belmont, CA: The Wadsworth Statistics/Probability Series1984.

3.              Quinlan, J. R.  “Induction of Decision Trees”. Machine Learning; 1986,pp. 81-106.

4.              Quinlan, J. R. Simplifying “Decision Trees. International Journal of Man-Machine Studies" ;1987, 27:pp. 221-234.

5.              Gama, J. and Brazdil, P. “Linear Tree. Intelligent   DataAnalysis”,1999,.3(1): pp. 1-22.

6.              Langley, P. “Induction of Recursive Bayesian Classifiers”. In BrazdilP.B. (ed.), Machine Learning: ECML-93;1993, pp. 153-164. Springer,Berlin/Heidelberg~lew York/Tokyo.

7.              Witten, I. & Frank, E,"Data Mining: Practical machine learning toolsand techniques", 2nd Edition, Morgan Kaufmann, San Francisco, 3,4, pp 45-100.

8.              Yang, Y., Webb, G. “On Why Discretization Works for Naive-BayesClassifiers”, Lecture Notes in Computer Science, vol. 2003, pp. 440 –452.

9.              H. Zantema and H. L. Bodlaender, “Finding Small Equivalent Decision Trees is Hard”, International Journal of Foundations of Computer Science; 2000, 11(2):343-354.

10.           Huang Ming, Niu Wenying and Liang Xu , “An improved Decision Tree classification algorithm based on ID3 and the application in score analysis”, Software
Technol. Inst., Dalian Jiao Tong Univ., Dalian, China, June 2009.

11.           Chai Rui-min and Wang Miao, “A more efficient classification scheme for ID3”,Sch. of Electron. & Inf. Eng., Liaoning Tech. Univ., Huludao, China; 2010,Version1, pp. 329-345.

12.           Iu Yuxun and Xie Niuniu “Improved ID3 algorithm”,Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China;2010,pp. ;465-573.

13.           Chen Jin, Luo De-lin and Mu Fen-xiang,” An im pr oved ID3 decision tree algorithm”,Sch. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China, page; 2009, pp.  127-134.

14.           Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, 2nd edition, Morgan Kaufmann, 2006, ch-3, pp. 102-130.




Gaurav Vashisth, A. D. Prasad

Paper Title:

Leveling of DEM Generated from Satellite Data for Mosaicking

Abstract:      Digital Elevation Model finds use in wide range of applications. Often the study area in such applications is very large, which needs the mosaicking of the adjacent smaller DEM tiles. When adjacent DEMs are mosaicked together then systematic errors such as vertical offset and tilt between the DEM tiles can produce visible discontinuities along the borders of the overlapping areas. The standard mosaicking procedures reduces just the inconsistencies at the boundaries of the areas of overlap; the remaining portion of DEM tiles is left uncorrected. The method proposed in the paper uses the cell values present in the overlap region to reduce the vertical offsets and the tilt present in the DEM tiles so that they can be subsequently used for preparing mosaic of DEM tiles.

 GIS, DEM, Leveling, Mosaic, Errors, Cartosat.


1.                 ASSESSMENT OF DEM MOSAIC ACCURACY, volume 37 of The International archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, 2008. ISPRS.
2.                 M. Constantini, F. Malvarosa, E. Minati, and E. Zapitelli. A data fusion algorithm for dem mosaicking: Building a global dem with srtm-x and ers data. In Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on, pages 3861 – 3864, 2006.

3.                 Dowman and P. Balan. Mapping Hazardous Terrain using Remote Sensing. Geological Society of London, 2007.

4.                 T AEndreny, E W Wood, and A Hsu. Correction of errors in spot-derived dem’s using gtopo30 data. IEEE Transactions on Geoscience and Remote Sensing, 38:1234 – 1241, May 2000

5.                 IEEE International Geoscience and Remote Sensing Symposium 2000. Modeling and estimation of the spatial distribution of elevation error in high resolution DEMs from stereo-image processing., number 2902-2905, Honolulu, Hawaii, USA, 2000.

6.                 NRSA. Cartosat-1 Data User’s Handbook. ISRO, 9 2006.

7.                 K Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2:559–572, 1901.

8.                 Robert Joseph Peckham and Gypzo Jordan, editors. Digital Terrain Modelling: Development and Applications in a Policy Support Environment(Lecture Notes in Geoinformation and Cartography). Springer, 2007.




Supiah Bt Shamsudin, Azmi Ab Rahman, Zaiton Bt Haron,  Salisu Danazumi, Siti Hawa Bt Jalil, Mohd Azizi B Zakaria

Paper Title:

Rainwater Harvesting Design Uncertainty Using Fuzzy Logic

Abstract:   One of the biggest environmental challenges that face Malaysia today is the scarcity of water. The increased need of water resources caused by population growth and rapid industrialization has created the need to manage water resources more efficiently and effectively. Many methods are developed to increase the source of water supply; one alternative source is rainwater harvesting. Rainfall harvesting from catchments has not received large attention in Malaysia. This method is one of the proactive action can be taken to avoid shortage of water resources in the future. The analysis results showed the maximum and minimum flow rate according to return year obtained using Fuzzy membership functions establishment. Retention times obtained from MATLAB are 7.27 days. Using a Rational method, the relationship between flow rate and intensity is created. The relationship yields an equation that is y = 0.0000317x and R2 = 0.9855. Then, the relationship between coefficient and intensity yields an equation that is y = -0.0031 x + 1.317 and R2 = 0.9238. All assumptions and calculations in this study are based on studies undertaken. In general, the findings can be summarized as good based on the results obtained.

 fuzzy membership function, MATLAB,  overflow, rainwater harvesting and  tank size.


1.             Fayez. 2009. Roof Rainwater Harvesting Systems for Household Water Supply in Jordan. Water Resources and Environmental Engineering Program, Jordan university of Science & Technology.
2.             Ishaku, H. T. et al. 2010. Planning For Sustainable Water Supply Through Partnership Approach In Wukari Town Taraba State Nigeria. Journal Of Water Resource And Protection. 2010(2) :916-922.

3.             Che Ani et al. 2009. Rainwater Harvesting as Alternative Water Supply in the Future. European Journal of Scientific Research.

4.             Norazman Mohamad Nor et al. 2011. Rainwater Harvesting System for Link Houses. American Journal of Scientific Research. 19(2011): 51-57.

5.             Jitender Dev Sehgal. 2005-2006. A Guide To Rainwater Harvesting in Malaysia. Rotary Club of Johor Bahru.

6.             Fabian et al. 1997. Detention Time Selection for Stormwater Quality Control Ponds. Can. Journal Civil Engineering. 26(1999): 72-82.




Kuldeep Kumar Srivastava, Saquib Shakil, Anand Vardhan Pandey

Paper Title:

Harmonics & Its Mitigation Technique by Passive Shunt Filter

Abstract:    This paper presents the study of harmonics present in the system & harmonics filter of primarily used in electrical system & worldwide. Through this paper we come to know about the harmonics & their significance effect on the system regarding the harmonics distortion caused by non- linear load such as computers, Variable frequency drives, & rectifier circuit. In this presentation we presented some of simple but not the least harmonics filter which is given as PASSIVE SHUNT FILTERS are also introduced with their respective designing methods. The basic object of this paper is to study about the designing of passive shunt type filter for the distribution system. These filters are very much affective in reduction of harmonics & distortion in voltage caused by non linear load. Here we studied design process of single tuned & high pass filter.  The major objective in this study will tell us the use of shunt passive filter, their designing criteria.

  Fundamental frequency, Harmonics, Harmonics filter, linear load, Non-linear load, Passive shunt filter


1.              Analysis & control of harmonic current using Passive filtersJ.C. Das, Member TappiSimons-Engineering Inc..
2.              J. Arrillaga, D. A. Bradley, and P. S. Bodger, .Power System Harmonics. New York: Wiley, 1985

3.              D. A. Gonzalez and J. C. McCall, .Design of filters to reduce harmonic distortion in industrial power systems,. IEEE Trans. Ind. Applicat.,vol.IA-23, pp. 504.512, May/June 1987

4.              Dugan, E.C., McGranagham, M.F., Santoso, S., Beaty, H.W.,.Elecirical power systems quality., McGraw-Hill, 2002

5.              IEEE Guide for Application and Specification of HarmonicFilters., IEEE Std. 1531-2003

6.              Recommended Practice and Requirements for HarmonicControl in Electrical Systems, IEEE Std.

7.              B. Singh, K. Al-Haddad, and A. Chandra, “A review of activefilters for power quality improvement”, IEEE Trans. Ind. Electron. 46 (6), 960–971 (1999).

8.              F.Z. Peng, “Harmonic sources and filtering approaches”, IEEE Ind. Appl. Mag. 7, 18–25 (2001).

9.              F.Z. Peng, H. Akagi, and A. Nabae, “Compensation characteristics of the combined system of shunt passive and series active filters”, IEEE Trans. Ind. Appl. 29 (1), 144–152 (1993).

10.           H. Fujita and H. Akagi, “A practical approach to harmonic compensation in power systems: series connection of passive and active filters”, Conf. Rec. IEEE-IAS Ann. Meeting, 1107–1112 (1990), (IEEE Trans. Ind. Appl. 27 (6), 1020–1025 (1991)).

11.           Takahashi, and Y. Omura, “High power active filter using LC tuned filter”, JIEE Trans. Ind. Appl. D 112 (9), 823–828 (1992), (in Japanese).

12.           Harmonic filter design using actual recored data”, IEEE Trans. Ind. Appl. 29 (6), 1176–1183 (1993).

13.           Lecture notes: harmonic analysis Russell Brown,Department of mathematics University of Kentucky Lexington, KY 40506-0027 12 June 2001

14.           Harmonics, Sources, Effects and Mitigation Techniques Ali I. Maswood and M.H. Haque School of EEE, Nanyang Technological University Second International Conference on Electrical and Computer Engineering ICECE 2002, 26-28 December 2002, Dhaka, Bangladesh





Paper Title:

Performance Analysis of Different Feed Forward Networks in Non-Linear Classification

Abstract:     Artificial Neural Networks (ANN) are recognized extensively as a powerful tool for most of the research applications including classification of heterogeneous data using function approximators. Identifying better neural classifier architecturefor a given input data depends on many factors, including the complexity of theproblem, the training set, the number of weightsand biases in the network and the error goal. Feedforward networks frequently exercise classification techniques for complex non-linear data. This paper presents a comparative study of different type of Feedforward neural networks such as Simple Feedforward networks, Pattern recognition networks and Cascade forward networks in classifying the global carbon emissions data. In this study the percapita carbon emissions of several countries are classified into low, medium and high category. Levenberg-Marquardt learning algorithm is used to train these networks as it is the fastest and first choice supervised learning algorithm with less training errors. Hyperbolic tangent activation function is used in this study because of their massive interconnectivity and enhanced processing performance. Experimental results show that simple Feedforward network outperformed in less number of epochs with higher classification accuracy.

   Green House Gases (GHG), Feed Forward network, Pattern Recognition Network, Cascade forward network


1.              Thomas Stocker , University of Bern, Switzerland and Gian-Kasper Plattner, The Physical Science Basis of Climate Change:- IPCC Working Group I: University of Bern  presented in Copenhagen Climate summit, 9 December 2009.
2.              World Meteorological Organization (WMO), Atmospheric Environment Research Division, Geneva, Vol. No.7, November 2011.

3.              Guoqiang Peter Zhang, Neural Networks for Classifcaiton: A survey, IEEE Transactions on systems,man and cybernetics-Applications and Reviews, Vol.30,No.4, Nov 2000.

4.              H KurtulusOzcan, Osman N Ucan, UlkuSahin, Mehmet Borat , Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz-Odayeri landfill site, Journal of Scientific & Industrial Research, Vol. 65, February 2006, pp. 128-134.

5.              Kropp, J.  A neural solution: a data driven assessment of global climate and vegetation classes,Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference ,Digital Object Identifier: 10.1109/ICONIP.1999.844000

6.              Imran Maqsood, Muhammad Riaz Khan, Ajith Abraham, An ensemble of neural networks for weather forecasting, Neural Computing & Applications, pp 112-122, DOI 10.1007/s00521-004-0413-4 

7.              James J. Simpson and Timothy J. McIntire, “A Recurrent Neural Network Classifier for Improved Retrievals of Areal Extent of Snow Cover,” IEEE transactions on Geosciences And Remote Sensing, Vol. 39, No. 10, October 2001

8.              I.S. Isa, S. Omar, Z. Saad, M.K. Osman, Performance Comparison of Different Multilayer Perceptron Network Activation Functions in Automated Weather Classifcation, 2010 Fourth Asia International Conference on Mathematical/Analytical Modeling and Computer Simulation, Digital Object Identifier: 10.1109/AMS.2010.27 

9.              Mario Trejo-Perea1, Gilberto Herrera-Ruiz, Jose Rios-Moreno, Rodrigo Castañeda Miranda And Edgar Rivasaraiza, “Greenhouse Energy Consumption prediction using neural network models,” International journal of agriculture & biology, Vol. 11, No. 1, 2009.

10.           Soria D, Garibaldi J.M, Biganzoli E, Ellis I.O, A Comparison of Three Different Methods for classification of Breast Cancer Data, Machine Learning and Applications, 2008,ICMLA ’08, Seventh International Conference, Digital Object Identifier: 10.1109/ICMLA.2008.97