Volume-4 Issue-1

Download 25
Total Views 712
File Size 4.00 KB
File Type unknown
Create Date September 5, 2017
Last Updated September 6, 2017

 Download Abstract Book

S. No

Volume-4 Issue-1, June 2014, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Mohamed DYABI, Abdelmajid HAJAMI, Hakim ALLALI

Paper Title:

CATP: An Enhanced MANETs Clustering Algorithm Based on Nodes Trusts and Performances

Abstract:  A mobile ad hoc network (MANET) is a wireless network without the support of any fixed infrastructure. Security is one of the main challenges in ad hoc network due to dynamic topology and mobility of nodes. Organizing mobile nodes into manageable clusters can limit the amount of secure routing information. Under a cluster structure, mobile nodes are managed by nodes called cluster heads. The cluster head role is resource consuming since it’s always switched on and is responsible for the long-range transmission, for example  to send a bit over 10 or 100 m distance, Manet’s nodes consume resources that can perform thousands to millions of arithmetic operations. In this work, we present a clustering algorithm based on node trust and performances called (CATP) , where the clusters are formed around the trustworthy , the densest and the most powerful nodes.

  Adhoc, Clustering, OLSR, trust.


1.       http://www.ietf.org
2.       T. CLAUSEN ET P. JACQUET. Optimized Link State Routing Protocol (OLSR).http://www.ietf.org/rfc/rfc3626.txt,RFC 3626

3.       S. Sarkar, T. G. Basavaraju, and C. Puttamadappa, Ad Hoc Mobile Wireless Networks: Principles, Protocols and  Applications New York: Auerbach Publications, 2007

4.       Anju Sharma, Shini Agarwal and Ravindra Singh Rathore  “Cluster Based Routing in Mobile Ad hoc Wireless Networks Using Neuro-Genetic Paradigm”, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012

5.       Dr. Nasib Singh Gill, Swati Atri  ,Jaideep Atri “Clustering Approach Based on ant Colony Optimization” International Journal of Advanced Research in Computer Science and Software Engineering , Volume 4, Issue 2, February 2014

6.       Hajami, K. Oudidi, M. Elkoutbi. “A Distributed Key Management Scheme based on Multi Hop Clustering Algorithm for MANET“,IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.2, February 2010

7.       Satu Elisa Virtanen and Pekka Nikander. Local clustering for hierarchical ad hoc networks. In Proceedings of WiOpt'04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pages 404–405, Los Alamitos, CA, USA, 2004.

8.       E. M. Belding-Royer, “Hierarchical Routing in Ad Hoc MobileNetworks,” Wireless Commun. and Mobile Comp., vol. 2, no.5, 2002, pp. 515–32.

9.       Anju Sharma, Shini Agarwal and Ravindra Singh Rathore  “Cluster Based Routing in Mobile Ad hoc Wireless Networks Using Neuro-Genetic Paradigm”, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012

10.    Er and W. Seah, “Mobility-based d-hop clustering algorithm for mobile ad hoc networks,” in Wireless Communications and Networking Conference, 2004.

11.    Karamjeet Singh “energy efficiency in mobile ad -hoc networking using cluster head routing protocol” Vol. International Journal of Advanced Research in IT and Engineering 2 No. 5  May 2013

12.    M. L. Jiang, J. Y. Li, and Y. C. Tay, “ Cluster Based Routing Protocol (CBRP) Functional Specification.” draft-ietfmanet- cbrp-spec-01.txt, Aug. 1999.

13.    E. Baccelli. “OLSR Trees: A simple Clustering Mechanism for OLSR.” Mediterranean Workshop on Ad-Hoc Networks (MED-HOC-NET), Porquerolles, France, June 2005.

14.    Y. Lacharite, M. Wang, P. Minet, T. Clausen. “ Hierarchical OLSR “ draft-lacharite-manet-holsr-02.txt July 13, 2009

15.    M. Gerla, J.T.C.Tsai, “Multicluster, mobile, multimediaradio network”, Wireless Networks 1(3), pp. 255, 1995.

16.    Roy, B. (2005). An overview of MCDA techniques today: paradigms and challenges. In: Figueira, J., Greco, S. and Ehrgott, M. (eds) Multiple criteria decision analysis: state of the art surveys.

17.    Barron, F.H. 1992. Selecting a best multiattribute alternative with partial information about attribute weights.

18.    http://www.ncsu.edu/nrli/decision-making/MCDA.php

19.    Butler J, Olson DL. Comparison of centroid and simulation approaches for selection sensitivity analysis. Journal of Multi-Criteria Decision Analysis 1999;8:146–61.
20.    Jia J, Fischer GW, Dyer JS. Attribute weighting method and decision quality in the presence of response error: a simulation study. Journal of Behavioral Decision Making

21.    StillwellWG, Seaver DA, EdwardsW. A comparison of weight approximation techniques in multiattribute utility decision making. Organization Behavior and Human Decision Processes 1981;28:62–77.

22.    Network Simulator NS2 http://www.isi.edu/nsnam/ns/

23.    Bidaki, Moazam; Masdari, Mohammad  “Reputation-Based Clustering Algorithms in Mobile Ad Hoc Networks.” International Journal of Advanced Science & Technology . May2013, Vol. 54, p1-11

24.    Sohail Abbas “A Survey of Reputation Based Schemes for MANET” PGNet  2010

25.    younghwan yoo and dharma p.agrawal “why does it pay to be selfish in a manet ? “ ieee wireless communications  december 2006

26.    Yao Yu+, Lincong Zhang “A Secure Clustering Algorithm in Mobile Ad Hoc Networks”  IPCSIT vol. 29  2012






Mojtaba Atabakhsh, Mahmoud Ebadian, Majidreza Naseh

Paper Title:

Transient Stability Enhancement of Wind Farms using Flexible AC Transmission Technology (Comparison of SVC and STATCOM)

Abstract: Uncontrollable nature of wind power causes using wind turbine induction generators. From the viewpoint of stability, induction generators consume reactive power similar to the induction motor, and it has a negative impact on short-term voltage stability and system voltage profile. This main issue of wind turbines that equipped with doubly fed induction generators (DFIGs) becomes bold in the grid faults. In this thesis, a new solution for uninterrupted operation of wind turbine driving a DFIG has been proposed during fault condition in the grid. A fault current limiter (FCL) is placed in series with the rotor circuit. During fault condition FCL enters a huge solenoid in the rotor circuit to inhabit increasing of current in the rotor circuit. When the fault is cleared the FCL bypasses the solenoid. A static synchronous compensator (STATCOM) and a static VAR compensator (SVC) have been applied for supplying required reactive power in faults and steady states. Capability and modeling accuracy of the proposed method confirmed with simulating a sample power system in MATLAB/Simulink software.

   FACTS, Wind power, Transient stability, Doubly fed induction generators, Power system.


1.      Z. Saad-Saoud, M.L. Lisboa, J. B. Ekanayake, N. Jenkins, G. Strbac, “Applications of STATCOM to the Wind Farms”, IEE Proc.-Gener. Transm. Distrib, Vol. 145, No. 5. September 1998, pp. 511-516.
2.      Chong Han; A.Q. Huang,; W. Litzenberger, L. Anderson, A. A. Edris, M. Baran; S. Bhattacharya; A. Johnson; “STATCOM Impact Study on the Integration of a Large Wind Farm into a Weak Loop Power System”, IEEE Trans. On Energy Conversion, Digital Object Identifier 10.1109/TEC.2006.888031.

3.      M. Molinas, J. A. Suul, T. Undeland, “Wind farms with increased transient stability margin provided by a STATCOM”, Proc. 2006 5th International Power Electronics and Motion Control Conference, Vol. 1, Aug 2006, pp. 1-7.

4.      Lie Xu, Liangzhong Yao, and Christian Sasse, “Comparison of Using SVC and STATCOM for Wind Farm Integration”, Proc. 2006 International Conference on Power System Technology, Chongqing, China, Oct. 2006, pp. 1-7.

5.      Paulo Fischer de Toledo, Hailian Xie, “TOPIC 7:  WIND FARM IN WEAK GRIDS COMPENSATED WITH STATCOM”, Nordic PhD course on Wind Power, Smøla, Norway, June 5 - 11, 2005.

6.      Chen, Z.; Blaabjerg, Frede; Hu, Y; “Stability Improvement of Wind Turbine Systems by STATCOM”, 2006 32nd IEEE Industrial Electronics, pp. 4213-4218.

7.      H. Gaztanaga, I. Etxeberria-Otadui, D. Ocnasu, S. Bacha, “Real-Time Analysis of the Transient Response Improvement of Fixed-Speed Wind Farms by Using a Reduced-Scale STATCOM Prototype”, IEEE Trans. On Power Systems, Vol. 22, No. 2, May 2007, pp. 658-666.

8.      V. Akhmatov, H. Knudsen, A.H. Nielsen, J.K. Pedersen, and N.K. Poulsen, "A dynamic stability limit of grid-connected induction generators". Proc. International IASTED Conference on Power and Energy Systems, Marbella, Spain, September 2000.

9.      Divya, K.C.; Rao, P.S.N.; “Study of dynamic behavior of grid connected induction generators”, 2004 IEEE Power Engineering General Meeting, Vol. 2, June 6-10, 2004, pp. 2200-2205.

10.   M. Steurer, J. Langston, S. Suryanarayanan, P. Ribeiro, R. Meeker, P. Sorensen, “Model Validation and Voltage Deviation Analysis of an Existing Wind Farm Using High Fidelity Real Time Digital Simulation”, 19th International Conference on Electricity Distribution, Vienna, May 21-24, 2007.

11.   B. Chen, et. al., “Emitter turnoff (ETO) thyristor: an emerging, lower cost power semiconductor switch with improved performance for converterbased transmission controllers,” in Proc. IEEE-IECON, pp. 662 – 667, Nov. 2005

12.   M. Steurer, “PEBB based High-Power Hardware-In-Loop Simulation Facility for Electric Power Systems ,” in Proc. IEEE PES GM 2006, Montreal, Canada

13.   F. Peng, J. Lai, “Dynamic Performance and Control of a Static Var Generator Using Cascade Multilevel Inverters”, IEEE Trans. On Industry Applications, Vol. 33, No. 3, May/June 1997, pp 748-755.

14.   Y. Liu, S. Bhattacharya, W. Song, A. Huang, “Control Strategy for Cascade Multilevel Inverter based STATCOM with Optimal Combination Modulation”, 3rd SPEC Annual Power Electronics and Power System Seminar, Raleigh, NC, May 15, 2007.

15.   T. Ackermann, Wind Power in Power Systems, Wiley, 2005.

16.   Federal Energy Regulatory Commission, Interconnection for Wind Energy and Other Alternative Technologies, Jan 24, 2005, [Online] http://www.ferc.gov/whats-new/comm-meet/011905/E-1.pdf.





Aassia Mohammad Ali Jassim Al-a'Assam

Paper Title:

Design and Improvement the Performance of LTE Transceiver based OFDM Wavelet Signals and Turbo Coder

Abstract:  LTE, a term of Long Term Evolution, marketed as 4G LTE, is a standard for wireless communication of high-speed data for mobile phones and data terminals. It is based on the GSM/EDGE and UMTS/HSPA network technologies, increasing the capacity and speed using a different radio interface together with core network improvements. In this paper a new technique based on the Discrete Wavelet Transform (DWT) for implementing the OFDM in LTE is proposed. The proposed scheme is tested in different SUI channels. The results explain that the proposed system overcome the conventional method based on the Fast Fourier transform (FFT) and give lower BER compared with the conventional method based on FFT.

Turbo Coder, LTE, 3GPP, OFDM, FFT, DWT, SUI.


1.       3GPP releases. Retrieved June, 2008 from Available from World Wide Web http://www.3gpp.org/
2.       3GPP TR 25.943 v6 .0.0(2004-12) , Technical report, 3rd generation Partnership Project, Technical specification group radio access network .Deployment aspects (Release 6).

3.       3GPP TR 25.943 v5 1.0(2002-06) , Technical report, 3rd generation Partnership Project, Technical specification group radio access network Deployment aspects (Release 5).

4.       Rohde & Schwarz: White Paper 1MA169 “LTE-Advanced Technology Introduction”.

5.       Tara Ali-Yahiya, “Understanding LTE and its Performance”, Springer Science Business Media, ISBN 978-1-4419- 6456-4, 2011.

6.       Guangyi LIU, Jianhua ZHANG, Feng Jiang, and Weidong WANG, “Joint Spatial and Frequency ProportionalFairness Scheduling for MIMO OFDMA Downlink”, International Conference on Wireless Communications, Networking and Mobile Computing, Wi-Com, IEEE Conference Publications, 2007.

7.       Samuel C. Yang, “OFDMA System Analysis and Design”, ARTECH House, ISBN-13: 978-1-60807-076-3, 2010.

8.       JurajGazda, Peter Drot´ar, PavolGalajda, and DuˇsanKocur, “Comparative evaluation of OFDMA and SC-FDMA based transmission systems”, 8th IEEE International Symposium on Applied Machine Intelligence and Informatics, Harlan, Slovakia ,SAMI, 2010.

9.       Henrik Schulze, and Christian Luders, “Theory and Applications of OFDM and CDMA, Wideband WirelessCommunications”, John Wiley & Sons Ltd, ISBN-13 978-0-470-85069-5, 2005.

10.    P.Balasundaram, S.Nandakumar, J.Ajanthkumar, and K.G.Lingesh, “Radio Resource Management and InterferenceAnalysis for Downlink OFDMA in LTE”, International   Journal of Computer Applications (0975 – 8887), Volume 22– No.2, May 2011.

11.    G. Monghal, K. I. Pedersen, I. Z. Kovács, and P. E. Mogensen, “QoS Oriented Time and Frequency Domain Packet Schedulers for the UTRAN Long Term Evolution”, IEEE Vehicular Technology Conference, VTC Spring 2008, Page(s): 2532 – 2536, 2008.

12.    3GPP Technical Specification TS 36.420 “E-UTRAN; Physical channels and modulation”, Version 1.0.0.

13.    Preben Mogensen, et al, “LTE Capacity compared to the Shannon Bound,” IEEE 65thVehicular Technology Conference, 2007. VTC2007-Spring. April 2007.

14.    Manish J. Manglani, “Wavelet Modulation in Gaussian and Rayleigh Fading Channels,” Msc.   Thesis, Faculty of the Virginia Polytechnic Institute and State University, July 2001.

15.    Jim Zyren ,Dr. Wes McCoy, Technical Editor, “Overview of the 3GPP Long Term. Evolution     Physical Layer.”, White Paper 3GPPEVOLUTIONWP, 07/2007.

16.    C. Berrou, A. Galvieux and P. Thitimajshima, “Near Shannon Limit Error-Correcting Coding and Decoding: Turbo Codes,” Proceedings ICC 93, Geneva Switzerland, May 1993, pp. 1064-1070.

17.    Daniel S. Baum, Stanford University, Simulating the SUI Channel Models, 2001,  IEEE.






Makamure C, Chinofunga D, Usai T, Mutonhodza B

Paper Title:

Determining the Efficacy of Protocols Employed in Replacement /Artificial Feeding using Commercial Infant Formula in, Harare Zimbabwe

Abstract: The study determined the efficacy of protocols employed in replacement/artificial feeding using commercial infant formula. The study was carried out in the different suburban locations of Harare, Zimbabwe. A sample size of 20 mothers/caregivers giving commercial infant formula to their babies at between 0-6 months was targeted; convenience and snowball sampling techniques were used to identify the participants. Interviews using a structured questionnaire were conducted and complemented by direct observation of the participants as they prepared the infant formula. The results were tallied against a checklist of recommended practices and label instructions. The results established that there were short falls in the preparation procedures as employed by the caregivers, mainly the mixing order of powder and water, temperature of the water for reconstitution and handling of left over formula after feed; 50 percent of caregivers were not adhering to the label instructions as given by the manufacturers and to recommendations proposed by World Health Organisation. Poor hand washing was indicative in 80 percent of cases, bottle feeding was predominant (n = 16) compared to cup feeding (n = 4) and the population practicing artificial feeding were mostly the young (90%), married (80%), educated (100%) and working group (90%). The researcher  recommends that health providers strengthen efforts to ensure that adequate information /counselling and consistent advice  of optimal benefit to the infant-mother pair be given and that the Ministry of Health and Child Welfare , Nutrition unit must strictly monitor the activities and the information  given out by infant formula manufacturers as stipulated by the International Code of Marketing of Breastmilk Substitutes and also giving them the responsibility of following  up on the appropriate use of their products.

commercial infant formula, infants, caregivers.


1.       Brown, R.E. (1973). Breastfeeding in Modern Times. American journal of clinical nutrition.
2.       Food and Agriculture Organization of the United Nations/World Health Organization. (2006).Enterobacter sakazakii and Salmonella in powdered infant formula. Microbiological Risk Assessment Series.

3.       Iversen, C, Forsythe S. (2004). Isolation of Enterobacter sakazakii and other Enterobacteriaceae from powdered infant formula milk and related products.

4.       Li Ma, Goudong Z, Balasbur S, Doyle & Bowen, A. (2009). Efficacy of Protocols for Cleaning and Disinfecting Infant Feeding Bottles in Less Developed Communities. Atlanta: Center of Food Safety, University of Georgia.

5.       Riordan, J.M.1997).The Cost of not Breastfeeding: A commentary.
6.       U.S. Food and Drug Administration. What is an infant formula.
7.       UNICEF. (2010).The Community Infant and Young Child Feeding Counseling Package. Key messages booklet.

8.       UNICEF/WHO.2009.Baby Friendly Hospital Initiative, Revised Updated And Expanded For Integrated Care Manual. A 20hr course for maternity staff.

9.       WHO & FAO. (2007).Guidelines for the safe preparation, storage and handling of powdered infant formula.

10.    WHO/UNICEF [United Nations Children's Fund]. (2003). The Global Strategy for Infant and Young Child Feeding. 






Aamir Eftikhar Bondre, Meenakshi Ananth, Nishu Nandita, Sriragh Karat, Sadashiva V Chakrasali

Paper Title:

Comparative Analysis of Different Windowing Techniques in MFCC Speaker Recognition

Abstract:  Speaker recognition is the process of automatically recognising the speaker on the basis of individual information included in speech waves. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Speaker recognition technology can be used in many services such as voice dialling, banking by telephone, telephone shopping, database access  services, information services, voice mail, security control for confidential information areas, and remote access to computers. Feature extraction is an important process in speaker recognition. In this paper Mel Frequency Cepstrum Coefficients method is used in order to design a text dependent speaker recognition system. Different types of windowing methods are used during feature extraction. In this paper, a comparative analysis of different windowing techniques is done in order to determine the most effective windowing technique for MFCC speaker recognition.

Speaker, MFCC, Mel, Frequency, Cepstrum, Coefficients.


1.        K.K. Paliwal and B.S. Atal, ”Frequency related representation of speech.” in Proc. EUROSPEECH,p.p.65-68 Sep. (2003).
2.        Vibha Tiwari, ”MFCC and its applications in speaker recognition” International Journal on Emerging Technologies ISSN : 0975-8364.

3.        T. Fukuda, M. Takigawa and T. Nitta, ”Peripheral features for HMM based speech recognition” in Proc.ICASSP,1: 129-132(2001).

4.        M. Pandit and J. Kittler, ”Feature selection for a dtw-based speaker verification system” Proceedings of IEEE Int.Conf. Acoust.   And Signal Processing,2: 769-772 (1998).

5.        Dr. H.B. Kekre, Ms. Tanuja K. Sarode, ”Vector Quantized Codebook Optimization using K-Means”,International Journal on Computer Science and Engineering,Vol.1(3), 2009, 283-290.
6.        Darshan Mandalia and Pravin Gareta,”Speaker Recognition Using MFCC and Vector Quantization Model”.
7.        Atal, B.S. and S.L. Hanauer,”Speech analysis and synthesis by linear prediction of the speech wave”,Journal of the acoustical society of America,50: 637-655(1971)

8.        Speaker recognition using MFCC by S. Khan, Mohd Rafibul lslam, M. Faizul, D. Doll, IJCSES (International Journal of Computer Science and Engineering System)2(1): 2008.

9.        Molau, S, Pitz, M, Schluter, R, and Ney, H., ”Computing Mel frequency coefficients on Power Spectrum”,Proceedings of IEEE ICASSP-2001,1: 73-76(2001).

10.     Lawrence Rabiner and Biing-Hwang Juang, Fundamentals of Speech Recognition Prentice-Hall, Englewood Cliffs, N.J.,(1993).

11.     Bhupinder Singh, Rupinder Kaur, Nidhi Devgun, Ramandeep Kaur,”The process of Feature Extraction in Automatic Speech Recognition System for Computer Machine Interaction with Humans: A Review”,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 2, February 2012 ISSN: 2277 128X

12.     Leigh D. Alsteris and Kuldip K. Paliwal,”Importance Of Window Shape For Phase-Only Reconstruction Of Speech”,presented in International Conference on Acoustics,Speech and Signal Processing

13.     J.B. Allen and L.R. Rabiner,” A unified approach to short time Fourier analysis and synthesis”Proc. IEEE, Vol. 65, No.11, pp. 1558 1564, 1977

14.     Premakanthan and W.B. Mikhael, Speaker verification/ recognition and the importance of selective feature extraction: Review,Proceedings of the 44th IEEE 2001,Midwest Symposium, 1:14-17(2001).

15.     Goutam Saha and Malyaban Das,On Use of Singular Value Ratio Spectrum as Feature Extraction Tool in Speaker Recognition Application, CIT-2003, pp. 345-350,Bhubaneswar, Orissa, India, (2003).






Rajni B. Kinalkar, M.S. Harne

Paper Title:

A Review on Various Cooling System Employed in Grinding

Abstract:   Grinding is most commonly used as a finishing process to provide good surface, dimensional and geometrical quality. As thermal damage is one of the main limitations of grinding process. Cooling plays a crucial role in grinding to avoid thermal damage to the workpiece surface. Cooling and lubrication are especially important to ensure workpiece quality in grinding, because of high friction and intense heat generation involved in the process. This paper focused on Different approaches of cooling system as per the surface quality requirement for different types of material. Also it discusses the recent trends in cooling system.

 Grinding, Cooling system, Cryo grinding, Slotted grinding wheel, MQL, Hybrid MQL.


1.          Z.W. Zhong, V.C. Venkatesh, Recent Developments in Grinding of Advanced Materials, International Journal of Advanced Manufacturing and Technology, 41(2009) 468-480.
2.          Snoeys, R., Maris, M., Peters, J., 1978, Thermally Induced Damage in Grinding, Annals of the CIRP, 27/2:571-581.

3.          Torrance, A. A., 1978, Metallurgical Effects Associated with Grinding, Proceedings of the 19th International Machine Tool Design and Research Conference, 637-644.

4.          Srivastava, A.K. et al. (1992). Surface finish in robotic disk grinding. International Journal of Machine Tools & Manufacture, vol. 32, p. 269-297.

5.          Chen, X., Brian, W. (1996). Analysis and simulation of the grinding process, Part II: Mechanics of grinding. International Journal of Machine Tools & Manufacture, vol. 36, p. 883-896.

6.          Srihari, G., Lal, G.K. (1994). Mechanics of vertical surface grinding. Journal of Materials Processing Technology, vol. 44, p. 14-28.

7.          Huang, L. et al. (1999). Effect of tool/chip contact length on orthogonal turning performance. Journal of Industrial Technology, vol. 15, p. 88-91.

8.          Anne Venu gopal, P.V.Rao,Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding, International Journal of Machine Tools & Manufacture 43 (2003) 1327-1336.

9.          [43] Tang, J. S., Pu, X. F., Xu, H. J., Zhang, Y. Z., 1990, Studies on Mechanisms and Improvement of Workpiece Burn during Grinding of Titanium Alloys, Annals of the CIRP, 39/1:353-356.

10.       3] Tarasov, L. P., 1950, Some Metallurgical Aspects of Grinding, Machining Theory and Practice, ASM, 409-464.

11.       [44] Malkin, S., Cook, N. H., 1971, The Wear of Grinding Wheels, Part I, Attritious Wear, ASME Journal of Engineering for Industry, 93:1120-1128.

12.       Malkin, S., 1974, Thermal Aspects of Grinding, Part 2 - Surface Temperatures and Workpiece Burn, ASME Journal of Engineering for Industry, 96:184- 1191

13.       Fedoseev, O. B. and Malkin, S., 1991, Analysis of Tempering and Rehardening for Grinding of Hardened Steels. ASME Journal of Engineering for Industry, 113:388-394.

14.       Eda, H., Yamauchi, S., 1993, Computer Visual Simulation on Structural Changes of Steel in Grinding Process and Experimental Verification, Annals of the CIRP, 42/1:389-392.

15.       Anon, 1960, Grinding Stresses - Cause, Effect, and Control, Collected Papers, Grinding Wheel Institute, Cleveland, Ohio.

16.       Littman, W. E., 1967, Control of Residual Stresses in Metal Surfaces, Proceedings of the International Conference on Manufacturing Technology, ASTME, 1303-1317.

17.       Littman, W. E.,1967, The Influence of Grinding on Workpiece Quality, ASTME Paper MR67-593.

18.       Wakabayashi, M., Nakayama, M., 1979, Experimental Research on Elements Composing Residual Stresses in Surface Grinding, Bull. Japan Soc. Prec. Engg., 13:75.
19.       Hahn, R. S., 1976, On the Loss of Surface Integrity and Surface Form due to Thermoplastic Stress in Grinding Operations, Annals of the CIRP, 25/1:203-207.
20.       Lenning, R. L., 1968, Controlling Residual Stresses in Cylindrical Grinding, Abrasive Engineering, December: 24.

21.       Winter, P. M., McDonald, W. J., 1969, Biaxial Residual Surface Stresses from Grinding and Finish Machining 304 Stainless Steel Determined by a New Dissection Technique, ASME Journal of Basic Engineering, 91:15-23.

22.       Elliot S. Nachtman, Tower Oil & Technology Company,  Metal Cutting and Grinding Fluids, Volume 16 Machining,ASM handbook, 244-247

23.       S. PAUL and A. B. CHAITOPADHYAY , “A study of effects of cryo-cooling in grinding” ‘,  Int. 1. Mach. Tools Manufact. Vol. 35, No. 1, pp. 109-117, 1995

24.       Jan C. Aurich and Benjamin Kirsch, “Improved coolant supply through slotted grinding wheel” , CIRP Annals - Manufacturing Technology 62 (2013) 363–366.

25.       Leonardo R. Silva et. al. , “Environmentally friendly manufacturing: Behavior analysis of minimum quantity of lubricant - MQL in grinding process” , Journal of Cleaner Production  January 2013

26.       R. Alberdi et.al. , “Strategies for optimal use of fluids in grinding” ,  International Journal of Machine Tools & Manufacture 51 (2011) 491–499

27.       Eduardo Garcia et.al. , “Strategies for optimal use of fluids in grinding” , International Journal of Machine Tools & Manufacture 51 (2011) 491–499

28.       Jan C. Aurich et.al, “ Hydraulic design of a grinding wheel with an internal cooling lubricant supply” , Prod. Eng. Res. Devel. (2011) 5:119–126.






Sabna Sharma, Ratika Pradhan

Paper Title:

Classification Methods for Land use and Land Cover Pattern Analysis

Abstract: The importance of mapping of land use and land cover is highlighted in this paper. The paper discusses image classification as one way of mapping land use and land cover. Image classification is the process of sorting all the pixels into in an image into a finite number of individual classes .Image classification is further classified into supervised and unsupervised classification. This paper also highlights the numerous ways for image classification.

 Image classification, Mapping, Supervised, Unsupervised..


1.       Mr. Anand Upadhyay, Dr. S. K. Singh, Dr. Varsha Turkar, 1 May 2014,Classification Of IRS LISS-III Image Using Artificial Neural Network, International Journal Of Pure And Applied Research In Engineering And Technology, Volume 2 (8), pp.100-108 .
2.       Haval M. SIDQI and Jamal F. KAKBRA, January – February 2014,Image Classification Using K-mean Algorithm, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, pp.38-42.

3.       Dr. C. Jothi Venkateswaran, R.Vijaya  ,A.M.Saravanan, Jun-Jul 2013, A Fuzzy Based Approach to Classify Remotely Sensed Images, International Journal of Engineering and Technology, Vol 5 , pp.3051-3055.

4.       Shivakumar.B.R, Pallavi.M, June 2013, Fuzzy Logic Based RS Image Classification Using Maximum Likelihood and Mahalanobis Distance Classifiers,  International Journal of Current Engineering and Technology, Vol.3, pp.378-382.

5.       Hayder Abd Al-Razzaq Abd, Husam Abdulrasool Alnajjar, June 2013,Maximum Likelihood for Land-Use/Land-Cover Mapping and Change Detection Using Landsat Satellite Images: A Case Study “South Of Johor”, International Journal of Computational Engineering Research, Volume-03, pp. 26-33.

6.       Vini Malik, Aakanksha Gautam, Aditi Sahai, Ambika Jha, Ankita Ramvir Singh, May 2013, Satellite Image Classification Using Fuzzy Logic, International Journal of Recet Technology and Engineering (IJRTE), Volume-2, pp.204-207.

7.       Manibhushan, Nilanchal Patel, Gadadhar Sahoo & Anil Kumar Singh, 2013, Image Classification for Different Land Use and Land Covers Using Fuzzy Logic for the Improvement of Accuracies, Journal of Agricultural Science, Vol. 5,pp. 278-283.

8.       Ms.Chinki Chandhok, Mrs.Soni Chaturvedi, Dr.A. A Khurshid, August 2012, An Approach to Image Segmentation using K-means  Clustering Algorithm,  International Journal of Information Technology (IJIT), Volume – 1, pp.11-17.

9.       Asmala Ahmad and Shaun Quegan, August 2012, Analysis of Maximum Likelihood Classification on Multispectral Data, Applied Mathematical Sciences, Vol. 6, pp. 6425 – 6436.

10.    M.Renuka Devi, Dr.S. Santhosh Baboo, October 2011, Land use and Land Cover Classification using RGB&L Based Supervised Classification Algorithm, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 2 ,pp.167-180.

11.    P. Sathya and L. Malathi, October 2011, Classification and Segmentation in Satellite ImageryUsing Back Propagation Algorithm of ANN and K-Means Algorithm, International Journal of Machine Learning and Computing, Vol. 1, pp. 422-426.

12.    B  Sowmya and B  Sheelarani, April 2011, Land cover classification using reformed fuzzy C-means, Indian Academy of Sciences, Vol. 36, pp.153–165.

13.    M. K. Ghose , Ratika Pradhan, Sucheta Sushan Ghose, November 2010, Decision Tree Classification of Remotely Sensed Satellite Data using Spectral Separability Matrix, International Journal of Advanced Computer Science and Applications,Vol. 1, pp.93-101.

14.    Navdeep Kaur Johal, Samandeep Singh ,Harish Kundra, September 2010, A hybrid FPAB/BBO Algorithm for Satellite Image Classification, International Journal of Computer Applications ,Volume 6, pp. 31-36

15.    Anil Z Chitade, DR. S.K. Katiyar, 2010, Colour Based Image Segmentation using K-Means Clustering ,International Journal of Engineering Science and Technology ,Vol. 2(10) , pp.5319-5325.

16.    Nayak,S.,and Behera,M.D., june 2009,Improving land-Use and vegetation Cover Classification Accuracy using Fuzzy Logic-A Study in Pilibhit District,Uttar Pradesh,India, International Journal of Geoinformatics,Vol.5,pp.1-10 .

17.    Yusheng Shia,  Jieying Xiaoc, Yanjun Shen, 2008, Landscape pattern change and associated environmental implication in Haihe River Basin, China, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII, pp. 569-573.

18.    Mansoor D. Leh and Sreekala G. Bajwa, 2007, Land Use Change in NW Arkansas: Implications for Runoff Potential on the West Fork Watershed, IEEE,   pp.419-423.

19.    I. Nedeljkovic ,2004 ,Image classification based on Fuzzy logic, The International Archives of the Photogrammetric, Remote Sensing and Spatial Information Sciences, Vol. 34, Part XXX.






Ronak Malpani, Sachith Kumar Jegarkal, Rashmi Shepur, Ravi Kiran H. N, Veena Kumara Adi

Paper Title:

Effect of Marble Sludge Powder and Quarry Rock Dust as Partial Replacement for Fine Aggregates on Properties of Concrete

Abstract:  Concrete sustainability involves continuously choosing low impact building materials. Use of alternate aggregate materials has greater potential because 75% of concrete is composed of aggregates. The experimental study has been carried out to investigate the suitability of marble sludge powder and quarry rock dust as partial replacements for fine aggregates.  This paper reports the properties of concrete mixtures where in a portion of sand is replaced by marble sludge powder and quarry rock dust and mixtures of both. During this experiment, the properties of concrete were studied for eight series of concrete mixtures by replacing the portion of fine aggregates by marble sludge and quarry rock dust and mixtures of both. The chemical composition and some of the mechanical properties of marble sludge powder and quarry rock dust are reported with that of sand. The effect of quarry rock dust and marble sludge powder on the compressive strength and split tensile strength were recorded at the curing age of 7 and 28 days. All the data are tabulated and compared. It was observed that particular proportions of marble sludge powder and quarry rock dust displayed enhancing effect on the compressive strength.

 marble sludge powder, quarry rock dust, workability, compressive strength, split strength.


1.       Ilangovan R, Mahendran N and Nagamani K (2008), "Strength and durabilityproperties of concrete containing quarry rock dust as fine aggregates",ARPN Journal of Engineering and Applied Science, Vol.3(5), pp.20-26.
2.       Prof. Veena G. Pathan1, Prof. Md. Gulfam Pathan2, Feasibility and Need of use of Waste Marble Powder in Concrete Production IOSR Journalof Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684, p-ISSN: 2320-334X PP 23-26

3.       Corinaldesi V., Moriconi, G. and Naik T.R. 20075. Characterization of Marble Dust for its use in Mortar and Concrete. CANMET/ACI Three day International Symposium on Sustainable development of Cement and Concrete, October 5-7, Toronto, Canada.

4.       Wu K, Chen B, Yao W, Zhang D (2001). Effect of coarse aggregate type on mechanical properties of high-performance concrete. Cem. Conc.Res., 31(10): 1421-1425.

5.       Nisnevich M. Sirotin G. and Eshel Y. 2003. Light weight concrete containing thermal power station and stone quarry waste. Magazine of Concrete Research. pp. 313-320.

6.       Hudson B.P. 1997. Manufactured sand for Concrete. The Indian Concrete Journal. pp. 237-240.

7.       Ilangovan R. and Nagamani K. 2006. Application of quarry Rock dust as fine aggregate in concrete construction. National Journal on construction Management: NICMR. Pune. December. pp. 5-13.

8.       Babu K. K., Radhakrishnan R. and  Nambiar E. K. K. 1997. Compressive strength of Brick Masonary with Alternative-Aggregate Mortar. CE and CR journal, New Delhi. Pp. 25-29.

9.       Ms. Monica C. Dhoka. “Green Concrete: Using Industrial Waste of Marble Powder, Quarry Dust and Paper Pulp” International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 Volume 2 Issue 10ǁ October 2013 ǁ PP.67-70

10.    Joseph O. Ukpata, Maurice E. Ephraim and Godwin A. Akeke, Pg.No. 81 to 92, Compressive strength of concrete using lateritic sand and quarry dust as fine aggregates ARPN Journal of Engineering and Applied Science, Vol.7, No.1, January 2012.





Poonam M. Baikar

Paper Title:

Design of PID Controller based Information Collecting Robot in Agricultural Field

Abstract:   This project presents a design of a PID algorithm for driving agricultural robot motors. This approach has been proved with MATLAB simulation results. This kind of position control can be improved using adaptive algorithm. This project also described implementation of PID using PWM method. The robot prototype can move rapidly with the controller. Based on the study, the accuracy of the moving velocity of the robot can be further improved, such as the use of artificial neural networks and genetic algorithms for precise speed control. The results obtained from the PID simulation in MATLAB-Simulink shows that PID algorithm gives considerable precision in positioning compared to conventional motor control algorithms.



1.    Naiqian Zhang, Maohua Wang, Ning Wang. Precision agriculture-a worldwide overview, Computers and Electronics in Agriculture, 36(2002)113-132.
2.    Hui Fang, Yong He. A Pocket PG based field information fast collection system, Computers and Electronics in Agriculture, 61(2008)254-260.

3.    Y Nagasaka, Q Zhang, T.E.Grift, etal. Control System Design for an Autonomous Field Watching-dog Robot. Technology for Off-Road Equipment, Proceedings of the 7-8 October 2004 Conference, Kyoto, Japan.

4.    Bak, T. and H.Jakobsen.2004.Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering, 87(2):125-136.

5.    Blas M. Vinagre, Concepción A. and Monje etc. Fractional PID Controllers for Industry Application- a Brief Introduction. Journal of Vibration and Control, 2007, 7(13):1419-1429.






Niharika Mehta, Romika Choudhary

Paper Title:

Direction of Arrival Estimation on the Performance of WCMSR Technique

Abstract: This paper presents direction-of-arrival (DOA) estimation of wideband signals, and wideband covariance matrix sparse representation (W-CMSR) method is proposed. In W-CMSR, covariance matrix is taken such that the lower left triangular elements are aligned to form a new measurement vector. In W-CMSR technique we use constraint of sparsity, sparse representations are those representations that account for most or all information of a signal with a linear combination of a small number of elementary signals called atoms. Often the atoms are chosen from a so called over-complete dictionary. It means that given a signal firstly we form the dictionary which contains the atoms that represent the signal and then after that we find the smallest set of atoms from the dictionary to represent the signal. No prior information of the incident signal is required in W-CMSR, and no decomposition is done. Half-wavelength spacing restriction can be changed from the highest to the lowest frequency of the incident wideband signals.

 Direction-of-arrival (DOA) estimation, over complete representation, sparse representation, wideband signal, source localization.


1.       J. G. Proakis, Digital Communications, 4th ed. New York: McGraw- Hill, 2001.
2.       H. Wang and M. Kaveh, “Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wideband sources,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-33, no. 4, pp. 823–831, Aug. 1985.

3.       Z. M. Liu, Z. T. Huang, and Y. Y. Zhou, “Source number detection and direction estimation via sparsity-inducing representation of the array covariance matrix,” IEEE Trans. Aerosp. Electron. Syst., to be published.

4.       Sandeep Santosh, O. P. Sahu, Monika Aggarwal, “An Overview of Different Wideband Direction of Arrival (DOA) Estimation Methods” WSEAS TRANSACTIONS ON

5.       SIGNAL PROCESSING  Volume 5,2009, Print ISSN:1790-5052, E-ISSN: 2224-3488.

6.       Y. S. Yoon, L. M. Kaplan, and J. H. McClellan, “TOPS: New DOA estimator for wideband signals,” IEEE Trans. Signal Process., vol. 54,  no. 6, pp. 1977–1988, Jun. 2006.

7.       H. Krim and M. Viberg, “Two decades of array signal processing research: The  parametric approach,” IEEE Signal Process. Mag., vol. 13, no. 4, pp. 67–94, Jul.

8.       D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3010–3022, Aug. 2005.

9.       M. M. Hyder and K. Mahata, “A robust algorithm for joint-sparse recovery,” IEEE Signal Process. Lett., vol. 16, no. 12, pp. 1091–1094, Dec. 2009.

10.    S. Ejaz and M. A. Shaq, “Comparison of spectral and subspace algorithms for FM source estimation” Progress In Electromagnetics Research C, Vol. 14, 2010.

11.    J. S. Sturm, Using SeDuMi 1.02, A Matlab Toolbox for Optimization Over Symmetric Cones. Tilburg, The Netherlands, Dept. Econometrics, Tiburg Univ., 2010
[Online]. Available: http://fewcal.kub.nl/ ~strum.

12.    J. A. Tropp and S. J. Wright, “Computational methods for sparse solution of linear inverse problems,” Proc. IEEE, vol. 98, no. 6, pp. 948–958, Jun. 2010.

13.    Zhang-Meng Liu, Zhi-Tao Huang, and Yi-Yu Zhou, “Direction-of-Arrival Estimation of Wideband Signals via Covariance Matrix Sparse Representation” IEEE Transactions on signal processing, Vol. 59, No. 9, September 2011.






Boussaa Mohamed, Bennis Abdelattif, Atibi Mohamed

Paper Title:

Comparison Between Two Hardware Implementations of a Formal Neuron on FPGA Platform

Abstract:  The formal neuron is equivalent to a simple processor that performs a series of mathematical operations more or less complex on real data. The chosen representation to encode these data is the 32 bits floating point representation; this makes possible to achieve satisfactory precision in calculation. This paper presents a hardware comparison between two formal neurons, one is associated with the sigmoid activation function and the other to the gaussian activation function. This comparison is designed firstly to compare the hardware results obtained respectively from these two implementations with software results, and secondly, to make comparison between the two hardware implementations in terms of the consumed material resources and execution time. These neurons are implemented by using a number of specific blocks called megafunction, on an FPGA platform of Altera DE2-70 which offers several advantages, including flexibility, efficiency and speed.

  formal neuron, FPGA, hardware resources, execution time, mega function.


1.       Lin, C. J., & Lee, C. Y. (2011). Implementation of a neuro-fuzzy network with on-chip learning and its applications. Expert Systems with Applications, 38(1), 673-681.
2.       Shao, X., & Sun, D. (2007). Development of a new robot controller architecture with FPGA-based IC design for improved high-speed performance. Industrial Informatics, IEEE Transactions on, 3(4), 312-321.

3.       Bruti-Liberati, N., Martini, F., Piccardi, M., & Platen, E. (2008). A hardware generator of multi-point distributed random numbers for Monte Carlo simulation.Mathematics and Computers in Simulation, 77(1), 45-56.

4.       Salewski, F., &Kowalewski, S. (2008). Hardware/software design considerations for automotive embedded systems. Industrial Informatics, IEEE Transactions on, 4(3), 156-163.

5.       Bueno, E. J., Hernandez, A., Rodriguez, F. J., Girón, C., Mateos, R., &Cobreces, S. (2009). A DSP-and FPGA-based industrial control with high-speed communication interfaces for grid converters applied to distributed power generation systems. Industrial Electronics, IEEE Transactions on, 56(3), 654-669.

6.       Savich, A. W., Moussa, M., & Areibi, S. (2007). The impact of arithmetic representation on implementing MLP-BP on FPGAs: A study. Neural Networks, IEEE Transactions on, 18(1), 240-252.

7.       El Moukhlis, S., Elrharras, A., &Hamdoun, A. FPGA-Based Handwritten Signature Recognition System. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-11, April 2014

8.       Wolf, D. F., Romero, R. A., & Marques, E. D. U. A. R. D. O. (2001, November). Using embedded processors in hardware models of artificial neural networks. InVSimposioBrasileirode automaçãointeligente, Brazil.

9.       Bosque, G., del Campo, I., &Echanobe, J. (2014). Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades. Engineering Applications of Artificial Intelligence..

10.    Rostro-Gonzalez, H., Cessac, B., Girau, B., & Torres-Huitzil, C. (2011). The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks. Journal of Physiology-Paris, 105(1), 91-97.

11.    Tisan, A., &Cirstea, M. (2013). SOM neural network design–A new Simulink library based approach targeting FPGA implementation. Mathematics and Computers in Simulation, 91, 134-149.

12.    Online in: http://www.altera.com.






Vishvender Singh, Gunjan Agarwal, Mukesh Sharma

Paper Title:

Design and Analysis of Low Offset High Speed Low Power 1Kb SRAM Memory

Abstract:   This paper we present the design and analysis of 1Kb Static Random Access Memory (SRAM) at 180nm technology and main focusing on optimizing power consumption and delay factors are improved by varying the size of transistor used in Sense Amplifier. The present 1kb SRAM can be divided into main three block sense amplifier, basic cell and precharged circuit. Present 1kb SRAM design input decoupled sense amplifier. Presented Sense amplifier CMOS schematic is design tanner EDA S-edit, Simulate T-spice and 0.18µm technology.

 Sense amplifier, Driver transistor, Access transistor, load transistor.


1.       Adel S. Sedra and Kenneth C. Smith, “Microelectronics Circuits” Oxford University Press International Edition, New York, 5th Edition 2006. 
2.       Ardalan,S.;  Chen, D.;  Sachdev, M.; Kennings, A.; “Current mode sense amplifier” Circuits and Systems, 2005. 48th Midwest Symposium  Vol. 1,  7-10 Aug. 2005  Page(s):17 – 20.

3.       Himanshu, “Design of a low power and high speed sense amplifier”, Master thesis , Thapar University,2010.

4.       Hwang-Cherng Chow,Shu-Hsien Chang; “high performance sense amplifier circuit for low power SRAM APPLICATION S: Circuits and

5.       Tegze P. Haraszti, Microcirc Associates “CMOS Memory Circuits”, kluwer academic  publishers New York, boston , dordrecht, London, Moscow. Pages  238-239.     

6.       Chun-Lung Hsu; Mean-Horn Ho; “High-speed sense amplifier for SRAM applications”Volume 1,  6-9 Dec. 2004 Page(s):577 - 580

7.       H. Mahmoodi, S. Mukhopadhyay, and K. Roy, “Estimation of delay variations due to random-dopant fuctuations in nanoscale CMOS circuits,” IEEE J. Solid-State Circuits, vol. 40, pp. 1787 1796, Sept. 2005 

8.       E. Seevinck et al., “Current-Mode Techniques for High-Speed VLSI Circuits with Application to Current Sense Amplifier for CMOS SRAM,” IEEE JSSC, vol. 26, no.4, pp. 525-536, 1991.

9.       Singh, R.; Bhat, N., “An offset compensation technique for latch type sense amplifiers in high-speed low-power SRAMs” Volume 2000, paper 11.3.4, p. 12,  Issue  6,  June 2004 Page(s):652 – 657..

10.    J. Bhavnagarwala, X. Tang, and J. D. Meindl, “The impactof intrinsic device fluctuations on CMOS SRAM cell stability” IEEE J. Solid-State Circuits, vol. 36, pp. 658–665, Apr. 2001 .

11.    Ardalan,S.;  Chen, D.;  Sachdev, M.; Kennings, A.; “Current mode sense amplifier” Circuits and Systems, 2005. 48th Midwest Symposium  Vol. 1,  7-10 Aug. 2005  Page(s):17 – 20     

12.    R. Sarpeshkar, J.L. Wyatt, N.C. Lu, and P.D. Gerber, “Analysis of Mismatch Sensitivity in a Simultaneously Latched CMOS Sense Amplifier”, IEEE Trans. on Circuits and Systems-II, Vol. 39, No.5, Muy 1992.

13.    Agarwal, B. Paul, S. Mukhopadhyay, and K. Roy, “Process variation in embedded memories: Failure analysis and variation aware architecture”,IEEE J. Solid-State Circuits, vol. 40, pp. 1804 1813, 2005.

14.    Kiyoo Itoh, “VLSI Memory Chip Design” Springer-Verlag Berlin Heidelberg New York,  p.p. 110, 2001.

15.    Ying-Chuan Liu, Hung-Yu Wang, Yuan-Long Jeang and Yu-Wei Huang, “A CMOS Current Mirror with Enhanced Input Dynamic Range”, 3rd International Conference on Innovative Computing Information and Control (ICICIC'08) , 2008.

16.    R. Menchaca, and H. Mahmoodi, "Impact of transistor aging effects on sense amplifier reliability in nano-scale CMOS," in 13 rd International Symposium on Quality Electronic Design, pp. 342-6, 2012.

17.    Sreerama Reddy G M and P Chandrasekhara Reddy,“Design and Implementation of 8K-bits Low Power SRAM in 180nm Technology”, Proceedings of the International Multi Conference of Engineers and Compute Scientists 2009 Vol. II IMECS2009, March -20, 2009, Hong Kong.

18.    Kiyoo Itoh, “VLSI Memory Chip Design” Springer-Verlag Berlin Heidelberg New York, p.p. 110, 2001

19.    Andrei Pavlov and Manoj Sachdev, “CMOS SRAM CircuitDesign and Parametric Test in Nano-Scale Technologies”, 2008 Springer Science Business Media B.V.ISBN 978-1-4020-8362-4 e-ISBN 978-1-4020-8363-1.