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Volume-1 Issue-4: Published on September 10, 2012
54
Volume-1 Issue-4: Published on September 10, 2012
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S. No

Volume-1 Issue-4, September 2012, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Zhenxing Luo

Paper Title:

A New Direct Search Method for Distributed Estimation in Wireless Sensor Networks

Abstract:   Distributed estimation is a popular research topic in wireless sensor networks (WSNs). A maximum likelihood estimation (MLE) method is widely used in WSNs for distributed estimation. However, the MLE method is a computationally intensive method. To overcome this problem, in this paper, a new direct search method will be presented. This method has much lower computation complexity while can achieve estimation results similar to the results given by the MLE method.

Keywords:
 Direct search, maximum likelihood estimation, wireless sensor networks.


References:

1.       I. Akyildiz, W. Su, Y, Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Commun. Mag., vol. 40, pp. 102-114, 2002.
2.       Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

3.       Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

4.       Z. X. Luo and T. C. Jannett, “Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

5.       Z. X. Luo and T. C. Jannett, “Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

6.       Z. X. Luo and T. C. Jannett, “Optimal threshold for locating targets within a surveillance region using a binary sensor network”, Proc. of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec., 2009.

7.       Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks”, Journal of Engineering and Technology, 2012, no 2, pp. 69-74.

8.       Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks deployed in hostile environments”, to appear in International Journal of Engineering and Advanced Technology, vol. 1, no. 6, Aug. 2012.

9.       Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of Byzantine attacks”, International Journal of Innovative Technology and exploring Engineering, vol. 1, no. 3, Aug. 2012.

10.     Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks”, to appear in International Journal of Soft Computing and Engineering, vol. 2, no. 4, Sept. 2012.

11.     Z. X. Luo, “Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

12.     Z. X. Luo, “Distributed estimation in wireless sensor networks based on decisions transmitted over Rayleigh fading channels”, accepted by International Journal of Electrical engineering and Communication Engineering for Applied Research.

13.     Z. X. Luo, “Overview of Applications of Wireless Sensor Networks”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.
14.     X. Sheng and Y. H. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol.53, no.1, pp. 44-53, Jan. 2005.
15.     R. X. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data”, IEEE Transactions on Signal Processing, vol. 54, pp. 4519-4528, Dec. 2006.

16.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp.1131-43, March 2006.

17.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part II: Unknown Probability Density Function,” IEEE Transactions on Signal Process., vol. 54, no. 7, pp. 2784-96, July 2006.

18.     G. Liu, B. Xu, M. Zeng, and H. Chen, "Distributed Estimation over Binary Symmetric Channels in Wireless Sensor Networks," IET Wireless Sensor Systems, vol. 1, pp. 105-109, 2011.

19.     W. Tao, and C. Qi, “Distributed estimation over fading channels using one-bit quantization”, IEEE Trans. Wireless Commun. vol. 8, no. 12, Dec. 2012.


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2.

Authors:

Zhenxing Luo

Paper Title:

Overview of Applications of Wireless Sensor Networks

Abstract:   Wireless sensor networks (WSNs) have become a popular research topic recently due to their wide applications. Such wide applications also drive the development of WSNs because usually, the development of WSNs comes from challenges in real applications. Therefore, it is worthwhile to review some real applications to see how WSNs can be used and developed in the future to address more practical challenges.

Keywords:
 Applications, medical area, wireless sensor networks.

References:

1.       M. A. Batalin, M. Rahimi, Y. Yu, D. Liu, A. Kansal, G. S. Sukhatme, W. J. Kaiser, M. Hansen, G. J. Pottie, M. Srivastava, and D. Estrin, “Call and response: experiments in sampling the environment,” in Proceedings of the 2nd international conference on Embedded networked sensor systems, Baltimore, MD, USA, 2004, pp. 25-38.
2.       K. Romer and F. Mattern, "The design space of wireless sensor networks," IEEE Wireless Commun., vol. 11, no. 6, pp. 54-61, 2004.

3.       A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, "Wireless sensor networks for habitat monitoring," in the Proc. of the 1st ACM international workshop on Wireless sensor networks and applications, Atlanta, Georgia, USA, 2002.

4.       Z. Ying, "Design of the node system of wireless sensor network and its application in digital agriculture," in Proceedings of 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), 2011, pp. 29-35.

5.       A. Tiwari and P. Ballal, "Energy-efficient wireless sensor network design and implementation for condition-based maintenance," ACM Trans. Sen. Netw., vol. 3, no. 1, 2007.

6.       N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A wireless sensor network for structural monitoring,” in Proceedings of the 2nd international conference on Embedded networked sensor systems, Baltimore, MD, USA, 2004, pp. 13-24.

7.       V. Shnayder, B. Chen, and K. Lorincz, "Sensor networks for medical care," Technical Report, Harvard University, 2005, pp. 314–314. Avaiable: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.3639&rep=rep1&type=pdf

8.       A. Milenkovic, C. Otto, and E. Jovanov, "Wireless sensor networks for personal health monitoring: Issues and an implementation," Comput. Commun., vol. 29, pp. 2521-2533, 2006.

9.       S. Chen, H. Lee, C. Chen, H. Huang, and C. Luo, "Wireless body sensor network with adaptive low-power design for biometrics and healthcare applications," IEEE Systems Journal, vol. 3, no. 4, pp. 398-409, Oct. 2009.

10.     P. A. Morreale, "Wireless sensor network applications in urban telehealth," in Proceedings of 21st International Conference on Advanced Information Networking and Applications Workshop (AINAW 2007), Niagara Fall, Ontario, Canada, May 2007, pp. 810-814.

11.     K. Kim, J, Jun, S. Kim, and B. Y. Sung, "Medical asset tracking application with wireless sensor networks," in Proceedings of Second International Conference on Sensor Technologies and Applications (SENSORCOMM 2008), Cap Esterel, France, August 2008, pp. 531-536.

12.     R. McSweeney, C. Spagnol, E. Popovici, and L. Giancardi, "Implementation of source and channel coding for power reduction in medical application wireless sensor network," in Proceedings of Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009), Athens/Glyfada, Greece, June 2009, pp. 271-276.

13.     K. Sha, W. Shi, and O. Watkins, "Using wireless sensor networks for fire rescue applications: requirements and challenges," in the Proc. of the 2006 IEEE International Conference on Electo/information Technology, East Lansing, MI, USA, May 7-10, 2006, pp. 239-244.
14.     A. Ko, H. Y. K. Lau, and R. P. S. Sham, "Application of distributed wireless sensor network on humanitarian search and rescue systems," in Proceedings of second International Conference on Future Generation Communication and Networking (FGCN 2008), Hainan Island, China, Dec. 13-15, 2008, pp. 328-333.
15.     L. Hoehmann and A. Kummert, "Mobility support for wireless sensor networks simulations for road intersection safety applications," in Proceedings of 52nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2009), Cancun, Mexico, Aug. 2-5, 2009, pp. 260-263.

16.     J. Chen, Z. Pang, Z. Zhang, J. Gao, Q. Chen, and L. Zheng, "A novel acceleration data compression scheme for wireless sensor network application in fresh food tracking system," in Proceedings of 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009), Biejing, China, Aug. 16-19, 2009, pp. 3-1-3-5.

17.     S. Lee, D. Yoon, and A. Ghosh, "Intelligent parking lot application using wireless sensor networks," in Proceedings of International Symposium on Collaborative Technologies and Systems (CTS 2008), Irvine, California, USA, May 19-23, 2008, pp. 48-57.

18.     I. Akyildiz, W. Su, Y, Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Commun. Mag., vol. 40, pp. 102-114, 2002.

19.     Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and WirelessSymposium, Santa Clara, CA, Jan. 2012.

20.     Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

21.     Z. X. Luo and T. C. Jannett, “Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

22.     Z. X. Luo and T. C. Jannett, “Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

23.     Z. X. Luo and T. C. Jannett, “Optimal threshold for locating targets within a surveillance region using a binary sensor network”, Proc. of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec., 2009.

24.     Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks”, Journal of Engineering and Technology, 2012, no 2, pp. 69-74.

25.     Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks deployed in hostile environments”, to appear in the International Journal of Engineering and Advanced Technology, vol. 1, no. 6, Aug. 2012.

26.     Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of Byzantine attacks”, to appear in the International Journal of Innovative Technology and exploring Engineering, vol. 1, no. 3, Aug. 2012.

27.     Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks”, to appear in International Journal of Soft Computing and Engineering, vol. 2, no. 4, Sept. 2012.

28.     Z. X. Luo, “Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

29.     Z. X. Luo, “Distributed estimation in wireless sensor networks based on decisions transmitted over Rayleigh fading channels”, accepted by International Journal of Electrical engineering and Communication Engineering for Applied Research

30.     X. Sheng and Y. H. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol.53, no.1, pp. 44-53, Jan. 2005.

31.     R. X. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data”, IEEE Transactions on Signal Processing, vol. 54, pp. 4519-4528, Dec. 2006.

32.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp.1131-43, March 2006.

33.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part II: Unknown Probability Density Function,” IEEE Transactions on Signal Process., vol. 54, no. 7, pp. 2784-96, July 2006.

34.     G. Liu, B. Xu, M. Zeng, and H. Chen, "Distributed Estimation over Binary Symmetric Channels in Wireless Sensor Networks," IET Wireless Sensor Systems, vol. 1, pp. 105-109, 2011.

35.     Z. X. Luo, “A new direct search method for distributed estimation in wireless sensor networks”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

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3.

Authors:

Pushpendra Singh, Om Prakash Yadav, Yojana Yadav

Paper Title:

ECG Signal Compression Validation by a New Transform Technique

Abstract:   Electrocardiogram signal compression algorithm is needed to reduce the amount of data to be transmitted, stored and analyzed, without losing the clinical information content. This work investigates a set of ECG signal compression schemes to compare their performances in compressing ECG signals. These schemes are based on transform methods such as discrete cosine transform (DCT), fast Fourier transform (FFT), discrete sine transform (DST), and their improvements. An improvement of a discrete cosine transform (DCT)-based method for electrocardiogram (ECG) compression is also presented as DCT-II. A comparative study of performance of different transforms is made in terms of Compression Ratio (CR) and Percent root mean square difference (PRD).The appropriate use of a block based DCT associated to a uniform scalar dead zone quantiser and arithmetic coding show very good results, confirming that the proposed strategy exhibits competitive performances compared with the most popular compressors used for ECG compression. Each specific transform is applied to a pre-selected data segment from the Physiobank ATM database, and then compression is performed.

Keywords:
 Compression Ratio, Compression factor, Compression time, ECG, PRD.


References:

1.        B. A. Rajoub, “An efficient coding algorithm for the compression of ECG signals using the wavelet transform,” IEEE Transactions on Biomedical Engineering, 49 (4): 355–362, 2002.
2.        O. O. Khalifa, S. H. Harding, A. A. Hashim, “Compression Using Wavelet Transform” Signal Processing: An International Journal (SPIJ), pp. 17 – 26, 2008.

3.        J. Cox, F. Nulle, H. Fozzard, and G. Oliver, “AZTEC, a preprocessing program for real-time ECG rhythm analysis,” IEEE. Trans. Biomedical Eng., BME-15: 128–129, 1968.

4.        R.N. Horspool and W.J. Windels, “ECG compression using Ziv-Lempel techniques, Comput” Biomed. Res., 28: 67–86, 1995.

5.        B. R. S. Reddy and I. S. N. Murthy, “ECG data compression using Fourier descriptors,” IEEE Trans. Biomed. Eng., BME-33 (4): 428–434, 1986.

6.        H. A. M. Al-Nashash, “ECG data compression using adaptive Fourier coefficients estimation,” Med. Eng. Phys., 16: 62–66, 1994.

7.        S. C. Tai, “Improving the performance of electrocardiogram sub-band coder by extensive Markov system,” Med. Biol. Eng. And Computers, 33: 471–475, 1995.

8.        J. Chen, S. Itoh, and T. Hashimoto, “ECG data compression by using wavelet transform,” IEICE Trans. Inform. Syst., E76-D (12): 1454–1461, 1993.

9.        A. Cohen, P. M. Poluta, and R. Scott-Millar, “Compression of ECG signals using vector quantization,” in Proc. IEEE-90 S. A. Symp. Commun. Signal Processing
COMSIG-90, Johannesburg, South Africa, pp. 45–54, 1990.

10.     G. Nave and A. Cohen, “ECG compression using long-term prediction,” IEEE. Trans. Biomed. Eng., 40: 877–885, 1993.

11.     A. Iwata, Y. Nagasaka, and N. Suzumura, “Data compression of the ECG using neural network for digital Holter monitor,” IEEE Eng. Med. Biol., Mag, pp. 53–57, 1990.

12.     L. Auslander, E. Feig and S. Winograd (1984): Abelian Semi-simple Algebras and Algorithms for the Discrete Fourier Transform. In Advances in Applied Mathematics.5, 31-55.

13.     Tinku Acharya and Ajoy K. Roy. Image Processing Principles and Applications. John Wiley.

14.     S. Chan and K. Ho (1990): Direct Methods for computing discrete sinusoidal transforms. IEEE Proceedings, 137, 433-442.

15.     G. Steidl and M. Tasche (1991): A Polynomial approach to Fast algorithms for Discrete Fourier –cosine and Fourier-sine Transforms. In Mathematics in Computation, 56 (193), 281-296.

16.     E. Feig and S. Winograd (1992): Fast Algorithms for Discrete Cosine Trnsforms. IEEE Tran. On Signal Processing.vol-40(9), pp 2174-2193.

17.     Xuancheng Shao and Steven G. Johnson (May 10, 2007): Type-II/III DCT/DST algorithms with reduced number of arithmetic operations. Preprint submitted to Elsevier.

18.     J. Abenstein and W. Tompkins (1982): A new data-reduction algorithm for real time ECG analysis. IEEE Tran. On Biomed. Engg., 29(BME-1):4, 3-8.

19.     K. R. Rao and P. Yip (1990): Discrete cosine transform – algorithms, advantages, applications, San Diego: Academic Press.

20.     Al-Nashash, H. A. M., 1994, "ECG data compression using adaptive Fourier coefficients estimation", Med. Eng. Phys., Vol. 16, pp. 62-67

21.     Bradie, Brian., 1994, "Wavelet Packet Based Compression of Single Lead ECG", Scheduled to appear in IEEE Transactions on Biomedical Engineering

22.     Hamilton, Patrick S., 1991, "Compression of the Ambulatory ECG by Average Beat Subtraction and Residual Differencing", IEEE Transactions on Biomedical Engineering, Vol. 38, No. 3., pp. 253-259.

23.     Pranob K. Charles and Rajendra Prasad K. (2011): A Contemporary Approach For ECG Signal Compression Using Wavelet Transforms. Signal and Image Processing: An International Journal (SIPIJ). Vol. 2, No. 1, 178-183.

24.     Okada M, A digital filter for the QRS complex detection, IEEE Trans on BME, vol. 26, no 12, pp 700-703, December 1979.

25.     Murthy I S N & Prasad G S D, Analysis of ECG from Pole-zero models, IEEE Trans on BME, vol. 39, no. 7, pp 741-751, 1992.

26.     Pan J & Tompkins W J, A real- time QRS detection algorithm, IEEE Trans on BME, vol.32, no 3, pp 230-236 March 1985.  


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4.

Authors:

B.C.Patle, D.V. Bhope

Paper Title:

Stress Analysis of Plate With Oblique Hole

Abstract:   In this study, the wok is carried out to analyzed, the stresses of plate with oblique hole with the Finite Element Analysis. Stress analysis of a series of flat plates with oblique holes subjected to axial tension has been carried out using the finite element method (FEM). Different plate hole diameter-width ratios, angles of hole obliquity have been considered to provide stress concentration factors at such holes. The work covers plate hole diameter- width (d/w)  ratios from 0.1 to 0.9, hole obliquity angles from 00 to 800 and inclination of hole axis in widthwise, lengthwise and diagonal wise direction, 300 direction and 600 direction.

Keywords:
 Finite Element Method, Oblique Hole, Stress Analysis.


References:

1.       McKENZIE D. J. WHITE “Stress concentration caused by an oblique round hole in a flat plate under uniaxial tension.” Journal of Strain Analysis, Vol 3, No 2 ,1968. 
2.       P Stanley and A G Starr has find out “Stress concentration at an oblique hole in a thick plate”. Journal of Strain Analysis, Vol 35, No 2, 2000. A TAFRESHI AND T.E.THORPE “NUMERICAL ANALYSIS OF STRESSES AT OBLIQUE HOLES IN PLATE SUBJECTED TO TENSION AND BENDING”. JOURNAL OF STRAIN ANALYSIS, VOL 30, NO 4, 1995.


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5.

Authors:

Aqueel Ahmed, Shaikh Abdul Hannan

Paper Title:

Data Mining Techniques to Find Out Heart Diseases: An Overview

Abstract:   Heart disease is a major cause of morbidity and mortality in modern society.  Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently.  Although significant progress has been made in the diagnosis and treatment of heart disease, further investigation is still needed. The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. There is a huge data available within the healthcare systems.  However, there is a task of effective analysis tools to discover hidden relationships and trends in data.  Knowledge discovery and data mining have found numerous application in business and scientific domain.  Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. This research paper proposed to find out the heart diseases through data mining, Support Vector Machine (SVM), Genetic Algorithm, rough set theory, association rules and Neural Networks. In this study, we briefly examined that out of the above techniques Decision tree and SVM is most effective for the heart disease. So it is observed that, the data mining could help in the identification or the prediction of high or low risk heart diseases.  

Keywords:
 Data Mining, Heart Disease, SVM, rough sets techniques, association rules & clustering.


References:

1.        Mrs. Bharati M. Ramageri, “Data Mining Techniques And Applications”,Bharati M. Ramageri / Indian Journal of Computer Science and Engineering Vol. 1 No. 4 301-305
2.        Heart disease from “http://www.nhlbi.nih.gov/educational/hearttruth/lower-risk/what-is-heart-disease.htm”

3.        Bala Sundar V, Bharathiar, “Development of a Data Clustering Algorithm for Predicting Heart” International Journal of Computer Applications (0975 – 888) Volume 48– No.7, June 2012

4.        http://heart-disease.emedtv.com/ coronary-artery-disease/coronary-artery-disease.html

5.        R. Gupta, V. P. Gupta, and N. S. Ahluwalia, “Educational status, coronary heart disease, and coronary risk factor prevalence in a rural population of India”, BMJ. pp 1332–1336, 19 November 1994.

6.        Panniyammakal Jeemon & K.S. Reddy, “Social determinants of cardiovascular disease outcomes in Indians”, pp 617-622, November 2010.

7.        A Mathavan, MD, A Chockalingam, PhD, S Chockalingam, BSc, B Bilchik, MD, and V Saini, MD, “Madurai Area Physicians Cardiovascular Health Evaluation Survey (MAPCHES) – an alarming status”, The Canadian Journal of Cardiology; 25(5): 303–308, May 2009.

8.        Vamadevan S. Ajay & Dorairaj Prabhakaran, “Coronary heart disease in Indians: Implications of the INTERHEART study”, Indian J Med Res 132, pp 561-566, November 2010.

9.        K.S.Kavitha,K.V. Ramakrishnan, Manoj Kumar Singh “Modeling and design of evolutionary neural network for heart disease detection” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, 1694-0814, September 2010.

10.     Rajeswari K, Vaithiyanathan V, P. Amirtharaj “Application of Decision Tree Classifiers in Diagnosing Heart Disease using Demographic Data” American Journal of Scientific research ISSN 2301-2005 pp. 77-82  EuroJournals  Publishing,  2012.

11.     Yanwei Xing “Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease” IEEE Transactions on Convergence Information Technology,  pp(868 – 872), 21-23 Nov. 2007

12.     Ordonez C,” Association rule discovery with the train and test approach for heart disease prediction”, IEEE Transactions on  Information Technology in Biomedicine, P(334 – 343),  April 2006

13.     AbuKhousa, E “Predictive data mining to support clinical decisions: An overview of heart disease prediction systems”, IEEE Transaction on Innovations in Information Technology (IIT), pp(267 - 272) March 2012. 
14.     Srinivas, K.,”Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques”, IEEE Transaction on Computer Science and Education (ICCSE), p(1344 - 1349), 2010.
15.     Shen, Z., Clarke, M. Jones, R. Alberti, T.  “A neural network approach to the detection of coronary artery disease”,IEEE Transaction on Computers in Cardiology, 5-8 Sep 1993.

16.     Karaolis, M.A. Moutiris, J.A.  Hadjipanayi, D.  Pattichis, C.S.,  “Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees” IEEE Transactions on Information Technology in Biomedicine, pp 559 -566, May 2010.

17.     Frawley and Piatetsky-Shapiro, 1996. Knowledge Discovery in Databases:An Overview. The AAAI/MIT Press, Menlo Park, C.A.

18.     Miller, A., B. Blott and T. Hames, 1992. Review of neural network applications in medical imaging and signal processing. Med. Biol. Engg. Comp., 30: 449-464.

19.     Chen, J., Greiner, R.: Comparing Bayesian Network Classifiers. In Proc. of UAI-99, pp.101–108,1999.

20.     Shaikh Abdul Hannan,  V. D. Bhagile  R. R. Manza, R. J. Ramteke,  “Diagnosis and Medical Prescription of Heart Disease Using Support Vector Machine and
Feedforward Backpropagation technique”, International Journal on Computer Science and Engineering, pp 2150-2159, 2010.


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6.

Authors:

Guropinder Singh, Parvinder Singh

Paper Title:

Strong/Weak Muscle Fiber Analysis by Pattern Recognition of SEMG Based On BP and RBF Neural N/W

Abstract:   In this paper, we use both BP neural network and RBF neural network to identify SEMG from human upper arm (Bicep). In the experiments, we study the SEMG signal  strength by different algorithm We use two electrodes to extract SEMG signal from the upper arm biceps, then analyze this signal using the peak value of SEMG signal , put this value vectors into BP neural network and RBF neural network to complete strength recognition. The results of the experiments using the method introduced in this paper show that the average recognition rate of strength of muscle are above 94 % for BP and is above 99% for RBF neural network.

Keywords:
 BP neural network; pattern recognition; RBF neural network; Surface Electromyography Signal.


References:

1.        ZHANG Qing-ju, LUO Zhi-zeng, YE Ming.Based on Power Spectrum and RBF Neural Network to Classify Surface Electromyography[J]. Mechanical & Electrical Engineering Magazine.2005,22(11),35-38.
2.        Qing Yu,Jihai Yang,Xiang Chen,Xu Zhang.Gestures action surface EMG pattern recognition based on BP neural network. Biomedical Engineering Research. 2009,28(1):06-10.

3.        Baofeng Sun,Wanzhong Chen,Yantao Tian, The Pattern Recognition of Surface EMG Based on Wavelet Transform and BP Neural Network, 978-1-4244-5089-3/11/ ©2011 Crown

4.        Nigg B.M., & Herzog W., 1999. Biomechanics of the Musculo-Skeletal system. Wiley. Page:349.

5.        Zhu Xizhi, Study of Surface Electromyography Signal based on Wavelet Transform and Radial Basis Function Neural Network, 978-0-7695-3561-6/08 $25.00 © 2008 IEEE DOI

6.        Chengjian Liu.Based on fuzzy neural network analysis ofEMG[J]. Chinese Medical Devices.1999,23(2):80-82.

7.        CUI Jian-guo, WANG Xu, LI Zhong-hai etc. Application of Support Vector Machine in Pattern Classification of Surface EMG[J]. Journal of  Northeastern University (Natural Science),2006, 27(3), 280-284.


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7.

Authors:

Abhishek Gandhar, Balwinder Singh, Rintu Khanna

Paper Title:

Impacts of Facts Technology-A State of Art Review

Abstract:    FACTS (Flexible AC Transmission Systems) means a whole family of controllers and devices for increase the use and flexibility of power systems. These controllers are installed in many places and improving the capabilities of different power systems.. This paper presents a review on the research and developments in the area of FACTS controllers and their contributions. This paper will treat benefits of FACTS devices installed in power systems such as increment in power transmission capability and a reduction in transmission losses. And improved transient and dynamic stability, This paper also includes the main barriers of voltage instability and power transmission structure. Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of voltage stability improvement by using FACTS controllers

Keywords:
  FACTS, SVC, STATCOM, UPFC.


References:

1.        H. G. Kwatny, A. K. Pasrija, and L. Y. Bahar, “Loss of steady state stability and voltage collapse in electric power systems,” PTOCIE. EE Conference on Decision and Control, Ft. Lauderdale, FL, Dec. 1985, pp. 804-811 [2] T. V. Cutsem and C. Vournas, Voltage Stability of Electric PowerSystem, Kluwer Academic Publisher, 1998.
2.        WSCC RRWG, “Proposed voltage stability guidelines, undervoltage load shedding strategy, and reactive power reserve monitoring methodology,”, Final Report, September 1997

3.        P. Kundur, Power System Stability and Control. New York: McGraw-Hill, Inc., 1993.

4.        C. W. Taylor, Power System Voltage Stability. New York: McGraw-Hill, Inc., 1994.IEEE Guide for synchronous generator modeling practices in stability analyses, IEEE Standard 1110-1991.

5.        Anderson P., Fouad A., Power system control and stability, Revised Printing, IEEE Press, ISBN 0-7803-1029-2, 1994

6.        Anderson P., “A stability taxonomy,” Proceedings of the IEEE PES

7.        2. John J. Paserba, Fellow, IEEE, “How FACTS Controllers Benefit AC Transmission Systems” IEEE Transactions on Power Delivery,Vol, No 1  pages 949-957,2003.

8.        Machowski J., Bialek J., Bumby J., Power system dynamics and stability,John Wiley & Sons, ISBN 0471 97174 X (PPC), 1997

9.        Kundur P., Power system stability and  control, EPRI McGraw-Hill, ISBN 0-07-035958-X, 1994

10.     Taylor C., Reactive power compensation and voltage stability: removing transmission limitations, Seminar Book, February 1997 Winter Meeting, Panel Session on Stability Terms and Definitions, New York, USA, February 1997

11.     N.G. Hingorani, L. Gyugyi, Understanding FACTS, Concepts and Technology of Flexible AC Transmission systems, IEEE Press 2000.

12.     Y. H. Song and A. T. Johns, Flexible AC Transmission System (FACTS), IEE Power and Energy Series 30, 1999.
13.     IEEE Power Engineering Society, FACTS Overview. IEEE Special Publication 95TP108, 1995

14.     IEEE Power Engineering Society, FACTS Applications. IEEE Special Publication 96TP116-0, 1996

15.     R. M. Mathur and R. S. Basati, Thyristor-Based FACTS Controllers forElectrical Transmission Systems. IEEE Press Series in Power Engineering, 2002.

16.     Edris A., “FACTS technology development: an update,” IEEE PowerEngineering Review, vol. 20, no. 3, March 2000, pp. 4-9

17.     Caldon R., Mari A., Paolucci A., Turri R., "An efficient modular model for static and dynamic analysis of FACTS performances on electric power systems," Proceedings of the IEEE Power Tech'99 Conference -BPT'99, Budapest, Hungary, August 1999, paper BPT99-138-44

18.     Avramović B., Fink L., “Energy management systems and control of FACTS,” Electrical Power & Energy Systems, vol. 17, no. 3, June 1995,pp. 195-198

19.     Povh D., Renz K., Teltsch E., Witzmann R., “Integrated operation of HVDC and FACTS,” Proceedings of 12th Conference on the Electric Power Supply Industry 12CEPSI, Pattaya, Thailand, November 1998,pp. 64-71, 34-09

20.     Pilotto L., Ping W., Carvalho A., Wey A., Long W., Alvarado F., Edris A., “Determination of needed FACTS controllers that increase asset utilization of power systems,” IEEE Trans. Power Delivery, vol. 12, no.1, January 1997, pp. 364-371

21.     N. G. Hingorani, "FACTS-Flexible AC Transmission System,"Proceedings of 5th International Conference on AC and DC PowerTransmission-IEEE Conference Publication, 1991, pp. 1-7.

22.     Mihalič R., Povh D., Žunko P., “Increased loadability of long transmission lines by insertion of FACTS devices,” Electrotechnical Review, Ljubljana, Slovenia, vol. 60, no. 1, 1993, pp. 7-12 (in Slovenian)

23.     Pilotto L., Ping W., Carvalho A., Wey A., Long W., Alvarado F., Edris A., “Determination of needed FACTS controllers that increase asset utilization of power systems,” IEEE Trans. Power Delivery, vol. 12, no.1, January 1997, pp. 364-371\

24.     N. Mithulananthan, C. Canizares, J. Roeeve, and G. Rogers,“Comparison of PSS, SVC, and STATCOM for Damping Power System Oscillations,” IEEE Trans. On Power Systems, Vol. 18, No. 2, May 2003.

25.     I. Papic, P. Zunko, D. Povh and W. Weinhold, “Basic control of unified power flow controller”, IEEE Trans. on PS, Vol. 12, No. 4, pp 1734-1739, 1997.

26.     D. Povh, “FACTS Controller in Deregulated Systems”, SEPOPE’98Power Systems Symposium, Rio de Janeiro, Brazil, May 1998.

27.     K.Matsuno, I. Iyoda, and Y. Oue, “An Experience of FACTS Development 1980s and 1990s”, IEEE PES Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, 2(6-10)(2002), pp. 1378 –1381.

28.     S.M. Bamasak and M. A Abido, “Assessment Study Of Shunt Facts- Based Controllers Effectiveness On Power System Stability Enhancement,” 39th UPEC Proceedings, Vol. 1, pp 65-71, Sept 2004.

29.     R. Byerly, D. Poznaniak, and E. Taylor, "Static Reactive Compensation for Power Transmission System," IEEE Trans. PAS-101, 1982, pp.3998-4005

30.     V. Sitnikov, W. Breuer, D. Povh, D. Retzmann, M. Weinhold, “Benefits of FACTS forLarge Power Systems”, Cigré Conference, 17-19. Sept. 2003, St.-Petersburg, Russia.

31.     A. E. Hammad, "Analysis of Power System Stability Enhancement by Static VAR Compensators," IEEE Trans. PWRS, Vol. 1, No. 4, 1986,pp. 222-227.

32.     Noroozian. M., Angquist L. Ghandhari. M., and Anderson. G.: 'Improving power system dynamics by series connected FACTS devices', IEEE Trms. Puirer Delic., pp. 163-1641, 1997

33.     H. F. Wang and F. J. Swift, "Capability of the Static VAr Compensator in Damping Power System Oscillations,” IEEE Proc. Genet. Transm.Distrib., Vol. 143, No. 4, 1996, pp. 353-358.

34.     K. R. Padiyar and R. K. Varma, "Damping Torque Analysis of Static VAR System Oscillations," IEEE Trans. PWRS, Vol. 6, No. 2, 1991, pp.458-465

35.     Sandeep Gupta, R. K. Tripathi, and Rishabh Dev Shukla,“ Voltage Stability Improvement in Power Systems using Facts Controllers: State-of-the-Art Review,” IEEE Trans. On Power Systems, 2010

36.     Bompard E., Carpaneto E., Chicco G., Napoli R., “Asynchronous motor models for voltage stability analysis,” Proceedings of the InternationalSeminar on Bulk Power System Voltage Phenomena - III, Voltage Stability, Security and Control, Davos, Switzerland, August 1994, pp.489-499.


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8.

Authors:

Zeiad El-Saghir, Hamed S. El-Ghety, M. AbdelAziz

Paper Title:

Efficient Implementation model for Public Geographic Information System: Case Study

Abstract:   Public Geographic Information system (GIS) is a solution that automates the process of collecting community contributions of spatial data, cross-referenced to base maps. It provides the capabilities of analyzing such spatial data giving effective decision support information. On the other hand, the most important implementation models for GIS used recently are Client-Server and Software as a Service (SaaS). This paper focus on giving typical situation as a case study for exploring the advantages of SaaS implementation model over Client server one where public GIS services are implemented first as client server model and secondly when implemented as SaaS. In our new urban City state office, SaaS implementation achieved huge reduction on the total cost of ownership for users of public GIS solution rather than previous GIS Client-Server implementation. High level of cooperative collaboration between departments has been achieved (e.g. survey dept. , land use dept.). Achieved huge reduction on front-up cost required to start using the GIS solution (typically it costs 4.26% of the total front-up cost in case client-server model is used which was 214000USD), and reduction in maintenance /installation hours needed to fix a bug or install new feature across department rather than previous GIS Client Server implementation where the same maintenance /installation hours must be replicated to install new batch containing the fix for new feature. Hence, SaaS implementation model for public GIS overcomes Client Server model in many different aspects and increased the Return On Investment (ROI) value for public GIS solution as it empowers departments in state office to focus in delivering more spatial business's value rather than being busy with having a dedicated data center to operate and manage separate installation of GIS software in client server model.

Keywords:
 Cloud Computing, Public Geographic Information System, Software as a service, Client Server.


References:

1.       http://www.egyptsoft.com/framework/app/upload/article/81_L_gis.jpg
2.       Marc Schlossberg: Asset Mapping and Community Development Planning with GIS, Kluwer Academic Publishers, University of Michigan, USA.

3.       Lasse Berntzen: Enhanced e-Services through Partnerships, IEEE Proceedings of the First International Conference on the Digital Society (ICDS'07), 2007

4.       http://www.marketingautomationsoftware.com/guides/is-web-based-software-right-for-you/#ixzz25bYo5FBn

5.       K. Layne, J. Lee, “Developing fully fuctional E-Government: A four stage model,” Government Information Quarterly, vol. 18, p. 122-136.

6.       L. Berntzen, “Citizens Meet Politicians on the Internet – Debriefing Report,” Proc. 4th European Conference of e-Government, Academic Conferences, London, 2004, p. 79-84

7.       L. Berntzen, M. Winsvold, “A Web-based Tool to Support Citizen Initiative”, Workshop and Poster Proceedings of the Fourth International EGOV Conference 22-26 August 2005, Denmark, Schriftenreie Informatik, Band 15, Trauner Verlag, p. 241-248

8.       S. Stephens, P. McCusker, A.M. Logue, D. O’Donnell, “On the Road from Consultation Cynicism to Energising e-Consultation”, Proc. 6th European Conference on e-Government, Academic Conferences, London, 2006, p. 411-420.


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9.

Authors:

Virendra Swarnkar, K. J. Satao

Paper Title:

Understanding Transmission Control Protocols: Basic Survey

Abstract:   Data transfer from one system to another has always been a challenging task. Multiple protocols have been developed to transmit data from one system to another, considering the security, convenience, and speed criteria. TCP (transmission control protocol) stands to be the most widely used and accepted protocol. In this paper we have discussed various commonly employed protocols for data transfer. Many algorithms and protocols have been stated which are capable in providing high speed data transfer along with security, especially in terms of congestions (little or no congestion is desirable). We have studied various transmission control protocols in this paper.

Keywords:
 TCP, HSTCP, Scalable TCP, SCTP


References:

1.       http://en.wikipedia.org/wiki/Transmission_Control_Protocol
2.       Comer, Douglas E. (2006). Internetworking with TCP/IP:Principles, Protocols, and Architecture. 1 (5th ed.). Prentice Hall. ISBN 0-13-187671-6.

3.       http://tools.ietf.org/html/rfc793

4.       Tanenbaum, Andrew S. (2003-03-17). Computer Networks (Fourth ed.). Prentice Hall. ISBN 0-13-066102-3.

5.       http://en.wikipedia.org/wiki/HSTCP

6.       http://en.wikipedia.org/wiki/Network_congestion

7.       http://www.deneholme.net/tom/scalable/

8.       Tom Kelly, Scalable TCP: Improving Performance in Highspeed Wide Area Networks. Computer Communication Review 32(2), April 2003

9.       RFC 2960 October 2000

10.     http://tools.ietf.org/html/rfc2960

11.     Ong, Lyndon; Randall R. Stewart; Qiaobing Xie (March 2000). Tunneling of SCTP over Single UDP Port. IETF. Retrieved 2011-07-15.

12.     Bickhar, Ryan; Paul D. Amer; Randall R. Stewart (2007). "Transparent TCP-to-SCTP Translation Shim Layer" (PDF). Retrieved 2008-09-13.
13.     "Transport". Diameter Base Protocol. IETF. sec. 2.1. RFC 3588. Retrieved 2012-05-18.
14.     RFC 5351 Section 4.2

15.     http://tools.ietf.org/html/rfc5351


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10.

Authors:

Sagar Krishna Sivvam, Solomon Gotham

Paper Title:

A Hybrid and Memory Efficient Multiplier and Accumulator Design Using Radix -4 Algorithm

Abstract:  In this paper we proposed a new architecture for high speed MAC operation. By combining multiplication and addition and devising a hybrid type of Carry save adder, the performance was improved. The proposed CSA uses 1’s complement based radix-2 booth algorithm The multiplication and accumulation unit  provides  high speed  multiplication along  with accumulative  addition. And for final addition some final such as CLA, Kogge stone adder and then adders compare their performance characteristics. The one most effective way to increase the speed of a multiplier is to  reduce  the  number  of  the  partial  products.  Although  the number  of  partial  products  can  be  reduced  with  a  higher radix booth  encoder,  but  the  number of  hard  multiples that are expensive to generate also increases simultaneously. To increase the speed and performance, many parallel MAC architectures have been proposed. The design was implemented on Xilinx Xc3s500E fpga and the device utilized 13% of the total LUT’s and the total power utilization was 0.041mW.

Keywords:
 Radix-4 Booth multiplier, CLA, multiplier and- accumulator (MAC).


References:

1.        J. J. F. Cavanagh, “Digital Computer Arithmetic”, New York: McGraw- Hill, 2004.
2.        K. Hwang. Computer Arithmetic – Principles, Architecture and Design. School of Electrical Engineering 1979.

3.        Chung Nan Lyu & David W. Matula Redundant Binary Booth Recoding Proceedings of the 12th Symposium on Computer Arithmetic (ARITH '95) 1995 IEEE.

4.        B.Cherkauer and E. Friedman. A Hybrid Radix-4/Radix-8 Low Power, High Speed Multiplier Architecture for Wide Bit Widths. In  IEEE  International  Symposium  on  Circuits  and  Systems,  volume  4,  pages 53–56, 1996.

5.        Yajuan  He  and  Chip-Hong  Chang,  “A  New  redundant binary  booth encoding  for  fast  2n-bit  multiplier  design”,  IEEE  Transaction  on  circuit and systems, Vol. 56, No.6, June 2009.

6.        A. R. Omondi, Computer Arithmetic Systems . Englewood Cliffs, NJ: Prentice-Hall, 1994.

7.        D. Booth, “A signed binary multiplication technique,” Quart. J. Math., vol. IV, pp. 236–240, 1952.

8.        S. Wallace, “A suggestion for a fast multiplier,”IEEE Trans. Electron Comput. , vol. EC-13, no. 1, pp. 14–17, Feb. 1964.

9.        R. Cooper, “Parallel architecture modified Booth multiplier,” Proc.Inst. Electr. Eng. G , vol. 135, pp. 125–128, 1988.

10.     N. R. Shanbag and P. Juneja, “Parallel implementation of a 4  4-bit multiplier using modified Booth’s algorithm,”IEEE J. Solid-State Circuits , vol. 23, no. 4, pp. 1010–1013, Aug. 1988.

11.     G. Goto, T. Sato, M. Nakajima, and T. Sukemura, “A 54   54 regularstructured tree multiplier,” IEEE J. Solid-State Circuits, vol. 27, no. 9,pp. 1229–1236, Sep. 1992.

12.     J. Fadavi-Ardekani, “M   N Booth encoded multiplier generator using optimized Wallace trees,”IEEE Trans. Very Large Scale Integr. (VLSI) Syst. , vol. 1, no. 2, pp. 120–125, Jun. 1993.
13.     N. Ohkubo, M. Suzuki, T. Shinbo, T. Yamanaka, A. Shimizu, K. Sasaki, and Y. Nakagome, “A 4.4 ns CMOS 54  54 multiplier using pass-transistor multiplexer,” IEEE J. Solid-State Circuits, vol. 30, no. 3, pp. 251–257, Mar. 1995.
14.     A. Tawfik, F. Elguibaly, and P. Agathoklis, “New realization and implementation of fixed-point IIR digital filters,” J. Circuits, Syst., Comput., vol. 7, no. 3, pp. 191–209, 1997.

15.     Tawfik, F. Elguibaly, M. N. Fahmi, E. Abdel-Raheem, and P. Agathoklis, “High-speed area-efficient inner-product processor,” Can. J. Electr. Comput. Eng. , vol. 19, pp. 187–191, 1994.

16.     F. Elguibaly and A. Rayhan, “Overflow handling in inner-product pro- cessors,” in Proc. IEEE Pacific Rim Conf. Commun., Comput., Signal Process., Aug. 1997, pp. 117–120. IEEE J. Solid-State Circuits, vol. 25, no. 2, pp. 584–594, Feb. 1990.


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11.

Authors:

Sandeep Sivvam, Solomon Gotham

Paper Title:

A Concurrent Self Repair Scheme for Defects in Random Access Memories

Abstract:   Built-in self-repair (BISR) techniques are widely used for repairing embedded random access memories (RAMs). One key component of a BISR module is the built-in redundancy-analysis (BIRA) design. This paper presents an effective BIRA scheme which executes the 2-D redundancy allocation based on a 1-D local bitmap. Two BIRA algorithms for supporting two different redundancy organizations are also proposed. Simulation results show that the proposed BIRA scheme can provide high repair rate (i.e.,the ratio of the number of repaired memories to the number of defective memories) for the RAMs with different fault distributions. Experimental results show that the hardware overhead of the BIRA design is only about 2.9% for an 8192 64-bit RAM with two spare rows and two spare columns. The design is implemented on Xiliinx Spartan3E FPGA and the device used 532 flip-flops out of 3840 available and 439 LUT’s out of 3840 and the number of IO blocks used is 13.  Moreover, the time overhead of  redundancy  analysis  is  very  small. Embedded  memories  are  among  the  most  widely  used  cores  in  current system-on-chip  (SOC)  implementations. Total power utilized by the device was0.041mW.  Memory  cores  usually occupy  a  significant  portion  of  the  chip  area,  and  dominate  the manufacturing  yield  of  the  chip. The BIRA module executes the proposed redundancy analysis (RA) algorithm for RAM with a 2-D redundancy structure, i.e., spare rows and spare columns.

Keywords:
 BIRA, ReBIRA


References:

1.        S. E. Schuster, ―Multiple word/bit line redundancy for semiconductor memories, IEEE Journal of Solid- State  Circuits,  vol.  13,  no.  5, pp.  698–703.
2.        M.  Horiguchi,  J.  Etoh,  M.  Masakazu, K. Itoh, and T. Matsumoto, ―A  flexible  redundancy  technique  for  high-density  DRAM’s, IEEE  Journal of Solid-State Circuits, vol. 26, no. 1, pp. 12–17.

3.        T. Yamagata, H. Sato, K. Fujita, Y. Nishimura, and K. Anami, ―A  distributed globally replaceable redundancy scheme for sub-half-micron  ULSI memories and beyond, IEEE Journal of Solid-State Circuits, vol.  31, no. 2, pp. 195–201, Feb. 1996.

4.        I. Kim, Y.  Zorian,  G.  Komoriya,  H.  Pham,  F.  P.  Higgins, and  J.  L. Lweandowski, ―Built in self repair for embedded high density SRAM,  in Proc. Int. Test Conf. (ITC), Oct. 1998, pp. 1112–1119.

5.        S. Runyon, ―Testing big chips becomes an internal affair, IEEE Spectrum, pp. 49–55, Apr. 2006.

6.        C.-T. Huang, J.-R. Huang, C.-F. Wu, C.-W. Wu, and T.-Y. Chang, ―A programmable BIST core for embedded DRAM, IEEE Design & Test  of Computers, vol. 16, no. 1, pp. 59–70, Jan.-Mar. 2009.  


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12.

Authors:

B.Rajani Kumari, K.V.Ramana Rao.

Paper Title:

Dynamic Power Suppression Technique in Booth Multipliers

Abstract:   The SPST has been applied on both the modified Booth decoder and the compression tree of multipliers to enlarge the power reduction. This paper provides the experience of applying an advanced version of our former spurious power suppression technique (SPST) on multipliers for high-speed and low-power purposes. To filter out the use-less switching power, there are two approaches, i.e., using registers and using AND gates, to assert the data signals of multipliers after the data transition. The simulation results show that the SPST implementation with AND gates owns an extremely high flexibility on adjusting the data asserting time which not only facilitates the robustness of SPST but also leads to a 40% speed improvement. Adopting a Xilinx Spartan 3 Xc3s200 board the proposed SPST-equipped multiplier dissipates only 0.0121 mW per MHz in H.264 texture coding applications, and obtains a 40% power reduction and the overall utilization of the resources reduced to 26%.

Keywords:
 low-power multiplier, spurious power suppression technique (SPST)


References:

1.        J. Choi, J. Jeon, and K. Choi, “Power minimization of functional units by partially guarded computation,” in Proc. IEEE Int. Symp. Low Power Electron. Des., 2000, pp. 131–136.
2.        O. Chen, R. Sheen, and S. Wang, “A low-power adder operating on effective dynamic data ranges,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 10, no. 4, pp. 435–453, Aug. 2002.

3.        O. Chen, S.Wang, and Y. W.Wu, “Minimization of switching activities of partial products for designing low-power multipliers,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 11, no. 3, pp. 418–433, Jun. 2003.

4.        L. Benini, G. D. Micheli, A. Macii, E. Macii, M. Poncino, and R. Scarsi, “Glitching power minimization by selective gate freezing,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 8, no. 3, pp. 287–297, June 2000.

5.        S. Henzler, G. Georgakos, J. Berthold, and D. Schmitt-Landsiedel, “Fast power-efficient circuit-block switch-off scheme,” Electron. Lett., vol. 40, no. 2, pp. 103–104, Jan. 2004.

6.        Z. Huang and M. D. Ercegovac, “On signal-gating schemes for lowpower adders,” in Proc. 35th Asilomar Conf. Signal, Syst., Comput., 2001, pp. 867–871.

7.        Z. Huang, “High-level optimization techniques for low-power multiplier design,” Ph.D. dissertation, Dept. Comput. Sci., Univ. California, Los Angeles, 2003.

8.        Z. Huang and M. D. Ercegovac, “High-performance low-power left-toright array multiplier design,” IEEE Trans. Comput., vol. 54, no. 3, pp. 272–283, Mar. 2005

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13.

Authors:

Prasad Munasa, P.Jayanagalakshmi

Paper Title:

Single Dictionary based Cache Compression and Decompression Algorithm

Abstract:   Computer systems and micro architecture researchers have proposed using hardware data compression units within the memory hierarchies of microprocessors in order to improve performance, energy efficiency, and functionality. All work on cache compression, has made unsubstantiated assumptions about the performance, power consumption, and area overheads of the proposed compression algorithms and hardware. It is not possible to determine whether compression at levels of the memory hierarchy closest to the processor is beneficial without understanding its costs. Furthermore, as we show in this paper, raw compression ratio is not always the most important metric. In this paper, we present a lossless compression algorithm that has been designed for fast on-line data compression, and cache compression in particular. The algorithm has a number of novel features tailored for this application, including combining pairs of compressed lines into one cache line and allowing parallel compression of multiple words while using a single dictionary and without degradation in compression ratio. Apart from that we reduced the proposed algorithm to a register transfer level hardware implementation on Xilinx xc3s500E fpga permitting performance, power consumption, and area estimation. The total power consumption of the device was estimated to be 0.081W.

Keywords:
 Cache compression, pair matching, parallel compression, hardware implementation


References:

1.        R. Alameldeen and D. A. Wood, “Adaptive cache compression for high-performance processors,” inProc. Int. Symp. Computer Architecture, June 2004.
2.        E. G. Hallnor and S. K. Reinhardt, “A compressed memory hierarchy using an indirect index cache,” inProc. Wkshp. Memory Performance Issues, 2004.

3.        A. R. Alameldeen and D. A. Wood, “Interactions between compression and prefetching in chip multiprocessors,” in Proc. Int. Symp. High-Performance Computer Architecture, Feb. 2007.

4.        Moffat, “Implementing the PPM data compression scheme,” in IEEE Trans. on Communications, Nov. 1990.

5.        M. Burrows and D. Wheeler, “A block sorting lossless data compression algorithm,” Digital Equipment Corporation, Tech. Rep. 124, 1994.

6.        Tremaine, et al., “IBM memory expansion technology,” IBM J.Research and Development, vol. 45, no. 2, pp. 271–285, Mar. 2001.

7.        J. L. N´u˜nez and S. Jones, “Gbit/s lossless data compression hardware,” IEEE Trans. VLSI Systems, vol. 11, no. 3, pp. 499–510, June 2003.

8.        A. Alameldeen and D. A. Wood, “Frequent pattern compression: A significance-based compression scheme for l2 caches,” Dept. of Computer Sciences, University of Wisconsin-Madison, Tech. Rep., Apr. 2004.


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14.

Authors:

S.Rajeswari, P.Deepthi, K.V.Ramana Rao

Paper Title:

Image Compression Technique using Two Dimensional Discrete Cosine Transform

Abstract:   This paper presents an architecture for the fast computation of the 8×8 two dimensional (2D) Inverse Discrete Cosine Transform. The proposed method is the permanent storage of the Basis Matrices of the 8×8 2D Discrete Cosine Transform (DCT). The sparseness property of the 2D DCT coefficient matrix, the computational time decreases as the number of nonzero coefficients decreases. The proposed structure computes all 64 pixel luminance values of an 8×8 block simultaneously. The design was implemented in Xilinx Xc3s500 board and the design used 23% LUT’s and 33%  of the total slices. The total power consumed by the device was 0.081W.

Keywords: 2D IDCT, image processing, sparse matrices

References:
1.        N. Ahmed, T. Natarajan, and K. R. Rao ,  “Discrete cosine transform,”  IEEE Transactions on Computers , vol. C-23, no. 1, pp. 90-93, January  1974.
2.        K. Choi, S. Lee, and E. S. Jang,  “Zero coefficient-aware IDCT algorithm  for fast video decoding,”  IEEE Transactions on Consumer Electronics,  vol. 56, no. 3, pp. 1822-1829, August 2010.

3.        C. - P. Fan, “Fast algorithm designs for low-complexity 4×4 discrete  cosine transform,”  IEICE Transactions on Fundamentals of Electronics,  Communications and Computer Sciences, vol. E88-A, no. 11, November 2005.

4.        J. Nikara, R. Rosendahl, K. Punkka, and J. Takala, “Implementation of  two-dimensional discrete cosine transform and its inverse,”  XII  European Signal Processing Conference (Eusipco 2004), pp. 1537- 1540, September 2004.

5.        T. Xanthopoulos and A. P. Chandrakasan, “A low-power IDCT  macrocell for MPEG-2 MP@ML exploiting data distribution properties  for minimal activity,” IEEE Journal of Solid-State Circuits , vol. 34, no.  5, pp. 693-703, May 1999.

6.        H. Jeong, J. Kim, and W. – K. Cho, “Low-power multiplierless DCT  architecture using image correlation,”  IEEE Transactions on Consumer  Electronics, vol. 50, no. 1, pp. 262-267, February 2004.

7.        J. Huang and J. Lee, “Efficient VLSI architecture for video  transcoding,”  IEEE Transactions on Consumer Electronics,  vol. 55, no.  3, pp. 1462-1470, August 2009.

8.        L. McMillan and L. Westover, “A  forward-mapping realization of the  inverse discrete cosine transform,”  Data Compression Conference 1992  (DCC ‘92), pp. 219-228, March 1992.

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15.

Authors:

Asha Latha P, Rambabu B

Paper Title:

A New Binary Tree approach of Huffman Code

Abstract:   Lossless compression of a sequence of symbols is important in Information theory as well as today’s IT field. Huffman coding is lossless and is most widely used. However, Huffman coding has some limitations depending on the stream of symbols appearing in a file. In fact, Huffman coding generates a code with very few bits for a symbol that has a very high probability of occurrence and a larger number of bits for a symbol with a low probability of occurrence [1]. In this paper, we present a novel technique that subdivides the original symbol sequence into two or more sub sequences. We then apply Huffman coding on each of the sub sequences. This proposed scheme gives approximately 10-20% better compression in comparison with that of straightforward usage of Huffman coding. The target FPGA deivce for implementing the design is Xilinx Xc3s500E.The devices utilises 9% and 17% of the total flip flops and LUT’s in the FPGA. The total power consumed the device 0.041W.

Keywords:
 Huffman decoding, Table lookup


References:

1.        D. Huffman, “A method for the construction of minimum redundancy code”, Proc. IRE, vol. 40, pp. 1098-1101, 1952.
2.        S. Choi, and M. Lee, “ High Speech Pattern Matching for a Fast Huffman Decoder”, IEEE Transactions on Consumer Electronics, vol. 41, pp. 97-103, February 1995. [3] S. Cho, T. Xanthopoulos, and A. Chandrakasan, “A lowpower Variable Length Decoder for MPEG-2 Based on Non uniform Fine-Grain Table Partitioning”, IEEE Transactions on VLSI systems, vol. 7, no. 2, pp. 249-257, June 1999.

3.        S. Lei, and M. Sun, “ An entropy coding system for digital HDTV applications”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 2, No. 1, pp. 147- 155, March 1991.

4.        ISO/iec 11172-3:1993 “Information Technology – coding of Moving Pictures and associated audio for Digital Storage Media at up to about 1.5 Mbit/s –part 3 : Audio ”

5.        A. Gersho, and A. Gray, “Vector Quantization and Signal Compression”, Kluwer Academic Publications, 1991.

6.        CCITT Recommendation T.81, “Digital compression and coding of continuous-tone still images”, 1992.


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16.

Authors:

Vasantha Sudheer N, Venu Gopal B

Paper Title:

FPGA Implementation of 64 Point FFT Processor

Abstract:  A power efficient Fast Fourier Transform (FFT) processor for use in the Direction of Arrival (DOA) estimation of a wideband waveform is presented. The target device for implementation is a Xilinx Spartan-3 Xc3s200 Field Programmable Gate Array (FPGA).  The FFT processor was developed using the Xilinx ISE in Verilog code. Although the parallel and pipelined architecture uses a large portion of the available FPGA resources, the architecture does yield a high throughput. The total power cosumed by the design is 0.081W

Keywords:
 Direction of Arrival (DOA), Fast fourier tansform (FFT),FPGA.


References:

1.        M.A. Ringer, G.J. Frazer and S.J. Anderson “Waveform Analysis of Transmitters of Opportunity for Passive Radar” DSTO Electronics and Surveillance Research Laboratory, 1999
2.        Pollard, Robert. “How Passive Radar Sensors can Support Air Traffic Control” BAE Systems, Advanced Technology Centre, 2006

3.        Hsio-Feng, Chiang,”Waveform Generation for Ultra-Wideband Radar System”, Naval Postgraduate School, Montrey, California, 1993

4.        R. O. Schmidt, “Multiple emitter location and signal parameter estimation” IEEE Transactions on   Antennas  and  Propagation,  vol. AP-34, No. 3, pp. 276-280,
March 1986.

5.        Hong, Wang, “Multiple-target direction finding”, Graduate School of University of Minnesota,   PhD Thesis, July 1985.

\6.        Proakis, J.G. “Digital Signal Processing: Principles, Algorithms, and  Applications”, Prentice Hall Inc., 1996.

7.        J.W. Cooley and J.W. Tukey, “An Algorithm for the Machine Computation of the Complex Fourier Series,” Math. Computation, Vol. 19, pp. 297-301, April 1965. 

8.        Chi-hau Chen, “Signal Processing Handbook”, CRC Press, 1988.

9.        Chidambaram, Ramesh. “A Scalable and High-Performance FFT Processor, Optimized for UWB-OFDM”. July 2005. Technische Universiteit Delft, Department of Electrical Engineering

10.     T. Pitkanen, T. Partanen, J. Takala.  “Low-Power Twiddle Factor Unit for FFT Computation” SAMOS 2007

11.     C. Chang, C. Wang “Efficient VLSI Architectures for Fast Computation of the Discrete Fourier   Transform  and  Its  Inverse.   IEEE Transactions on Signal Proc essing, vol. 48 No. 11 Nov  2000

12.     M. Hasan, T. Arslan, J.S. Thompson “A Novel Coefficient based Lower Power Pipelined Radix-4 FFT Processor for Wireless LAN Applications"

13.     M. Hasan, T. Arslan and J.S. Thompson, “A Novel Coefficient Ordering based Low Power Pipelined Radix-4  FFT Processor for Wireless LAN Applications”, IEEE Transactions on Consumer Electronics, Vol. 49, No. 1 February 2003.

14.     www.xilinx.com

15.     C. Wang and Y. Lin, “An Efficient FFT Processor for DAB Receiver Using Circuit-Sharing Pipeline Design”, IEEE Transactions on Broadcasting, Vol. 53, No. 3, September 2007

16.     J. J. Fuster and K. S. Gugel, “Pipelined 64-Point Fast Fourier Transform For Programmable Logic Devices”, Dept. of Electrical and Computer Engineering, University of Florida, Gainsville, FL

17.     Dillon Engineering, www.dilloneng.com.

18.     J. Palmer and B. Nelson, “A Parallel FFT Architecture for FPGAs”, Dept. of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, FPL 2004, LNCS 3203, pp. 948-953, 2004.

19.     Omar Sattari, “Fast Fourier Transforms on a Distributed Digital Signal Processor”, University of California-Davis, 2004.

20.     J. Takala and K. Punkka, “Scalable FFT Processors and Pipelined Butterfly Units”, Tampere University of Technology, Tampere, Finland, SAMOS 2004, LNCS 3133, pp. 373-382, 2004.

21.     W. Li and L.Wanhammar ,“Efficient Radix-4 and Radix-8 Butterfly.


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17.

Authors:

Kuldeep Niranjan, Sanjay Srivastava, Jaikaran Singh, Mukesh Tiwari

Paper Title:

Carbon Nanotube Field Effect Transistor: Fabrication of Thin Film of SiO2-Based Micro Cantilevers Dielectric Layer between the Channel and Substrate by Anisotropic Chemical Etching of (100) Single Crystal Si

Abstract:   The performance of the CNT-FET with variable channel length was modified by using the micro-cantilever/micro-bridge of SiO2. The etching technique was used to prepare the micro cantilever of SiO2 from the Si-substrate. We focus the idea about for the fabrication of the nano-device, in order to reduce the dielectric layer thickness. The channel length of the FET was altered along with the dimension of the substrate. One of the possibilities to reduce the thickness of the dielectric layer is either by etching processes or growing the oxide layer from the substrate through etching process. In this case Laser leaching process was used to reduce the thickness of the substrate. Various electrical properties like gate voltage, drain current, mobility, and device performance have been investigated. A better I-V characteristic was obtained with higher mobility in between the channel and used dielectric layer.  

Keywords:
 Anisotropy, CNT-FET, Lateral growth, Micro-cantilever


References:

1.        G. Moore 1975,“Progress in Digital Electronics,” IEDM Tech Digest, 1975, pp 11-13.
2.        “International Technology Roadmap for Semiconductors” 2009 Edition.

3.        S.J.Wind, J.Appenzeller, and Ph. Avouris,“Lateral scaling in carbon-nanotube field-effect transistors," Phys. Rev. Lett., 91(5), pp .1058301-1058304, (2003).

4.        Chun-Wei Chen and Ming-Hsien Leede,“pendency of work function on the geometries of single-walled carbonnanotubes,” 2004 Nanotechnology 15 480 doi:10.1088/0957-4484/15/5/013.

5.        J. Wildoer, L.Venema, A. Rinzler, R. Smalley, and C. Dekker, "Electronic structure of atomically resolved carbon nanotubes," Nature, vol. 391, pp. 59-62, January 1998.

6.        R. Bacon, "Growth, structure, and properties of graphite whiskers," Journal of Applied Physics, vol. 31, pp. 283-290, February 1960.
7.        M. Madou, “Fundamentals of micro fabrication,” CRC Press, Boca Raton, 1997, p. 145.
8.        O. Powell, H. B. Harrison, J. Micromech. Microen., “Fabrication of SiO2-based micro cantilevers by anisotropic chemical etching of (100) single crystal Si,” 11 (2001) 217. 

9.        G. Wilk, R.M. Wallace, J.M. Anthony, “High-κ gate dielectrics: Current status and materials properties considerations,” J. Appl. Phys. 89, 5243 (2001).

10.     J.D. Plummer, P.B. Griffin, “Material and process limits in silicon VLSI technology,” Proc. IEEE 89, 240 (2001).

11.     M. Copel, M. Gribelyuk, E. Gusev, “Reaction of SiO2 with hafnium oxide in low oxygen pressure,” Appl. Phys. Lett. 76, 436 (2000).

12.     M. Gutowski, J.E. Jaffe, C.L. Liu, M. Stoker, R.I. Hegde, R.S. Rai, P.J. Tobin, “Structure and stability of ultrathin zirconium oxide layers on Si(001),”  Appl. Phys. Lett. 80, 1897 (2002).


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18.

Authors:

Ohaneme C.O., Idigo V.E., Oguejiofor O.S. and Nnebe S.U.

Paper Title:

Analysis of Ambient Noise Level and its Impact on the Capacity and Coverage of CDMA System

Abstract:   Communication services have been the most intriguing things to network users in recent times. Hence there is the need to provide adequate communication facilities such as robust technologies in order to sustain the number of subscribers that are connected to wireless network daily. Therefore this paper provides the best platform to study the effect of ambient noise on wireless cellular network and how it affects the capacity and coverage of the cellular system. Special consideration is given to the Visafone CDMA network cellular environment of South-East Nigeria for the study from where the received signal levels from base stations are taken at various locations. The system is simulated using Matlab to give the vivid account of the effects of ambient noise within the cellular network and how it can be reduced. The simulation results show that at lower ambient noise, an advantage of a network capacity is achieved.

Keywords:
 Ambient noises, wireless network, electromagnetic waves, thermal noise, reuse efficiency and ionosphere.


References:

1.        Nathan Blaunstein and Christos Christodoulou, “Radio Propagation and Adaptive Antennas for Wireless Communication Links: Terrestial, Atmospheric and Ionosperic”, John Wiley & Sons Inc., 2007, pp 1-21.
2.        T.S Rappaport, “Wireless Communications Principles and Practice”, Second Edition, PHI Learning Private Limited, 2008, pp 138-157.

3.        John D. Klaus and Daniel A. Fleich, “Electromagnetics with Applications, 5th Edition”, McGraw-Hill International Editions, Electrical Engineering Series, 1999.

4.        Andreas F. Molisch, “Wireless Communications”, John Wiley and Sons Ltd, 2006, pp 35-37. 

5.        Lee W.C.Y., “Mobile Communications Engineering, Theory and Application”, 2nd Edition, Tata McGrawHill Ltd 2008 pp 520-527.

6.        Wikipedia, the free encyclopedia, “Electromagnetic Radiation”, August 2012, pp 1-21.

7.        QUALCOM Inc., “CDMA Capacity 2.1 Test Report”, August 1993

8.        Joseph Wolf, “Phase Noise Measurement with Spectrum Analyzer of the FSE Family”, Rhode & Schwarz, Dec., 1995.


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19.

Authors:

Zhenxing Luo

Paper Title:

Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors

Abstract:   In this paper, a robust distributed estimation method in wireless sensor networks (WSNs) with heterogeneous sensors is presented. Particularly, a single parameter is estimated based on decisions from heterogeneous sensors, which have different signal gains. The sensor gains follow uniform distributions. Using the distributions of sensor gains, we calculated the probability density function of the signal received by sensors. Then, the overall likelihood function for a given decision vector is derived and a maximum likelihood estimation (MLE) method is used to estimate the unknown parameter. To evaluate estimation performance, the Cramer-Rao lower bound (CRLB) is also derived. Simulation results showed that if the range of sensor gains was narrow, the RMS errors were close to CRLB. If the range of sensor gains was wide, the RMS errors deviated from CRLB.

Keywords:
 Distributed estimation, maximum likelihood estimation, wireless sensor networks.


References:

1.       D. Li, K. D. Wong, Y.H.Hu, and A. N. Sayeed, "Detection, Classification, and Tracking of Targets", IEEE Signal Processing Magazine, vol.19, no. 3, pp. 17-29, Mar. 2002.
2.       Z. X. Luo and T. C. Jannett, “Optimal Threshold for Locating Targets within a Surveillance Region Using a Binary Sensor Network”, in Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009.

3.       D. Li and Y. H. Hu, "Energy Based Collaborative Source Localization Using Acoustic Microsensor Array", EURASIP Journal on Applied Signal Processing, no. 4, pp.321-337, 2003.

4.       X. Sheng and Y. H. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol.53, no.1, pp. 44-53, Jan. 2005.

5.       Z. X. Luo and T. C. Jannett, “Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012, in press.

6.       Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

7.       Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

8.       R. X. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data”, IEEE Transactions on Signal Processing, vol. 54, pp. 4519-4528, Dec. 2006.

9.       O. Ozdemir, R. X. Niu, and P. K. Varshney, "Channel Aware Target Localization with Quantized Data in Wireless Sensor Networks," IEEE Trans. Signal Process., vol. 57, pp. 1190-1202, 2009.

10.     H. Ochiai, P. Mitran, H. V. Poor, and V. Tarokh, "Collaborative Beamforming for DistributedWireless Ad Hoc Sensor Networks," IEEE Transactions on Signal Processing, vol.53, no.11, pp. 4110- 4124, Nov. 2005

11.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp.1131-43, March 2006.

12.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part II: Unknown Probability Density Function,” IEEE Transactions on Signal Process., vol. 54, no. 7, pp. 2784-96, July 2006.

13.     C. Hao and P. K. Varshney, "Nonparametric One-Bit Quantizers for Distributed Estimation," IEEE Transactions on Signal Processing, vol. 58, pp. 3777-3787, 2010.

14.     C. Hao and P. K. Varshney, "Performance Limit for Distributed Estimation Systems With Identical One-Bit Quantizers," IEEE Transactions on Signal Processing, vol. 58, pp. 466-471, 2010.

15.     G. Liu, B. Xu, M. Zeng, and H. Chen, "Distributed Estimation over Binary Symmetric Channels in Wireless Sensor Networks," IET Wireless Sensor Systems, vol. 1, pp. 105-109, 2011.

16.     Z. X. Luo and T. C. Jannett, “Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proc. of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

17.     Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks”, Journal of Engineering and Technology, 2012, no 2, pp. 69-74.

18.     Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks deployed in hostile environments”, to appear in International Journal of Engineering and Advanced Technology, vol. 1, no. 6, Aug. 2012.

19.     Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of Byzantine attacks”, International Journal of Innovative Technology and exploring Engineering, vol. 1, no. 3, Aug. 2012.

20.     Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks”, to appear in International Journal of Soft Computing and Engineering, vol. 2, no. 4, Sept. 2012.

21.     Z. X. Luo, “A new direct search method for distributed estimation in wireless sensor networks”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

22.     Z. X. Luo, “Distributed estimation in wireless sensor networks based on decisions transmitted over Rayleigh fading channels”, to appear in International Journal of Electrical engineering and Communication Engineering for Applied Research.

23.     Z. X. Luo, “Overview of Applications of Wireless Sensor Networks”, to appear in International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.


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