International Journal of Soft Computing and Engineering(TM)
Exploring Innovation| ISSN:2231-2307(Online)| Reg. No.:61903/BPL/CE/2011| Published by BEIESP| Impact Factor: 3.76
Home
Articles
Conferences
Editors
Scopes
Author Guidelines
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-2 Issue-4: Published on September 05, 2012
28
Volume-2 Issue-4: Published on September 05, 2012

Download Abstract Book

S. No

Volume-2 Issue-4, September 2012, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Hamdy Mohamed Soliman, S. M.EL. Hakim

Paper Title:

Identification of Stator Winding Faults and Remedial Operation of Permanent Magnet Synchronous Motors with Suppress Noise and Ripple

Abstract:    The reliability of the drive system is very important in critical systems. The faults in these systems are unwanted and the drive system must be operated under the fault conditions. If fault occurs this may be lead to loss of the human life and capital so the detection of this fault, separation the faulty part and method invention for remedial operation is very important. In this paper the performance of a permanent magnet synchronous motor drive under a stator winding fault is studied and a negative sequence is used to detect the different types of the faults in that winding. This paper is suggested two models for solving these faults. The control in these models depends upon the controlling in each phase separately. The first model doesn’t contain any special tools to improve the torque ripple and THD. The second model contains 2PI current controllers to improvement the performance at fault and remedial operation. One is for the torque and the other is for the flux. The first PI controller is feeding from the torque error between the reference and estimated torques to get new q-axis current component representing modifier current arises from uncertain things inside the machine and drive system. This current will add to reference q-axis current to get robust new q-axis current to satisfy the drive requirement and solve the torque problem (ripple torque). With robust current, the total harmonic distortion is a decrease but doesn’t reach the best value so the other PI controller is used to adjust the THD. In this PI controller, the d-axis flux is compared to rotor permanent magnet flux to solve this problem arises from non-sinusoidal of the magnetic flux. The output of the PI controller is introduced to the reference d-axis current. The new d-axis current will reach the best value of THD. The simulation of the second controller is compared to the simulation of first controller to show if the adding the 2 PI current controllers is profit or not. Here the matlab simulink is used to simulate the drive system.

Keywords:
  fault detection, PI controller, remedial Operation, stator winding fault, Torque ripple


References:

1.        B. C. Mecrow, A. G. Jack, J. A. Haylock, J. Coles, “Fault-tolerant permanent magnet machine drives”, IEE Proceedings on Electric Power Applications, Vol. 143(6), Nov. 1996, pp. 437-442.
2.        J. A. Haylock, B. C. Mecrow, A. G. Jack, and D. J. Atkinson, “Operation of a fault tolerant PM drive for an aerospace fuel pump application”, IEE Proceedings on Electric Power Applications, Vol. 145(5), September 1998, pp. 441-448.

3.        B. C. Mecrow, A. G. Jack, D. J. Atkinson and S. R. Green, “Design and testing of a four-phase fault-tolerant permanent-magnet machine for an engine fuel pump”, IEEE Transactions on Energy Conversion, Vol. 19(4), Dec. 2004, pp. 671-678.

4.        N. Ertugrul, W. Soong, G. Dostal, and D. Saxon, “Fault tolerant motor drive system with redundancy for critical application”, IEEE Power Electronics Specialists Conference, Cairns, June 2002.

5.        Dinyu Qin, Xiaogang Luo, and T. A. Lipo, " Reluctance Motor Control For Fault - Tolerant Capability", EEE IEMDC '97, Milwaukee, WI, USA, 1997.

6.        Mecrow B.C., Jack A.G., Haylock J.A. and Coles J., Fault Tolerant Permanent Magnet Machine Drives, IEE Proceedings Electric Power Application; Vol 143, No. 6, pp 437-441

7.        L. Parsa and H. A. Toliyat, “Fault- tolerant interior- permanent- magnet machines for hybrid electric vehicle applications,” IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 1546–1552, Jul. 2007.

8.        N. Bianchi, S. Bolognani, and M. D. Pre, “Impact of stator winding of a five-phase permanent-magnetmotor on postfault operations,” IEEE Trans. Ind. Electron., vol. 55, no. 5, pp. 1978–1987, May 2008.

9.        Jahns, T. M.: Improved Reliability in Solid-State AC Drives by Means of Multiple Independent Phase-Drive Units. In: IEEE Trans. on Ind. Appl., Vol. IA-16, No. 3, May/June 1980, pp. 321 – 331

10.     Mecrow, B. C. et al.: Fault-tolerant Permanent Magnet Machine Drives. In: IEE Proc. - Electr. Power Appl. Vol 143, No. 6, 1996, pp. 437 – 441

11.     Spée, R.; Wallace, A. K.: Remedial Strategies for Brushless DC Drive Failures. In: IEEE Trans. on Ind. Appl., Vol. 26, No. 2, 1990

12.     Elch-Heb, T.; Fan, Y.; Hautier, J. P.: Reliability Improvement of Field-oriented Controlled Three-phase AC Drives by Means of Two-phase Remedial Operation. In: Proc. Int. Conf. on Electric Machines (ICEM), Paris, 1994, vol. 2, pp. 194 - 198


1-10

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

2.

Authors:

Daulat Singh, Shiv Kumar, Rakesh Shrivastava, Dinesh Varshney

Paper Title:

Edusat Satellite Based Education: Study of Scope for Enhancement of Audio-Video Quality- A Case Study of Madhya Pradesh Bhoj (Open) University

Abstract:    Edusat Satellite is the satellite dedicated for fulfillment the needs of education and its effective usage requires a network comprising Hub, Teaching End (Studio) and Receiving Terminals. The present study is related with Edusat-network of Madhya Pradesh Bhoj (Open) University, which includes Hub dedicated for higher and distance education, Studio and 40 Satellite Interactive Terminals with Edusat-network provided by Indian Space Research Organization (ISRO). These SITs have been installed in urban, rural and tribal areas. Detail study of equipments of hub, studio and receiving terminals was taken up. Target group was chosen and most easy and suitable way of virtual class was identified. Various factors affecting quality of audio/video were drawn from the study and content/presentation of video lectures were analysed. Recorded video lectures and live lectures were telecasted for 177 working days and observations related to BER (Bit Error Rate) correlating problems encountered during operation of terminals, audio-visual quality of lectures, skill and response of operators were made. We aimed at identifying the scope of enhancement in audio-video quality of the material telecasted through Edusat. The study is based on the network which has been created mainly for the students of rural and tribal areas. On the basis of observations and BER data collected from hub, the present study makes findings and suggests those possibilities which can enhance the audio-video quality of the telecast without any major change in satellite’s band width and hub, and with minimum increase in cost and expenditure.

Keywords:
   Edusat- Satellite dedicated for education, SIT- Satellite Interactive terminals, BER- Bit Error Rate, RCST- Return Channel Satellite Terminal, LNB- Low Noise Block down converter, GCU- (Gateway Cannel Unit), Gramsat- Satellite based project for villages, TDCC- Training and Development Communication Channel.


References:

1.       idsp.nic.in/idsp/UserManaula/ModuleB.pdf
2.       S. K. Pandey, (1999) "Handbook of Satellite communications" Authors Press, Delhi, pp135

3.       D. C. Agrawal, (2004) "Digital Satellite Communication" Khanna Publication, Delhi, pp

4.       V. Palsule, (2006). EDUSAT Network Configurations, Papers presented at EDUSAT Users’ Meet: Southern Region January 19 & 20, 2006, jointly organized by DECU-ISRO, Ahmedabad & SAPNET, Hyderabad 

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


11-20

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

Manoj Bora, Tridiv Jyoti Neog, Dusmanta Kumar Sut

Paper Title:

Some New Operations of Intuitionistic Fuzzy Soft Sets

Abstract:    In this paper, we have defined disjunctive sum and difference of two intuitionistic fuzzy soft sets and study their basic properties. The notions of   - cut soft set and   - cut strong soft set of an intuitionistic fuzzy soft set have been put forward in our work. Some related properties have been established with proof, examples and counter examples.

Keywords:
   Intuitionistic Fuzzy Set, Intuitionistic Fuzzy Soft Set, Disjunctive Sum, Difference, - cut soft set, -  cut strong soft set..


References:

1.       B. Ahmad and A. Kharal, “On Fuzzy Soft Sets”, Advances in Fuzzy Systems, Volume 2009, pp. 1-6, 2009.
2.       D. A. Molodtsov, “Soft Set Theory - First Result”, Computers and Mathematics with Applications, Vol. 37, pp.19-31, 1999.

3.       K. Atanassov, “Intuitionistic fuzzy sets”, Fuzzy Sets and Systems 20

4.       (1986 ), 87-96

5.       P. K. Maji, R. Biswas and A. R. Roy, “Fuzzy Soft Sets”, Journal of Fuzzy Mathematics, Vol 9, No. 3,pp. 589-602, 2001.

6.       P. K. Maji and A. R. Roy, “Soft Set Theory”, Computers and Mathematics with Applications 45 (2003) 555 – 562.

7.       P. K. Maji, R. Biswas, A. R. Roy, “Intuitionistic fuzzy soft sets”, The journal of fuzzy mathematics 9(3)( 2001 ), 677-692

8.       T. J. Neog, D. K. Sut, “Some New Operations of Fuzzy Soft Sets”, Accepted for publication in Journal of Mathematical and Computational Sciences.


21-26

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

4.

Authors:

B. Srinivas, K.Venkata Rao, P. Suresh Varma

Paper Title:

Movie Piracy Detection Based on Audio Features Using Mel-Frequency Cepstral Coefficients and Vector Quantization

Abstract:    Along with the increase in the advancement of technology in movie industry over internet, there is also an increase in the movie piracy via internet which affects factors like economy and repudiation of movie industry. Internet movie piracy is the most common means for pirates as well as downloader’s to break copyright laws by anonymous illegal uploads/downloads. In this paper we proposed an automated internet movie piracy detection mechanism based on audio fingerprint, which implements two famous algorithms, one is Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and the other is Vector Quantization (VQ) for classification. Our trained system initially looks for the sites which offer illegitimate copies of movies and if there is any suspicion based on a particular movie which is similar to the database of copyrighted movies that are registered with our trained system, it simply compares the fingerprints that are generated by implementing the above specified algorithms for both the trained and suspected movies. We collected various audio samples of different movies and we also extracted audio samples of pirated movies via internet with and without noises and trained and tested with our system. Finally, our system rendered efficient results with few error rates. We collected 52 audio samples without noise and 48 samples with noise and the resulted success classification is 96% and 92% respectively.

Keywords:
   Classification, Code Book, Movie Piracy, MFCC, VQ.


References:

1.        Satyanand Singh and Dr. E G Rajan. Article: MFCC VQ based Speaker Recognition and Its Accuracy Affecting Factors. International Journal of Computer Applications 21(6):1–6, May 2011.
2.        Lindasalwa Muda, Mumtaj began and I.Elamvazuthi, Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient(MFCC) and Dynamic Time Wrapping(DTW) Techniques. Journal of Computing, Volume 2, March 2010.

3.        Satyanand Singh, Dr.E.G.Rajan, Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC. International Journal of Computer Applications(0975-8887) March 2011

4.        Michael A. Lewis, Ravi P. Ramachandran, Cochannel speaker count labeling based on the use of cepstral and pitch prediction derived features, Pattern Recognition (the journal of Pattern Recognition society) 34 499-507, 2001

5.        Shi-Huang Chen and Yu-Ren Luo, Speaker verification Using MFCC and Support Vector Machine, International MultiConference of Engineers and Computer Scientists 2009 Vol 1, IMECS 2009, March 18-20, 2009, Hong Kong

6.        Laura E. Boucheron, Phillip L. De Leon and Steven Sandoval, Low Bit-Rate Speech Coding through Quantization of Mel-Frequency Cepstral Coefficients. IEEE, 2011

7.        Kristleifur, Herwig, Fridrik, Jonsson, Laurent,   Videntifier: Identifying pirated movies in real-time.

8.        Marc Fetscherin, Movie piracy on peer-to-peer networks- the case of KaZaA, Elsevier, Telematics and Informatics 22 (2005) 57-70

9.        T. F. Quatieri, Discrete Time Speech Signal Processing. Prentice Hall, 2002.

10.     Ashish Jain, Hohn Harris, Speaker identification using MFCC and HMM based techniques, university Of Florida, April 25,2004.

11.     Z. Jun, S. Kwong, W. Gang, Q. Hong.: Using MelFrequency Cepstral Coefficients in Missing Data Technique. EURASIP Journal on Applied Signal Processing, Vol.2004, No. 3 (2004) 340346.

12.     J. C. Brown, A. HodginsDavis, PJO. Miller: Classification of vocalizations of killer whales using dynamic time warping. The Journal of the Acoustical Society of America, Vol. 119, (2006) EL34EL40.

13.     A.M. Youssef, T.K. AbdelGalil, E.F. ElSaadany, M.M.A. Salama.: Disturbance classification utilizing dynamic time warping classifier. IEEE Transactions on Power Delivery, Vol. 19, No. 1, (2004) 272278.

14.     L. Lu, S. Li, and H.J. Zhang, “Content-based audio segmentation using support vector machines,” Proceeding of ICME 2001, pp. 956 – 959, 2001, Tokyo, Japan.

15.     Y. Linde, A. Buzo, and R. M. Gray.: „An algorithm for vector quantizer design,” IEEE Trans. Commun., vol. COM-28, no. 1, pp. 84-95, 1980.


27-31

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

5.

Authors:

Kapil Kumar, Prateek Sharma, Ajay Kumar Singh

Paper Title:

Configuring the System to Share Internet from Single User to Multi-user with Single Internet Dongle

Abstract:    Now a days everywhere there is the need of internet. Everyone wants to access internet on mobile phones, system and laptops. Internet can be accessed on a system either wired or wireless. Internet dongle is used to access internet on systems wirelessly. The aim of this paper is to make single user internet dongle to multi-user by sharing the internet connection. This internet connection is shared using Local Area Network connection that is created in between laptop and other systems. Hardware as well as software configuration is required for this purpose.

Keywords:
   Bandwidth, Internet Sharing, Internet dongle, Local Area Network.


References:

1.       Marco Chirico, Anna Marina Scapolla and Andrea Bagnasc, “A New and Open Model to Share Laboratories on the Internet,” IEEE Trans. on Instrumentation and
Measurement, vol. 54, no. 3, pp. 1111-1117, June 2005.

2.       Miguel A. Ruiz-Sanchez, Ernst W. Biersack and Walid Dabbous, “Survey and Taxonomy of IP Address Lookup Algorithms,” IEEE Trans. on Network, pp. 8-23, March/April 2001.

3.       Behrouz A. Forouzan, “Data Communications and Networking,” McGraw Hill, 2006.

4.       Mellquist “Automatic Internet Protocol (IP) Address Allocation and Assignment,” United States Patent, Patent no. 6,115,545.

5.       Ralph Droms, “Automated Configuration of TCP/IP with DHCP,” IEEE Internet Computing, pp. 45-53, July /Aug. 1999.

6.       Steven Cheung, “Denial of Service against the Domain Name System,” IEEE Trans. on Security & Privacy, pp. 40-45, 2006.

7.       Hyokyung Bahn, “A Shared Cache Solution for the Home Internet Gateway,” IEEE Trans. on Consumer Electronics, vol. 50, no. 1, pp. 168-172, Feb. 2004.

8.       Kensuke Fukuda, Hideki Kakayasu and Misako Kakayasu, “Spatial and Temporal Behavior of Congestion in Internet Traffic,” Fractals, World  Scientific Publishing Company, vol. 7, no. 1, pp. 23-31, 1999.

9.       J. E. McGeehan, Saurabh Kumar, Deniz Gurkan, S. M. R. Motaghian Nezam, Alan Eli Willner, K. R. Parameswaran, M. M. Fejer, J. Bannister, and Joseph D. Touch,  “All-Optical Decrementing of a Packet’s Time-to-Live (TTL) Field and Subsequent Dropping of a Zero-TTL Packet,” IEEE Journal of Lightwave Technology, vol. 21, no. 11, pp. 2746-2752, Nov. 2003.

10.     S P Maj, W Makasiranondh and D Veal, “An Evaluation of Firewall Configuration Methods,” IJCSNS International Journal of Computer Science and Network Security, vol. 10, no. 8, pp. 1-7 Aug. 2010.

11.     Jane Grimson, Gaye Stephens and Benjamin Jung, “Sharing Health-Care Records over the Internet,” IEEE Internet Computing, pp. 49-58, May/June 2001.

12.     Yi Sun, “Bandwidth-Efficient Wireless OFDM,” IEEE Journal on Selected Areas in Communications, vol. 19, no. 11, pp. 2267-2278,  Nov. 2001.

32-35

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

6.

Authors:

N. Ebrahimi, A.Gharaveisi

Paper Title:

Optimal Fuzzy Supervisor Controller for an Active Suspension System

Abstract:    In this paper, an optimal fuzzy supervisor controller is developed to improve the performance of active suspension system. Fuzzy logic is used to tune each parameter of PID Controller and input membership function of fuzzy controller optimized by Discrete Action Reinforcement Learning Automata (DARLA) technique. Through simulation in MATLAB, it is shown that the performance of active suspension system has improved significantly compare to conventional PID controller which is tuned by Zigler- Nichols method.

Keywords:
   Active suspension system, Discrete Action Reinforcement Learning Automata (DARLA), Fuzzy supervisor, PID controller


References:

1.       Bijan Ranjbar-Sahraie, Mohammad Soltani and Mehdi Roopaie, “Control of Active Suspension System:An Interval Type-2 Fuzzy Approach” World Applied Sciences Journal 12 (12): pp.2218-2228, 2011, ISSN 1818-4952
2.       A.G. Thompson, "Design of active suspensions", Proc. Instn. Mech. Engrs., 185:pp.553–563, 1970–1971.

3.       R. Pitcher, H. Hillel, and C.H. Curtis," Hydraulic suspensions with particular reference to public service vehicles", In Public Service Vehicles Conference. Mechanical Engineering Publications,1977

4.       D. Hrovat and M. Hubbard, "Optimal vehicle suspensions minimizing rms rattle space, sprung mass acceleration and jerk. Trans", of the ASME, pp.228–236, 1981.

5.       Lin, J.-S.  and I.  Kanellakopoulos (1997). "Nonlinear Design of Active Suspensions," IEEE Control   Systems, Vol.17, No. 3, pp. 45-59.

6.       Ming-Chang Chen, Wei-Yen Wang, Shun-Feng Su, and Yi-Hsing Chien, “Robust T–S fuzzy-neural control of uncertain active suspension systems” International Journal of Fuzzy Systems, Vol. 12, No. 4, December 2010

7.       MohamedM.ElMadany “Control and Evaluation of Slow-Active Suspensions with Preview for a Full Car” Hindawi Publishing Corporation Mathematical Problems in Engineering, 2012.

8.       Milad Geravand and  Nastaran Aghakhani, “Fuzzy sliding mode control for applying to active vehicle suspentions” WSEAS Transaction on Systems and Control

9.       A.H. Shirdel ,E. Gatavi  and Z. Hashemiyan, “Comparison of H-∞ and optimized-LQR controller in active suspension system” Second International Conference on Computational Intelligence, Modeling and Simulation

10.     Mariagrazia Dotoli, Bruno Maione and  Biagio Turchiano, “Fuzzy-Supervised PID Control: Experimental Results”

11.     R. K. Pekgökgöz, M. A. Gürel, M. Bilgehan and M. Kısa, “Active suspension of cars using fuzzy logic controller optimized by genetic algorithm” International Journal of Engineering and Applied Sciences (IJEAS) Vol.2, Issue 4,pp.27-37, 2010

12.     Bingül, Z., Matlab ve Simulik’le Modelleme/Kontrol I-II, birinci bas., Birsen Yayınevi, İstanbul, 2005.

13.     Zulfatman1 and M. F. Rahmat, “Application of self-tuning fuzzy PID controller on industrial hydraulic actuator using system identification approach” International Journal on smart sensing and intelligent systems, Vol.2, No.2, June 2009

14.     Li-Xin Wang. “A Course in Fuzzy Systems and Control”. Prentice-Hall International, Inc, 1997.

15.     M. Kashki, A. Gharaveisi, and F. Kharaman, “Application of CDCARLA Technique in Designing Takagi-Sugeno Fuzzy Logic Power System Stabilizer (PSS)” First International Power and Energy Conference PECon , Putrajaya, Malaysia,  pp.28-29, 2006

16.     M. N. Howell, G. P. Frost, T. J.Gordon and Q. H. Wu, "Continuous action reinforcement learning applied to vehicle suspension control,"Mechatronics, pp. 263-276, 1997.


36-39

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

7.

Authors:

Hamdy Mohamed Soliman, S.M.EL. Hakim

Paper Title:

Improved Hysteresis Current Controller to Drive Permanent Magnet Synchronous Motors through the Field Oriented Control

Abstract:    Hysteresis current controller is used in many industrial applications because it has many advantages as fast, high dynamic performance and doesn’t require any information about load parameters. The draw back of this current controller is varying switching frequency. This paper presents adaptive hysteresis current controller to control the inverter. It is used to reduce the ripple, total harmonic distortion and improvement the switching frequency through design of PI current controller. The performance of the drive system due to improvement in the hysteresis current controller is simulated through the matlab simulink. The modified hysteresis current controller is compared to conventional hysteresis controller under steady state and transient conditions with fixed load, sudden applied and sudden removal load and reversing load to show the effectiveness of this modification.

Keywords:
   Hysteresis Current Controller, PI Controller, PMSM, Torque Ripple.


References:

1.        M. P. Kazmierkowski and H. Tunia (1994) "Automatic control of converter-fed drives", Amsterdam, The Netherlands: Elsevier.
2.        Calin RUSU, Iulian BIROU 8th international conference on development and application systems "DSP based control of pm synchronous motor used in robot motion applications" Suceava, Romania, 25–27May, 2006, ppt. 45-50

3.        C. Mademlis and N. Margaris, "Loss minimization in vector-controlled interior permanent-magnet synchronous motor drives", Industrial Electronics, IEEE Transactions on, vol. 49, pp. 1344-1347, 2002.

4.        X. Jian-Xin, S. K. Panda, P. Ya-Jun, L. Tong Heng, and B. H. Lam, "A modular control scheme for PMSM speed control with pulsating torque minimization", Industrial Electronics, IEEE Transactions on, vol. 51, pp. 526-536, 2004.

5.        P. L. Jansen and R. D. Lorentz, "Transducerless position and velocity estimation in induction and salient AC machines", IEEE Trans. Ind. Applicat., vol. 31, pp. 240–247, Mar./Apr. 1995.

6.        P. L. Jansen, R. D. Lorenz, and D. W. Novotny, "Observer-based direct field orientation: Analysis and comparison of alternative methods",” IEEE Trans. Ind. Applicat., vol. 30, pp. 945–953, July/Aug. 1994.

7.        M. P. Kazmierkowski, and L. Malesani, "Current control techniques for three-phase voltage-source PWM converters: a survey", IEEE Trans. Ind. Electron., vol. 45, no. 5, October, 1998, pp. 691-703.

8.        P. Rathika and Dr. D. Devaraj,"Fuzzy logic – based approach for adaptive hysteresis band and dc voltage control in shunt active filter", International Journal of Comuter and Electrical Engineering, Vol. 2, No. 3, June2010, pp. 1793-8163.

9.        Zare, Firuz and Zabihi, Sasan and Ledwich, Gerard F., "An adaptive hysteresis current control for a multilevel inverter used in an active power filter". In Proceedings of European Conference on Power Electronics and applications, Aalborg, Denmark, Sept. 2007, pp. 1-8.

10.     B. k. Bose, "An adaptive hysteresis-band current control technique of a voltage - fed PWM inverter for machine drive system", IEEE Trans., on Ind. Appl., Vol.IA-37, pp.402-408, 1990

11.     Tae-Won Chun; Meong-Kyu Choi;"Development of adaptive hysteresis band current control strategy of PWM inverter with constant switching frequency" Applied power electronics conf. and exposition, APEC. vol.1,pp.194-199, 1996

12.     M.P.Kazmierkowski, H.Tunia "Automatic Control of Converter-Fed Drives", Warszawa 1994


40-46

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

8.

Authors:

Geeta Chandna, Mohit Bansal, Saloni, Saru Sehgal

Paper Title:

Multimedia Streaming Technology in 4G Mobile Communication Systems

Abstract:    Popularity and evolution of mobile devices like laptops, mobile phones and Personal Digital Assistants (P.D.A.), and the evolution of fast mobile networks in the last decade, have made it possible to increase the complexity of mobile applications and services provided to end-users. It is also a spectacular growth in multimedia communication especially via the World Wide Web. This paper explore some of the current technology of mobile devices, mobile networks and multimedia systems, and is based on the exploration outline some issues for design and development of mobile multimedia systems in 4G Mobile Communication System. Fourth-generation mobile communication systems will combine standardized streaming with a range of unique services to provide high-quality content (Multimedia) that meets the specific needs of the rapidly growing mobile market. By offering higher data-transmission rates up to 20 Mbps more than 3G for wide-area coverage and local-area coverage, 4G systems will be able to provide high quality streamed content to the rapidly growing mobile market.

Keywords:
  4G, Streaming, Code Division Multiple Access C.D.M.A. , Global System For Mobile (G.S.M.)


References:

1.       G.J. Conklin et al., _Video Coding for Streaming Media Delivery on the Internet,_ IEEE Trans. Circuits and Systems for Video Technology, Mar. 2001, pp. 2 69-281.
2.       2. D. Wu et al., _Streaming Video over the Internet:

3.       Approaches and Directions,_ IEEE Trans. Circuits and Systems for Video Technology, Mar. 2001, pp. 282-300.

4.       3. J. Vass, S. Zhuan, and X. Zhuang, _Scalable, Error- Resilient, and High- Performance Video.

5.       Communications in Mobile Wireless Environments,_ IEEE Trans. Circuits and Systems for Video Technology, July 2001, pp. 833-847.

6.       4. L. Boman, _Eriksson’s Service Network: A _Melting Pot_ for Creating and Delivering Mobile Internet

7.       Service,_ Ericsson Rev., vol. 78, no. 2, 2001, pp. 62-67.


47-51

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

9.

Authors:

Ramander Singh, Vinod Kumar, Ajay Kumar Singh, Santosh Kumar Upadhyay

Paper Title:

Identifying Faulty Node and Alternative Path in a Network

Abstract:    This Paper describes a good algorithm to find the Faulty node in any given complex Network and provides the alternative path and also tells us the number of faulty node. As the Technological advances increasing number of node day by day in a Network. If a node fails, the system continues to operate with degraded performance until the faulty node is repaired. If the repair operation will take an unacceptable amount of time, it is useful to replace the faulty node with a spare node. However, the appropriate procedures must be followed and precautions must be taken so you do not interrupt I/O operations and compromise the integrity of your data that we have presented in our paper.

Keywords:
 Alternative path, Faulty node, Buffer, Computer


References:

1.       Boncheol Gu, Jinman Jung, Kyongdong Kim, Junyoung Heo, Namhoon Park,Gwangil Jeon, and Yookun Cho., “SWICOM: An SDR-Based Wireless Communication Gateway for Vehicles,” vol. 59,  no. 4,  pp. 1593-1605, May 2010.
2.       Yan Li, Jianping Wang, Chunming Qiao, Ashwin Gumaste, Yun Xu, and Yinlong Xu, “Integrated Fiber-Wireless (FiWi) Access Networks Supporting Inter-ONU Communications,” vol. 28, no. 5, pp. 714-724 ,  March 1, 2010.

3.       Howard Bowman, Lynne Blair, Gordon S. Blair and Amanda G. Chetwynd, “A formal description technique supporting expression of quality of service and media synchronization, ” Lecture Notes in Computer Science, vol.  882, Multimedia Transport and Teleservices, pp. 145-167, 1994.

4.       L. Cappietti and P. Aminti, “Rehabilitation of Highly Protected Beaches by Using Environment-Friendly Structures,” NATO Science Series: IV: Earth and Environmental Sciences, vol. 53, Environmentally Friendly Coastal Protection, Chapter 2, pp.  163-175, 2006.

5.       Julia Poncela Casasnovas, “The Prisoner’s Dilemma on Static Complex Networks,” Springer Theses, Evolutionary Games in Complex Topologies, Part 1, pp. 51-76, 2012.

6.       A. A. Kozlov, V. A. Nosov and A. E. Pankratiev, “Matrices and graphs of essential dependence of proper families of functions,” Journal of Mathematical Sciences, vol. 163, no. 5, pp. 534-542., 2009.

7.       Xudong Wang, Ping Yi., “Security Framework for Wireless Communications in Smart Distribution Grid,” vol. 2, no. 4, pp. 809-818, Dec 2011.

8.       Yv Mei Liao and Jie Zhong, “FLOYD Algorithm Based on the Shortest Path in GIS,” Communications in Computer and Information Science, Information and Business Intelligence, Part 4, pp. 574-579.

9.       Barry J. Welch, “Aluminum production paths in the new millennium,” Journal of the Minerals (JOM), Metals and Materials Society, vol. 51, no 5, pp. 24-28, 1999.

10.     Travis Russell, “Signaling System” Publisher: McGraw-Hill, Fifth Edition, ISBN: 9780071468794, 2006.


52-55

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

10.

Authors:

Zhenxing Luo

Paper Title:

A Coding and Decoding Scheme for Energy- based Target Localization in Wireless Sensor Networks

Abstract:    A coding and decoding scheme for energy-based target localization in wireless sensor networks (WSNs) is presented in this paper. This scheme can improve target localziation performance when WSNs are deployed in noisy environments. Simulation results showed that the energy-based target localization method using this coding and decoding scheme could produce better localization performance than the energy-based target localziation method which did not use this scheme. Moreover, the root mean square (RMS) errors given by the proposed method were close to the Cramer-Rao lower bound (CRLB).

Keywords:
   Cramer-Rao lower bound, maximum likelihood estimation, quantization, 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.       Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks”, To appear in Journal of Engineering and Technology.

4.       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.

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, “Modelling 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.

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.       M. P. Michaelides and C. G. Panayiotou, "Fault tolerant maximum likelihood event localization in sensor networks using binary data," IEEE Trans. Signal Process, vol. 16, no. 5, pp. 406-409, May 2009.

10.     M. P. Michaelides and C. G. Panayiotou, "SNAP: Fault tolerant event location estimation in sensor networks using binary data," IEEE Trans. Comput., vol. 58, no. 9, pp. 1185-1197, Sept. 2009.

11.     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.

12.     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.

13.     Y. Chien, P. N. Chen, Y. T. Wang, Y. S. Han, P. K.  Varshney, “Performance Analysis and Code Design for Minimum Hamming Distance Fusion in Wireless Sensor Networks,” IEEE Transactions on Information Theory, vol. 53, no. 5, pp. 1716-1734, 2007.

14.     V. W. Cheng, and W. Tsang-Yi, “Performance Analysis of Distributed Decision Fusion Using a Censoring Scheme in Wireless Sensor Networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 6, pp. 2845-2851, 2010.


56-59

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

11.

Authors:

El Sayed M. Saad, Medhat H. Awadalla, Hosam Eldin I. Ali, Rasha F. A. Mostafa

Paper Title:

Voice-Based Humanoid Robot Interaction

Abstract:    Recently, the interest in service robots endowed with communicative capabilities has been increased. These robots should operate in cluttered and uncluttered environments and interact with humans using natural language to perform a variety of service-oriented tasks. Recognizing and fetching of a user-specified object can be considered as one of the major tasks for a humanoid robot. To get the robot capable of identifying the geometric shapes and colors of the objects, a vision system is proposed. Furthermore, the paper proposes a natural language understanding system, where the robot will be able to effectively communicate with human through a dialogue developed in Arabic language. The developed dialogue and a dynamic object model are used for learning the semantic categories and object descriptions. In this paper, a robot will be developed to interact with the users performing some specified actions. Moreover, integration between the proposed vision and natural language understanding systems has been presented. Finally, a voice-based dialogue between the user and robot will be developed. Intensive experiments have been conducted indoor to address the validity of the complete proposed system. The achieved results show that the overall system performance is high compared with the related literature to the theme of this paper.

Keywords:
   Vision System,  Speech system, object category recognition, Object Detection, Color detection, Natural Language Understanding, Ontology, Syntax, knowledge Representation, Semantic Networks.


References:

1.        M. Vukobratović, "Humanoid Robotics, Past, Present State, Future", Director Robotics Center,  Mihailo Pupin    Institute, 11000 Belgrade, P.O. Box 15, Serbia,E-mail: vuk@robot.imp.bg.ac.yu,SISY 2006 • 4th Serbian-Hungarian Joint Symposium on Intelligent Systems, pp 13-27.
2.        V. Graefe, R. Bischoff, "Past, Present and Future of Intelligent Robots",  Intelligent Robots Lab , LRT  6, Bundeswehr University Muenchen, 85577 Neubiberg, Germany, http://www.UniBw- Muenchen.de/campus/LRT6,CIRA 2003, Kobe, pp 1-10.

3.        C.Pasca, "History of Robotics", University of Ottawa, ENRICHMENT MINI-COURSE, Robotics – Intelligent Connection of the Perception to Action,May 5, 2003, pp1-46 .

4.        R. JARVIS,"INTELLIGENT ROBOTICS: PAST, PRESENT AND FUTURE",International Journal of Computer Science and Applications, Vol. 5, No. 3,pp 23 – 35, 2008.

5.        M. Takizawa, Y. Makihara, N. Shimada, J. Miura and Y. Shirai," A Service Robot with Interactive Vision- Object Recognition Using Dialog with User - ", Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan, E-mail: shimada@eng.osaka-u.ac.jp,2003.

6.        H.J.C. Luijten,"Basics of color based computervision implemented in Matlab",TechnischeUniversiteit  Eindhoven, Department Mechanical Engineering, Dynamics and Control Technology Group, Eindhoven, June, 2005, pp 1-24.

7.        E. Menegatti, S. Behnke, C. Zhou," Humanoid soccer robots", Robotics and Autonomous Systems, contents lists available at ScienceDirect, journal homepage: www.elsevier.com/locate/robot, Robotics and Autonomous Systems 57 (2009) 759_760.

8.        J.Sinapov and Al. Stoytchev,"Object Category Recognition by a Humanoid RobotUsing Behavior-Grounded Relational Learning", Developmental Robotics Laboratory, Iowa State University,{jsinapov, alexs}@iastate.edu,2011,pp 1-7.

9.        MathworksMatlab Image Processing function list,

10.     H.Holzapfel, D.Neubig, A.Waibel,"A dialogue approach to learning object descriptions and semantic categories", Contents lists available at ScienceDirect, Robotics and Autonomous Systems 56 (2008) 1004_1013.

11.     J. Carbonell, Towards a self-extending parser, in: Annual Meeting of the Association for Computational Linguistics, 1979.

12.     R. Becher, P. Steinhaus, R. Zöllner, R. Dillmann, "Design and implementation of an interactive object modelling system", in: Proceedings of ISR 2006 and Robotik 2006, Düsseldorf, 2006.

13.     M. Khalifa, V. Liu," KNOWLEDGE ACQUISITION THROUGH COMPUTERMEDIATEDDISCUSSIONS: POTENTIAL OF SEMANTIC NETWORK REPRESENTATIONS AND EFFECT OFCONCEPTUAL FACILITATION RESTRICTIVENESS ",Twenty-Sixth International Conference on Information Systems,2005, pp 221-232.

14.     P. Tanwar , T. V. Prasad, M. S. Aswal,"Comparative Study of Three Declarative Knowledge Representation Techniques", PoonamTanwar et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2274-2281.

15.     S. H¨uwel, B. Wrede, and G. Sagerer, "Robust Speech Understanding for Multi-Modal Human-Robot Communication", Faculty of Technology, Applied Computer Science Bielefeld University, 33594 Bielefeld, Germany, 2006.

16.     Al. Ramsay, H. Mansour," Towards including prosody in a text-to-speech system for modern standard Arabic", Received 13 March 2006; received in revised form 22 June 2007; accepted 22 June   2007 Available online 6 August 2007, Science Direct, Computer Speech and Language 22 (2008) 84–103.


60-65

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

12.

Authors:

Vinay Dawar, Ritu Sharma

Paper Title:

Reduction in Bit Error Rate from Various Equalization Techniques for MIMO Technology

Abstract:   The effect of fading and interference effects can be combated with equalizer for a MIMO system. MIMO systems exploit the multipath propagation in rich scattering environment using multiple transmit and receive antennas to increase the capacity of a link. The matrix channel plays a pivotal role in the throughput of a MIMO link since the modulation, data rate, power allocation and antenna weights are dependent on the channel gain. In this case independent, identically distributed (i.i.d.) Rayleigh fading MIMO channel with m-transmit and n-receive antennas has been taken, the diversity order is almost same i.e. m x n for ZF, MRC and MMSE-filtering-based decoding. The error probability analysis also yields a design criterion for optimizing transmitt power allocations. This paper analyses the performance of MMSE, ZF equalizer and MRC based receiver for MIMO wireless channel .The BER characteristics for the various transmitting and receiving antennas simulated in MATLAB tool box and many advantages and disadvantages the system is described. The simulation is carried out in signal processing lab, which shows that the MRC equalizer based receiver is a good choice for removing some ISI and minimizes the total noise power. The results show that the BER decreases as the m x n antenna configurations is increased.

Keywords:
   Equalizer, Bit error rate, Signal to noise ratio (Eb/N0), transmitting antenna, receiving antenna


References:

1.        H. Zhang, H. Dai, Q. Zhou, and B. L. Hughes, 2006 On the “diversity-multiplexing tradeoff for ordered SIC receivers over MIMO channels,” IEEE International Conference on Communications (ICC), Istanbul, Turkey.
2.        K. Cho and D. Yoon, 2002 , On the general BER expression of one and two-dimensional amplitude modulations," IEEE Transactions on Communications, vol. 50.

3.        Matthew R. McKay, Alex J. Grant, and Iain B. Collings, “Performance Analysis of MIMO-MRC in Double-Correlated Rayleigh Environments” IEEE Transactions on
Communications (Submitted November 2005) .

4.        N. Sathish Kumar, Dr. K. R. Shankar Kumar “Performance Analysis of M*N Equalizer based Minimum Mean Square Error (MMSE) Receiver for MIMO Wireless Channel” International Journal of Computer Applications (0975 – 8887) Volume 16– No.7, February 2011.

5.        D. Tse and P. Viswanath, 2005 Fundamentals of Wireless Communications. Cambridge Press. 1074-1080

6.        N. Jindal, 2005 High SNR analysis of MIMO broadcast channels," Proc. IEEE Int. Symp. Information Theory, Adelaide,Australia.

7.        X. Zhang and S. Kung, 2003, Capacity analysis for parallel and sequential MIMO equalizers," IEEE Transactions on Signal Processing, vol. 51. 2989-3003

8.        B. Hassibi, 2000, A fast square-root implementation for BLAST," Thirty-Fourth Asilomar Conf. Signals, Systems and Computers. 1255-1259

9.        N. Prasad and M. K.Varanasi, 2004, Outage capacities of space-time architecture," Information Theory Workshop, SanAntonio, Texas. 24-29

10.     M. Varanasi, 1995, Group detection for synchronous Gaussian code-division multiple-access channels," IEEE Transactions on Information Theory, vol. 41. pp. 1083-1096.


66-70

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

13.

Authors:

Neena Malhotra, Shivani Sehgal

Paper Title:

Power Factor Improvement in a Sugar Mill:  An Analysis

Abstract:   To reduce losses in the distribution system, and to reduce the electricity bill, power factor correction, usually in the form of capacitors, is added to neutralize as much of the magnetizing current as possible..To improve the power factor in sugar mill, it is required to install the capacitors of required capacitor ratings as near to the load as possible. This paper reports a case of sugar mill where the induction motors having a power factor of 0.8 has a potential to improve the power factor to 0.95, by installing suitable power factor improvement capacitors.

Keywords:
   Distribution losses, induction motors, inductive load, power factor, sugar mill


References:

1.        “Power Factor”, Wikipedia, the Free Encyclopaedia, Available:  http://en.wikipedia.org/wiki/Power_factor
2.        “Reducing Power Factor Cost”, U. S. Department of Energy.

3.        K. R. Govindan, “Power Factor Improvement”, Kavoori Consultants, 2002

4.        Bureau of Energy Efficiency (BEE), Energy Efficiency in Electrical Utilities: Electrical system, Available:  http://emt-india.com/BEE-Exam/GuideBooks/book3.pdf

71-73

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

14.

Authors:

Hetal Bhavsar, Amit Ganatra

Paper Title:

A Comparative Study of Training Algorithms for Supervised Machine Learning

Abstract:   Classification in data mining has gained a lot of importance in literature and it has a great deal of application areas from medicine to astronomy, from banking to text classification.. It can be described as supervised learning algorithm as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. The classification techniques are help to learn a model from a set of training data and to classify a test data well into one of the classes. This research is related to the study of the existing classification algorithm and their comparative in terms of speed, accuracy, scalability and other issues which in turn would help other researchers in studying the existing algorithms as well as developing innovative algorithms for applications or requirements which are not available.

Keywords:
   classification, decision tree, nearest neighbour, neural network, SVM, Supervised learning.


References:

1.        M. Javier ,M. Moguerza, “Support Vector Machines with Applications,” Statistical Science , vol. 21, no. 3, pp. 322-336, 2006.
2.        J. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, 1998.

3.        Cover, T. , Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, 1967.

4.        E. Rumelhart, G. E. Hinton and R. I. Williams, “Learning internal representation by error propagation,” Parrallel Distrubuted Processing, 1986.

5.        Duda R, Hart P, "Pattern Classification and Scene Analysis," John Wiley and Sons, New York, 1973.

6.        F. Rosenblatt, “The perceptron: A probabilisticc model for informtaion storage and organization in the brain,” Psychological Review, vol. 65, pp. 386-498, 1958.

7.        Friedman, N., Geiger, D., Goldazmidt, “Bayesian Network Classifiers,” Machine Learning, vol. 29, pp. 131-163, 1997.

8.        Gao, Jiawei Hen and Jing, “Classification and  regression trees,” Wadsworth, Belmont, 1984.
9.        J. Han and M. Kamber, Data Mining Concepts and Techniques, Elevier, 2011.
10.     J. R. Quinlan, “Discovering rules by induction from large collections of examples,” Expert Systems in the Microelectronic age, pp. 168-201, 1979.

11.     J. R. Quinlen, “Introduction of Decision Trees,” Machine Learning, vol. 1, pp. 81-106, 1986.

12.     J. R.Quinlan, “C4.5: Programs for machien learning,” Morgan Kaufmann, San Fransisco, 1993.

13.     Jensen, “An Introduction to Bayesian Networks,” Springer, 1996.

14.     K. P. Soman, Insight into Data Miining Theory and Practice, New Delhi: PHI, 2006.

15.     Klaus_Robert Muller, Sebastian Mika, Gunnar Ratsch, Koji Tsuda, Bernhard Scholkopf, “An Introduction to Kernel Based Learning Algorithms,” CRC Press, 2002.
16.     Robert Burbodge, Bernard Buxton, “An introduction to Support Vector Machines for Data Mining,” Computer Science Dept., UCL, UK.
17.     S. B. Kotsiantis, “Supervised Machine Learning: A Riview of Classification Techniques,” Informatica, vol. 31, pp. 249-268, 2007.

18.     Thair N. Phyu, “Survey of Classification echniques in Data Mining,” in International Multiconference of Engineers and Computer Scientists, Hong Kong, 2009.

19.     Vapnik, Corinna Cortes and Vladimir, “Support Vector Network,” Machine Learning, vol. 20, pp. 273-297, 1995.

20.     XindongWu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg, “Top 10 algorithms in data mining,” Knowledge Information system, vol. 14, pp. 1-37, 2008.


74-81

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

15.

Authors:

Ankur Kumar Shrivastava, Abhinav Kumar, Richa Bhatnagar, Nidhi Chaudhary, Mariya Khurshid Ansari, Amod Tiwari

Paper Title:

An Elaborative Approach To Enhance Access Control Model: Demostrated By 7-Tier Architecture

Abstract:    Access control is a security process, which work as a facilitator between every initiated resource and access request to determine whether the system allows or deny the request. Access control is important for preventing theft of data and resources for ensuring that they are safe and well kept at all times. Thus in rapidly growing IT environment, unauthorized access becomes a form of threat. An organization or industry may possess a wealth of critical resources, but those resources are not at all available to each and every employee, customer or partner. Industries and organizations must implement access control mechanism to ensure that each user whether inside or outside of an organization had only necessary access to the required resources. We discuss here various architectures, policies and models for access control, which are trying to illustrate why they are so much crucial for any organizations. In this paper we are providing an elaborative approach to enhance access control architecture. Also we discuss some key aspect for designing access control architecture.

Keywords:
   MAC (Mandatory access control), DAC (Directory access control), RBAC (Role based access control).


References:

1.       M. Abadi, M. Burrows, B. Lampson, and G. Plotkin. A calculus for access control in distributed systems. ACM Transactions on Programming Languages and Systems, 15:706–734,1993.
2.       R. Ahad, J. David, S. Gower, P. Lyngbaek, A. Marynowski, and E. Onuebge. Supporting access control in an object-oriented database language. In Proc. of the Int. Conference on Extending Database Technology (EDBT), Vienna, Austria, 1992.

3.       G. Ahn and R. Sandhu. The RSL99 language for role-based separation of duty constraints. In Proc. of the fourth ACM Workshop on Role-based Access Control, pages 43–54, Fairfax, VA, USA, October 1999.

4.       P. Bonatti, S. De Capitani di Vimercati, and P. Samarati. A modular approach to composing access control policies. In Proc. of the Seventh ACM Conference on Computer and Communications Security, Athens, Greece, 2000.
5.       D.F.C. Brewer and M.J. Nash. The Chinese wall security policy. In Proc. IEEE Symposium on Security and Privacy, pages 215–228, Oakland, CA, 1989.
6.       D.D. Clark and D.R. Wilson. A comparison of commercial and military computer security policies. In Proceedings IEEE Computer Society Symposium on Security and Privacy, pages 184–194, Oakland, CA, May 1987.

7.       D. Ferraiolo and R. Kuhn. Role-based access controls. In Proc. of the 15th NIST-NCSC Naional Computer Security Conference, pages 554–563, Baltimore, MD, October 1992.

8.       http://spdp.dti.unimi.it/papers/sam-fosad.pdf.

9.       http://www.fas.org/irp/nsa/rainbow/tg10.pdf.

10.     http://en.wikipedia.org/wiki/Access_control.

11.     http://www.eit.lth.se/fileadmin/eit/courses/eit060/lect/Lect8.pdf.


82-86

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

16.

Authors:

Subhra Rani Patra, R. Jehadeesan, S. Rajeswari, S. A.V. Satya Murty, M. Sai Baba

Paper Title:

Development of Genetic Algorithm based Neural Network model for parameter estimation of Fast Breeder Reactor Subsystem

Abstract:   This work provides the construction of Genetic Algorithm based Neural Network for parameter estimation of Fast Breeder Test Reactor (FBTR) Subsystem. The parameter estimated here is temperature of Intermediate Heat Exchanger of Fast Breeder Test Reactor.  Genetic Algorithm based Neural Network is a global search algorithm having less probability of being trapped in local minimum problem as compared to Standard Back Propagation algorithm which is a local search algorithm. The various development stages of Genetic Algorithm based Neural Network such as the preparation of the training set, weight extraction from the genetic population, training of the neural network and validation phase etc have been described in detail.

Keywords:
   Genetic Algorithm based Neural Network, Fast Breeder Test Reactor, Intermediate Heat Exchanger, Multi layer Perceptron.


References:

1.       Rajasekaran S., Vijayalakshmi Pai G. A., PHI learning Pvt. Ltd, 2008, pp. 305-327
2.       Rao H., Ghorpade V.G., Mukherjee A., Genetic algorithm based weight determination for back propagation networks, Proceedings of Trends in Computing, Tata McGraw-Hill, 1996, pp. 73-80

3.       Internal report on “Primary Sodium Main Circuit System Manual”, Report No. FBTR/FRG/32000-DN-S-RS-06

4.       Internal report on “Heat Exchangers”, FRG/FBTR/TM

5.       Erdogan E., Gecknli M., Annals of Nuclear Energy, Vol. 30, 2003,pp.  35-53

6.       Ahmed A.A., Alfishawy N.A., Albrdini M.A., Mahmoud I.I., Nature and Science, Vol. 5, 2011,pp.  64-74

7.       Burse K., Manoria M., Kirar V. P. S., World Academy of Science, Engineering and Technology, Vol. 72, 2010

8.       Gupta J. N.D., Sexton R. S., Omega, Vol. 27, 1999,pp.  679-684 

9.       Montana D. J., Davis L., Proceedings of the 11th international joint conference on Artificial intelligence, Vol. 1, 1989, pp. 762-767

10.     Montana D. J., John Wiley & Sons, 1995,pp.  85-104

11.     Ileana I., Rotar C., Incze A., Proceedings of the International Conference on Theory and Applications Mathematical and Informatics, Thessaloniki, Greece, 2004, pp. 223-24

87-90

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

17.

Authors:

Fahian Ahmed, Saddam Quirem, Gak Min and Byeong Kil Lee

Paper Title:

Hotspot Analysis Based Partial CUDA Acceleration of HMMER 3.0 on GPGPUs

Abstract:    With the introduction of many-core GPUs, there is widespread interest in using GPUs to accelerate non-graphics applications such as bioinformatics, energy, finance and several research areas. Even though the GPUs provide highly parallel processing capability, the communication interface between CPU and GPU could be a performance bottleneck due to heavy data transfer. If data transfer time is overwhelming the computation time on GPU, it would be better keep the computation on CPU instead of using GPUs. In this paper, we characterize the HMMER 3.0 and investigate performance hotspot functions. The HMMER is a bioinformatics application which is used in searching sequence databases for protein sequences. For our experiment, we use Nvidia CUDA that abstracts the GPU hardware. Based on the hotspot analysis of HMMER 3.0, we consider two factors for partial CUDA acceleration: one is the performance impact of major hotspot functions and the other one is data transfer overhead. Also, we verified that hotspot analysis based partial CUDA acceleration could provide better performance than full CUDA implementation.

Keywords:
   CUDA acceleration, GPGPU, HMMER, Many-core processors 


References:

1.       Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., and Phillips, J. C. GPU computing. IEEE Proceedings, 879-899, May 2008.
2.       Jakub Kurzak, Alfredo Buttari, Piotr Luszczek, Jack Dongarra, "The PlayStation 3 for High-Performance Scientific Computing," Computing in Science and Engineering, vol. 10, no. 3, pp. 84-87, May/June, 2008.

3.       P. Bakkum and K. Skadron “Accelerating SQL database operation on a GPU with CUDA,” in proceedings of the 3rd workshop on general purpose computation on graphics processing units, ACM, pp.94-103, 2010.

4.       Steven Derrie and Patrice Quinton, “Parallelizing HMMER for Hardware Acceleration on FPGAs”, Proceedings in IEEE 18th International Conference Application-specific Systems, Architectures and Processors, 2007.

5.       Daniel Horn, Mike Houston and Pt Hanrahan, “ClawHMMER: A Streaming HMMer-Search Implementation”, presented at Supercomputing 2005, Washington, D.C., 2005.

6.       D.Schaa and D. Kaeli, “Exploring the multiple GPU design space,” in International Parallel and Distributed Processing Symposium., pp. 1–12, May 2009.

7.       J. Owens, M. Houston, D. Luebke, S. Green, J. Stone, and J. Phillips, “GPU computing,” Proceedings of the IEEE, vol. 96, no.5, pp. 879–899,May 2008.

8.       Z. Fan, F. Qiu, A. Kaufman, and S. Yoakum-Stover, “GPU cluster for high performance computing,” in ACM/IEEE Conference on Supercomputing, Pittsburgh, PA, pp. 47–58, November 2004.

9.       J. Cohen and M. Molemaker, “A fast double precision CFD code using CUDA,” in Parallel Computational Fluid Dynamics: Recent Advances and Future Directions, Moffett Field, CA, pp. 414–429, May 2009.

10.     G. Dotzler, R. Veldema, and M. Klemm, “JCUDAmp: OpenMP/Java on CUDA,” in 3rd International Workshop on Multicore Software Engineering, pp. 10–17, May 2010.

11.     Ali Bakhoda, George L. Yuan, W.L. Fung, Henry Wong and Tor M. Aamodt, “Analyzing CUDA workloads using a detailed GPU Simulator,” 2009 IEEE International Symposium on Performance Analysis of Systems and Software, 2009.

12.     Vtune: Intel Performance Analyzer,

13.     http://www.software.intel.com/en-us/intel-vtune/


91-95

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

18.

Authors:

Parul Sinha, Monika Arora, N.M. Mishra

Paper Title:

Framework for a Knowledge Management Platform in Higher Education Institutions

Abstract:    Effective implementation of Knowledge Management is considered as an increasingly important tool facilitating organisations to gain a competitive advantage. Educational Institutes are not far behind, they have also realised that Knowledge is now a driving force for organisational change and innovation, which are a survival tool in today’s dynamic environment. As a result, Educational Institutions are exercising radical changes and are at  varying stages of planning and implementing knowledge-based strategies in effort to improve their competitiveness, productivity, organisational effectiveness and better service to the nation by producing skilled leaders for tomorrow. However, KM initiatives are both expensive and risky propositions. Financial resources put a constraint on what can be expended on knowledge activities. This necessitates a re-look at knowledge management initiatives in Educational Institutions, which can be considered to be knowledge intensive organisation. This paper presents a framework which can be adopted for building a Knowledge Management platform in Higher Education Institutions. It lists the steps to implement a KM solution/portal and discusses the sub parts of the portal, which can cater to the needs of the stakeholders of an Academic Institution. It also discusses the factors influencing the success of knowledge management initiatives in a Higher Education Institution, which help them to distinguish themselves in the academic market place. 

Keywords:
   Higher Education Institutions (HEI), Knowledge Management (KM), Portal.


References:

1.       Agarwal S., Sharma P.B.and Kumar M(2008) , Knowledge Management Framework for improving Curriculum Development Processes in Technical Education, Third 2008 International Conference on Convergence and Hybrid Information  Technology, IEEE Xplore.
2.       Anthony J. Delmonte,  Kennedy Space Center, Jay E. Aronson, The University of Georgia, The Relationship Between Social Interaction And Knowledge Management System Success ,Journal of Knowledge Management Practice, August 2004.

3.       Basu,B.,& Sengupta,K.,(2007). Assessing Success Factors of Knowledge Management Initiatives of Academic Institutions – a Case of an Indian Business School” The Electronic Journal of Knowledge Management Volume 5 Issue 3, pp 273 - 282, available online at www.ejkm.com.

4.       Cranfield, D., & Taylor, J. (2008), Knowledge management and higher education: a UK case study, Electronic Journal of Knowledge Management, 6, pp.1-116.

5.       Davenport, T. H., & Prusak, L., (1998,p.12). Working knowledge: How organizations manage what they know, Boston, Harvard Business School Press.

6.       K., Ramakrishnan, N., Mohd., Yasin (2012),Knowledge Management System and Higher Education Institutions, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia, International Conference on Information and Network Technology (ICINT 2012),IPCSIT vol. 37 (2012) © (2012) IACSIT Press, Singapore.

7.       Kidwell, J.J., Vander Linde, M.K., Johnson, L.S. (2000), ‘Applying Corporate Knowledge Management Practices in higher education’, EDUCAUSE QUARTERY, no. 4, pp. 28- 33.

8.       Kumar A. & Kumar A., “ IT based KM for Institutions of Higher Education- A Need “ University News, A weekly Journal of Higher Education in India from Association of Indian Universities, New Delhi India Vol. 43, No. 30, July 25-31, 2005, pp. 4 – 9.

9.       Kumar A. & Kumar A.,(2006), “ IT Based KM In Indian Higher Education System: Addressing Quality Concerns And Setting The Priorities Right” , Journal of Knowledge Management Practice, 7(3).

10.     Naidu,S., Bernath, U., Training the Trainers in the Essentials of Online Learning, Downloaded from

11.     http://www.col.org/pcf2/papers/naidu_1.pdf

12.     Petrides, L. A., & Guiney, S. Z. (2002).,”Knowledge management for school leaders: an ecological framework for thinking schools”, Teachers College Record, 104 (8).


96-100

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

19.

Authors:

L.P.Bhaiya, Arif Ullah Kha

Paper Title:

Hindi Speaking Person Identification using Zero Crossing rate and Short-Term Energy

Abstract:   Language is man’s most important means of communication and speech its primary medium. Speech recognition is the ability of a computer to recognize general, naturally flowing utterances from a wide variety of users. Differences of physiological properties of the glottis and vocal tracts are partly due to age, gender and/or person differences. Since these differences are related in the speech signal, acoustic measures related to those properties can be helpful for speaker identification. Acoustic measure of voice sources were extracted from 5 utterances spoken by10 peoples including 5 male and 5 female talkers (aged 19 to 25 years old). The differences of speech long term features including zero crossing rate and short term energy for different person is studied.

Keywords:
   Since these differences are related in the speech signal, acoustic measures related to those properties can be helpful for speaker identification

References:

1.        Yiu - Kei Lau and Chok- Ki Chan,”Speech recognition based on zero-crossing rate”, IEEE Transactions on acoustics, speech and signal processing, Vol.ASSP-33, No.1.
2.        Costas panagiotakis and George tziritas, “A speech/music discriminator based on RMS and zero-crossings”, IEEE transactions on multimedia.vol.7, no.1, February 2005.

3.        Sumit Kumar Banchhor, Om Prakash Sahu, Prabhakar, “A Speech/Music Discriminator based on Frequency energy, Spectrogram and Autocorrelation”, IJSCE, Volume-1, Issue-6, January 2012

4.        Sumit kumar Banchhor and Arif Khan, “Musical Instrument Recognition using Zero Crossing Rate and Short-time     Energy”, Volume 1– No.3, February 2012.

5.        Bachu R.G, Kopparthi S, Adapa B, Barkana B.D, “Separation of voiced and unvoiced using zero crossing rate and energy     of the signal”.

6.        Sumit kumar Banchhor and S. K. Dekate, “Text-dependent Method for Gender Identification through synthesis of voiced segments”,IJEST, Volume- 3, No. 6,  June 2011

7.        Dimitrios Ververidis and Constantine Kotropoulos, “Emotional Speech Recognition: Resources, Features, and methods”.

8.        Nicolas Cummins, Julien Epps, Miachael Breakspear, and Roland Goecke, “ An Investigation on Depressed Speech Detection: Features and Normalization”.


101-104

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

20.

Authors:

Yogendra Singh, R K Agarwal, C. Srivastava

Paper Title:

Effect of Energy Conservation on Environmental Loads

Abstract:    In the present paper, a theoretical investigation on energy conservation and their effect on green house gases will be discussed. The result shows the reduction of thermal (roof) load if the space between roof and false ceiling is ventilated by outside air, a certain portion of the roof load may be carried away by this air due to convective heat transfer between roof and air. Reduction in thermal load leads to reductions in the amount of environmental loads. The saving is determined between 9am to 6 pm. The other parameters are air velocities of 1.25, 1.75 and2.25 m/s; roof length in the direction flow as 10, 20 and 30m; inclination of roof at 00,300 and 450 at a location of  290 latitude.

Keywords:
   roof heat load, environmental loads


References:

1.        Zhang W, Wu J, Wei Y & Gao X, Research on the effect of  Planting Roof  on the Thermal Load of a Business Building, Envelope Technologies for Building Energy Efficiency, Vol- 2 (2006) 4-2
2.        Higuchi Y & Udagawa M, Effects of Trees on the Room temperature and Heat Load of Residential Building, proceedings: Buildings Simulation, 2007

3.        Charan V & Agarwal R K, Effect of ventilation of space between roof and false ceiling on A/C Load , International  CB .W 67 symposium on Energy, Moisture and  Climate in buildings held at Rotterdam, Netherlands, September ,1990

4.        Duffie J.A., & Beckman W.A, Solar engineering of thermal processes,(John Wiley & Sons, New York), 1980

5.        Arora C.P., Refrigeration and air-conditioning, (Tata McGraw Hill, New Delhi), 1984

6.        Holman J.P., Heat transfer, (Tata McGraw Hill, New Delhi), 2004

7.        Gupta C.P. &  Prakash R., Engineering Heat Transfer, (Nem Chand and Bros., Roorkee), 1999

8.        Garg H. P. & Prakash J, Solar Energy Fundamentals and Applications, (Tata McGraw Hill, New Delhi), 2000

9.        Agarwal R.K., Sharma B.C., Effect of ventilation of space between roof and false ceiling on roof  heat  load  -  A   case  study,  International  Journal  on  Mechanical  and  Automobile Engineering (IJMAE) ISSN  0974231X , 2009


105-106

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

21.

Authors:

Ashish Raj, Akanksha Deo, Mangesh S. Tomar, Manoj Kumar Bandil

Paper Title:

Analysing the Inclusion of Soft Computing Techniques in Denoising EEG Signal

Abstract:    The electrical nature of the human nervous system has been innovated for more than a century. It is prominent that the variation of the surface potential distribution on the scalp reflects function and activities emerging from the underlying brain. This variation of the surface potential can be recorded by placing an array of electrodes to the scalp, and measuring the voltage between pairs of these electrodes. These measured voltages are then filtered, amplified, and recorded. The resulting data is called the EEG. As per the usefulness, EEG has proved to be an important tool for diagnosis, monitoring and managing various nervous disorders. The electrical activity of brain changes in accordance with various parameters inside & outside environment. A number of severe disorders in human body which were impossible to be traced in early stages are easily being signal processing stages are being predicted with help of EEG. But there are certain artifacts which are present in raw EEG recording. These raw signals are firstly processed with help of mathematical tools in order to make them more and more informative. The informative signal thus calculated from recording is known as ERP (event related potential). These ERP data are very specific and changes with every physiological & biological change in human body. Thus the analysis of ERP has got numerous clinical importance. But there are certain artifacts which are present in raw EEG recording. These artifacts make the ERP contaminated and it introduces inconsistency in the output. These artifacts in EEG signals arise due to two types of factors; Biological factors and External factors. The Biological factors are caused by EOG (Elecro-oculogram), ECG (Electrocardiogram), EMG (Electromyogram) and Respiratory (PNG).The external factors are caused due to line-interference, leads and electrodes. These noises have an adverse effect on EEG signals and act as a contamination to obtain clear cut information from EEG signals .Thus it is perquisite to eliminate these artifacts from the EEG. The ERP generated from artifacts free EEG are most suitable for versatile researches and efficient diagnosis. The clinical information thus obtained is of considerable importance in identifying different pathologies. Thus artifact rejection is most important preliminary stage before ERP analysis. This is a paper scrutinizing different soft computing methods for removing artifacts with illustrating characteristics of a good informative EEG signal. In this paper we have discussed about inclusion of several soft computing techniques with the conventional artifact removal approaches.

Keywords:
   EEG; EMG; ECG; ocular artifacts; muscular artifacts; spike detection; Wavelet transform; Neural network., Fuzzy logic; Genetic Algorithm


References:

1.        ERP lecture, Dr.John J. Curtin,  University of Wisconsin-Madison.
2.        Ian Daly, Floriana Pichiorri, Josef Faller, Vera Kaiser, Alex Krielinger, Reinhold Scherer and Gernot M¨uller-Putz,” What does clean EEG look like”,EMBC 2012.

3.        Ashish Raj, Akanksha Deo, Manoj Kumar bandil, Canvassing Various Techniques for Removal of Biological Artifact in EEG,IJRTE, Vol-1, 2012.

4.        G.geetha, Dr.S.N.Geethalakshm,” EEG De-noising using SURE Thresholding based on Wavelet Transforms”, International Journal of Computer Applications, Volume 24– No.6, June 2011

5.        R. Romo Vázquez, H. Vélez-Pérez , R. Ranta , V. Louis Dorr , D. Maquin, L. Maillard,” Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling”, Biomedical Signal Processing and Control volume-7.

6.        Janett Walters-Williams & Yan Li” Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger”.

7.        Mika S.: Numerical metody algebry. SNTL, 1985

8.        Hyarinen A., Karhunen J., Oja E.: Independent Component Analysis. Wiley Interscience, 2001

9.        N.L. Nikolayev and H. Iba, “Automated discovery of polynomials by inductive genetic programming”, in Principles of Data Mining and Knowledge Discovery (PKDD’99), J. Zutkow, and J. Ranch, Springer, Berlin, pp. 456-461, 1999.

10.     The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns, Vitaly Schetinin and Joachim Schult.

11.     A. Asadi Ghanbari, M. R. Nazari Kousarrizi, M. Teshnehlab, and M. Aliyari, AN Evolutionary Artifact Rejection Method For Brain Computer Interface Using ICA.

12.     G.kezi Selva, P.Kanagasabapathy, Stanley Johnson, and Vinodh Ewards, Efficient Cancellation of artifacts in EEG signal using ANFIS, International journal Of soft computing, 2007.

13.     Jacqueline Fairley, George Georgoulas, Chrysostomos Stylios, and David Rye, A Hybrid Approach for Artifact Detection in EEG Data.


107-112

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

22.

Authors:

L. Sumalatha, G. RoslineNesa Kumari, V.Vijaya Kumar

Paper Title:

A Simple Block Based Content Watermarking Scheme for Image Authentication and Tamper Detection

Abstract:   Digital watermarking techniques have been proposed for handling applications like Copy protection, Content authentication of digital images. Any tiny change to the content is not acceptable in images especially when they are used to store secret information in the form of an invisible digital watermark. To address this present paper proposes a simple block based content checksum watermarking (BCCW) method for image authentication and tamper localization. The proposed BCCW is a hierarchical and block based method. In BCCW the image is divided into sub blocks of size 4×4. Then a hierarchical relationship is established by dividing each 4×4 as a set of four 2×2 blocks. A Checksum of 8 bits is computed from pixels of 4×4 block and the checksum is placed intelligently on the selected 2×2 block pixels. In the proposed BCCW if any block or even a pixel is tampered then the block checksum does not match with the extracted bit sequence. The main advantage of the BCCW scheme is, it can identify effectively in which blocks the tampering has occurred. The experimental results show that the quality of the embedded image is very high, and the positions of the tampered parts are located correctly. The proposed BCCW method is compared with several other methods.

Keywords:
   Block Based, Content Checksum, Image Authentication.


References:

1.       Schyndel, R.G., Tirkel, A.Z., Osbome, C.F.,”A digital watermark”, In Proceedings of the IEEE International Conference on Image Processing, Austin, Texas, 1994,vol. 2, pp. 86–90.
2.       Walton, S. “Information authentication for a slippery new age”,      Dr. Dobbs J. 1995, 20 (4), pp.18–26.

3.       Wolfgang, R.B., Delp, E.J., “A watermark for digital images”, In Proceedings of IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996, vol. 3, pp. 219–222.

4.       Wong, P.W., ”A public key watermark for image verification and authentication.” In Proceedings of IEEE International Conference on Image Processing, Chicago, IL, 1998.  vol. 1, pp. 425–429.

5.       Sujoy Roy and Qibin Sun, “Robust hash for detecting and localizing image tampering”, in Proc. IEEE International Conference on Image Processing, Sep 2007.

6.       C.-C. Chang, Y.-S. Hu, T.-C. Lu, “A watermarking-based image ownership and tampering authentication scheme”, in Pattern Recognition Lett. 2006, 27 (5), pp.439--446.

7.       V. Skala and M. Kucha, “The hash function and the principle of duality” in Proceedings of Computer Graphics International 2001, pp. 167–174.

8.       S. Halevi and H. Krawczyk, Strengthening digital signatures via randomized hashing, Advances in Cryptology-CRYPTO 2006, pp. 41–59.

9.       P.L.Lin, C.H., Hsieh, W.S., “Applying projection and B-spline to image authentication and remedy” in IEEE Trans. Consumer Electron. 2003., 49 (4), pp.1234–1239.

10.     Sergio Bravo-Solorio and Asoke K. Nandi.” Fragile watermarking with improved tampering localization and self-recovery capabilities”. In EUSIPCO 2010, pp.820-824.


113-117

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

23.

Authors:

Kishore M, H M Guruprasad, S M Shashidhar

Paper Title:

A Novel Snapshot Based Approach for Direction of Arrivial Estimation with Least Bias

Abstract:    Adaptive array smart antenna involves the array processing to manipulate the signals induced on various antenna elements in such way that the main beam directing towards the desired signal and forming the nulls towards the interferers. Such smart antennas are widely used in wireless mobile communications as they can increase the channel capacity and coverage range. In adaptive array smart antenna, to locate the desired signal, various direction of arrival (DOA) estimation algorithms are used. This paper investigates Novel Snapshot Based approach using Estimation of Signal Parameters via Rotational Invariance Technique. ESPRIT algorithms provide high angular resolution and hence they are explored much in detail by varying various parameters of smart antenna system.

Keywords:
   Smart antenna, ESPIRT, DOA, AOA


References:

1.        T.B. Lavate. V.K. Kokate, Dr. A. M. SapkalL ,“Performance     Analysis of MUSIC and ESPRIT  DOA Estimation algorithms for adaptive array smart antenna in mobile communication” IEEE,second international conference on computer and network technology,pg 308-311,2010
2.        M. Mouhamadou and P. Vaudon, “Smart Antenna Array Patterns Synthesis: Null Steering and multi-user  Beamforming”, Progress in Electromagnetic research, June 2006, PIER-60, pp 95-106.

3.        J.M. Samhan, R.M. Shubair and M.A.Al-qutayriz , “Design and implementation of an adaptive smart antenna array system”, Innovations in information technology, November 2006,  pp 1-4.

4.        C.S. Nemai, C. Karmakar, “Direction of Arrival Estimation based on a Single-Port Smart Antenna using MUSIC Algorithm with periodic signals”, International Journal of  Signal Processing,  March 2005, Vol-1, No 2, pp153-162.

5.        C.S. Nemai, C. Karmakar, “Direction of Arrival Estimation with a Novel single port smart antenna”, EURASIP Journal on applied Signal Processing, Sept 2004, Vol-2004, pp 1364-1375.

6.        T. K. Sarkar, S. Park, J. Koh,  R.A. Schneible, “A Deterministic Least Squares Approach to Adaptive Antennas”, Digital Signal Processing, A Review Journal-6, March 1996, Vol- 49 , pp 185-194.

7.        S. Choi, H. M. Son, T. K. Sarkar, “Implementation of a Smart Antenna System on a General-Purpose Digital Signal Processor Utilizing a Linearized CGM”, Digital Signal Processing Journal-7, March 1997,Vol- 7, Issue-8,pp 105-119.


118-121

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

24.

Authors:

Mustapha Ben Saidi, Anas Abou Elkalam, Abderrahim Marzouk

Paper Title:

TOrBAC: A Trust Organization Based Access Control Model for Cloud Computing Systems

Abstract:   The access control models like DAC, MAC, RBAC, TBAC, TMAC, DomBAC, etc. mainly model security policies for information systems having a centralized governance. Moreover, they only specify permissions and prohibitions, sometimes obligations. Besides that, they generally do not allow the establishment of activated, dynamic and adapted rules.  However, such rules are highly useful in a cloud environment where IT governance is shared, used or managed by different entities. In this paper, we propose a new model for specifying such security policies. This model called Trust Organization Based Access Control (TOrBAC) which relies on the use of a recursive formula for calculating a confidence index. We also formalize our work using a language based on first order logic and we apply it to a cloud computing (CC) related use case.

Keywords:
   Cloud computing; Access model control; security.


References:

1.        Abou El Kalam, P. Balbiani, S. Benferhat, F. Cuppens, Y. Deswarte, R. El-Baida, A. Miège, C. Saurel, G. Trouessin “Organization-Based Access Control”, 4th International Workshop on Policies for Distributed Systems and Networks (Policy’03), Côme, Italie, 4-6 june 2003, IEEE Computer Society Press, pp. 120-131.
2.        Abou El Kalam, “A Research Challenge in Modeling Access Control Policies: Modeling Recommendations”, IEEE International Conference on Research Challenges inInformation Science, 3-6 Jun 2008, Marrakech, Morocco.

3.        Anas Abou El Kalam, Yves Deswarte, Amine Baina, Mohamed Kaaniche, PolyOrBAC: a Security Framework for Critical Infrastructures, Rapport LAAS N°09087, 28 pp., International Journal on Critical Infrastructure Protection, Elsevier, vol. 2(4), Decembre 2009, 37pp, LNCS.

4.        DomBAC: An access control model for moder collaborative systems Antonios Gouglidi s *, Ioannis Mavridis Department of Applied Informatics, University of Macedonia, 156 Egnatia Str., 54006 Thessaloniki, Greece

5.        TBAC : R.  Thomas  et  R.  Sandhu.  Task-based  Authorization  Controls  (TBAC):  A  Family  of Models  for  Active  and  Enterprise-oriented Authorization  Management.  11th  IFIP  Working Conference  on  Database  Security,  Lake  Tahoe, California, USA, 1997.

6.        RBAC : ESEP 2011: 9-10 December 2011, Singapore Role-Based Access Control Model of Cloud Computing Chen Jincui  and  Jiang Liqun  China University of Mining and Technology, Xuzhou221008, China 

7.        www.ORBAC.org

8.        ESEP 2011: 9-10 December 2011, Singapore Role-Based Access Control Model of Cloud Computing Chen Jincui and Jiang Liqun China University of Mining and Technology, uzhou221008, China

9.        A. Abou El Kalam, P. Balbiani, “A Policy Language for Modelling Recommendations”, IFIP TC-11 International Information Security Conference, (IFIP SEC 2009), Cyprus, 18-20 juin 2009, Springer.

10.     Livre Cloud : Cloud Computing - 2ème éd - Une rupture décisive pour l'informatique d'entreprise : Guillaume PLOUIN.

11.     Livre blanc MIBS et Pierre Audouin Consultants : " Les infrastructures critiques : bien maîtriser le socle du patrimoine informatique de l´entreprise " .

12.     Recent Advances in Cloud Security Jiyi Wu 1,2 1.Key Lab of E-Business and Information Security, Hangzhou Normal University,Hangzhou,China  2.School of Computer Science and Technology, Zhejiang  university, Hangzhou, China & al.

13.     Luo et al. 2011, C.L., A hierarchy attribute - based access control model for cloud storage, in Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 3, pp. 1146 - 1150, IEEE, 2011. 

14.     Law 2002-303 related to the patient’s rights and to the quality of healthcare systems, Article L. 1111-7, March 2002.

15.     Recommendations of the Council of Europe, R(97)5, On The Protection of Medical Data Banks, Council of Europe, Strasbourg, 13 February 1997.

16.     Book: Cloud Computing Bible : Author: Sosinsky, Barrie Edition: John Wiley & Sons Publication: 2011.

17.     Livre blanc : Quelles tendances pour  le Cloud Computing en 2011 ?

18.     Livre blanc produit par Euro Cloud France  November 2011.

19.     J.I. Andrew Jones, J.S. Marek, “On the Characterization of Law and Computer Systems: The Normative Systems Perspective.” In John-Jules Ch.Meyer and Roel J.Wieringa, Deontic Logic in Computer Science: Normative System Specification, John Wiley and Sons, Chichester,England, 1993.

20.     B.F. Chellas, “Modal Logic: An Introduction”, Cambridge University Press, 1980, ISBN 0-521-29515-7, 295 pp.

21.     S. Kripke, “Semantical Consideration in Modal Logic”, Acta Philosophical Logic, vol. 16, 1963, pp. 83-94.

22.     R. Sandhu, D.F. Ferraiolo, D, R. Kuhn (2000), "The NIST Model for  Role Based Access Control:  Toward a Unified Standard,"  Postscript  PDF   Proceedings,  5th ACM Workshop on Role Based Access Control, July 26-27, 2000, Berlin, pp.47-63 

23.     [Abou El Kalam & Deswarte, 2009b] A. Abou El Kalam, Y. Deswarte, "Poly-OrBAC: An Access Control Model fior Inter-Organizational Web Services", Handbook of Research on Semantic Technologies and Web Services, ISBN: 978-1-60566-650-1, May 2009, IGI-Global Editor, <http://www.igi-global.com/reference/details.asp
ID=34405>

24.     [Abou El Kalam et al. Cloud Computing - 2ème éd - Une rupture décisive pour l'informatique d'entrepriseInternational Journal on Critical Infrastructure Protection, Elsevier, vol. 2(4), Décembre 2009, 37pp, LNCS.


122-130

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

25.

Authors:

Sonali Gandhi, Deeba Khan, Vikram Singh Solanki

Paper Title:

A Comparative Analysis of Selection Scheme

Abstract:    Selection scheme is an important part of genetic algorithms, which chooses a chromosome from the current generation’s population for inclusion in the next generation’s population, is the main subject of this paper. A selection operator selects the best chromosome using fitness function. Selection scheme is used to improve chances of the survivals of the fittest individuals. This paper recommends a number of selection (reproduction) methods most commonly used in genetic algorithms and analyzes them. These methods are: roulette wheel, rank selection, Boltzmann selection, tournament selection, steady state selection and elitism are compared on the basis of performance and takeover time computations .the analysis provides approximate or exact solutions. The paper recommends practical application and analyses a number of ways for more detailed analytical investigation of selection schemes.

Keywords:
   Roulette Wheel, Rank selection, Boltzmann Selection, Tournament selection, Steady State Selection, Elitism.


References:

1.       Goldberg, D. E. (1990). A note on Boltzmann tournament selection forgenetic algorithms and population-oriented simulated annealing. Complex Systems,
2.       Ankenbrandt, C. A. (1990). An extension to the theory of convergence and a proof of the time complexity of genetic algorithms (Technical Report CS/CIAKS- 90-0010/TU) New Orleans: Center for Intelligent and Knowledge-based Systems, Tulane University.

3.       Introduction to Genetic Algorithms: http://www.obitko.com/tutorials/genetic-algorithms/selection.php

4.       VOL. 3, NO.7, July 2012 ISSN 2079-8407 Journal of Emerging   Trends in Computing and Information Sciences: Comparison of Selection Methods and Crossover Operations using Steady State Genetic Based Intrusion Detection System Firas Alabsi,Reyadh Naoum  Middle East University(2012)

5.       Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. Proceedings of the Second International Conference on Genetic Algorithms, 14-21.

6.       David E. Goldberg and Kalyanmoy Deb Department of General Engineering University of Illinois at Urbana-Champaign 117 Transportation Building 104 South Mathews Urbana, IL 61801- 2996.A Comparative Analysis of Selection Schemes used in Genetic algorithm.

7.       Www2.chemie.uni-erlang... Genetic Algorithm : Flowchart of the hybrid algorithm

8.       Fundamentals of Genetic Algorithms www.myreaders.info09_Genetic_Algorithms

9.       Introduction to Soft Computing (Neural Network , Fuzzy Logic and Genetic Algorithm):Ashish Kumar Sharma (Dhanpat Rai & co.)

10.     A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing .David E. Gold berg University of Illinois at Urbana-Champaign, Urbana IL 61801 USA

11.     Davis and M. Steenstrup, "Genetic algorithms and simulat ed annealing: An overview." In Genetic Algorithms and Simulated Annealing, 1. Davis, ed. (Pitman, London, 1989)


131-134

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

26.

Authors:

P.Satheesh, B.Srinivas, A.Satish Kumar

Paper Title:

Generation of Frequent Patterns with Weights Over Continuous Flow of Data Efficiently

Abstract:    Mining data streams for knowledge discovery has been used in many applications like web click stream mining, network traffic monitoring, network intrusion detection, and dynamic tracing of financial transactions. In this paper, by analyzing characteristics of date stream, we propose an efficient algorithm weighted frequent pattern (WFP) mining that discovers more knowledge compared to traditional frequent pattern mining. The existing algorithms cannot apply for stream of data because those algorithms require multiple database scans. This technique uses a single database scan for mining stream of data. Our technique is efficient for web applications for mining web records and also discovers valuable knowledge compared to other techniques.

Keywords:
   Data stream, weight, weighted frequent pattern mining.


References:

1.        James Cheng, Yiping Ke, and Wilfred Ng, "A Survey on algorithms for mining frequent itemsets over data streams,"
2.        C.Raissi, P.Poncelet, and M.Teisseire, "Towards a new approach for mining frequent itemsets on data stream," Journal of Intelligent Information Systems, vol. 28, pp.23-36, 2007.

3.        A.MetwaIly, D.Agrawal, and A.E.Abbadi, "An integrated efficient solution for computing frequent and top-k elements in data streams," ACM Transactions on database systems, vol. 31, pp.l 095-1133, 2006.

4.        N.Jiang, L.Gruenwald, "Research issues in data stream association rule mining," SIGMOD Record, vol 35, pp. 14-19, 2006.

5.        C.K.-S.Leung, Q.I.Khan, "DSTree: A tree structure for the mining of frequent sets from data streams," Proc.Sixth IEEE Int'l Conf. on Data Mining, pp.928-932, 2006.

6.        U.Yun, J.J.Leggett, "WFIM: weighted frequent itemset mining with a weight range and a minimum weight," Proc.Fifth SIAM Int. Conf. on Data Mining, pp.630-640, 2005.

7.        U.Yun, "Efficient mining of weighted interesting patterns with a strong weight and/or support affinity," Information Sciences, vol. 177, pp.3477-3499, 2007.

8.        R.Agrawal, A. Swami, "Fast algorithm for mining association rules," In Proc. Of the 20th IntI. Conf. on Very Large Data Bases, September 1994.

9.        Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, and Byeong Soo Jeong, "Efficient mining of weighted frequent patterns over data streams," 2009 11th IEEE International Conference on High Performance Computing and Communications.


135-139

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

27.

Authors:

Munaf S. N. Al-Din

Paper Title:

Decomposed Fuzzy Controller for Reactive Mobile Robot Navigation

Abstract:   An Autonomous Mobile Robot is an artificially intelligent vehicle that is capable of traveling in unknown, unstructured environments independently. Among the proposed approaches in the literature to handle the navigation problem of a mobile robot is the simple fuzzy reactive approach. This approach, however, occasionally suffers from two problems the combinatorial explosion of the if-then rules in the inference engine, and finally the lack of a unified fuzzy rule-based system procedure. This paper offers an approach to handle the first two problems. In this paper a new approach to the design of simple fuzzy navigation systems is presented. The proposed approach is based on decomposing MIMO fuzzy logic controller into a number of SISO controllers. This approach has the advantage of greatly reducing the number of if-then rules by introducing weighting factors for the sensor inputs, thus inferring the reflexive conclusions from each input to the system rather than putting all the possible states of all the inputs to infer a single conclusion. Simulation and experimental results are presented to prove the efficiency of the proposed approach for mobile robot navigation in unstructured unknown environment.

Keywords:
   Decomposed fuzzy controller, Mobile robot, Autonomous navigation


References:

1.       O. Khatib, "Real-Time Obstacle Avoidance for Manipulators and Mobile Robot"; International Journal of Robotic Research; Vol. 5, No.1, 1986, pp90-99.
2.       J. Brrenstein and Y. Koren, "Obstacle Avoidance with Ultrasonic Sensors"; IEEE Trans. On Robotics and Automation 4, 1988, pp. 213-218.

3.       M. Maeda, Y. Maeda and S. Murakami, "Fuzzy Drive Control o fan Autonomous Mobile robot", Fuzzy Sets and Systems, Vol. 39, No. 2, 1991, pp. 195-204.

4.       K. Fujimura and H. Samet, "Planning a Time Minimal Motion Among Moving Obstacles", Algorithmical, Vol. 10, No. 1, 1993, pp. 41-63.

5.       A. Martinez, E. Tunstel and Mo. Jamshidi, "Fuzzy Logic Based Collision Avoidance for a Mobile Robot", Robotica Vol 12 Part 6, pp. 521-527, 1994.

6.       W. L. Xu and S. K. Tso, "real-Time Self-Reaction of a  Mobile Robot in Unstructured Environments Using Fuzzy reasoning", Engng Applic.-Artif. Intell., Vol. 9, No. 5, 1996, pp. 475-485.

7.       S. Lee, T. M. Adams, and B. Y. Ryoo, "A fuzzy Navigation System for Mobile Construction Robot", Automation in Construction 6, 1997, pp. 97-107.

8.       A. Saffiotti, "Autonomous Robot Navigation", In Handbook of Fuzzy computation, Eds, E. Ruspini, P. Bonissone, and W. Pedrycz, Oxford University Press, 1998.

9.       I. Ulrich and J. Borenstein, "VFH*: Local Obstacle Avoidance with Look-Ahead verification", Proc. of IEEE International Conf. on Robotics and Automation, San Francisco, April 2000, pp. 2505-2511.

10.     F. Hoffmann, "Soft Computing Techniques for the Design of Mobile Robot Behaviors", Information Sciences 1222, 2000, pp. 241-258.

11.     R. Chatterjee and F. Matsuno, "Use of Single Side Reflex for Autonomous Navigation of Mobile Robots in Unknown Environments", Robotics and Autonomous Systems 35, 2001, pp. 77-96.

12.     L. McFetridge and M. Y. Ibrahim, "new Technique of Mobile robot Navigation Using a Hybrid Adaptive Fuzzy-Potential Field Approach", Computers Ind. Engng, Vol. 35, Nos. 3-4, 1998, pp. 471-474.

13.     Y. Koren and J. Brrenstein, "Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation", Proc. of IEEE Conf. on Robotics and Automation, Sacramento, California, 1991,  pp. 1398-1404.

14.     I. Ulrich and J. Borenstein, "VFH+ : Reliable Obstacle Avoidance for fast Mobile Robots", Proc. of IEEE International Conf. on Robotics and Automation, Leuven, Belgium, 1998, pp. 1572-1577.

15.     S. M. Noorhossini and A.S. Malowany, "Gorp: A New Method for Mobile Robot Path Planning Problem", SPIE 1831, Mobile Robots VII, 1992, pp. 37-44.

16.     Z. Q. Ma and Z. R. Yuan, "Real Time Navigation and Obstacle Avoidance Based on Grid Method for Fast Mobile Robots", Engng. Applic.  Artif. Intell. 8, No. 1, 1995, 91-95.

17.     G. Antonelli, S. Chiaverini, R. Finotello and R. Schiavon, " Real-Time Path Planning and Obstacle Avoidance for RAIS: An Autonomous Underwater Vehiacle" IEEE Jornal of Ocenic Engng, Vol. 26, No. 2, 2001, pp. 216-277.

18.     S. Thrun, "An Approach to Learning Mobile Robot Navigation", Robotics and Autonomous Systems 15, 1995, pp. 301-319.

19.     H. Kanoh, T.H. Bui, A. Kashiwazaki, N. Kato, and S. Nishihara, "Real-Time Rout Selection Using Genetic Algorithms for Car Navigation Systems",  Proc. of IEEE International Conf. on Intelligent Vehicles, 1998, pp. 207-212.

20.     Gerke M., "Genetic Path Planning for Mobile Robots", Proc. of American Control Conf., Vol. 4, 1999, 2424-2429.

21.     Mo. Jamshidi, " Autonomous Control of Complex Systems: Robotic Applications", Applied Mathematics and Computation 120, 2001, pp. 15-29.

22.     F. Michaud, "Selecting Behaviors Using Fuzzy Logic", IEEE, International Conf. on Fuzzy Systems, Spain, July 1997, pp.

23.     A. Saffiotti, E.H. Ruspini, and K. Konolige, "Using Fuzzy Logic for Mobile Robot Control", In Practical Applications of Fuzzy Technologies, Edt. by H.J. Zimmermann, Kluwer Academic, 1999, pp. 185-206.

24.     S. Thongchai, S. Suksakulchai, D.M. Wilkes and N. Sarkar, "Sonar Behavior-Based Fuzzy Control for Mobile Robot", Proc. of IEEE International Conf. on Systems, Man, and Cybernetics, Nashville, Tennessee, Oct. 2000.

25.     E. Aguirre and A. Gonzalez, "Fuzzy Behaviors for Mobile Robot Navigation: Design, Coordination and Fusion", International Journal of Approximate Reasoning, Vol. 25, Issue 3, 2000, pp. 255-289.

26.     P.G. Zavlangas, S.G. Tzafestas and K. Althoefer, "Fuzzy Obstacle Avoidance and navigation for Omnidirectional Mobile Robots", ESIT 2000, Aachen, Germany, 2000, pp. 375-382.

27.     M.B. Montaner and A. Ramirez-Serrano, "Fuzzy Knowledge-Based Controller Design for Autonomous Robot Navigation", Expert Systems with Applications 14, 1998, pp.179-186.

28.     Chia-Han Lin, Ling-Ling Wang, "Intelligent Collision Avoidance by Fuzzy Logic Control", Robotics and Autonomous Systems 20, 1997, pp.61-83.

29.     W.L. Xu, S.K. Tso and Y.H. Fung, "Fuzzy Reactive Control of a Mobile Robot Incorporating a Real/Virtual Target Switching Strategy", ibid. 23, 1998, pp.171-186.

30.     W.L. Xu, " A Virtual Target Approach for Resolving the Limit Cycle Problem in Navigation of a Fuzzy Behavior-Based Mobile Robot", ibid. 30 , 2000, pp. 315-324.

31.     M. golob, " Decomposition of Fuzzy Controller Based on the Inference Break-Up Method", Intelligent Data Analysis 3, 1999, pp. 127-137.

32.     Y. Koren and J. Brrenstein, , "Noise Rejection for Ultrasonic Sensors in Mobile Robot Applications", Proc. of IEEE International Conf. on Robotics and Automation, Sacramento, California, 1992,  pp. 1727-1732


140-149

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

28.

Authors:

Gaurav Kansal, Maneesha

Paper Title:

ADATPM: Analysis and Design of Author’s Trait Processing Module for textual Data

Abstract:    Trait theory is a major approach to the study of human personality.  Personality is the branch of psychology which is concerned with providing a systematic account of the ways by which we can differentiate one-another. Individuals differ from one another in a variety of ways: their anatomical and physiognomic characteristics, their personal appearance, grooming, manner of dress, their social backgrounds, roles and other demographic characteristics, their effect on others or social stimulus value and their temporary states, moods, attitudes and activities at any given moment in time.. In this paper we have designed a system that takes text input and returns the author’s trait accordingly. Since human tendencies are largely dependent on environmental and situational consistencies, we have considered five different traits in our identification. These are High Extrovert, Low Extrovert, High Introvert, Low Introvert and Ambivert. Our algorithm refines the author’s text under eight different properties. The text undergoes POS tagger where each word is assigned a tag. After analyzing the tag we generate Feature Vector matrix (FVM), we use this FVM for our analysis as well as for the classification. We have applied our proposed algorithm on different 280 files. These files are also annotated by human.  We compare the result got from human annotation and proposed algorithm and we found that the accuracy of our algorithm is 84.26%.

Keywords:
   FVM, POS, SVM, Trait Theory.


References:

1.        Brown, G. and Yule, G. Discourse Analysis. Cambridge: Cambridge University Press Buchanan, T. 2006.
2.        Cohn, M., Mehl, M.R. and Pennebaker, J.W. Linguistic markers ofpsychological change surrounding  Psychological Science, 15,687-693 2001.

3.        Costa, P., and McCrae, R.R. Professional Manual. Psychological Assessment Resources, Odessa, FL1992.

4.        Efimova, L., and de Moor, A. Beyond personal web publishing: Anexploratory study of conversational blogging practises. Proceedingsof the 37th Annual HICSS Conference. Big Island, Hawaii 2005.

5.        Glance, N., Hurst, M., and Tomokiyo, T. BlogPulse: AutomatedTrend Discovery for Weblogs. in Proceedings of WWW  New York,US 2004..

6.        J. Oberlander and S. Nowson. Whose thumb is it anyway?Classifying author personality from weblog text. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics, Sydney, Australia, 2006.

7.        Alastair J. Gill, Jon Oberlander, and Elizabeth Austin. Rating e-mailpersonality at zero acquaintance. Personality and Individual Differences, 40:497–507 2006.

8.        James W. Pennebaker and Laura King. Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77:1296–1312 1999.

9.        S. Nowson and J. Oberlander. The identity of bloggers: Openness and gender in personal weblogs. AAAI Spring Symposium, Computational Approaches to Analyzing Weblogs, Stanford University, 2006.

10.     I. H. Witten and E. Frank. Data Mining: Practical

11.     L. R. Goldberg. The structure of phenotypic personality traits.American Psychologist, 48(1):26–34, 1993.

12.     Ying Li Ching Y. SuenTypeface Personality Traits and Their Design Characteristics DAS Boston, MA, USA June 9-11, 2010.

13.     K. H.-Y. Lin, C. Yang and H.-H. Chen. What Emotions NewsArticles Trigger in Their Readers? Proceedings of SIGIR, 733-734, 2007.

14.     Guiying Wei, Xuedong Gao, Sen Wu Study of text classificationmethods for data sets with huge features, 2nd International Conference on Industrial and Information Systems 2010.

15.     Xiaojin Zhu. Semi-Supervised Learning Literature Survey,Computer Science, University of Wisconsin -Madison, 2008.

16.     H Jia-wei, M Kamber. Data mining: concepts and techniques, 2ndedition, New York: Morgan Kaufmann Press, 2006.

17.     AH-HWEE TAN, “Text Mining: The State of the Art and the Challenges”[C], PA KDD’99 Workshop on Knowledge Discovery from Advanced DatabasesKDAD’99, Beijing, 1999.

18.     C. Sanderson and S. Guenter. Short text authorship attribution via sequence kernels, markov chains and author unmasking. In Proceeding of 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 482491. Association for Computational Linguistics, 2006.

19.     A. Abbasi and H. Chen. Applying authorship analysis toextremist-group web forum messages.IEEE INTELLIGENT SYSTEMS, pages 67–75, 2005.

20.     Abbasi and H. Chen. Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Transaction Information Systems, 26(2):1– 29, 2008.

21.     M. Koppel and J. Schler. Exploiting stylistic idiosyncrasies for authorship attribution. In Proceedings of IJCAI’03 Workshop on Computational Approaches to Style Analysis and Synthesis, pages 69–72, 2003.

22.     M. Gamon. Linguistic correlates of style: authorship classification with deep linguistic analysis features. In Proceedings of the 20th international conference on Computational Linguistics.Association for Computational Linguistics Morristown, NJ, USA, 2004.

23.     ShlomoArgamon, Marin Saric, and Sterling S. Stein. Style mining of electronic messages for multiple authorship discrimination: first results. In Proceedings of SIGKDD, pages 475–480 2003.

24.     ShlomoArgamon, SushantDhawle, Moshe Koppel, and James W. Pennebaker..Lexical predictors of personality type.In Proceedings of the 2005 Joint Annual Meeting of the Interface and the Classification Society of North America 2005.

25.     Satanjeev Banerjee and Ted Pedersen. The design, implementation, and use of the ngram statistics package. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, pages 370–381, Mexico City 2003.

26.     Tom Buchanan. Online implementation of an IPIP five factor personality inventory 2001. [27]. Paul T. Costa and Robert R. McCrae, Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI): Professional Manual. Odessa, FL: Psychological Assessment Resources 1992.

27.     Francis Heylighen and Jean-Marc Dewaele. Variation in the contextuality of language: an empirical measure. Foundations of Science, Volume 7, pages 293–340, 2002.

28.     Douglas Biber. Variation across Speech and Writing. Cambridge University Press, Cambridge, 1988.

29.     Max Louwerse, Philip M. McCarthy, Danielle S. Mc- Namara and Arthur C. Graesser. Variation in language and cohesion across written and spoken registers. In Proceedings of the 26th Annual Conference of the Cognitive Science Society, pages 1035–1040, Hillsdale, NJ, 2004.

30.     Scott Nowson, Jon Oberlander Differentiating Document Type and Author Personality from Linguistic Features Proceedings of the 11th Australasian Document Computing Symposium, Brisbane, Australia, December 11, 2006.

31.     Chen, H., and Dumais, S. Bringing order to the web: automatically categorizing search results. CHI, 145-152, 2000.

32.     Dumais, S., Chen, H. Hierarchical classification of Web content, In Proc. SIGIR, 256-263, 2000.

33.     Yang, Y., Zhang, J., and Kisiel, B. A scalability analysis of classifiers in text categorization.SIGIR, 96-103, 2003.

34.     Hersh, W., Buckley, C., Leone, T., and Hickam, D.OHSUMED: An interactive retrieval evaluation and new large test collection for research. SIGIR, 192-201, 1994.

35.     Cai, L. and Hofmann, T. Hierarchical Document Categorization with Support Vector Machines, CIKM, 78-87, 2004.

36.     Granitzer, M. Hierarchical text classification using methods from machine learning, Master's Thesis, Graz University of Technology, 2003.

37.     Sun, A. and Lim, E. Hierarchical Text classification and evaluation, ICDM, 521-528, 2001.

38.     Yang, Y., Zhang, J., and Kisiel, B. A scalability analysis of classifiers in text categorization.SIGIR, 96-103, 2003.

39.     Allport, F. H. &Allport, G. W. Personality traits: heir Classification and Measurement. Journal of Abnormal and Social Psychology, 16, 1–40 1921.

40.     Allport, G. W. Personality –A psychological interpretation. New York: Henry Holt and Company 1937.

41.     Allport, G. W. The use of personal documents in psychological science. New York: Social Science Research Council 1941.

42.     Allport, G. W. Becoming. Basic considerations for a psychology of personality. New Haven: Yale University Press 1955.

43.     Allport, G. W. Letters from Jenny. New York: Harcourt, Brace & World, Inc 1965.

44.     Allport, G. W. Traits revisited. American Psychologist, 21, 1-10 1966.

45.     Cartwright, D. S. Introduction to personality. Chicago: Rand McNally College Publishing Company 1974.

46.     Robert J. Harvey, William D. Murry, Steven E. Markham A “Big Five” Scoring System for the Myers-BriggsType Indicator Annual Conference of the Society for Industrial and Organizational Psychology Orlando in May 1995.

47.     Barkhuus, L. and Csank, P Allport’s Theory of traits-  a critical review of the theory and two studies A Technical report, Concordia University 1999.


150-158

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

29.

Authors:

Saurabh Yadav, Ajay Kumar Singh

Paper Title:

NoC Based Approach to Enhance the Existing Network Architecture

Abstract:    Now a day’s performance is really one of the greatest issue. We want to send the packet from source to destination with a highest throughput, even though there is any fault in the intermediate node or router we don’t want to compromise with the packet loss as it leads to wastage of bandwidth. In this paper we have tried to improve the architecture of existing system. That yield better performance, in order to improve the performance we have taken the approach of shortest path.

Keywords:
   Dijkstra Algorithm, NoC, Packet Switching, Pyramid Network, Topology.


References:

1.       Nevin Kırman, Meyrem Kırman, Rajeev K. Dokania, Jose´ F. Martı´nez, Alyssa B. Apsel, Matthew A. Watkins, David H. Albonesi , “On-Chip Optical Technology In Future Bus-Based Multicore Designs,” IEEE Computer Society, pp. 56-66, 2007.
2.       Ciprian Seiculescu, Srinivasan Murali, Luca Benini, and Giovanni De Micheli, “Comparative Analysis of NoCs for Two-Dimensional Versus Three-Dimensional SoCs Supporting Multiple Voltage and Frequency Islands,” IEEE Transactions on Circuits and Systems—Ii: Express Briefs, vol. 57, no. 5, pp. 364-368, May 2010.

3.       Raffaele Bruno, Marco Conti, and Enrico Gregori, “Mesh Networks: Commodity Multihop Ad Hoc Networks,” IEEE Communications Magazine, pp. 123-131, March 2005.

4.       M.J. Quinn, “Designing Efficient Algorithms for Parallel Computers, McGraw-Hill,” International Edition, 1987.

5.       Keqin Li Suny, “New Divisible Load Distribution Methods using Pipelined Communication Techniques on Tree and Pyramid Networks ,”  IEEE Transactions on Aerospace and Electronic Systems vol. 47, no. 2, pp. 806-819, April 2011.

6.       P. Guerrier and A. Grenier, “A Generic Architecture for On-Chip Packet-Switched Interconnections,”Proc. IEEE Design Automation and Test in Europe (DATE 2000), IEEE Press, Piscataway, N.J., pp. 250-256, 2000.

7.       Kanishka Lahiri, Anand Raghunata  and Sujit Dey, “System-Level Performance Analysis for Designing On-Chip Communication Architectures,”  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 20, no. 6, pp. 768-783, June 2001.

8.       Se-Joong Lee, Kangmin Lee, Seong-Jun Song, and Hoi-Jun Yoo, “Packet-Switched On-Chip Interconnection Network for System-On-Chip Applications,” IEEE Transactions on Circuits and Systems—Ii: Express Briefs, vol. 52, no. 6, pp. 308-312, June 2005.

9.       Lauri Savioja and Vesa Välimäki, “Reducing the Dispersion Error in the Digital Waveguide Mesh Using Interpolation  and Frequency-Warping Techniques ,” IEEE Transactions on Speech and Audio Processing, vol. 8, no. 2, pp. 184-194, March 2000.

10.     R. Miller and Q. Stout, “Data Movement Techniques for the Pyramid Computer,” SIAM Journal on Computing 16, pp. 38–60, 1987.

11.     V. Sanju, N.N. Chiplunkar, B.Y.Baby, “Design of A Generic Network on Chip Frame Work for Store and Forward Routing for 2d Mesh Topology,” International Conference on  Emerging Trends in Electronic and Photonic Devices & Systems, pp. 104 - 107, Dec. 2009.

12.     Larry Peterson, Yitzchak Gottlieb, Mike Hibler, Patrick Tullmann, Jay Lepreau, Stephen Schwab, Hrishikesh Dandekar, Andrew Purtell, and John Hartman, “An OS  Interface for Active Routers ,” IEEE Journal on Selected Areas in Communications, vol. 19, no. 3, pp.  473-487, March 2001.

13.     T. Theis, “The Future of Interconnection Technology,” IBM J. Research and Development, pp. 379-390, May 2000.

14.     Hailong Yao, Yici Cai, Qiang Zhou, and Xianlong Hong, “Multilevel Routing With Redundant Via Insertion,” IEEE Transactions on Circuits and Systems—Ii: Express Briefs, vol. 53, no. 10, pp. 1048-1052, Oct. 2006.


159-162

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

30.

Authors:

Himabindu Vallabhu, R V Satyanarayana

Paper Title:

Biometric Authentication as a Service on Cloud: Novel Solution

Abstract:    Authenticating the user based on behavior based biometrics is more reliable than the more traditional means of password authentication .Since Biometric identification is unique and slow intrusive .Biometric systems provide the solution to ensure that the rendered services are accessed only by a legitimate user and no one else. Biometric systems identify users based on behavioral or physiological characteristics. The advantages of such systems over traditional authentication methods, such as passwords and IDs, are well known; hence, biometric systems are gradually gaining ground in terms of usage. As security is the main concern in using cloud computing fused  biometric authentication technique which  can be used as single sign on so that the services can be more secure and reliable ,and that  biometric authentication is provided as a  service by a cloud provider.

Keywords:
   Biometrics,cloud ,authentication, Single Sign On, fused


References:

1.        http://en.wikipedia.org/wiki/Biometrics
2.        J.L.Wayman, "Fundamentals of Biometric Authentication Xhtp:/ww.engr.sisu.edu/biometrics/nbtcw.p

3.        http://www.indexbiometrics.com/physiological_or_behavioral.htm

4.        http://www.fi.muni.cz/reports/files/older/FIMU-RS-2000-08.pdf

5.        http://www.netsecurity.org/secworld.php?id=8922

6.        http://ntrg.cs.tcd.ie

7.        http://www.sans.org/reading_room/whitepapers/authentication/biometric-scanning-technologies-finger-facial-retinal-scanning

8.        NIST Special Publication 800-145 “The NIST Definition of Cloud Computing”  Peter Mell Timothy Grance http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf

9.        Cloud Computing Security: From Single to Multi-Clouds http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=614956

10.     S.L. Garfinkel, "Email-based identification and authentication: An    alternative to PKI?", IEEE Security and Privacy, 1(6), 2003

11.     S.L. Garfinkel, "An evaluation of amazon’s grid computing services: EC2, S3, and SQS", Technical Report TR-08-07, Computer Science Group, Harvard University, Citeseer, 2007


163-165

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

31.

Authors:

Raza Abdulla Saeed, Loay Edwar George

Paper Title:

Apply Pruning Algorithm for Optimizing Feed Forward Neural Networks for Crack Identifications in Francis Turbine Runner

Abstract:   In this study the Feed Forward Artificial Neural Networks (FFANN) for crack identification and estimates the turbine operating conditions in Francis turbine type was investigated. The sets of vibration data were used as vibrational signatures for studied mechanical structure, and they fed to FFANN as input vector for identification purpose. Different arrangements of FFANN were taken into consideration to find out the best topology which can produce identification results with acceptable accuracy levels. In order to examine the performance of the FFANN and obtain the satisfactory arrangements, different numbers of input data sets are tested. The test results showed that the use of very large number of input data will cause a large increase in training time beside to it may lead to unstable FFANN with over-fitting. To avoid these deteriorated results, different data reduction techniques have been proposed for reducing dimensionality of the input data to achieve an acceptable data reduction level.  The conducted results indicated that the FFANN models have been successfully employed for crack identification and estimates the turbine operating conditions using vibration data sets. Moreover the results revealed that the pruning mechanism which is based on the data reduction mechanism can led to satisfactory results.

Keywords:
   Crack Identifications, Feed Forward Artificial Neural Networks, Francis Turbines Runner, Pruning Algorithm


References:

1.       Angelakis, C., Loukis, E.N., Pouliezos, A.D., Stavrakakis, G.S.: Neural Network Based Method for Gas Turbine Blading Fault Diagnosis. International Journal of Modelling and Simulation 21(1): 51-60 (2001).
2.       Araújo dos Santos, J.V., Mota Soares, C.M., Mota Soares, C.A.,Maia, N.M.M. :Structural Damage Identification in Laminated Structures Using FRF Data. Composite Structures 67(2): 239-249(2005)

3.       Bamnios, G., Trochides, A.: Dynamic Behaviour of a Cracked Cantilever Beam. Applied Acoustics 45(2): 97-112 (1995)

4.       Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford, Oxford University Press (1995)

5.       Choubey, A., Sehgal, D.K., Tandon, N.: Finite Element Analysis of Vessels to Study Changes in Natural Frequencies Due to Cracks. International Journal of Pressure Vessels and Piping 83(3): 181-187 (2006)

6.       Hu, Y., Hwang, J.N. : Handbook of Neural Network Signal Processing. Washington, USA, CRC Press (2002)

7.       Huynh, D., He, J., Tran,  D.: Damage Location Vector: a Non-Destructive Structural Damage Detection Technique. Computers and Structures 83(28-30): 2353-2367 (2005)

8.       Kuo, R.J.: Intelligent Diagnosis for Turbine Blade Faults Using Artificial Neural Network and Fuzzy Logic. Engineering Application of Artificial Intelligence 8(1): 25-34 (1995)

9.       Liu, S.W., Huang, J.H., Sung, J.C., Lee, C.C.: Detection of Cracks Using Neural Networks and Computational Mechanics. Computational Methods in Applied Mechanical Engineering 191(25): 2831-2845(2002)

10.     Maclntyre, J., Tait, J., Kendal, S., Smith, P., Harris, T., Brason, A.: Neural Networks Applications in Condition Monitoring. Applications of Artificial Intelligence in Engineering, WIT press. 6: 37-49 (1994)

11.     Marwala, T., Hunt, H.: Fault Identification Using Finite Element Models and Neural Networks. Mechanical Systems and Signal Processing 13(3): 475-490 (1999)

12.     Oberholster, A.J., Heyns, P.S.: On-Line Fan Blade Damage Detection Using Neural Networks. Mechanical Systems and Signal Processing 20(1): 78-93 (2006)

13.     Owolabi, G.M., Swamidas, A.S.J., Seshadri, R.: Crack Detection in Beams Using Changes in Frequencies and Amplitudes of Frequency Response Functions. Journal of Sound and Vibration 265(1): 1-22 (2003)

14.     Saeed, R. A., Galybin, A. N., Popov, V.: Modelling of flow induced stresses in a Francis turbine runner”, Journal of Advances in Engineering Software 41(12):1245-1255 (2010)

15.     Salawu, O.S.: Detection of Structural Damage Through Changes in Frequency: A Review. Engineering Structures 19(9): 718-723(1997)

16.     Suresh, P.V., Chaudhmf, D.: Artificial Neural Network Approach for Multiple Fault Diagnosis: a case study. Applications of Artificial Intelligence in Engineering, WIT press (1994)

17.     Xia, Y., Hao, H.: Statistical Damage Identification of Structures with Frequency Changes. Journal of Sound and Vibration 263 (4): 853-870 (2003)

18.     Yun, C.B., Bahng, E.Y.: Substructural Identification Using Neural Networks. Computers and Structures 77(1): 41-52 (2000)

19.     Zang, C., Imregun, M.: Structural Damage Detection Using Artificial Neural Networks and Measured FRF Data Reduced Via Principal Component Projection. Journal of Sound and Vibration 242(5): 813-827 (2001)

20.     Zapico, J.L., González, M.P., Worden, K.: Damage Assessment Using Neural Networks. Mechanical systems and signal processing 17(1): 119-125 (2003)


166-175

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

32.

Authors:

Manjunath M., Sunitha S.L., B.N.Shobha

Paper Title:

Flexible Arbiter Based On Dynamic Arbitration Scheme for Ml-Ahb Busmatrix

Abstract:    The multi-layer advanced high-performance bus (ML-AHB) matrix proposed by ARM is an excellent architecture for applying embedded systems with low power. However, there is one clock cycle delay for each master in the ML-AHB Bus Matrix of the advanced microcontroller bus architecture (AMBA) design kit (ADK) whenever a master starts new transactions or changes the slave layers. The total area and power consumption of the ML-AHB Bus Matrix of an ADK is increases due to the heavy input stages. Due to heavy input stage the cost of   arbitration scheme becomes high. The computation time of each master is predictable, but it is not easy to foresee the data transfer time since the on-chip bus is usually shared by several masters. Previous works solved this issue by minimizing the latencies of several latency-critical masters, but a side effect of these methods is that they can increase the latencies of other masters; hence, they may violate the given timing constraints. It is better to apply improved Bus Matrix to some applications that do not require the time division multiple accesses to the slaves. This paper adapts improved ML-AHB Bus Matrix to multimedia applications such as a video phone, MPEG-4, and H.264 codec and presents flexible arbiter based on the Dynamic Arbitration scheme for the ML-AHB bus matrix. The arbiter supports three priority policies-fixed priority, round-robin, and dynamic priority-and three approaches to data multiplexing-transfer, transaction, and desired transfer length. Experimental results show that, although the area of the proposed Dynamic Arbitration scheme is 9%–25% larger than those of other arbitration schemes, the arbiter scheme improves the throughput by 14%–62% compared with other schemes.

Keywords:
   Multilayer AHB (ML-AHB) bus matrix, on-chip bus, Dynamic arbitration scheme, slave side arbitration, system-on-a-chip (SoC).


References:

1.       M. Drinic, D. Kirovski, S. Megerian, and M. Potkonjak, “Latency guided on-chip bus-network design,” IEEE Trans. Comput.-Aided Design Integer, Circuits Syst., vol. 25, no. 12, pp. 2663–2673, Dec. 2006.
2.       S. Y. Hwang, K. S. Jhang, H. J. Park, Y. H. Bae, and H. J. Cho, “An ameliorated design method of ML-AHB bus matrix,” ETRI J., vol. 28, no. 3, pp. 397–400, Jun. 2006.

3.       ARM, “AHB Example AMBA System,” 2001

4.       IBM, New York, “32-bit Processor Local Bus Architecture Specification,” 2001.

5.       R. Usselmann, “WISHBONE interconnect matrix IP core,” Open- Cores, 2002 [6] N.J. Kim and H.J. Lee, “Design of AMBA wrappers for multiple clock operations,” in Proc. Int. Conf. ICCCAS, Jun. 2004, vol. 2, pp. 1438–1442

6.       D. Flynn, “AMBA: Enabling reusable on-chip designs,” IEEE Micro,vol.17,no.4,pp.20–27,Jul./Aug.1997.

7.       S. Y. Hwang, H.J. Park, and K.S. Jhang, “Performance analysis of slave-side arbitration schemes for the multi-layer AHB bus matrix,” J. KISS, Comput Syst. Theory, vol. 34, no. 5, pp. 257–266, Jun. 2007.

8.       S. S. Kallakuri and A. Doboli, “Customization of arbitration policies and buffer space distribution using continuous-time Markov decision processes,” IEEE Trans. Very Large Scale Integer (VLSI) Syst., vol. 15, no. 2, pp. 240–245, Feb. 2007.

9.       D. Seo and M. Thottethodi, “Table-lookup based crossbar arbitration for minimal-routed, 2D mesh and torus networks,” in Proc. Int. Conf. IPDPS, Mar. 2007, pp. 1–10.


176-179

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

33.

Authors:

Mahadevaswamy V P, Sunitha S L, B N Shobha

Paper Title:

Implementation of Fault Tolerant Method Using BCH Code on FPGA

Abstract:    The Fault tolerance degradation is the property that enables a system (often computer-based) to continue operating properly in the event of the failure of (or one or more faults within) some of its components. To designing a new 32-bit Arithmetic Logic Unit (ALU) that is secure against many attacks or faults and able to correct any 5-bit fault in any position of its 32 bits input register of ALU. Because the radiation effects on electronic circuits may cause to be inverted data bits of registers or memories. If one bit of main storage system is changed the mission of system would be completely different. The high motivation in choice of BCH (Bose, chaudhuri, and Hocquenghem) codes is that, it is able to correct multiple errors and these classes of codes are kind of powerful random error correcting cyclic codes. In comparison with area penalty methods, 32-bit fault tolerant ALU using BCH code is a better choice in terms of area as compared to Triple Modular Redundancy (TMR) and Residue code. This is due to the fault tolerant method for 32-bit ALU using TMR with single or triplicated voting need single voting scheme or tripled voter and two extra 32-bit ALU which has been increased the hardware overhead by 202% and 208% respectively.  The Residue code requires hardware overhead of 148.9%. However, in comparison with TMR and Residue code, BCH code needs the  hardware overhead is 70 to 75%, which causes that the overall cost and power consumption will get reduces. Thus proposed fault tolerant hardware overhead has lower hardware and multiple error correction when compared to the other techniques.

Keywords:
   Fault Tolerant, BCH codes, ALU, Residue code, TMR, Encoder, Decoder, FPGA.


References:

1.        S.Bourdarie and M.Xapsos, senior member, IEEE, “The near earth space radiation environment”, IEEE Transaction on Nuclear Science, August, 2008.
2.        Veeravalli, V.S.  “Fault tolerant Arithmetic and Logic Unit”, IEEE international conference, Rutgers State Univ. of  New Jersey, Piscataway, NJ, USA, March,      2009.

3.        R.Hentschke, F.Marques, F.Lima, L.Carro et al.  “Analyzing area and performance penalty of   protecting   different  digital  modules  with Hammnig  code and Triple Modular redundancy”. IEEE International Conference on Integrated Circuits and Systems Design, 2002.

4.        Fernanda Lima Kastensmidt, L.Carro, R.Reis, “Fault tolerant techniques for SRAM-  Based FPGA” June, 2006.

5.        Israel Koren and C.Mani K rishna. “Fault-  Tolerant  System”.Morgan   Kaufmann Publishers    2007.

6.        W.W.  Peterson, “Encoding and error-correction  procedures  for the Bose-Chaudhuri Codes”, IRE  Trans.  Inf. Theory, IT-6, pp. 459-470, September 1960.

7.        Lin,   Shu,   and   Daniel   J.   Costello,   Jr.,   “Error Control   Coding:  Fundamentals and Applications”, Englewood Cliffs, NJ, Prentice- Hall, 1983.

8.        Vahid Khorasani, B.Vousoghi et al. “Designing a secure 32-bit ALU using (63, 36) BCH code”,  Worldcomp conference, July, 2011.

180-182

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

34.

Authors:

Raj Kumar, Rajesh Verma

Paper Title:

Classification Rule Discovery for Diabetes Patients by Using Genetic Programming

Abstract:    The learning algorithms have great application in knowledge discovery. Learning algorithm offers new beneficiary ways in for real-world applications. Genetic Programming (GP) have some advantages due to which it become suitable for classification in data mining for Knowledge Discovery(KDD)  This paper focuses on the classification by using the Genetic Programming. There are various types of the traditional classification techniques like Naïve Bayesian, ID3, C4.5, CART, kNN, k-mean, SVM etc. The proposed algorithm is implemented on the Diabetes data set and excremental results are compared with traditional approach

Keywords:
   Classification, DM, GP, KDD,


References:

1.       Fayyad, Piatetsky-Shapiro, Smyth and Uthurusamy”, Advances in Knowledge Discovery and Data Mining”, (Chapter 1), AAAI/MIT Press 1996.
2.       Raj Kumar, Dr. Anil Kr. Kapil, Anupam Bhatia ,“Modified Tree Classifiation in Data Mining Global Journal of Computer Science and Technology, Vol. 12, Issue 2 (Ver. 1.0), 2012, pp. 59-62

3.       http://www.liacc.up.pt/ML/statlog/datasets/diabetes/diabetes.doc.html

4.       R.Quinlan, “C.5: Programs for  Machine learning,” Morgan Kaufimann, 1993

5.       Jaiwei Han, Micheline Kkamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006, pp 360-361


183-185

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

35.

Authors:

Shilpa Tiwari, Sunil Jalwania, Ajay Kumar Bairwa

Paper Title:

A New Design of Borosilicate Crown Glass Photonic Crystal Fiber to Minimize the Dispersion

Abstract:    In this design we present a defected core photonic crystal fiber using borosilicate crown glass .This paper shows that how the central defected air hole ,controls the  dispersion  of a photonic crystal fiber .For this purpose ,Finite difference time domain (FDTD) method with the perfectly matched layers (PML) boundary conditions has been used to investigate the result. It is possible to have high negative dispersion, by varying the size of defected core. It is also shown that borosilicate glass PCF provides much higher negative dispersion as compared to silica PCF of the same structure, so such PCF have high potential to be used as a dispersion compensating fiber in optical communication.

Keywords:
   Borosilicate Crown glass, Dispersion, Photonic Crystal Fiber, Scalar Effective Index Method.


References:

1.        W Jin ,HF Xuan and H L Ho ;sensing with hollow-core photonic bandgap fibers, Meas Sci. Technol. 21(2010) 094014(12 pp)
2.        T.A. Birks, J.C. Knight, P.S.J. Russell, Endlessly single-mode photonic crystal fiber, Opt. Lett. 22 (13) (July 1997) 961–963.

3.        S. Haxha, H. Ademgil, Novel design of photonic crystal fibers with low confinement losses, nearly zero ultra-flatted chromatic dispersion, negative chromatic dispersion and improved effective mode area, Opt. Commun. 281 (2) (January 2008) 278–286.

4.        MATSUI T., JIAN ZHOU, NAKAJIMA K., SANKAWA I., Dispersion-flattened photonic crystal fiber with large effective area and low confinement loss, Journal of Lightwave Technology 23(12), 2005, pp. 4178–4183.

5.        FERRANDO A., SILVESTRE E., MIRET J.J., ANDRES P., Nearly zero ultraflattend dispersion in photonic crystal fibers, Optics Letters 25(11), 2000, pp. 790–792.

6.        TZONG-LIN WU, CHIA-HSIN CHAO, A novel ultraflattened dispersion photonic crystal fiber, IEEE Photonics Technology Letters 17(1), 2005, pp. 67–69.

7.        FERRANDO A., SILVESTRE E., ANDRES P., MIRET J., ANDRES M., Designing the properties of dispersion--flattened photonic crystal fibers, Optics Express 9(13), 2001, pp. 687–697.

8.        REEVES W.H., KNIGHT J.C., RUSSELL P.ST.J., ROBERTS P., Demonstration of ultra-flattened dispersion in photonic crystal fibers, Optics Express 10(14), 2002, pp. 609–613.

9.        SAITOH K., KOSHIBA M., HASEGAWA T., SASAOKA E., Chromatic dispersion control in photonic crystal fibers: Application to ultra-flattened dispersion, Optics Express 11(8), 2003, pp. 843–852. Theoretical design of a large effective mode area ... 683

10.     RENVERSEZ G., KUHLMEY B., MCPHEDRAN R., Dispersion management with microstructured optical fibers: Ultra-flattened chromatic dispersion with low losses, Optics Letters 28(12), 2003, pp. 989–991.

11.     FLOROUS N., SAITOH K., KOSHIBA M., The role of artificial defects for engineering large effective mode area, flat chromatic dispersion, and low leakage losses in photonic crystal fibers: Towards high speed reconfigurable transmission platforms, Optics Express 14(2), 2006, pp. 901–913.

12.     Saeed Olyaee and Fahimeh Taghipour; A new design of photonic crystal fiber with ultra-flattened dispersion to simultaneously minimize the dispersion and confinement loss;Journal of Physics: conference series – 276(2011)012080.

13.     Shahran Mohammadnejad, Nasrin.Ehteshami ;Novel design to compensate dispersion for index-guiding photonic crystal fiber with defected core; 2nd International Conference on Mechanical and Electronics Engineering (ICMEE 2010)

14.     Kunimasa. Saitoh, Nikolaos .Florous, and Masanori.Koshiba, Ultraflattened chromatic dispersion controllability using a defected- core photonic crystal fiber with low confinement losses, Optics Express, Vol. 13, No. 21, pp,8365-837I,2005,

15.     G.P.Agarwal ,Non linear Fiber Optics, third ed. (Academic Press, New York, 1995)

16.     Razzak S M A, Khan M A G, Namihira Y, and Hussain M Y 2008 Optimum design of a dispersion managed photonic crystal fiber for nonlinear optics applications in telecom systems 5th International Conference on Electrical and Computer Engineering ICECE 2008, Bangladesh, IEEE 2008

17.     Reeves W H, Knight J C, Russell P S J, and Roberts P J 2002 Demonstration of ultra-flattened dispersion in photonic crystal fibers Opt. Express, 10, 609


186-189

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

36.

Authors:

Manas Kumar Parai, Debajyoti Misra, Banasree Das

Paper Title:

CPLD Based Speed Controller of a DC Motor Operated Through Cellphone

Abstract:    In this paper a new method has been developed to control the operation of a DC motor remotely. It becomes very much advantageous if a DC motor is controlled using a cellphone. In order to do that the proposed method uses a complex programmable logic device (CPLD). The control input of CPLD is given by Dual Tone Multi-Frequency Signaling (DTMF) decoder. The decoder is connected with a cellphone. It becomes very much beneficial as range of controlling becomes very much wider i.e equivalent to coverage area of the service provider. It is known that CPLD provides quick implementation and fast hardware verification. It gives facilities of reconfiguring the design construct unlimited number of times. The function of CPLD is to process the data coming from the output of DTMF decoder and to create proper duty cycle PWM output, which will control the speed of DC Motor. Hardware is implemented using CPLD trainer kit (model: UNI-BSX-M1) and software is written using VHSIC hardware description language (VHDL) as a pattern tool. 

Keywords:
   DC motor, CPLD, VHDL, PWM, DTMF.


References:

1.       S. M. Bashi, I. Aris and S.H. Hamad, “Development of Single Phase Induction Motor Adjustable Speed Control Using M68HC11E-9 Microcontroller,” Journal of Applied Sciences 5 (2), pp. 249-252.
2.       T. Kenjo and A. sugawara, “stepping motors and their Microprocessor controls”, 2nd Edition, Oxford university press, 1994.

3.       Kevin Skahill, VHDL for programmable logic , Addition-Wesley Longman Publishing Co, boston, MA, USA, 1996.

4.       Power Semiconductor Applications , DC Motor Application notes , Chapter 3  Philips Semiconductors.

5.       Bhasker J.,VHDL Primer, Englewood Clit. Nj: Prentice hall 3rd edition 1999.

6.       Mohini Ratna Chaurasia, Nitin Naiyar , “Stepper Motor Controller using XC9572 CPLD through Mobile As a Remote” , International Journal of Soft Computing and Engineering, ISSN: 2231-2307, Volume-1, Issue-6, January 2012.

7.       Suman Khakurel, Ajay Kumar Ojha , Sumeet  Shrestha, Rasika N. Dhavse “Mobile  Controlled Robots for regulating DC motor s and their Domestic Application” International Journal of Scientific and Engineering Reserch, Volume 1, Issue 3, December 2010 1 ISSN 2229-5518.

8.       Using Xilinx CPLDs as Motor Controllers, Application Note: Xilinx CPLDs, XAPP940 (v1.0.1) March 23, 2009.

9.       http://www.datasheetcatalog.org/datasheet/texasinstruments/l293d.pdf

10.     Zoonubiya Ali and R.V.Kshirsagar “An open loop stepper motor controller based on CPLD” International Journal of Electronic Engineering Research ISSN 0975 - 6450 Volume 2 Number 2 (2010) pp. 219–228.

11.     Mohini Ratna Chaurasia, Nitin Naiyar , “A research of a new Technique of open loop control algorithm for stepper motor using CPLD” , International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-1, Issue-1, April- 2012.

12.     Douglas.L.Perry “VHDL Programming by Example” Mc.Grawh. USA: Academic 2002, pp. 842- 868


190-193

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

37.

Authors:

Mohammadreza Shahlaei, Seyyed Mohsen Hashemi

Paper Title:

Toward a Pattern Language for a Allocation View in SOA

Abstract:   Using pattern as proved solution helps to achieve SOA strategic goals. Maturity models are parental framework for SOA roadmaps, and Architecture is an important dimension for maturity models. Architecture has different views, Usually Reference architecture and pattern languages present runtime view of architecture dimension. This paper presents pattern language that creates allocation view for service oriented architecture. This view shows mapping between services and enterprise parts.

Keywords:
   service oriented architecture, pattern language, reference architecture, allocation view


References:

1.       F.Meier, Service Oriented Architecture Maturity Models: A guide to SOA Adoption, University of Skovde Sweden, 2006
2.       The Open Group Service Integration Maturity Model (OSIMM), Version 2, The Open Group, 2011

3.       F. Buschmann, R. Meunier, H. Rohnert, P.Sommerlad and M.Stal. Pattern-Oriented Software Architecture, John Wiley&Son, 1996

4.       P.Clements, F.Bachmann, L.Bass, D.Garlan, J.Ivers, R.Little, P. Merson, R.Nord & A. Stafford, Documenting Software Architectures: Views and Beyond, Second Edition. Addison- Wesley  Professional, 2010

5.       A.Arsanjani, Toward a pattern language for Service Oriented Architecture and Integration, Part 1: Build a service eco-system,2005
6.       H.Kilaru , A pattern language for service-oriented architecture, San Jose State University,2006
7.       Thomas Erl, SOA Design Patterns, Prentice Hall, 2009

8.       The Open Group SOA Reference Architecture, Technical Standard,  October 2010 (C104), available at:

9.       www.opengroup.org/bookstore/catalog/c104.htm.

10.     The OASIS Reference Architecture for SOA, Version 1.0, OASIS Standard, 23 April 2008, available at:

11.     http://docs.oasis-open.org/soa-rm/soa-ra/v1.0/soa-ra-pr-01.pdf.

12.     Recommended Practice for Architectural Description of Software- Intensive Systems, 2000, IEEE Product No.: SH94869-TBR.: IEEE Standard No. 1471-2000


194-197

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

38.

Authors:

El-Sayed T. El-kenawy, Ali Ibraheem El-Desoky, Mohamed F. Al-rahamawy

Paper Title:

Extended Max-Min Scheduling Using Petri Net and Load Balancing

Abstract:    Max-min algorithm is based on comprehensive study of the impact of RASA algorithm in scheduling tasks and the atom concept of Max-min strategy. An Improved unique version of Max-min algorithm is proposed to outperform scheduling map at least similar to RASA map in total complete time for submitted jobs. Improved Max-min is based on the expected execution time instead of complete time as a selection basis. We employ Petri nets which are well suited for modeling the concurrent behavior of distributed systems. Experimental results show availability of load balance in small cloud computing environment and total small makespan in large-scale distributed system; cloud computing. In turn scheduling tasks within cloud computing using Improved Max-min demonstrates achieving schedules with comparable lower makespan rather than RASA and original Max-min. 

Keywords:
   Distributed System, Job Dispatching Algorithms and Cloud Computing. Petri net, Load Balance, Quality of Service, Meta Task Scheduling, Max-min Algorithm, Min-min Algorithm


References:

1.        Salim Bitam, "Bees life algorithms for job scheduling in cloud computing", International Conference on Computing and Information Technology, 2012.
2.        Saeed Parsa and Reza Entezari-Maleki , "RASA: A New Grid Task Scheduling Algorithm", International Journal of  Digital Content Technology and its Applications,Vol. 3, pp. 91-99, 2009.

3.        I. Foster, and C. Kesselman, The Grid 2: Blueprint for a New Computing Infrastructure,  Second Edition, Elsevier and Morgan Kaufmann Press, 2004.

4.        L. Chunlin, et al, "QoS based resource scheduling by computational economy in computational grid,"  Journal of Information Processing Letters, Vol. 98, pp. 119-126, 2006.

5.        X. He, X-He Sun, and G. V. Laszewski, "QoS Guided Min-min Heuristic for Grid Task Scheduling," Journal of Computer Sci. & Technology, Vol. 18, pp. 442-451, 2003.

6.        L. Mohammad Khanli, and M. Analoui,  "Resource Scheduling in Desktop Grid by Grid-JQA," The 3rd International Conference on Grid and Pervasive Computing, IEEE, 2008.

7.        L. Mohammad Khanli, and M. Analoui, "Grid_JQA: A QoS Guided Scheduling Algorithm for Grid Computing," The Sixth International Symposium on Parallel and Distributed Computing (ISPDC’07), IEEE, 2007.

8.        E. Elmroth, et al, "Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions," J. of Future Generation Computer Systems, Vol. 24, pp.585-593, 2008.

9.        B.T. Benjamin Khoo, B. Veeravalli, T. Hung, and C.W. Simon See, "A multi-dimensional scheduling scheme in a Grid computing environment," Journal of  Parallel and Distributed Computing, Vol. 67, pp. 659-673, 2007.

10.     B. Yagoubi, and Y. Slimani, "Task Load Balancing Strategy for Grid Computing," Journal of Computer Science, Vol. 3, No. 3, pp. 186-194, 2007.

11.     M. Maheswaran, Sh. Ali, H. Jay Siegel, D. Hensgen, and R. F. Freund, "Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems, Journal of Parallel and Distributed Computing, Vol. 59, pp. 107-131, 1999.

12.     William Stallings, Operating Systems, 6th Ed. Chapter 6 - Concurrency: Deadlock and Starvation, Pearson Education International, 2008

13.     T. D. Braun, H. Jay Siegel, N. Beck, L. L. Boloni, M.Maheswaran, A. I. Reuther, J. P. Robertson, M. D.Theys, and B. Yao, "A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems, "Journal of Parallel and Distributed Computing, Vol. 61, pp. 810-837, 2001.

14.     F. Dong, J. Luo, L. Gao, and L. Ge, "A Grid Task Scheduling Algorithm Based on QoS Priority Grouping,"  In the  Proceedings of the Fifth International Conference on Grid and Cooperative Computing (GCC’06), IEEE, 2006.

15.     E. Ullah Munir, J. Li, and Sh. Shi, 2007. QoS Sufferage Heuristic for Independent Task Scheduling in Grid. Information Technology Journal, 6 (8): 1166-1170.

16.     K. Etminani, and M. Naghibzadeh, "A Min-min Max-min Selective Algorithm for Grid Task Scheduling,"The Third IEEE/IFIP International Conference on Internet, Uzbekistan, 2007.

17.     A. Afzal, A. Stephen McGough, and J. Darlington, "Capacity planning and scheduling in Grid computing environment," Journal of Future Generation Computer Systems, Vol. 24, pp. 404-414, 2008.

18.     P. Brucker, Scheduling Algorithms, Fifth Edition, Springer Press, 2007.

19.     R. Buyya, and M. Murshed, "GridSim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing," Journal of Concurrency and Computation Practice and Experience, pp 1175–1220, 2002.

20.     D.I. George Amalarethinam and P. Muthulakshmi, "An Overview of the scheduling policies and algorithms in Grid Computing ", International Journal of Research and Reviews in Computer Science, Vol. 2, No. 2, pp. 280-294, 2011.

21.     T. Kokilavani and Dr. D.I. George Amalarethinam, "Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing", International Journal of Computer Applications, Vol. 20, No. 2, pp. 43-49, 2011.

22.     G. Rozenburg, J. Engelfriet, Elementary Net Systems, in: W. Reisig, G. Rozenberg (Eds.), Lectures on Petri Nets I: Basic Models - Advances in Petri Nets, volume 1491 of Lecture Notes in Computer Science, Springer,1998, pp. 12-121

198-203

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

39.

Authors:

Kuldeep Singh, Rajesh Verma, Ritika Chehal

Paper Title:

Modified Prime Number Factorization Algorithm (MPFA) For RSA Public Key Encryption

Abstract:    The Public key encryption security such as RSA scheme relied on the integer factoring problem. The security of RSA algorithm based on positive integer N, which is the product of two prime numbers, the factorization of N is very intricate. In this paper a factorization method is proposed, which is used to obtain the factor of positive integer N. The present work focuses on factorization of all trivial and nontrivial integer numbers as per Fermat method and requires fewer steps for factorization process of RSA modulus N. By experimental results it has been shown that factorization speed becomes increasing as compare to traditional Trial Division method, Fermat Factorization method, Brent’s Factorization method and Pollard Rho Factorization method.

Keywords:
   Factorization Problem, MPFA, Public Key Cryptography, RSA Scheme.


References:

1.        Rivest, R.; A. Shamir; L. Adleman. "A Method for Obtaining Digital Signatures and Public-Key Cryptosystems", Communications of the ACM 21 (2): 120–126, doi: 10.1145/359340.359342, 1977
2.        Bell, E. T. "The Prince of Amateurs: Fermat.", New York: Simon and Schuster, pp. 56-72, 1986.

3.        João Carlos Leandro da Silva, “Factoring Semi primes and Possible Implications”, IEEE in Israel, 26th Convention, pp. 182-183, Nov. 2010.

4.        Sattar J Aboud, “An efficient method for attack RSA scheme”, IEEE 2009.

5.        L. Scripcariu, M.D. Frunza, "A New Character Encryption Algorithm", Proceedings of the Intern. Conference on Microelectronics and Computer Science, Chisinau, (Republica Moldova), ICMCS 2005, pp. 83 - 86, Sept., 2005.

6.        B. Schneier, Applied cryptography, second edition, NY: John Wiley & Sons, Inc., 1996.

7.        J. Pollard, "Monte Carlo methods for index computation (mod p)",Math. Comp., Vol. 32, pp.918-924, 1978.

8.        R. P. Brent, “An improved Monte Carlo factorization algorithm”, BIT 20 (1980), 176-184. MR 82a:10007, Zbl 439.65001. rpb051.


204-206

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

40.

Authors:

Virender Kumar, G.C. Lall

Paper Title:

A Comparative Study of MIPv6, HMIPv6 & IMS in terms of Cost

Abstract:    Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6) both are the mobility management solution proposed by the Internet Engineering Task Force (IETF) to support IP Mobility. There are various types of parameters which have been proposed and used to describe the system performance in the form of mobility of MIPv6 and HMIPv6. In this paper a comparative study has been described in which the performance of MIPv6 and HMIPv6 with Intelligent Mobility Support (IMS) scheme in terms of cost has been demonstrated using an analytical model. Numerical results demonstrate the performance of MIPv6 and HMIPv6 when certain parameters are changed.

Keywords:
   Access Route, Fast Mobile IPv6, Hierarchical Mobile IPv6, Mobile IPv6, Mobility Anchor Point.


References:

1.        D. Johnson, C. Perkins, and J. Arkko, “Mobility Support in IPv6,” RFC3775, June 2004.
2.        H. Soliman, C. Castelluccia, K. El Malki, and L. Bellier,“Hierarchical Mobile IPv6 Mobility Management (HMIPv6),” RFC4140, 2005.

3.        Shengling Wang, Yong Cui, Sajal K. Das, Wei Li, and Jianping Wu, “Mobility in IPv6: Whether and How to Hierarchize the Network?” 1045-9219/11/$26.00 , 2011 IEEE

4.        Shengling Wang, Yong Cui, Sajal K.Das   “Intelligent Mobility support for IPv6”. 978-1-4244-2413-9/08/$25.00 ©2008 IEEE 

5.        H. Tzeng and T. Przygienda. “On Fast     Address-Lookup Algorithms”. IEEE J. Selected Areas in Communications, vol. 17, no. 6, pp. 1067-1082, 1999.

6.        Y. Chen and M. Huang. “A Novel MAP Selection Scheme by Using Abstraction Node in Hierarchical MIPv6”. Proc. IEEE International Conference on Communications, 2006. pp:5408-5413

7.        H. Xie, S. Tabbane, and D.J. Goodman. “Dynamic Location Area Management and Performance Analysis”. Proc. 43rd IEEE Vehicular Technology Conference, 1993.pp:536-539

8.        M. Yabusaki. “Mobility/Traffic Adaptive Location Management”. Proc. IEEE 56th Vehicular Technology Conference, Vancouver, 2002. pp:1011-1015

9.        W.R. Stevens, TCP/IP Illustrated, Volume 1: The Protocols. Addison Wesley Longman, Inc., 1994.    

10.     J Xie. and I.F.Akyildiz. “A Novel Distributed Dynamic Location Management Scheme for Minimizing Signaling Costs in Mobile IP”. IEEE Transactions on Mobile Computing, vol. 1, no. 3, pp.163-175,2002.


207-210

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

41.

Authors:

I.Krishna Rao, K.santhosh Kumar

Paper Title:

Performance Analysis of Multi User Detectors for Synchronous Ds-Cdma Systems

Abstract:    A Direct sequence code division multiple access (DS-CDMA) is a popular wireless technology. This system suffers from Multiple Access Interference (MAI) caused by Direct Sequence users and near –far effect. Multi-User Detection schemes are used to detect the users’ data in presence of MAI and near- far problem. In this dissertation, we present comparative study between linear multiuser detectors and conventional single user matched filter in DS-CDMA system. Analysis and simulations are conducted in synchronous AWGN channel, and Gold sequence and Kasami sequence are used as the spreading codes. Simulation results depict the performance of three detectors, conventional detector, Decorrelating detector and MMSE (Minimum Mean Square Error) detector. It shows that the performance of these detectors depends on the length of PN code used and Number of users. Linear multiuser detectors perform better than the conventional matched filter in terms of BER performance.

Keywords:
   AWGN CHANNEL, BER, MAI, MMSE.


References:

1.        S. Verdu “Minimum Probability Of Error For Asynchronous Gaussian Multiple Access Channel”. IEEE Transactions on Information Theory,Vol. IT-32, pp.85-96, Jan 1986.
2.        R Lupas and S. Verdu “Linear Multiuser Detectors for Synchronous Code Division Multiple   Access Channels “IEEE Transactions on Information Theory, Vol. 35, pp. 23-136, Jan 1989.

3.        Z. Xie , R. T. Short and C. K. Rushforth “ A Family Of Suboptimum Detectors for Coherent   Multiuser Communications “IEEE Journal on Selected Areas In Communications, Vol. 8, pp.  683-690, May 1990.

4.        M. Honig, U. Madhow and S. Verdu “Blind Multiuser Detection” IEEE Transactions on Information Theory, Vol.41, pp. 944-960, July 1995.

5.        X. Wang and H. V. Poor “Blind Multiuser Detection: IEEE Transactions On Information Theory, Vol.44, pp.677-690, Mar 1998.


211-214

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

42.

Authors:

Sitanath Biswas, Subrat S. Pattnaik, Sweta Acharya

Paper Title:

Improving DDM Performance By Combining Distributed Data Mining And Multi-Agent System

Abstract:    Autonomous agents and multi agent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.

Keywords:
   multi-agent systems, distributed data mining, clustering, privacy, agent- based ddm.


References:

1.        Ajith Abraham, Crina Gros an, and Vitorino Ramos, editors. Swarm Intelligence in Data Mining, volume 34 of Studies in Computational Intelligence. Springer, 2006.
2.        Sung W. Baik, Jerzy W. Bala, and Ju S. Cho  Agent based distributed data mining. Lecture Notes in Computer Science, 3320:42–45, 2004.

3.        S. Bailey, R. Grossman, H. Sivakumar, and A. Turinsky. Papyrus: a system for data mining over local and wide area clusters and super-clusters. In Supercomputing ’99: Proceedings of the 1999 ACM/IEEE conference on Supercomputing (CDROM), page 63, New York, NY, USA, 1999. ACM.

4.        R. J. Bayardo, W. Bohrer, R. Brice, A. Cichocki, J. Fowler, A. Helal, V. Kashyap, T. Ksiezyk, G. Martin, M. Nodine, and Others. InfoSleuth: agent-based semantic integration of information in open and dynamic environments. ACM SIGMOD Record, 26(2):195–206, 1997.

5.        F. Bergenti, M. P. Gleizes, and F. Zambonelli. Methodologies And Software Engineering For Agent Systems: The Agentoriented Software Engineering Handbook. Kluwer Academic Publishers, 2004.

6.        A. Bordetsky. Agent-based Support for Collaborative Data Mining in Systems Management. In Proceedings Of The Annual Hawaii International Conference On System Sciences, page 68, 2001.

7.        R. Bose and V. Sugumaran. IDM: an intelligent software agent based data mining environment. 1998 IEEE Internationa Conference on Systems, Man, and Cybernetics, 3, 1998.

8.        L. Cao, C. Luo, and C. Zhang. Agent-Mining Interaction: An Emerging Area. Lecture Notes in Computer Science, 4476:60, 2007.

9.        L. Cao, J. Ni, J. Wang, and C. Zhang. Agent Services-Driven Plug and Play in the FTRADE. In 17th Australian Joint Conference on Artificial Intelligence, volume 3339, pages 917–922. Springer, 2004.

10.     J. Dasilva, C. Giannella, R. Bhargava, H. Kargupta, and M. Klusch. Distributed data mining and agents. Engineering Applications of Artificial Intelligence, 18(7):791–807, October 2005.

11.     S. Datta, K. Bhaduri, C. Giannella, R. Wolff, and H. Kargupta. Distributed data mining in peer-to-peer networks. Internet Computing, IEEE, 10(4):18–26, 2006.

12.     W. Davies and P. Edwards. Distributed Learning: An Agent-Based Approach to Data-Mining. In Proceedings of Machine Learning 95 Workshop on Agents that Learn from Other Agents, 1995.

13.     U. Fayyad, R. Uthurusamy, and Others. Data mining and knowledge discovery in databases. Communications of the ACM, 39(11):24–26, 1996.

14.     C. Giannella, R. Bhargava, and H. Kargupta. Multi-agent Systems and Distributed Data Mining. Lecture Notes in Computer Science, pages 1–15, 2004.

15.     V. Gorodetskiy. Interaction of agents and data mining in ubiquitous environment. In Proceedings of the 2008 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08), 2008.

16.     V. Gorodetsky, O. Karsaev, and V. Samoilov. Multi-Agent Data and Information Fusion. Nato Science Series Sub Series Iii Computer And Systems Sciences, 198:308, 2005.

17.     V. Gorodetsky, O. Karsaev, and V. Samoilov. Infrastructural Issues for Agent-Based Distributed Learning. In Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology, pages 3–6. IEEE Computer SocietyWashington, DC, USA, 2006.

18.     V. Gorodetsky, O. Karsaev, V. Samoylov, and S. Serebryakov. P2P Agent Platform: Implementation and Testing. In Proceedings International Workshop ”Agent and Peer-to  Peer Computing”(AP2PC-2007) associated with AAMAS-07. Honolulu, Hawaii, pages 21–32, 2007.

19.     V. Gorodetsky and I. Kotenko. The Multiagent Systems for Computer Network Security Assurance: frameworks and case studies. In IEEE International Conference on.


215-221

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

43.

Authors:

Arvind Kumar Upadhyay, A. K. Misra

Paper Title:

Prioritizing Test Suites Using Clustering Approach in Software Testing

Abstract:    Prioritizing test cases enables test suites to be scheduled in a manner that optimize the objective of reducing effort to test exhaustively. Of various techniques/methods available, a need is felt to further improve existing schemes. In this paper, we propose clustering based prioritization and support our effort with average percentage of fault detection (APFD) measure. We target the significant test suites to get priority. Our method can considerably help realization of overall clustering approach.  

Keywords:
   APFD


References:

1.        Gregg Rothermel, Roland H.Untch,Mary Jean Harrold,”Prioritizing Test Cases For Regression Testing,” IEEE Transaction on Software Engineering, Vol.27, No.10 October 2001.
2.        G. J. Myers. The Art of Software Testing. Revised and Updated by Tom Badgett and Todd M.Thomas with Corey Sandler, John Wiley & Sons, Inc, Second Edition. 2004, pp. 1-255.

3.        Boris Beizer. Software System Testing and Quality Assurance. Van Nostrand, New York, 1984.

4.        Boris Beizer. Software Testing Techniques. Van Nostrand Reinhold, Inc, New York NY, 2nd edition, 1990. ISBN 0-442-20672-0.

5.        Richard A. DeMillo, W. Michael McCracken, Rhonda  J. Martin, and John F. Passafiume.Software and Evaluation.Benjamin/Cummings, Menlo Park CA,  1987.

6.        Siripong Roongruangsuwan, Jirapun Daengdej,”Test Case prioritization techniques,” Journal of theoretical and applied informational technology,2005

7.        Mao ye, boqinFeng, yao Lin 7Li Zhu. “Neural Networks Based Test Case Selection” Proc of IEEEtransactions,2006.

8.        T.Y. Chen, Pak-lok poon, t.h. Tse.”A choice Relation framework for supporting Category-partition Test Case generation” IEEE transactions on software Engineering, vol.29, No.7, July 2003.

9.        Sebastian Elbaum, Alexey G.Malishevsky, Gregg Rothermel.”Test Case Prioritization” IEEE transactions on software Engineering, vol.28, No.2, February 2002.

10.     Kuo –Chung Tainand Yu Lei. “A Test generation strategy for Pairwisetesting” IEEE transactions on software Engineering, vol.28, No.1, January 2002.

11.     Christoph C. Michael, gary McGraw, Michael A. Schatz. “Generating software test data by  Evolution”. IEEE transactions on software Engineering, vol.27, No.12, December 2001.

12.     Shino yahoo & Mark Harman ,Paolo tonella & Angelo susi, “Clustering test cases to Effective & scalable prioritisation incorporating  Expert knowledge,” ISSTA 09,July 19-23, 2009, Chicago, USA.

13.     Gregg rothermel , roland h. untch, chengyun chu, mary jean harrold, “ Test case prioritization : An Empirical study,” Proceedings of the international conference on software maintenance, oxford ,U.K., September, 1999, IEEE  

14.     Wei-Tek Tsai and Lian Yu, Feng Zhu, Ray Paul. “Rapid embedded system testing using verification patterns” . IEEE software 2005.

15.     S. G. Elbaum, A. G. Malishevsky, and G. Rothermel. Prioritizing test cases for regression testing. In International Symposium on Software  Testing and  Analysis, pages 102–112. ACM Press, 2000.

16.     Martina marre and Antonia Bertolino, “using spanning sets for coverage testing”. IEEE transactions on software Engineering, vol.29, No.11,  November 2003.

17.     H. Do, S. G. Elbaum, and G. Rothermel. Supporting controlled experimentation with testing techniques: an infrastructure and its potential impact. Empirical Software Engineering, 10 (4): 405–435, 2005.

18.     H. Do, G. Rothermel, and A. Kinneer. Prioritizing JUnit Test cases: an empirical assessment and cost-benefits analysis.Empirical Software Engineering, 11: 33–70, 2006.

19.     P. M. Duvall, S. Matyas, and A. Glover. Continuous Integration: Improving Software Quality and Reducing Risk. Addison Wesley, Upper Saddle River, NJ, 2007.

20.     S. G. Elbaum, A. G. Malishevsky, and G. Rothermel.Prioritizing test cases for regression testing. ACM SIGSOFT Software Engineering Notes, 25 (5): 102–112, 2000.

21.     S. G. Elbaum, A. G. Malishevsky, and G. Rothermel. Test case prioritization: a family of empirical studies. IEEE Transactions on Software Engineering, 28 (2): 159–182, 2002.

22.     S. G. Elbaum, G. Rothermel, S. Kanduri, and A. G. Malishevsky. Selecting a cost-effective test case Prioritization technique. Software Quality Control, 12 (3):   185–210, 2004.

23.     David Gustafson,”Theory and Problem of Software Egineering,” Computing and Information science Department Kansas state University.


222-226

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

44.

Authors:

Ugwuishiwu C.H, Udanor C., Ugwuishiwu B.O

Paper Title:

Application of ICT in Crop Production

Abstract:    The rapid advancement in Information and Communications Technologies (ICTs) has given rise to new applications that were impossible just few years ago. Agriculture is an important sector with the majority of the rural population in developing countries depending on it. The sector faces major challenges of enhancing production in a situation of dwindling natural resources necessary for production. ICT plays an important role in challenging and uplifting the livelihoods of the rural populace using an agro computer-based information system. This paper proposes an Agro-Information System that enables a farmer to have relevant information about a crop, such as the varieties and other requirements like soil type, temperature, type and quantity of fertilizer, time of planting, time of maturity, planting distance, diseases, pest, pest and Disease control measures, rainfall, sunshine, etc. of that crop. The level of application of this information determines the volume and efficiency of the crop yield. An AIS software is designed and implemented which helps the farmer achieve the afore-mentioned objectives.

Keywords:
   Agriculture, Crop yield, ICT, Crop requirements, Agro Information System (AIS).


References:

1.        Anyanwu A.C; Anyanwu B.O.; Anyanwu V. A.(1999). A textbook of Agriculture Sciencefor schools and colleges. Fifth Edition 1999. ISBN 975-175-350-1
2.        Krishna Kumar P.S. Mapping & Preliminary Evaluation of ICT Applications Supporting  Agricultural Development retrieved from www.acdivoca.org/ accessed on 2nd  feb, 2012

3.        Huggan, R.D.; E.D. Hunt; and M.C. Van den Berg (1994).  From research through information… into production In: New information technologies in agriculture, “Quarterly

4.        Uguru, F.N.(2001). Information technology: Access capability and use among Administrators of Agro-Technologies Transfer program- mmes in South-Eastern Nigeria Ph.D Thesis.

5.        Chowdhury, N. (2001).  Appropriate technology, for Stainable Food Security: ICTs. In Pinstrup- Anderson, P.(Ed) Appropriate technology for sustainable food security.  2020 Vision Focus7. IFPRI, Washington DC.

6.        What is ICT retrieved from tutor2u.net/business/ict/intro_what_is_ict.htm - Cached accessed on 3rd  Feb, 2012

7.        ICT in Agriculture retrieved from en.wikipedia.org/wiki/ ICT_in_agriculture – Cached accessed on 2nd  Feb, 2012

8.        Odigboh, E.U. and Onwualu, A.P. (1994). Mechanization of Agriculture in Nigeria:  a critical appraisal “Journal of Agricultural Technology” 2 (2): 1-58.

9.        How ICT can make a difference in agricultural livelihoods retrieved from www.iicd.org/files/ICT%20and%20agricultural%20livelihoods.pdf accessed on 2nd  Feb, 2012

10.     ICT in Agriculture retrieved from www.ekrishinaip.in/ - Cached accessed on 2nd  Feb, 2012

11.     Ugwuishiwu C.H. (2004). Application of Information and Communication Technology in Agriculture. BSc. Project

12.     Adeyinka, F.M.(1997).Technology response of firms to telecommunication development in Nigeria .An interim project report submitted to the African Technology studies (ATPS), Ibadan: NISER.


227-231

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

45.

Authors:

R.Hari Kumar, B.Vinoth Kumar, K.Karthik, Jagdsh L.K.Chand, C.Navin Kumar

Paper Title:

Performance Analysis of Singular Value Decomposition (SVD) and Radial basis Function (RBF) Neural Networks for Epilepsy Risk Levels Classifications from EEG Signals

Abstract:   The objective of this paper is to compare the performance of Singular Value Decomposition (SVD) method and Radial Basis Function (RBF) Neural Network for optimization of fuzzy outputs in the epilepsy risk level classifications from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient.  SVD and RBF neural network is exploited on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s epilepsy risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV).

Keywords:
  Singular Value Decomposition, Radial Basis Function Neural Network, Fuzzy Techniques, EEG Signals, Epilepsy risk levels.


References:

1.       Joel.J etal, “Detection of seizure precursors from depth EEG using a sign periodogram transform,” IEEE Transactions on Bio Medical Engineering, vol, 51 no,4, pp-449-458, April 2004.
2.       D.Zumsteg and H.G.Wieser, “Presurgical evaluation: current role of invavasive EEG,”Epilepsia,vol.41, No. suppl 3,pp, 55-60,2000.

3.       K P Adlassnig, “Fuzzy Set Theory in Medical diagnosis”, IEEE Transactions on Systems Man Cybernetics, 16 pp 260-265, March 1986.

4.       Alison A Dingle et al, “A Multistage system to detect epileptic form activity in the EEG”, IEEE Transactions on Biomedical Engineering, 40(12) pp 1260-1268, December 1993.

5.       Haoqu and Jean Gotman, “A patient specific algorithm for detection onset in long-term EEG monitoring possible use as warning device”, IEEE Transactions on Biomedical Engineering, February 1997, 44(2): 115-122.

6.       R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharathi, “Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,” I.E. India Journal of Interdisciplinary panels, vol.86, no.1, May 2005.

7.       Sankeun lee, Monson, “Properties of the Singular Value Decomposition for Efficient Data Clustering,” IEEE Signal Processing Letters,11(11), pp 862-866, November 2004.            

8.       Nurettin Acir etal., “ Automatic Detection of Epileptiform Events In EEG by A Three-Stage Procedure Based on Artificial Neural Networks,” IEEE transaction on Bio Medical Engineering  52,1 pp30-40,January 2005.

9.       Hwang et al., “Recognition of Unconstrained Handwritten Numerals by A Radial Basis Function Network Classifier,” Pattern Recognition Letters,   18, pp-657-664, 1997.

10.     Drazen.S.etal., “Estimation of difficult –to- Measure process variables using neural networks,”  Proceedings  of  IEEE  MELECON 2004,May 12-15,2004 Dubrovnik,Croatia,pp-387-390.

11.     Moreno.L. etal., “ Brain maturation estimation using neural classifier,” IEEE Transaction of Bio Medical Engineering ,42,2 pp-428-432,April 1995.

12.     Tarassenko.L, Y.U.Khan, M.R.G.Holt, “Identification of inter-ictal spikes in the EEG using neural network analysis,” IEE Proceedings –Science Measurement Technology, 145,6 pp-270-278,November 1998.

13.     H.Demuth and M..Beale, “Neural network tool box: User’s guide, Version 3.0,” the math works, Inc., Natick, MA, 1998.

14.     G.Fung etal, “Fault Detection In Inkjet Printers Using Neural Networks,” Proceedings of IEEE  SMC, 2002.

15.     Dr. (Mrs). R.Sukanesh R.Harikumar, “A Simple Recurrent Supervised Learning Neural Network for Classification of Epilepsy Risk Levels from EEG Signals,” I.E. India
Journal of Interdisciplinary panels, vol.87, no.2,pp 37-43, November 2006.

16.     Dr. (Mrs). R.Sukanesh R.Harikumar, “A Patient Specific Neural Networks (MLP) for Optimization of Fuzzy Outputs in Classification of Epilepsy Risk Levels from EEG Signals,” Journal of Engineering Letters  (EL),vol.13, No:2 ,pp 50-56,Sep 2006

17.     Dr. (Mrs). R.Sukanesh R.Harikumar, “A Comparison of Genetic Algorithm and Neural Network(MLP) In Patient Specific Classification of Epilepsy Risk Levels from EEG Signals,” Journal of Engineering Letters (EL), vol.14, No:1 ,pp 96-104,March 2007.

18.     Dr. (Mrs). R.Sukanesh R.Harikumar, “Analysis of Fuzzy Techniques and Neural Networks (RBF&MLP) in Classification of Epilepsy Risk Levels from EEG Signals ,” IETE Journal of Research 2007 vol.53, no 5, pp  465-474,Sep-Oct 2007.

19.     Dr.(Mrs). R.Sukanesh R.Harikumar, “A Comparison of Elman and MLP feed forward neural networks for Classification of Epilepsy Risk Level using EEG Signals,” AMSE Journal Modeling C vol.67, no 1, pp  43-60, 2006.


232-236

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

46.

Authors:

Katkar Anjali S., Kulkarni Raj B.

Paper Title:

Web Vulnerability Detection and Security Mechanism

Abstract:    Web applications consist of several different and interacting technologies. These interactions between different technologies can cause vast security problems. As organizations are taking their businesses online they make their systems accessible to the world. They might have a firewall in place and possibly even their web server is running an up-to-date version of its software but that is not enough to protect their resources. The research areas of this paper outline the major publicly reported security vulnerability in recent year’s strong growth of the web applications. Unvalidated Input, Broken Access Control, Broken Authentication and Sessions Management, Insecure Configuration Management, Improper Error Handling, Parameter Modification, Cookie Modification and Directory Traversal have been the most dominant class of web vulnerabilities. Further, the research includes methods for detecting the vulnerabilities and then providing security mechanism to protect web application from those vulnerabilities. The result shows the security mechanisms against the attacks and vulnerabilities. Securing the websites against these vulnerabilities is very difficult and challenging task as day to day new techniques for attacks are invented, so the study of various types of vulnerabilities, detecting the attacks and providing solution for these vulnerabilities is essential part in internet world.

Keywords:
   Security, Vulnerability detection and Web applications.


References:

1.        Top 10 web security threats part-1 URL:http://www.emate-econtent.org/security/top-10-web-security-threats-part-1/
2.        Top 10 web security threats part-2 URL: http://www.emate-econtent.org/security/top-10-web-security-threats-part-2/

3.        A Survey on Web Application Vulnerabilities (SQLIA, XSS) Exploitation and Security Engine for SQLInjectionCommunication Systems and Network Technologies (CSNT), 2012 International Conference on Date of Conference: 11-13 May 2012 Author (s):  Johari, R.;Sharma, P. USIT, GGSIP Univ., Delhi, India

4.        Security vulnerabilities in modern web browser architecture MIPRO, 2010 Proceedings of the 33rd International Convention Date of Conference: 24-28 May 2010 Author(s): Silic, Marin;Krolo, Jakov ;  Delac, Goran  Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Croatia

5.        Testing and Comparing Web Vulnerability Scanning Tools for SQL Injection and XSS Attacks Dependable Computing, 2007.PRDC 2007. 13th Pacific Rim International Symposium on Date of Conference: 17-19 Dec. 2007 Author(s): Fonseca, J. CISUC - Polytechnic Inst. of Guardia, Guardia Vieira, M. ;  Madeira, H.

6.        Using web security scanners to detect vulnerabilities in web services Dependable Systems& Networks, 2009. DSN' 09. IEEE/IFIP International Conference on Date of Conference: June 29 2009-July 2 2009 Author(s): Vieira, M.;Antunes, N. ;  Madeira, H. Dept. of Inf. Eng., Univ. of Coimbra, Coimbra, Portugal

7.        OPEN WEB APPLICATION SECURITY PROJECT; OWASP Top 10-2010 PDF URL:


237-241

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

47.

Authors:

Sunitha Yeddula, K.Lakshmaiah

Paper Title:

Investigation of Techniques for Efficient & Accurate Indexing for Scalable Record Linkage & Deduplication

Abstract:    Record linkage is the process of matching records from several databases that refer to the same entities. When applied on a single database, this process is known as deduplication. Increasingly, matched data are becoming important in many applications areas, because they can contain information that is not available otherwise, or that is too costly to acquire. Removing duplicate records in a single database is a crucial step in the data cleaning process. and also, the complexity of the matching process becomes one of the major challenge.  Various indexing techniques have been developed for record linkage and deduplication. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious non-matching pairs, while at the same time maintaining high matching quality. This paper presents a survey of variations of six indexing techniques. Their complexity is analyzed, and their performance and scalability is evaluated within an experimental framework using both synthetic and real data sets.

Keywords:
   Data matching, data linkage, entity resolution, index techniques, blocking, experimental evaluation, scalability.


References:

1.        T. Churches, P. Christen, K. Lim, and J. X. Zhu, “Preparation of name and address data for record linkage using hidden Markov models,” BioMed Central Medical Informatics and Decision Making, vol. 2, no. 9, 2002.
2.        R. Baxter, P. Christen, and T. Churches, “A comparison of fast blocking methods for record linkage,” in ACM SIGKDD’03 workshop on Data Cleaning, Record Linkage and Object Consolidation, Washington DC, 2003, pp. 25–27.

3.        W. E. Winkler, “Overview of record linkage and current research directions,” US Bureau of the Census, Tech. Rep. RR2006/02, 2006.

4.        . P. Fellegi and A. B. Sunter, “A theory for record linkage,” Journal of the American Statistical Society, vol. 64, no. 328, 1969.

5.        D. E. Clark, “Practical introduction to record linkage for injury research,” Injury Prevention, vol. 10, pp. 186–191, 2004.

6.        C. W. Kelman, J. Bass, and D. Holman, “Research use of linked health data – A best practice protocol,” Aust NZ Journal of Public Health, vol. 26, pp. 251–255, 2002

7.        H. Hajishirzi, W. Yih, and A. Kolcz, “Adaptive near-duplicate detection via similarity learning,” in ACM SIGIR’10, Geneva, Switzerland, 2010, pp. 419–426.

8.        W. Su, J. Wang, and F. H. Lochovsky, “Record matching over query results from multiple web databases,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 4, pp. 578–589, 2009.

9.        I. H. Witten, A. Moffat, and T. C. Bell, Managing Gigabytes, 2nd ed. Morgan Kaufmann, 1999.

10.     M. A. Hernandez and S. J. Stolfo,“The merge/purge problem for large databases,” in ACM SIGMOD’95, San Jose, 1995.

11.     P. Christen, “Towards parameter-free blocking for scalable record linkage, “Department of Computer Science, The Australian National University, Canberra, Tech. Rep. TR-CS-07-03, 2007.

12.     A. Aizawa and K. Oyama, “A fast linkage detection scheme for multi-source information integration,” in WIRI’05, Tokyo, 2005.

13.     W. W. Cohen and J. Richman, “Learning to match and cluster large high-dimensional data sets for data integration,” in ACM SIGKDD’02, Edmonton, 2002, pp. 475–480.

14.     A. McCallum, K. Nigam, and L. H. Ungar, “Efficient clustering of high-dimensional data sets with application to reference matching,” in ACM SIGKDD’00, Boston, 2000, pp. 169–178.

15.     L. Jin, C. Li, and S. Mehrotra, “Efficient record linkage in large data sets,” in DASFAA’03, Tokyo, 2003, pp.137.


242-246

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

48.

Authors:

Suvanam Sasidhar Babu, A. Chandra Sekhara Sarma, Yellepeddi Vijayalakshmi, N.V.Kalyankar

Paper Title:

Scalability of Multi Tier Transactions Towards Data Confidentiality For Cloud Applications

Abstract:    Cloud computing provides dynamically scalable and virtualized resources as a service over the network at a nominal initial investment. Data-center works as backbone in cloud computing there a large number of servers are networked to host computing & storage needs of the users. We study and seek to improve the confidentiality of application data stored on third-party computing clouds. Scalable database services allow data query only by primary key rather than supporting secondary-key or join queries. We propose to identify and encrypt all functionally encrypt able data, sensitive data that can be encrypted without limiting the functionality of the application on the cloud. Many data intensive applications produce enormous amounts of data which travel on cloud network. As the cloud users grow, cloud architecture should accommodate movement of voluminous data to avoid data congestion in the network. Many other applications such as payment and online auction services cannot afford any data inconsistency. Cloud computing model provides benefits for private enterprise environments where a significant physical infrastructure already exists. Private cloud management platforms have been emerging in the last several years providing new opportunities for efficient management of internal infrastructures leading to high utilization.

Keywords:
  Cloud computing, Green computing, Data confidentiality, Program analysis, Dynamic resource allocation, Scalability, Performance, ACID, SAAS (Software as a Service).


References:

1.        Chetna Dabas and J.P Gupta, “A Cloud Computing Architecture Framework for Scalable RFID,” Proceeding of the International Multi Conference of Engineers and Computer Scientists (IMECS 2010,) Hong Kong, Vol 1, March 2010, pp 217-220.
2.        Google AppEngine: http://code.google.com/appengine/.

3.        Amazon Web Service: http://aws.amazon.com/.

4.        Eucalyptus: http://www.eucalyptus.com/.

5.        Windows Azure: http://www.microsoft.com/windowsazure/. CLAUSE, J., AND ORSO, A. Penumbra: automatically identifying failure-relevant inputs using dynamic tainting. In Proc. of ISSTA (2009).

6.        MOLNAR, D., AND SCHECHTER, S. Self Hosting vs. Cloud Hosting: Accounting for the security impact of hosting in the cloud. In Proceedings of the Ninth Workshop on the Economics of Information Security (2010).

7.        M. Armbrust, A. Fox and R.Griffith etc. Above the Clouds: A Berkeley View of Cloud Computing. Technical Report No. UCB/EECS-2009-28. University of California at Berkley, USA, Feb. 10, 2009.

8.        Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms,  Volume 41, Number 1, Pages: 23-50, New York, USA, January, 2011.


247-250

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

49.

Authors:

Dhowmya Bhatt, Ekata Gupta

Paper Title:

Security Approaches in Wireless Home Networks – The Incipient drifts and Surviving Glitches

Abstract:    The usage of wireless Networks have almost become inevitable for the human community. From a simple point to point communication to extra-large-area covering networks, wireless communication is a significant part and parcel of technological progress that mankind has ever made. As the vast convention of wireless has increased than ever before, it also becomes the matter of concern of how to protect these networks from attacks and attackers and ensure the users safe and secured communication. It is worthwhile to note that the Right to the protect  the moral and material interests resulting from any scientific production owned by an individual is one of the basics of Human rights declaration. Hence it is very much essential to implement and practice methods to secure wireless networks in a very strict manner so as to ensure confidentiality of the information transmitted and ensure people involved communication some absolutely reliable safety. The attempt to safeguard home wireless networks will greatly benefit mankind which not otherwise would be a huge loss to the entire human community. This Paper is an effort to throw light on the upcoming trends in securing wireless networks and the external threats to these networks. Possible future directions are also addressed towards the end.

Keywords:
   security, privacy, attacks, intruders


References:

1.        C. Rapier and B. Bennett, "High speed bulk data transfer using the database chainingl", MG '08: Proc. of 15th ACM Mardi Gras Conference. pp. 1-7, 2008.
2.        M. Mathis, J. Heffner, P. O'Neil, P. Siemsen, "Pathdiag: Automated SVChosting", PAM 2008.

3.        A. Adams, M. Mathis, "A System for Flexible Network Performance Measurement," Proceedings of INET 2000, July 2000.

4.        V. Paxson, A. Adams, M. Mathis, " Experiences with chaining," Proceedings of the Passive and Active Measurement Workshop 2000, April 2000.

5.        A. Adams, A. J. Lee, and D. Mossé, "Receipt-Mode Trust Negotiation: Efficient Authorization Through Outsourced  database Interactions," in Proceedings of the Sixth ACM Symposium on Information, Computer, and Communication Security (ASIACCS 2011), March 2011.

6.        J. C. Honig, D. Katz, M. Mathis, Y. Reckhter and J. Y. Yu, “Applications of database chaining in the Internet”, June 1990, RFC1164 USC/Information Sciences
Institute.

7.        R. L. Clay, J. Mahdavi, G. J. McRae, “Scheduling in the Presence of Uncertainty in database chaining. The Linear Assignment Problem,” Proceedings of AICHE National Meeting, August, 1991.

8.        www.quikr.com.

9.        http://dbmmo.com/

10.     www.it.iitb.ac.in/~palwencha/assg/wlan_sec.pdf www.ece.tamu.edu/~reddy/ee689.../indira-monica.pdf

11.     www.wireless-technology-advisor.com/ wireless-technology-trends.html

12.     www1.cse.wustl.edu/~jain/cse574-06/ftp/j_2trn.pdf
13.     www.afn.org/~afn48922/downs/wireless/wireless_wan.pdf
14.     www.smallbusinesscomputing.com/.../Wireless-Network-Security-A

251-254

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

50.

Authors:

Suresh Velaga, Sridhar Kovvada

Paper Title:

Efficient Techniques for Denoising of Highly Corrupted Impulse Noise Images

Abstract:   In this paper, different types of impulse noise removal techniques are presented. Because of adaptive nature of mask size depending on the noise quantity in the image, adaptive median filter works better in removing the salt and pepper noise. To show the performance of Adaptive Median filter, median filter, Lee filter, Frost and Kuan filters and DWT and Duqal tree Complex Wavelet Transform are considered. Adaptive median filter is compared with other filters and also the transformations. The superiority of Adaptive Median filter in removing the Highly Corrupted with impulse noise in images are presented. Graphs are drawn between the input PSNR and output PSNR for impulse noise removal techniques.

Keywords:
   Dual-Tree CWT, Adaptive median Filter, DWT.


References:

1.        V. S. Frost, J. A. Stiles, K. S. Shanmugan, J. C. Holtzman. A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 2, pp. 157{166, 1982.
2.        D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel. Adaptive Noise Smoothing Filter for Images with Signal dependent Noise. IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 7, no. 2, pp. 165{177, 1985.

3.        J. S. Lee. Digital Image Enhancement and Noise Filtering by Use of Local Statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 2, pp. 165{ 168, 1980.

4.        M. Dai, C. Peng, A. K. Chan, D. Loguinov. Bayesian Wavelet Shrinkage with Edge Detection for SAR Image Despeckling. IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1642{1648, 2004.

5.        I.A.Ismail, and T.Nabil, "Applying Wavelet Recursive Translation Invariant to Window Low-Pass Filtered Images,". International Journal  of Wavelets, Multiresolution And Information Processing, Vol.2, No.1, March (2004) , p.p.99-110.

6.        Yuan S.Q. and Tan Y.H., "Difference-type Noise Detector for Adaptive Median Filter", Electronics Letters, 42, No.8, pp. 454 – 455, 2006.

7.        Wenbin Luo, "An Efficient Detail-Preserving Approach for Removing Impulse Noise in Images", IEEE Signal Processing Letters, 13, No.7, pp. 413 – 416, 2006.

8.        Deng Ze-Feng, Yin Zhou-Ping, and Xiong You-Lun, "High Probability Impulse Noise-Removing Algorithm Based on Mathematical Morphology", IEEE Signal Processing Letters., 14, No.1, pp.31-34., 2007.

9.        Srinivasan K. S. and Ebenezer D., "A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises" IEEE Signal Processing Letters, 14, No. 3, pp.189 -192, 2007.

10.     Krishnan Nallaperumal, Justin Varghese et.al., “Iterative Adaptive Switching Median Filter”, in proc. of IEEE ICIEA 2006, Singapore, May 2006.

11.     Krishnan Nallaperumal, Justin Varghese et.al. “Salt & pepper impulse noise removal using Adaptive Switching Median Filter”, in proc. Of IEEE Oceans 2006, Singapore, May 2006.

12.     14 Raymond H. Chan, Chung-Wa Ho, and Mila Nikolova, Salt-and- Pepper Noise Removal by Median-type Noise Detectors and Detail- preserving Regularization," July, 2004.

13.     Besdok E. and Emin Yuksel M., Impulsive Noise suppression for images with Jarque-Bera test based median filter, International Journal of Electronics and Communications, Vol.59, pp. 105-110, 2005.

14.     Chen T, Wu HR, Adaptive impulse detection using center weighted median Filters, IEEE Signal Processing Letter, Vol.8, pp. 1-3, 2001.


255-258

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

51.

Authors:

M.P.Singh, Rajnish Vyas

Paper Title:

Requirements Volatility in Software Development Process

Abstract:    Changes in requirements do occur during the software development life cycle. The changes may take place from the initial design phase up to the implementation phase. These change that creep during the development process pose risk to cost and quality of the product, but at the same time provide an opportunity to add value. This paper discusses the requirement, volatility in requirements, causes of requirement volatility and then the impact of requirement volatility on Project Schedule, Project Cost, Project Performance, Software Quality and Software Maintenance. We also try to explore the positive implications (if any) of the requirement changes. The purpose of this paper is to discuss aspects related to requirement volatility.

Keywords:
  Requirements change; requirements management; project management; card sorting; software evolution; development; maintenance.


References:

1.       Mundlamuri Sudhakar, “Managing the Impact of Requirements Volatility”, Master Thesis, 2005, Department of Computing Science, Umeå University, SE-90187 Umeå, Sweden.
2.       “Effective Requirements Definition and Management”, April 2006, http://www.borland.com/resources/en/pdf/solutions/rdm_whitepaper. pdf

3.       D. Zowghi, “A Longitudinal Study of Requirements Volatility in Software Development”, in the ASMA/SQA Meeting, 2005.

4.       Zowghi, N. Nurmuliani, “A study of the Impact of requirements volatility on Software Project Performance”, Proceedings of the Ninth Asia-Pacific Software Engineering Conference , APSEC 2002, Gold Cost, Queensland, Australia,04-06 Dec 2002, pp:3-11.      

5.       Donald Firesmith: “Prioritizing Requirements”,  in Journal of Object Technology, vol. 3, no. 8, September-October 2004, pp. 35-47.http://www.jot.fm/issues/issue_2004_09/column4

6.       Lamswede, A. Requirements Engineering in the Year 00: A research perspective. In proceeding of the 22nd International conference on Software Engineering (ICSE’2000), Limerick, Ireland, 5-19, ACM Press.
7.       D.Zowghi, Susan. P. Williams, “Requirements Volatility and its Impact on Change Effort: evidence-based Research in software Development Projects”, in AWRE2006
8.       Ian Soumerville, Software Engineering, 7th Edition, Pearson Education

9.       Pfleeger, S.L . Software Engineering Theory and Practice, Prentice Hall, 1998.

10.     Jones, C. (ed) (1997): Software Quality: Analysis and guidelines for Success, International Thomson Computer Press.

11.     George Stark, et al. “An Examination of the Effects of  requirements Changes on Software Maintenance Releases”,  Journal of Software Maintenance

12.     Standish Group Report (CHAOS), 1995.


259-264

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

52.

Authors:

C.M.Sheela Rani, V.VijayaKumar, B.V.Ramana Reddy

Paper Title:

Improved Block Based Feature Level Image Fusion Technique Using Multiwavelet with Neural Network

Abstract:    Image fusion is defined as the process of combining two or more different images into a new single image retaining the important features from each image with extended information content. To overcome the spectral distortions in the fused image, many multiresolution based approaches have been proposed. They include different pyramid transforms and discrete scalar wavelet transforms. The multiwavelet transform (MWT) produces a non-redundant image representation, which provides better spatial and spectral localization of image formation. This paper has derived an efficient block based feature level multiwavelet transform with neural network (BFMN) model for image fusion. The proposed (BFMN) model integrates MWT with neural network, which is one of the feature extraction or detection machine learning applications.  In the proposed BFMN model, the two fusion techniques, multiwavelet transform (MWT) and neural network (NN) are discussed for fusing the IRS-1D images using LISS III scanner about the locations Hyderabad, Vishakhapatnam, Mahaboobnagar and Patancheru in Andhra Pradesh, India. Also QuickBird image data and Landsat 7 image data are used to perform experiments on the proposed BFMN model. The features under study are contrast visibility, spatial frequency, energy of gradient, variance and edge information.  Feed forward back propagation neural network is trained and tested for classification since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is then used to fuse the pair of source images. The proposed BFMN model is compared with other techniques to assess the quality of the fused image. Experimental results clearly prove that the proposed BFMN model is an efficient and feasible algorithm for image fusion.

Keywords:
  Image Fusion, Multiwavelet Transform, GHM multiwavelet, Mutual information, Performance Analysis.


References:

1.        K. Kannan, S. Arumuga Perumal, K. Arulmozhi, “Performance Comparison of various levels of Fusion of Multi-focused Images using Wavelet Transform”, 2010 International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 6.
2.        Jiang Dong, Dafang Zhuang, Yaohuan Huang and Jingying Fu, “Advances in Multi-sensor data fusion: algorithm and applications", Sensors 2009, 9, 7771,  7784; doi:10.3390/ s91007771.

3.        Kai-Chieh Liang, Jin Li and C.-C. Jay Kuo, “Image Compression with Embedded Multiwavelet Coding”.

4.        Michael B. Martin and Amy E. Bell, “New Image Compression Techniques Using Multiwavelets and Multiwavelet Packets”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 4, APRIL 2001.

5.        Fernando Pérez Nava and Antonio Falcón Martel, “Planar shape representation based on  Multiwavelets”.

6.        Abdul Basit Siddiqui, M.Arfan Jaffar, Ayyaz Hussain, Anwar M. Mirza, “Block-based Feature-level Multi- focus Image Fusion”, 5th International Conference on Future Information Technology (FutureTech), 2010 IEEE, 978-1-4244-6949-9/10.

7.        G. Piella, “A region-based multiresolution image fusion algorithm”, Information Fusion, 2002.  Proceedings of Fifth International Conference on Information Fusion, 1557-1564.

8.        “Effectiveness of Contourlet vs Wavelet Transform on Medical Image Compression: a Comparative Study”, Negar Riazifar, and Mehran Yazdi, World Academy of Science, Engineering and Technology, 49, 2009.

9.        Block feature based image fusion using multi wavelet transforms - Maziyar Khosravi, Mazaheri Amin - International Journal of Engineering Science and Technology (IJEST) - ISSN: 0975-5462 Vol. 3 No. 8 August 2011, 6644.

10.     J. Lebrun and M. Vetterli, “Balanced multi wavelets and design,” IEEE Trans. on Signal Processing, Vol. 46, No.4, pp. 1119-1124, Apr. 1998. 

11.     H. Wang, J. Peng, and W. Wu, “Fusion algorithm for multi sensor images based on discrete multi wavelet transform,” IEE Proc. Vis. Image Signal Process., Vol. 149, No. 5, pp. 283-289, Oct. 2002.

12.     Louis, E.K.; Yan, X.H. A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote Sens. Environ. 1998, 66, 153–165.

13.     Dong. J.; Yang, X.; Clinton, N.; Wang, N. An artificial neural network model for estimating crop yields using remotely sensed information. Int. J. Remote Sens. 2004, 25, 1723–1732.

14.     Hossein Sahoolizadeh, Davood Sarikhanimoghadam, and Hamid Dehghani, “Face Detection using Gabor Wavelets and Neural Networks”, World Academy of Science, Engineering and Technology 45, 2008.

15.     Shutao, L.; Kwok, J.T.; Yaonan W. Multifocus image fusion using artificial neural networks. Pattern Recognit. Lett.2002, 23, 985–997.

16.     B.A.David and Y.David Solomon Raju, “Multi scale image fusion using GHM multi-wavelet”, Global Journal of Advanced Engineering Technologies-Vol1, Issue1-2012.

17.     S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, Second Edition, 1998.

18.     J. S. Geronimo, D. P. Hardin, and P. R. Massopust, Fractal functions and wavelet expansions based on  several scaling functions," J. Approx. Theory, 1994.

19.     G. Donovan, J. S. Geronimo, D. P. Hardin, and P. R. Massopust,\ Construction of orthogonal wavelets using fractal interpolation functions," preprint, 1994.

20.     I Daubechies, Ten Lectures on Wavelets, pp. 251{254, SIAM, 1992.

21.     T. Xia and Q. Jiang, “Optimal multifilter banks: Design, related symmetric extension transform and application to image compression,” IEEE Trans. Signal
Processing, vol. 47, pp. 1878–1889, July 1999.

22.     S. S. Goh, Q. Jiang, and T. Xia, Construction of biorthogonal multiwavelets using the lifting scheme, preprint, 1998.

23.     J. Y. Tham, L.-X. Shen, S. L. Lee, and H. H. Tan, “A general approach for analysis and application of discrete multiwavelet transforms,” IEEE Trans. Signal
Processing, vol. 48, pp. 457–464, Feb. 2000.

24.     V. Strela and A. T.Walden, “Orthogonal and biorthogonal multiwavelets for signal denoising and image compression,” Proc. SPIE, vol. 3391, pp. 96–107, 1998.

25.     T. N. T. Goodman and S. L. Lee, “Wavelets of Multiplicity”, Trans. Of the Amer. Math. Soc., Vol. 342, pp.307-324,1994.

26.     Maziyar Khosravi and Mazaheri Amin, “Block Feature Based Image Fusion using Multi Wavelet Transforms”, International Journal of Engineering Science and Technology (IJEST) Vol. 3 No. 8 August 2011.

27.     C.M.Sheela Rani, V.VijayaKumar, B.Sujatha “An Efficient Block based Feature level image fusion technique using Wavelet transform and Neural network”, “International Journal of Computer Applications”, (0975-8887), Vol.52, No.12. 


265-271

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

53.

Authors:

Amarjeet Kaur, Himani Malik, Asha Lather, V.K.Lamba

Paper Title:

Effect of Communication Frequency on Specific Absorption Rate Of Electromagnetic Radiations In Human Body

Abstract:    In this paper a novel approach for analyzing penetration of electromagnetic radiations in human tissue at different frequency is presented. Moreover, we concentrated our work on analyzing the other related factors describing system performance like return loss& Voltage Standing Wave Ratio (VSWR). consider We use different communication frequencies to simulate antenna for analyzing variation in Specific Absorption Rate (SAR) of electromagnetic radiations (produced by handheld communication devices) in Human tissues at different communication frequencies.

Keywords:
   Electromagnetic Interaction, Electromagnetic radiations, EM waves, Return Loss, Specific Absorption Rate, SAR, VSWR, XFDTD


References:

1.       D. A. A. Mat, F. Kho, A. Joseph, K. Kipli, S. Sahrani, K. Lias & A. S. Wani Marzuki, “Electromagnetic Radiation from Mobile Phone near Ear-skull Region” in International Conference on Computer and Communication Engineering (ICCCE 2010), 11-13 May 2010, Kuala Lumpur, Malaysia
2.       M. T. Islam, M. R. I. Faruque, N. Misran, “Reduction Of Specific Absorption Rate (SAR) In the Human Head With Ferrite Material And Metamaterial” in Progress In Electromagnetics Research C, Vol. 9, 47-58, 2009

3.       Chang-xia Sun, Yong Liu, Fei Liu, “The Research of 3G Mobile Phone Radiation on the Human Head”, IEEE Trans., 2011

4.       D. A. A. Mat, F. Kho, A. Joseph, K. Kipli, S. Sahrani, K. Lias & A. S. Wani Marzuki, “The Effect of Headset and Earphone on Reducing Electromagnetic Radiation from Mobile Phone toward Human Head”

5.       Minseok lung, Bomson Lee, “SAR Reduction for Mobile Phones Based on Analysis of EM Absorbing Material Characteristics”, IEEE Trans., 2003

6.       Lisheng Xu, Max Q.-H. Meng, Hongliang Ren, “Effect of Subject Size on Electromagnetic Radiation from Source in Human Body Following 2450MHz Radio Frequency Exposure” in Proceedings of the 2007 IEEE International Conference on Integration Technology, March 20 - 24, 2007, Shenzhen, China

7.       Salah I. Al-Mously, “Assessment Procedure of the EM Interaction between Mobile Phone Antennae and Human Body”

8.       Pedro Pinho12, Amelia Lopes1, Joao Leite1 and Joao Casaleiro, “SAR determination and influence of the human head in the radiation of a mobile antenna for two different frequencies”, IEEE Trans., 2009

9.       Ali Zamanian and Cy Hardiman, “Electromagnetic Radiation and Human Health: A Review of Sources and Effects” in High Frequency Electronics,16-26,  July 2005

10.     Shalatonin V. I., “Mobile Phones and Health: The Key Role Of Human Body Flids in Bioeffects of Non-Thermal EM Radiation” in 200818th Int. Crimean Conference "Microwave Telecommunication Technology" (CriMiCo'2008). 8-12 September, Sevastopol, Crimea, Ukraine© 2008: CriMiCo'2008 Organizing Committee; CrSTC. ISBN: 978.966.335.166.7. IEEE Catalog Number: CFP08788

11.     B. Blake Levitt and Henry Lai, “Biological effects from exposure to electromagnetic radiation emitted by cell tower base stations and other antenna arrays” in Environ. Rev. 18: 369–395 (2010)

12.     P. Salonen, L. Sydanheimo, M. Keskilammi, M. Kivikoski,"A small planar inverted-F antenna for wearable applications," Wearable Computers, 1999. Digest of Papers. the Third International Symposium on, pp. 95-100, 1999.


272-274

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

54.

Authors:

Pankaj Varshney

Paper Title:

High Temperature Electrical Characterization of Single Crystal of N-Type CDSE Optimally Annealed in a molten CD

Abstract:   In this paper high quality single crystal of n-type CDSE is grown from the vapor phase by the piper polich method. X-ray laue photographs from different directions are taken. DC galvanomagnetic properties are measured and analyzed in the temperature range 75 to 450 K in an undoped single crystal of n-type CDSE, which was optimally annealed in high purity molten CD at 850 0C for a period of ~ 400 h. α- the polaron coupling constant is estimated from the measured Hall mobility (µH) data at low and intermediate temperature by identifying a temperature region, wherein only the polar optical phonon scattering operates in the conduction band. The deformation potential Eb¬ in the conduction band of CDSE is obtained from the measured µH data in the temperature region wherein the deformation potential also operates effectively. It is shown that a change in the microscopic mobility through photo excitation indicate a change in scattering.

Keywords:
   CDSE, Methodology


References:

1.       N.KH. ABRIKOSOV et al., Semiconductor Compounds, Synthesis, and Properties, Nauka, Moseow 1967 (P.210) (In Russian).
2.       M. AVEN and J.S. PRENER, (Ed.), Physics and Chemistry of II-VI Compounds, North-Holland Publ. Co., Amsterdam 1967(P.588).

3.       B.F. ORMOUNT, Introduction To Physical Chemistry and Chemistry Of Crystalline Semiconductors, Vysshaya Shkola, Moscow 1968 (P.108)(In Russian).

4.       A.M. GURVICH and R.V.KOTOMINA, Fiz.Tekh. Poluprov. 5, 1351(1971)

5.       F. CHERMOW, E. COURTENS, M. DOUMA, and L. GOODMAN, Appl. Phys. Lett.9,145 (1966)

6.       F. CHERNOW ,G.ELDRIDGE,G. RUSE and L.WAHLIN, Appl. Phys.Lett.12,339(1968)

7.       W.W.ANDERSON and J.T.MITCHELL, Appl. Phys. Letters 12,334 (1968)

8.       M.LICHTENSTEIGER, I.LAGNATO, and H.C.GATOS, Appl. Phys. Letters 15,418(1969)

9.       C.H HENRY, K.NASSAU, and J.W.SHIEVER, Phys. Rev. B4,2453 (1971)

10.     N.R.KULISH, M.P.LISITSA, A.F.MAZNICHENKO, and B.M. BULAKH, Soviet Phys.Semicond.12,585(1978)

11.     N.R.KULISH, A.F.MAZNICHENKO, and B.M. BULAKH, Ukr. Fiz. Zh.25,666 (1980)

12.     N.R.KULISH, A.F.MAZNICHENKO, and B.M. BULAKH, Soviet Phys.-Semicond.14,409(1980)

13.     M.P.LISITSA, N.R.KULISH, A.F.MAZNICHENKO, B.M. BULAKH,  Soviet Phys.Semicond.16,171(1982)

14.     H.H.WOODBURY.Phys.Rev.B9,5188(1974)

15.     G.JONES and J.WOODS,J.Phys.D9,799(1976)

16.     B.R.SETHI, P.C.MATHUR and J.WOODS, J.Appl. Phys 49, 3618 1978)

17.     B.R.SETHI, P.C.MATHUR and J.WOODS, J.Appl. Phys.50,353 (1979)

18.     D.M.FINLAYSON, J.IRVINE, and L.S.PETERKIN, Phil. Mag. B39,253(1979)

19.     D.LRODE, Phys.Rev.B2,4036(1970)

20.     R.P.KHOSLA, J. S.FICHER, and B.C BURKEY, Phys. Rev. B7,2551 (1973)

21.     W.W.PIPER and S.J.POLICH, J.Appl.Phys.32,1278(1961)

22.     M.AVEN and H.WOODBURY, Appl.Phys.Letters1,53(1962)

23.     M.AVEN, J.Appl.Phys.42,1204(1971)

24.     J.E.ROWOE and R.A.FORMAN, J.Appl.Phys.38,1917(1968)

25.     E.H PUTLEY, The Hall Effect and Semiconductor Physics, Dover Publ. Inc. Newyork,1960(Chap.2)

26.     B.R.SETHI, O.P.SHARMA,P.K.GOYAL, and P.C. MATHUR, J.Phys.C14,1649(1981)

27.     E. G. S. PAIGE, Progress In Semiconductors,Vol.8, Ed. A.F.GIBSON, and R.E.BURGEN, JOHN WIELY & Sons, New York 1964(P.47)

28.     H.FROHLICH, Adv.Phys.3,325(1954)

29.     P.FISHER and H.FAN, Bull.Amer.Phys.Soc.4,409(1959)

30.     R.B.PARSON, W.WARDZYNSKI, and A.D. YOFFE, Proc. Roy. Soc.8,542(1963)

31.     D.J.HOWARTH and E.H SONDHEIMER, Proc.Roy. Soc.262a, 120(1961)

32.     R.E.HALSTED, M.AVEN, and H.D.GOGHIU, Bull.Amer. Phys. Soc.8,542(1963)

33.     H.EHRENREICH, J. Phys. Chem. Solids 8,130(1959)

34.     C.KITTEL, Quantum Theory Of Solids ,John Wiley & Sons ,New York 1963.(P.137)
35.     C. HERRING,Phys.Rev.96,1164(1964)
36.     IRENEUSZ STRZALKOWSKI, S.JOSHI, and C.R. CROWELL, Appl. Phys. Letters 28, 350 (1976)

37.     E.F.TOKOREV, G.S.PADO,L.A.CHERNOZATONSKII, and  V.V. DRACHEV, Soviet Phys.- Solid State 15, 1064(1973)

38.     D.L.RODE, Phys. Rev.B2, 1012(1970)

39.     M. BALKANSKI, In: II- VI Semiconducting Compounds, Ed. D.G.THOMAS, W.A. BENJAMIN, Inc., New York 1967 (P.81)

40.     J.BARDEEN and W.SHOCKLEY, Phys.Rev.80, 72(1950)

41.     H.EHRENREICH, J. Phys. Chem. Solids 2,131 (1957)

42.     E.HAGA and H.KIMURA, J. Phys. Soc. Japan 18, 777 (1963)

43.     K.W.NILL and A.L MCWHORTER, J. Phys. Soc. Japan (Suppl.) 21, 755 (1966).

44.     S.M.PURI, Phys.Rev.139, A995(1965).

45.     K.K. KULSHRESTHA, A.PANDYA, P.K. GOYAL and P.C. MATHUR, Phys. Stat. Sol. (a) 97, 557(1986).

46.     R.H.BUBE, H.E.MACDONALD and J.BLANC, Journal of physics and chemistry of solids vol. 22, December 1961 (P. 173-180).

47.     LEE M J & Lee S C, Solid State Electr, 43 (1999) 883.

48.     KLEMENT U, ERNST F, BARETZKY B, & PLITZKO J M, Mater Sci & Engg, B94 (2002) 123.

49.     SEBASTIAN P J & SIVARAMAKRISHNAN V, Phys Rev, B40 14-15 (1989) 9767.

50.     ARUN PANDAYA, K.K.KULSHRESTHA, P.C. MATHUR, Journal of Materials Science 25 (1990) 22-28
51.     M N BORAH, S CHALIHA, P C SARMAH* & A RAHMAN, Indian Journal of Pure & Applied Physics, Vol. 45, August 2007, pp. 687-691.
52.     A.ABD-EL MONGY, Egypt. J. Sol., (27), No. (1), (2004).

53.     SHASHI BHUSHAN & ANJALI OUDHIA, Indian Journal of Pure & Applied Physics Vol. 47, January 2009, pp. 60-65.


275-278

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

55.

Authors:

Rajesh Kumar Tripathi, Minakshi Gaur

Paper Title:

Scattered Domination In Graphs

Abstract:    In this paper, we want to compute an optimal scattered domination for domination graph. In this paper, we also show optimal broadcast domination is in path P. We first prove that every graph has an optimal scattered domination in which the subset of vertices dominated by the same vertex is ordered in a path or a cycle. Using this, we give a polynomial time algorithm for computing optimal broadcast domination of arbitrary graphs.

Keywords:
   Domination graph, scattered domination graph, path, broadcast domination of graph.


References:

1.        J. Bar, G. Kortsarz and D.Peleg. How to allocate network centers. J. Algorithms 15,385-415, (1997).
2.        J.R.S. Blair, SHorton. Broadcast domination algorithms for interval graphs and trees. J.Graph Theory, 169, 55-77(1999).

3.        J.E.Dunbar, T.W. Haynes; Broadcast in graphs .J. Graph Theory, 30, 99-107(2000).

4.        D.J. Ewin. Dominating broadcasts in graphs. J. Comp. Sci.April, 42, 89-105(2001).
5.        M.R. Garey and D.S.Johnson, Computer and intra stability, J.Comp. Sci., 23, 446-450(2002).
6.        T.W. Haynes, Domination in graphs J. Combinatory, 9, 23-29, (2003).

7.        T.W. Haynes, S.T. Hedetniemi, Fundamentals of domination in Graphs , J. Graph theory , Vol.3, 52-59, (2004).

8.        M.A. Henning, Distance domination in graphs, J. Combinatory, 321-349, (2005).

9.        S.B. Horton, A. Mukherjee; a Computational study of the broadcast domination problem, J. Of Discrete Maths, 131-147, (2005).

10.     P.J. Slater, R-domination in graphs, J. Computer Science, 23, 446-450, (2006).

11.     M. Yannakaki’s , F. Gavril, “Edge dominating sets in graphs, SIAM J. Applied Maths, 38, 364-372, (2007).


279-283

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

56.

Authors:

Himani Malik, Amarjeet Kaur, Asha Lather, V.K.Lamba, Bhupesh Kharb

Paper Title:

Effect of Thickness and Material on Electronic Properties of GAA-MuGFETs

Abstract:    We present a CMOS compatible n-type gate-all-around (GAA) silicon nanowire (NW) MOSFETs with excellent electrostatic scaling. This paper investigates the sensitivity of gate-all-around (GAA) nanowire (NW) to process variations in silicon film thickness and material i.e. Si and Ge with multigate devices using analytical solutions of Poisson's equation verified with device simulation Our study indicates that the GAA nanowire (NW) has the smallest threshold voltage (Vth) dispersion caused by process variations in silicon film thickness. Specifically, the GAA NW shows better immunity to channel thickness variation than multigate devices because of its inherently superior surrounding gate structure. To explore the optimum design space for (GAA) silicon nanowire (NW) MOSFETs were performed with three variable device parameters: channel width, material, and silicon film thickness. The efficiency of the GAA gate structures is shown to be dependent of these parameters.

Keywords:
   GAA gate FETs, MOS devices


References:

1.        S. B. Park, Y. W. Kim, Y. G. Ko, K. I. Kim, I. K. Kim, H. S. Kang, J. O. Yu, and K. P. Suh, “A 0.25-_m, 600 MHz, 1.5-V, fully depleted SOI CMOS 64-bit microprocessor,” IEEE J. Solid-State Circuits, vol. 34, pp. 1436–1445, 1999.
2.        R. Chau, J. Kavalieros, B. Roberds, A. Murthy, B. Doyle, D. Barlage, M. Doczy, and R. Arghavani, “A 50 nm depleted-substrate CMOS transistor (DTS),” in IEDM Tech. Dig., 2001, pp. 621–623.

3.        T. Ernst, D. Muteanu, S. Cristoloveanu, T. Ouisse, N. Hefyene, S. Horiguchi, Y. Ono, Y. Takahashi, and K. Murase, “Ultimately thin SOI MOSFETs: Special characteristics and mechanisms,” in Proc. IEEE Inte. SOI Conf., pp. 92–93, 2008.

4.        J. P. Colinge, M. H. Gao, A. Romano-Rodriguez, H. Maes, and C. Claeys, “Silicon-on-insulator gate-all-around device,” in IEDM Tech. Dig., 1990, pp. 595–598.

5.        H.-S. P. Wong, K. K. Chan, and Y. Taur, “Self-align (top and bottom) double-gate MOSFET with a 25 nm thick silicon channel,” in IEDM Tech. Dig., 1997, pp. 427–430.

6.        J. P. Denton and G. W. Neudeck, “Fully depleted dual-gated thin-film SOI P-MOSFET’s fabricated in SOI islands with an isolated buried poly-silicon back gate,” IEEE Electron Device Lett., vol. 17, pp.509–511, Nov. 1996.

7.        (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue).  Available: http://www.(URL)
8.        J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com
9.        (Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given.            Available: http://www.(URL)

10.     R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880.   Available: http://www.halcyon.com/pub/journals/21ps03-vidmar


284-286

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

57.

Authors:

Pankaj Varshney, Kapil Kumar Bansal, Jyotshana Gaur

Paper Title:

Wavelets for the Fast Solution of Ordinary Differential Equations

Abstract:   In this paper, wavelets have shown to be a powerful tool and a potential substitute for the Fourier transform in many problems. It is natural to use them for the solution of differential equations. In this chapter, we show how to use wavelets in the numerical solution of boundary value ordinary differential equations. Rather than using algebraic wavelets, we adapt the wavelets to the specific operator at hand. We want their construction to be easy to implement and computationally inexpensive in order to build a general solver

Keywords:
   FFT, Wavelet, Boundary Value


References:

1.       G. Aharoni, A. Averbuch, R. Coifman, and M. Israeli. Local cosine transform |A method for the reduction of the blocking effect in JPEG. J. Math. Imag. Vision, 3:7{38, 1993.
2.       A. Aldroubi and M. Unser. Families of wavelet transforms in connection with Shannon's sampling theory and the Gabor transform. In [35], pages509{528.

3.       Aldroubi and M. Unser. Families of multiresolution and wavelet spaces with optimal properties. Numer. Funct. Anal. Optim., 14:417{446, 1993.

4.       B. Alpert. A class of bases in L2 for the sparse representation of integral operators. SIAM J. Math. Anal., 24(1):246{262, 1993.

5.       Jian-Go and Simon P. Schurr, Lecture Notes For Applied Mathematics And Scientific Computational Program, AMSC/CMSC 660,Scientific Computing I,University Of Meryland, pp.1-4, December2001

6.       B. Alpert, G. Beylkin, R. Coifman, and V. Rokhlin. Wavelet-like bases for the fast solution of second-kind integral equations. SIAM J. Sci. Comput., 14(1):159{184, 1993.

7.       K. Amaratunga and J. R. Williams. Wavelet based Green's function approach to 2D PDEs. Engrg. Comput., 10(4), 1993.

8.       L. Andersson, N. Hall, B. Jawerth, and G. Peters. Wavelets on closed subsets of the real line. In [160], pages 1{61.

9.       L. Andersson, B. Jawerth, and M. Mitrea. The Cauchy singular     integral operator and Clifford wavelets. In [20], pages 525{546.

10.     P. Auscher and Ph. Tchamitchian. Conjecture de Kato sur les ouverts de R. Rev. Mat. Iberoamericana, 8(2):149{199, 1992.

11.     P. Auscher, G. Weiss, and V. Wickerhauser. Local sine and cosine bases ofCoifman and Meyer and the construction of smooth wavelets. In [35], pages 237{256.

12.     wavelet transforms and numerical algorithms i. Comm. Pure Appl. Math., 44:141{183, 1991.


287-291

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html