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: 4.96
Home
Articles
Conferences
Editors
Scopes
Author Guidelines
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-2 Issue-2: Published on May 05, 2012
31
Volume-2 Issue-2: Published on May 05, 2012

Download Abstract Book

S. No

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

Page No.

1.

Authors:

Rajesh. P, Priya. S, Priyanka. R

Paper Title:

Modified Energy Efficient Backup Hierarchical Clustering Algorithm Using Residual Energy for Wireless Sensor Network

Abstract:    Clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability, lifetime and power level. In this paper, we integrate the multi-hop technique with a backup-based clustering algorithm using the residual energy to organize sensors. By using an adaptive backup strategy as well as the residual energy, the algorithm not only realizes load balance among sensor node, but also achieves dynamic cluster head distribution across the network in a timeout manner. Simulation results also demonstrate our algorithm is more energy-efficient compared to other algorithms. Our algorithm is also easily extended to avoid the formation of forced cluster heads, thereby it achieves better network management, energy-efficiency and scalability.

Keywords:
  dynamic cluster,   forced cluster head,  load balance, residual energy.


References:

1.        Wang J, Cao YT, Xie JY et al. Energy efficient backoff hierarchical clustering algorithms for multi-hop wireless sensor networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 26(2): 283{291 Mar. 2011. DOI 10.1007/s11390- 011-1131-x
2.        Yu M, Leung K K, Malvankar A. A dynamic clustering and energy efficient routing technique for sensor networks. IEEE Transactions on Wireless Communications, Aug. 2007, 6(8): 3069-3079.

3.        Chen Y P, Liestman A L, Liu J. A hierarchical energy efficient framework for data aggregation in wireless sensor networks. IEEE Trans. VT, May, 2006, 55(3): 789-796.

4.        Cao Y, He C. A distributed clustering algorithm with an adaptive backoff strategy for wireless sensor networks. IEICE Transactions on Communications., 2006, 89-B(2): 609-613.

5.        Sundararaman B, Buy U, Kshemkalyani A D. Clock synchronization for wireless sensor networks: A survey. Ad Hoc Networks, 2005, 3(3): 281-323.

6.        Younis O, Fahmy S. Distributed clustering in ad-hoc sensor networks: A hybrid, energy efficient approach. In Proc. IEEE INFOCOM, Hong Kong, China, Mar. 7
11, 2004.

7.        Zhao F, Guibas L. Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann, 2004.

8.        Bandyopadhyay S, Coyle E J. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proc. IEEE INFOCOM2003, San Francisco, USA, Mar. 30-Apr. 3, April 2003, pp.1713-1723.

9.        Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application specific protocol architecture for wireless microsensor networks. IEEE Tran. Wireless Communications,  Oct. 2002, 1(4): 660-670.

10.     Akyildiz I F, Su W, Sankarasubramaniam Y et al. A survey on sensor networks. IEEE Communications Magazine, 2002, 40(8): 102-114.

11.     Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. ACM/IEEE Int. Conf. Mobile Computing and Networking (MOBICOM), Boston, USA, Aug. 6-11,2000, pp.56-67.

12.     Pottie G J, Kaiser W J. Wireless integrated network sensors.Communications of the ACM, 2000, 43(5): 51-58.

13.     Amis A D, Prakash R, Vuong T H P, Huynh D T. Max-Min D-cluster formation in wireless ad hoc networks. In Proc. IEEEINFOCOM2000, Tel Aviv, Israel, Mar. 26, 2000, pp.32-41.

14.     “ Wireless Sensor Networks for Early Detection of Forest Fires “ by Mohamed Hefeeda and Majid Bagheri.

1-5

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

2.

Authors:

Mopsy Dhiman, Pawan Kapur, Abhijit Ganguli, Madan Lal Singla

Paper Title:

Impedance Study of Drinking Water and Tastants Using Conducting Polymer and Metal Electrodes

Abstract:    In this study the sensing capabilities of a combination of metals and conducting polymer electrodes for drinking water and dissolved tastants using an AC-impedance mode  in frequency range 102 to 105 Hz at 0.1 V potential has been carried out. Classification of seven different bottled and municipal drinking water samples along with various tastants dissolved in DI water (DI water) for KCl (5mM) (salty), HCl (5 mM) (sour) quinine (0.1 mM) (bitter), sucrose (5 mM) (sweet), black tea liquor, black tea liquor with sucrose (2% sugar solution), and a bottle of “packed” orange juice has been made using six different working electrodes in a multi electrode setup using PCA. Working electrodes of Platinum (Pt), Gold (Au), Silver (Ag), Glassy Carbon (GC) and conducting polymer electrodes of Polyaniline (PANI) and Polypyrrole (PPY) grown on an ITO surface potentiostatically have been deployed in a three electrode set up. The impedance response of these water samples using number of working electrodes shows a decrease in the real and imaginary impedance values presented on nyquist plots depending upon the nature of the electrode and amount of dissolved salts present in water/tastants. The different sensing surfaces allowed a high cross-selectivity in response to the same analyte. From PCA plots it was possible to classify drinking water in 3-4 classes using conducting polymer electrodes; however tastants were well separated from the PCA plots employing the impedance data of both conducting polymer and metal electrodes.

Keywords:
   Sensing electrodes, AC-impedance, Principal component analysis, Drinking water, tastants, conducting polymers.


References:

1.        B Jakosky, “The Search for Life on Other Planets”, Cambridge University Press, Cambridge, 1998.
2.        D Rajkumar,  and K Palanivelu , “Electrochemical treatment of industrial wastewater”. Journal of Hazardous Materials B, vol.113, no.4 pp.123–129, 2004,

3.        Kiyoshi, Toko. “Taste sensors”. Sensors and Actuators B: Chemical, vol. 64, no.1, pp. 205-215. 2000

4.        C Di Natale, A Macagnano, F Davide, A Damico,  A Legin, Y Vlasov, A Rudnitskaya, and B Selenzenev, “Multi component analysis on polluted water by means of impedance study”. Sensors and Acurators B: Chemical, vol. 44, no.1-2, pp. 423-28, 1997

5.        A Legin,  A Smirnova,  A Rudnitskaya,   L Lvova,   E Suglobova  and  Y Vlasov,   “Chemical sensor array for multi component analysis of biological liquids”. Analytica Chimica Acta,,vol. 385,no.1, pp.131-135, 1999

6.        Y Vlasov, A V Legin,  A M Rudnitskaya, A  Di D'Amico, and C Natale, “Impedance study new analytical tool for liquid analysis on the basis of non-specific sensors and methods of pattern recognition”. Sensors and Actuators B: Chemical, vol.65, pp.235-236, 2000

7.        C Di Natale, R Paolesse, A Macagnano, A Mantini , A D'Amico, M Ubigli, A Legin, L Lvova, A Rudnitskaya and  Y Vlasov, “Application of a combined artificial olfaction and taste system to the quantification of relevant compounds in red wine”. Sensors and Actuators B: Chemical, vol.69, no.3, pp.342–347, 2000

8.        H Sakai, S Iiyama, and K Toko, “Evaluation of water quality and pollution using multichannel sensors”, Sensors and Actuators B: Chemical, vol. 66, pp.251-255, 2000

9.        V Martina,  K Ionescu,  L Pigani,  F Terzi,  A Ulrici,  C Zanardi,  and R Seeber, “ Development of an electronic toungue based on a PEDOT modified voltammetric sensor”, Anal. Bioanal. Chem.,vol. 387, no.6,pp.2101-2110. 2007

10.     F Winquist, S Holmim, C Krantz-Rulcker, P Wide, and I Lundstorm, “A hybrid impedance study”. Analytica Chemical Acta, vol.406, no.2 , pp.147-157, 2000

11.     C Krantz-Rulcker, M Stenberg, F Winquist, and I Lundstorm, “Electronic toungues for environmental monitoring based on arrays and pattern recognition: a review”. Analytica Chimica Acta, vol.426, pp.217-226, 2001

12.     A Riul Jr., D. S dos Santos, Jr., K Wohnrath, R Di Tommazo, A C P L F Carvalho, F J Fonseca, O N Olivera, Jr., D M Taylor, and L H. C Mattoso, “Artificial taste sensor: - efficient combination of sensor from Langmuir-Blodgett films at conducting polymer and ruthenium complex and self assembled films of an azobenzene containing polymer”, Langmuir,vol. 18,no.1, pp.239-245. 2002

13.     RT Kurulugama, O Wlpf David, A Takacs Sara, S Pongmayteegul, P A Garris and J E Baur, “Scanning Electrochemical Micrscopy of Model Neurons: Constant Distance Imaging”. Anal. Chem., vol.77, no.4, pp.1111 -1117, 2005

14.     P Mirtaheri, S Grimnes and Ǿ G Martinsen, “ Electrode Polarization Impedance in Weak NaCl Aqueous Solutions”, IEEE Transactions on Biomedical Engineering, vol.52,no.12, pp.2093-93,2005

15.     L Scribner and R Taylor, “The Measurement and Correction of Electrolyte Resistance in Electrochemical Test”, Philadelphia. ASTM Pub., 1990.

16.     H Fricke, The theory of electrolyte polarization. Phil. Mag., vol. 7, pp. 310–318, 1932

17.     H P Schwan, “Electrode polarization impedance and measurements in biological materials”, Ann. New York Acad. Sci., vol.148, no.1 pp. 191–209, 1968

18.     E Stussi, R Stella and D De Rossi, “Chemo resistive conducting polymer based odour sensors: Influence of thickness changes on their sensing properties”, Sensors and Actuators B: Chemical, vol.43, no. 1, pp.180-185. 1997

19.     Z Mark, “The effect of the PANI-free volume on impedance response”. Journal of Electroanalytical Chemistry, vol. 610, no. 1, pp. 57–66. 2007

20.     K Rossberg, G Paasch, L Dunsch, and S Ludwig, “The influence of porosity and the nature of charge storage capacitance on the impedance behavior of electropolymersized PPY film”, Journal of Electroanalytical Chemistry, vol. 443,no.1 , pp.49-62. 1998

21.     T Ragheb and L. A Geddes, “The polarization impedance of common electrode metals operated at low current density”, Ann. Biomed. Eng, vol. 19, no. 2  pp.151–163,1991

22.     V Freger and S Bason, “Characterization of ion transport in thin film using electrochemical impedance spectroscopy: I .Principles and theory, Journal of Membrane Science, vol. 302, no.1-2, pp.1-9. 2007

23.     M A Vorotyntsev, L I Daikhin and M D Levi, “Modelling the impedance properties of electrodes coated with electroactive polymer films”, Journal of Electroanalytical Chemistry , vol. 364, no. 1-2, pp. 37-49, 1994

24.     SC Lenore, EG Arnold and RT Rhodes, “Standard methods for the examination of water and wastewater-17th edition”, APHA publication, Washington DC., ISBN 087553 161-X. 1989

25.     D M Taylor and A G MacDonald, “AC admittance of the metal/insulator/electrolyte interface”. Journal of Physics D: Applied Physics, vol.20, no.10, pp.1277-1283, 1987

26.     P Ferloni, M Mastragostino, and L Maneghello, “Impedance analysis of electrochemically conducting polymer”, Electrochimica Acta, , vol.41,no.1 ,pp. 27-33. 1996

27.     Genz, M M Lohrengel and J W Schultze, “Potentiostatic pulse and impedance investigations of the redox process in PPY films”, Electrochimica Acta, vol. 39,no.2, pp.179-185. 1994

28.     G Inzelt, G Láng, V Kertész and J Bácskai, “Effect of the temperature on the conductivity and capacitance of poly(aniline) film electrodes”, Electrochimica Acta, vol. 38,no.17, pp. 2503-2510, 1993

29.     A P Bhondekar, M Dhiman, A Sharma, A Bhakta, A Ganguli, S S Bari, R Vig, P Kapur and M L Singla, “A novel iTongue for Indian black tea discrimination”, Sensors and Actuators B: Chemical, vol.148,no.2,  pp.601–609, 2010.


6-11

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

3.

Authors:

Mopsy Dhiman, Pawan Kapur, Madan Lal Singla, Abhijit Ganguli

Paper Title:

Classification of Epigallocatechin and Catechin in Impedometric Mode Using PCA

Abstract:   Due to the presence of innumerable compounds and their diverse contribution to tea quality an assessment of tea quality is a difficult task. As a result tea samples are assessed by experienced tea tasters and an instrumental evaluation of tea quality is not practiced in the industry. There had been a very few reports where instruments like electronic tongue/electronic nose has been used for the discrimination of taste of tea samples. In this paper, an Impedance study has been carried out at Epigallocatechin and Catechin levels present in black tea using Glassy Carbon electrode and its fingerprint mapping was done using Principal Component Analysis. Similar data has been generated from the known individual antioxidant compounds and the respective mixture. The antioxidant level has been also extracted from the complex structure of the other antioxidants present in black tea. It has been found that impedance data and their PCA have been able to clearly discriminate the presence of these two compounds. The reproducibility has been studied continuously for about month’s time which lies within the + 2% of the output.

Keywords:
   Fingerprint Mapping, Principal Component Analysis, Antioxidants, Impedance.


References:

1.       M Naczk, and F Shahidi, “Phenolics in cereals, fruits and vegetables: occurrence, extraction and analysis”, J. Pharm. Biomed. Anal., vol. 41, no., pp. 1523–1542, 2006.
2.       Hara, Y, “Green tea: Health benefits and applications”, New York: Marcel Dekker, 2001.

3.       L D Mello, A A Alves, K V Macedo, L T Kubota, “Peroxidase-based biosensor as a tool for a fast evaluation of antioxidant capacity of tea”, Food Chemistry, vol. 92, no. pp. 515–519. 2005

4.       N Khan, H Mukhtar, “Tea polyphenols for health promotion”, Life Sci., vol. 81, no.,  pp. 519–533, 2007

5.       L Yao, X Liu, Y Jiang, N Caffin, B D’Arcy, R Singanusong, N Datta and Y Xu, “Phenolic compounds in tea from Australian supermarkets”, Food Chemistry, vol. 96, no., pp.614–620, 2006

6.       R G Santana, G A Orner, A Amantana, C Provost, S-Y Wu and R H Dashwood, “Potent antimutagenic activity of white tea in comparison with green tea in the Salmonella assay”, Mutation Research, vol. 495, no., pp. 61–74, 2001

7.       Y S Zhen, Z M Chen, S J Cheng and M L Chen, “Tea Bioactivity and Therapeutic Potential” , London: Taylor & Francis, 2002
8.       K Hosoda, M F Wang, M L Liao, C K Chuang, M Iha, B.Clevidence and S Yamamoto, “Anti-hyperglycemic effect of Oolong tea in type 2 diabetes”, Diabetes Care, vol. 26 no.,, pp. 1715–1718, 2003

9.       B Halder, S Pramanick, S Mukhopadhyay and A K Giri, “Anticlastogenic effects of black tea polyphenols theaflavins and thearubigins in human lymphocytes in vitro”, Toxicology in Vitro, vol. 20, no., pp. 608–613, 2006

10.     C H Lu, L S Hwang, “Polyphenol contents of pu-erh teas and their abilities to inhibit cholesterol biosynthesis in Hep G2 cell line”, Food Chemistry, vol. 111, no., pp. 67–71, 2008

11.     D Wang, K Kubota, A Kobayashi, and I-M. Juan, “Analysis of glycosidically bound aroma precursors in tea leaves.3. Change in the glycoside content of tea leaves during the oolong tea manufacturing process”, Journal of Agricultural and Food Chemistry, vol. 49, no., pp. 5391–5396, 2001

12.     M M Camouse, D S Domingo, F R Swain, E P Conrad, M S Matsui, D Maes, L Declercq, K D Cooper, S R Stevens, and E D Baron, “Topical application of green and white tea extracts provides protection from solar-simulated ultraviolet light in human skin”, Experimental Dermatology, vol. 18, no., pp. 522–526, 2009

13.     N Togari, A Kobayashi and T Aishima “Relating sensory properties of tea aroma to gas chromatographic data by chemometric calibration methods”, Food Res. Int., vol. 28, no. , pp. 485–493, 1995

14.     Y Zuo, HChen and Y Deng, “Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector”, Talanta, vol. 57, no. , pp. 307–316, 2002

15.     H Horie, T Mukai and K Kohata, “Simultaneous determination of qualitatively important components in green tea infusions using capillary electrophoresis”, J. Chromatogr. A., vol.758, no. , pp. 332–335,1997

16.     B Banerjee, Tea Production and Processing. New Delhi, India, Oxford & IBH Publishing Co, 1993

17.     P Ivarsson, S Holmin, N E Hojer, C  Krantz-Rulcker and F Winquist, “Discrimination of tea by means of a voltammetric electronic tongue and different applied waveforms”, Sens. Actuators B. Chem., vol. 76, no. , pp. 449–454, 2001

18.     V Parra, A A Arrieta, J A Fernandez-Escudero, H Garcia, C Apetrei, M L Rodriguez-Mendez, J A De Saja, “E-tongue based on a hybrid array of voltammetric sensors based on phthalocyanines, perylene derivatives and conducting polymers: Discrimination capability towards red wines elaborated with different varieties of grapes”, Sens. Actuators B. Chem., vol. 115, no. , pp. 54–61, 2006

19.     B A Lawton and R Pethig, “Determining the fat content of milk and cream using AC conductivity measurements”, Meas. Sci. Technol., vol. 4, no. , pp. 38–41, 1993

20.     A Legin, A Rudnitskaya,  Y Vlasov, C Di Natale, F Davide and A.D’Amico, “Tasting of beverages using an electronic tongue”, Sens. Actuators B. Chem., vol. 44, no. , pp. 291–296, 1997

21.     Q Chen, J Zhao and S Vittayapadung, “Identification of the green tea grade level using electronic tongue and pattern recognition”, Food Res. Int., vol. 41, no. , pp. 500-504, 2008

22.     P Ciosek, Z Brzózka, and W Wróblewski, “Electronic tongue for flowthrough analysis of beverages”, Sens. Actuators B. Chem., vol. 118, no. , pp.  454–460, 2006

23.     A Scozzari, N Acito, and G Corsini, “A novel method based on voltammetry for the qualitative analysis of water”, IEEE Trans. Instrum. Meas.,vol. 56, no. , pp. 2688–2697, 2007

24.     K Beullens, D Kirsanov, J Irudayaraj, A Rudnitskaya, A Legin, , B M Nicolai, and J Lammertyn, “The electronic tongue and ATR-FTIR for rapid detection of sugars and acids in tomatoes”, Sens. Actuators B. Chem., vol. 116, no. , pp.107–115, 2006

25.     M Scampicchio, S Benedetti, S Brunetti, and S Mannino, “Amperometric electronic tongue for the evaluation of the tea astringency”, Electroanalysis, vol. 18,
no. , pp.1643–1648, 2006

26.     J Olsson, F Winquist, and I Lundstrom, “A self polishing electronic tongue”, Sens. Actuators B. Chem., vol. 118, no. , pp. 461–465, 2006.

VI.     L LVOVA, A LEGIN, Y VLASOV, AND G S CHA, H NAM, “MULTICOMPONENT ANALYSIS OF KOREAN GREEN TEA BY MEANS OF DISPOSABLE ALL-SOLID-STATE POTENTIOMETRIC ELECTRONIC TONGUE MICROSYSTEM”, SENS. ACTUATORS B. CHEM., VOL. 95, NO. ,PP. 391–399, 2003

27.     M J C Pontes, S R  Santos, M C U Araujo, L F Almeida, R A C Lima, E Gaiao, and U Souto, “Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry”, Food Research International, vol. 39, no. , pp.182–189, 2006

28.     A P Bhondekar, M Dhiman, A Sharma, A Bhakta, A Ganguli, S S Bari, R Vig, P Kapur, and M L Singla, “A novel iTongue for Indian black tea discrimination”, Sen. Act. B. Chem., vol.148, no. , pp. 601–609, 2010

29.     T L Alexandre, M S Bueno, K Goraieb, and M Izabel, ”Quality control of beverages using XRS allied to chemometrics: determination of fixed acidity, alcohol and sucrose contents in Brazilian cachaca and cashew juice”, X-Ray Spectrometry, vol. 39, no. ,  pp. 285-290, 2010.


12-16

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

4.

Authors:

Rakesh Kumar, Tapesh Parashar, Gopal Verma

Paper Title:

Genetic Algorithm and DWT Based Multilevel Automatic Thresholding Approach for Vehicle Extraction

Abstract:    Vehicle Extraction from aerial images is an important research topic in surveillance, traffic monitoring and military applications. In this paper, an approach based on Automatic Multilevel Thresholding has been proposed for extracting vehicles from aerial imagery. The approach combines Genetic Algorithm with DWT to make segmentation faster and geometric feature of vehicles for vehicle extraction. This algorithm analyses the color and connected properties of pixels to extract the outline of vehicles. In this research, UAV colour imagery is examined experimentally. After analysis, it is examined that proposed method provides the vehicle position accurately.

Keywords:
   Histogram, Thresholding, Genetic Algorithm, Discrete Wavelet Transform, Morphological Processes, Edge Detection, Aerial Imagery


References:

1.       Grejner-Brzezinska, D., C. Toth and E. Paska, “Airborne remote sensing supporting traffic flow estimates”, C. V. Tao and J. Li (eds) Advances in Mobile Mapping Technology, Taylor & Francis, London, 2007.
2.       Tao Zhao and Ram Nevatia, “Car Detection in Low Resolution Aerial Images”. In Image and Vision Computing, 2001, pages 710–717.

3.       Stefan Hinz, “Detection of Vehicles and Vehicle Queues in High Resolution Aerial Images”. In Photogrammetrie - Fernerkunding - Geoinformation (PFG) 3, 2004, pages 203–215.

4.       ZuWhan Kim and Jitendra Malik, “Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking”, Computer Vision, IEEE International Conference on Computer Vision, 2003, pages 524-531.

5.       J.C. Yen, F.J. Chang, S. Chang, “A new criterion for automatic multilevel thresholding”, IEEE Trans. Image Process. IP-4, 1995, pages 370–378.

6.       B.-G. Kim, J.-I. Shim, D.-J. Park, “Fast image segmentation based on multi-resolution analysis and wavelets”, Pattern Recognit. Lett. 24, 2003, pages 2995–3006.

7.       P.-Y. Yin, L.-H. Chen, “A fast iterative scheme for multilevel thresholding methods”, Signal Process. 60, 1997, pages 305–313.

8.       D.E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison-Wesley, 1989.

9.       Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Pearson Education, Second Edition, 2002.
10.     Li Yu, “Vehicle Extraction Using Histogram and Genetic Algorithm based Fuzzy Image Segmentation from High Resolution UAV   Aerial Imagery”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing, 2008.

17-21

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

5.

Authors:

Ashish Patel, Vasundhara Misal, Pankaja Alappanavar, Ronak Agrawal

Paper Title:

Unified Operating Systems

Abstract:    Every Operating System has its different way of operation. When a novice user wants to perform some operations with the Operating System he is not acquainted with, the problem arises. The Novice user has to learn about the basic operations about the system to perform the intended task.  However, this is time consuming job and often leads to frustration when needs to be done frequently. Hence, leads to reduced productivity. One answer to the above mentioned problem can be a generic interface which would allow user to perform his task irrespective of the underlying Operating System. Under these circumstances this paper proposes a system which implements the above mentioned interface as the core concern. The unique feature that the above implemented system will provide is the same input and output syntax for performing the intended tasks under the scope of the system. Studied statistics show that this system is capable of achieving an Operating System independent interface on all JAVA supported systems.

Keywords:
   Operating System, Working Platform, Java Swing, GUI.


References:

1.        Moving from Solaris to Red Hat Enterprise Linux | Michael Solberg
2.        Forouzan, B.A. (2000). TCP/IP: Protocol Suite. 1st ed. New Delhi, India: Tata McGraw-Hill Publishing Company Limited.

3.        APPNOTE.TXT - .ZIP File Format Specification, Version: 4.5, 2001-11-01, retrieved 2012-01-05

4.        Unifying Operating System published in NTSACT-2012 (ISBN 978-1-4675-1444-6)


22-25

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

6.

Authors:

K. Porkumaran, S. Manimurugan, Pradeep P Mathew

Paper Title:

An Assessment on Irrevocable Compression of Encrypted Grayscale Image

Abstract:   This paper may deals with the miscellaneous troubles that may be occurs during the irrevocable compression applied on an encrypted grayscale image. This work is a comparative learn with diverse methods of irrevocable compression such as Compressive sensing technique and Iterative reconstruction technique on encrypted grayscale image. But they practiced a multiplicity of limitations. The major obscurity is to achieve higher compression ratio as well as the better quality of the reconstructed image. The higher compression ratio and the smoother the original image may furnish the better quality of the reconstructed image.

Keywords:
   Image compression, image encryption, image decryption, image decompression, image reconstruction.


References:

1.       Xinpeng Zhang, “Lossy Compression and Iterative Reconstruction for Encrypted Images,” IEEE Trans. Information Forensics and Security., Vol. 6, No. 1, pp. 53-58, Mar. 2011.
2.       A. Kumar and A. Makur, “Lossy compression of encrypted image by compressing sensing technique,” in Proc. IEEE Region 10 Conf.  (TENCON 2009), 2009, pp. 1–6.

3.       Z. Xiong, A. D. Liveris and S. Cheng, ”Distributed source coding for sensor networks”, IEEE Signal Processing Magazine, Vol. 21, pp. 80-94,  Sep. 2004.

4.       M. Johnson, P. Ishwar, V. Prabhakaran, D. Schonberg and K. Ramachandran, ”On compressing encrypted data”, IEEE Trans. Signal Processing Vol. 52, pp.
2992-3006, Oct. 2004.

5.       D. Schonberg, S. Draper and K. Ramachandran, ”On compression of encrypted images”, Proc. International conference on Image processing, Atlanta, GA, pp. 269-272, Oct. 2006.

6.       A. Kumar and A. Makur, ”Distributed source coding based encryption and lossless compression of gray scale and color images”, Proc. IEEE 10th workshop on multimedia and signal processing, Cairns, Australia,  pp. 760-764, Oct. 2008.

7.       Y. Rachlin and D. Baron, ”The secrecy of compressive sensing measurements”, Proc. 46th Allerton conference on commun., control, and computing, Monticello, IL, Sep. 2008.

8.       E. J. Candes and M. B. Wakin, ”An introduction to compressive sampling”, IEEE signal processing magazine, Vol. 25, pp. 21-30, Mar. 2008.

9.       S. S. Chen, D. L. Donoho, and M. A. Saunders, ”Atomic decomposition by basis pursuit”, SIAM J. Sci. Comput., Vol. 20, No. 1, pp. 3361, 1988.

10.     R. Baranuik, ”Compressive Sensing”, IEEE signal processing magazine, Vol. 24, pp. 118-120, July 2007.

11.     A. M. Bruckstein, D. L. Donoho and M. Elad, ”From sparse solutions     of systems of equations to sparse modeling of signals and images”, to  appear in SIAM review.

12.     A. Mitra, Y. V. S. Rao and S. R. M. Prasanna, ” A new image encryption  approach using combinational permutation techniques”, International J. of comp. Sc., Vol. 1, pp. 127-131, May 2006.

13.     S. Sridharan, E. Dawson and B. Goldburg, ”Fast Fourier transform based speech encryption system”, IEE Proceedings - I, Vol. 138, pp. 215-223,  June 1991.

14.     Y. Wu and B. P. Ng, ”Speech scrambling with hadamard transform in transform domain”, Proc. 6th International conference on signal  processing, pp. 1560-1563, Aug. 2002.

15.     M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, “On compressing encrypted data,” IEEE Trans. Signal Process., Vol. 52, No. 10, Pt. 2, pp. 2992–3006, Oct. 2004.

16.     R. G. Gallager, “Low Density Parity Check Codes,” Ph.D. dissertation,  Mass. Inst. Technol., Cambridge, MA, 1963.

17.     D. Schonberg, S. C. Draper, and K. Ramchandran, “On blind compression of encrypted correlated data approaching the source entropy rate,” in Proc. 43rd Annu. Allerton Conf., Allerton, IL, 2005.

18.     R. Lazzeretti and M. Barni, “Lossless compression of encrypted grey-level and color images,” in Proc.16th Eur. Signal Processing Conf. (EUSIPCO 2008), Lausanne, Switzerland, Aug. 2008 [Online] Available: http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/pap-ers/1569105134.pdf

19.     W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Image Process., Vol. 19, No. 4,  pp. 1097–1102, Apr. 2010.

20.     D. Schonberg, S. C. Draper, C. Yeo, and K. Ramchandran, “Toward  compression of encrypted images and video sequences,” IEEE Trans. Inf. Forensics Security, Vol. 3, No. 4, pp. 749–762, Dec. 2008.

21.     T. Bianchi, A. Piva, and M. Barni, “Composite signal representation for fast and storage-efficient processing of encrypted signals,” IEEE Trans. Inf. Forensics
Security, Vol. 5, No. 1, pp. 180–187, Mar. 2010.

22.     T. Bianchi, A. Piva, and M. Barni, “On the implementation of the discrete fourier transform in the encrypted domain,” IEEE Trans. Inf. Forensics Security, Vol. 4, No. 1, pp. 86–97, Mar. 2009.

23.     J.-C. Yen and J.-I. Guo, “Efficient hierarchical chaotic image encryption algorithm and its VLSI realization,” Proc. Inst. Elect. Eng., Vis. Image Signal Process., Vol. 147, No. 2, pp. 167–175, 2000.

24.     N. Bourbakis and C. Alexopoulos, “Picture data encryption using SCAN patterns,” Pattern Recognit., Vol. 25, No. 6, pp. 567–581, 1992.


26-29

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

7.

Authors:

Afsane Fathi, Amir Hassan Monadjemi, Fariborz Mahmoudi

Paper Title:

Defect Detection of Tiles with Combined Undecimated Wavelet Transform and GLCM Features

Abstract:    Development of an automatic defect detection system has a major impact on the overall performance of ceramic tile production industry. With this in mind, in this paper, a new algorithm has been offered for segmentation of defects in random texture tiles. firstly, by using undecimated discrete wavelet Transform (UDWT), frequency features of textures which are robust towards transition could be extracted. Then a co-occurrence matrices of sub-bands, in order to extract texture information, is obtained. Finally, after obtaining special characteristics from the combination of the two new methods, a back propagation neural network is applied for segmentation which is the final product of this. The results, both visually and computationally, show a higher accuracy while using this method than the conventional wavelet method and co-occurrence matrices that was utilized previously. The reason could be its independent from scale and rotation nature compared to the typical transform. Different locations of defects make different wavelet coefficients and ultimately increase the defect segmentation performance of a wide variety of defects.

Keywords:
   Defect detection, Wavelet Transform, Undecimated Wavelet Transform, Co-occurrence Matrices, Back-Propagation Neural Network.


References:

1.       A. Monadjemi, B. Mirmehdi,and T. Thomas, “Reconstructured Eginfilter Matching for Novelty Detection in Random Texture,” In Proceedings of the 15th british Machine Vision Conference, 2004, pp. 637-646.
2.       X. Xie and M. Mirmehdi, “TEXEMS: Texture Examplers for Defect Detection on Random Textured Surfaces,” IEEE Transaction on PAMI, Vol.29, No.8, Aug.2007, pp.1454-1464.

3.       I. Novak, Z. Hocenski, “Texture Feature Extraction for a Visual Inspection of Ceramic Tiles,” IEEE ISIE, June. 2005, Dubrovnik, Croatia, pp.1279-1283.

4.       G. Loum, C. T. Haba, J. Lemoine, and P. Provent, “Texture charecterisation and classification using full wavelet decomposition,” J. Applied Sci, Vol.7, pp. 1563-1573 .

5.       S.C. Kim and T. J. Kang, “Texture Classification and Segmentation using Wavelet Packet Frame and Gaussian Mixture Model,” Vol. 28, 2007, pp. 1566-1573.

6.       A. L. Amet, A. Ertuzun, and A.Ercil, ”An efficient method for texture defect detection: Sub-band domain co-occurrence matrices,” Vol. 28, 2000, pp. 543-553.

7.       S.Rimac, A. Keller, and Hocenski, “Neural Network Based Detection of Defects in Texture Surfaces,” IEEE ISIE,2005, Dubrovnik, Croatia, pp.1255-1260 .

8.       M. Ghazvini, S. A. Monadjemi, N. Movahedinia, and K. Jamshidi, “Defect Detection of Tiles Using 2D-Wavelet Transform and Statistical Features,” World Academy of Science, Engineering and Technology, 2009.

9.       N. G. Kingsburg, “Complex Wavelets and Shift Invariance,” Proceedings IEE Colloquiom on Time-Scale and Time-Frequency Analysis and Applications, London, 2000.

10.     M. Mirmehdi, X. Xie, and S. Jasjit, ed. Texture Analysis. London: Imperial college, 2008.

11.     S. Abdelmounaime, F. B. Mohamed, I. Tahar, “La Transform en ondelettes pour l’extraction de la texture-couleur. Application a la classification combinee des images (HRV) de SPOT,” International Journal of Remote Sensing, Vol.28, No. 18, 2006, pp. 3977-3990.

12.     J. Bonnel, A. Khademi, S. Krishnan, and C. Loana, “Small bowel image image classification using Cross-co-occurrence matrices on wavelet domain,” Journal of Biomedical Signal Processing and Control, Vol. 4, 2009, pp. 7-15. 

13.     H. Zhang, Image Processing Via Undecimated Wavelet Systems, Doctor of Philosophy Thesis, March, 2000, Rice University.

14.     S. Mallat, “Multiresolution approximation and wavelets orthonormal bases of L2(r),” Trans Amer. Math. Soc, Vol. 315, September 1989, pp. 69-87.

15.     M. J. Shensa, “The discrete wavelet transform: wedding the a trous and mallat algorithm,” IEEE Transactions on Signal Processing, Vol. 40, No. 10, December 1992, pp. 2464-2482.

16.     R. Haralick, “Statistical and Structural Approaches to Texture,” Proceedings of the IEEE, Vol. 67, No 5, 1979, pp. 786-803.

30-34

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

8.

Authors:

A. Ganguly, Manoj K Kowar, H. Chandra

Paper Title:

Preventive Maintenance of Rotating Machines Using Signal Processing Techniques

Abstract:   This paper presents a method for analyzing the vibration signals of rotating machines and diagnoses preventive maintenance requirements using Vibration Signature Analysis Technique. The concept of Vibration Signature Analysis of Rotating Machines lies on the fact that all rotating machines in good condition have a fairly stable vibration pattern, which can be considered its 'Signature'. Under any anomalous condition of working of such machines, the vibration pattern gets changed. The amount of variation can be detected and the nature of anomalies can be analyzed to get an idea about the malfunctioning of the rotating machine. In order to develop the technique to be applied, it is proposed to simulate the vibration signals of a rotating machines using MATLAB to store the signature of rotating machines under healthy conditions. Deformation can now be introduced in the signature or can be acquired from other sources. Such deformed signals are to be processed in order to know the type of defect the rotating parts of the machine is suffering from. Based on the type of defect, preventive maintenance schedule can be proposed. This paper also aims at overcoming the limitations of traditional Vibration Signature Analysis techniques.

Keywords:
   Vibration Signals, Signature Analysis, Signal Processing, Rotating machines, Preventive Maintenance.


References:

1.        Jack,L.B. and Nandi,A.K., Genetic Algorithm for feature selection in machine condition monitoring with vibration signals, IEE Proceedings on Vision, Image and Signal Processing, 147; 2000; 205-212.
2.        Li,B.,Chow, M. Y., Tipuswan and Y. Hung, J.C., Neural Network based Motor Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics, 47(5); 2000; 1060-1069.

3.        Mellor P.H., Roberts D., Turner D. R., Lumped parameter thermal model for electrical machines of TEFC design, IEE Proceedings B, 138; 1995; 208-219

4.        O. I. Okoro, Steady and transient states thermal analysis of induction machine at blocked rotor operation, IEE Proceedings B, 20(4); 2005, 730-736.

5.        Randall R.B. Vibration Signature Analysis : Techniques & Instrument Systems, Proceedings of Noise, Shock and Vibration Analysis at Monash University, Melbourne, May 1974.

6.        J. Ellison , C. J. Moore and S. J. Yang, Methods of measurement of acoustic noise radiated by an electric machine, Proceeding of IEE, 118(1); 1971; 174-184.

7.        Neelam Mehala and Ratna Ddahiya, An approach of condition monitoring of induction motor using MCSA, International Journal of Systems Applications, Engineering and Development, 1(1); 2007; 13 – 17.


35-40

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

9.

Authors:

Ravi M. Potdar, Manoj K. Kowar, Amit Biswas, Mayur Amtey

Paper Title:

Multi-Scale Domain Classification Based Heart Sound Compression

Abstract:   In recent days, fractal compression has gained a wide popularity due to its inherent features and efficiency in compressing data. In the present communication, fractal compression technique has been applied on heart sound signals for effective compression. Fractal heart sound coding based on the representation of a heart sound signal (1D or vector) by a contractive transform, on the sound data, for which the fixed point (reconstructed heart sound) is close to the original heart sound. The work is intended to provide an approach on this process by introducing the idea of multi-scale Domain pool classification using Variance Fractal Dimension (VFD) based on complexity of the heart sound data. A pre-processing analysis of the heart sound data by VFD to identify the complexity of each sound data samples block for classification has been undertaken. The performance result of the present work has focused in terms of good fidelity signal reconstruction versus encoding time and amount of compression.

Keywords:
   Phonocardiogram, Fractal Compression, Variance Fractal Dimension, Domain Classification.


References:

1.        S.P. Collins, P. Arand, CJ Lindsell, WF Peacock, AB Storrow, “Prevalence of the third and fourth heart sound in asymptomatic adults,” In Congestive Heart Failure, 2005,Vol. 11(5), pp. 242-247.
2.        M. Kompis and E. Russi, "Adaptive Heart-Noise Reduction of Lung Sounds Recorded by a Single Microphone," In Proceedings of International Conference on Engineering in Medicine and Biology Society , 1992,Vol. 2, pp. 691-692.

3.        B. N. Robert. Noninvasive Instrumentation and Measurement in Medical Diagnosis, CRC Press, 2002.

4.        Chissanuthat Bunluechokchai, Weerasak Ussawawongaraya, “A Wavelet-Based Factor for Classification of Heart Sounds with Mitral Regurgitation”, International Journal of Applied Biomedical Engineering, 2009,Vol. 2, No. 1.

5.        M. F. Barnsley, L. Hurd, “Fractal Image Compression”, Wellesley 1993.

6.        J. C. Hart, “Fractal Image Compression and Recurrent Iterated Function Systems”, IEEE Computer Graphics and Applications, 1996, Vol. 16, No. 4, 25-40.

7.        Bi-Qiang Du, Gui-Ji Tang, “Fractal Data Compression Algorithm for Vibration Signal in Fault Diagnosis”, IEEE proceedings  international conference on Wavelet Analysis and  Pattern Recognition, Beijing, China, 2-4 Nov. 2007.

8.        El-Bahlul Fgee, W. J. Phillips and W. Robertson, “Comparison Audio Compression using Wavelets with other Audio Compression Schemes”, proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, Alberta, Canada, May 9-12, 2009.

9.        J. Gnitecki, Z. Moussavi, H. Pasterkamp, “Classification of Lung Sounds during Bronchial Provocation Using Waveform Fractal Dimensions”, San Francisco, CA, USA, Proceedings of the 26th Annual International Conference of the IEEE EMBS, Sep. 2004.

10.     Dietmar Saupe, Matthias Ruhl, “Evolutionary Fractal Image Compression”, IEEE International Conference on Image Processing (ICIP'96), Lausanne, Sept. 1996.

41-44

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

10.

Authors:

Bidishna Bhattacharya, Kamal K.Mandal, Niladri Chakravorty

Paper Title:

Cultural Algorithm Based Constrained Optimization for Economic Load Dispatch of Units Considering Different Effects

Abstract:    This paper introduces an efficient evolutionary programming based approach of cultural algorithm which is a probabilistic optimum search method using genetics and evolution theory to solve different economic load dispatch problems. The proposed algorithm is a powerful population-based algorithm in the field of evolutionary computation which can efficiently search and actively explore solutions. Also it may be employed to handle the equality and inequality constraints of the ELD problems. The salient features of its knowledge space make the proposed cultural algorithm attractive in large-scale highly constrained nonlinear and complex systems. In this paper cultural algorithm combines with evolutionary programming technique to take care of economic dispatch problem involving constraints like power balance constraints, generator limit constraints, valve point loading effect, ramp rate limits, prohibited operating zone, and transmission losses etc because of cultural algorithm's flexibility. The effectiveness and feasibility of the proposed method is tested with one example of thirteen generator system considering valve point effect and one example of three generator system considering ramp rate limits, prohibited operating zone and transmission losses. Additionally the proposed algorithm was compared with other evolutionary methods like particle swarm optimization technique, genetic algorithm, evolutionary programming etc. It is seen that the proposed method can produce comparable results. 

Keywords:
   Cultural algorithm, cultural based evolutionary algorithm, evolutionary programming, ELD, prohibited operating zone, ramp rate limits, valve-point loading.


References:

1.        D.P. Kothari and J.S. Dhillon, Power System Optimization, Prentice-Hall  of India, 2006.
2.        A.J.Wood,  and B.F. Wollenberg, Power Generation, Operation and  Control, John Wiley & Sons, New York, 1984.

3.        C.L.Chen,  and S.C. Wang, "Branch-and bound scheduling for thermal generating units," IEEE Trans. on Energy Conversion, Vol. 8, No. 2, pp. 184-189, June 1993.

4.        K.Y.Lee, e t al., "Fuel cost minimization on for both real- and reactive power dispatches," IEE b.C, Gener. Trsns. & Distr., 131, (3). pp. 85- 93,1984.

5.        G.B. Sheble, and K. Brittig, "Refined genetic algorithm-economic dispatch  example," IEEE Paper 94 WM 199-0 PWRS, presented at the IEEE/PES  1994 Winter Meeting.

6.        D.C.Walters, and G.B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. on Power Systems, Vol. 8,  NO.  3, p ~1.32 5-1332, August 1993

7.        A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, “Optimal power flow by enhance genetic algorithm,” IEEE Trans. Power Systems, vol. 17, pp. 229–236,
May 2002.

8.        D. B. Fogel, “An introduction to simulated evolutionary optimization,” IEEE Trans. Neural Networks, vol. 5, pp. 3–14, 1994.

9.        D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of   Machine Intelligence. Piscataway, NJ: IEEE Press, 1995.

10.     K. Chellapilla and D. B. Fogel, “Two new mutation operators for enhanced search and optimization in evolutionary  programming,” in SPIE Int. Symp. Optical Science and Engineering Instrum. Conf., 3165:Applic. Soft Comput.. Bellingham, WA: SPIE Press, pp. 260–269

11.     K. P. Wong and Y. W. Wong, “Thermal generator scheduling using  hybrid  genetic/simulated annealing approach,” in IEE Proc. C, vol. 142,  July1995, pp. 372
380.

12.     K. P. Wong and C. C. Fung, “Simulated annealing based economic dispatch algorithm,” in Proc. Inst. Elect. Eng. C, vol. 140, 1992, pp. 544– 550.

13.     R.G.Reynolds, “An Introduction to Cultural Algorithms, ” in   of the 3rd Annual Conference on Evolutionary Programming, World Scientific Publishing, pp 131–139, 1994

14.     J. Liu, H. Gao, J. Zhang, and B. Dai, “Urban power network substation optimal planning based on Geographic Cultural Algorithm,” The 8th Int. Conf. Power Engineering. pp. 500-504, 2007.

15.     X. Yuan, and Y. Yuan, “Application of Cultural Algorithm to generation  scheduling of hydrothermal systems,” Energy Conversion and Management, vol. 47, issue 15-18, pp. 2192-2201, 2006.

16.     A R. Seifi, “A new Hybrid Optimization method for optimum distribution capacitor planning,” Modern Applied Science, CCSE, vol. 3, no. 4, pp.  196-202, April 2009.

17.     Coelho L.D.S.,Souza R.C.T.,Mariani V.C., Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems, Mathematics and Computers in Simulation 79, 3136-3147.(2009)

18.     Chan-Jin Chung,.R.G.Reynolds , “CAEP:AnEvolution-based Tool for real valued Function Optimization using Cultural algorithms”, International Journal on Artificial Intelligence Tools,7(3).(1998)

19.     Y.H.Song,, G.S.Wang,, P.Y.Wang,, and Johns, A. T., “Environmental/Economic Dispatch using Fuzzy Logic Controlled Genetic Algorithm,” Proc.I.E.E Generation, Transmission and  Distribution, Vol. 144, No. 4,pp. 377–382.(1997)

20.     IEEE Committee Report, "Present practices in the economic operation of  power systems," IEEE Trans. on Power Apparatus and Systems, Vol. PAS-90, pp. 1768-1775, July/Aug. 1971

21.     Wang C. and S.M. Shahidehpour, "Effects of ramp-rate limits on unit  commitment and economic dispatch," IEEE Trans. on Power Systems, Vol. 8, NO. 3, pp.
1341-1350, August 1993.

22.     Po-Hung Chen and Hong-Chan Chang, Large-Scale Economic Dispatch by Genetic Algorithm, IEEE Transactions on Power System. Vol. 10.  No.  4. November 1995

23.     T.A.A..Victoire ,A.E.Jeyakumar , Hybrid PSO-SQP for economic dispatch with valve-point effect, Electric Power system research 71    (1)51-59. (2004).

24.     C. L. Chiang, “Improved genetic algorithm for power economic dispatch  of units with valve-point effects and multiple fuels”, IEEE Transactions  on Power Systems, Vol. 20, no. 4, pp. 1690-1699, 2005.

25.     N. Sinha, R. Chakrabarti and P. K.Chattopadhyay, “Evolutionary programming techniques for economic load dispatch”, IEEE    Transactions  on EvolutionaryComputation, Vol. 7, no. 1, pp. 83-94, 2003.

26.     Noman N,Iba H,Differential Evolution for economic load dispatch    problem, Electric Power Systems Research 78(3)1322-1331(2008)

27.     B. K. Panigrahi, V. R. Pandi, and S. Das, “Adaptive particle swarm optimization approach for static and dynamic economic load  dispatch,”Energy Convers. Manage., vol. 49, no. 6, pp. 1407–1415,  2008.

28.     R. Naresh, J. Dubey, and J. Sharma, “Two phase neural network based modeling framework of constrained economic load dispatch,” Proc.Inst. Elect. Eng., Gen., Transm., Distrib., vol. 151, no. 3, pp. 373–378,May 2004.

45-50

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

11.

Authors:

Ram Kumar Singh, Akanksha Balyan

Paper Title:

Approach to Software Maintainability Prediction Versus Performance

Abstract:    The software maintainability is one of the most significant aspects in software evolution for the software product. Due to the complexity of chase maintenance demeanor, it is difficult to accurately anticipate the price and risk of maintenance afterward delivery of the software products. The value of a software system results from the interaction between its functionality and quality attribute (performance, reliability and security) and the market-place. The software maintainability is viewed considered as an inevitable evolution procedure driven through maintenance demeanor. Traditional product cost model have focused on the short term development cost of the software product. A HMM (Hidden Markov Model) is applied to simulate the maintenance demeanor demonstrated as their potential occurrence probabilities. The software metric function is the measurement of the software quality products and its measurements results of a software product existence delivered combined to from health index of the software product. When the occurrence probabilities of maintenance demeanor reach certain number which is calculate as the denotation of worsening position of software product, the software product can be considered as obsolete. The longer time, more beneficial the maintainability would be. We believe on the architectural approach to price-modeling will be able to capture these concerns so that the software can reason about the risk I the system and price of mitigating them.

Keywords:
   Software maintainability, HMM (Hidden Markov Model), Performance modes between availability and Software metrics.


References:

1.        S. Balsamo, P. Inverardi and B. Selic, Proc. Third ACM Workshop on software and Performance, Performance, Italy Rome, July 24-27, 2002.
2.        Boehm B. , “Software Engineering Economics”, Prentice Hall (1981).

3.        M. Hilton, “Information Technology Workers in the New Economy”, Monthly Labor Review, June 2001, pp. 41-45.

4.        ISO/IEC 14764:2006 http://www.iso.org/iso/catalogue_detail.htm?csnumber=39064

5.        R.L Glass, “Frequently Forgotten Fundamental Facts about Software Engineering”, IEEE Software, May/June 2001.

6.        Cem Kaner, and Walter P. Bond “Software Engineering Metrics: What Do They Measure and how Dow We Know?”, 10TH INTERNATIONAL SOFTWARE METRICS
SYMPOSIUM METRIS, 2004.

7.        Lawrence R. Rabiner (February 1989). “A tutorial on Hidden Markov Models and selected applications in speech recognition”, Proceedings of the IEEE 77 (2); 1989.

8.        Steve Cornwell, “Code Complete: A  Practical Handbook of  Software Construction”, Microsoft Press; 2nd edition (June 9, 2004).

9.        Thomas McCabe, “A complicity Measure”, IEEE Transaction on Software Engineering, VOL, SE-2, No. 4, 1986.


51-54

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

12.

Authors:

R. Vijay

Paper Title:

Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem

Abstract:   Bacterial Foraging optimization (BFO) is a swarm intelligence technique used to solve problem in power systems. The algorithm is based on the group foraging behaviour of Escherichia coli (E-Coli) bacteria present in human intestine. This social foraging behaviour of E.coli bacteria has been used to solve optimization problems. In this paper, an overview of the biology of bacterial foraging and the pseudo-code that models this process also explained. This paper presents a novel BFO to solve Economic Load Dispatch (ELD) problems. The results are obtained for a test system with three and thirteen generating units. In this paper the performance of the BFO is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results clearly show that the proposed method gives better optimal solution as compared to the other methods.

Keywords:
   Bacteria Foraging Optimization, Escherichia coli Economic load Dispatch, Genetic Algorithm, Particle Swarm Optimization.


References:

1.       Kevin M.Passino, “Bacterial Foraging optimization,” International Journal of Swarm Intelligence Research, pp. 1-16, Jan-Mar 2010.
2.       Selvakumar, and K. Thanushkodi, "A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems," IEEE Trans. Power Systems, vol. 22, no. 1, pp. 42-51, Feb. 2007.

3.       K.P Wong and J.Yuryevich, "Evolutionary Programming Based Algorithm for Environmentally Constrained Economic Dispatch",IEEE transaction on Power Systems”, Vol. 13,No.2,pp 301,May 1998

4.       H. Chowdhury and S. Rahman, "A review of recent advances in economic dispatch," IEEE Trans. Power Syst., vol. 5, no. 4, pp. 1248- 1259, Nov. 1990.

5.       BijayaKetanPanigrahi,Yuhui Shi, and Meng-Hiot Lim “Handbook of Swarm Intelligence Concepts, Principles and Applications,” Springer pp.487-502, 2011.

6.       Jason Brownlee “Clever Algorithms: Nature-Inspired Programming Recipes,” Jason Brownlee pp.257-264, 2011.

7.       VeyselGazi and Kevin M. Passino “Swarm Stability and Optimization,” Springer pp.233-249, 2010.

8.       J. Wood, and B. F. Wollenberg, “Power generation operation and control,” John Willey and Sons, 2nd Ed, pp. 39-43, 1996.

9.       T. Yang, P. C. Yang, and C. L. Huang, “Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions,” IEEE Trans. Power Syst., vol. 11, no. 1, pp. 112–118, Feb. 1996

10.     Y. Liu and K. M. Passino, “Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors,” Journal Optimal. Theory Appl., Vol. 115, no. 3, pp. 603–628, Dec. 2002

11.     SibylleD.Muller, JarnoMarchetto, StefanoAiraghi, and PetrosKoumoutsakos,”Optimization Based on Bacterial Chemotaxis,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, February 2002.

12.     Kevin M.Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., Vol. 22, no. 3, pp. 52–67, Jun. 2002.

13.     N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch,” IEEE Trans. Evol. Comput., vol. 7, no. 1, pp. 83–94, Feb. 2003.

14.     Kevin M. Passino “Biomimicry for Optimization, Control, and Automation,” Springer Verlag London, pp. 768-816, 2005.

15.     J.-B. Park, K.-S. Lee, J.-R. Shin, and K. Y. Lee, “A particle swarm optimization for economic dispatch with nonsmooth cost functions,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 34–42, Feb. 2005.

16.     S. Das, A.Biswas, S. Dasgupta, and A. Abraham, “Foundations of Computational Intelligence,”Vol 3: Global Optimization, chapter Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, pages 23–55. Springer, 2009.

17.     J. Hazra, A.K.Sinha, “Environmental Constrained Economic Dispatch using Bacteria Foraging Optimization,” IEEE Transactions on Evolutionary Computation,2009.

18.     Swagatam Das, S.Dasgupta, A. Biswas, Ajith Abraham, “On Stability of the Chemotactic Dynamics in Bacterial-Foraging Optimization Algorithm,” IEEE Transactions on systems, man, and cybernetics, Vol. 39, No. 3, May 2009.

19.     Sambarta Dasgupta,ArijitBiswas,Swagatam Das, BijayaKetanPanigrahi and Ajith Abraham, “A Micro-Bacterial Foraging Algorithm for High-Dimensional Optimization,” IEEE Congress on Evolutionary Computation, pp.785-792, 2009.

20.     M. E. El-Hawary and G. S. Christensen, “Optimal Economic Operation of Electric Power System”, New York: Academic, 1979.


55-59

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

13.

Authors:

Srikanth.S, M.Jagadeeswari

Paper Title:

High Speed VLSI Architecture for Multilevel Lifting 2-D DWT Using MIMO

Abstract:    The Discrete Wavelet Transform (DWT) Lifting architecture is a powerful signal analysis technique for non-stationary data. High speed implementation of this architecture is a challenging task. This paper proposes an efficient multi-input/multi-output VLSI architecture (MIMO) for two-dimensional lifting-based discrete wavelet transform (DWT). Computing time for this high speed architecture is as low as N2/M for an N X N image with controlled increase of hardware cost. M is the throughput rate. The experimental results show that proposed architecture provides high throughput and power consumption compared to the conventional architecture.

Keywords:
   Discrete Wavelet Transform Lifting Scheme, MIMO, Memory Buffer, SISO.


References:

1.        S. Mallat, “A Theory for Multiresolution Signal Decomposition: The  Wavelet Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674–693, July 1989.
2.        W. Sweldens, “The Lifting Scheme: A Custom-Design Construction of  Biorthogonal Wavelets,” Applied and Computational Harmonic Analysis, Vol. 3, pp.186-200, 1996.

3.        B.F.Wu and C.F. Lin, “High-performance memory-efficient pipeline architecture for the 5/3 and 9/7 discrete wavelet transform of JPEG 2000 codec,” IEEE Trans. on Circuit and System for Video Technology, vol.15, no.12, pp.1615-1628, Dec. 2005.

4.        Andra, K. Chakrabarti, C. and Acharya, T. “A VLSI Architecture for Lifting-based Forward and Inverse Wavelet Transform,” IEEE Trans.Signal Process., Vol. 50, No. 4, pp. 966–977, 2001.

5.        Wu, P.C.  and Chen, L.G. “An Efficient Architecture for Two-Dimensional Discrete Wavelet transform,” IEEE Trans. Circuits Syst. Video Technol., Vol. 11,  No. 4, pp. 536–545.

6.        Huang, C.T. Tseng, P.C. and Chen, L.G. “Generic RAM-based Architectures  for two-Dimensional Discrete Wavelet Transform with Line based method,” IEEE  Trans. Circuits Syst. Video Technol., Vol. 15, No. 7, pp. 910–920, 2005.

7.        Huang, C.T. Tseng, P.C. and Chen, L.G. “Analysis and VLSI architecture for 1 D and 2 D DWT,” IEEE Trans. Signal Process., Vol.53, No.4, pp.1575-1586, Apr. 2005.

8.        Xin Tian , Lin Wu , Yi-Hua Tan “Efficient Multi-Input/Multi-Output VLSI Architecture for Two-Dimensional Lifting-Based Discrete Wavelet Transform,” IEEE Transactions on Computers, vol. 60, no. 8, August 2011.

9.        Chrysafis C. and Ortega A., “Line-Based, Reduced Memory, wavelet Image Compression,” IEEE Trans. Image Processing., vol. 9, no. 3, pp. 378-389, Mar.2000.

10.     Dillen G. et al., “Combined Line-Based Architecture for the 5-3 and 9-7  Wavelet Transform of JPEG2000,” IEEE Trans. Circuits and Systems for Video Technology, vol. 13, no. 9, pp. 944-950, Sept. 2003.

11.     Barua, S.  Carletta J.E., Kotteri K.A., and Bell A.E., “An Efficient Architecture for Lifting-Based Two-Dimensional Discrete Wavelet Transform, “Integration, the VLSI J., vol. 38, no. 3, pp. 341-352, 2005.

12.     Cheng C. and Parhi K.K., “High-Speed VLSI Implementation of 2D Discrete Wavelet Transform,” IEEE Trans. Signal Processing, vol. 56, no. 1, pp. 393-403, Jan. 2008.

13.     Meher, P. K. Mohanty,  B. K.  and Patra, J. C.( 2008) ‘Memory Efficient Modular VLSI Architecture for High throughput and Low-Latency Implementation of   Multilevel Lifting 2-D DWT’. IEEE Transactions on Signal processing, vol. 59, no. 5, may 2011.


60-64

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

14.

Authors:

Mamatha. T

Paper Title:

Network Security for MANETS

Abstract:   A mobile ad hoc network (MANET) is a network consisting of a collection of nodes capable of communicating with each other without the help from a network infrastructure. Although security issues in mobile ad hoc networks have been a major focus in the recent years, the development of fully secure schemes for these networks has not been entirely achieved till now. MANETs have a unique characteristics and constraints that make traditional approaches to security inadequate. The lack of an infrastructure exacerbates the situation of using shared secret keys or authentication among members. Therefore, the issues of authentication, key distribution and intrusion detection require different methods, which are discussed here. In this paper, we propose to combine efficient techniques from elliptic curve cryptography (ECC) and a distributed intrusion detection system (IDS) based on threshold cryptography. And also propose to use a distributed certifying authority (CA) along with per-packet per-hop authentication for addressing the issues mentioned above. The model assumes that no single node can be trusted and relies instead on a distributed trust model.

Keywords:
   mobile ad hoc network (MANET), elliptic curve cryptography (ECC), distributed certifying authority, certifying authority (CA), threshold cryptography, intrusion detection (ID)


References:

1.        B. Mukherjee, T. L. Heberlein, and K. N. Levitt, “Network  Intrusion Detection,” IEEE Network, 8(3): 26–41, 1994.
2.        J. S. Balasubramaniyan et al., “An Architecture for Intrusion Detection using Autonomous Agents,” Proceedings of the Fourteenth Annual Computer Security Applications Conference, 1998    .

3.        M. Asaka et al., “A Method of Tracing Intruders by Use of Mobile Agents,” in proceedings of the Internet Society,  1999

4.        S. Kumar and E. Spafford, “An Application of Pattern Match in Intrusion Detection,” Technical Report 94-013, Dept. of Computer Science, Purdue University, 1994.

5.        M. Crosbie and G. Spafford, “Active Defense of a Computer System Using Autonomous Agents,” Technical Report 95-008, COAST Group, Dept. of Computer Science, Purdue University,

6.        Y. Desmedt, “Some Recent Research Aspects of Threshold Cryptography,” in proceedings of the First International Workshop  on Information Security: 158–173, 1997.

7.        H. Luo, P. Zerfos, J. Kong, S. Lu, and L. Zhang, “Self-Securing Ad Hoc Wireless Networks,” in proceedings of the 2002 IEEE Symposium on Computers and Communications, Italy, July  2002.

8.        A. Khalili, J. Katz, and W. Arbaugh, “Toward Secure Key Distribution in Truly Ad-Hoc Networks,” in proceedings of the 2003 Symposium on Applications and the Internet Workshops

9.        G.V.S. Raju, G. Hernandez, and Q. Zou, “Quality of service routing in adhoc networks,” IEEE WCNC 2000, Vol. 1, 2000

10.     G.V.S. Raju and G. Hernandez, “Routing in Ad hoc networks”, in proceedings of the IEEE–SMC International Conference, 

11.     G.V.S. Raju and J. Charoensakwiroj, “Wireless Communications,” Annual Review of Communications, Vol. 57 (Chicago: IEC, 2004).

12.     L. Zhou and Z. Haas, “Securing Ad Hoc Networks”, IEEE  Network Magazine, 13(6), November/December 1999 .

13.     P Papadimitratos and Z. Haas, “Secure Routing for Mobile Ad hoc Networks,” in proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference, January 2002.

14.     A. Perrig, R. Szewcyzk, V. Wen, D. Culler, and J.D. Tygar,  “Spins: Security Protocols for Sensor Networks,” in proceedings of Mobile Computing and Networking 2001.

15.     G.V.S. Raju and R. Akbani, “Elliptic Curve Cryptosystems and its Applications,” in the proceedings of the IEEE-SMC Conference, October 2003.

16.     V.S. Miller, “Use of Elliptic Curves in Cryptography,” in Advances in Cryptology (Proceedings of CRYPTO 1985), Springer Verlag Lecture Notes in Computer Science 218, 1986,  pp. 417–426.

17.     G.V.S. Raju and Rehan Akbani, “Some Security Issues in Mobile Ad-hoc Networks,” in proceedings of the Cutting Edge Wireless and IT Technologies Conference, November 2004.


65-68

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

15.

Authors:

Ponrajan. P, Jebarani Evangeline. S, Jayakumar. J

Paper Title:

ANFIS Based Torque Control of Switched Reluctance Motor

Abstract:    This paper develops an ANFIS based torque control of SRM to reduce the torque ripple.  The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. This controller realizes a good dynamic behavior of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI). The above controller was realized using MATLAB/Simulink.

Keywords:
   ANFIS, Torque Control, Switched Reluctance Motor.


References:

1.        I. Husain, and M. Ehsani,“Torque Ripple minimization in   Switched Reluctance Motor Drives by PWM Current Control”,  IEEE Transaction on Power Electronics., vol.11, no. 1, pp. 83-88, 1996.
2.        N. C. Sahoo,“A Study on Application of Modern Control techniques for Torque Control of Switched Reluctance Motors,” Ph.D. Thesis, National University of
Singapore, 2001.

3.        R.S. Wallace, D.G. Taylor, “Low-torque-ripple switched reluctance motors for direct-drive robotics,” IEEE Trans. on Robotics and Automation, vol. 7, no. 6 , pp. 733-742, Dec 1991.

4.        I. Husain,“Minimization of torque ripple in SRM drives”, IEEE  Transaction on Industrial Electronics”, vol. 49, no. 1, pp. 28-39, Feb. 2002.

5.        K.J. Tseng, Shuyu Cao, “A SRM variable speed drive with torque ripple minimization control”,  IEEE APEC vol. 2, pp. 1083-1089, 2001.

6.        C.  Shang,  D.  Reay,  and  B.  Williams,  “Adapting  CMAC  neural networks  with  constrained  LMS  algorithm  for  efficient  torque  ripple reduction  in  switched  reluctance  motors,”  IEEE  Transactions  on Control Systems Technology, vol. 7, No. 4, pp. 401-413, July 1999.

7.        Z.  Lin, D.  S.  Reay, B.  W.  Williams and  X.  He,  “Torque  ripple reduction  in  switched  reluctance  motor  drives  using  B-spline  neural networks,” IEEE Transactions on Industry  Applications, vol. 42, no. 6, pp. 1445-1453, Nov./Dec. 2006.

8.        J. G. O' Donovan, P. J. Roche, R. C. Kavanagh, M. G. Egan, and J. M. D.  Murphy, “Neural  network  based  torque  ripple  minimisation   in  a switched  reluctance  motor,”  in  20th  International  Conference  on Industrial  Electronics,  Control  and  Instrumentation,  vol.2,  pp.  1226- 123, 1994.

9.        K.  M.  Rahman, A.  V.  Rajarathnam  and  M.  Ehsani,  “Optimized instantaneous  torque  control  of  switched  reluctance  motor  by  neural network,” IEEE Industry Application Society Annual Meeting, pp. 556-563, 1997.

10.     Y. Cai and C. Gao, “Torque ripple minimization in switched reluctance motor  based  on  BP  neural  network,”  in  2nd  IEEE  Conference  on Industrial Electrics and Applications, pp.1198-1202, 2007.

11.     M.  Brown,  K.  M.  Bossley,  D.  J.  Mills,  and  C.  J.  Hams,  “High Dimensional  Neurofuzzy  Systems:  Overcoming  the  curse  of  Dimensionality,”  IEEE  International  Conference. on Fuzzy  Systems, vol.4,  pp.2139- 2146, 1995. 

12.     C.  T.  Lin  and  C.  S.  George,  Neural  Fuzzy  Systems:  A  Neuro-Fuzzy Synergism  to  Intelligent  Systems,  1st  ed.,  New  Jersey:  Prentice  Hall PTR, 1996, p.242.


69-72

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

16.

Authors:

L. Sreenivasulu Reddy 

Paper Title:

A New Modal of Hill Cipher Using Non – Quadratic Residues

Abstract:   This paper is  improved the security  on Hill cipher by using Non-Quadratic residues of a prime number p≥53. In Hill Cipher, a plain text is encrypted using a fixed value ‘26’ during the computation.  The paper explains how using Non-Quadratic residues during encryption improves security.             

Keywords:
Modular arithmetic inverse, inner key, outer key, linear congruence’s, Quadratic residues, Non-Quadratic residues, GL (n, Z).


References:

1.        Introduction to Analytic Number Theory, fifth edition.  T. Apostol .Undergraduate Texts in Mathematics, Springer-Verlag, New York, 1995
2.        An introduction to the theory of numbers, 5th ed.,I. Niven, H. S. Zuckerman and H. L. Montgomery, Wiley, New York, 1991.

3.        Cryptography and Network security, William stallings, 3rd Edition, pearson Education

4.        On the Modular Arithmetic Inverse in the cryptology of Hill cipher, 2005. V.U.K. sastry, V.Janaki, proceedings of North American Technology and Business conference, canada

5.        Hill’s  System of Data Encryption prepared by” Ben Kohler and Michael Ziegler”

6.        A.Vanstone. Handbook of Applied cryptography Menezes, Alfred, paul C.Van Oorschot, and scott .New York: CRC press,1997

7.        Saroj KumarPanigrahy,Bindudendra Acharya and  debasish Jena,Image Encryption Using Self-Invertible Key Matrix of Hill Cipher Algorithm, 1st International Conference on Advances in Computing, Chikhli, India, 21-22 February 2008.

8.        G.Sivagurunathan, V.Rajendran and Dr.T.Purusothaman. Classification of Substitution Ciphers using Neural Networks . IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.3, March 2010.


73-74

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

17.

Authors:

L. Sreenivasulu Reddy  

Paper Title:

Efficient on-Board -RSA Key Generation with Smart Cards

Abstract:    Public key cryptography gained increasing attention from both companies and the end users who wish to use this emerging technology to secularize a wide variety of applications. A major consequence of this trend has been the growing significance of the public-key smart cards. A smart card is a tiny secure crypto processor embedded within a credit card-size or smaller(like the GSM SIM) card which provide encryption, decryption as well as key generation within it’s security perimeter. -RSA is a simple and easy to implement public key cryptographic algorithm. Today -RSA key keys range from 512 bits to 2048 bits and some bodies envision 4096-bit -RSA keys in near future, like RSA key. In this paper, I will present a study of efficient algorithms involved in on-board -RSA key generation[1].

Keywords:
  RSA , Jordan arithmetic function,  Prime Number and Co-primes


References:

1.       J. J. Quisquater and B. Schneier, Smart Card  Crypto- Coprocessors for Public-Key Cryptography, vol. 1820 of Lecture Notes in Computer Science, Springer Verlag, 2000.
2.       C,. K. Koc,, "High-Speed RSA Implementation," Tech. Rep. TR 201, RSA Laboratories,  73 pages, November 1994.

3.       M. Joye, P. Paillier, and S. Vaudenay, "Efficient Generation of Prime Numbers," Cryptographic Hardware and Embedded Systems, pp. 340- 354, Aug. 2000.

4.       J. F. Dhem, Design of an Efficient Public-Key Cryptographic Library in RISC-based Smart Cards, Ph.D. thesis, Unbiversit Catholique de Louvain, May 1998.

5.       Chenghuai Lu, A. L. M. Santos, and F. R. Pimentel, "Implementation of Fast RSA Key Generation in Smart Cards," in Proceedings of the 2002-ACM Symposium on Applied computing. 2002, pp. 214-220, ACM Press.

6.       N. Feyt and M. Joye, "A better use of smart cards in pkis," Gemplus Developer Conference, Nov. 2002.   

7.       N. Feyt, M. Joye, and P. Paillier, "Off-line/on-line generation of RSA keys with smart cards." 2nd International Workshop for Asian Public Key Infrastructures, pp. 153-158, Oct. 2002.


75-79

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

18.

Authors:

L. Sreenivasulu Reddy, V. Vasu, M. Usha Rani

Paper Title:

Scheduling Algorithm Applications to Solve Simple Problems in Diagnostic Related Health Care Centers

Abstract:   Scheduling algorithms focuses on the applications of analytical methods to facilitate better decision making. This paper aims to raise the awareness of diagnostic specialists with regard to practical scheduling algorithm applications. Scheduling algorithm applications used as part of mainstream decision making by diagnostic centre specialists. Common people in the real world facing so many solvable problems each and every day in diagnostic centers for malaria parasite checkup. If diagnostic specialist takes proper care then it is solvable simple problems. Also it is a good encouragement to everyone for checking their blood whether it is infected with parasite or not. It’s also helpful to supporting staff. We explained basic applications along with problems with suitable simple solutions through scheduling algorithm techniques and graph theory approach too. 

Keywords:
   Microscopy, scheduling algorithms, waiting time, image processing, Malaria parasite.


References:

1.        Andrew G Dempster and F Boray Tek: Computer vision for microscopy diagnosis of malaria. Malar J. 2009; 8: 153.
2.        Brailsford S: Overcoming the barriers to implementation of operations research simulation models in healthcare. Clinical and Investigative Medicine, 28, (6), 312-315, year 2005.

3.        Cayirli T,Veral E,Rosen H:Designing  appointment scheduling systems for ambulatory care services. Health Care Manage Sci 2006;9:47-58.

4.        Crane AB. Management engineers: a scientific approach to pinpoint a hospital’s problems and find common-sense solutions. Hosp Health Netw 2007; 81:50-4 .

5.        Fone D,Hollinghurst S, Temple M,Round A,Lester N,Weightman A: Systematic review of the use and value of computer simulation modeling in population health and health care delivery. Journal of public health medicine, 25 (4). pp. 325-335. ISSN 1741-3842

6.        Krithi Ramamritham and John A. stankovic: Scheduling Algorithms and Operating SystemsSupport for Real-Time Systems. Proceedings of the IEEE :1994.

7.        P.M.Rubesh Anand,  Vidhyacharan Bhaskar, G.Bajpai and Sam M.Job: Detection of the  malarial parasite infected blood images by 3D-Analysis of the cell curved surface. In the Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering, Kuala Lumpur, June 2008.

8.        Proudlove N,Boaden R,Jorgensen J: Developing bed managers:the why and the how. Journal of Nursing Management Volume 15, Issue 1, pages 34–42, January 2007.

9.        Osamuyimen Igbinosa, Owen Igbinosa, Chenyi Jeffery: A Sequential review on accuracy of detecting malaria parasitemia in developing countries with large restriction on resources. Journal of Medicine and Medical Sciences Vol. 1(9) pp. 385-390 October 2010 .


80-82

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

19.

Authors:

Mukhwinder Kaur, Bhawna, G.C.Lall

Paper Title:

An Architecture of Integration Of  802.11 WLAN Network & UMTS

Abstract:    In Wireless network different technologies used for different purposes like Wireless LAN  used for data services and UMTS are used for  cellular networks such as  provide various voice and data services, WLAN provides data services at high speed.   Integration of UMTS and WLAN allows Operator to deploy used services at low cost and high speed. WLAN also allow covering hotspot areas Furthermore the architecture of WLAN and UMTS integration permits a mobile node to continue data connection (packet switch) through WLAN and voice connection (circuit switch) in parallel. In this paper the main features we are explaining WLAN and UMTS architecture along with its advantages and challenges facing during integration  and handover scheme from WLAN to UMTS  is being proposed.

Keywords:
   CIRCUIT SWITCH, PACKET SWITCH, HOTSPOT, UMTS, WLAN


References:

1.        W. Song , H. Jiang, W. Zhuang, and Xuemin Shen , "Resource management for Qos support in cellular/WLAN interworking," Network, IEEE , vol.19, no.5, pp. 12- 18, Sept.-Oct. 2005.
2.        W.Song,W.Zhuang,A.Saleh,” Interworking of 3G cellular networks and Wireless LAN” ,International Journal of Wireless and Mobile Computing, vol.2, no. 4, pp. 237-247, 2007.

3.        Matthew Gast, 802.11 Wireless Networks – The Definitive Guide, O’Reilly, 2002.

4.        IEEE Std. 802.11b, Supplement to ANSI/IEEE Std. 802.11,1999 Edition, IEEE Standard for Wireless LAN MAC and PHYSpecifications, PDF: ISBN 0-7381-1812-5, January 2000.

5.        Aziz, A.; Saad, N.M.;   Samir, B.B.; Dept. of Elect. & Electro Eng, Univ. Teknol. Petronas, Tronoh, Malaysia “A comparative analysis of integration schemes for UMTS and WLAN networks “,Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference on 6-9 Dec. 2010.

6.        Christine E. Jones, Krishna M. Siva lingam, Prathima Agrawal, Jyh Cheng Chen, A survey of energy efficient network protocols for wireless networks, Wireless Networks 7 (4) (2001) 343–358.

7.        A. Helmy, and M. Jaseemuddin, Efficient Micro-Mobility using Intra-domain Multicast-based Mechanisms (M&M), USCCS-TR-01-747, August 2001.

8.        A Comparative Analysis of Integration Schemes for UMTS and WLAN Networks Safdar Rizvi, Asif Aziz, N.M. Saad, Brahim Belhaouari Samir, Department of Electrical and Electronic Engineering, University Technology Petrona  31750 Tronoh, Perak, Malaysia, 978-1-4244-7456-1/10, 2010 IEEE.

9.        M.A. Amara,”Performance of WLAN and UMTS integration at the hot spot location using opnet“, 2003-2006

10.     An Architecture for Integrating UMTS and 802.11 WLAN Networks”, Muhammad Jaseemuddin Dept. of Electrical & Computer Engineering, Ryerson University, 2009

11.     J. Alba-Laurila, J. Mikkonen, and J. Rinnemaa, Wireless LANAccess Network Architecture for Mobile Operators, IEEE Communications, pp. 82-89, Vol. 39, No. 11, November 2001

12.     M. Bauer et al, Comparison of Different Strategies for UMTS and WLAN integration, IPCN 2002.

13.     A.K. Salkintzis, "Interworking techniques and architectures forWLAN/3G integration toward 4G mobile data networks," Wireless Communications, IEEE, vol.11, no.3, pp. 50- 61, June 2004

14.     Rastin Pries, Andreas M¨ader, Dirk Staehle, and Matthias Wiesen “On the Performance of Mobile IP in Wireless LAN Environments, In Wireless Systems and Mobility in Next Generation Internet”, LNCS vol. 4369, Sitges, Spain, June 2006.

15.     G. Dommety, “Fast Handovers for Mobile IPv6”, Internet Draft, July 2001.

16.     A. Campbell, J. Gomez, S. Kim, A. Valko, C. Wan, Z.Turanyi, Design, Implementation, and Evaluation of Cellular IP,IEEE Personal Communications, Vol. 7, No. 4, pp. 42-49,August 2000.

17.     Vahid Solouk, Borhanuddin Mohd Ali, Daniel Wong “Vertical Fast Handoff in Integrated WLAN and UMTS Networks “, ICWMC 2011, the Seventh International Conference on Wireless and Mobile Communications, 2011

18.     F. Zarai, N. Boudriga, M.S. Obaidat. “WLAN-UMTS Integration: Architecture, Seamless Handoff, and Simulation Analysis”. SIMULATION, 82(6): 413-424, 2006

19.     A. H. Zahran, B. Liang, A. Saleh, “Signal Threshold Adaptation for Vertical Handoff in Heterogeneous Wireless Networks”. Mobile Networks and Applications, 11: 625-640, 2006

20.     Hacker, H. Labiod, G. Pujolle, H. Afifi, A. Serhrouchni, P. Urien. “A New Access Control Solution for a Multi-Provider Wireless Environment”, Telecommunication Systems, 29(2): 131-152, 2005

21.     Zhi Ren, Guangyu Wang, Qianbin Chen, Hongbin Li” Modeling and simulation of Rayleigh fading, path loss, and shadowing fading for wireless mobile networks”, Simulation Modeling Practice and Theory 19 (2011)

22.     V. Dasarathan, M. Muthukuma, K.N. Elankumaran, Outdoor channel measurement, path loss modeling and system simulation of 2.4 GHz WLAN IEEE802.11g in Indian rural environments, in: Asia-Pacific Microwave Conference, 2007.

23.     N. Alsindi, B. Alavi, K. Pahlavan, “Empirical path loss model for indoor relocation using UWB measurements”, IET Electronics Letters 43 (7) (2007)

24.     Celal Ceken, Serhan Yarkan ,Huseyin Arslan,” Interference aware vertical handoff decision algorithm for quality of service support in wireless heterogeneous
networks”, Computer Networks 54 (2010)

25.     S. Yarkan, A. Maaref, K.H. Teo, H. Arslan, “Impact of Mobility on the Behavior of Interference in the field of Cellular Wireless Networks”, Global Telecommunications Conference, 2008.

26.     Upendra Rathnayake, Maximilian Ott, Aruna Seneviratne, “Network availability prediction with hidden context”, Performance Evaluation 68 (2011)

27.     Zhu, H. Yu, Xining Wang, and H. Chen, Improvement of Capacity and Energy Saving of VoIP over IEEE 802.11 WLANs by A Dynamic Sleep Strategy, IEEE GLOBECOM09 (2009)

28.     Qixiang Pang, S.C. Liew, V.C.M. Leung, Performance improvement of 802.11Wireless network with TCP ACK agent and auto-zoom backoff algorithm, in: IEEE Vehicular Technology Conference, 2005

29.     M. van Der Schaar, N. Sai Shankar, Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms, IEEE Wireless Communications 12 (4) (2005) 50–58.


83-87

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

20.

Authors:

S. B. Rashmi, Siva S. Yellampalli

Paper Title:

Design of Phase Frequency Detector and Charge Pump for  High Frequency PLL

Abstract:  A simple new phase frequency detector and charge pump design are presented in this paper. The proposed PFD uses only 4 transistors and preserves the main characteristics of the conventional PFD. Both PFD and charge pump are implemented using cadence 0.18 μm CMOS Process. The maximum frequency of operation is 5 GHz when operating at 1.8V voltage supply. It has free dead zone. It can be used in high speed and low power consumption applications. This makes the proposed PFD more suitable to low jitter applications.

Keywords:
   PFD, PLL, High speed.


References:

1.       A Simple CMOS PFD for High Speed Applications Nesreen Ismail Institute of Micro-Engineering and Nano-Electronics University Kebangsaan Malaysia, MalaysiaMasuri Othman Institute of Micro-Engineering and Nano-Electronics University Kebangsaan Malaysia, Malaysia
2.       Leenaerts, D., J. V. D. Tang, and C. S. Vaucher, 2001. “Circuit Design for RF Transceivers”,Kluwer Academic Publishers, USA, pp. 243-258.[2] Best, R. E., 1993. “Phase-Locked Loop Design, Simulation, and Application”, 2nd edition,McGraw Hill, New York.

3.       Johansson, H., 1998. “Simple Precharged CMOS Phase Frequency Detector”, IEEE Journal of solid state circuits, Vol. 33, No. 2, pp. 295-299.

4.       Arshak, K., O. Abubaker, and E. Jafer, 2004. “Design and Simulation Difference Types CMOS Phase Frequency Detector for High Speed and Low Jitter PLL”, proceedings of 5th IEEE International Caracas Conference on Devices, Circuits, and Systems, Dominican Republic, Vol. 1, Nov.3-5, pp.188-191.

5.       Johnson, T., A. Fard, and D. Aberg, 2004. “An Improved Low Voltage Phase-Frequency  Detector with Extended Frequency Capability”, The 47th IEEE International Midwest Symposium on Circuits and Systems, pp. 181-184.

6.       Lee, G. B., P. K. Chan, and L. Siek, 1999.“A CMOS Phase Frequency Detector for Charge Pump Phase-Locked  loop”, IEEE 42nd Midwest Symposium on Circuits and Systems, Vol.2, pp. 601 – 604.

7.       Kondoh, H., H. Notani, T. Yoshimura, H. Shebata, and Y. Matsuda, 1995. “A 1.5-V 250-MHz to 3-V 622-MHz operation CMOS Phase-Locked Loop with Precharge type Phase Detector”, IEICE Trans. Electron, Vol. E78-C, no.4, pp 382-388.

8.       Razavi, 2003.“Design of Integrated Circuits for Optical Communications”, McGraw-Hill, USA.

9.       El-Hage, M., and F. Yuan, 2003. “ Architectures and Design Consideration of CMOS Charge Pump for Phase-Locked Loops”, Electrical and Computer engineering, IEEE CCECE Canadian Conference, ON, Vol. 1, pp. 223 – 226.

10.     Barrett, Curtis. Fractional/Integer-N PLL Basics. Texas Instruments, Wireless Communication Business Unit, August 1999.

11.     Chou, Chien-Ping, Lin, Zhi-Ming,and Chen, Jun-Da. “ A 3-PS Dead-Zone Double- Edge-checking Phase-Frequency-Detector With 4.78 GHz Operation Frequency.” The 2004 IEEE Asia-Pacific Conference on Circuits and Systems conference. (2004) : Volume 2, Page(s): 937 – 940.


88-92

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

21.

Authors:

Sergey Panasenko, Sergey Smagin

Paper Title:

On Use of Lightweight Cryptography in Routing Protocols

Abstract:   Cryptographic algorithms become more complex and “heavyweight” every year. This is completely correct from the viewpoint of security. But at the same time such growth increases resource requirements of the algorithms and the complexity of their implementation. This also essentially increases expenses of energy required to perform cryptographic procedures. In this paper we review applications of cryptographic algorithms in routing protocols. Also we analyze the possibilities of use of a lightweight block cipher as a cryptographic kernel to mount various types of cryptographic algorithms which do not require significant resources together over it. We propose to enlarge the set of cryptographic algorithms required to be implemented within IPsec protocol and to include lightweight encryption and authentication algorithms into the set. Implementation of lightweight algorithms to apply in IPsec and related network protocols allows to provide adequate moderate security level in various applications where it is not required to use heavy and strong cryptography; it also allows to save energy and reduce the cost of implementation.

Keywords:
   Lightweight cryptography, KATAN, block cipher, hash function, routing protocol, RIPv2, IPsec.


References:

1.       A. Poschmann. Lightweight Cryptography from an Engineers Perspective. Workshop on Elliptic Curve Cryptography (ECC 2007).
2.       J. Troutman and V. Rijmen. Green Cryptography: Cleaner Engineering Through Recycling. IEEE Security & Privacy, vol. 7, no. 4, pp. 71-73, July/August, 2009.

3.       S. Kent, K. Seo. RFC 4301. Security Architecture for the Internet Protocol. December 2005.

4.       G. Malkin. RFC 1388. RIP Version 2. Carrying Additional Information. January 1993.

5.       C. Hedrick. RFC 1058. Routing Information Protocol. June 1988.

6.       F. Baker, R. Atkinson. RFC 2082. RIP-2 MD5 Authentication. January 1997.

7.       R. Rivest. RFC 1321. The MD5 Message-Digest Algorithm. April 1992.

8.       R. Atkinson, M. Fanto. RFC 4822. RIPv2 Cryptographic Authentication. February 2007.

9.       FIPS PUB 180-2. Secure Hash Standard. National Institute of Standards and Technology, U. S. Department of Commerce – August 2002.

10.     H. Krawczyk, M. Bellare, R. Canetti. RFC 2104. HMAC: Keyed-Hashing for Message Authentication. February 1997.

11.     G. Malkin, R. Minnear. RFC 2080. RIPng for IPv6. January 1997.

12.     S. Kent. RFC 4302. IP Authentication Header. December 2005.

13.     S. Kent. RFC 4303. IP Encapsulating Security Payload (ESP). December 2005.

14.     V. Manral. RFC 4835. Cryptographic Algorithm Implementation Requirements for Encapsulating Security Payload (ESP) and Authentication Header (AH). April 2007.

15.     G. Malkin. RFC 2453. RIP Version 2. November 1998.

16.     C. De Cannière, O. Dunkelman, M. Knežević. KATAN & KTANTAN – A Family of Small and Efficient Hardware-Oriented Block Ciphers. CHES’09, LNCS, vol. 5747, pp. 272-288. Springer, 2009.

17.     S. Panasenko, S. Smagin. Energy-efficient cryptography: application of KATAN. SoftCOM 2011. 19. International Conference on Software, Telecommunications & Computer Networks. Split – Hvar – Dubrovnik, September 15-17, 2011. Proceedings (SS2 – Special Session on Green Networking).

18.     J. Patarin, L. Goubin, M. Ivascot, W. Jalby, O. Ly, V. Nachef, J. Treger, E. Volte. CRUNCH. Specification. // Available at http://csrc.nist.gov – October 28, 2008.

19.     E. Volte. CRUNCH. A SHA-3 Candidate. // Available at http://www.voltee.com – 27 February 2009.

20.     S. Bradner. RFC 2119. Key words for use in RFCs to Indicate Requirement Levels. March 1997.

21.     S. Frankel, R. Glenn, S. Kelly. RFC 3602. The AES-CBC Cipher Algorithm and Its Use with IPsec. September 2003.

22.     R. Pereira, R. Adams. RFC 2451. The ESP CBC-Mode Cipher Algorithms. November 1998.

23.     R. Housley. RFC 3686. Using Advanced Encryption Standard (AES) Counter Mode With IPsec Encapsulating Security Payload (ESP). January 2004.

24.     C. Madson, R. Glenn. RFC 2404. The Use of HMAC-SHA-1-96 within ESP and AH. November 1998.

25.     S. Frankel, H. Herbert. RFC 3566. The AES-XCBC-MAC-96 Algorithm and Its Use With IPsec. September 2003.

26.     C. Madson, R. Glenn. RFC 2403. The Use of HMAC-MD5-96 within ESP and AH. November 1998.

27.     NIST Special Publication 800-38A. Recommendation for Block Cipher Modes of Operation. Methods and Techniques. National Institute of Standards and Technology, U. S. Department of Commerce – December 2001.

93-97

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

22.

Authors:

Biswapati jana, Pabitra Pal, Jaydeb Bhaumik

Paper Title:

New Image Noise Reduction Schemes Based on Cellular Automata

Abstract:   This paper presents noise filtering technique of noisy image using cellular automata (CA). Two new approaches to reduce noise form a noisy image have been proposed. In the first approach, difference values of Moore neighbors form center pixel are calculated, then sorted in ascending order and the center pixel value is updated depending on the present pixel values using CA rule. In second approach, all pixels value of Moore neighbor including center pixel are sorted in ascending order. Then the minimum and maximum values are eliminated form sorted pixel values and the center pixel value is updated using CA rule. Results are compared with other existing filtering technique in terms of Peak Signal to Noise Ratio ( PSNR). This comparisons shows that a filter based on CA provides significant improvements over the standard filtering methods.

Keywords:
   Cellular Automata (CA), Image processing, Noise reduction, Peak signal-to-noise ratio (PSNR).


References:

1.       Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, 2001, pp.220 – 243.
2.       A. Marion, “An Introduction to Image Processing”, Chapman and Hall, 1991, pp. 274.

3.       Pratt. William. K, “Digital image processing”, 2nd Edition. John Willey & Sons Inc., 2001, pp. 150-157.

4.       K. R. Castleman, “Digital Image Processing”. Prentice Hall, Upper Saddle River, NJ (1995).

5.       J. C. Russ. “The Image Processing Handbook”. CRC Press, Boca Raton, FL, thirded. (1998).

6.       A. K. Jain. “Fundamentals of Digital Image Processing”. Prentice Hall, Engle wood Cliffs, NJ (1989).

7.       NING Chun-yu, LIUShu-fen, QUMing  “Research on Removing Noise in Medical Image Based on Median Filter Method”, China, pp 384-388, 2009

8.       F. Bergholm. “Edge focusing,” in Proc. 8th Int. Conf. Pattern Recognition, Paris, France, pp. 597- 600, 1986.

9.       Marr. D and Hilderth. “Theory of Edge Detection”, Proc.R.Soc. London, vol. B 207, pp 187-217, 1980.

10.     J. F. Canny. “A computational approach to edge detection”, IEEETrans. On Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986).

11.     Renyan Zhang, Guoliang Zhao and Li Su, “A New Edge Detection Method in Image Processing”, In: IEEE Proceedings of the ISCIT’05 Oct 12-14, 1: pp. 445-448, 2005. 

12.     Davis, L. S., “Edge detection techniques: Computer Graphics Image Process”, (4), pp. 248-270, 1995.

13.     D. Marr and E. Hildreth, “Theory of edge detection”. Proceedings of the Royal Society of London, Series B 207, 187–217 1980.

14.     L. S. Davis, “A Survey of Edge Detection Techniques ", Computer Graphics and Image Processing, 12, 1975, 248-270.

15.     C. G. Harris and M. Stephens. “A combined corner and edge detector”. In C. J. Taylor, editor, “4th Alvey Vision Conference”, pp.147–151, Manchester 1988.

16.     Ziou, D. and Tabbone, “Edge detection techniques an overview, Pattern Recognition and Image Analysis 8 (4)”, pp. 537–559, 1998.

17.     A. Popovici and D. Popovici, “Cellular automata in image processing,” in Proceedings of the 15th International Symposium on the Mathematical Theory of Networks and Systems, D. S. Gilliam and J. Rosenthal, Eds., 2002, electronic proceedings.

18.     Christopher D Thomas, Riccardo Poli, “Evolution of Cellular Automata for Image Processing”, Thesis, School of Computer Science, University of Birmingham (UK), April 2000. 

19.     P. L. Rosin, “Training cellular automata for image processing,” in Proceedings of the 14th Scandinavian Conference on Image Analysis, H. Kalviainen, J. Parkkinen, and A. Kaarna, Eds., 2005, pp. 195–204.

20.     Stephen Wolfram, “statistical mechanics of Cellular Automata”, Rev Mod Phys.55, 601-644 (July 1983).

21.     W. Pries, “A Thanailakis and H.C.  Card, Group properties of cellular automata and VLSI Application,” IEEE Trans on computers C-35, 1013-1024, Dec 1986.

22.     N.H. Packard and S. Wolfram, “Two dimensional cellular automata,” Journal of Statistical Physics, 38 (5/6) 901-946, 1985.

23.     N. Ganguly, P. Maji, S. Dhar, B. K. Sikdar, P. P. Chaudhuri, “Evolving Cellular Automata as Pattern Classifier”, ACRI 2002, LN CS2493, Springer-Verlag Berlin Heidelberg (2002) pp. 56-68.

24.     P. P. Chaudhuri, D. R. Chaudhuri, S. Nandi, S. Chattrrjee, “Additive Cellular Automata, Theory and Applications”, Vol. 1, IEEE Computer Society Press, Los Alamitos, California, ISBN-0-8186-7717-1. (1997).

25.     P. Jebaraj Selvapeter and Wim Hordijk, “cellular automata for image noise filtering”.

26.     Von Neumann, J.: “Theory of Self-Reproducing Automata”, chapter in Essays on Cellular Automata. University of Illinois Press, Urbana, Illinois, 1970.

27.     M. Delorme, “An introduction to cellular automata”: Some basic definition and concepts by LIP ENS Lyon 46, Allee d’Italie, 69364 Lyon Cedex 07, France.

28.     Nie, Harald. “Introduction to Cellular Automata: Organic Computing”, USA.

29.     J. E. Hanson, J. P. Crutchfield, “Computational mechanics of cellular automata: an example, Physics D103: 1-4 (1997)”, pp.169-89.

30.     W.R. Ashby, “Principles of the self-organizing system, Principles of Self-organization”, pp.255-278, 1962.

31.     T. Gramss, S. Bornholdt, M. Gross, M. Mitchell, and T. Pellizzari, “Computation in Cellular Automata: A selected Review of Non standard Computation”, pp.95–140. Weinheim: VCH Verlagsge sells chaft, 1998.

32.     Wolfram, S.:  “Cellular Automata as Models of Complexity”, Nature, 311, pp. 419-424, 1984.

33.     David J. Eck. “Introduction to one dimensional cellular automaton”.

34.     A. S. Mariano, G.M.B.de Oliveira, “Evolving one-dimensional radius-2 cellular automata rules for the synchronization task”, AUTOMATA-2008 Theory and Applications of Cellular Automata, Luniver Press (2008), pp.514-526.

35.     P. P. Choudhury, B. K. Nayak, S. Sahoo, S.P. Rath, 2008. “Theory and Applications of Two-dimensional, Null-boundary, Nine-Neighborhood, Cellular Automata Linear Rules”, in: arXiv: 0804.2346, cs.DM; cs.CC; cs.CV. (2008).

36.     D.R. Chowdhury, I.S. Gupta and P.P. Chaudhury, “A class of two-dimensional cellular automata and applications in random pattern testing”, Journal of Electronic Testing: Theory and Applications 5, 65-80, (1994).

37.     Abdul Raouf Khan, “On Two Dimensional Cellular Automata and its VLSI applications”, Department of Computer Sciences, KING FAISAL UNIVERSITY, SAUDI ARABIA.

38.     T. Sunand Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Letters, vol.15, no. 4,pp. 341–347, 1994.

39.     H. Hwangand, R. A. Haddad, “Adaptive median filters: new algorithms and results,” IEEE Transactions on Image Processing, vol.4, no.4, pp.499–502, 1995.


98-103

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

23.

Authors:

Prashant, Sarika Gupta

Paper Title:

Simplifying Use Case Models Using CRUD Patterns

Abstract:   In this paper, we have presented CRUD, a use-case patterns that is proven useful for developing maintainable and reusable use-case models. These patterns focus on designs and techniques used in high-quality models, and not on how to model specific usages. In CRUD we merge short, simple use cases such as Creating, Reading, Updating, and Deleting pieces of information into a single use case forming a conceptual unit.

Keywords:
   Create data, delete data, information handling, merge use cases, read data, short flow, short use case, simple operation, update data.


References:

1.        Adolph, S., and P. Bramble . 2002. Patterns for effective use cases.Addison-Wesley.
2.        Alexander, C., S. Ishikawa, and M. Silverstein . 1977. A pattern language: towns, buildings, construction. Oxford University Press.

3.        Bass, L., P. Clements, and R. Kazman . 2003. Software architecture in practice. Addison-Wesley.

4.        Bittner, K., and I. Spence . 2002. Use case modeling. Addison-Wesley.

5.        Buschmann, F., R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal . 1996. Pattern-oriented software architecture, volume 1: a system of patterns. John Wiley and Sons.

6.        Jacobson, I.  Concepts for modeling large real time systems. Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden.

7.        Jacobson, I."Object-oriented development in an industrial environment." Proceedings of OOPSLA'87. Sigplan Notices 22(12) :183191.

8.        Jacobson, I. 2003 (March). "Use cases yesterday, today, and tomorrow." The Rational Edge.

9.        Jacobson, I., G. Booch, and J. Rumbaugh . 1999. The unified software development process. Addison-Wesley.

10.     Jacobson, I., M. Christerson, P. Jonsson, and G. Övergaard . 1993. Object-oriented software engineering: a use- case driven approach. Addison-Wesley.


104-106

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

24.

Authors:

Manish Ranjan Pandey, Manoj Kapil, Sohan Garg

Paper Title:

Beginning of an Effective E-Governance in India by using Informative and Communicative Mechanism

Abstract:  Good governance is characterized by skill, collaboration, transparency and openness which are the results of effective communication. Three key areas Communication Planning Process (GCPP), Government Communication Assessment Process (GCAP) and Government Communication Improvement Process (GCIP) have been identified and the catalytic impact that ICT has in these key area has been discussed. Government communication is the exchange of government-citizen specific information to citizens (G2C, C2G) and government (G2G) that serves some useful purpose of either government or citizen or both. As the interaction between the citizen and the government is crucial in democracy analyzing the role of governmental officials as service and information providers and the need for improvement in the government – citizen relationship becomes essential [1]. An effective communication mechanism will solve the variety of issues and challenges faced by governments in their efforts to apply 21st century capabilities to e-Government initiatives [2]. According to Moon [3] e-Government was initially envisioned as a means of enhancing intra-governmental communications via an intranet system. The available research on the role of communications in governance is fragmented across multiple disciplines with often conflicting priorities [4, 5].

Keywords:
   GCPP, GCAP, G2C, C2G,


References:

1.        Luht K., 2002, Reforming government – citizen relationship in the information age, Tallinn 2002.
2.        Sinha S., 2002, “Competition Policy in Telecommunications: The Case of the India”, International Telecommunication Union.

3.        Moon, M. J., 2002, “The Evolution of E-Government Among Municipalities: Rhetoric or Reality?” Public Administration Review, 62: 4. pp. 424-433.

4.        Ojo A., Janowski T., Estevez E., Khan I. K., Human Capacity Development for e-Government, April 2007, UNU-IIST Report No. 362.

5.        Owen A., Johnson, Stephen F., King, Best Practice in Local E-Government: A Process Modelling Approach, E government Workshop ’05 (Egov05), September 13 2005, Brunel University, West London, Uk

6.        Norris P., 2001, “Digital divide: Civic engagement, information, poverty and the Internet worldwide”, Cambridge University Press, Cambridge, pp. 232

7.        O.Looney, J. A., 2002, “Wiring governments: Challenges and possibilities for public managers”, Westport: Quorum Books.
8.        Subramanian M., 2007, Theory and practice of e-governance in India: a gender perspective, ACM International Conference Proceeding Series; Vol. 232.

9.        Thomas J.C., Streib G., “The New Face of Government: Citizen- Initiated Contacts in the Era of E-Government,” Journal of public administration: research and theory, vol.13, No.1, pp.83-102, 2003.

10.     Wilson. M., Warnock K., Schoemaker M., 2007, At the Heart of Change: The Role of Communication in Sustainable Development, Panos Institute, London.

11.     Kumar T., 2010, “E-SANCHAR (e-Speech Application through Network for Communication, Help and Response”, 13th National Conference on e-Governance, http://indiagovernance.gov.in/files/E-sanchar.pdf.


107-109

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

25.

Authors:

Bhawna, Mukhwinder Kaur, G.C.Lall

Paper Title:

Automatic Modulation Recognition for Digital Communication Signals

Abstract:  Different modulation techniques are used for different signal transmission. These techniques give versatility to the transmission medium as well as make user easy to work in such computational field. With prior no knowledge of data transmitted and various unspecified parameters at receiver side like the carrier frequency, phase offsets and signal power etc., blind detection of the modulation is challenging. This becomes more difficult at the time of fading. That’s why recognizing these modulation schemes is useful for various technical purposes and especially quite significant for the military, wireless and COMINT applications.  Digital modulation recognition is based on some parameters especially statistical parameters.  Till now various recognition algorithms have been developed and still developing. The recognition algorithms can be divided into two major groups ‘maximum likelihood approach (MLA) and pattern recognition approach (PRA). In this paper we are emphasizing on the theoretical information of these techniques of modulation recognition along with ANN modulation recognizer for m-ary modulation techniques. A general application of modulation recognition in field of SDR is also proposed.

Keywords:
   Maximum likelihood, Pattern Recognition, Modulation Detection Scheme, Software Defined Radio, Artificial neural network


References:

1.        E.E Azzouz and A.K. Nandi, “Automatic Modulation Recognition of Communication Signals”, Kluwar Academic Publishers, 1996
2.        D. Linda Essentials of cognitive radio, Cambridge Wireless Essentials Series, Cambridge University Press, 2009

3.        O.A. Dobre and Y. Bar-Ness Blind Modulation Classification: A Concept Who’s Time has Come IEEE/Sarnoff Symposium, pp. 223U˝ 228 April 18U˝ 19, 2005

4.        D. L. Guen, A. Man sour, “Automatic Recognition Algorithm for Digitally Modulated Signals”, International Conference on Signal Processing, Pattern Recognition, and Applications Crete, Greece, 25-28 June,2002

5.        K .N. Haq, A. Mansur, Sven Nordholm, “Comparison of digital modulation classification based on statistical approach”, 10thPostgraduate Electrical and Computer Symposium Perth Australia, September 2009

6.        S.S. Soliman and Z.S. Hsue, “Signal classification using statistical moments,”IEEE Transactions on Communications, vol. 40(5), pp. 908–916, May 1992

7.        Z.S. Hsue and S.S. Soliman, “Automatic modulation classification using zero-crossing IEEE Proc. Part F, Radar and signal processing, vol. 137 (6), pp. 459–464, December 1990

8.        J.E. Hipp, “Modulation Classification based on Statistical moments”, IEEE Proc. Military Communication Conference, vol2, pp. 20.2.1-20.2.6, October 1986

9.        G.Acosta, “OFDM simulation using Mat Lab”, Report, Smart Antenna Research Laboratory Georgia Institute of Technology, Georgia, USA, August 2000

10.     H. Zhang, “Orthogonal Frequency Division Multiplexing for Wireless Communication Thesis, Georgia Institute of Technology, Georgia, November 2004

11.     Li Tieying, Cui yan,”A design of neural classifier based on rough sets” [J]. Computer Engineering and Applications, 2005, 32

12.     Martin P. DeSimio, Glenn E. Prescott “Adaptive Generation of Decision Functions For Classıfıcatıon of Digitally Modulated Signals” NAECON, 1988

13.     Adel Metref, Daniel Le Guennec, Jacques Palicot “A new digital modulation recognition technique using the phase detector reliability “2010

14.     HU You-qiang, LIU Juan, TAN Xiao-hang “Digital modulation recognition based on instantaneous information “June 2010

15.     Hua-Kui Wang, Bin Zhang, Juan-Ping Wu, Ying-Zhuang Han, Xiao-Wei Wu, Roué-Si Jia “A Research on Automatic Modulation Recognition with the Combination of the Rough Sets and Neural Network” 2010 DOI 10.1109/PCSPA.2010.24880\

16.     Fatima K. Faek,” Digital Modulation Classification Using Wavelet Transform and Artificial Neural network” (JZS) Journal of Zankoy Suleiman 2010

17.     Asoke K. Nandi, E. E. Azzouz “Algorithms for Automatic Modulation Recognition of Communication Signals” IEEE Transactions On Communications, Vol. 46, No. 4, April 1988

18.     Khandker Nada Haq, Ali Mansur, Sven Nordholm” Recognition of Digital Modulated Signals based on Statistical Parameters “, 4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010)

19.     Octavia A. Dobre, Ali Abdi, Yeheskel Bar-Ness and Wei S “A Survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends “, Vol. 46, No. 4, April 2010

20.     Liang Hong K.C. Ho” Identification of Digital Modulation Types Using the Wavelet Transform”, vol2, pp. 20.2.1-20.2.6, October 2010

21.     Azzedine Zerguine,” Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems “, December 2009

22.     Z Chaozhu, Yang Lianbai, Wang Xin,” Discrete wavelet neural network group system for digital modulation recognition”, IEEE 3rd international conference, May 2011

23.     Cheng Yuanzeng, zang Hailong, Wang Yu,” Research on modulation recognition of the communication signal based on statistical model” ICMTMA, IEEE 3rd international conference May 2011

24.     K Hassan, Nzeza CN,” Blind Modulation identification for MIMO system “, IEEE Global telecom conference, Dec 2010

25.     N Ahmadi, “, Modulation classification based on constellation using TTSAS approach”, Journal of recognition research, May 2010

26.     Mobien shoaib, Alharbi Harza, Alturki Fahd “Robustness of digital modulated signals against variation in Hf noise model”, EURASIP journal on wireless communication network, 2011

27.     Wu Min. The research of Rough Set attribute reduction algorithm in numeral character recognition [D].Hefei University of Technology Master Dissertation, 2009

28.     Zhao F., Hu Y. and SH., Hao, ’Classification using wavelet packet decomposition and support vector machine for digital modulation’ Journal of system Engineering and Electronics, August 2009, 19,914-918.

29.     Khandker Nada Haq, Ali Man sour, Sven Nordholm” Recognition of Digital Modulated Signals based on Statistical Parameters “, 4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010)

30.     Z.S. Hsue and S.S. Soliman, “Automatic modulation classification using zero-crossing IEEE Proc. Part F, Radar and signal processing, vol. 137 (6), pp. 459–464, December 1990.


110-114

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

26.

Authors:

R M Potdar, Anup Mishra, Soma Kala Sammidi, Akula Nagesh

Paper Title:

Controlling Induced Draft Fan of Power Plant Using Labview

Abstract:   In this proposed work, design and development of  controlling induced Draft fan in a power plant which is presently working on  DCS technique  has been accomplished by using high  computing software Lab VIEW and results has been shown with suitable examples. The goal of this work is to control the Induced Draft Fan in a different way. A set of six interlock conditions were provided for this purpose. The objective was to design and implement the controlling of ID Fan in Lab VIEW that will control the ID Fan similar to the DCS technique. Since DCS is applicable only for big system not less than 5000 input and output but this is costly. It consists of separate server, processor and computers where as Lab VIEW does not require a separate processor, no workstation, no operator  station here directly connect interfacing card with computer itself. Proposed system can cost less than two hundred times than a DCS.

Keywords:
   Induced Draft Fan; LAB VIEW,Power Plant (WHRB), Software Control.


References:

1.       Gregory K. McMillan, Douglas M. Considine (Ed), Process/Industrial Instruments and Controls Handbook Fifth Edition, McGraw-Hill, 1999 ISBN 0-07-012582-1 Section 3 Controllers
2.       Li, Nan; Teng, Fei System Design Electro-motor Rotational Speed Control Based on of Lab VIEW . Computer Measurement & Control, p794-799. 2006. 14(6).

3.       Prime, J.B. Valdes, J.G., “use of ladder diagram in discrete system of PLC”, IEEE Transaction, Vol. PAS-100, pp-143-153, January 1989.

4.       IEEE Guide for AC Motor Protection IEEE, Std C37.96-2000 (Revision of IEEE Std C37.96-1988)

5.       National Instruments Corporation. Getting started with LabVIEW [Z]. Part   No.323427A-01. April 2003 Edition.

6.       Peter A. Blume: The LabVIEW Style Book, February 27, 2007, Prentice Hall. Part of the National Instruments Virtual Instrumentation Series series. ISBN 0-13
145835-3

7.       Jeffrey Travis, Jim Kring: LabVIEW for Everyone: Graphical Programming Made Easy and Fun, 3rd Edition, July 27, 2006, Prentice Hall. Part of the National Instruments Virtual Instrumentation Series. ISBN 0-13-185672-3.


115-120

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

27.

Authors:

H.S. Behera, Abhishek Ghosh, Sipak Ku.Mishra

Paper Title:

A New Improved Hybridized K-MEANS Clustering Algorithm with Improved PCA Optimized with PSO for High Dimensional Data Set

Abstract:   The day to day computation has made the data sets and data objects to grow large so it has become important to cluster the data in order to reduce complexity to some extent. K-means clustering algorithm is an efficient clustering algorithm to cluster the data, but the problem with the k-means is that when the dimension of the data set becomes larger the effectiveness of k-means is lost. PCA algorithm is used with k-means to counter the dimensionality problem. However K-means with PCA does not give much optimisation. It can be experimentally seen that the optimisation of k-means gives more accurate results. So in this paper we have proposed a PSO optimised k-means algorithm with improved PCA for clustering high dimensional data set.

Keywords:
  Data mining, Clustering, Particle Component Analysis, Centred vector, Squared Sum Error, Lower bound, Bound Error, Particle Swarm Optimisation.


References:

1.        Dash et.al , “A Hybridized k-Means Clustering Algorithm for High Dimensional Dataset”, International Journal of Engineering, Science and Technology, vol. 2, No. 2, pp.59-66, 2010.
2.        H.S. Beheraet.al. “An Improved Hybridized K-Means Clustering Algorithm (IHKMCA) For High dimensional Dataset &its Performance Analysis International” Journal on Computer Science and Engineering (IJCSE) vol 3 no 3 march 2011

3.        P.Prabhuet et al. “Improvising the performance of K-means clustering for high dimensional data set” International journal on computer science and engineering vol 3, Jun 2011

4.        ”Dimensionality reduction: A comparative review”, by Maaten L.J.P., Postma E.O. and Herik H.J. van den, Tech. rep.University of Maastricht ,2007.

5.        Davy Michael and Luz Saturnine, 2007. “Dimensionality reduction for active learning with nearest neighbour classifier” in text categorization problems, Sixth International Conference on Machine Learning and Applications, pp. 292-297

6.        ”Performance analysis of K-means with different initialization for high dimensional data” by Tanjunisha and Saravan International journal of Artificial Intelligence and application vol1 no.4, October 2010.

7.        ”New method of dimensionality reduction using K-means clustering algorithm for high dimensional data set” by D Napoleon and S.Paralakodi international journal of computer science application vol13 no.7, January 2011.

8.        ”An efficient method to improve clustering performance for high dimensional data by principal component analysis and modified K-means” by Tanjunisha and SaravanInternationaljournalofdatabase management system vol3 no.1, February 2011.

9.        ”Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging”.byJakob R. Olesen, Jorge Cordero H., and YifengZeng.Cao et al. (Eds.): ADMI 2009, LNCS 5680, pp. 69–83, 2009 Springer-Verlag Berlin Heidelberg 2009.

10.     ”Particle Swarm Optimization Methods, Taxonomy and Applications” by DavoudSedighizadeh and EllipsMasehian, International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December 20091793-8201.


121-126

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

28.

Authors:

Nagamani .K , A G Ananth

Paper Title:

Evaluation of SPIHT Compression Scheme for Satellite Imageries Based on Statistical Parameters

Abstract:   Non reversible and lossy image compression techniques is known to be computationally more complex as they grow more efficient, confirming the constraints of source coding theorems in information theory that a code for a (stationary) source approaches optimality the limit of infinite computation (source length). It has been observed that when a variety of images of different types are compressed using a fixed wavelet filter, the peak signal to noise ratios (PSNR) vary widely from image to image. This variation in PSNR can be attributed to the nature and inherent statistical characteristics of image. To explore the effect of various image features on the coding performance, a set of gray level image statistics have been analyzed by using SPIHT (Set Partitioning In Hierarchical Trees) algorithm. The Mean Square Error (MSE) and Peak Signal to Noise Ratios (PSNR) determined for an image depends on the statistical properties of the image and the compression scheme applied. The efficiency of the compression scheme can be evaluated by examining the statistical parameters of the image. In this paper various statistical parameters associated with the SPIHT compression scheme are derived for three different types of images namely standard Lena, satellite urban and rural imageries based on higher order statistics. The statistical parameters include higher order image statistics like Rate Distortion and Skewness and Kurtosis which describe the shape and symmetry of the image. The statistical parameters derived for a fixed rate and fixed level of decomposition for three types of images have been are used for the explanation of the Compression Ratio and Peak Signal to Noise Ratio (PSNR) achieved for the satellite imageries. The results show that urban images are better suited for SPIHT compression scheme compared to that of satellite rural image. The results of the analysis are presented in the paper.

Keywords:
   Compression ratio, EZW, MSE, SPIHT, PSNR.


References:

1.        S. Lewis and G. Knowles, “Image Compression Using the 2-D Wavelet Transform”, IEEE Trans. on Image Processing, Vol. 1, No. 2, pp. 244-250, April (1992).
2.        J.M.Shapiro,“Embedded Image Coding Using Zerotrees of Wavelet Coefficients”, IEEE Trans. on Signal Processing, Vol. 41, pp 3445-3462, (1993)

3.        A Said and W.A. Pearlman, “A New, Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Trans. on Circ and Syst for Video Tech, Vol 6, no. 3, pp 243-250, June 1996.

4.        A Said and A. Pearlman, “An Image Multiresolution Representation for Losssless and Lossy Compression.” IEEE Trans. Image Processing, Vol. 5, No. 9, pp 243-250, Sept. 1996.

5.        Richa Jindal ,Sonika Jindal Navdeep Kaur , Analyses of Higher Order Metrics for SPIHT Based Image  Compression , International Journal of Computer Applications , Volume 1 – No. 20, 2010.

6.        Sunhasis Saha and Rao Vemuri, “How do Image Statistics Impact Lossy Coding Performance?” Proceedings. International Conference of Information Technology: Coding and Computing   Pages 42 - 47, 2000.

7.        Sunhasis Saha and Rao Vemuri, An Analysis on the Effect of Image Features on Lossy Coding Performance,  IEEE Signal Processing Letters, Volume. 7, No. 5, Pages 104-108, May 2000.


127-130

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

29.

Authors:

B. Amarendra Reddy, Praveen Adimulam, M. Sujatha

Paper Title:

Signal Flow Graph Analysis of Linearized Takagi-Sugeno Fuzzy PI Controller

Abstract:   A systematic procedure for developing the signal flow graph model of linearized Takagi-Sugeno (TS) fuzzy PI controller is presented in this paper. This proposed method provides ease of model formulation and avoids the mathematical complexity involved in obtaining the linearized model from a non-linear model. As a first step in constructing the signal flow graph, the analytical structures of TS-fuzzy PI controller is needed. Triangular/trapezoidal membership functions are considered for input variables, Zadeh fuzzy logic AND operation and centroid defuzzifier, structural analysis of TS-fuzzy pi controller are considered. A TS-fuzzy PI controller is represented as a non-linear TS-fuzzy PI controller which is linearized around an operating point using perturbation method. For the linearized fuzzy TS-fuzzy PI controller signal flow graphs are developed.

Keywords:
   TS-fuzzy, PI controller.


References:

1.       Yongsheng Ding; Hao Ying; Shihuang Shao; “Theoretical analysis of a Takagi-Sugeno fuzzy PI controller with application to tissue hyperthermia   therapy” Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on Volume:  1 On page(s): 252 -
257 vol.1

2.       The Simplest Fuzzy controllers using different inference methods are different nonlinear Proportional-Integral controllers with variable gains. Automatica, Vol 29, No.6, pp.1579-1589, 1993.

3.       Alwadie,A., H. Ying, and H.Shah “A Practical Two-Input Two-Output Takagi-Sugeno Fuzzy Controller” International Journal of Fuzzy Systems, Vol 5, No.2, June 2003.

4.       Hao Ying, Senior Member, IEEE  “Deriving Analytical Input-Output Relationshipfor Fuzzy Controllers Using Arbitrary InputFuzzy Sets and Zadeh Fuzzy AND Operator”. IEEE TRANSACTION ON FUZZY SYSTEMS, VOL 14, NO.5, OCTOBER 2006.

5.       Hao Ying, William Siler and James J. Buckleys “Fuzzy Control Theory: A Nonlinear Case”. Automatica, Vol 26, No.3, pp.513-520,1990.

6.       Hao Ying “Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme”. Information Sciences 123 (2000) 281-293.

7.       Hao Ying, Senior Member, IEEE “Constructing Nonlinear Variable Gain Controllers via the Takagi-Sugeno Fuzzy Control” IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 6, NO. 2, MAY 1998.

8.       Y.Ding, H.Ying, S.Shao “Structure and stability of a Takagi-Sugeno fuzzy PI controller with application to tissue hyperthermia therapy” Soft Computing 2(1999) 183-190© Springer-Verlag 1999.

9.       Hao Ying “The Takagi-Sugeno Fuzzy Controllers Using Simplified Linear Control Rules are Nonlinear Variable Gain Controllers” Automatica, Vol 34, No.2, pp.157-167, 1998.

10.     A.V. Patel, B.M. Mohan “Some numerical aspects of center of area defuzzification method” Fuzzy Sets and Systems 132 (2002) 401 – 409


131-136

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

30.

Authors:

Binitha S, S Siva Sathya

Paper Title:

A Survey of Bio inspired Optimization Algorithms

Abstract:    Nature is of course a great and immense source of inspiration for solving hard and complex problems in computer science since it exhibits extremely diverse, dynamic, robust, complex and fascinating phenomenon. It always finds the optimal solution to solve its problem maintaining perfect balance among its components. This is the thrust behind bio inspired computing. Nature inspired algorithms are meta heuristics that mimics the nature for solving optimization problems opening a new era in computation .For the past decades ,numerous research efforts has been concentrated in this particular area. Still being young and the results being very amazing, broadens the scope and viability of Bio Inspired Algorithms (BIAs) exploring new areas of application and more opportunities in computing. This paper presents a broad overview of biologically inspired optimization algorithms, grouped by the biological field that inspired each and the areas where these algorithms have been most successfully applied.

Keywords:
   Bio Inspired Algorithm, Optimization algorithms.


References:

1.       Back, T. 1996: Evolutionary algorithms in theory and practice. Oxford University Press.
2.       J.H. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Comput. 2 (2) (1973) 88–105

3.       Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press.

4.       Beyer, H.G. and Schwefel, H.P. 2002: Evolution strategies. Natural Computing 1,3–52.

5.       R. Storn, K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (1997) 341–359.

6.       Upeka Premaratne , Jagath Samarabandu, and Tarlochan Sidhu, “A New Biologically Inspired Optimization Algorithm”,Fourth International Conference on Industrial and Information Systems, ICIIS 2009,28-31 December 2009, Sri Lanka.

7.       Bonabeau, E., Dorigo, M. and Theraulaz, G.1999: Swarm intelligence. Oxford University Press

8.       Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.

9.       Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26, 29–41.

10.     D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (2007) 459–471

11.     X. Li, Z. Shao, J. Qian,Anoptimizing method base on autonomous animates: fish- swarm algorithm, Systems Engineering Theory and Practice 22 (2002) 32–38.

12.     Shah_Hosseini, H.  Shahid Beheshti Univ., Tehran Problem solving by intelligent water drops IEEE Congress on Evolutionary Computation, 2007. CEC 2007.

13.     K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., vol. 22, no. 3, pp.52–67, Jun. 2002

14.     D. Dasgupta, Artificial Immune Systems and Their Applications, Springer, Berlin, 1999.

15.     X.S Yang, “Fire fly algorithm for multimodal optimization”,in proceedings of the stochastic Algorithms. Foundations  and Applications(SAGA 109) vol.5792of Lecture notes in Computer Sciences Springer,Oct.2009

16.     S.He,Q.H.Wu, Senior Member IEEE and J.R Saunders.A novel group search optimizer inspired by Animal Behavioral Ecology;2006,IEEE Congress on Evolutionary Computation,1272-1278

17.     Eusuff MM and K.E Lansey; Optimization of water distribution network design using SFLA(2003)

18.     Hanning Chen , Yunlong Zhu, Optimization based on symbiotic multi-species coevolution;journal on Applied Mathematics and Computation 205 (2008)

19.     A.R. Mehrabian, C. Lucas, A novel numerical optimization algorithm inspired from weed colonization, Ecological Informatics 1 (2006) 355–366.

20.     Simon, D., 2008. Biogeography-based optimization. IEEE Transactions on Evolu- tionary Computation. 12 (6), 702–713.

137-151

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

31.

Authors:

Debabrata Samanta, Goutam Sanyal

Paper Title:

Statistical Approach for Classification of SAR Images

Abstract:    The statistical parameters contain high order image statistics which portray the outline and symmetry of the different image region. The good feat of recognition algorithms based on the quality of classified image. The main problem in SAR image function is accurate classification. In this paper a novel methodology has been carried out to classify SAR images using the statistical approach based on skewness. A comparison has been carried out with histogram based classification on same images for measuring the accuracy.

Keywords:
   SAR image, Skewness, symmetrical, normal distribution.


References:

1.       Lee J S, Grunes M R, Kwok R. “Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution”, Int. J. Remote Sensing, 1994, 15(11): 2299-2311.
2.       Bischof H, Schneider W, Pinz A J.”Multispectral classification of landsat-images using neural networks ”, IEEE Trans. Geosci. Remote Sensing, 1992, 30(3): 482-490.

3.       Chen C T, Chen K S, Lee J S. “The use of fully polarimetric information for the fuzzy neural classification of SAR images”, IEEE Trans. Geosci. Remote Sensing, 2003, 41(9 Part I): 2089-2100.

4.       Du L, Lee J S. “Fuzzy classification of earth terrain covers using complex polarimetric SAR data”, International Journal of Remote Sensing, 1996, 17(4): 809-826.

5.       Fukuda S, Hirosawa H. “A wavelet-based texture feature set applied to classification of multi frequency polarimetric SAR images”. IEEE Trans. Geosci. Remote Sensing, 1999, 37(5): 2282-2286.

6.       Debabrata Samanta and Goutam Sanyal, “Development of Edge Detection Technique for Images using Adaptive Thresholding”, Fifth International Conference on Information Processing (ICIP-2011), CCIS 157, pp. 671-676, 5-7 Aug.2011. @ Springer-Verlag Berlin Heidelberg.

7.       Chih-Chang Lai,Ching-Chih Tasi,A Modified Stripe-RGBW TFT-LCD with Image-Processing Engine for Mobile Phone Displays, IEEE Transaction on Computer Electronics ,Vol.- 53,No. 4,Nov-2007.

8.       Debabrata Samanta, Mousumi Paul and Goutam Sanyal , ”Segmentation Technique of SAR Imagery using Entropy”, International Journal of Computer Technology and Applications, Vol. 2 (5), pp.1548-1551, 2011.

9.       J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, R. Zabih, Image Indexing Using Color Correlograms, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 762-768, 1997.

10.     M. J. Swain, S. H. Ballard,” Color Indexing”, Int. Journal of Computer Vision , Vol.7, No. 1, pp. 11-32, 1991.

11.     W. Y. Ma, H. J. Zhang,, “Content-based Image Indexing and Retrieval, In Handbook of Multimedia Computing”, Borko Furht. Ed, CRC Press, 1998.

12.     Theodor Richardson, Improving the Entropy Algorithm with Image Segmentation, [Online document] 2003, Available at HTTP: http://www.cse.sc.edu/songwang/CourseProj/proj2003/Richardson/richardson.pdf.

152-155

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

32.

Authors:

Virender Kumar, G.C. Lall, Rishipal

Paper Title:

Optimum Efficient Fast Handover Support for IPv6

Abstract:    International Engineering Task Force (IETF) proposed MIPv6 and HMIPv6 both mobility management solutions to support the IP mobility. Although HMIPv6 is an extension of MIPv6 still there is handover latency and packet loss in HMIPv6.In this paper a scheme is presented that supports a fast handover efficiently in hierarchical mobile IPv6 networks (HMIPv6). In HMIPv6 when a mobile node (MN) moves from a one MAP region to another, then there is a interruption of connection as well as packet loss due to long handover latency. To overcome these problems, an efficient fast handover scheme is adopted from FMIPv6 to optimize the performance of the inter-MAP handover. In this paper the handover latency for MIPv6 & HMIPv6 is compared to the proposed scheme with analytical model. By analysis and by simulations, we show that the proposed scheme has better performance compared to MIPv6 & HMIPv6 in terms of handover latency and packet loss.

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”, RFC 3775, 2004.
2.        H.S. Flarion, C. Castelluica, K. El-Malki, and L. Bellver, “Hierarhical Mobile IPv6 mobility mangement (HMIPv6)”, RFC 4140, 2005.

3.        R. Koodli, ”Fast Handovers for Mobile IPv6”, RFC 4068, 2005

4.        H.Y. Jung, S. Koh, “Fast Handover Support in Hierarchical Mobile IPv6”, Proc. Int. Conf. on Advanced Communication Technology, Feburary 2004, Phoenix Park,
Vol. 2, pp. 551–554.

5.        J. Vatn, “An experimental study of IEEE 802.11b handover performance and its effect on voice traffic”, Technical Report TRITA-IMIT-TSLAB R 03:01, Royal Institute of Technology, Stockholm, Sweden, 2003.

6.        T. Kwon, M. Gerla, and S. Das, “Mobility Management for VoIP Service: Mobile IP vs. SIP”, IEEE Wireless Communications, 2002, 9, (5), pp. 66–75.

7.        H. Fathi, R. Prasad, and S. Chakraborty, “Mobility Management for VoIP in 3G System: Evaluation of Low- Latency Handoff Schemes”, IEEE Wireless Communications, 2005, 12, (2), pp. 96–104.

8.        H. Fathi, S. Chakraborty, and R. Prasad, “Optimization of Mobile IPv6-based Handovers to Support VoIP Services in Wireless Heterogeneous Networks”, IEEE Trans. Vehicular Tech., 2007, 56, (1), pp. 260–270.

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

10.     Zheng Wang, Xiaodong Li, Baoping Yan “Fast inter-MAP handover in HMIPv6” 978-0-7695-3557-9/09 $25.00 © 2009 IEEE


156-160

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

33.

Authors:

Meenu Gupta, Ajay Rana

Paper Title:

Hybrid Evolutionary Techniques to Restricted Feed Forward Neural Network with Distributed Error for Recognition of Handwritten Hindi ‘MATRAS’

Abstract:  This paper evaluates the performance of restricted feed forward neural network trained by hybrid evolutionary algorithm with generalized delta learning rule for distributed error to obtain the pattern classification for the given training set of Handwritten Hindi ‘MATRAS’. Generally, the feed forward neural network considers the performance index as back-propagated instantaneous unknown error for output of hidden layers. Within this proposed endeavor, we are considering the performance index of distributed instantaneous unknown errors i.e. different errors for different layers. In this case, the convergence is obtained only when the minimum of every error on different layer is determined. The simulation for the performance evaluation is conducted for hand-written ‘MATRAS’ of Hindi language scripted by five different people. These samples are stored as scanned images. The MATLAB is used to determine the densities of these scanned images after partitioning each image into 16 portions. These 16 densities for each character are used as an input pattern of training set. We consider five trials for each learning method and results are presented with their mean value.

Keywords:
  Genetic Algorithm, Handwritten Hindi MATRAS, Multilayer Feed Forward Neural Network, Pattern ecognition.


References:

1.        Du K.L. and Swamy, M.N.S. (2006),” Neural Networks in a Soft computing Framework”, Springer-Verlag London Limited
2.        Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996), “Neural Network Design,” PWS Publishing Co., Boston, MA

3.        Kim, H.B., Jung, S.H., Kim, T.G., and Park, K.H. (1996), “Fast Learning method for back propagation neural network by evolutionary adaption of learning rates”, Neurocomputing, vol. 11(1), pp. 101-106

4.        Mangal M. and Singh,M.P. (2007), ‘Analysis of multidimensional XOR classification problem with evolutionary feed-forward neural networks.”, International Journal on Artificial Intelligence Tools , volume 16(1),pp. 111-120.

5.        Mangal,M. and Singh,M.P. (2007), “Analysis of pattern classification for the multidimensional parity-bit-checking problem with hybrid evolutionary feed-forward neural network.”, Neurocomputing, volume 70,pp. 1511-1524.

6.        Mangal,M. and Singh,M.P. (2006), “Handwritten English vowels Recognition using Hybrid Evolutionary Feed-Forward Neural Networks.”, Malaysian Journal of computer science, vol. 19(2), pp. 169-187.

7.        Pradeep J, Srinviasan E & Himavathi S (2010 ) “Diagonal Feature Extraction Based Handwritten Character System Using Neural Network”, International Journal of Computer Applications Volume 8– No.9

8.        Rumelhart, D.E., Hinton G.E., and Williams R.J. (1986), “Learning internal representations by error propagation.”, MIT Press, Cambridge, vol.  1,pp. 318–362.

9.        Shrivastava, S. and Singh, M.P. (2011), “Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets”, Journal of Applied Soft Computing, vol. 11, pp. 1156-1182

10.     Shrivastava, S., and Singh, M. P. (2007), “ Analysis of soft computing techniques for minimizing the problem of local minima in back-propagation for handwritten English alphabets”, Proc Int. Con. of soft computing and intelligent systems, vol. 2, pp. 307-313


161-169

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

34.

Authors:

Muhammad Ahmad, Sungyoung Lee, Ihsan Ul Haq, Qaisar Mushtaq

Paper Title:

Hyperspectral Remote Sensing: Dimensional Reduction and End member Extraction

Abstract:    In this work, we present an algorithm to overcome the computational complexity of hyperspectral (HS) image data to detect multiple targets/endmembers accurately and efficiently by reducing time and complexity. In order to overcome the computational complexity standard deviation and chi square distance metric methods are considered. The number of endmembers is estimated by unbiased iterative correlation method. Hyperspectral remote sensing is widely used in real time applications such as; Surveillance, Mineralogy, Physics and Agriculture.

Keywords:
   Hyperspectral data, chi square, correlation, unbiased, Mat lab


References:

1.       F. H. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing”, in Airborne Imaging Spectrometer Data Analysis workshop, JPL Publication, vol. 228, pp. 22-29, 1985
2.       Green. R. O, Pavri. B, Faust. J, and Williams. O, “AVIRIS Radiometric Laboratory Calibration, In flight Validation, and a Focused Sensitivity Analysis”, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 1998.

3.       Haq. U. I, and Xu. X, 2008, A New Approach to Band Clustering and Selection for Hyperspectral Imagery, IEEE Proceedings on Signal Processing, , pp.1198-1202, ICSP, 2008.

4.       Muhammad Ahmad et al, “A new statistical approach for band clustering and band selection using K-Means clustering”, International journal of engineering and technology (IJET), vol. 3 (6), p.p. 606-614, 2001.

5.       S. Serpico and L. Bruzzone, “A new search algorithm for feature selection in  hyperspectral remote sensing images”, IEEE Transactions on Geosciences and Remote Sensing, vol. 39, pp. 1360-1367, 2001.

6.       Nascimento, J., Dias, J, “Does independent component analysis play a role in unmixing Hyperspectral data”, IEEE Transaction, vol 43, issue 1, p.p. 175-187, 2005.

7.       Nascimento. J and Dias. J, “Vertex component analysis: A fast algorithm to unmix Hyperspectral data”, IEEE Transaction, vol 43, issue 4, p.p. 898-910, 2005.

8.       Boardman J, “Automating spectral unmixing of AVIRIS data using convex geometry concepts”, In Jet Propulsion Laboratory (JPL) Publications, AVIRIS Workshop, vol 1, ppno 93-26, 1993.

9.       Winter, M, “an algorithm for fast autonomous spectral end-member determination in HyperSpectral data”, In Process of SPIE. vol 3753, 1999.

10.     N. Keshava and J. Mustard, \Spectral unmixing," IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 44-57, 2002.

11.     N. Keshava, \Best bands selection for detection in hyperspectral processing," in IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 3149-3152, IEEE, IEEE, 2001.

12.     J. M. P. Nascimento and J. M. B. Diasb, \Independent component analysis applied to unmixing hyperspectral data," in Image and Signal Processing for Remote Sensing (L. Bruzzone, ed.), vol. 5238, pp. 306-315, SPIE, SPIE, Bellingham, WA, 2004.

13.     J. M. P. Nascimento and J. M. B. Dias, \Vertex component analysis: a fast algorithm to unmix hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 898-910, April 2005.

14.     A. R. Gillespie, M. O. Smith, J. B. Adams, S. C. Willis, A. F. Fisher, and D. E. Sabol, \Interpretation of residual images: Spectral mixture analysis of aviris images, owens valley, california," vol. 2 of AVIRIS Workshop, JPL, 1990.

15.     M. O. Smith, J. B. Adams, and D. E. Sabol, Spectral mixture analysis-New strategies for the analysis of multispectral data. Brussels and Luxemburg, Belgium: Kluwer Academic Publishers, 1994.

16.     C. Chang, X. Zhao, M. L. G. Althouse, and J. J. Pan, \Least squares subspace projection approach to mixed pixel classi_ation for hyperspectral images," IEEE Transection on Geoscience Remote Sensing, vol. 36, pp. 898-912, May 1998.

17.     C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. 2003.

18.     C. Chein. I., \Estimation of number of spectrally distnct signal sources in hyperspectral imagery," IEEE Transection on Geoscience and Remote Sensing, vol. 42, March 2004.

19.     A. Huck and M. Guillaume, \Independent component analysis-based estimation of anomaly abundances in hyperspectral images," pp. 168-177, 2007.

20.     D. Manolakis, \Taxonomy of detection algorithms for hyperspectral imaging applications," IEEE Signal Processig Magazine, pp. 29{43, January 2002.

21.     D. Manolakis, \Taxonomy of detection algorithms for hyperspectral imaging applications," Optical Engineering, vol. 44, no. 6, p. 066403, 2005.

22.     A. Ifarraguerri and C.-I. Chang, \Hyperspectral image segmentation with convex cones," Proceedings of ISSSR, 1997.

23.     Winter and M. E., \Fast autonomous spectral end-member determination in  hyperspectral data," in Proceedings of the Thirteenth International Conference on Applied Geologic Remote Sensing, vol. 11, (Vancouver, BC, Canada), pp. 337-344, 1999.

24.     H. H. Harmon, Modern Factor Analysis. Chicago: University of Chicago Press, 1967.

25.     C. S. DimitrisManolakis and G. Shaw, \Hyperspectral subpixel target detection using the linear mixing model," IEEE Transaction on Geoscience and Remote Sensing, vol. 39, July 2001.

26.     M. Lennon, G. Mercier, M. C. Mouchot, and L. Hubert-Moy, \Independent  component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images," SPIE Remote Sensing, vol. 4541, pp. 2893-2895, Sept 19-21 2001.

27.     R. B. Singer and T. B. McCord, \Mars: large scale mixing of bright and dark surface materials and implications for analysis of spectral reectance," in 10th conference on Lunar Planet Science, pp. 1835-1848, 1979.

28.     W. Wei and T. Adali, \Detection using correlation bound in a linear mixure model," Signal Processing, vol. 87, pp. 1118-1127, 2007.

29.     M. P. Jos_e and M. B.-D. Jos_e, \Blind hyperspectral unmixing," in Image and Signal Processing for Remote Sensing XIII (Lorenzo, ed.), vol. 6748, SIE, 2007.

30.     B. Hapke, \Bidirectional reactance spectroscopy. i-theory," Geophysical Research, vol. 86, pp. 4571-4586, June 1986.

31.     S. Kay, Fundamentals of Statistical Signal Processing. Englewood Cli_s, NJ: Prentice Hall, 1998.

32.     J. B. Adams and M. O. Smith, \Spectral mixture modeling : a new analysis of rock and soil types at the vicking lander site [j]," Geophysical Research, vol. 91, no. B8, pp. 8098{8112, 1986.

33.     J. C. Harsanyi and C. I. Chang, \Hyperspectral image classi_cation and dimensionality reduction: An orthogonal subspace projection approach," IEEE Transaction Geoscience and Remote Sensing, vol. 32, pp. 779-785, July.

34.     A. Schaum and A. Stocker, \Spectrally-selective target detection," in SPIE Proceeding on ISSSR, vol. 3071, 1997.

35.     A. Stocker and A. Schaum, \Application of stochastic mixing models to hyperspectral detection problems," vol. 3071, SPIE, April 1997.

36.     J. Ellis, “Searching for oil seeps and oil-impacted soil with hyperspectral imagery”, Earth Observation Magazine, January 2001.

37.     R. Smith, “Introduction to hyperspectral imaging with tmips”, MicroImages Tutorial Web site: www.microimages.com/documentation/Tutorials/hyprspec.pdf, July 2006.

38.     G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen and W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS)”, Remote Sensing of the Environment, vol. 44, pp. 127–143, 1993.

39.     G. Swayze, S. S. R.N. Clark and A. Gallagher, “Ground-truthing AVIRIS mineral mapping at Cuprite Nevada”, in Third Annual JPL Airborne Geosciences Workshop, pp. 47–49, 1992.


170-175

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

35.

Authors:

B. Babypriya, N. Devarajan

Paper Title:

Simulation and Analysis of a DFIG Wind Energy Conversion System with Genetic Fuzzy Controller

Abstract:   The behavior of a grid connected, wind energy conversion system (WECS) is simulated using MATLAB in this paper. This analysis is presented for different fault conditions like line to ground faults, line to line faults, double line to ground faults and three phase symmetric faults. A genetic algorithm based fuzzy controller is incorporated into the Doubly fed Induction Generator (DFIG) Wind Energy Conversion System. The dynamic behavior of a DFIG Wind Energy Conversion system with genetic fuzzy controller is simulated for different fault conditions and the results are compared to that of the system with PI Controllers. The comparison shows that the incorporation of the Genetic fuzzy controller results in an improvement in the dynamic behavior of the system under transient conditions.

Keywords:
   Doubly fed Induction Generator, Wind Energy Conversion System, Genetic Fuzzy Controller.


References:

1.       Anderson P.M. and Anjan Bose (1983), ‘Stability Simulation of Wind Turbine Systems’, IEEE Transactions on power apparatus and systems, Vol. PAS-102, No. 12, pp. 3791-3795.
2.       Hinrichsen E.N. (1983), ‘Integration of Wind Turbine and Power System Controls’, Sixth Biennial Wind Energy Conference and Workshop, pp. 887- 898.

3.       Nayar C.V. and Bundell J.H. (1987), ‘Output Power Controller for a Wind Driven Induction Generator’, IEEE Transactions on Aerospace and Electronic Systems, Vol. Aes-23, No. 3, pp. 388-400.

4.       Filho R., Sanchez E., Sanchez B. and Armando V. (1997), ‘Control of wound rotor induction machine’, International Conference on Power Electronics and Drives, Systems Proceedings IEEE, Vol. 1, pp. 97-102.

5.       Nakra H.L. and Benoit Dube (1988), ‘Slip Power Recovery Induction Generators for Large Vertical Axis Wind Turbines’, Winter Meeting of the IEEE Power Engineering Society, pp. 733-737.

6.       Pena R., Clare J.C. and Asher G.M. (1996), ‘A Doubly Fed Induction Generator Using back-to-back PWM Converters Supplying an Isolated Load from a Variable Speed wind Turbine’, IEEE Proc. B, Electric Power Applications, Vol. 143, pp. 380-387.

7.       Datta R. and Ranganathan V.T. (2002), ‘Variable Speed Wind Power using Doubly Fed Wound Rotor Induction Machine - A Comparison with Alternative Scheme’, IEEE Trans Energy Conversion, Vol. 17, pp. 414-421.

8.       Torbjorn Thiringer (2002), ‘Grid Friendly Connecting of constant speed wind turbines using external resistance’, IEEE Transactions on Energy Conversion, Vol. 17 , No. 4 , pp. 537-542

9.       Renewable Energy Sources and Technologies (TERI Energy Data Directory and Yearbook, 2005), pp. 175-183.

10.     Riaz M. (1959), ‘Energy-Conversion Properties of Induction Machines in Variable-Speed Constant Frequency Generating Systems’, Applications an Industry.


176-182

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

36.

Authors:

Isha Garg

Paper Title:

Multi-Area Load Frequency Control Implementation in Deregulated Power System

Abstract:   In power system, the main goal of load frequency control (LFC) or automatic generation control (AGC) is to maintain the frequency of each area and tie- line power flow within specified tolerance by adjusting the MW outputs of LFC generators so as to accommodate fluctuating load demands. In this paper, attempt is made to make a scheme for automatic generation control within a restructured environment considering effects of contracts between DISCOs and GENCOs to make power system network in normal state. This scheme is tested on two area system with considering deregulation using MATLAB simulink tool. The results are shown in frequency and power response for two area AGC system in restructured environment.

Keywords:
   Automatic generation control, load frequency control, two area control in deregulated power system.


References:

1.       Javad Sadeh and Elyas Rakshani, “ Multi area load frequency control on deregulated power system using optimal output feedback method,” IEEE Transaction, 978-1-4244-1744-5/08.
2.       O. I. Elgerd, C. Fosha, “Optimum Megawatt-Frequency control of multiarea Electric Energy Systems”, IEEE Transaction  on Power Apparatus & Systems, Vol. PAS-89, No. 4, April 1970.

3.       R. Christie, A. Bose, “Load-Frequency control Issues in Power Systems sOperations AfterDeregulation”, IEEE Transactions on Power sSystems, Vol 11, Aug 1996, Pages. 1191-1200.

4.       V. Donde, M. A. Pai, I. A. Hiskens ,“ Simulation of Bilateral Contracts in an AGC System After Restructuring” IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST.

5.       C. Fosha, O. I. Elgerd, “The Megawatt-Frequency control Problem: A New approach via Optimal control Theory”, IEEE Transactions on Power Apparatus & Systems, Vol. PAS-89, No. 4, April 1970.

6.       E. Rakhshani, and J. Sadeh. “Load Frequency Control of Multi area Restructured Power system” IEEE 978-1-4244-1762-9/08.

7.       B. Venkata Prasanth, S. V. Jayaram Kumar,“Load Frequency Control For A Two  Area aInterconnected Power System ssUsing Robust Genetic Algorithm Controller” Journal of Theoretical and Applied Information Technology, pp 1204-1212.

8.       Richard D. Christie, Anjan Bose, “Load Frequency Control Issues In Power System     Operations After Deregulation”  IEEE 0-7803-2663-619.

9.       E. Rakhshani, and J. Sadeh, “A Reduced-Order Estimator with Prescribed Degree of Stability for Two-Area LFC System in a Deregulated Environment” IEEE 978-1-4244-3811-2/09.

10.     IEEE Recommended Definitions of Terms for Automatic Generation Control   on Electric Power Systems, Approved September 26,1991 Standard Board.

11.     S.N. Singh, S.C. Srivastava, “Electric Power Industry Restructuring in India:Present Scenario and Future Prospect” IEEE Intemational Conference on Electric Utility Deregulation, Restructuring and Power Technologies (DRPT2004) April 2004, pp 20-23

12.     F. Liu, Y.H. Song, J. Ma, S. Mei and Q. Lu, “Optimal load-frequency control in restructured power systems” IEE Proc.-Gmer. Transm. Distrib &. Vol. 150, No.1 January 2003, pp s87-95.

13.     A. P. Sakis Meliopoulos, George J. Cokkinides, A. G. Bakirtzis, “Load-Frequency Control Service in a Deregulated Environment” IEEE 1060-3425/98.


183-187

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

37.

Authors:

A K Malik, Yashveer Singh, S K Gupta

Paper Title:

A Fuzzy Based Two Warehouses Inventory Model for Deteriorating Items

Abstract:    In real life situations, especially for new products, the probability is not known due to lack of historical data and adequate information. Then these parameters and variables are treated as fuzzy parameters. Fuzzy set theory is now applied to problems in engineering, business, medical and related health sciences and natural sciences. Over the years there have been successful applications and implementations of fuzzy set theory in production management. In this study, a fuzzy based two warehouses inventory model has been developed with exponential demand. Deterioration rates of two warehouses are considered to be different due to change in environment. The holding cost in RW is assumed to be higher than those in OW. To reduce the inventory costs, it will be economical for firms to store goods in OW before RW, but clear the stocks in RW before OW. The parameters such as holding costs, ordering cost and deteriorating cost for two warehouses are considered as fuzzy number. We considered the triangular fuzzy number to represents the fuzzy parameters. The total inventory cost is obtained in crisp environment as well as fuzzy sense with the help of Signed distance method.

Keywords:   Exponential demand, linear deterioration, Fuzzy model, Crisp model, Signed distance.


References:

1.        Ghare, P.M. and Schrader, G.P. (1963), “A model for exponentially decaying inventory”, Journal of Industrial Engineering (J.I.E.), 14, 228-243.
2.        Covert, R.P. and Philip, G.P. (1973), “An EOQ model for items with Weibull distribution deterioration”, AIIE Trans., 5(4), 323-329.

3.        Hertely V. Ronald. (1976), “On the EOQ model two levels of storage”, Opsearch, 13,190-196.

4.        Bhunia, A.K. and Maiti, M. (1998), “A two-warehouse inventory model for deteriorating items with a linear trend in demand and shortages”, J.O.R.S., 49 (3), 287–292.

5.        Lee, C.C. and Hsu, S. L. (2009), “A two-warehouse production model for deteriorating inventory items with time-dependent demands, European Journal of Operational Research, 194, 700-710.

6.        Singh, S.R., Malik, A.K., (2009). Effect of inflation on two warehouse production inventory systems with exponential demand and variable deterioration, International Journal of Mathematical and Applications, 2, (1-2), 141-149.

7.        Lee, Y. Y., Kramer, B. A. and Hwang, C. L. (1990) Part-period balancing with uncertainty: a fuzzy sets theory approach, International Journal of Production Research, 28(10), 1771-1778.

8.        Park, K. S. (1987) Fuzzy-set theoretic interpretation of economic order quantity, IEEE Transactions on Systems, Man and Cybernetics, 17(6), 1082-1084.

9.        E. A. Silver, R. Peterson, (1985) Decision Systems for Inventory Management and Production Planning, John Wiley & Sons, New York.

10.     H. J. Zimmermann. (1985) Fuzzy Set Theory and Its Applications. Kluwer-Nijho, Hinghum, Netherlands.

11.     M. Papadrakakis, N. D. Lagaros. (2003) Soft Computing Methodologies for Structural Optimization. Applied Soft Computing, vol. 3, no. 4, pp. 283-300.

12.     R. P. Sundarraj, S. Talluri. (2003) A Multi-period Optimization Model for the Procurement of Component-based Enterprise Information Technologies. European Journal of Operational Research, vol. 146, no. 2, pp. 339-351.

13.     Singh S.R.  and Singh C.  (2008). Fuzzy Inventory Model for finite rate of Replenishment using Signed Distance Method.  International Transactions in Mathematical Sciences and Computer, 1(1), 27-34.
14.     Yao J.S. and Lee H.M., (1999). Fuzzy inventory with or without backorder for fuzzy order quantity with trapezoidal fuzzy number. Fuzzy Sets and Systems, 105, 311-337.
15.     Yung, K. L., W.  Ip and D. Wang (2007). Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing System. International Journal of Automation and Computing, 04 (1), 80-87.

16.     Y.W. Zhou, (2003). A Multi-warehouse Inventory Model for Items with Time-varying Demand and Shortages. Computers & Operations Research, vol. 30, no. 14, pp. 2115-2134.

17.     Chang, H., C., Yao, J., S., and Quyang, L.Y., (2004) “Fuzzy mixture inventory model with variable lead-time based on probabilistic fuzzy set and triangular fuzzy number”, Computer and Mathematical Modeling, 39, 287-304.

18.     Chang, H., C., Yao, J., S., and Quyang, L.,Y., (2006) “Fuzzy mixture inventory model involving fuzzy random variable, lead-time and fuzzy total demand”, European Journal of Operational Research, 69  65-80.

19.     Dutta, P., Chakraborty, D., and Roy, A.R.,(2007) “Continuous review inventory model in mixed fuzzy and stochastic environment”, Applied Mathematics and Computation, 188, 970-980.

20.     Maiti, M.K., and Maiti, M., (2007) “Two-storage inventory model with lot-size dependent fuzzy lead-time under possibility constraints via genetic algorithm”, European Journal of Operational Research, 179, 352-371.

21.     Bellman, R. E. & Zadeh, L. A. (1970). Decision-making in a fuzzy environment.  Management Science, 17, B141-B164.

22.     Goni, A. & Maheswari, S. (2010). Supply chain model for the retailer’s ordering policy under two levels of delay payments in fuzzy environment. Applied Mathematical Sciences, 4, 1155-1164.

23.     Halim, K.A., Giri, B.C. & Chaudhuri, K.S. (2008). Fuzzy economic order quantity model for perishable items with stochastic demand, partial backlogging and fuzzy deteriorating rate. International Journal of Operational Research, 3, 77-96.

24.     Halim, K.A., Giri, B.C. & Chaudhuri, K.S. (2010). Lot sizing in an unreliable manufacturing system with fuzzy demand and repair time.  International Journal of Industrial and Systems Engineering, 5, 485-500.

25.     Hsieh, C.H. (2002). Optimization of fuzzy production inventory models. Information Sciences, 146, 29-40.

26.     Mahapatra, N. K. & Maiti, M.  (2006). A fuzzy stochastic approach to multi-objective inventory model of deteriorating items with various types of demand and time dependent holding cost, Journal of the Operational Research Society of India, 43 (2), 117-131.

27.     Mahata, G. C. & Goswami, A. (2006). Production lot size model with fuzzy production rate and fuzzy demand rate for deteriorating item under permissible   delay in payments.  Journal of the Operational Research Society of India, 43 (4), 358-375.

28.     Zadeh, L. A. (1965). Fuzzy Sets, Information and Control, 8, 338-353.


188-192

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

38.

Authors:

R. Valarmathi, S. Palaniswami, N. Devarajan

Paper Title:

Simulation and Analysis of Wind Energy and Photo Voltaic Hybrid System

Abstract:    This paper models a hybrid system consisting of a wind turbine and a photovoltaic array as main energy sources and this is simulated using MATLAB. To connect the PV system to the grid the only adaptation required is to adjust the DC bus voltage to the conventional/isolated grids characteristics. Both energy sources are parallely linked to a common PWM voltage source inverter through individual AC/DC and DC/DC converters. A AC/DC converter transforms the 3 phase variable frequency wind turbine AC power, into variable DC power. A DC/DC converter controls variable power from the solar array DC. Though all sources have their individual controllers they have a common configuration. A VLSI based fuzzy logic controller ensures constant voltage needed for the load through the convertor’s PWM signals. The wind turbine and photovoltaic array voltage are controlled through error signal which is fed to the controller to generate pulses for the dc-dc converter. Simulation results reveal that the hybrid system provides a constant power to the load.

Keywords:
   Photovoltaic array, Wind turbine, VLSI, Fuzzy logic controller.


References:

1.       J. M. Carrasco, L. G. Franquelo, J. T. Bialasiewicz, E. Galvan, R. C. PortilloGuisado, M. A. M. Prats, J. I. Leon, and N.Moreno-Alfonso, “Power-electronic systems for the grid integration of renewable energy sources: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002– 1016, Jun. 2006.
2.       F. Valencaga, P. F. Puleston, and P. E. Battaiotto, “Power control of a solar/wind generation system without wind measurement: A passivity/ sliding mode approach,” IEEE Trans. Energy Convers., vol. 18, no. 4, pp. 501–507, Dec. 2003.

3.       R. Chedid and S. Rahman, “Unit sizing and control of hybrid wind-solar power systems,” IEEE Trans. Energy Convers., vol. 12, no. 1, pp. 79–85, Mar. 1997.

4.       W. D. Kellogg, M. H. Nehrir, G. Venkataramanan, and V. Greez, “Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems,” IEEE Trans. Energy Convers., vol. 13, no. 1, pp. 70–75, Mar. 1998.

5.       Y. Atat and N.-E. Zergainoh, “Simulink-Based MPSoC Design: New Approach to Bridge the Gap between Algorithm and Architecture Design,” Proc. IEEE CS Ann. Symp. VLSI, pp. 9-14, 2007.

6.       D. Soderman and Y. Panchul, “Implementing C Designs in Hardware: A Full-Featured ANSI C to RTL Verilog Compiler in Action,” Proc. Int’l Verilog HDL Conf. and VHDL Int’l Users Forum, pp. 22-29, 1998.

7.       P. Banerjee et al., “Overview of a Compiler for Synthesizing MATLAB Programs onto Fpgas,” IEEE Trans. VLSI Systems, vol. 12, no. 3, pp. 312-323, Mar. 2004.

8.       MATLAB, “The MATLAB Website,” http://www.mathworks. com, 2007.

9.       J.S. Kim et al., “TANOR: A Tool for Accelerating N-Body Simulations on Reconfigurable Platform,” Proc. Int’l Conf. Field Programmable Logic and Applications (FPL ’07), pp. 68-73, Aug. 2007.

10.     S. Jayasoma, S. J. Dodds, and R. Perryman, “An FPGA implemented PMSM servo drive: Practical issues,” in Proc. Int. Universities Power Eng. Conf., 2004, vol. 1, pp. 499–503.

11.    R.C.Bansal (2005), “Three phase Self Excited Induction Generators: An Overview”, IEEE Transactions On Energy Conversion, Vol. 20, No. 2, pp.292-299


193-200

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

39.

Authors:

Ravindra Kumar Sharma, Kirti Vyas, Ajay Kumar Bairwa

Paper Title:

Flattened Dispersion of Hexagonal Chalcogenide As2Se3 Glass Photonic Crystal Fiber with a Large Core

Abstract:    In this paper, we have proposed a novel structure of   the fabrication of a chalcogenide As2Se3 glass photonic crystal fiber (PCF) with increased core diameter. As comparision with the normal PCFs in which silica glass is used as core material, the proposed PCF has following feature; firstly we have used the chalcogenide As2Se3 glass as core material in which the first ring area contains no air holes. Then the proposed PCF has a large core area chalcogenide As2Se3 glass photonic crystal fiber. There are low chromatic dispersion in the proposed PCF comparied to normal As2Se3 glass PCF. The chromatic dispersion is almost flat in the range of 2.4 micrometer to 4.0 micrometer range when the air hole diameter ‘d’ is 1.0 micrometer and air hole space ‘˄’ is 2.0 micrometer.

Keywords:
   chalcogenide As2Se3 glass, chromatic dispersion, photonic crystal fiber.


References:

1.       Knight, T.A. Birks, P. St. J. Russel, and D.M. Atkin, “ All silica single- mode optical fiber with photonic crystal cladding”, Opt. Lett, 21, pp. 1547 – 1549 (1996).
2.       J. Broeng, D. Mogilevstev , S.E. Barkou, and A. Bjarklev, “Photonic crystal fibers: a new class of optical waveguides”, Optical fiber Technology, 5, pp. 305-330(1999).

3.       M.J. Gander, et.al. “Experimental measurement of group velocity dispersion in photonic crystal fiber”, Electron, let. 35, pp. 63-64 (1999).

4.       A.V. Husakou, J. Hermann, Appl. Phys. B77 (2003)227.

5.       D.I. Yeom, E.C. Magi, M.R.E. Lamont, M.A.F. Roelens, L. Fu, B.J. Eggleton, Opt. Lett. 33(2008) 660.

6.       G.P. Agarwal, Nonlinear Fiber Optics, third ed., Academic Press, New York, 1995.

7.       G. Boudebs, S. Cherukulappurath, M. Guignard, J. Troles, F. Smektala, and F. Sanchez,” Linear optical characterization of chalcogenide glasses”,Opt. common. 230, 331- 336 (2004).

8.       L.P. Shen, W.P. Huang and S.S. Jain, “Design of photonic crystal fibers for dispersion – related applications”, J. Lightwave Technol, 21pp. 1644- 1651 (2003).

9.       K. Thyagarajan, R.K. Varshney, P. Palai, A.K. Ghatak, and I.C. Goyal, “ A noval design of a dispersion compensating fiber”, IEEE Photon Technol, Lett. 8, pp. 1510- 1512 (1996).

10.     Bhawana Dabas , R.K. Sinha, “ Dispersion characteristic of hexagonal and square lattic chalcogenide As2Se3 glass photonic crystal fiber”, opt. Comm. 283, 1331- 1337 (2010).


201-203

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

40.

Authors:

A. Thirueelakandan, T. Thirumurugan

Paper Title:

An Approach towards Improved Cyber Security by Hardware Acceleration of Open SSL Cryptographic Functions.

Abstract:    Providing improved Information Security to the rapidly developing Cybernet System has become a vital factor in the present technically networked world.  The information security concept becomes a more complicated subject when the more sophisticated system requirements and real time computation speed are considered. In order to solve these issues, lots of research and development activities are carried out and cryptography has been a very important part of any communication system in the recent years. Cryptographic algorithms fulfill specific information security requirements such as data integrity, confidentiality and authenticity. This work proposes an FPGA-based VLSI Crypto-System, integrating hardware that accelerates the cryptographic algorithms used in the SSL/TLS protocol. SSL v3 and TLS v1 protocol is deployed in the proposed system powered with a Nios-2 soft-core processor. The cipher functions used in SSL-driven connection are the Scalable Encryption Algorithm (SEA), Message Digest Algorithm (MD5), Secured Hash Algorithm (SHA2). These algorithms are accelerated in the VLSI Crypto-System that is on an Altera Cyclone III FPGA DE2 development board. The experimental results shows that, by hardware acceleration of  SEA, MD5 and SHA2 cryptographic algorithms, the VLSI Crypto-System performance has increased in terms of speed, optimized area and enhanced  level security for the target Cybernetic application.

Keywords:
   Cryptographic algorithm, Hardware accelerator, SSL/TLS protocol, CtoH Compiler, VLSI Crypto - System.


References:

1.       Mohamed Khalil-Hani, Vishnu P., Nambiar M., Marsono N., (2010) “Hardware Acceleration of OpenSSL cryptographic functionsfor high-performance Internet Security”International Conference on Intelligent Systems, Modelling and Simulation.
2.       Nambiar V. P., Khalil-Hani M., and Zabidi M. M, (IJCTS 2009), “Accelerating the AES encryption function in OpenSSL for embedded systems,” International Journal of Information and Communication Technology, vol. 2, no. 1/2, pp. 83–93.

3.       Khalil-Hani M., Nazrin M., and Hau Y. W., (ICED 2008) “Implementation of SHA-2 hash function  for a digital signature System-on-Chip in FPGA,” in International Conference on Electronic Design.

4.       Praveen Kumar B., Ezhumalai P., Ramesh P., Dr SankaraGomathi S., Dr.Sakthivel P., (Febraury 2010), “Improving the Performance of a Scalable Encryption Algorithm (SEA) using FPGA”, IJCSNS International Journal of Computer Science and Network Security, VOL. 10  No.2.

5.       Maharak C. and Sowanwanichakul B., (in TENCON 2004), “Security methods for Web- based    applications on embedded system,” 2004 IEEE Region 10 Conference, vol. C, 2004, pp.56–59 Vol. 3.

6.       Colleen E. Garcia, Naval Postgraduate School, Monterey, California, (June 2010) “Regulating nation-state cyber attacks in Counter terrorism operations” – Master Thesis. 

7.       EkawatHomsirikamol, MarcinRogawski, Kris Gaj, in George Mason University, (2010) “Comparing Hardware Performance of Fourteen Round Two SHA-3 Candidates Using FPGAs” – Master Thesis.

8.       Jury: Prof.Y.Willems ,voorzitter in atholiekeuniversiteitleuven, Kasteelpark, Arenberg 10, B–3001 Heverlee, (May 2007), “Analysis and design of symmetric encryption algorithms” -  Master Thesis .

9.       Pravir Chandra, Matt Messier, John Viega, (June 2002) Publisher: O'ReillyPub Date: ISBN : 0-596-00270. Network Security with OpenSSL..

10.     Pascal junod, in EcolePolytechnique, Federale De Lausanne, (2005)“Statistical Cryptanalysis of Block Ciphers” – Master Thesis.

11.     Stephen A. Weis in Massachusett Institute of Technology, (May 2006), “New Foundations for Efficient Authentication, Commutative Cryptography, and Private Disjointness TestinG”.

12.     Saar Drimer in University of Cambridge United Kingdom, (November 2009)  “Security for volatile FPGAs” – Master Thesis

13.     Wollinger .T, J. Guajardo, C. Paar, (2003) “Cryptography in Embedded Systems: An Overview,” in  Proc. of the Embedded World 2003 Exhibition and Conference.

14.     William Stallings 3’rd Edition, Publisher: Pearson Education.“Cryptography and Network Security– Principles and Practices”.

15.     “Hacking Techniques – High Tech Crime Brief” An Article by Australian Institute of Criminology, 2005.

16.     “2010 Data Breach Investigations Report” A study conducted by the Verizon Business RISK team in cooperation with the United States Secret Service.

17.     www.openssl.org and www.cryptography.org

204-207

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

41.

Authors:

Sandeep Kumar, Puneet Verma

Paper Title:

Comparison of Different Enhanced Image Denoising with Multiple Histogram Techniques

Abstract:  There are different techniques for enhance an image by using gray scale manipulation, histogram equalization and filtering. Out of different enhancement techniques HE became a popular technique because, it is simple and effective. For preserving the input brightness of the image, there is a segment to avoid the generation of non-existing artifacts in the output image. So, these methods are used for preserving the input brightness with the significant contrast enhancement. They may produce an image which is not look like input image. HE method is used for re-mapping of the gray level and tends to introduce some annoying artifacts and unnatural enhancement. To preserve from these drawbacks brightness preserving techniques are used such as CLAHE, DSIHE and DHE. But after the enhancement some noise is also there which is further reduce for better result. Enhanced Image Denoising comparative analysis with the different techniques is carried out. In this comparison some subjective and objective parameters are used. For subjective parameter visual quality and computation time and for objective parameter PSNR and MSE are used.

Keywords:
   Contrast enhancement, HE, PSNR, MSE, visual quality.


References:

1.        S. Lau, “Global image enhancement using local information,” Electronics Letters, vol. 30, pp. 122–123, Jan. 1994.
2.        J. Zimmerman, S. Pizer, E. Staab, E. Perry,  W. McCartney, and B. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Transactions on Medical Imaging, pp. 304-312, Dec. 1988.

3.        Yu Wan, Qian Chen and Bao-Min Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Transactions Consumer Electron., vol. 45, no. 1, pp. 68-75, Feb. 1999.

4.        Yeong-Taeg Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consumer Electronics, vol. 43, no. 1, pp. 1-8, Feb. 1997.

5.        M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae, “A dynamic histogram equalization for image contrast enhancement”, IEEE Transactions. Consumer Electron., vol. 53, no. 2, pp. 593- 600, May 2007.

6.        Y. Wang, Q. Chen, and B. Zhang, Soong-Der Chen, and Abd. Rahman Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement”, IEEE Transactions Consumer Electron. vol. 49, no. 4, pp. 1310-1319, Nov. 2003.

7.        WANG Zhiming, TAO Jianhua, “A Fast Implementation of Adaptive Histogram Equalization”, IEEE 2006, ICSP 2006 Proceedings.

8.        Md. Foisal Hossain, Mohammad Reza Alsharif, “Image Enhancement Based on Logarithmic Transform Coefficient and Adaptive Histogram Equalization”, 2007 International Conference on Convergence Information Technology, IEEE 2007.

9.        Alex Stark “Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization”, IEEE Transactions on Image Processing, Vol. 9, No. 5, May 2000.

10.     Wang Yuanji. Li Jianhua, Lu E, Fu Yao and Jiang Qinzhong, “Image Quality Evaluation Based On Image Weighted Separating Block Peak Signal To Noise Ratio”, IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003.

11.     Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall, 2002.

12.     Stephen M. Pizer, R. Eugene Johnston, James P. Ericksen, Bonnie C. Yankaskas, Keith E. Muller, “Contrast-Limited Adaptive Histogram Equalization Speed and Effectiveness”, ”, IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003.

13.     Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall, 2002.

14.     Ashok Saini, International Journal of Electronics Engineering, 3 (2), 2011, pp. 275– 277,” Reduction of     Noise from Enhanced Image Using Wavelets”.

15.     Rafael. E. Herrera, Robert J. Sclabassi, “Single trial visual event related potential EEG analysis     using wavelet transform” proceedings of the first joint BMES/EMB conference serving humanity advance technology Oct. 13-16, 99, ATLANTA USA.

16.     Sudha, G.R.Suresh, and R. Sukanesh ,  “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance”, International Journal of Computer Theory and Engineering, Vol.1, No.1, April 2009.

208-213

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

42.

Authors:

Santosh Kumar Gupta, S. Baishya

Paper Title:

Modeling and Simulation of Triple Metal Cylindrical Surround Gate MOSFETs for Reduced Short Channel Effects

Abstract:    Due to the continuous scaling of the MOS transistors it has become absolute necessary to investigate for the new transistor architectures for better control of SCEs and HCEs. In literature triple metal and double metal gate structure has been proposed to reduce the SCEs and HCEs due to scaling of the MOS transistors. The double metal and triple metal structures screen the effect of drain voltage change on the source/channel barrier reducing the SCE. The triple metal gate structure however induces an electrical junction on source and drain side which works as ultra shallow source/drain junctions. Since the surround gate structures have been found to have best control over the channel a cylindrical surround gate structure with triple metal was recently proposed by Cong Li et al. In this paper we present the physically based analytical model for the surface potential of triple metal cylindrical surround gate MOSFET. The model takes into account for the drift-diffusion currents and continuity equations. In the latter part of the paper some 2D simulation results of triple metal gate MOS transistor has been shown. The device has also been explored for the suitable channel doping in terms of subthreshold slope, DIBL, transconductance etc.

Keywords:
   Cylindrical Surround Gate MOSFETs, Surface Potential, TCAD, Short Channel Effects, Analog.


References:

1.       Chaudhry and M. J. Kumar, “Controlling Short-Channel Effect in Deep-Submicron SOI MOSFETs for Improved Reliability: A Review”  IEEE Trans. Device and Materials Reliability, vol. 4, pp. 99-109, Mar. 2004.
2.       G.V.Reddy and M. J. Kumar,"A New Dual-Material Double-Gate (DMDG) Nanoscale SOI MOSFET – Two-dimensional Analytical Modeling and Simulation,"  IEEE Trans. on Nanotechnology, Vol.4, pp.260 - 268, March  2005.

3.       J.-P. Colinge, “Silicon-On-Insulator: Material to VLSI,” Amsterdam,  Kluwer Academic Publishers, 2004.

4.       K. K. Ng and G. W. Taylor, “Effects of hot-carrier trapping in n- and p-channel MOSFETs,” IEEE Trans. Electron Devices, vol. ED-30, pp. 871-876, 1983.

5.       M. J. Kumar and A. A. Orouji, "Two-Dimensional Analytical Threshold Voltage Model of Nanoscale Fully Depleted SOI MOSFET with Electrically Induced Source/Drain Extensions,"  IEEE Trans. on Electron Devices, vol. 52, no. 7, pp. 1568-1575, July 2005.

6.       Ali A. Orouji and M. Jagadesh Kumar, "Nanoscale SOI-MOSFETs with Electrically Induced Source/Drain Extension: Novel attributes and Design considerations for Suppressed Short-channel Effects," Superlattices and Microstructures, Vol.39, pp. 395-405, May 2006.

7.       Y. Taur and T. H. Ning, Fundamentals of Modern VLSI Devices. Cambridge, U. K. Cambridge Univ. Press, 1998.

8.       S. R. Banna, P. C. H. Chan, P. K. Ko, C. T. Nguyen, and M. Chan, “Threshold voltage model for deep-submicrometer fully depleted SOI MOSFETs,” IEEE Trans. Electron Devices, vol.42, no.11, pp.1949–1955, Nov.1995.

9.       Biswajit Ray , Santanu Mahapatra “A New Threshold Voltage Model for Omega Gate Cylindrical Nanowire Transistor”, 21st  International Conference on VLSI Design, 1063-9667/08, DOI 10.1109/VLSI.2008.52, pp. 447-452.

10.     Cong Li, Yiqi Zhuang, Ru Han “Cylindrical surrounding-gate MOSFETs with electrically induced source/drain extension”, Microelectronics Journal, vol. 42, issue 2, February 2011, pp. 341-346.

11.     Hamdy AbdEl Hamid, Benjamin Iñíguez, Jaume Roig Guitart  “Analytical Model of the Threshold Voltage and Subthreshold Swing of Undoped Cylindrical Gate-All-Around-Based MOSFETs”, IEEE Transactions on Electron Devices, Vol.54, No.3, March 2007, pp. 572-579

12.     Hyun-Jin Cho, James D. Plummer “Modeling of Surrounding Gate MOSFETs With Bulk Trap States”, IEEE Transactions On Electron Devices, Vol. 54, No. 1, January 2007, pp. 166-169.

13.     Sentaurus TCAD User’s Manual, 2009.

14.     Cong Li, Yiqi Zhuang, Ru Han, Gang Jin, Junlin Bao, “Analytical threshlod voltage model for cylindrical surrounding gte MOSFET with electrically induced source/drain extensions”, Microelectronics Reliability, vol. 51, issue 12, December 2011, pp.2053-2058.

15.     Santosh Kumar Gupta and S. Baishya, “Design Considerations of Electrically Induced Source/Drain Junction SOI MOSFETs for the Reduced Short Channel and Hot Carrier Effects”, International Journal of Computer and Electrical Engineering, vol. 3, No. 6, December 2011, pp. 869-872.

16.     Santosh Kumar Gupta, Achinta Baidya and S. Baishya, “Simulation and Analysis of Gate Engineered Triple Metal Double Gate (TM-DG) MOSFET for Diminished Short Channel Effects”, International Journal of Advanced Science and Technology, vol. 38, January 2012, pp. 15-24.

17.     Santosh Kumar Gupta, Srimanta Baishya, “3D-TCAD Simulation Study    

18.     of an Electrically Induced Source/Drain Cylindrically Surrounding Gate 

19.     MOSFETs for reduced SCEs and HCEs”, IEEE 3rd International          

20.     Conference on Electronics Computer Technology, 8-10 April, 2011,

21.     Kanyakumari, India, vol. 2, pp. 429-432.


214-221

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

43.

Authors:

Gurpreet Kaur, Devesh Mahor, Anil Kamboj

Paper Title:

CDMA vs. OFDM- Comparison and Hybrid OFDM- the Solution for the Next Generation

Abstract:    This paper investigates the effectiveness of OFDM  and proven in other conventional (narrowband) commercial radio technologies (e.g. DS-CDMA in cell phones) (e.g. OFDM in IEEE 802.11a/g). The main aim was to assess the suitability of OFDM as a modulation technique for a fixed wireless phone system for rural areas. However, its suitability for more general wireless applications is also assessed. Most third generation mobile phone systems are proposing to use Code Division Multiple Access (CDMA) as their modulation technique. For this reason, CDMA is also investigated so that the performance of CDMA could be compared with OFDM on the basis of various wireless parameters. At the end it is concluded that the good features of both the modulation schemes can be combined in an intelligent way to get the best modulation scheme as a solution for wireless communication high speed requirement, channel problems and increased number of users.

Keywords:
   CDMA, OFDM, PN Sequence, Peak Power Clipping.


References:

1.       L. Hanzo, M. Mu¨nster, B. J. Choi, and T.  Keller,“OFDM and  MC-CDMA for Broadband  Multi-User Communications, WLANs and  Broadcasting”. Piscataway, NJ: IEEE  Press/Wiley, (2003).
2.       R. V. Nee and R. Prasad “OFDM for Wireless  Multimedia Communications”, London, U.K.:  Artech House, 2000.

3.       J. A. C. Bingham “Multicarrier modulation for  data transmission: An idea whose time has  come”, IEEE Commun. Mag., vol. 28, no.  5,  pp. 5–14, May 1990.

4.       Datacomm Research  Company, Using MIMO-OFDM Technology to Boost WirelessLAN  Performance Today, White Paper, St.   Louis,  MO, Jun. 2005.

5.       R. S. Blum, Y. Li, J. H. Winters, Q. Yan and  “Improved space-time coding for MIMO-OFDM  wireless communications”, IEEE  Trans. Commun., vol. 49, no. 11,pp. 1873– 1878, Nov. 2001.

6.       K.  S.  Gilhousen,  I.  M.  Jacobs,  R.  Padovani,  A.  J.  Viterbi,  L.   A.  Weaver, Jr., and C. E. Wheatley, III, “On the capacity of a  cellular  CDMA  system,”  IEEE  Trans.  Veh.  Technol.,  vol.  40,  no.  2,  pp.  303-312, May 1991.

7.       L.  Liu,  J.  Tong,  and  Li  Ping,  “Analysis  and  optimization  of  CDMA  systems  with  chip-level  interleavers,”  IEEE  J.  Select.  Areas Commun., vol. 24, no. 1, pp. 141-150, Jan. 2006

8.       S.  Verdu  and  S.  Shamai,  “Spectral  efficiency  of  CDMA  with  random spreading,” IEEE Trans. Inform. Theory, vol. 45, no. 2, pp.  622-640, Mar. 1999.

9.       I. Cosovic, S.  Kaiser, M. Schnell, and A. Springer, “Performance of coded uplink MC-CDMA with  combined-equalization in fading channels,”  in  Proc.  IST  Mobile  &  Wireless  Commun.  Summit  (IST’04), Lyon, France, pp. 692-696, June, 2004.

10.     M.  Moher,  “An  iterative  multiuser  decoder  for  near-capacit communications,”  IEEE  Trans.  Commun.,  vol.  46,  pp.  870- 880,  July,1998.


222-225

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

44.

Authors:

Nirosha Joshitha J, R. Medona Selin

Paper Title:

Image Fusion using PCA in Multifeature Based Palmprint Recognition

Abstract:  Biometric technology offers an effective approach to identify personal identity by using individual’s unique, reliable and stable physical or behavioral characteristics. Palmprint is a unique and reliable biometric characteristic with high usability. The composite algorithm used estimates the orientation field of the palmprint from which multiple features is extracted. Fusion increases the system accuracy and robustness in person recognition. The first kind of fusion is multiple features from one palmprint image. The existing system uses this technique through multiple features like minutiae, density map orientation, and principal line map from each palmprint image. The proposed paper uses multi-image fusion. The PCA-based image fusion technique adopted here improve resolution of the images in which images to be fused are firstly decomposed into sub images with different frequency and then the information fusion is performed and finally these sub images are reconstructed into a result image with plentiful information. The PCA algorithm builds a fused image of several input images as a weighted superposition of all input images. The resulting image contains enhanced information as compared to individual images. This image is used for palmprint recognition. A database containing multiple images of the same palmprint is used. The task of palmprint matching is to calculate the degree of similarity between an input test image and a training image from database. A normalized Hamming distance method is adopted to determine the similarity measurement for palmprint matching.

Keywords:
   Density map, Hamming distance, Multi-image fusion, Minutiae, PCA, Principal line map.


References:

1.       Jifeng Dai and Jie Zhou, “Multifeature- Based High Resolution Palmprint Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 945-957, May 2011.
2.       A. Jain, P. Flynn, and A. Ross, “Handbook of Biometrics,” Springer, 2007.

3.       PolyU Palmprint Database.

4.       A. Jain and J. Feng, “Latent Palmprint Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1032- 1047, July 2009.

5.       W. Kong, D. Zhang, and M. Kamel, “Palmprint Identification Using Feature Level Fusion,” Pattern Recognition, vol. 39, no. 3, pp. 478-487, 2006.

6.       D. Zhang, W. Kong, J. You, and M. Wong, “Online Palmprint Identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sept. 2003.
7.       W. Kong, D. Zhang, and W. Li, “Palmprint Feature Extraction Using 2-D Gabor  Filters,” Pattern Recognition, vol. 36, no. 10, pp. 2339-2347, 2003.

8.       N. Duta, A. Jain, and K. Mardia, “Matching of Palmprints,” Pattern Recognition Letters, vol. 23, no. 4, pp. 477-486, 2002.

9.       A.Jain, P.Flynn, and A.Ross, Handbook of Biometrics. Springer,2007 & Wikipedia, free Encyclopedia.

10.     Nagesh kumar.M, Mahesh.PK and M.N. Shanmukha Swamy,”An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image”, IJCSI International Journal of Computer Science Issues, vol. 2, pp 49-53, 2009.

11.     Naidu & Raol,”Pixel-Level Image Fusion Using Wavelets And Principal Component Analysis”, Defence Science Journal, Vol. 58, No. 3, May 2008, Pp. 338-352.

12.     K.Y. Rajput, Melissa Amanna, Mankhush Jagawat and Mayank Sharma,“Palmprint Recognition Using Image Processing”, International Journal of Computing Science and Communication Technologies, Vol. 3, No. 2, Jan. 2011. (ISSN 0974-3375), Pp 618-621.

13.     J. B. O. Souza Filho, L. P. Caloba, J. M. Seixas,”An Accurate and Fast Neural Method for PCA Extraction“, Proc. IJCNN 2003, Portland, USA.


226-230

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

45.

Authors:

R. Vinothkanna, Amitabh Wahi

Paper Title:

A Novel Approach for Extracting Fingerprint Features from Blurred Images

Abstract:  Biometrics is the science and technology of authentication by identifying the living individual’s physiological or behavioral attributes. Fingerprint identification is one of the most well known and published biometrics. Normally in blurred fingerprints the extraction of ridges becomes very difficult. But the extraction of valleys instead of ridges from the same blurred fingerprint images will produce better results. In this paper, we have tried the extraction of features with different types of filters like Median filter, Gaussian filter, Wiener filter, Kalman filter and Gabor filter. We noticed that the extraction of valleys instead of ridges from blurred fingerprints will produce more features for forth coming processes like post-processing and matching process.

Keywords:
   Biometrics, Fingerprints, Valley Extraction, Ridge Extraction, and Gabor filter.


References:

1.       A.K. Jain, R. Bolle, S. Pankanti (Eds.), “Biometrics: Personal Identification in Networked Society”, Kluwer Academic Publishers, Boston, 1999.
2.       L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, S. Tsutsui (Eds.), “Intelligent Biometric Techniques in Fingerprint and Face Recognition”, CRC Press, Boca Raton, 1999.

3.       Nishiuchi. N, Soya. H, “Cancelable Biometric Identification by Combining Biological Data with Artifacts”, Biometrics and Kansei Engineering (ICBAKE), 2011 International Conference on 19-22 Sept. 2011, 61-64.

4.       Nalini Ratha, Rudd Bolle, “Automatic Finger print Recognition system”, Springer New York 2004.

5.       J.R. Parker,” Gray level thresholding in badly illuminated images”, IEEE Trans. Pattern Anal. Mach. Intell. 13 (8) (1991) 813–819.

6.       Chowdhury. A, “An Effectual Thinning Algorithm”, Electronics Computer Technology (ICECT), 2011 3rd International Conference on 8-10 April 2011,1, 183 –
187.

7.       Luping Ji, Zhang Yi, Lifeng Shang, Xiaorong Pu, “Binary Fingerprint Image Thinning Using Template Based PCNNs”, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on Oct. 2007, Volume :  37 ,  Issue:5 , 1407 – 1413.

8.       Zhang Jinhai, “Fingerprint Image Enhancement based on Gabor Function” Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 2011 , 2, 1414 – 1417.

9.       Heeyeol Yu, Mahapatra R, Bhuyan L, “A Hash-Based Scalable IP lookup using Bloom and Fingerprint filters”, Network Protocols, 2009. ICNP 2009. 17th IEEE International Conference on 13-16 Oct. 2009, 264 – 273.

10.     Takenga, C. Tao Peng, Kyamakya K, “Post-Processing of Fingerprint Localization using Kalman filter and Map-Matching Techniques” , Advanced Communication Technology, The 9th International Conference on 12-14 Feb. 2007,3, 2029 – 2034.

11.     Chitresh Saraswat and Amit Kumar, “An Efficient Automatic Attendance System using Fingerprint Verification Technique”, International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 264-269.

12.     Medeiros. L.X, Flores. E.L, Arantes Carrijo. G, Paschoarelli Veiga. A.C, “Optimization of Calculation of Field Orientation Time and Binarization of Fingerprint Images”, Latin America Transactions, IEEE (Revista IEEE America Latina) Sept. 2011, Volume :  9 ,  Issue:5 , 868 – 874.

13.     Chaur-Chin Chen and Yaw-Yi Wang, “An AFIS Using Fingerprint Classification,” Image and Vision Computing, 2003.

14.     Jie-Cherng Liu, Yang-Lung Tai, “ Design of 2-D Wideband Circularly Symmetric FIR Filters by Multiplierless High-OrderTransformation”, Circuits and Systems, IEEE Transactions on April 2011,vol.58,issue 4, 746 – 754.

15.     Rajapaksha, Nilanka T, Madanayake, Arjuna, “Asynchronous – QDI 2D IIR Digital Filter Circuits”, Circuits and Systems (ISCAS), 2011 IEEE International Symposium on 15-18 May 2011, 665 – 668.

16.     John C. Ross, “Image Processing Hand Book”, CRC Press. 1994.

17.     S.Jayaraman, S.Esakkirajan, T.Veerakumar, “Digital Image Processing”, Tata McGraw Hill Education privateLtd,NewDelhi,2009.

18.     Gang Cao; Yao Zhao; Rongrong Ni; Lifang Yu; Huawei Tian, “ Forensic Detection of Median Filtering in Digital Images”, Multimedia and Expo (ICME), 2010 IEEE International Conference on 19-23 July 2010, 89 – 94.

19.     Kanagalakshmi K, Chandra E, “Performance Evaluation of Filters in Noise Removal of Fingerprint Image”, Electronics Computer Technology (ICECT), 2011 3rd International Conference on 8-10 April 2011,1, 117 – 121.

20.     Wan.S, Raju. B.I, Srinivasan. M.A, “Robust deconvolution of high-frequency ultrasound Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on Oct.wavelets” images using higher-order spectral analysis and, 2003, Volume :  50 ,  Issue:10 , 1286 – 1295.

21.     Sun Chun-Jung, Kuo Hong-Yi, Lin Chin E, “A Sensor Based Indoor Mobile Localization and Navigation Using Unscented Kalman Filter”, Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION 4-6 May 2010, 327 – 331.

22.     Feng Zhao, Xiaoou Tang, “Preprocessing and Postprocessing for skeleton-based fingerprint minutiae extraction,” Pattern Recognition 40(4): 1270-1281 (2007).

23.     Pham. T.Q, Perry. S.W, Fletcher. P.A, Ashman. R.A, “Paper Fingerprinting using alpha-masked image matching”, Computer Vision, IET, July 2011, Volume:  5 ,  Issue:4 , 232 – 243.





231-235

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

46.

Authors:

Sarnali Basak, Md. Imdadul Islam, M. R. Amin

Paper Title:

Detection of Virtual Core Point of A Fingerprint: A New Approach

Abstract:    In a fingerprint the profile of ridges are flowed by ridge orientation curves. The slope of each point of a ridge orientation curve varies with the radius of curvature of the line. The change in gradient will attain its maximum value when the curve changes its slope from positive to negative or vice versa which occurs on immediate left and right of maxima or minima point. Every ridge on a fingerprint will provide such point of maximum gradient and the mean value of those points is considered as the virtual core point. This paper presents a new model to determine the virtual core point based on changed in gradient of maxima and minima points, so that this core point is considered to be the reference point to select the region of interest (ROI) of a fingerprint for further processing. The results of the paper show that, the proposed method can provide the virtual core point from different types of fingerprint very efficiently and consequently simplifies the fingerprint recognition system.

Keywords:
   Change in gradient, maxima and minima points, non-minutia and minutia based detection, ridge orientation, ROI.


References:

1.        B. Tan, S. Schuckers, “New approach for liveness detection in fingerprint scanners based on valley noise analysis,” Journal of Electronic Imaging,  vol. 17(1), pp. 011009-1- 011009-9, Jan.-March 2008.
2.        D. Batra, G. Singhal and S. Chaudhury, “Gabor filter based fingerprint classification using support vector machines,” IEEE India Annual Conference, pp. 256-261, Dec. 2004.

3.        E. R.Henry, Classification and uses of finger prints, London: George Rutledge & Sons, Ltd., pp. 17-18, 1900.

4.        S. W. Lee and B. H. Nam, “Fingerprint recognition using wavelet transform and probabilistic neural network,” International Joint Conference on Neural Network, vol. 5, pp. 3276 – 3279, 1999.

5.        A. K. Jain, S. Prabhakar and L. Hong, “A multichannel approach to fingerprint classification,” IEEE Trans. PAMI, vol. 21, no. 4, pp. 348-359, Apr. 1999.

6.        W. Zhang and Y. Wang, "Singular point detection in fingerprint image," Asian Conference on Computer Vision, vol. 2, pp. 793-796, Jan. 2002.

7.        A. Mishra and M. Shandilya, “Fingerprint core point detection using gradient field mask,” International Journal of Computer Applications, vol. 2, no. 8, June 2010.

8.        Julasayvake and S. Choomchuay, “An Algorithm For Fingerprint Core Point Detection,” International Symposium on Signal Processing and Its Applications, pp. 1-4, Feb. 2007.

9.        M.S. Khalil, D. Muhammad, M. K. Khan and K. Alghathbar., “Singular points detection using fingerprint orientation field reliability,” International Journal of Physical Sciences, vol. 5(4), pp. 352-357, April 2010.

10.     J. C. Yang and D. S. Park, “A fingerprint verification algorithm using tessellated invariant moment features”, Neurocomputing, vol. 71, pp. 1939-1946, 2008.

11.     Q. Zhao, D. Zhang, L. Zhang and N. Luo, “Adaptive fingerprint pore modeling and extraction”, Pattern Recognition, vol. 43, pp. 2833-2844, 2010.

12.     M. U. Munir and M. Y. Javed, “Fingerprint Matching using Gabor Filters,” National Conference on Emerging Technologies, Pakistan, pp. 147-152, 2004.

13.     T. Hsieh1, S.R. Shyu1 and K.M. Hung, ‘An Effective Method for Fingerprint Classification,” Tamkang Journal of Science and Engineering, vol. 12, No. 2, pp. 169-182,2009.

14.     A. Jain, L. Hong, and R. Bolle, “On-line fingerprint verification,” IEEE Transactions on Pattern Analysis and Machine Intelligence,  vol. 19, pp. 302–314, Apr. 1997.

15.     A.K. Jain, S. Prabhakar, L. Hong and S. Pankanti, “Filterbank-based fingerprint matching,” IEEE Transactions on Image Processing, vol. 9, no. 5, pp. 846-859, 2000.


236-239

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

47.

Authors:

Soumen Biswas, Sarosij Adak

Paper Title:

Back-Gate Biasing of the DG Transistors

Abstract:   DG-MOSFET programmable logic circuits have noteworthy features such as the ease of re-programming techniques and fewer transistors used in an IC package. Dynamic and reconfigurable threshold logic gates based on DG-MOSFETs are explored. Multiple functions are obtained on a single Boolean static logic circuit built with DG-MOSFETs. Our proposed work is to reconfigurable static and dynamic Boolean logic gates, as well as threshold logic gates designed with DG-MOSFETs. For reconfiguration in these circuits, a systematic back-gate biasing approach is utilized.

Keywords:
   CMOS integrated circuits, double-gate (DG) transistors, logic circuits,


References:

1.        S. Hauck, “The roles of FPGAs in reprogrammable systems,” Proc.   IEEE, vol. 86, no. 4, pp. 615–638, Apr. 1998.
2.        B. Yu, H. Wang, A. Joshi, Q. Xiang, E. Ibok, and M.-R. Lin, “15-nm gate length planar CMOS transistor,” in Tech. Dig. IEDM , 2001, p. 937.

3.        H.-S. P.Wong, “Beyond the conventional MOSFET,” in Proc. 31st Eur. Solid-State Device Research Conf., 2001, p. 69.

4.        P. Beckett, “Low-power spatial computing using dynamic threshold devices,” in Proc. IEEE Int. Symp. Circuits Syst., 2005, p. 2345.

5.        S. Kaya and W. Ma, “Optimization of RF linearity in DG-MOSFETs,” IEEE Electron Devices Lett., p. 308, 2004.

6.        G. Pei and E. C.-C. Kan, “Independently driven DG MOSFETs for mixed-signal circuits—I: Quasi-static and nonquasi-static channel coupling,”IEEE Trans. Electron
Dev., vol. 51, no. 12, pp. 2086–2093, Dec.2004.

7.        M. V. R. Reddy, D. K. Sharma, M. B. Patil, and V. R.Rao,“Power-area evaluation of various double-gate RF mixer topologies,” IEEE Electron Devices Lett., vol. 26, p. 664, 2005.

8.        L. Mathewet al., “CMOS vertical multiple independent gate field effect transistor (MIGFET),” in Proc. IEEE SOI Conf., 2004, p. 187.

9.        S.Varadharajan and S. Kaya, “Study of dual-gate SOI MOSFETs as RF mixers,” in Proc. Int. Semiconductor Dev. Res. Symp. (ISDRS), Dec. 7–9, 2005, p. 7.

10.     J. M. Rabaey, A. Chandrakasan and B Nikolic, “Digital integrated circuits”, Second Edition, Prentice Hall, 2003.

11.     K. Bernstein and N. J. Rohrer, “SOI circuit design concepts”, Kluwer Academic Publishers, 2000.104

12.     A. Marshall and S. Natarajan, “SOI design: analog, memory and digital techniques”, Kluwer Academic Publishers, 2002.

13.     “ISE TCAD Suite,” Synopsis, Mountain View, CA, 2006 [Online]. Available: http://www.synopsis.com

14.     S. Mukhopadhyay, H. Mahmoodi and K. Roy, “A Novel High-Performance and Robust Sense Amplifier Using Independent Gate Control in Sub-50-nm Double-gate MOSFET,” IEEE Trans. Very Large Scale Integration Systems (VLSI), vol. 14, no.2, pp. 183-192, Feb. 2006.

15.     S. L. Hurst, “Threshold logic”, Mills & Boon Ltd. London, 1971.

16.     S. Kaya, H. F.A. Hamed, D.T. Ting and G. Creech, "Reconfigurable Threshold Logic Gates with Nanoscale DG-MOSFETs, "Solid-State Electronics, vol. 51, no.7, pp. 1301-1307, Oct. 2007.

17.     Y. Taur, “Analytic solutions of charge and capacitance in symmetric and asymmetric double-gate MOSFETs,” IEEE Trans. Electron Devices, vol. 48, no.12, Dec. 2001.


240-245

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

48.

Authors:

K.Gupta, P. T. Das, T. K. Nath, P.C.Jana, A.K.Meikap

Paper Title:

Polymer Coated Manganites and Its Magnetic Properties

Abstract:    Synthesis and analysis of magnetic properties of polypyrrole coated La0.9-xSmxSr0.1MnO3 (x= 0.2) nanoparticles is the main aim of this investigation. About 60% magneto resistance (MR) is obtained for La0.9-xSmxSr0.1MnO3 nanoparticles and it decreases with increasing temperature. Enhanced spin-polarized tunneling between two adjacent grains at the grain boundary may increase the MR. Oscillating type of MR is obtained for polypyrrole coated La0.9-xSmxSr0.1MnO3. A core shell type model is attributed to an intermediate exchange coupling between the shell (surrounding) and antiferromagnetic core mainly on the basis of uncompensated surface spins. Samples may be used as multifunctional spintronic devices and magnetic recording medium.

Keywords:
   A. Manganites, B. Polypyrrole, C. Oscillating magneto resistance.


References:

1.        J.S. Zhou, J.B. Goodenough, A. Asamitsu, Y. Tokura, Phys. Rev. Lett. 79(1997) 3234
2.        R.A. Rao, D. Lavric, T.K. Nath, C.B. Eom, L. Wu, F. Tsui, Appl. Phys. Lett. 73(1998) 3294

3.        Y. Tokura, Colossal Magnetoresistive Oxides (Gordon and Breach, New York, 2000)

4.        D. C. Worledge, G. Jeffrey Snyder, M. R. Beasley, T. H. Geballe, J. Appl. Phys. 80(1996) 5158

5.        A. Gupta, G. Q. Gong, Gang Xiao, P. R. Duncombe, P. Lecoeur, P. Trouilloud, Y. Y. Wang, V. P. Dravid, J. Z. Sun, Phys. Rev. B 54(1996) R15629

6.        C. N. R. Rao, B. Raveau, Colosal magnetoresistance, charge ordering and related properties of manganese oxides,  (World Scientific 1998).

7.        S. Satpathy et al J.Appl.Phys 79(1996) 4555 

8.        Solovyev et al Phys Rev Lett 76 (1996) 4825) 

9.        C.Zener, Phys.Rev. 82(1951) 403

10.     P. Dey, T. K.  Nath, Phys. Rev. B 73(2006) 214425

11.     P. Dey, T. K.  Nath, U. Kumar, P.K.Mukhopadhyay,  J. Appl. Phys 98(2005) 014306

12.     H. Naarman, Science and Application of Conducting Polymers (Adam Hilger, Bristol, 1991)

13.     Joo, A. Epstein, Appl. Phys. Lett. 65(1994) 2278

14.     N. Asim, S. Radiman, M.A. Yarmo, Mater. Lett. 62(2008)1044

15.     T. Skothemin, R. Elsenbaumer, Handbook of Conducting Polymers (Marcel Dekker, New York, 1998).

16.     H. S. Nalwa, Handbook of organic Conductive Molecules and polymers (Wiley, 1997, Vol-2, Chapter 12)

17.     Gupta, P. C. Jana, A. K. Meikap, T. K. Nath 107(2010)073704

18.     Chang, J. B. Xia , F. M. Peeters Phys. Rev. B 65(2002) 115209

19.     C. Mamani, G. M. Gusev, O. E. Raichev, T. E. Lamas, and A. K. Bakarov  Phys. Rev. B 80(2009)075308

20.     Q.Xie, B.Lv,P. Wang,P. Song,X. Wu, Materials Chem and Physics 114(2009)636

21.     P. Raychaudhuri, K. Sheshadri, P. Taneja, S.    Bandyopadhyay, P. Ayyub, A.K.Nigam, R. Pinto, Phys. Rev. B 59(1999) 13919

22.     P.Raychaudhuri, T.K. Nath, A.K. Nigam, R. Pinto, J. Appl. Phys. 84(1998)2048 

23.     J.S. Helman, B. Abeles, Phys. Rev. Lett. 37(1976)1429 


246-250

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

49.

Authors:

Shobha Sharma

Paper Title:

22nm High K Metal Gate Inverter Comparative Analysis of Substrate Biasing Effect on Low Power And High Performance Ptm Models

Abstract:    This paper analysis the low power and high performance models of PTM with Hi-K  metal gate cmos technology by using them in an cmos inverter. Also the effect of substrate body biasing is analysed on the output characteristics. The comparison tables are drawn on Voltage Transfer Characteristic  in normal biasing as well as in  nsubstrate and psubstrate biasing with input voltage sweeping from minimum to maximum voltage, at 22nm technology node. This analysis gives an insight into unusual leakages in the gate and supply terminal at 22nm node.  All the simulations are being done with Hspice simulator using PTM models of 22nm cmos HiK-metal gate of Arizona state University, USA.

Keywords:
  22nm,  body biasing,BSIM473, ptm,  scaling issue.


References:

1.        Bohr et al, “nanotechnology goals & challenges for electronic, “IEEE T_nano, vol1_1, pp.56-62, March2002
2.        Ronen et al, “Coming challenges in Architecture”, Proceedings of IEEE, Vol89, March 2011

3.        S Borkar et al, ‘Design Challenges in scaling”. IEEE Micro, Vol19 Aug1999

4.        K roy et al, “Low power VLSI Design”, John wiley, inc 2000

5.        Borkar et al, “Obeying Moore’s law beyond. 18micron’, Proceedings of IEEE intl ASIC sept 2000

6.        Brooks et al, “Power aware architecture, “IEEE micro, vol20 Dec2000

7.        Flynn, “deep submicron microprocessor design issues, ”IEEE Micro, Vol19 Aug1999

8.        Chanderakasan et al, “Low power CMOS digital Design, “Kluwer Academic Publications

9.        Slawsby et al, “Trends in Mobile device power consumption”, Intel tech, June 2002

10.     Usami et al, “automated low power techniques, “IEEE Journal of Solid State circuits, Vol 33, March 1998.

11.     Kursun et al, “CMOS voltage interface circuit ..,” proceedings of IEEE int’l symp um, Vol3, May 2002

12.     Chen et al,” performance and Vdd scaling” IEEE journal of solid state, Vol33, 1998

13.     Kuroda et al” .9V 150MHz Core Processor with Variable threshold”, IEEE J SSC, Vol31 Nov1996.

14.     Huang et al, “scalability and biasing” Proceeding of the IEEE int’l symposium, June2001

15.     Keshaveri et al, “Technology scaling behavior “Proceeding of the IEEE int’l symposium, June 2006

16.     Keshaveri et al, “Effectiveness of Reverse body bias for leakage control”, Proceeding of the IEEE int’l symposium, August 2001 Miyazaki et all, “a 1.2 GIPS microprocessor using adaptive Threshold Voltage with Forward bias,” IEEE SSC, Vol37, Feb2002

17.     Kuroda et al, Variability supply scheme for Low power CMOS design, “IEEE  SSC Vol33, March 1998

18.     Kao et al, “175mv Multiply Accumulate unit using Adaptive supply voltage”, IEEE J of SSC, Vol37, Nov 2002

19.     G. E. Moore, “Progress in Digital integrated Electronics,” IEEE International Electron Devices Meeting, pp. 11-13, December 1975,.

20.     S. Borkar, “Obeying Moore’s Law Beyond 0.18 Micron, “Proceedings of 13th Annual IEEE International ASIC/SOC Conference, pp. 26-31, September 2000.

21.     World Technology Working Groups, International Technology Roadmap for semiconductors. Semiconductor Industry Association. 1999.

22.     K. Wang, S. Thomas, and M. Tanner, “SiGe Band Engineering for MOS, CMOS and Quantum Effect Devices, “Journal of Materials Science: Materials in Electronics, Vol. 6, No.5, pp.311-324, October 1995.

23.     R. Bate and M. Reed, “Prospects for Quantum Integrated Circuits,” Proceedings of SPIE – Symposium on Quantum Well and Superlattice Physics, Vol. 792, pp.26-35, March 1987.

24.     B. Davari and G. Shahidi, “CMOS scaling for High Performance and Low Power-the Next Ten Years,” Proceedings of the IEE, Vol. 83, No.4, pp.596-606, April 1995.

25.     Y. Taur, D. Buchanan, and II. Wong, “CMOS scaling into the Nanometer Regime, “Proceedings of the IEEE. Vol. 85, No. 4, pp. 486-504, April 1997.

26.     K. Wang and W. Lynch, “Scenarios of CMOS scaling, “Proceedings of the IEEE interactional Conference on Solid-state and integrated Circuit Technology. pp. 12-16, October 1998.

27.     II Wong. D Frank, and J Welset, “Nanoscals CMOS, “Proceedings of the IEEE, Vol. 87, No.4, pp. 537 570, April 1999 R.C.      PR Smith, and Y. L. Lowe, “inductive Crosstalk Between Integrated Passive Components M RF Wireless Modules,” Proceedings of the IEEE International.

28.     Conference on Multichip Modules and High Density Packaging, pp. 496-500, April 1998.

29.     A. Deutsch, H. Smith, G.A. Katopis, W.D. Becker, P.W. Coteus, and P.J. Reslte, “ The Importance of Inductance and Inductive Coupling for on-chip Wiring,” Proceedings of the IEEE Electrical performance of Electronic Packaging Conference, pp. 53-56, October 1997.

30.     T. Sakurai, “Closed-Form Expressions for interconnection Delay, Coupling, and Crosstalk in VLSi’s,” IEEE Transactions on Electron Devices, Vol. 40, No.1, pp.118-124, January 1994.

31.     D. H. Cho, Y. S. Eo, and H. S. Park, “Interconnect Capacitance, Crosstalk, and Signal Delay for 0.35 pm CMOS Technology, “Proceedings of the IEEE International Electron Devices Meeting, pp. 619-622, December 1996.

32.     K. M. Fukuda, S. Macda, T. Tsukuda, and T. Matsuura, “Substrate Noise Reduction Using Active Guard Band Filters in Mixed-Signal Integrated Circuits,” IEICE Transactions on Fundamentals of Electronics, Communications, and computer Scvences, Vol. E80-A, No.2, pp. 313-320, February 1997.

33.     D. K. Su and B. A. Wooley, “experimental Results and Modeling Techniques for substrate Noise in Mixed- Signal Integrated Circuits,” IEEE Journal of Solid-State Circuits, Vol. 28, No. 4, pp. 420-430, April 1993.

34.     X. Aragones and A. Rubio, “Analysis and Modeling of Parasitic Substrate Coupling in CMOS Circuits,” IEE Proceedings-Circuits, Devices and Systems, Vol. 142, No.5, pp. 307-312, October 1995.


251-256

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

50.

Authors:

Shobha Sharma

Paper Title:

Comparative Analysis of Low Power and High Performance PTM Models of CMOS with HiK-Metal Gate Technology at 22nm

Abstract:   This paper analysis the low power and high performance models of PTM with Hi-K  metal gate cmos technology by using them in an cmos inverter at 22nm technology node.The characteristics are compared with cmos bulk technology as well. This analysis gives an insight into leakages  when the input voltage is sweeping from minimum to maximum voltage.The aim of HiK metal gate technology is to reduce the leakage at sub 32nm node and is a good alternative to cmos bulk technology having high leakage and power dissipation as seen in this paper’s comparative analysis. All the simulation is done with hspice simulator at 22nm technology node with PTM models of Arizona state university.

Keywords:
  22nm cmos, body biasing, Scaling issues, ptm models.


References:

1.       Dennard A. R. LeBlanc et al, SSC 256.
2.       Hiwai,ohmi microelectronics reliab 1251 42 (2002)

3.       www.itrs.net

4.       H lwai IEDM 2008

5.       Ono et al,ieee transaction on elec devices,42 ,1510

6.       www.freescale.com  

7.       Pille et al,Issscc Dig tech 2007, p322-323

8.       Kukushima et al, iwdtf,2008

9.       Kuhn et al, IEDM tech  dig 2007 p471

10.     Bohr et al Proceedings ICSICT 2008 p13

11.     www.intel.com
12.     Morita et al, Symp on VLSI circuit 2007,p256

13.     Hlwai IWJT 2008

14.     Zimmermann and W. Fichtner, \Low-power logic styles," IEEEJSSC, vol. 32, no. 7, pp. 1079 1090, R1997.

15.     G. E. Moore et al., Cramming more components onto integrated circuits," Proceedings of the IEEE, vol. 86, no. 1, pp. 82  85, 1998.


257-261

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

51.

Authors:

Krishnendu Chattopadhyay, Santanu Das, Sekhar Ranjan Bhadra Chaudhuri

Paper Title:

Bandwidth Enhancement of A Micro strip Line Fed Hexagonal Wide-Slot Antenna Using Fork-like Tuning Stub

Abstract:   In this paper, a printed hexagonal wide slot antenna, fed by a microstrip line with fork like tuning stub for bandwidth enhancement is proposed and experimentally investigated. The impedance, radiation and gain characteristics of this antenna are studied. Simulation and experimental results indicate that a 1.5:1 VSWR bandwidth, of about 1 GHz and 2:1 VSWR bandwidth of 1.34 GHz is achieved at operating frequency around 2.5 GHz, which is about three times larger than a microstrip line fed hexagonal wide slot antenna, with normal tuning stub, considered as reference antenna.

Keywords:
   Fork-like tuning stub, Hexagonal wide-slot, Microstrip line fed, Method of moment, wide band.


References:

1.       M. Kahrizi, T.K. Sarkar and Z.A. Maricevic , “Analysis of a wide radiating slot in the ground plane of a microstrip line” IEEE Trans. Microwave Theory and Techniques, vol.41,No. 1, pp. 29-37.Jan.1993.
2.       J.Y. Jan, J.C. Kao, “Novel printed Wide-band Rhombus-Like slot antenna with an offset microstrip-fed line”, IEEE Antennas and Wireless Propagation Lett. Vol.6, pp.249-251, 2007.

3.       J.Y. Jan, J.W. Su, “Bandwidth Enhancement of a printed wide slot Antenna with a rotated slot” IEEE Trans. Antennas and Propagation.vol.53, No.6, pp.2111-2114,2005.

4.       Y.W. Jang, “Experimental study of large bandwidth Three-offset microstripline-fed slot antenna” IEEE Microwave and wireless components Lett. Vol.11,No.10,pp.425-427,oct.2001.

5.       J.Y. Jan, L.C. Wang, “Printed wideband Rhombus slot antenna with a pair of parasitic strips for Multiband applications” IEEE Trans. Antennas and Propagation, vol.57, No.4, pp.1267-1270, 2009.

6.       Y. Sung, “A printed wide slot antenna with a modified L-shaped microstrip line for wideband applications” IEEE Trans. Antennas and Propagation,vol.59,No.10,pp. 3918-3922,2011.

7.       J.Y. Sze, K.L. Wong, “Bandwidth enhancement of a microstrip-line-fed printed wide-slot antenna” IEEE Trans. Antennas and Propagation, vol.49, No.7, pp.1020-1024, 2001.

8.       L. Zhu, R. Fu, K.L. Wu, “A novel broadband microstrip-fed wide slot antenna with double rejection zeros” IEEE Antennas and Wireless Propagation Lett., vol.2,pp.194-196,2003.

9.       Y.F .Liu, K.L. Lau, Q. Xue, C.H. Chan, “Experimental studies of printed wide slot antennas for wideband applications” IEEE Antennas and wireless Propagation Lett., vol.3,pp.273-275,2004.

10.     H. Kim, Y. J. Yoon, “Microstrip-fed slot antennas with suppressed harmonics” IEEE Trans. Antennas and Propagation,vol.53,No.9, pp. 2809-2817,2005.

11.     P.H. Rao, V.F. Fusco,R. Cahill, “Linearly polarized radial stub fed high performance wideband slot antenna” Electronics Lett., vol.37,No.6,pp.335-337,March 2001.


262-267

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

52.

Authors:

R.Parvathi , C.Malathi

Paper Title:

Arithmetic Operations on Symmetric Trapezoidal Intuitionistic Fuzzy Numbers

Abstract:   In this paper, Symmetric Trapezoidal Intuitionistic Fuzzy Numbers (STIFNs) have been introduced and their desirable properties are also studied. A new type of intuitionistic fuzzy arithmetic operations for STIFN have been proposed based on -cuts. A numerical example is considered to elaborate the proposed arithmetic operations. These operations find applications in solving linear programming problems in intuitionistic fuzzy environment and also to find regression coefficient in intuitionistic fuzzy environment.

Keywords:
Intuitionistic Fuzzy Index, Intuitionistic Fuzzy Number, Intuitionistic Fuzzy Set, Symmetric Trapezoidal Intuitionistic Fuzzy Number, -cuts.


References:

1.        K. T. Atanassov, “Intuitionistic Fuzzy Sets: Theory and Applications”, Physica-Verlag, Heidelberg, New York, 1999.
2.        K. T. Atanassov, “Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, vol. 20, 1986, pp. 87-96.

3.        K. T. Atanassov, “More on intuitionistic fuzzy sets”, Fuzzy Sets and Systems, vol. 33, 1989, pp. 37-46.

4.        R. E. Bellman and L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, vol. 17, 1970, pp. 141-164.

5.        H. Bustine and P. Burillo, “Vague sets are intuitionistic fuzzy sets”, Fuzzy Sets and Systems, vol. 79, 1996, pp. 403- 405.

6.        K. Ganesan and P. Veeramani, “Fuzzy Linear Programs with Trapezoidal Fuzzy Numbers”, Ann.Oper.Res., vol. 143, 2006, pp. 305-315.

7.        W. L. Gau and D. J. Buehrer, “Vague sets”, IEEE Transactions on Systems, Man and Cybernetics, vol. 23, 2, 1993, pp. 610-614.

8.        B. S. Mahapatra and G. S. Mahapatra, “Intuitionistic Fuzzy Fault Tree Analysis using Intuitionistic Fuzzy Numbers”, International Mathematical Forum, vol. 5, 21, 2010, pp. 1015 –             1024.

9.        H. M. Nehi, “A New Ranking Method for Intuitionstic Fuzzy Numbers”, International Journal of Fuzzy Systems, vol. 12, 1, 2010, pp. 80-86.

10.     M. H. Shu, C. H. Cheng and J. R. Chang, “Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly”, Microelectronics Reliability, vol. 46, 2006, pp. 2139-2148.

11.     J. Q. Wang, “Overview on fuzzy multi-criteria decision-making approach”, Control and decision, vol. 23, 6, 2008, pp. 601-606.

12.     H. J. Zimmermann, “Fuzzy Set Theory and its Applications”, Kluwer Academic Publishers, London, 1991.

13.     H. J. Zimmermann, “Fuzzy programming and linear programming with several objective functions”, Fuzzy Sets and Systems, vol. 1, 1978, pp. 45-55.

268-273

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

53.

Authors:

R. Sudha, Vishwas Vats, Gaurav Pathak, Jayabarathi T

Paper Title:

Optimal Placement of Phasor Measurement Units Using Modified Invasive Weed Optimization

Abstract:    A modified Invasive Weed based methodology for optimal measurement of Phasor measurements units (PMUs) for complete observability of Power system is presented in this paper. The prime objective of this Optimization problem is to reduce the number of PMUs and to maximize the redundancy at power system bushes. In this paper MIWO (Modified Invasive Weed Algorithm is implemented for three bush systems namely 7, 9, IEEE 14 standard bus systems. The proposed algorithm is very easy to understand and it’s result is as satisfactory as results of other algorithm methods.

Keywords:
   Invasive Weed Algorithm, Phasor Measurement Units, Observability, Optimal Placements.


References:

1.        K Mazlumi and H. Vahedi “ Optimal Placement of PMUs in   power system based on Bacterial Foraging Algorithm” Proceedings of ICEE 2010, May 11-13, 2010 ..
2.        State Estimation and voltage Security Monitoring using Synchronized Phasor Measurement by Reynaldo Francisco Nuqui.

3.        Abhinav Sadu, Rajesh G. Kavasser, Rajesh Kumar, “Optimal Placement of Phasor Measurement Units using Particle Swarm Optimization”, IEEE, 2009, 978-1-4244-5612, World Congress on Nature &  Biologically Inspired Computing.

4.        B.Dadalipour, A.R. Mallahzadeh  and  Z. Davoodi-Rad   “APPLICATION OF THE INVASIVE WEED   OPTIMIZATION TECHNIQUE FOR ANTENNA CONFIGURATIONS” 2008 Loughborough Antennas & Propagation Conference.


274-277

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

54.

Authors:

Shah Murtaza Rashid Al Masud

Paper Title:

An Extended and Granular Classification of Cloud’s Taxonomy and Services

Abstract:    In the recent time cloud computing has come forwarded as one of the most admired computing model in knowledge domain that concerns about the distributed information systems to support the whole world as a cloud community. Distributed, virtualization and service-oriented nature have given ascendancy to cloud computing to distinguish from its core descendants like grid computing, geographical information systems, and distributed system. Although cloud computing dominants the e-society, but it is still in under research, progress. The architecture of cloud’s taxonomy and its services are very significant issues for cloudifications because every day some new advancements and developments are adjoined under its umbrella. In this paper we proposed an extended and granular classification of taxonomy for cloud computing and specified services that is a detailed ontology of cloud, which will be helpful for researchers and stakeholders in better understanding, developing, and implementing cloud technology and services to their lives.

Keywords:
   Cloud computing, Distributed system, Granular classification, Taxonomy. 


References:

1.        M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “Above the Clouds: A Berkeley View of Cloud Computing,” EECS Dept., Uni. of California, Berkeley, Tech. Rep. UCB/EECS-2009-28, Feb ’09.
2.        L. Qian, Z. Luo, Y. Du, and L. Guo, “Cloud computing: An overview,” in CloudCom ’09: Proceedings of the 1st International Conference on Cloud Computing.
Springer-Verlag, 2009, pp. 626–631.

3.        G. Lin, D. Fu, J. Zhu, and G. Dasmalchi, “Cloud computing: It as a service,” IT Professional, vol. 11, no. 2, pp. 10–13, 2009.

4.        P. Murray, “Enterprise grade cloud computing,” in WDDM ’09: Proceedings of the Third Workshop on Dependable Distributed Data Management. New York, NY, USA: ACM, 2009, pp. 1–1.

5.        A. Weiss, “Computing in the clouds,” NetWorker, vol. 11, no. 4, pp. 16–25, 2007.

6.        D. Hilley, “Cloud computing: A taxonomy of platform and infrastructure-level offerings,” CERCS, Georgia Institute of Technology, Tech. Rep. GIT-CERCS-09-13, April 2009.

7.        cloud-outlook-for-2012.html: http://cloudonomic.blogspot.com/2011/12/cloud-outlook-for-2012.html#!/2011/12/cloud-outlook-for-2012.html

8.        T. Jowitt, “Four out of five enterprises giving cloud a try,” August 27, 2009. Computerworld UK, (visited: 2010, May 7). [Online]. Available: http://www.computerworlduk.com/management/ it-business/services-sourcing/news/index.cfm?newsId=16355

9.        R. Grossman, “The case for cloud computing,” IT Professional, vol. 11, no. 2, pp. 23 –27, march-april 2009.

10.     P. Mell and T. Grance, “The NIST definition of cloud computing (v15),” National Institute of Standards and Technology, Tech. Rep., 2009.

11.     A. Plesser, “CC is hyped and overblown, Forrester’s Frank Gillett.....Big tech companies have ’cloud envy’,” Sep ’08, interview video (visited: 2010, Apr 23). [Online]. Available: http://www.beet.tv/2008/ 09/cloud-computing.html

12.     Bhaskar Prasad Rimal, Eunmi Choi, “A Conceptual Approach for Taxonomical Spectrum of Cloud Computing”,  Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, Dec 2009. ICUT '09.

13.     D. Gottfrid, “Self-service, Prorated Super Computing Fun” Available from: http://open.blogs.nytimes.com/2007/11/01/self-service-proratedsuper- computing-fun/

14.     M. Crandell, “Defogging Cloud Computing: A Taxonomy”, Available: http://gigaom.com/2008/06/16/defogging-cloud-computing-ataxonomy/

15.     P. Laird, “Different Strokes for Different Folks: A Taxonomy of Cloud Offerings”, Enterprise Cloud Submit, INTEROP, 2009.

16.     Cloud Computing Use Case Discussion Group, “Cloud Computing use Case”, White Paper version 1.0, 5 August, 2009

17.     S. Ried, “Yet Another Cloud – How Many Clouds Do We Need?” Available from: Forrester Research. http://www.forrester.com/

18.     M. Crandell, “Defogging Cloud Computing: A Taxonomy”, Available: http://gigaom.com/2008/06/16/defogging-cloud-computing-ataxonomy/

19.     P. Laird, “Different Strokes for Different Folks: A Taxonomy of Cloud Offerings”, Enterprise Cloud Submit, INTEROP, 2009.

20.     Cloud Computing Use Case Discussion Group, “Cloud Computing use Case”, White Paper version 1.0, 5 August, 2009

21.     S. Ried, “Yet Another Cloud – How Many Clouds Do We Need?” Available from: Forrester Research. http://www.forrester.com/

22.     Cloud computing conference in China, May 2012, “http://www.ciecloud.org/2012/schedule03_en.html”

23.     Cloud outlook for 2012, http://cloudonomic.blogspot.com/2011/12/cloud-outlook-for-2012.html#!/2011/12/cloud-outlook-for-2012.html. 

24.     Gurdev Singh, and others, “cloud computing future solution for educational systems”: http://www.ijecbs.com/January2012/29.pdf, Vol. 2 Issue 1 January 2012.

25.     Medical cloud: http://www.backup-technology.com/5988/the-medical-cloud/

26.     Kevin L. Jackson, “Government Cloud Computing”, SOA-R Cloud Computing, Dataline, LLC.

27.     “Salesforce Customer Relationships Management (CRM) system,” http://www.salesforce.com/.

28.     “GOOGLE Apps,” http://www.google.com/apps/business/index.html.

29.     “Apex: Salesforce on-demand programming language and framework,” http://developer.force.com/.

30.     “Hadoop,” http://hadoop.apache.org/.

31.     C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, “Pig latin: a not-so-foreign language for data processing,” in SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 2008, pp. 1099–1110. [Online]. Available: http://dx.doi.org/10.1145

32.     S. Ghemawat, H. Gobioff, and S.-T. Leung, “The google file system,” SIGOPS Oper. Syst. Rev., vol. 37, no. 5, pp. 29–43, 2003.

33.     K. Petersen, M. Spreitzer, D. Terry, and M. Theimer, “Bayou: replicated database services for world-wide applications,” in EW 7: Proceedings of the 7th workshop on ACM SIGOPS European workshop. New York, NY, USA: ACM, 1996, pp. 275–280.

34.     G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, “Dynamo: amazon’s highly available key-value store,” in SOSP ’07. New York, NY, USA: ACM, 2007, pp. 205–220.

35.     “Amazon simple storage service,” http://aws.amazon.com/s3/.

36.     “EMC Managed Storage Service,” http://www.emc.com/. /1376616.1376726

37.     W. J. et al., “Network Communication as a Service-Oriented Capability,” High Performance Computing and Grids in Action, Advances in Parallel Computing, vol. 16, March 2008.

38.     A. H. et al., “Perfsonar: A service oriented architecture for multi-domain network monitoring,” in ICSOC, ser. Lecture Notes in Computer Science, B. B. et al., Ed., vol. 3826. Springer, 2005, pp. 241–254.

39.     J. Hofstader, “Communications as a Service,” http://msdn. microsoft.com/en-us/library/bb896003.aspx.

40.     “Microsoft Connected Service Framework,” http: //www.microsoft.com/serviceproviders/solutions/ connectedservicesframework.mspx.

41.     Symantec:http://www.symantec.com/theme.jsp?themeid=symantec-cloud 

42.     Security: https://wiki.cloudsecurityalliance.org/guidance/index.php/Cloud_Computing_Architectural_Framework

43.     Foster and C. Kesselman, “Globus: A metacomputing infrastructure toolkit,” International Journal of Supercomputer Applications, 1997.

44.     T. Tannenbaum and M. Litzkow, “The condor distributed processing system,” Dr. Dobbs Journal, February 1995.

45.     R. W. et al., “Grid resource allocation and control using computational economies,” in Grid Computing: Making the Global Infrastructure a Reality. John Wiley & Sons, 2003, pp. 747–772.

46.     T. Severiens, “Physics portals basing on distributed databases,” in IuK, 2001.

47.     P. Smr and V. Novek, “Ontology acquisition for automatic building of scientific portals,” in SOFSEM 2006: Theory and Practice of Computer Science: 32nd Conference on Current Trends in Theory and Practice of Computer Science. Springer Verlag, 2006, pp. 493–500. [Online]. http://www.fit.vutbr.cz/research/view pub.php?id=8004

48.     M. Chau, Z. Huang, J. Qin, Y. Zhou, and H. Chen, “Building a scientific knowledge web portal: the nanoport experience,” Decis. Support Syst., vol. 42, no. 2, pp. 1216–1238, 2006.

49.     M. Christie and S. Marru, “The lead portal: a teragrid gateway and application service architecture: Research articles,” Concurr. Comput. : Pract. Exper., vol. 19, no. 6, pp. 767–781, 2007.

50.     D. G. et al., “Building Grid Portals for e-Science: A Service Oriented Architecture,” High Performance Computing and Grids in Action, IOS Press, Amsterdam, 2007.

51.     D. Gannon, J. Alameda, O. Chipara, M. Christie, V. Dukle, L. Fang, M. Farellee, G. Fox, S. Hampton, G. Kandaswamy, D. Kodeboyina, C. Moad, M. Pierce, B. Plale, A. Rossi, Y. Simmhan, A. Sarangi, A. Slominski, S. Shirasauna, and T. Thomas, “Building grid portal applications from a webservice component architecture,”
Proceedings of the IEEE (Special issue on Grid Computing), vol. 93, no. 3, pp. 551–563, March 2005.

52.     Rough Type. http://www.roughtype.com

53.     “Morgan Stanley, IBM ink utility computing deal,” http://news. cnet.com/2100-7339-5200970.html.

54.     “Preboot Execution Environment (PXE) Specifications, Intel Technical Report, September 1999.”

55.     “Das U-Boot: The Universal Boot Loader,” http://www.denx. de/wiki/U-Boot/WebHome.

56.     J. Appavoo, V. Uhlig, and A. Waterland, “Project Kittyhawk: building a global-scale computer: Blue Gene/P as a generic computing platform,” SIGOPS Oper. Syst. Rev., vol. 42, no. 1, pp. 77–84, 2008.

57.     Oracle cloud strategy, http://www.oracle.com/us/products/consulting/resource-library/cloud-servi es-ds-507966.pdf

58.     Truffle Capital (2007), ‘Truffle Capital: European Commission Recognises the Need for a “European Strategy for Software” – Commenting on the 2007 Truffle 100 Europe, Viviane Reding Calls on Europe to Develop a Leadership Position in Software’ - available at http://www.businesswire.com/news/google/20071122005070/en

59.     DG Information Society and Media - Directorate for Converged Networks and Service (2009), ‘Towards A European Software Strategy - Report Of An Industry Expert Group’ - available at http://www.nessi-europe.com/Nessi/LinkClick.aspx?fileticket=7teEO5hz wY%3D&tabid=304 &mid=1571

60.     Strategy: http://cordis.europa.eu/fp7/ict/ssai/docs/cloud-report-final.pdf  


278-286

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

55.

Authors:

Purno Mohon Ghosh, Md. Anwar Hossain, A.F.M. Zainul Abadin, Kallol Krishna Karmakar

Paper Title:

Comparison Among Different Large Scale Path Loss Models for High Sites in Urban, Suburban and Rural Areas

Abstract:  Radio propagation is essential for emerging technologies with appropriate design, deployment and management strategies for any wireless network. It is heavily site specific and can vary significantly depending on terrain, frequency of operation, velocity of mobile terminal, interface sources and other dynamic factor. Accurate characterization of radio channel through key parameters and a mathematical model is important for predicting signal coverage. Path loss models for macro cells such as Hata Okumura, Walfisch-Ikegami and Lee models are analyzed and compared their parameters. The received signal strength was calculated with respect to distance and model that can be adopted to minimize the number of handoffs. This paper proposes path loss models for high sites in urban, suburban and rural areas.

Keywords:
   Cellular mobile, Propagation model, Path loss, Received signal strength.


References:

1.        Armoogum.V, Soyjaudah.K.M.S, Fogarty.T and Mohamudally.N,  “Comparative Study of Path Loss using Existing Models for Digital Television Broadcasting for Summer Season in the North of Mauritius”, Proceedings of Third Advanced IEEE International Conference on Telecommunication, Mauritius Vol. 4, pp 34-38, May 2007.
2.        Abhayawardhana V. S, Wassell.I.J, Crosby D, Sellars. M.P. and Brown. M.G, “Comparison of empirical propagation path loss models for fixed wireless access systems”, Proceedings of IEEE Conference on Vehicular Technology , Stockholm, Sweden, Vol. 1, pp 73-77, June 2005.

3.        K.Ayyappan, P. Dananjayan, “Propagation Model for Highway in Mobile Communication System”,

4.        Ahmed H.Zahram, Ben Liang and Aladdin Dalch, “Signal threshold adaptation for vertical handoff on heterogeneous wireless networks”, Mobile Networks and application, Vol.11, No.4, pp 625- 640, August 2006.

5.        A. Hecker, M. Neuland, and T. Kuerner, “Propagation models for high sites in urban areas”, Adv. Radio Sci., 4, pp. 345-349, 2006.

6.        Vijay K. Garg, “Wireless Communications and Networking”, Morgan Kaufmann Publishers,  pp 66-68, 2007.

7.        Gordon L. Stüber, “Principles of Mobile Communication”, Second Edition, Kluwer Academic Publishers, pp 105-109, 2002.

8.        T.S. Rappaport, “Wireless Communications”,  Pearson Education, 2003.

9.        William C.Y. Lee, “Mobile Cellular Telecommunications”, McGraw Hill International Editions, 1995.


287-290

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

56.

Authors:

Seyyed Ashkan Ebrahimi, Peiman Keshavarzian, Saeid Sorouri, Mahyar Shahsavari

Paper Title:

Low Power CNTFET- Based Ternary Full Adder Cell for Nanoelectronics

Abstract:    In a VLSI circuit, about 70 percent of area occupies by Interconnection. Such a large number of area occupation leads to many limitations of fabricating and applying in binary circuit implementation. Multiple-valued logic is one of the most proper way to improve the ability of value and data transferring in binary systems. Nowadays as small portable devices consuming are largely increased, applying low power approaches are considerably taking into account. In this paper we suggest and evaluate a novel low power ternary full adder cell which is built with CNTFETs (Carbon Nano-Tube Field Effect Transistors). Using beneficial characteristics of CNTFET in our design and implementation notably increased the efficiency of this adder cell. Simulation results using HSPICE are reported to show that the proposed TFA (ternary full adder) consume significantly lower power and impress improvement in term of the power delay product compare to previous work.

Keywords:
   CNTFET, Low Power,Nanoelectronic, Ternary Full Adder, Ternary Logic.


References:

1.       X. Wu, F. P. Prosser: 'CMOS ternary logic circuits', Circuits, Devices and Systems, IEE Proceedings G., 1990, 137, (1), pp. 21–27
2.       X. Wu, X. Deng, and s. Ying: 'Design of ternary current-mode CMOS circuits based on switch-signal theory', Journal of Electronics., 1993, 10, (3), pp. 193–202

3.       A. Raychowdhury and K. Roy: 'Carbon-nanotube-based voltage-mode multiple-valued logic design', Nanotechnology, IEEE Transactions on., 2005, 4, (2), pp. 168–179

4.       P. Keshavarzian and K. Navi: 'Efficient carbon nanotube galois field circuit design', IEICE Electronics Express., 2009, 6, (9), pp. 546–552

5.       K.Nepal: 'Dynamic circuits for Ternary computation in Carbon Nanotube based Field Effect Transistors', NEWCAS Conference (NEWCAS), 2010 8th IEEE International, June 2010:  pp. 53-56

6.       R. Mariani, F. Pessolano, , and R. Saletti: 'A new CMOS ternary logic design for low-power low-voltage circuits', in PATMOS, Louvain-la-Neuve, Belgium, September 1997

7.       S. Lin, Y.-B. Kim, and F. Lombardi: 'A novel CNTFET-based ternary logic gate design', Circuits and Systems, 2009. MWSCAS '09. 52nd IEEE International Midwest Symposium on, Aug. 2009:  pp. 435–438

8.       K. Navi,  M. Rashtian,  A. Khatir,  P. Keshavarzian, and O. hashemipour: 'High Speed Capacitor-Inverter Based Carbon Nanotube Full Adder', Nanoscale Res. Lett. Springer., 2010, 5, (5), pp. 859-862

9.       S. Lin, Y. Kim, and F. Lombardi: 'CNTFET-Based Design of ternary logic Gates and Arithmetic Circuits', Nanotechnology, IEEE Transactions on., 2011, 10, (2), pp. 217-225

10.     M. H. Moaiyeri, R. FaghihMirzaee, K. Nani and O. Hashemipour, "Efficient CNTFET-based ternary full adder cell for nanoelectronics", Nano-Micro Lett. 2011, 3, (1), pp. 43-50

11.     Stanford University CNFET model Website: Stanford University, Stanford, CA.Available: http://nano.stanford.edu/model.php?id=23, accessed April 2012

12.     S. Iijima: 'Helical microtubules of graphitic carbon', Nature., 1991, 354, pp. 56–58

13.     Leonardo C. Castro, D. L. John, D. L. Pulfrey , Mahdi Purfath , Andreas Gehring ,Hans Kosina , Method of Predicting FT for Carbon Nanotube FETs,IEEE TRANSACTION on NANOTECHNOLOGY , VOL , 4 NO ,62005.

14.     S. J. Tans, A. R. M. Verschueren, and C. Dekker: 'Room-Temperature Transistor based on a Single Carbon Nanotube', Nature., 1998, 393,  pp. 49-52

15.     V. Derycke, R. Martle, J. Appenzeller, and P. Avouris: 'Carbon Nanotubes Inter- and Intermolecular Logic Gates', Nano Letters., 2001, 1, (9), pp. 453-456

16.     S. Frank, P. Poncharal, Z. L.Wang, andW. A. de Heer: 'Carbon nanotube quantum resistors', Science., 1998, 280, pp. 1744–1746

17.     T. Rueckes, K. Kim, E. Joselevich, G. Y. Tseng, C. Cheung, and C. M. Lieber: 'Carbon nanotube based nonvolatile random access memory for molecular computer', Science., 2000, 289, pp. 94–97

18.     P. Avouris, J. Appenzeller, R. Martel, and S. J. Wind: 'Carbon Nanotube Electronics', Proceedings of the IEEE, 2003, 91, (11), pp. 1772-1784

19.     P. L. McEuen, M. S. Fuhrer, and H. Park : 'Single Walled Carbon Nanotube Electronics', Nanotechnology, IEEE Transactions on., 2002, 1, (1), pp. 78-85

20.     M. Kameyama, S. Kawahito, and T. Higuchi: 'A multiplier chip with multiple-valued bidirectional current-mode logic circuits', Computer., 1988, 21, (4), pp. 43–56.

21.     P. Keshavarzian, and K. Navi: 'Universal ternary logic circuit design through carbon nanotube technology', International Journal of Nanotechnology., 2009, 6, (10), pp. 942-953.

22.     J. Deng and H.-S. P.Wong: 'A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part I: Model of the intrinsic channel region', Electron Devices, IEEE Transactions on., 2007, 54, (12), pp. 3186–3194

23.     J. Deng and H.-S. P.Wong: 'A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part II: Full device model and circuit performance benchmarking', Electron Devices, IEEE Transactions on., 2007, 54 (12), pp. 3195–3205.


291-295

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

57.

Authors:

Peiman Keshavarzian, Mahla Mohammad Mirzaee

Paper Title:

A Novel Efficient CNTFET Gödel Circuit Design

Abstract:    Carbon nanotube field effect transistors (CNFETs) are being extensively studied as possible successors to Silicon MOSFETs. Implementable CNTFET circuits have operational characteristics to approach the advantage of using MVL in voltage mode. In this paper we used CNTFETs to implement the improved Gödel basic operators. This paper presents arithmetic operations, implication and multiplication in the ternary Godel field through carbon nanotube field effect transistors (CNFETs). Consequently, in the novel Gödel circuit design, the simulation results demonstrate an improvement in the circuit parameters such as delay, power and power delay product.

Keywords:
   CNTFET, MVL, TVL, Gödel.


References:

1.        Z. Yao, h. W. Postma, L. Balents, and C. Dekker.Carbon nanotube intramolecular junctions. Nature.1999; 402:273-276.
2.        W. Z. Li, S. S. Xie, L. X. Qian, B. H. Change, B. S. Zou, W. Y. Zhou, R.A. Zhao, and G. Wang.Large scale synthesis of aligned carbon nanotubes.Science.1996; 274(5293): 1701-1703.

3.        M. Mukaidono .Regular ternary logic functions - ternary logic functions suitable for treating ambiguity. IEEE Trans. Computers .1986 ;C-35(2):179 - 183.

4.        X.Wu,F .p .Prosser, Cmos ternary logic circuits. IEEE Pro. G. Electronic Circuits and Systems.1990; 137:21-22.

5.        P. Keshavarzian,K. Navi. Efficient carbon nanotube galois field circuit design .IEICE Electronic Express.2009;6 (9): 546-552.

6.        P . Keshavarzian and K. Navi. An improved CNTFET galois circuit design as a basic MVL field. IEICE Electronic Express.2009; 6(9): 546-552.

7.        P.Keshavarzian and M.M.Mirzaee.A novel efficient CNTFET galois design as a basic ternary valued logic field.Nanotechnology ,sience and applications.2012,vol 5,p 1-11.

8.        S. LIN, Y.-B. Kim,F. Lombardi.novel CNTFET-based ternary logic gate design.IEEEInt.MidwestSymp. Circuits Syst.2009:435-438.

9.        P. Keshavarzian, K. Navi, “Universal Ternary Circuit Design Through Carbon Nanotube Technology” Int. J. Nanotechnol., Vol. 6, Nos. 10/11, p. 942, 2009.

10.     P. Keshavarzian, K. Navi, “efficient carbon nanotube lukasiewicz circuit design,in proceeding of  3’rd international conference on nanosrsucture, kish Island,p.1022, 2010.

11.     S. Iijima.Helical microtubules of graphic carbon.Nature.1991;345: 56-58.

12.     S. LIN, Y.-B. Kim,F. Lombardi.novel CNTFET-based ternary logic gate design.IEEEInt.MidwestSymp. Circuits Syst.2009:435-438.

13.     S. J. Tans, A. R. M. Verschueren,C.Dekker.Room-temperature transistor based on a single carbon nanotube,Nture.1998;393:49-52.   

14.     P. L. McEuen, M. S. Fuhrer,H. Park.Single-walled carbon nanotube electronics.IEEE Tran. On Nanotechnology,2002;1(1):78-85.

15.     P. Avouris, J. Appenzeller, R. Martel,andS. J. Wind.Carbonnanotube electronics.Proceeding of IEEE.2004;91(11):1772-1784.

16.     J. S. Hwang etal.Electronic transport properties of a single-wall carbon nanotube field effect transistor with deoxyribonucleic acid conjugation. Physica E: Low
dimensional Systems and Nanostructures. 2008;40(5): 1115-1117.

17.     M. Pourfath, et al. Numerical Analysis of Coaxial Double Gate Schottky Barrier Carbon Nanotube Field Effect Transistors.Journal of Computational Electronics;2005,4: 75–78.

18.     J. Guo, A. Javey, H. Dai, S. Datta,M. Lundstrom.Predicted Performance advantages of carbon nanotube transistors with doped nanotubes source/drain. Phys. Rev. B, Condens.Matter;2003,cond-mat/0 309 039.

19.     J. Deng, H.S. P. Wong. A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part I: Model of the Intrinsic Channel Region. IEEE Trans. Electron Devices; 2007.54:3186-3194.

20.     J. Deng, H.S. P. Wong. A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part II: Full DeviceModel and Circuit Performance Benchmarking.IEEE Trans. Electron Devices;2007. 54: 3195-3205.

296-300

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

58.

Authors:

Harshal J. Jain, M. S. Bewoor, S. H

Paper Title:

Context Sensitive Text Summarization Using K Means Clustering Algorithm

Abstract:   The field of Information retrieval plays an important role in searching on the Internet. Most of the information retrieval systems are limited to the query processing based on keywords. In the information retrieval system matching of words with huge data is core task. Retrieval of the relevant natural language text document is of more challenging. In this paper we introduce the concept of OpenNLP tool for natural language processing of text for word matching. And in order to extract meaningful and query dependent information from large set of offline documents, data mining document clustering algorithm are adopted. Furthermore performance of the summary using OpenNLP tool and clustering techniques will be analysed and the optimal approach will be suggested.

Keywords:
   K means algorithm, Document graph, Context sensitive text summarization.


References:

1.        Ramakrishna Varadarajan, Vangelis Hristidis,”A System for Query-Specific Document Summarization”
2.        Ravindranath Chowdary P Sreenivasa Kumar “An Incremental Summary Generation System”

3.        Regina Barzilay and Michael Elhadad,”Using Lexical Chains for Text Summarization”

4.        Mohamed Abdel Fattah, and Fuji Ren,”utomatic Text Summarization”

5.        Jackie CK Cheung,”Comparing Abstractive and Extractive Summarization of Evaluative Text: Controversialist and Content Selection”

6.        Jie Tang, Limin Yao, and Dewei Chen,”Multi-topic based Query-oriented Summarization”

7.        R.M.Aliguliyev,”Automatic Document Summarization by Sentence Extraction”

8.        Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Proceedings of the ACL-04 Workshop: Text Summarization Branches Out, pages 74–81, Barcelona, Spain.

9.        Luhn H. P. 1958, the automatic creation of literature abstracts, IBM Journal, pages 159-165

10.     http://opennlp.apache.org/

11.     L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons,


301-304

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

59.

Authors:

Hossein Etemadi, Morvarid F. Dabiri, Peiman Keshavarzian, Tahere Panahi

Paper Title:

Design of CNTFET-Based Invertor Inspired BiCMOS Technology

Abstract:   In this paper we present a new combination of Carbon NanoTube Field Effect Transistors (CNTFETs) and bipolar transistors which named Bi CNTFET and used to design a fast and low power inverter. New inverter proposes and compare to existing Bipolar-CMOS (BiCMOS) design. Propose Bi CNTFET inverter has advantages such as large load drive capabilities, low static power dissipation, fast switching and high input impedance. Extensive simulation using HSPICE to investigate the power consumption and delay of propose inverter. Simulation result shows that the propose inverter using carbon nanotube has better performance in terms of delay and power consumption, in compared to BiCMOS counterpart. Furthermore the new design reduces the chip area because of using carbon nanotubes.

Keywords:
   CNTFET, Nanoelectronic, Bi-CNTFET.


References:

1.       Dong-Shong Liang; Kwang-Jow Gan; Jenq-Jong Lu; Cheng-Chi Tai; Cher-Shiung Tsai; Geng-Huang Lan; Yaw-Hwang Chen, “Multiple-Valued Memory Design by Standard BiCMOS Technique,” Computer Science and Information Engineering, Volume 7 , Issue 3, pp. 596 – 599, April 2009.
2.       Xiaohui Hu;   Jizhong Shen; City Coll., Sch. of Inf. & Electr. Eng., Zhejiang Univ., Hangzhou, “The structure of dynamic BiCMOS circuit and its switch-level design,” IEEE International conference, Issue 11, pp 319 – 322,Dec 2008.     

3.       J. Appenzeller, “Carbon Nanotubes for High-Performance Electronics—Progress and Prospect,” Proc. IEEE, Volume 96, Issue 2, pp. 201 - 211, Feb. 2008.

4.       A. Raychowdhury,; K. Roy, “Carbon-nanotube-based voltage-mode multiple-valued logic design,” IEEE Trans. Nanotechnol., Volume 4, Issue 2, pp. 168 – 179, March 2005.

5.       Y. Li, W. Kim, Y, Zhang, M, Rolandi, D. Wang, “Growth of Single-Walled Carbon Nanotubes from Discrete Catalytic Nanoparticles of Various Sizes,” J. Phys.
Chem., Vol. 105, pp. 11 424, 2001.

6.       J. Appenzeller, et al. Appl. Phys. Lett. 78, 3313 (2001); C.T. White and T. N. Todorov, Nature (London) 393, 240 (1998).

7.       M. S. Fuhrer, M. Forero, A. Zettl, and P. L. McEuen , AIP Conference Proceedings, Electronic Properties of Novel Materials – Molecular Nanostructures, (Editors: H. Kuzmany, J. Fink, M. Mehring and S. Roth) 2001, p. 401.

8.       J. Appenzeller et al. submitted.

9.       Y. Bok Kim, Y. B. Kim and F. Lombardi, Proc.” Design and analysis of a high-performance CNTFET-based Full Adder” IEEE International Midwest Symposium on Circuits and Systems 1130 (2009).

10.     J. Deng and H. SP Wong,” A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part I: Model of the Intrinsic Channel Region” IEEE T.Electron. Volume :  54,  Issue:12, pp  3186 – 3194, Nov. 2007.

11.     J. Deng and H. SP Wong,” A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part II: Full Device Model and Circuit Performance Benchmarking,” IEEE T. Electron.volome:54, Issue: 12, pp 3195 – 3205,  Nov. 2007.

12.     (2008). Stanford University CNTFET model Website. Stanford University, Stanford, CA [Online].Available: http://nano.stanford.edu/model.php? id=23

13.     Keivan Navi, Rabe'e Sharifi Rad, Mohammad Hossein Moaiyeri and Amir Momeni, “A low-voltage and energy-efficient full adder cell based on carbon nanotube technology” Vol. 2, No. 2 114-120 (2010).


305-308

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

60.

Authors:

S.Sujitha, C.Venkatesh

Paper Title:

Design and Analysis of Standalone Solar Assisted Switched Reluctance Motor Drives

Abstract:    Switched Reluctance Motor (SRM) is a simple, low cost, robust structure, reliability, controllability and high efficiency, So that it is used in variable speed and high speed applications. Renewable energy sources are a great improvement in many applications. In this paper, a switched reluctance motor with PV modeling is introduced. The implemented design is based on the optimization of solar PV modules arranged in array, integrated with rechargeable battery with existing converter models to drive the switched reluctance motor. The results of the investigations compare with SRM driven by DC source offers superior performance in terms of simulation analysis.

Keywords:
   Battery, Charger, Converter, PV Panel, Switched Reluctance Motor.


References:

1.       Tsai HL. Insolation-oriented model of photovoltaic module using MATLAB/Simulink. Solar Energy 2010; 84:1318 - 26.
2.       Tsai HL, Tu CS, Su YJ. Development of generalized photovoltaic model using MATLAB/Simulink. In: Proceedings of the world congress on engineering and computer science, 2008, San Francisco, 2008. p. 1 - 6.

3.       C.S. Chin , A. Babu, W. McBride, Design, modeling and testing of a standalone single axis active solar tracker using MATLAB/Simulink. In: Renewable Energy 2011; 36: 3075 – 90

4.       Villalva MG, Gazoli JR, Filho ER. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics 2009; 5:1198 - 208.

5.       C.S.Solanki, Solar Photovoltaics: Fundamentals, Technologies and Applications. New Delhi, PHI learning Pvt. Ltd., 2011.

6.       Lopes LAC, Lienhardt AM. A simplified nonlinear power source for simulating PV panels. In: IEEE 34th annual conference on power electronics specialist; 2003. p. 1729 - 34.

7.       Kroposki B, DeBlasio R. Technologies for the new millennium: photovoltaics as a distributed resource. In: IEEE power engineering society summer meeting; 2000. p. 1798 - 801.

8.       Ji Keyan, Zhang Zhuo. Study on direct torque control system of Switched Reluctance motor, In: ICCSE 2011; 0904 – 08.

9.       Z.Zhang, N.C.Cheung. Analysis and design of cost effective converter for SRM drives using Component sharing. In: 4th International conference on power electronics system and Applications; 2011. p. 099 - 104.

10.     Mehrdad Ehsani, Ramani, James. H. Galloway. Dual Decay Converter for SRM Drives in Low voltage Applications. In: IEEE Transaction on Power Electronics, April 1993. P. 224 – 230.

309-312

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

61.

Authors:

Neelapala Anil Kumar, Mehar Niranjan Pakki

Paper Title:

Analyzing The Severity of The Diabetic Retinopathy and Its Corresponding Treatment

Abstract:    Diabetic-related eye disease is a major cause of blindness in the world. It is a complication of diabetes which can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. Regular screening is essential to detect the early stages of diabetic retinopathy for timely treatment and to avoid further deterioration of vision. This project aims to detect the presence of abnormalities in the retina such as the structure of blood vessels, micro aneurysms and exudates using image processing techniques by automating the detection of Diabetic retinopathy (DR). This Process is achieved by the fundus images using morphological processing techniques to extract features such as blood vessels, micro aneurysms and exudates and then we calculate the area of each extracted feature. Depending on the area of each feature we classify the severity of the disease. Then finally by knowing the severity of the disease corresponding treatment measures can be analyzed. It will surely help to reduce the risk and increase efficiency for ophthalmologists.

Keywords:
   Blood Vessels, De-noising, Diabetic Retinopathy, Disease Severity, Enhancement, Exudates, Fundus Camera, Micro-aneurysms, Morphological Operations, Segmentation, Treatment.


References:

1.        U R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura. Computer-based detection of diabetes retinopathy stages using digital fundus images.
2.        Singapore Association of the Visually Handicapped. http://www.savh.org.sg/info_cec_diseases.php.

3.        What is Diabetic Retinopathy? http://www.news-medical.net/health/What-is-Diabetic-Retinopathy.aspx.

4.        Diabetic Retinopathy. http://www.hoptechno.com/book45.htm.

5.        James L. Kinyoun, Donald C. Martin, Wilfred Y. Fujimoto, Donna L. Leonetti. Opthalmoscopy Versus Fundus Photographs for Detecting and Grading Diabetic Retinopathy.

6.        Salvatelli A., Bizai G., Barbosa G.Drozdowicz and Delrieux (2007), ‘A comparative analysis of pre-processing techniques in colour retinal images’, Journal of Physics: Conference series 90.

7.        Andrea Anzalone, Federico Bizzari, Mauro Parodi, Marco Storace (2008), ‘A modular supervised algorithm for vessel segmentation in red-free retinal images’, Computers in Biology and Medicine, Vol. 38, pp. 913-922.

8.        Daniel Welfer, Jacob Schacanski, Cleyson M.K., Melissa M.D.P., Laura W.B.L., Diane Ruschel Marinho (2010), ‘Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach’, Journal on Computers in Biology and Medicine”, Vol. 40, pp. 124-137.

9.        Cemal Kose, Ugur Sevik, Okyay Gencalioglu (2008), ‘Automatic segmentation of age-related macular degeneration in retinal fundus images’, Computers in
Biology and Medicine,Vol.38, pp. 611-619.

10.     Dietrich Paulus and Serge Chastel and Tobias Feldmann (2005), ‘Vessel segmentation in retinal images’, Proceedings of SPIE, Vol. 5746, No.696.

11.     Ana Maria Mendonca and Aurelio Campilho (2006), ‘Segmentation of Retinal Blood Vessels by Combining the Detection of centerlines and Morphological Reconstruction’, IEEE Transaction on Medical Imaging, Vol. 25, No. 9, pp. 1200-1213.

12.     Jagadish Nayak, Subbanna Bhat (2008), ‘Automated identification of diabetic retinopathy stages using digital fundus images’, Journal of medical systems, Vol.32, pp. 107-115.

13.     Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, Thomas H.Williamson (2008), ‘Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods’, Computerized Medical Imaging and Graphics, Vol. 32, pp.720-727.

14.     ANOVA test for severity of disease. Available: http://afni.nimh.nih.gov/pub/dist/HOWTO/howto/ht05_group/html/background_ANOVA.shtml


313-315

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

62.

Authors:

K. Srinivas, A. A. Chari

Paper Title:

ECDC: Energy Efficient Cross Layered Congestion Detection and Control Routing Protocol

Abstract:   Here in this paper A MAC layer level congestion detection mechanism has been proposed. The proposed model aims to deliver an energy efficient mechanism to quantify the degree of congestion at victim node with maximal accuracy. This congestion detection mechanism is integrated with a Two-Step Cross Layer Congestion Control Routing Protocol. The proposed model involves controlling of congestion in two steps with effective energy efficient congestion detection and optimal utilization of resources. Packet loss in network routing is primarily due to link failure and congestion. Most of the existing congestion control solutions do not possess the ability to distinguish between packet loss due to link failure and packet loss due to congestion. As a result these solutions aim towards action against packet drop due to link failure which is an unnecessary effort and may result in loss of resources. The other limit in most of the existing solutions is the utilization of energy and resources to detect congestion state, degree of congestion and alert the source node about congestion in routing path.   Here in this paper we propose cross layered model of congestion detection an control mechanism that includes energy efficient congestion detection, Zone level Congestion Evaluation Algorithm [ZCEA] and Zone level Egress Regularization Algorithm [ZERA], which is a hierarchical cross layer based congestion detection and control model in short we refer this protocol as ECDC(Energy Efficient Congestion Detection and Control). This paper is supported by the experimental and simulation results show that better resource utilization, energy efficiency in congestion detection and congestion control is possible by the proposed protocol.

Keywords:
   Ad-hoc networks, cross-layer design, optimization, random access, wireless networks.


References:

1.        Michael Gerharz, Christian de Waal, and Matthias Frank, “A Practical View on Quality-of-Service Support in Wireless Ad Hoc Networks”, BMBF
2.        Xiaoqin Chen, Haley M. Jones, A .D .S. Jayalath, “Congestion-Aware Routing Protocol for Mobile Ad Hoc Networks”, IEEE, 2007

3.        Hongqiang Zhai, Xiang Chen, and Yuguang Fang, “Improving Transport Layer Performance in Multihop Ad Hoc Networks by Exploiting MAC Layer Information”, IEEE, 2007

4.        Yung Yi, and Sanjay Shakkottai, “Hop-by-Hop Congestion Control Over a Wireless Multi-Hop Network”, IEEE, 2007

5.        Tom Goff, Nael B. Abu-Ghazaleh, Dhananjay S. Phatak and Ridvan Kahvecioglu, “Preemptive Routing in Ad Hoc Networks”, ACM, 2001

6.        Xuyang Wang and Dmitri Perkins, “Cross-layer Hop-byhop Congestion Control in Mobile Ad Hoc Networks”, IEEE, 2008.

7.        Dzmitry Kliazovich, Fabrizio Granelli, “Cross-layer Congestion Control in Ad hoc Wireless Networks,” Elsevier, 2005

8.        Duc A. Tran and Harish Raghavendra, “Congestion Adaptive Routing in Mobile Ad Hoc Networks”, 2006

9.        Nishant Gupta, Samir R. Das. Energy-Aware On-Demand Routing for Mobile Ad Hoc Networks, OPNET Technologies, Inc. 7255 Woodmont Avenue Bethesda, MD 20814 U.S.A., Computer Science Department SUNY at Stony Brook Stony Brook, NY 11794-4400 U.S.A.

10.     Laura, Energy Consumption Model for performance analysis of routing protocols in MANET,Journal of mobile networks and application 2000.

11.     LIXin MIAO Jian –song, A new traffic allocation algorithm in AD hoc networks, “The Journal of ChinaUniversity of Post and Telecommunication”, Volume 13. Issue3. September 2006.

12.     Chun-Yuan Chiu; Wu, E.H.-K.; Gen-Huey Chen; "A Reliable and Efficient MAC Layer Broadcast Protocol for Mobile Ad Hoc Networks," Vehicular Technology, IEEE Transactions on , vol.56, no.4, pp.2296-2305, July 2007

13.     Giovanidis, A.  Stanczak, S., Fraunhofer Inst. for Telecommun., Heinrich Hertz Inst., Berlin, Germany This paper appears in: 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 2009. WiOPT 2009

14.     Outay, F.;   Vèque, V.;   Bouallègue, R.; Inst. of Fundamental Electron., Univ. Paris-Sud 11, Orsay, France This paper appears in: 2010 IEEE 29th International Performance Computing and Communications Conference (IPCCC)

15.     Yingqun Yu; Giannakis, G.B.; , "Cross-layer congestion and contention control for wireless ad hoc networks," Wireless Communications, IEEE Transactions on , vol.7, no.1, pp.37-42, Jan. 2008

16.     http://www-lih.univ-lehavre.fr/~hogie/madhoc/

17.     Prof.K.Srinivas and Prof.A.A.Chari. Article: Cross Layer Congestion Control in MANETs and Current State of Art. International Journal of Computer Applications 29(6):28-35, September 2011. Published by Foundation of Computer Science, New York, USA

18.     Prof. K. Srinivas, Dr. A. A. Chari;"ZCEA&ZERA: Two-Step Cross Layer Congestion Control Routing Protocol (pp. 36-44)", Vol. 9 No. 12 December 2011 International Journal of Computer Science and Information Security.


316-322

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

63.

Authors:

Arshdeep Kaur, Amrit Kaur

Paper Title:

Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System

Abstract:   Fuzzy inference systems are developed for air conditioning system using Mamdani-type and Sugeno-type fuzzy models. The results of the two fuzzy inference systems (FIS) are compared. This paper outlines the basic difference between the Mamdani-type FIS and Sugeno-type FIS. It also shows which one is a better choice of the two FIS for air conditioning system.

Keywords:
   Air Conditioning, Fuzzy Inference System (FIS), Fuzzy Logic, Mamdani.


References:

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

3.       G. S. Sandhu and K. S. Rattan, “Design of a neuro-fuzzy controller”, IEEE International Conference on Systems, Man, Cybern., 1997.

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

5.       M. Du, T. Fan, W. Su, H. Li, “Design of a new practical expert fuzzy controller in central air conditioning control system”, IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008

6.       S. Li, J. Liu, J. Liu, “Design on the central air-conditioning controller based on LabVIEW”, ICCASM IEEE proc., 2010.

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

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


323-325

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

64.

Authors:

Anand Bora, Abrar Chapalgaonkar,Vanita More

Paper Title:

Ultrasonic 3 Dimensional Mouse

Abstract:    With the advent of 3D technology in our daily lives, need of the hour is to develop 3D interactive devices. In this paper, review an air mouse that interacts with PC in 3 Dimensions. The device will not only need any contact surface, but also provide the user with three degrees of freedom. Setup of the project consists of a non-echo ultrasonic system with three receivers at different corners of the display screen and one hand held transmitter, which acts as the mouse. Upon measuring the three distances, position of transmitter in three dimensions can be determined. Above calculated distances will be sent to the PC serially. A 3D image is used to demonstrate the functionality in three dimensions, and changes in transmitter coordinates will result in corresponding changes in 3D image.

Keywords:
  3D, mouse, ultrasonic, spatial, interrupts.


References:

1. Karl Gluck and David DeTomaso, “UltraMouse 3D”. Available: http://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2009/kwg8_dmd54/kwg8_dmd54/index.html
2.       Ronald E.Milner, United States Patent – “Sonic Positioning Device ”, Patent Number: 4862          152

3.       Dave Johnson, “40KHz Ultrasound Receiver”. Available: http://www.discovercircuits.com/DJ-Circuits/40kultrasoundrvr2.htm.


326-328

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

65.

Authors:

Ashita S. Bhagade, Parag. V. Puranik

Paper Title:

Artificial Bee Colony (ABC) Algorithm for Vehicle Routing Optimization Problem

Abstract:   This paper involves Bee Colony Optimization for travelling salesman problem. The ABC optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This biological phenomenon when applied to the process of path planning problems for the vehicles, it is found to be excelling in solution quality as well as in computation time. Simulations have been used to evaluate the fitness of paths found by ABC Optimization. The effectiveness of the paths has been evaluated with the parameters such as tour length, bee travel time by Artificial Bee Colony Algorithm. In this article, the travelling salesman problem for VRP is optimized by using nearest neighbor method; evaluation results are presented which are then compared by the artificial bee colony algorithm. The pursued approach gives the best results for finding the shortest path in a shortest time for moving towards the goal. Thus the optimal distance with the tour length is obtained in a more effective way.

Keywords:
   Artificial Bee Colony algorithm, Bee travel time,  Nearest neighbor method, Tour length, Travelling Salesman Problem


References:

1.       Karaboga, D. Artificial Bee Colony Algorithm. Scholarpedia 2010, 5,6915.Availableonline:http://www.scholarpedia.org/article/Artificial_bee_colony_algorithm/ (accessed on 27 May 2011).
2.       Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. Milos Subotic Faculty of Computer Science University Megatrend Belgrade Bulevar umetnosti.

3.       J. F. Cordeau, M. Gendreau, G. Laporte, J. Y. Rotvin, F. Semet. A  guide to vehicle routing heuristics. Journal of the Operational Research Society, 2002, 53(5): 512-522.

4.       P.-W. TSai, J.-S. Pan, B.-Y. Liao, and S.-C. Chu, “Enhanced artificial bee colony optimization,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 12, 2009.

5.       Chaotic Bee Swarm Optimization Algorithm for Path Planning of Mobile Robots Jiann-Horng Lin and Li-Ren Huang Department of Information Management I-Shou University, Taiwan 2009

6.       Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem.Adil Baykasolu1, Lale Özbakır2 and Pınar Tapkan2 1University of Gaziantep, Department of Industrial Engineering 2Erciyes University, Department of Industrial Engineering Turkey,2007.

7.       Bee colony optimization: the applications survey Duˇsan teodorovi´c University of Belgrade, faculty of transport and traffic engineering Tatjana davidovi´c Mathematical institute, Serbian academy of sciences and arts And Milica ˇselmi´c University of Belgrade, faculty of transport and traffic engineering.

8.       Nearest neighbor method by Sofiya Cherni, ¤Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD 57701-3995).

9.       An Effective Refinement Artificial Bee Colony Optimization Algorithm Based On Chaotic Search and Application for PID Control Tuning Gaowei YAN †, Chuangqin LI College of Information Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

10.     Artificial bee colony (abc), harmony search and bees algorithms on Numerical optimization D. Karaboga, b. Akay Erciyes University, the dept. Of computer engineering, 38039, melikgazi, kayseri, turkiye

11.     Elitist artificial bee colony For constrained real-parameter optimization Efr´en mezura-montes member, ieee and ramiro ernesto velez-koeppel

12.     The bee colony-inspired algorithm (bcia) – a two-stage Approach for solving the vehicle routing problem with Time windows Sascha hackle Faculty of economics and business Administration Chemnitz university of technology Chemnitz, Germany Shae@hrz.tu-chemnitz.de Patrick dippold Faculty of economics and business Administration Chemnitz university of technology Chemnitz, Germany  padi@hrz.tu-chemnitz.de

13.     Optimization of multiple vehicle routing problems using approximation algorithms.R. Nallusami1, K.Duraiswamy2, R. Dhanalaxmi3and P. Parthiban4. 1,2Department of computer science and engineering, K S Rangasamy college of technology, Tiruchengode-637215, India.Email:nallsam@rediffmail.com 3D-Link India Ltd, Bangalore, India. 4Department of production engineering, National institute of technology, Tiruchirapalli, India

14.     Bee colony optimization – a cooperative learning Approach to complex transportation problems Dušan teodorović1,2, mauro dell’ orco3

15.     An improved artificial bee colony algorithm for the capacitated vehicle routing problem With time-dependent travel times Ping ji1 yongzhong wu1,2 1 department of industrial and systems engineering, the hongkong polytechnic University, hongkong 2 school of business administration, south china university of technology, Guangzhou, p.r., china.

16.     An Efficient Bee Colony Optimization Algorithm for Traveling Salesman Problem using Frequency-based Pruning Li-Pei Wong† Malcolm Yoke Hean Low‡ School of Computer Engineering, Nanyang Technological University Nanyang Avenue, Singapore 639798. Email: †wonglipei@pmail.ntu.edu.sg, ‡yhlow@ntu.edu.sg Chin Soon Chong Singapore Institute of Manufacturing Technology 71 Nanyang Drive, Singapore 638075. Email:  cschong@SIMTech.a-star.edu.sg

17.     P. Curkovic, B. Jerbic, Honey-bees optimization algorithm applied to path planning problem, International Journal of Simulation Modelling, pp. 137-188, 2007.

18.     D. Karaboga and B. Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, In Press, 2009.

19.     Bee Colony Optimization with Local Search for Traveling   Salesman Problem i Li-Pei Wong, ii Malcolm Yoke Hean Low, iii Chin Soon Chong i,ii School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, SINGAPORE 639798. iii Singapore Institute of Manufacturing Technology, 71 NanyangDrive,SINGAPORE638075.wonglipei@pmail.ntu.edu.sg, ii yhlow@ntu.edu.sg, iii cschong@SIMTech.a-star.edu.sg

20.     A bee colony optimization algorithm with the fragmentation statetransition rule for traveling salesman problem L.P. Wonga,        M.Y.H. Lowa, C.S. Chongb a School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798.b Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075. Email: cschong@simtech.a-star.edu.sg


329-333

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

66.

Authors:

Ashish kumar Dewangan, Majid Ahmad Siddhiqui

Paper Title:

Human Identification and Verification Using Iris Recognition by Calculating Hamming Distance

Abstract:   A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favorable conditions, and there have been no independent trials of the technology.  The work presented in this paper involved developing an ‘open-source’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system one databases of digitized grayscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.

Keywords:
   Automatic segmentation, Biome