An improvement on Routh approximation technique

A modification of the Routh approximant is introduced which results in a considerable improvement over the conventional Routh approximation technique. Since some amount of arbitrariness is available in the time moment expressions of the conventional Routh approximant, it can be utilized to minimize the step response error while preserving the stability and moment matching property of the conventional Routh approximant. A numerical example illustrates the procedure.     

New type of variable-frequency RC oscillator

A technique of realising a variable-frequency RC oscillator is proposed. The frequency of oscillation is found to be the difference of two RC functions, which permits a very-low-frequency realisation. The frequency variation is obtained by controlling a single resistance in the network.     

A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique     

Genetic operators for combinatorial optimization in TSP and microarray gene ordering

This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes. Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta, Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India. His research interests include bioinformatics, evolutionary computation, neural networks, and data mining. Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology (IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998. She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering, University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty. Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy (INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics, Parallel & Distributed Systems and VLSI. Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, and Bioinformatics. He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement . Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98, 2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.     

On solute dispersion from an elevated line source in an open-channel flow

The streamwise dispersion of contaminant molecules due to a turbulent shear flow over a gravel-bed surface is examined when the solute is released from an elevated line source. A finite-difference, implicit method is used to solve the unsteady turbulent convection-diffusion equation, employing a combined scheme of central and four-point upwind differences for the steady-state condition and the Alternating Direction Implicit (ADI) method for the unsteady equation. It is shown how the mixing of contaminant is effected by the mean velocity due to the `log-wake law' and the corresponding eddy-diffusivity, when the solute is released in terms of a -function. The results for the steady-state condition are compared with existing experimental data and some other numerical results. The results obtained by the present method are in much better agreement with the experimental data than those obtained by the previous solution scheme, when the `log-wake-law' and the corresponding eddy-diffusivity are used. Far from the source, the concentration increases with time (the rate of increase is rapid near the source), asymptotically approaching a steady state after a certain time; concentrations are about two thirds of those near the source. The behaviour of iso-concentration lines in the vertical plane is also studied.     

New Generation Metal Matrix Composites

Metal matrix composite (MMC) materials have attracted considerable attention due to their ability to offer unusual combinations of stiffness, strength to weight ratio, high temperature performance, and hardness. Extensive research work in this area has led to the development of novel in situ processing techniques that are now being used to new generation metal matrix composites that can be used in wide ranging applications in diverse fields. The development and processing of these new generation metal matrix composites are highlighted in this report.     

Cryptanalysis and Enhancement of an Anonymous Self-Certified Key Exchange Protocol

Authentication protocols with anonymity have gained much popularity recently which allows users to access any public network without compromising their identity. Several key exchange protocols have been proposed in the literature using either public key infrastructure or identity-based cryptosystem. However, the former suffers from heavy computation cost and latter fails to prevent key escrow problem. Recently, Islam et al. have proposed a self-certified authenticated key agreement protocol based on ECC which removes the above limitations. However, through careful analysis, we found that their scheme lack anonymity and vulnerable to trace the attack, clogging attack, and fails to prevent the replay attack. To overcome these weaknesses, we propose an anonymous self-certified authenticated key exchange protocol by including the required security features. The scheme is formally proved using Automated Validation of Internet Security protocols and Applications software. Also, the formal authentication proofs using Burrows–Abadi–Needham logic ensures successful authentication. Furthermore, the performance analysis demonstrates that the proposed scheme accomplishes less computational cost and is applicable to a client–server architecture.     

Microstructural Investigation,Raman and Magnetic Studies on Chemically Synthesized Nanocrystalline Ni-Doped Gadolinium Oxide (Gd<Subscript>1.90</Subscript>Ni<Subscript>0.10</Subscript>O<Subscript>3−<Emphasis Type="Italic">δ</Emphasis></Subscript>)

Nanocrystalline Ni-doped gadolinium oxide (Gd_1.90Ni_0.10O_3?δ, GNO) is synthesized by co-precipitation method. The as-prepared sample is annealed in vacuum at 700°C for 6 h. Analyses of the x-ray diffractogram by Rietveld refinement method, transmission electron microscopy and Raman spectroscopy of GNO recorded at room temperature confirmed the pure crystallographic phase and complete substitution of Ni-ions in Gd₂O₃ lattice. Magnetization (M) as a function of temperature (T) and magnetic field (H) is measured by a superconducting quantum interference device magnetometer, which suggests the presence of ferromagnetic/antiferromagnetic phases together with a paramagnetic phase. From the M–T curve it can be shown that the ferromagnetic phase dominates over para-/antiferromagnetic phases in the temperature range of 300–100 K, but from 100 K to 50 K, the antiferromagnetic phase dominates over ferro-/paramagnetic phases. Hysteresis loops recorded at different temperatures indicate the presence of weak ferro-/antiferromagnetism, which dominates in the low field region (～ 4000 Oe), above which magnetization increases linearly. The sharp increase of magnetization in M–T curve observed in the temperature range of 50–5 K confirms the presence of dominating ferromagnetic plus paramagnetic phase over antiferromagnetic part. For the first time a combined formula generated from three-dimensional (3D) spin wave model and Johnston formula is proposed to analyze the coexistence of different magnetic phases in different temperature ranges. Interestingly, the combined formula successfully explains the co-existence of different magnetic phases along with their contribution at different temperatures. The onset of ferromagnetism in Gd_1.90Ni_0.10O_3?δ is explained by oxygen vacancy mediated F-centre exchange (FCE) coupling mechanism.