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Deyasi A.,RCC Institute of Information Technology | Bhattacharyya S.,Modern Institute of Engineering and Technology | Das N.R.,University of Calcutta
Superlattices and Microstructures | Year: 2013

In this paper, intersubband transition energy is computed for core-shell (normal and inverted) quantum dots (CSQD) of cubic and spherical geometries by solving time-independent Schrödinger equation using finite-difference technique. Sparse, structured Hamiltonian matrices of order N3 × N3 for cubic and N × N for spherical dots are produced considering N discrete points in spatial direction. The matrices are diagonalized to obtain eigenstates for electrons. Computed results for the lowest three eigenstates and intersubband transitions are shown for different structural parameters taking GaAs/AlxGa1-xAs based CSQD as example. Transition energy decreases with increase in core thickness. When compared, spherical CSQDs show higher transition energy between two subbands than cubic CSQDs of similar size and same material composition. Also, in inverted configuration, transition energy decreases for a cubic dot while increases for a spherical dot as core size is increased. Wide tuning range for intersubband transition by tailoring dot dimensions indicates important applications for optical emitters/detectors. © 2013 Elsevier B.V. All rights reserved.

Dey S.,Camellia Institute of Technology | Bhattacharyya S.,RCC Institute of Information Technology | Maulik U.,Jadavpur University
Swarm and Evolutionary Computation | Year: 2014

In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of gray-level images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter. © 2013 Elsevier B.V. © 2014 Elsevier Inc. © 2013ElsevierB.V.Allrightsreserved.

Dey S.,Camellia Institute of Technology | Bhattacharyya S.,RCC Institute of Information Technology | Maulik U.,Jadavpur University
Applied Soft Computing Journal | Year: 2016

The efficient meta-heuristic techniques, called ant colony optimization, differential evolution and particle swarm optimization, inspired by the fundamental features of quantum systems, are presented in this paper. The proposed techniques are Quantum Inspired Ant Colony Optimization, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization for Multi-level Colour Image Thresholding. These techniques find optimal threshold values at different levels of thresholding for colour images. A minimum cross entropy based thresholding method, called Li's method is employed as an objective (fitness) function for this purpose. The efficiency of the proposed techniques is exhibited computationally and visually on ten real life true colour images. Experiments with the composite DE (CoDE) method, the backtracking search optimization algorithm (BSA), the classical ant colony optimization (ACO), the classical differential evolution (DE) and the classical particle swarm optimization (PSO), have also been conducted subsequently along with the proposed techniques. Experimental results are described in terms of the best threshold value, fitness measure and the computational time (in seconds) for each technique at various levels. Thereafter, the accuracy and stability of the proposed techniques are established by computing the mean and standard deviation of fitness values for each technique. Moreover, the quality of thresholding for each technique is determined by computing the peak signal-to-noise ratio (PSNR) values at different levels. Afterwards, the statistical superiority of the proposed techniques is proved by incorporating Friedman test (statistical test) among different techniques. Finally, convergence curves for different techniques are presented for all test images to show the visual representation of results, which proves that the proposed Quantum Inspired Ant Colony Optimization technique outperforms all the other techniques. © 2015 Elsevier B.V.

Sarkar S.,RCC Institute of Information Technology | Das S.,Indian Statistical Institute | Chaudhuri S.S.,Jadavpur University
Pattern Recognition Letters | Year: 2015

We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite - the Barkley Segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm © 2014 Published by Elsevier B.V.

Bandyopadhyay S.,Indian Statistical Institute | Mallick K.,RCC Institute of Information Technology
IEEE/ACM Transactions on Computational Biology and Bioinformatics | Year: 2014

Gene Ontology (GO) consists of a controlled vocabulary of terms, annotating a gene or gene product, structured in a directed acyclic graph. In the graph, semantic relations connect the terms, that represent the knowledge of functional description and cellular component information of gene products. GO similarity gives us a numerical representation of biological relationship between a gene set, which can be used to infer various biological facts such as protein interaction, structural similarity, gene clustering, etc. Here we introduce a new shortest path based hybrid measure of ontological similarity between two terms which combines both structure of the GO graph and information content of the terms. Here the similarity between two terms t1 and t 2, referred to as GOSim-PBHM(t1,t2), has two components; one obtained from the common ancestors of t1 and t2. The other from their remaining ancestors. The proposed path based hybrid measure does not suffer from the well-known shallow annotation problem. Its superiority with respect to some other popular measures is established for protein protein interaction prediction, correlation with gene expression and functional classification of genes in a biological pathway. Finally, the proposed measure is utilized to compute the average GO similarity score among the genes that are experimentally validated targets of some microRNAs. Results demonstrate that the targets of a given miRNA have a high degree of similarity in the biological process category of GO. © 2004-2012 IEEE.

Bhattacharyya S.,RCC Institute of Information Technology | Pal P.,RCC Institute of Information Technology | Bhowmick S.,RCC Institute of Information Technology
Applied Soft Computing Journal | Year: 2014

Several classical techniques have evolved over the years for the purpose of denoising binary images. But the main disadvantages of these classical techniques lie in that an a priori information regarding the noise characteristics is required during the extraction process. Among the intelligent techniques in vogue, the multilayer self organizing neural network (MLSONN) architecture is suitable for binary image preprocessing tasks. In this article, we propose a quantum version of the MLSONN architecture. Similar to the MLSONN architecture, the proposed quantum multilayer self organizing neural network (QMLSONN) architecture comprises three processing layers viz., input, hidden and output layers. The different layers contains qubit based neurons. Single qubit rotation gates are designated as the network layer interconnection weights. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a quantum backpropagation algorithm. Results of application of the proposed QMLSONN are demonstrated on a synthetic and a real life binary image with varying degrees of Gaussian and uniform noise. A comparative study with the results obtained with the MLSONN architecture and the supervised Hopfield network reveals that the QMLSONN outperforms the MLSONN and the Hopfield network in terms of the computation time. © 2014 Elsevier B.V. All rights reserved.

Pan I.,RCC Institute of Information Technology | Samanta T.,Bengal Engineering and Science University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Digital Microfluidic Biochip has emerged as a revolutionary finding in the field of micro-electromechanical research. Different complex bioassays and pathological analysis are being efficiently performed on this miniaturized chip with negligible amount of sample specimens. Initially biochip was invented on continuous-fluid-flow mechanism but later it has evolved with more efficient concept of digital-fluid-flow. These second generation biochips are capable of serving more complex bioassays. This operational change in biochip technology emerged with the requirement of high end computer aided design needs for physical design automation. The change also paved new avenues of research to assist the proficient design automation. Droplet routing is one of those major aspects where it necessarily requires minimization of both routing completion time and total electrode usage. This task involves optimization of multiple associated parameters. In this paper we have proposed a particle swarm optimization based approach for droplet outing. The process mainly operates in two phases where initially we perform clustering of state space and classification of nets into designated clusters. This helps us to reduce solution space by redefining local sub optimal target in the interleaved space between source and global target of a net. In the next phase we resolve the concurrent routing issues of every sub optimal situation to generate final routing schedule. The method was applied on some standard test benches and hard test sets. Comparative analysis of experimental results shows good improvement on the aspect of unit cell usage, routing completion time and execution time over some well existing methods. Copyright © 2013 SPIE.

Kole A.,RCC Institute of Information Technology
International Journal of Automation and Control | Year: 2014

This paper reviews the historical background, present state, future challenges and opportunities of state-of-the-art power system protection, control and automation systems for thermal power plant. It presents latest high-performance, high-capacity process controller-based total plant automation system including standard control hardware and software to run the power plant reliably and efficiently with low emissions. It also focuses on the latest ABB make S+ Control HPC800 and ALSTHOM make ALSPA P320 power plant flexible automation system, distributed control architecture, high performance PROFIBUS and HART communication protocol and tolerance to data rushes during transients. Today's all subsystems of large thermal power plants can be controlled from central control room through state-of-the-art automation. In the future, it will be possible to modify or extend electrical systems without replacing the entire substation automation system. This paper reviews various aspects of the implementation of the standard in power plants and shows how devices integrate into distributed control system environment. © 2014 Inderscience Enterprises Ltd.

Bagchi S.,RCC Institute of Information Technology
Proceedings - 2014 6th International Conference on Computational Intelligence and Communication Networks, CICN 2014 | Year: 2014

In Cognitive Radio networks, a proper sensing of vacant frequency band or spectrum hole is necessary for an unlicensed user in order to avoid interference with the licensees. Noise distribution plays a significant role in spectrum hole identification process. Noise is often assumed to be AWGN but as it appears from various sources, the distribution often deviates from its AWGN nature. If the deviation is significant, the total detection process becomes unreliable. In this paper, an attempt has been made to detect the spectrum holes considering a continuous distribution as noise distribution instead of any specific parametric distribution. This approach is known as non-parametric approach the entire detection process is judged with two figures of merit, namely, probability of false alarm and probability of detection. Finally, some designing criteria are indicated based on these two parameters. © 2014 IEEE.

This paper focuses on the design of a real-time adaptive Takagi–Sugeno (T–S) fuzzy-based dynamic feedback tracking controller to deal with the metallic sphere position control of a magnetic levitation system (MLS), which is an intricate and highly nonlinear system involving plant uncertainties and external disturbances. The dynamic model of this MLS is first constructed based on the concepts of geometry and motion dynamics. The objective of this proposed control strategies is to design a real-time adaptive controller with the help of Takagi–Sugeno type fuzzy-based output feedback techniques and to directly ensure the asymptotic stability of the closed-loop controlled system by Lyapunov stability theorem without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers. Proposed adaptive tracking controller is developed in such a way such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error is as small as possible. In this paper, the controller consists of adaptive and robustifying components whose role is to nullify the effect of uncertainties and achieve a desired tracking performance. Here, separate adaptive control laws have been proposed to automatically take care of external disturbance and uncertainties by designing a two-port controller. The first part stabilizes the nominal plant; without modeling uncertainties. The second part of the controller is to reject modeling uncertainties. The good transient control performance and robustness to uncertainties of the proposed adaptive control scheme for the MLS is verified by numerical simulations and real-time experimental results. These results demonstrate that, the proposed adaptive controller yields favorable control performance superior to that of PID and and Neuro-fuzzy network controller in terms of overshoot, settling time, mean square error and steady-state error and also it can guarantee the system stability and parameter convergence with a pole placement algorithm. © 2014, Springer-Verlag Berlin Heidelberg.

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