Camellia Institute of Technology

Kolkata, India

Camellia Institute of Technology

Kolkata, India
SEARCH FILTERS
Time filter
Source Type

Mondal A.,Camellia Institute of Technology | Mitra S.,Indian Institute of Science
2016 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2016 | Year: 2017

Message authentication codes (MACs) are used to prevent the dissemination of unauthorized and corrupted message to avoid road accident in vehicular ad hoc network (VANET). VANET is a short-lived network due to high mobility of vehicles and hence can't afford any complex computation of existing MACs. A timestamp defined MAC (TDMAC) is proposed in the present work as a light weight security solution. Detailed security analysis shows that TDMAC also thwarts passive attack as well as active attack. Finally the comparative usability of the proposed algorithm in the said application domain is worked out and that shows the dominance of the scheme over the existing schemes. © 2016 IEEE.


Chatterjee H.K.,Camellia Institute of Technology | Gupta R.,University of Calcutta | Mitra M.,University of Calcutta
Computers in Biology and Medicine | Year: 2011

This paper illustrates a method for time-plane feature extraction from digitized ECG sample using statistical approach. The algorithm detects the position and magnitude of the QRS complex, P and T wave for a single lead ECG dataset. The processing is broadly based on relative comparison of magnitude and slopes of ECG samples. Then the baseline modulation in the dataset is removed. The R-peak detection and baseline modulation is tested MIT-BIH arrhythmia database as well as 12-lead datasets in MIT-PTB database (PTBDB) and available under Physionet. The overall accuracy obtained is more than 99%. © 2011 Elsevier Ltd.


Dey S.,Camellia Institute of Technology | Saha I.,Jadavpur University | Saha I.,Wrocław University | Bhattacharyya S.,RCC Institute of Information Technology | Maulik U.,Jadavpur University
Knowledge-Based Systems | Year: 2014

Image thresholding is well accepted and one of the most imperative practices to accomplish image segmentation. This has been widely studied over the past few decades. However, as the multi-level thresholding computationally takes more time when level increases, hence, in this article, quantum mechanism is used to propose six different quantum inspired meta-heuristic methods for performing multi-level thresholding faster. The proposed methods are Quantum Inspired Genetic Algorithm, Quantum Inspired Particle Swarm Optimization, Quantum Inspired Differential Evolution, Quantum Inspired Ant Colony Optimization, Quantum Inspired Simulated Annealing and Quantum Inspired Tabu Search. As a sequel to the proposed methods, we have also conducted experiments with the two-Stage multithreshold Otsu method, maximum tsallis entropy thresholding, the modified bacterial foraging algorithm, the classical particle swarm optimization and the classical genetic algorithm. The effectiveness of the proposed methods is demonstrated on fifteen images at the different level of thresholds quantitatively and visually. Thereafter, the results of six quantum meta-heuristic methods are considered to create consensus results. Finally, statistical test, called Friedman test, is conducted to judge the superiority of a method among them. Quantum Inspired Particle Swarm Optimization is found to be superior among the proposed six quantum meta-heuristic methods and the other five methods are used for comparison. A Friedman test again conducted between the Quantum Inspired Particle Swarm Optimization and all the other methods to justify the statistical superiority. Finally, the computational complexities of the proposed methods have been elucidated for the sake of finding out the time efficiency of the proposed methods. © 2014 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.


Mondal A.,Camellia Institute of Technology | Mitra S.,Bengal Engineering and Science University
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering | Year: 2012

The VANET should allow only the authentic vehicles to participate in the system for efficient utilization of its available resources. The proposed system architecture contains multiple base stations in the coverage area of a certifying authority. The base station verifies the identification of the vehicle and the certifying authority verifies the authentication of the vehicle using its vehicle identification number. The certifying authority also generates a digital signature for each authentic vehicle and assigns it to the corresponding vehicle through base station. The base station allocates a channel to each authentic vehicle. The channel remains busy as long as the vehicle is within the coverage area of this base station. So the base station is able to track an authentic vehicle by sensing the allocated channel within its coverage area. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.


Pal D.,Camellia Institute of Technology
Advances in Intelligent and Soft Computing | Year: 2012

It is a very common practice to use network simulators for testing different network performance parameters before the real-life deployment of such a network. Apart from ns-2, few other recent network simulators have come into existence today and are gaining in more popularity. In this paper, we survey some of the widespread network simulators that are in use today, and try to evaluate their performance over certain parameters by setting up identical network simulation scenarios. © 2012 Springer-Verlag GmbH.


In radiative transfer, the intensities of radiation from the bounding faces of a scattering atmosphere of finite optical thickness can be expressed in terms of Chandrasekhar's X- and Y-functions. The nonlinear nonhomogeneous coupled integral equations which the X- and Y-functions satisfy in the real plane are meromorphically extended to the complex plane to frame linear nonhomogeneous coupled singular integral equations. These singular integral equations are then transformed into nonhomogeneous Riemann-Hilbert problems using Plemelj's formulae. Solutions of those Riemann-Hilbert problems are obtained using the theory of linear singular integral equations. New forms of linear nonhomogeneous decoupled expressions are derived for X- and Y-functions in the complex plane and real plane. Solutions of these two expressions are obtained in terms of one known N-function and two new unknown functions N1- and N2- in the complex plane for both nonconservative and conservative cases. The N1- and N2-functions are expressed in terms of the known N-function using the theory of contour integration. The unknown constants are derived from the solutions of Fredholm integral equations of the second kind uniquely using the new linear decoupled constraints. The expressions for the H-function for a semi-infinite atmosphere are obtained as a limiting case. © Springer Science+Business Media B.V. 2009.


Kundu A.,Camellia Institute of Technology
Proceedings of the 2010 International Conference on Signal and Image Processing, ICSIP 2010 | Year: 2010

Motion estimation is the most important component of any video coding standard. So development of an efficient algorithm for fast motion estimation is the basic need for video encoder design. Block based motion estimation algorithms are used for fast motion estimation as block matching algorithms eliminate the temporal redundancy present in any video sequence and use optimized search area to reduce computational time. In this paper I have reviewed existing block based motion estimation algorithms like exhaustive full search (FS), three step search (TSS), adaptive dual cross diamond hexagon search (ADCDHS) and performed a comparative analysis of their performance. I have also proposed a Modified Diamond Hexagon Search which will use reduced search area compare to existing TSS, DAHSA, and ADCDHS and in turn it will reduce computational time. Application of proposed algorithm for local motion analysis is also discussed in this paper. ©2010 IEEE.


Bhattacharyya S.,University of Burdwan | Dey S.,Camellia Institute of Technology
Proceedings - 2011 International Conference on Computational Intelligence and Communication Systems, CICN 2011 | Year: 2011

A genetic algorithm inspired by the inherent features of parallelism and time discreteness exhibited by quantum mechanical systems, is presented in this article. The predominant interference operator in the proposed quantum inspired genetic algorithm (QIGA) is influenced by time averages of different random chaotic map models derived from the randomness of quantum mechanical systems. Subsequently, QIGA uses quantum inspired crossover and mutation on the trial solutions, followed by a quantum measurement on the intermediate states, to derive sought results. Application of QIGA to determine optimum threshold intensities is demonstrated on two real life gray level images. The efficacy of QIGA is adjudged w.r.t. a convex combination of two fuzzy thresholding evaluation metrics in a multiple criterion scenario. Comparative study of its performance with the classical counterpart indicates encouraging avenues. © 2011 IEEE.

Loading Camellia Institute of Technology collaborators
Loading Camellia Institute of Technology collaborators