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Maji C.,Bankura Unnayani Institute of Engineering
Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014 | Year: 2015

This paper focuses on minimization of interference to primary user (PU) through an optimal strategy of power allocation algorithm for source and relay nodes in multihop cognitive radio network (CRN) under the constraints of outage probability (successful delivery) and data rate over source-destination link. This objective is also studied in the framework of enhanced lifetime of the CRN. Extensive simulations are done for both energy aware (EA) and non-energy aware (NEA) power allocation schemes. Simulation results show that NEA based power allocation offers better capacity than EA scheme at the cost of slightly increased interference to PU. Simulation results also show a three dimensional (3D) relative trade-off performance among the data transmission capacity, network lifetime and total transmission power. © 2014 IEEE.


Manna P.,Bankura Unnayani Institute of Engineering | Si T.,Bankura Unnayani Institute of Engineering
Advances in Intelligent Systems and Computing | Year: 2016

This paper presents brain MRI segmentation for lesion detection using fire-fly based hard-clustering algorithm. First, MR images are denoised using median filter and denoised images are segmented using fire-fly based clustering algorithm. After segmentation, lesioned regions are extracted from segmented MR images. The performance of the proposed method is evaluated using quantitative measurement index. A comparative study is made with k-means and Fuzzy c-means algorithms. The experiment results demonstrate that the proposed method performs better than other two methods. © Springer India 2016.


Si T.,Bankura Unnayani Institute of Engineering | De A.,Dr. B. C. Roy Engineering College | Bhattacharjee A.K.,National Institute of Technology Durgapur
Advances in Intelligent Systems and Computing | Year: 2014

In this work, a new method for creating diversity in Particle Swarm Optimization is devised. The key feature of this method is to derive velocity update equation for each particle in Particle Swarm Optimizer using Grammatical Swarm algorithm. Grammatical Swarm is a Grammatical Evolution algorithm based on Particle Swarm Optimizer. Each particle updates its position by updating velocity. In classical Particle Swarm Optimizer, same velocity update equation for all particles is responsible for creating diversity in the population. Particle Swarm Optimizer has quick convergence but suffers from premature convergence in local optima due to lack in diversity. In the proposed method, different velocity update equations are evolved using Grammatical Swarm for each particles to create the diversity in the population. The proposed method is applied on 8 well-known benchmark unconstrained optimization problems and compared with Comprehensive Learning Particle Swarm Optimizer. Experimental results show that the proposed method performed better than Comprehensive Learning Particle Swarm Optimizer. © Springer International Publishing Switzerland 2014.


Si T.,Bankura Unnayani Institute of Engineering | Jana N.D.,National Institute of Technology Durgapur
International Journal of Intelligent Systems Technologies and Applications | Year: 2012

Particle swarm optimisation (PSO) is population-based optimisation algorithm having stochastic in nature. PSO has quick convergence speed but often gets stuck into local optima due to lacks of diversity. In this work, first mutation operator adopted from Differential Evolution (DE) algorithm is applied in PSO with decreasing inertia weight (PSO-DMLB). In second method, DE mutation is applied in another PSO variant, namely Comprehensive Learning PSO (CLPSO). The second method is termed as CLPSO-DMLB. Local best position of each particle is muted by a predefined mutation probability with the scaled difference of two randomly selected particle's local best position to increase the diversity in the population to achieve better quality of solutions. The proposed methods are applied on wellknown benchmark unconstrained functions and obtained results are compared to show the effectiveness of the proposed methods. © 2012 Inderscience Enterprises Ltd.


Chatterjee A.,National Institute of Technology Durgapur | Mahanti G.K.,National Institute of Technology Durgapur | Pathak N.,Bankura Unnayani Institute of Engineering
Progress In Electromagnetics Research B | Year: 2010

Scanning a planar array in the x-z plane directs the beam peak to any direction off the broadside along the same plane. Reduction of sidelobe level in concentric ring array of isotropic antennas scanned in the x-z plane result in a wide first null beamwidth (FNBW). In this paper, the authors propose pattern synthesis methods to reduce the sidelobe levels with fixed FNBW by making the scanned array thinned based on two different global optimization algorithms, namely Gravitational Search Algorithm (GSA) and modified Particle Swarm Optimization (PSO) algorithm. The thinning percentage of the array is kept more than 45 percent and the first null beamwidth (FNBW) is kept equal to or less than that of a fully populated, uniformly excited and 0.5λ spaced concentric circular ring array of same scanning angle and same number of elements and rings.


Si T.,Bankura Unnayani Institute of Engineering | Mandal B.,Bankura Unnayani Institute of Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Particle Swarm Optimizer is population based global search algorithm mimicking the behavior of fish–schooling, bird’s flocking etc. Recently the opposition based learning scheme is incorporated in Particle Swarm Optimizer to improve its performance. Till now opposition based Particle Swarm Optimizer is implemented with gbest topology. This paper proposes the opposition based Particle Swarm Optimizer with lbest or ring topology. The proposed method is applied on 20 benchmark unconstrained functions. The obtained results are compared with other well–known opposition based Particle Swarm Optimizers with statistical analysis. The experimental results with statistical analysis show that the proposed algorithm outperforms over other algorithms for most of the functions. © Springer International Publishing Switzerland 2015.


Mandal B.,Bankura Unnayani Institute of Engineering | Si T.,Bankura Unnayani Institute of Engineering
2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 | Year: 2015

Particle Swarm Optimizer is a swarm intelligent algorithm which simulates the behaviour of bird's flocking and fish schooling. This paper presents an improved opposition based Particle Swarm Optimizer. In the proposed method, generalized opposition based learning is incorporated first in population initialization and particle's personal best position. Second, a controlled mechanism of exploration and exploitation is employed through global best position of the swarm. The proposed method is applied on 28 CEC2013 benchmark problems. A comparative study is made with standard Particle Swarm Optimizer and its other opposition based variants. The experimental results show that the proposed method statistically outperforms other methods. © 2015 IEEE.


Chattopadhyay S.,Bankura Unnayani Institute of Engineering
Journal of Medical Imaging and Health Informatics | Year: 2012

Depression is a psychological disorder, which affects one's quality of life due to its chronic course. Its early screening is the key to curb the overall disease load. This paper is an attempt to develop a mathematical model that captures several common symptoms of depression using a Concept Hierarchical Tree (CHT) and based on the answers obtained, computes the Depression Load (DL) by a Connected Graph-based Approach (CGA) and multiple linear regressions. The proposed CHT-CGA model is implemented as a tool in a JavaScript and has been validated with 123 real-world depression cases. The prototype tool is able to diagnose 'mild,' 'moderate,' and 'severe' depression with 90%, 95%, and 94% accuracies, respectively with the average accuracy of 93%. It has been proposed that the tool might be deployed in rural health centres for initial screening and taking referral decision. Copyright © 2012 American Scientific Publishers.


Pathak N.,Bankura Unnayani Institute of Engineering | Basu B.,National Institute of Technology Durgapur | Mahanti G.K.,National Institute of Technology Durgapur
Progress In Electromagnetics Research M | Year: 2011

In this paper, the authors propose a method based on the combination of inverse fast Fourier transform (IFFT) and modified particle swarm optimization for side lobe reduction of a thinned mutually coupled linear array of parallel half-wave length dipole antennas with specified maximum return loss. The generated pattern is broadside (φ = 90 degree) in the horizontal plane. Mutual coupling between the half-wave length parallel dipole antennas has been taken care of by induced emf method considering the current distribution on each dipole to be sinusoidal. Directivity, first null beamwidth (FNBW), return loss of the thinned array is also calculated and compared with a fully populated array. Two cases have been considered, one with symmetric excitation voltage distribution and the other with asymmetric one. The method uses the property that for a linear array with uniform element spacing, an inverse Fourier transform relationship exists between the array factor and the element excitations. Inverse Fast Fourier Transform is used to calculate the array factor, which in turn reduces the computation time significantly. The element pattern of half-wave length dipole antenna has been assumed omnidirectional in the horizontal plane. Two examples are presented to show the flexibility and effectiveness of the proposed approach.


Si T.,Bankura Unnayani Institute of Engineering
Advances in Intelligent Systems and Computing | Year: 2016

Grammatical Evolution generates computer programs automatically in any arbitrary language using Backus-Naur Form of Context-free Grammar in automatic programming. Variable-length Genetic Algorithm is used as a learning algorithm in Grammatical Evolution. Fireworks algorithm is a recently developed new Swarm Intelligent algorithm used for function optimization. This paper proposes Grammatical Fireworks algorithm which uses Fireworks algorithm as a learning algorithm in place of variable-length Genetic Algorithm in Grammatical Evolution to evolve computer programs automatically. Grammatical Fireworks algorithm is applied on three well-known benchmark problems such as Santa Fe ant trail, symbolic regression and 3-input multiplexer problems. A comparative study is made with Grammatical Evolution, Grammatical Swarm, Grammatical Artificial Bee Colony, and Grammatical Differential Evolution. The experimental results demonstrate that the proposed Grammatical Fireworks algorithm can be applied in automatic computer program generation. © Springer Science+Business Media Singapore 2016.

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