S
Suman Kumar Saha
Researcher at National Institute of Technology, Raipur
Publications - 81
Citations - 1140
Suman Kumar Saha is an academic researcher from National Institute of Technology, Raipur. The author has contributed to research in topics: Particle swarm optimization & Infinite impulse response. The author has an hindex of 18, co-authored 63 publications receiving 838 citations. Previous affiliations of Suman Kumar Saha include National Institute of Technology, Durgapur.
Papers
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Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.
TL;DR: The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems and demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters.
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Optimal design of fractional order low pass Butterworth filter with accurate magnitude response
TL;DR: The proposed GSA based FOLBFs consistently achieve the best solution quality with the fastest convergence rate as compared with the designs based on Real coded Genetic Algorithm (RGA) and Particle Swarm Optimization (PSO).
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An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-Processor Design for QRS Detection
TL;DR: This paper presents an efficient infinite impulse response type digital fractional order differentiator (DFOD) based electrocardiogram (ECG) pre-processor to detect QRS complexes for the first time when the evolutionary algorithm based IIR-type DFOD is employed and establishing its performance superiority.
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Gravitation search algorithm: Application to the optimal IIR filter design
TL;DR: Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
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A new design method using opposition-based BAT algorithm for IIR system identification problem
TL;DR: When tested against standard benchmark examples, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm RGA, differential evolution DE and particle swarm optimisation PSO in terms of accuracy and convergence speed.