D
Debi Prasad Das
Researcher at Council of Scientific and Industrial Research
Publications - 61
Citations - 1042
Debi Prasad Das is an academic researcher from Council of Scientific and Industrial Research. The author has contributed to research in topics: Active noise control & Least mean squares filter. The author has an hindex of 16, co-authored 51 publications receiving 875 citations. Previous affiliations of Debi Prasad Das include Indian Institute of Technology Kharagpur & University of Adelaide.
Papers
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Journal ArticleDOI
Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm
Debi Prasad Das,Ganapati Panda +1 more
TL;DR: A novel filtered-s least mean square (FSLMS) algorithm based ANC structure, which functions as a nonlinear controller, is proposed in this paper and substantially reduces the number of operations compared to that of FSLMS as well as VFXLMS.
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Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes
TL;DR: This correspondence proposes a novel nonlinear adaptive algorithm named as filtered-s least mean square (FSLMS) algorithm for multichannel active control of nonlinear noise processes and a reduced complexity FSLMS algorithm using filter bank approach is suggested.
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New block filtered-X LMS algorithms for active noise control systems
TL;DR: In this article, the authors proposed a transform-domain active noise control (ANC) algorithm using only convolution machines, which is different from conventional block least-mean-square (LMS) algorithms that use both convolution and cross-correlation machines.
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Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification
TL;DR: The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path.
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Fast Adaptive Algorithms for Active Control of Nonlinear Noise Processes
TL;DR: The concept of reutilizing a part of the computations performed for the first sample while computing the next sample, for a block length of two samples, is exploited here to implement the fast and exact versions of the FSLMS and VFXLMS algorithms which are computationally efficient.