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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
Adaptive nonlinear active noise control algorithm for active headrest with moving error microphones
TL;DR: In this paper, a generalized filter bank based nonlinear active noise control (ANC) algorithm is proposed for active headrest application, which is used to control the noise at both ears by using two moving error microphones which are mounted on the head.
Proceedings ArticleDOI
A novel method of designing LVDT using artificial neural network
TL;DR: In this article, a simple and novel method of designing and developing LVDT based sensing system is proposed, where precise adjustment of windings is made to enhance the linearity range of LVDTs.
Journal ArticleDOI
Monitoring the fill level of a ball mill using vibration sensing and artificial neural network
TL;DR: In this paper, a vibration monitoring-based method is proposed and tested for estimating the fill level inside a laboratory-scale ball mill and the predicted fill level obtained by using different features are compared.
Proceedings ArticleDOI
An Energy Function Based Fuzzy Variable Step Size FxLMS Algorithm for Active Noise Control
TL;DR: Real-time implementation of a Lyapunov energy function based fuzzy adaptive step size filtered-x-LMS (FxLMS) algorithm for active control of noise in a single channel duct is described.
Journal ArticleDOI
Estimation of hydrogen flow rate in atmospheric Ar:H 2 plasma by using artificial neural network
TL;DR: A hydrogen flow rate estimation system is presented in this paper by using an artificial neural network (ANN) model fed with features of optical emission spectra of the plasma for estimating four different sets of hydrogen flow rates when the argon flow rate is constant at 10 lpm.