R
Rajib Lochan Das
Researcher at Dhirubhai Ambani Institute of Information and Communication Technology
Publications - 26
Citations - 401
Rajib Lochan Das is an academic researcher from Dhirubhai Ambani Institute of Information and Communication Technology. The author has contributed to research in topics: Adaptive filter & Compressed sensing. The author has an hindex of 10, co-authored 24 publications receiving 327 citations. Previous affiliations of Rajib Lochan Das include Indian Institute of Technology Guwahati & Indian Institute of Technology Kharagpur.
Papers
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Mathematical Modeling for Economic Evaluation of Electric Vehicle to Smart Grid Interaction
TL;DR: In this model the economic analysis has been done in such a way that the battery related liabilities do not become a financial burden to EV owners and the optimal cost of electricity is determined such that the grid, EV owners, and consumers are benefitted.
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Lorentzian Based Adaptive Filters for Impulsive Noise Environments
Rajib Lochan Das,Manish Narwaria +1 more
TL;DR: Simulation results show that the Lorentzian variable hard thresholding adaptive filtering (LVHTAF) outperforms the existing robust sparse adaptive algorithms by producing lesser steady state mean square error.
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Improving the Performance of the PNLMS Algorithm Using $l_1$ Norm Regularization
TL;DR: A rigorous convergence analysis of the proposed VSS zero attracting PNLMS algorithm is presented that expresses the steady state mean square deviation of both the active and the inactive taps in terms of a zero attracting coefficient of the algorithm.
Journal Article
HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction
TL;DR: A recognition model based on multiple Hidden Markov Models followed by few novel feature extraction techniques for a single character to tackle its different writing formats and a post-processing block at the final stage to enhance the recognition rate further is proposed.
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On Convergence of Proportionate-Type Normalized Least Mean Square Algorithms
TL;DR: A new convergence analysis is presented for a well-known sparse adaptive filter family, namely, the proportionate-type normalized least mean square (PtNLMS) algorithms, where, unlike all the existing approaches, no assumption of whiteness is made on the input.