Person:
Das, Rajib Lochan

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Rajib Lochan Das

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079-68261597

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Adaptive Signal Processing, Compressive Sensing, Machine Learning, Image Processing, Graph Signal Processing

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Biography

Rajib Lochan Das received the bachelor�s degree in electronics and communication engineering from the National Institute of Technology, Durgapur, India, in 2000, the master�s degree in control system engineering from Jadavpur University, Kolkata, India, in 2003, and the Ph.D. degree in electronics and electrical communication engineering from IIT, Kharagpur, India, in 2015. He is currently an Assistant Professor with the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. His research interests include adaptive signal processing, compressive sensing, and image processing. He has published several IEEE Transaction papers.

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2016 - 20203

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Now showing 1 - 3 of 3
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    Improving the Performance of the PNLMS Algorithm Using l1 Norm Regularization
    (IEEE, 01-07-2016) Das, Rajib Lochan; Chakraborty, Mrityunjoy; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; DA-IICT, Gandhinagar
    The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In this paper, we address this problem by introducing a carefully constructed l1�norm (of the coefficients) penalty in the PNLMS cost function which favors sparsity. This results in certain zero attracting terms in the PNLMS weight update equation which help in the shrinkage of the coefficients, especially the inactive taps, thereby arresting the slowing down of convergence and also producing lesser steady state excess mean square error (EMSE). A rigorous convergence analysis of the proposed 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. The analysis reveals that further reduction of the EMSE is possible by deploying a variable step size (VSS) simultaneously with a variable zero attracting coefficient in the weight update process. Simulation results confirm superior performance of the proposed VSS zero attracting PNLMS algorithm over existing algorithms, especially in terms of having both higher convergence speed and lesser steady state EMSE simultaneously.
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    An Adaptive Upper Threshold Based Gain Function for the ZA-PNLMS Algorithm
    (IEEE, 01-10-2020) Das, Rajib Lochan; Trivedi, Vishwas; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; Das, Rajib Lochan; DA-IICT, Gandhinagar; Trivedi, Vishwas (201611010)
    The recently proposed Zero-Attracting Proportionate Normalized Least Mean Square (ZA-PNLMS) algorithm improves the performance of the PNLMS algorithm for identifying sparse systems. In particular, it keeps the fast initial convergence rate of the PNLMS algorithm, and improves its transient performance by arresting the fall in the convergence rate at the later stage of the adaptation process, and it also improves the steady-state mean square error (MSE). However, the improvement in the steady-state performance is marginal. In this brief, we propose a novel gain function for the ZA-PNLMS algorithm by introducing an adaptive upper threshold parameter. It has two fold implications. First, the proposed threshold parameter truncates the proportional gains of highly active taps when they approach to their steady-states and helps the lesser active taps to converge faster by providing them more gains, and thereby the overall transient performance is further improved. Secondly, it also improves steady-state MSE significantly by reducing the fluctuation of the active taps at the steady-state. Extensive simulation studies have been carried out to justify the improvement obtained by the proposed algorithm.
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    Lorentzian Based Adaptive Filters for Impulsive Noise Environments
    (IEEE, 01-02-2017) Das, Rajib Lochan; Narwaria, Manish; DA-IICT, Gandhinagar
    In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive filtering (LAF) is derived from a sliding window type cost function with Lorentzian norm of past errors to combat adverse effect of impulsive noise on systems. The first and second order convergence analyses of the LAF algorithm are carried out in this paper. Then, to identify sparse systems in impulsive noise environment, l0�norm penalty is introduced to the cost function of the LAF algorithm leading to a new algorithm called Lorentzian hard thresholding adaptive filtering (LHTAF) which employs hard thresholding operator with a fixed hard thresholding parameter to obtain sparse solutions. The effect of the hard thresholding operator is further analyzed, and the analysis shows that a variable hard thresholding parameter offers significant improvement in the performance of the algorithm, and this result in the final algorithm called Lorentzian variable hard thresholding adaptive filtering (LVHTAF) where the hard thresholding parameter is adjusted adaptively. Simulation results show that the LVTHAF outperforms the existing robust sparse adaptive algorithms by producing lesser steady state mean square error.