The research interests of Prof. Chakraborty include Digital and Adaptive Signal Processing, VLSI Signal Processing, Compressive Sensing, Applied Linear Algebra and DSP applications to communications, speech and audio processing. The major contributions made by him in these are as follows :
- A. He has been the first to show how the block floating point (BFP) data format which provides floating-point like high dynamic range at fixed-point like low complexity, can be used for realizing adaptive filters. This is a major breakthrough, as before this, the usage of BFP was limited only to fixed coefficient filters.
B. He made a seminal theoretical contribution by showing that for multitonal signals, the filter weights in LMS-based adaptive filters can actually converge to their optimal values absolutely and not just in mean unlike for general signals.
C. In compressive sensing (CS), he developed new upper bound relations for convergence of the generalized orthogonal matching pursuit. Recently, he has determined iteration bounds for convergence of two important CS recovery algorithms, namely, CoSaMP and subspace pursuit.
D. He has been the first to introduce CORDIC and also canonic-signed-digit format to reduce multiplicative complexities substantially in adaptive filters.
E. His latest contributions include development of novel sparse adaptive filters based on convex combination and L1 norm regularization principles.
Apart from the above, his other contributions include development of novel adaptive filters for estimating and tracking the delay of sinusoids for applications in radar and sonar, pipelined architectures for adaptive decision feedback equalizers, new algorithms for multichannel ARMA modeling and filtering, and fast algorithms for cyclostationary processes.
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``On the Number of Iterations for Convergence of CoSaMP and Subspace Pursuit Algorithms" by Satpathi S., Chakraborty M. Journal of Applied and Computational Harmonic Analysis 43 568-576 (2017)
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A New Adaptive Filter for Estimating and Tracking the Delay and the Amplitude of a Sinusoid by Chakraborty M. IEEE Transactions on Instrumentation and Measurement 3049-3057 (2010)
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Sparse Adaptive Filtering by an Adaptive Convex Combination of the LMS and the ZA-LMS Algorithms by Das B. K., Chakraborty M. IEEE Transactions on Circuits and Systems, Part I 1499-1507 (2014)
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A Block Floating-Point Treatment to the LMS Algorithm : Efficient Realization and a Roundoff Error Analysis by Mitra A., Chakraborty M. , Sakai H. IEEE Transactions on Signal Processing 4536-4544 (2005)
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Convergence Analysis of a Complex LMS Algorithm with Tonal Reference Signals, M. Chakraborty and H. Sakai by Chakraborty M., Sakai H. IEEE Transactions on Speech and Audio Processing 286-292 (2005)
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Improving the Bound on the RIP Constant in Generalized Orthogonal Matching Pursuit by Satpathi S., Das R. L., Chakraborty M. IEEE Signal Processing Letters 1074-1077 (2013)
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An Efficient Algorithm for Solving General Periodic Toeplitz Systems by Chakraborty M. IEEE Transactions on Signal Processing 784-787 (1998)
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On Convergence of Proportionate-Type Normalized Least Mean Square Algorithms by Das R. L., Chakraborty M. IEEE Transactions on Circuits and Systems, Part II 62 491-495 (2015)
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An Improved L0-RLS Adaptive Filter by Das B. K., Chakraborty M. IET Electronic Letters 53 1650-1651 (2017)
Principal Investigator
- Data Acquisition, Learning and Decision Making under Resource Constraints in Large Scale Wireless Sensor Networks Scheme for Promotion of Academic and Research Collaboration (SPARC), Apex Committee of SPARC
- New Algorithms for Distributed Recovery of Sparse Signals from Compressed Measurements Science and Engineering Research Board (SERB)
Ph. D. Students
Ketan Atul Bapat
Area of Research: Signal Processing
Samparka Sanyal
Area of Research: Signal Processing and Applications
