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Advisor: Hema Mamidipaka Email: hema.asrith@gmail.com Chair: Bala Siva Saibaba Emani Email: balasiva.emani@gmail.com

Section: Publications & Resources

Join us in the second year of PROmotinG DiveRsity in Signal ProcESSing (PROGRESS), which is an initiative of the IEEE Signal Processing Society designed to motivate, provide information and support women and under-represented minorities in pursuing academic careers in signal processing. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, a smart pilot sequence assignment method is proposed to minimize inter-cell interference generated in a massive multi-input multi-output (MIMO) system due to pilot contamination in uplink TDD (Time Division Duplex) mode. The proposed method employs a zero-one integer linear programming method as the assignment algorithm.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We study distributed filtering for a class of uncertain systems over corrupted communication channels. We propose a distributed robust Kalman filter with stochastic gains, through which upper bounds of the conditional mean square estimation errors are calculated online. 

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

The papers in this special section focus on tensor decomposition for signal processing and machine learning. Tensor decomposition, also called tensor factorization, is useful for representing and analyzing multi-dimensional data. Tensor decompositions have been applied in signal processing applications (speech, acoustics, communications, radar, biomedicine), machine learning (clustering, dimensionality reduction, latent factor models, subspace learning), and well beyond.

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. 

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

Optimal rank selection is an important issue in tensor decomposition problems, especially for Tensor Train (TT) and Tensor Ring (TR) (also known as Tensor Chain) decompositions. In this paper, a new rank selection method for TR decomposition has been proposed for automatically finding near-optimal TR ranks, which result in a lower storage cost, especially for tensors with inexact TT or TR structures.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Despite the impressive technological strides made over the years, human lives still depend very much on the natural environment. Fortunately, technology can now be used to help address critical environmental concerns in air quality, soil condition, and weather events.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Signal processing (SP) is at the very heart of our digital lives, owing to its role as the pivotal enabling technology for advancement across multiple disciplines. Its prominence in modern data science has created a necessity to supply industry, government labs, and academia with graduates who possess relevant SP expertise and are well equipped to deal with the manifold challenges in current and future applications.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The effectiveness of teaching digital signal processing (DSP) can be enhanced by reducing lecture time devoted to theory and increasing emphasis on applications, programming aspects, visualization, and intuitive understanding. An integrated approach to teaching requires instructors to simultaneously teach theory and its applications in storage and processing of audio, speech, and biomedical signals.