Thesis Proposal: Arun Nair
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Title: Machine Learning for Collaborative Signal Processing in Beamforming and Compressed Sensing
Abstract: Life today has become inextricably linked with the many sensors working in concert in our environment, from the webcam and microphone in our laptops to the arrays of wireless transmitters and receivers in cellphone towers. Collaborative signal processing methods tackle the challenge of efficiently processing data from multiple sources. Recently, machine learning methods have become very popular tools for collaborative signal processing, largely due to the success of deep learning. The large volume of data created by multiple sensors pairs well with the data-hungry nature of modern machine learning models, holding great promise for efficient solutions.
This proposal extends ideas from machine learning to problems in collaborative signal processing. Specifically, this work will focus on two collaborative signal processing methods – beamforming and compressed sensing. Beamforming is commonly employed in sensor arrays for directional signal transmission and reception by combining the signals received in the array elements to enhance a signal of interest. On the other hand, compressed sensing is a widely applicable mathematical framework that guarantees exact signal recovery even at sub-Nyquist sampling rates if suitable sparsity and incoherence assumptions are satisfied. Compressed sensing accomplishes this via convex or greedy optimization to fuse the information in a small number of signal measurements.
The first part of this work was motivated by the common experience of attempting to capture a video on a mobile device but having the target of interest contaminated by the surrounding environment (e.g., construction sounds from outside the camera’s field of view). Fusing visual and auditory information, we propose a novel audio-visual zooming algorithm that directionally filters the received audio data using beamforming to focus only on audio originating from within the field of view of the camera. Second, we improve the quality of ultrasound image formation by introducing a novel beamforming framework that leverages the benefits of deep learning. Ultrasound images currently suffer from severe speckle and clutter degradations which cause poor image quality and reduce diagnostic utility. We propose to design a deep neural network to learn end-to-end transformations that extract information directly from raw received US channel data. Finally, we improve upon optimization-based compressed sensing recovery by replacing the slow iterative optimization algorithms with far faster convolutional neural networks.