Lecture by Dr. Ayush Bhandari @ 11.30am

Title: Digital Acquisition via Modulo Folding: Revisiting the Legacy of Shannon-Nyquist, Prony, Schoenberg, Pisarenko and Radon

Date and time: June 1, 2022; 11.30 AM
Venue: Multimedia Classroom, Electrical Engineering Department, IISc

Coffee will be served during the talk.

Abstract: Digital data capture is the backbone of all modern day systems and “Digital Revolution” has been aptly termed as the Third Industrial Revolution. Underpinning the digital representation is the Shannon-Nyquist sampling theorem and more recent developments such as compressive sensing approaches. The fact that there is a physical limit to which sensors can measure amplitudes poses a fundamental bottleneck when it comes to leveraging the performance guaranteed by recovery algorithms. In practice, whenever a physical signal exceeds the maximum recordable range, the sensor saturates, resulting in permanent information loss. Examples include (a) dosimeter saturation during the Chernobyl reactor accident, reporting radiation levels far lower than the true value, and (b) loss of visual cues in self-driving cars coming out of a tunnel (due to sudden exposure to light).

To reconcile this gap between theory and practice, we introduce a computational sensing approach—the Unlimited Sensing framework (USF)—that is based on a co-design of hardware and algorithms. On the hardware front, our work is based on a radically different analog-to-digital converter (ADC) design, which allows for the ADCs to produce modulo or folded samples. On the algorithms front, we develop new, mathematically guaranteed recovery strategies.

In the first part of this talk, we prove a sampling theorem akin to the Shannon-Nyquist criterion. Despite the non-linearity in the sensing pipeline, the sampling rate only depends on the signal’s bandwidth. Our theory is complemented with a stable recovery algorithm. Beyond the theoretical results, we also present a hardware demo that shows the modulo ADC in action.

Building on the basic sampling theory result, we consider certain variations on the theme. This includes different signal classes (e.g. smooth, sparse and parametric functions) as well as sampling architectures, such as One-Bit and Event-Triggered sampling. Moving further, we reinterpret the USF as a generalized linear model that motivates a new class of inverse problems. We conclude this talk by presenting a research overview in the context of single-shot high-dynamic-range (HDR) imaging, sensor array processing and HDR computed tomography based on the modulo Radon transform.

About the speaker:  Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2018, for his work on computational sensing and imaging which is being shaped as a forthcoming, co-authored book Computational Imaging in MIT Press. He is currently a faculty member with the Department of Electrical and Electronic Engineering, Imperial College London, U. K. He has held research positions at INRIA (Rennes), France, Nanyang Technological University, Singapore, the Chinese University of Hong Kong and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland among other institutes. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics), in 2019 by the Technical University of Munich.

He has been a tutorial speaker at various venues including the ACM Siggraph (2014,2015) and the IEEE ICCV (2015) and he was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar, Multimodal Sensing and Imaging (CoSeRa), 2018. Some aspects of his work have led to new sensing and imaging modalities which have been widely covered in press and media (e.g. BBC news). Applied aspects of his research have led to more than 10 US patents. His scientific contributions have led to numerous prizes, most recently, the Best Paper Award at IEEE ICCP 2020 (Intl. Conf. on Computational Photography) and the Best Student Paper Award (senior co-author) at IEEE ICASSP 2019 (Intl. Conf. on Acoustics, Speech and Signal Processing). In 2020, his doctoral work was awarded the Best PhD Dissertation Award from the IEEE Signal Processing Society. In 2021, he received the President’s Medal for Outstanding Early Career Researcher at Imperial College London.

Host: Prof. Chandra Sekhar Seelamantula (EE)