Speaker: Dr Ayush Bhandari
Affiliation: Assistant Professor, Imperial College, London
Talk Timings: April 30, 2021 (Friday) 1.30 PM-2.45 PM IST
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 sampling 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 followed by certain variations on the theme. We show that, remarkably, 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. Building on the basic result, we consider different signal classes, (e.g. smooth, sparse and parametric functions) as well as sampling architectures, such as One-Bit and Event-Triggered sampling. Beyond the theoretical results, we also present a hardware demo that shows our approach in action.
Moving further, we reinterpret the unlimited sensing framework 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.
Bio:
Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT) in 2018, for his work on computational sensing and imaging which is being shaped as a forthcoming, co-authored book in MIT Press. Since 2018, he has been a faculty member with the engineering department at Imperial College London. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics), in 2019 by the Technical University of Munich. Since 2020, he has been a UKRI Future Leaders Fellow. He has held research positions at INRIA (Rennes), France, NTU, Singapore, the CUHK Hong Kong and EPFL, Switzerland among other institutes. He has been a tutorial speaker at various venues including the ACM Siggraph (2014,2015) and the IEEE ICCV (2015). He was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar, Multimodal Sensing and Imaging (CoSeRa), 2018, SPIE Photonics West, 2021 as well as the upcoming CVPR 2021 workshop. 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.