DIFFERENTIATE-AND-FIRE TIME-ENCODING OF FINITE-RATE-OF-INNOVATION SIGNALS

Kamath, AJ and Seelamantula, CS (2022) DIFFERENTIATE-AND-FIRE TIME-ENCODING OF FINITE-RATE-OF-INNOVATION SIGNALS. In: 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 23 - 27 May 2022, Virtual, Online at Singapore, pp. 5637-5641.

PDF IEEE_ICASSP-2022_2022_5637-5641_2022 .pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Time-encoding or event-driven sampling of continuous-time signals is an alternative paradigm to uniform sampling. In this sampling scheme, the signal is encoded by a sequence of time-instants as opposed to a sequence of amplitudes in uniform sampling. Time-encoding is opportunistic by design - measurements are taken only when the signal exhibits significant variability. Consequently, the measurements are sparse, noise-robust, and require low power. However, standard processing and reconstruction methods do not apply. In this paper, we introduce a new time-encoding machine, namely, differentiate-and-fire time-encoding machine (DIF-TEM) inspired by the functioning of the human visual system. A DIF-TEM can be tuned to provide sampling sets with variable densities - sparse sets that mimic dynamic vision sensors (neuromorphic cameras) or dense sets that mimic classical time-encoding machines. We propose kernel-based time-encoding of finite-rate-of-innovation (FRI) signals using DIF-TEM via Fourier-domain analysis. We show that DIF-TEM measurements are sufficient for perfect signal reconstruction under certain conditions. We provide simulation results to substantiate our claims. © 2022 IEEE

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	differentiate- and-fire time-encoding machine; event-driven sampling; finite-rate-of-innovation; neuromorphic camera; Time-encoding machine
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	21 Jun 2022 10:35
Last Modified:	21 Jun 2022 10:35
URI:	https://eprints.iisc.ac.in/id/eprint/73939

Actions (login required)

View Item