Differentiate-and-Fire Time-Encoding of Finite-Rate-of-Innovation Signals
Abijith Jagannath Kamath, Chandra Sekhar Seelamantula
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.
BibTeX
@inproceedings{icassp2022_differentiateand,
title = {Differentiate-and-Fire Time-Encoding of Finite-Rate-of-Innovation Signals},
author = {Abijith Jagannath Kamath and Chandra Sekhar Seelamantula},
booktitle = {ICASSP 2022},
year = {2022}
}