Time encoding of bandlimited signals: reconstruction by pseudo-inversion and time-varying multiplierless FIR filtering

TL;DR

This paper introduces a new framework for reconstructing bandlimited signals from time encoding data using pseudo-inversion and time-varying FIR filtering, enabling efficient and potentially real-time processing.

Contribution

It presents a novel reconstruction method based on pseudo-inversion and convex set projections, with an efficient discrete implementation and a real-time approximate filtering approach.

Findings

Algorithm converges at a rate similar to previous methods.

Discrete implementation uses only scaling by powers of two.

Preliminary analysis of semi-convergence under noise.

Abstract

We propose an entirely redesigned framework of bandlimited signal reconstruction for the time encoding machine (TEM) introduced by Lazar and T\'oth. As the encoding part of TEM consists in obtaining integral values of a bandlimited input over known time intervals, it theoretically amounts to applying a known linear operator on the input. We then approach the general question of signal reconstruction by pseudo-inversion of this operator. We perform this task numerically and iteratively using projections onto convex sets (POCS). The algorithm can be implemented exactly in discrete time with multiplications that are all reduced to scaling by signed powers of two, thanks to the use of relaxation coefficients. Meanwhile, the algorithm achieves a rate of convergence similar to that of Lazar and T\'oth. For real-time processing, we propose an approximate time-varying FIR implementation, which avoids the splitting of the input into blocks. We finally propose some preliminary analysis of semi-convergence of the algorithm under data noise.