학술논문

Reconstructing Classes of Non-Bandlimited Signals From Time Encoded Information
Document Type
Periodical
Source
IEEE Transactions on Signal Processing IEEE Trans. Signal Process. Signal Processing, IEEE Transactions on. 68:747-763 2020
Subject
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Computing and Processing
Encoding
Timing
Kernel
Splines (mathematics)
Image coding
Image reconstruction
Neurons
Analog-to-digital conversion
non-uniform sampling
sub-Nyquist sampling
finite rate of innovation
time encoding
integrate-and-fire
crossing detector
cardinal splines
Language
ISSN
1053-587X
1941-0476
Abstract
We investigate time encoding as an alternative method to classical sampling, and address the problem of reconstructing classes of non-bandlimited signals from time-based samples. We consider a sampling mechanism based on first filtering the input, before obtaining the timing information using a time encoding machine. Within this framework, we show that sampling by timing is equivalent to a non-uniform sampling problem, where the reconstruction of the input depends on the characteristics of the filter and on its non-uniform shifts. The classes of filters we focus on are exponential and polynomial splines, and we show that their fundamental properties are locally preserved in the context of non-uniform sampling. Leveraging these properties, we then derive sufficient conditions and propose novel algorithms for perfect reconstruction of classes of non-bandlimited signals such as: streams of Diracs, sequences of pulses and piecewise constant signals. Next, we extend these methods to operate with arbitrary filters, and also present simulation results on synthetic noisy data.