Article
Version 1
Preserved in Portico This version is not peer-reviewed
Signal Filtering Using Neuromorphic Measurements
Version 1
: Received: 18 October 2023 / Approved: 19 October 2023 / Online: 19 October 2023 (10:48:30 CEST)
A peer-reviewed article of this Preprint also exists.
Florescu, D.; Coca, D. Signal Filtering Using Neuromorphic Measurements. J. Low Power Electron. Appl. 2023, 13, 63. Florescu, D.; Coca, D. Signal Filtering Using Neuromorphic Measurements. J. Low Power Electron. Appl. 2023, 13, 63.
Abstract
Digital filtering is a fundamental technique in digital signal processing, which operates on a digital
sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is done by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically we can achieve a similar accuracy compared to the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.
Keywords
event-driven sampling; time encoding machines; filter design
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
