Article
Version 1
This version is not peer-reviewed
1D Electronic Noise Filtering Using an Autoencoder
Version 1
: Received: 25 July 2024 / Approved: 26 July 2024 / Online: 26 July 2024 (06:39:01 CEST)
How to cite: Perotoni, M. B.; Lucio, L. F. 1D Electronic Noise Filtering Using an Autoencoder. Preprints 2024, 2024072138. https://doi.org/10.20944/preprints202407.2138.v1 Perotoni, M. B.; Lucio, L. F. 1D Electronic Noise Filtering Using an Autoencoder. Preprints 2024, 2024072138. https://doi.org/10.20944/preprints202407.2138.v1
Abstract
Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.
Keywords
Noise Filtering; Denoise; Deep Convolutional Autoencoder; Noise Generator
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.
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