Studies on 1D Electronic Noise Filtering Using an Autoencoder
Year: 2024
Type: article
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 is 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, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms.
Source: Knowledge
Institution: Universidade Federal do ABC
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,137
OpenAlex ID: W4404567258