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Quantized Information in Spectral Cyberspace
The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling meth...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047514/ https://www.ncbi.nlm.nih.gov/pubmed/36981308 http://dx.doi.org/10.3390/e25030419 |
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author | Garcés, Milton A. |
author_facet | Garcés, Milton A. |
author_sort | Garcés, Milton A. |
collection | PubMed |
description | The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications. |
format | Online Article Text |
id | pubmed-10047514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100475142023-03-29 Quantized Information in Spectral Cyberspace Garcés, Milton A. Entropy (Basel) Article The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications. MDPI 2023-02-26 /pmc/articles/PMC10047514/ /pubmed/36981308 http://dx.doi.org/10.3390/e25030419 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Garcés, Milton A. Quantized Information in Spectral Cyberspace |
title | Quantized Information in Spectral Cyberspace |
title_full | Quantized Information in Spectral Cyberspace |
title_fullStr | Quantized Information in Spectral Cyberspace |
title_full_unstemmed | Quantized Information in Spectral Cyberspace |
title_short | Quantized Information in Spectral Cyberspace |
title_sort | quantized information in spectral cyberspace |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047514/ https://www.ncbi.nlm.nih.gov/pubmed/36981308 http://dx.doi.org/10.3390/e25030419 |
work_keys_str_mv | AT garcesmiltona quantizedinformationinspectralcyberspace |