Kaplun, Dmitry, Voznesenskiy, Alexander, Romanov, Sergei, Nepomuceno, Erivelton and Butusov, Denis (2019) Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm. Entropy, 21 (9) (843). pp. 1-16. ISSN 1099-4300
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Abstract
In this paper, we consider the application of the matching pursuit algorithm (MPA) for
spectral analysis of non-stationary signals. First, we estimate the approximation error and the
performance time for various MPA modifications and parameters using central processor unit and
graphics processing unit (GPU) to identify possible ways to improve the algorithm. Next, we propose
the modifications of discrete wavelet transform (DWT) and package wavelet decomposition (PWD) for
further use in MPA. We explicitly show that the optimal decomposition level, defined as a level with
minimum entropy, in DWT and PWD provides the minimum approximation error and the smallest
execution time when applied in MPA as a rough estimate in the case of using wavelets as basis functions
(atoms). We provide an example of entropy-based estimation for optimal decomposition level in
spectral analysis of seismic signals. The proposed modification of the algorithm significantly reduces
its computational costs. Results of spectral analysis obtained with MPA can be used for various signal
processing applications, including denoising, clustering, classification, and parameter estimation.
Item Type: | Article |
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Keywords: | wavelet transform; digital signal processing; spectral analysis; matching pursuit algorithm; decomposition level; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 16737 |
Identification Number: | 10.3390/e21090843 |
Depositing User: | Erivelton Nepomuceno |
Date Deposited: | 22 Nov 2022 14:15 |
Journal or Publication Title: | Entropy |
Publisher: | MDPI |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/16737 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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