Adaptive Signal Processing Algorithms Based on EMD and ITD

In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD) algorithms deserved a variety of applications in various fields of science and engineering due to their obvious advantages compared to conventional (e.g. correlation- or spectral-based analy...

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Vydané v:IEEE access Ročník 7; s. 171313 - 171321
Hlavní autori: Voznesensky, Alexander, Kaplun, Dmitrii
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive algorithms
Adaptive filter bank
Adaptive filters
Algorithms
Approximation algorithms
Chirp modulation
Correlation analysis
denoising algorithms
Empirical analysis
empirical mode decomposition
Fourier transform
Frequency modulation
Hilbert-Huang transform
internal oscillations
intrinsic time-scale decomposition
Signal analysis
Signal processing
Signal processing algorithms
spectral analysis
Time-frequency analysis
wavelet transform
ISSN:2169-3536, 2169-3536
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Abstract In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD) algorithms deserved a variety of applications in various fields of science and engineering due to their obvious advantages compared to conventional (e.g. correlation- or spectral-based analysis) approaches like the ability of their direct application to non-stationary signal analysis. However, high computational complexity remains a common drawback of these otherwise universal and powerful algorithms. Here we compare similarly designed signal analysis algorithms utilizing either EMD or ITD as their core functions. Based on extensive computer simulations, we show explicitly that the replacement of EMD by ITD in several otherwise similar signal analysis scenarios leads to the increased noise robustness with simultaneous considerable reduction of the processing time. We also demonstrate that the proposed algorithms modifications could be successfully utilized in a series of emerging applications for processing of non-stationary signals.
AbstractList In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD) algorithms deserved a variety of applications in various fields of science and engineering due to their obvious advantages compared to conventional (e.g. correlation- or spectral-based analysis) approaches like the ability of their direct application to non-stationary signal analysis. However, high computational complexity remains a common drawback of these otherwise universal and powerful algorithms. Here we compare similarly designed signal analysis algorithms utilizing either EMD or ITD as their core functions. Based on extensive computer simulations, we show explicitly that the replacement of EMD by ITD in several otherwise similar signal analysis scenarios leads to the increased noise robustness with simultaneous considerable reduction of the processing time. We also demonstrate that the proposed algorithms modifications could be successfully utilized in a series of emerging applications for processing of non-stationary signals.
Author Voznesensky, Alexander
Kaplun, Dmitrii
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SubjectTerms Adaptive algorithms
Adaptive filter bank
Adaptive filters
Algorithms
Approximation algorithms
Chirp modulation
Correlation analysis
denoising algorithms
Empirical analysis
empirical mode decomposition
Fourier transform
Frequency modulation
Hilbert-Huang transform
internal oscillations
intrinsic time-scale decomposition
Signal analysis
Signal processing
Signal processing algorithms
spectral analysis
Time-frequency analysis
wavelet transform
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Title Adaptive Signal Processing Algorithms Based on EMD and ITD
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