Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm

Kaplun, Dmitry; Voznesenskiy, Alexander S.; Romanov, S. A.; Nepomuceno, Erivelton G.; Butusov, Denis N.

doi:10.3390/e21090843

Cited by 19 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decomposition level is another topic in wavelet transform, and there are also relative researches [36,37]. However, usually a reasonable decomposition level is enough.…”

Section: A Computation Results Of the Proposed Algorithmmentioning

confidence: 99%

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

et al. 2020

View full text Add to dashboard Cite

In electrical engineering, it is of great importance to monitor partial discharge (PD) of high voltage apparatus. However, on-site PD signal easily gets corrupted by white noise, and hence denoising the measured PD signal is necessary to acquire pure PD. In traditional method, single wavelet and regular thresholding are utilized. By combining wavelets of the same family, dual-tree complex wavelet pairs can be constructed. Specific combinations are approximately translation invariant, and they are also able to achieve better denoising effect without sophisticated computation. Meanwhile, considering the correlation between adjacent wavelet coefficients, the block thresholding is employed. In this paper, it is proposed to combine wavelet pair and block thresholding for PD denoising. There are four combinations based on single wavelet/wavelet pair and regular thresholding/block thresholding, and they are compared with each other. Based on a numerical study, the results demonstrate that the proposed algorithm outperforms the traditional method.

show abstract

“…The decomposition level is another topic in wavelet transform, and there are also relative researches [36,37]. However, usually a reasonable decomposition level is enough.…”

Section: A Computation Results Of the Proposed Algorithmmentioning

confidence: 99%

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

et al. 2020

View full text Add to dashboard Cite

show abstract

“…After choosing the candidate mother functions, the choice of decomposition level (DL) is most critical. We use the DL with minimum entropy as recommended in [40]. Additionally, the entropy of a signal is related to the number of coefficients needed to properly describe the signal [40].…”

Section: Wavelet Transformsmentioning

confidence: 99%

Automatic Detection of Electrodermal Activity Events during Sleep

Piccini,

August,

Noel Aziz Hanna

et al. 2023

Signals

View full text Add to dashboard Cite

Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and F1 score. We obtain F1 scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health.

show abstract

“…The wavelet transform is commonly used for analyzing non-stationary (seismic, biological, hydroacoustic etc.) signals, usually together with various spectral analysis algorithms [16,17].…”

Section: Harmonic Waveletsmentioning

confidence: 99%

“…Often the dictionary of atoms is full (all types of functions are used) and redundant (the functions are not mutually independent). One of the main problems in these techniques is the selection of basic functions and dictionary optimization to acheive optimal decomposition levels [17]. Decomposition levels for wavelets can be defined as:…”

Section: Harmonic Waveletsmentioning

confidence: 99%

Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

show abstract

Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm

Cited by 19 publications

References 16 publications

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

Automatic Detection of Electrodermal Activity Events during Sleep

Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

Contact Info

Product

Resources

About