Harmonic signal extraction from noisy chaotic interferencebased on synchrosqueezed wavelet transform

2015 ◽  
Vol 24 (8) ◽  
pp. 080203 ◽  
Author(s):  
Xiang-Li Wang ◽  
Wen-Bo Wang
Keyword(s):  
Wavelet Transform ◽  
Harmonic Signal ◽  
Signal Extraction ◽  
Synchrosqueezed Wavelet Transform

scholarly journals Harmonic signal extraction from chaotic interference based on synchrosqueezed wavelet transform

2015 ◽  
Vol 64 (10) ◽  
pp. 100201
Author(s):  
Wang Xiang-Li ◽  
Wang Bin ◽  
Wang Wen-Bo ◽  
Yu Min ◽  
Wang Zhen ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Harmonic Signal ◽  
Signal Extraction ◽  
Synchrosqueezed Wavelet Transform ◽  
Chaotic Interference

scholarly journals Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform

2020 ◽  
Vol 28 (2) ◽  
pp. 390-398 ◽  
Author(s):  
Takafumi Kinoshita ◽  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Keiko Ogawa ◽  
Yukiyoshi Sumi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Sleep Spindle ◽  
Synchrosqueezed Wavelet Transform

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

scholarly journals A New Method for Fundamental Signal Extraction Based on Wavelet Transform

Automatika ◽  
2016 ◽  
Vol 57 (2) ◽  
Author(s):  
Javad Modarresi ◽  
Eskandar Gholipour
Keyword(s):  
Wavelet Transform ◽  
New Method ◽  
Signal Extraction

Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I

2020 ◽  
Vol 20 (12) ◽  
pp. 2050138
Author(s):  
Wilson D. Sanchez ◽  
Jose V. de Brito ◽  
Suzana M. Avila
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Phase I ◽  
Damage Detection ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Detection Methods ◽  
State Of Health ◽  
Civil Structures ◽  
Synchrosqueezed Wavelet Transform

Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads. When a structure ages, it is necessary to know its state of health and make a decision of maintenance or replacement. When a structure such as a bridge or building is subjected to destructive environmental forces, determining its state of health becomes a priority since its recovery is urgently required to function normally. Structural Health Monitoring (SHM) is a technology that aims to prevent the collapse of structures and loss of human life through early diagnosis of the health status of a structure. There are a large number of damage detection methods that can be classified into (1) non-destructive testing methods, (2) dynamic characteristics-based damage detection methods, (3) dynamic response-based, (4) multi-scale damage detection method and (5) damage detection methods with consideration of uncertainties. In this work, it is implemented synchrosqueezed wavelet transform (SWT), which can be classified as a methods based on the dynamic response. To validate the robustness of the method it is identified first, the natural frequencies of the Benchmark Phase I without damage, which consists of a steel structure of 4-story [Formula: see text] bay 3D steel frame structure subjected to ambient vibrations. Subsequently, some damage patterns are validated according to IASC-ASCE SHM Task Group. The results obtained in the identification of natural frequencies are compared with those reported in literature. SWT was efficient, presenting a minimum error of 0.12[Formula: see text] and a maximum of 3.06[Formula: see text] in the identification of natural frequencies about the AISCE-ASCE group model. SWT overcomes some other damage detection methods, which are deficient in the identification of closely spaced frequencies, commonly present in many civil structures due to symmetric geometry or similar physical properties in different directions.

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