Wavelet Selection for EMG Based Grasp Recognition through CWT

2011 ◽  
pp. 119-129
Author(s):  
Adity Saikia ◽  
Nayan M. Kakoty ◽  
Shyamanta M. Hazarika
Keyword(s):  
Selection For ◽  
Wavelet Selection

scholarly journals Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

2020 ◽  
Vol 125 (1) ◽  
pp. 299-313
Author(s):  
Chaosheng Tang ◽  
Deepak Ranjan Nayak ◽  
Shuihua Wang
Keyword(s):  
Support Vector Machine ◽  
Hearing Loss ◽  
Least Square ◽  
Support Vector ◽  
Selection For ◽  
Wavelet Selection

BASE WAVELET SELECTION FOR BEARING VIBRATION SIGNAL ANALYSIS

2009 ◽  
Vol 07 (04) ◽  
pp. 411-426 ◽  
Author(s):  
RUQIANG YAN ◽  
ROBERT X. GAO
Keyword(s):  
Quantitative Measure ◽  
Vibration Signal ◽  
Critical Issue ◽  
Structural Defects ◽  
Machine Condition Monitoring ◽  
Bearing Vibration ◽  
Selection For ◽  
Wavelet Selection ◽  
Vibration Signal Analysis ◽  
Health Diagnosis

A critical issue to ensuring the effectiveness of wavelet transform in machine condition monitoring and health diagnosis is the choice of the most suited base wavelet for signal decomposition and feature extraction. This paper addresses this issue by introducing a quantitative measure to select an appropriate base wavelet for analyzing vibration signals measured on rotary mechanical systems. Specifically, the measure based on energy-to-Shannon entropy ratio has been investigated. Both the simulated Gaussian-modulated sinusoidal signal and an actual ball bearing vibration signal have been used to evaluate the effectiveness of the developed measure on base wavelet selection. Experimental results demonstrate that the wavelet selected using the developed measure is better suited than other wavelets in diagnosing structural defects in the bearing. The method developed provides systematic guidance in wavelet selection.

scholarly journals Suitable Mother Wavelet Selection for EEG Signals Analysis: Frequency Bands Decomposition and Discriminative Feature Selection

2020 ◽  
Vol 11 (1) ◽  
pp. 33-49
Author(s):  
Romain Atangana ◽  
Daniel Tchiotsop ◽  
Godpromesse Kenne ◽  
Laurent Chanel Djoufack Nkengfack
Keyword(s):  
Feature Selection ◽  
Mother Wavelet ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Discriminative Feature ◽  
Selection For ◽  
Wavelet Selection
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