Sequential Forward Mother Wavelet Selection Method for Mental Workload Assessment on N-back Task Using Photoplethysmography Signals

2021 ◽  
pp. 103966
Author(s):  
Tugba Aydemir ◽  
Mehmet Şahin ◽  
Onder Aydemir
Keyword(s):  
Mental Workload ◽  
Selection Method ◽  
Mother Wavelet ◽  
Wavelet Selection ◽  
Back Task

scholarly journals Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators

2020 ◽  
Vol 10 (6) ◽  
pp. 2162
Author(s):  
Yanan Li ◽  
Zhaohui Li
Keyword(s):  
Partial Discharge ◽  
Selection Method ◽  
Signal Denoising ◽  
Measured Signal ◽  
Wavelet Shrinkage ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Wavelet Selection ◽  
Core Idea ◽  
The One

Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

Wind Energy ◽  
2019 ◽  
Vol 22 (11) ◽  
pp. 1581-1592 ◽  
Author(s):  
Daniel Strömbergsson ◽  
Pär Marklund ◽  
Kim Berglund ◽  
Juhamatti Saari ◽  
Allan Thomson
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Discrete Wavelet Transform ◽  
Condition Monitoring ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Wavelet Selection

Comparing mother wavelet selection criteria for road pavements NDT monitoring

2019 ◽  
pp. 618-623 ◽  
Author(s):  
R. Fedele ◽  
F.G. Praticò ◽  
R. Carotenuto ◽  
F.G. Della Corte
Keyword(s):  
Selection Criteria ◽  
Mother Wavelet ◽  
Wavelet Selection

scholarly journals Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

2014 ◽  
Vol 7 ◽  
Author(s):  
Christian Herff ◽  
Dominic Heger ◽  
Ole Fortmann ◽  
Johannes Hennrich ◽  
Felix Putze ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Mental Workload ◽  
Back Task
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