A Scheme for Image Classification and Adaptive Mother Wavelet Selection

2006 ◽  
Author(s):  
B.S. Shajeemohan ◽  
V.K. Govindan ◽  
Baby Vijilin
Keyword(s):  
Image Classification ◽  
Mother Wavelet ◽  
Wavelet Selection

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 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.

scholarly journals Relationship Between Knee Extensors Power Output and Vastus Lateralis EMG Activation in Elderly Women: Influence of Mother Wavelet Selection

2017 ◽  
Vol 10 (1) ◽  
pp. 263-271
Author(s):  
João Pedro Pinho ◽  
Bruno Mezêncio ◽  
Desidério Cano Porras ◽  
Julio Cerca Serrão ◽  
Alberto Carlos Amadio
Keyword(s):  
Cross Correlation ◽  
Elderly Women ◽  
Vastus Lateralis ◽  
Biceps Femoris ◽  
Knee Extension ◽  
Knee Extensors ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Wavelet Selection ◽  
Mother Wavelets

Purpose:The main objective of this study was to compare frequency parameters produced by six mother wavelets pinpointing the most feasible to investigate electromyographic (EMG) parameters while producing knee extension power in elderly women. The influence of different load conditions in mother wavelet selection and power output were also analyzed.Methods:Thirteen sedentary elderly women (69.3 ± 4.1 years) took part in the study. Participants executed 6 repetitions of 3 load condition (30%, 50% and 70% of the maximal) with the concentric phase of the knee extension movement as quickly as possible. Kinematic data obtained by video analysis, an anthropometric model and Newtonian mechanics were used to calculate knee extensors’ power. A continuous wavelet analysis was used as a time-frequency transformation strategy of vastus lateralis and biceps femoris EMG data and six different mother wavelets were selected: Morlet; 4th, 8th and 44th order Daubechie, 4th order Coiflet and 5th order Symlet.Results:44th order Daubechie showed the highest maximal cross correlation value and no differences were seen between different mother wavelets and cross correlation at zero lag and in the lag variable. Although increased knee extensors peak power at higher loads were seen, no differences in vastus lateralis or biceps femoris root mean square values were obtained.Conclusion:44th order Daubechie mother wavelet was pinpointed as the most suitable to obtain EMG time-frequency parameters. We have also seen that different load conditions do not seem to have an influence on mother wavelet selection.

scholarly journals Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

2015 ◽  
Vol 31 (2) ◽  
pp. 148-159 ◽  
Author(s):  
Verônica Isabela Quandt ◽  
Edras Reily Pacola ◽  
Sérgio Francisco Pichorim ◽  
Humberto Remigio Gamba ◽  
Miguel Antônio Sovierzoski
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Border Effect ◽  
Mother Wavelet ◽  
Wavelet Selection
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