A Scheme for Image Classification and Adaptive Mother Wavelet Selection

Author(s):  
B.S. Shajeemohan ◽  
V.K. Govindan ◽  
Baby Vijilin
Wind Energy ◽  
2019 ◽  
Vol 22 (11) ◽  
pp. 1581-1592 ◽  
Author(s):  
Daniel Strömbergsson ◽  
Pär Marklund ◽  
Kim Berglund ◽  
Juhamatti Saari ◽  
Allan Thomson

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

2020 ◽  
Vol 10 (6) ◽  
pp. 2162
Author(s):  
Yanan Li ◽  
Zhaohui Li

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.


2017 ◽  
Vol 10 (1) ◽  
pp. 263-271
Author(s):  
João Pedro Pinho ◽  
Bruno Mezêncio ◽  
Desidério Cano Porras ◽  
Julio Cerca Serrão ◽  
Alberto Carlos Amadio

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.


2015 ◽  
Vol 31 (2) ◽  
pp. 148-159 ◽  
Author(s):  
Verônica Isabela Quandt ◽  
Edras Reily Pacola ◽  
Sérgio Francisco Pichorim ◽  
Humberto Remigio Gamba ◽  
Miguel Antônio Sovierzoski

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