Perfectly invertible, fast, and complete wavelet transform for finite-length sequences: the discrete periodic wavelet transform

1993 ◽  
Author(s):  
Neil H. Getz
Keyword(s):  
Wavelet Transform ◽  
Finite Length ◽  
Periodic Wavelet

Comparison of Hydraulic Pump Faults Diagnosis Methods: Wavelet vs. Spectral Analyses

2005 ◽  
Author(s):  
Yingjie Gao ◽  
Qin Zhang ◽  
Xiangdong Kong
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Finite Length ◽  
Data Sets ◽  
Analysis Method ◽  
Hydraulic Pump ◽  
Transform Method ◽  
Outlet Pressure ◽  
Spectral Analysis Method ◽  
Faults Diagnosis

This paper introduces two faults diagnosis methods, a conventional spectral analysis method and a wavelet transform method, for hydraulic pump applications. The fundamental technologies of both methods, as well as their performance in detecting a few common hydraulic pump defects, are described in this paper. The performance of both diagnoses methods were evaluated based on experimental results. In order to eliminate the effects of border distortion arising from applying wavelet transform to finite-length signals, the pump outlet pressure in this case, a preprocess on the obtained signals is carried to clean up the errors prior to faults diagnosis analysis. Validation results obtained from both methods in analyzing the same data sets indicated that the wavelet transform based method showed a more sensitive and robust detecting capability than that obtained from a spectrum analyses approach.

scholarly journals The Improvement of Discrete Wavelet Transform

2021 ◽  
Author(s):  
Zhihua Zhang
Keyword(s):  
Wavelet Transform ◽  
Decay Rate ◽  
Discrete Wavelet Transform ◽  
Wavelet Transforms ◽  
Polynomial Interpolation ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Periodic Wavelet ◽  
Improved Algorithm

Discrete wavelet transform and discrete periodic wavelet transform have been widely used in image compression and data approximation. Due to discontinuity on the boundary of original data, the decay rate of the obtained wavelet coefficients is slow. In this study, we use the combination of polynomial interpolation and one-dimensional/two-dimensional discrete periodic wavelet transforms to mitigate boundary effects. The decay rate of the obtained wavelet coefficients in our improved algorithm is faster than that of traditional two-dimensional discrete wavelet transform. Moreover, our improved algorithm can be extended naturally to the higher-dimensional case.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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