Effective Realizations of Biorthogonal Wavelet Transforms of Lengths $$2K + 1$$ / $$2K - 1$$ with Lattice Structures on GPU and CPU

2015 ◽  
pp. 130-137 ◽  
Author(s):  
Dariusz Puchala ◽  
Bartomiej Szczepaniak ◽  
Mykhaylo Yatsymirskyy
Keyword(s):  
Wavelet Transforms ◽  
Lattice Structures ◽  
Biorthogonal Wavelet

Image restoration using biorthogonal wavelet transforms

1994 ◽  
Vol 33 (7) ◽  
pp. 2378 ◽  
Author(s):  
Pierre Mathieu
Keyword(s):  
Image Restoration ◽  
Wavelet Transforms ◽  
Biorthogonal Wavelet

Rapid design of biorthogonal wavelet transforms

2000 ◽  
Vol 147 (5) ◽  
pp. 293 ◽  
Author(s):  
S. Masud ◽  
J.V. McCanny
Keyword(s):  
Wavelet Transforms ◽  
Biorthogonal Wavelet ◽  
Rapid Design

Fault Diagnosis of Gearbox Based on Wavelet Transforms and Neural Networks

2011 ◽  
Vol 148-149 ◽  
pp. 919-922
Author(s):  
Guang Bin Zhang ◽  
Yun Jian Ge ◽  
Yong Jiu Liu
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Failure Diagnosis ◽  
Biorthogonal Wavelet ◽  
Actual System ◽  
Network Learning ◽  
Learning Speed ◽  
Vanishing Moment ◽  
Marquardt Algorithm ◽  
Different Types

In this paper, an actual system based on wavelet transform and artificial neural networks was established to diagnose different types of fault in a gearbox. As a key step, biorthogonal wavelet was used to denoise in feature extraction of signals because of its properties of compact support, high vanishing moment and symmetry. Consequently, a multi-layer perceptron network were designed to diagnose the fault status with feature vectors as inputs. In order to improve the network learning speed and stability, Levenberg-Marquardt algorithm was used to train the network. The present classification accuracy indicates the effectiveness of gearbox failure diagnosis.

Biorthogonal Wavelet Transforms

2015 ◽  
pp. 215-237
Author(s):  
Amir Z. Averbuch ◽  
Pekka Neittaanmäki ◽  
Valery A. Zheludev
Keyword(s):  
Wavelet Transforms ◽  
Biorthogonal Wavelet
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