MEMS Gyro Denoising Based on Second Generation Wavelet Transform

2010 ◽  
Author(s):  
Mao Ben ◽  
Wu Jun Wei ◽  
Wu Jian Tong ◽  
Zhou Xue Mei
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Second Generation Wavelet Transform ◽  
Second Generation Wavelet

Nonlinear Enhancement Algorithm for Infrared Image Based on Second Generation Wavelet Transform

2009 ◽  
Vol 29 (2) ◽  
pp. 353-356 ◽  
Author(s):  
秦翰林 Qin Hanlin ◽  
周慧鑫 Zhou Huixin ◽  
刘上乾 Liu Shangqian ◽  
卢泉 Lu Quan
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Infrared Image ◽  
Second Generation Wavelet Transform ◽  
Second Generation Wavelet ◽  
Enhancement Algorithm

Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform

2011 ◽  
Vol 225 (4) ◽  
pp. 799-808 ◽  
Author(s):  
N Li ◽  
R Zhou ◽  
X Z Zhao
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Selection Criterion ◽  
Signal Denoising ◽  
Mechanical Equipment ◽  
Denoising Method ◽  
Second Generation Wavelet Transform ◽  
Non Linear ◽  
Early Fault Detection ◽  
Second Generation Wavelet

Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.

Construction of a Finite Element Multiresolution Method Based on the Second Generation Wavelets

2011 ◽  
Vol 291-294 ◽  
pp. 1161-1164
Author(s):  
Yun Lin ◽  
Yu Min He ◽  
Xiao Long Zhang
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
High Resolution ◽  
Second Generation ◽  
Singular Problems ◽  
Numerical Tests ◽  
Second Generation Wavelet Transform ◽  
High Flexibility ◽  
Second Generation Wavelet ◽  
Second Generation Wavelets

The second generation wavelet transform has shown the property of high flexibility in multiresolution analysis. Based on this powerful tool, a new finite element multiresolution method is proposed. By using this method, the equation can be resolved in the low-resolution space, and then the coarse solution can be refined by adding the detail solutions in the detail spaces gradually till the solution in a high-resolution space satisfies the expected accuracy. This method is easy to achieve self-adapting algorithm and is suited for solving the singular problems. Numerical tests demonstrate the validity of this method.

Second generation wavelet transform for data denoising in PD measurement

2007 ◽  
Vol 14 (6) ◽  
pp. 1531-1537 ◽  
Author(s):  
Xiaodi Song ◽  
Chengke Zhou ◽  
D.M. Hepburn ◽  
Guobin Zhang ◽  
M. Michel
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Second Generation Wavelet Transform ◽  
Second Generation Wavelet
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