scholarly journals NON-GAUSSIAN CHARACTERISTICS OF COASTAL WAVES

1984 ◽  
Vol 1 (19) ◽  
pp. 35 ◽  
Author(s):  
Michel K. Ochi ◽  
Wei-Chi Wang
Keyword(s):  
Statistical Analysis ◽  
Probability Distribution ◽  
Water Depth ◽  
Wave Characteristics ◽  
Coastal Waves ◽  
Gaussian Probability Distribution ◽  
Non Gaussian

This paper presents the results of a study on non-Gaussian characteristic of coastal waves. From the results of the statistical analysis of more than 500 records obtained in the growing stage of the storm, the parameters involved in the non-Gaussian probability distribution which are significant for predicting wave characteristics are clarified, and these parameters are expressed as a function of water depth and sea severity. The limiting sea severity below which the wind-generated coastal waves are considered to be Gaussian is obtained for a given water depth.

Signal Denoising of MEMS Microstructure Profile

2008 ◽  
Vol 381-382 ◽  
pp. 69-72
Author(s):  
Kai Hu ◽  
Xiang Qian Jiang ◽  
Xiao Jun Liu
Keyword(s):  
Wavelet Transform ◽  
Probability Distribution ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Complex Wavelet Transform ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Feature Information ◽  
Visual Artifacts ◽  
Non Gaussian

A new signal-denoising approach based on DT-CWT (Dual-Tree Complex Wavelet Transform) is presented in this paper to extract feature information from microstructure profile. It takes advantage of shift invariance of DT-CWT, non-Gaussian probability distribution for the wavelet coefficients and the statistical dependencies between a coefficient and its parent. This approach substantially improved the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts. A simulated MEMS microstructure signal is analyzed.

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