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.

Image Enhancement Method for Steel Surface Defects Based on DT-CWT

2013 ◽  
Vol 380-384 ◽  
pp. 3686-3689
Author(s):  
Zhi Xin Chen ◽  
Ping Wang ◽  
Shi Kun Xie
Keyword(s):  
Surface Defects ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Complex Wavelet Transform ◽  
Enhancement Method ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Simulation Results ◽  
Non Gaussian ◽  
Better Than

A method based on Dual-Tree Complex Wavelet Transform (DT-CWT) was proposed for enhancing the images. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking advantage of near shift-invariance of DT-CWT, it can obtain higher signal-to-noise ratio (SNR) than common wavelet denoising methods. The simulation results show that the proposed method is better than the traditional methods. It has a good enhancement performance which can improve the details of the image automatically.

Attenuation of noise and simultaneous source interference using wavelet denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V179-V190 ◽  
Author(s):  
Zhou Yu ◽  
Ray Abma ◽  
John Etgen ◽  
Claire Sullivan
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Wavelet Denoising ◽  
Complex Wavelet Transform ◽  
Low Velocity ◽  
Noise Characteristics ◽  
Interference Noise ◽  
Seismic Image ◽  
Simultaneous Source

High-resolution seismic imaging requires noise attenuation to achieve signal-to-noise ratio (S/N) improvements without compromising data bandwidth. Amplitude versus offset analysis requires good amplitude fidelity in premigration processes. Any nonreflected wavefield energy in the data will degrade the seismic image quality. Despite significant progress over the years, preserving low-frequency signals without compromising the S/N and avoiding the smearing of aliased signal are still a challenge for conventional methods. This problem is compounded when additional interference noise is added with simultaneous source acquisition. Because noise characteristics vary from shot to shot and receiver to receiver, we need a method that is robust and effective. In addition, we also want the method to be efficient and easy to use from a practical perspective. We have recently developed an approach using a wavelet transform to deterministically separate the primary signal from the noise, including simultaneous source interference. The goals are (1) improving the S/N without compromising bandwidth, (2) preserving the low-frequency and near-offset primaries without compromising the S/N, and (3) preserving the local primary wavefield while attenuating noise. For distance-separated simultaneous source acquisition, the goal is preserving long-offset primaries while removing interference. This wavelet denoising flow consists of a linear transformation and filtering using the complex wavelet transform (CWT). For reflection signals, normal moveout (NMO) is used. NMO transforms the low-velocity surface waves and the interference noise to where it is easily identified and rejected with a dip filter in the multidimensional CWT domain. Land field data examples have demonstrated significantly improved S/Ns and low-frequency signal preservation in migrated images after wavelet denoising. Since the numerical implementation of the CWT is as fast as a fast Fourier transform, this flow is able to suppress noise and interference simultaneously on the 3D land data much faster than the other inversion methods.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

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.

scholarly journals Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test

2018 ◽  
Vol 5 (9) ◽  
pp. 180436 ◽  
Author(s):  
Khuram Naveed ◽  
Bisma Shaukat ◽  
Naveed ur Rehman
Keyword(s):  
Wavelet Transform ◽  
Goodness Of Fit ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Complex Wavelet Transform ◽  
Goodness Of Fit Test ◽  
Denoising Method ◽  
Complex Wavelet

A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.

scholarly journals DT-CWT Robust Filtering Algorithm for The Extraction of Reference and Waviness from 3-D Nano Scalar Surfaces

2014 ◽  
Vol 14 (2) ◽  
pp. 87-93 ◽  
Author(s):  
Zhi Ying. Ren ◽  
ChengHui. Gao ◽  
GuoQiang. Han ◽  
Shen Ding ◽  
JianXing. Lin
Keyword(s):  
Computer Simulation ◽  
Wavelet Transform ◽  
Perfect Reconstruction ◽  
Robust Filtering ◽  
Directional Selectivity ◽  
Bilateral Filtering ◽  
Complex Wavelet Transform ◽  
Filtering Algorithm ◽  
Simulation Based ◽  
Shift Invariance

Abstract Dual tree complex wavelet transform (DT-CWT) exhibits superiority of shift invariance, directional selectivity, perfect reconstruction (PR), and limited redundancy and can effectively separate various surface components. However, in nano scale the morphology contains pits and convexities and is more complex to characterize. This paper presents an improved approach which can simultaneously separate reference and waviness and allows an image to remain robust against abnormal signals. We included a bilateral filtering (BF) stage in DT-CWT to solve imaging problems. In order to verify the feasibility of the new method and to test its performance we used a computer simulation based on three generations of Wavelet and Improved DT-CWT and we conducted two case studies. Our results show that the improved DT-CWT not only enhances the robustness filtering under the conditions of abnormal interference, but also possesses accuracy and reliability of the reference and waviness from the 3-D nano scalar surfaces.

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