scholarly journals The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Rogelio Ramos ◽  
Benjamin Valdez-Salas ◽  
Roumen Zlatev ◽  
Michael Schorr Wiener ◽  
Jose María Bastidas Rull
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Electrical Potential ◽  
Digital Processing ◽  
Noise Removal ◽  
Localized Corrosion ◽  
Electrical Noise ◽  
Discrete Wavelet ◽  
Electrical Measurements ◽  
Threshold Method

The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET) in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI), embedded systems, and the discrete wavelet transform (DWT). The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR) superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

scholarly journals SlantletTransformbased VideoDenoising

2011 ◽  
Vol 8 (2) ◽  
pp. 595-601
Author(s):  
Baghdad Science Journal
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Gaussian Noise ◽  
Noise Removal ◽  
Experimental Results ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Frame Sequence ◽  
Objective Quality ◽  
Original Frame

A technique for noise removal is proposed based on slantlet transform. The proposed algorithm tends to reduce the computational time by reducing the total number of frames through dividing the video film into sub films, finding master frames, applying the slantlet transform which is orthogonal discrete wavelet transform with two zero moments and with improved time localization. Thresholding technique is applied to the details coefficients of the slantlet transform .The denoised frame is repeated to retain the original frame sequence. The proposed method was applied by using MATLAB R2010a with video contaminated by white Gaussian noise .The experimental results show that the proposed method provides better subjective and objective quality, and obtain up to 5-6 dB PSNR improvement from the frames contaminated by noise.

scholarly journals ECG Signal Denoising by Discrete Wavelet Transform

2017 ◽  
Vol 13 (09) ◽  
pp. 51 ◽  
Author(s):  
Mounaim Aqil ◽  
Atman Jbari ◽  
Abdennasser Bourouhou
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Threshold Method ◽  
Baseline Drift ◽  
Ecg Denoising ◽  
The Mean ◽  
Power Line Interference ◽  
Electrocardiogram Ecg ◽  
Selection Of

<p>The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.</p>

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

IRBM ◽  
2014 ◽  
Vol 35 (6) ◽  
pp. 351-361 ◽  
Author(s):  
H.-Y. Lin ◽  
S.-Y. Liang ◽  
Y.-L. Ho ◽  
Y.-H. Lin ◽  
H.-P. Ma
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ecg Signals

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization
Sign in / Sign up

Export Citation Format

Share Document