Selection of best wavelet for discrete wavelet transform based PID controller connected with liquid level system and its performances analysis

2015 ◽  
Author(s):  
Rimi Paul ◽  
Anindita Sengupta
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Pid Controller ◽  
Liquid Level ◽  
Discrete Wavelet ◽  
Level System ◽  
Selection Of

On-Line Seam Detection in Rolling Processes Using Snake Projection and Discrete Wavelet Transform

2007 ◽  
Vol 129 (5) ◽  
pp. 926-933 ◽  
Author(s):  
Jing Li ◽  
Jianjun Shi ◽  
Tzyy-Shuh Chang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Projection Method ◽  
Discrete Wavelet ◽  
On Line ◽  
Feature Preserving ◽  
Two Parameters ◽  
T2 Control Chart ◽  
Speed And Accuracy ◽  
Selection Of

This paper describes the development of an on-line quality inspection algorithm for detecting the surface defect “seam” generated in rolling processes. A feature-preserving “snake-projection” method is proposed for dimension reduction by converting the suspicious seam-containing images to one-dimensional sequences. Discrete wavelet transform is then performed on the sequences for feature extraction. Finally, a T2 control chart is established based on the extracted features to distinguish real seams from false positives. The snake-projection method has two parameters that impact the effectiveness of the algorithm. Thus, selection of the parameters is discussed. Implementation of the proposed algorithm shows that it satisfies the speed and accuracy requirements for on-line seam detection.

scholarly journals A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 1420-1429
Author(s):  
Abdelouahad Achmamad ◽  
Atman Jbari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Neuromuscular Disorders ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Emg Signal ◽  
Selection Of

Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

scholarly journals Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5253
Author(s):  
Fthi M. Albkosh ◽  
Muhammad Suzuri Hitam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Rozniza Ali
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Texture Classification ◽  
Optimization Technique ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Bee Colony ◽  
Selection Of

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

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>

DIRECTIONAL SELECTION OF TWO-DIMENSIONAL COMPLEX DISCRETE WAVELET TRANSFORM AND ITS APPLICATION TO IMAGE PROCESSING

2010 ◽  
Vol 08 (04) ◽  
pp. 659-676 ◽  
Author(s):  
ZHONG ZHANG ◽  
NARIYA KOMAZAKI ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Directional Selection ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Directional Filter ◽  
Sampling Process ◽  
Frequency Components ◽  
Selection Of

In this study, a novel direction selection method using the two-dimensional complex discrete wavelet transform (2D-CDWT) is proposed. In order to achieve arbitrary direction selection, the directional filters are first designed. Calculation procedure of directional selection can be shown as follows: (1) The 16 sub-images are generally generated from the original image by the 2D-CDWT without a down-sampling process and the 12 sub-images that correspond to the high-frequency components are selected. (2) The 12 sub-images are filtered by using the designed directional filter. (3) The down-sampling process is carried out and the resulting images are obtained. Furthermore, this method is applied to the surface analysis of a wafer, and it is confirmed that our method is effective in detecting irregular direction components.

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