scholarly journals Discrete Wavelet Transform Based Selection of Salient EEG Frequency Band for Assessing Human Emotions

10.5772/20990 ◽  
2011 ◽  
Author(s):  
M. Murugappan ◽  
R. Nagarajan ◽  
S. Yaacob
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Frequency Band ◽  
Discrete Wavelet ◽  
Human Emotions ◽  
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.

Digital Watermarking Based on Wavelet and Chaotic Mapping

2014 ◽  
Vol 519-520 ◽  
pp. 520-523
Author(s):  
Yuan Song ◽  
Bo Zhou ◽  
Guo Jun Zhou
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Watermarking ◽  
Frequency Band ◽  
Image Watermarking ◽  
Chaotic Sequence ◽  
Experimental Results ◽  
Discrete Wavelet ◽  
Original Image ◽  
Chaotic Mapping

An algorithm of wavelet digital watermarking was proposed to improve the security of the watermark. First, the logistic chaotic sequence was used to generate an encrypted image watermarking, then discrete wavelet transform (DWT) was used to decompose the original image and the watermark was embedded into the selected frequency band of significant wavelet coefficients. Finally, an approach for extraction was adopted. Experimental results show that the watermark is robust to attacks if the chaotic key is unknown.

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