Forearm EMG Signal Classification Based on Singular Value Decomposition and Wavelet Packet Transform Features

2012 ◽  
Vol 433-440 ◽  
pp. 912-916 ◽  
Author(s):  
Mohammad Karimi
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Wrist Flexion ◽  
Emg Signal ◽  
Value Decomposition ◽  
Svd Method

The myoelectric signal (MES) with broad applications in various areas especially in prosthetics and myoelectric control, is one of the biosignals utilized in helping humans to control equipments. In this paper, a technique for feature extraction of forearm electromyographic (EMG) signals using wavelet packet transform (WPT) and singular value decomposition (SVD) is proposed. In the first step, the WPT is employed to generate a wavelet decomposition tree from which features are extracted. In the second step, an algorithm based on singular value decomposition (SVD) method is introduced to compute the feature vectors for every hand motion. This technique can successfully identify eight hand motions including forearm pronation, forearm supination, wrist flexion, wrist abduction, wrist adduction, chuck grip, spread fingers and rest state. These motions can be obtained by measuring the surface EMG signal through sixteen electrodes mounted on the pronator and supinator teres, flexor digitorum, sublimas, extensor digitorum communis, and flexor and extensor carpi ulnaris. Moreover, through quantitative comparison with other feature extraction methods like entropy concept in this paper, SVD method has a better performance. The results showed that proposed technique can achieve a classification recognition accuracy of over 96% for the eight hand motions.

ROBUST REFERENCE-WATERMARKING SCHEME USING WAVELET PACKET TRANSFORM AND BIDIAGONAL-SINGULAR VALUE DECOMPOSITION

2009 ◽  
Vol 09 (03) ◽  
pp. 449-477 ◽  
Author(s):  
GAURAV BHATNAGAR ◽  
BALASUBRAMANIAN RAMAN
Keyword(s):  
Singular Value Decomposition ◽  
Gaussian Noise ◽  
Copyright Protection ◽  
Wavelet Packet ◽  
Singular Values ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Noise Type ◽  
Value Decomposition ◽  
Watermarking Scheme

This paper presents a new robust reference watermarking scheme based on wavelet packet transform (WPT) and bidiagonal singular value decomposition (bSVD) for copyright protection and authenticity. A small gray scale logo is used as watermark instead of randomly generated Gaussian noise type watermark. A reference watermark is generated by original watermark and the process of embedding is done in wavelet packet domain by modifying the bidiagonal singular values. For the robustness and imperceptibly, watermark is embedded in the selected sub-bands, which are selected by taking into account the variance of the sub-bands, which serves as a measure of the watermark magnitude that could be imperceptibly embedded in each block. For this purpose, the variance is calculated in a small moving square window of size Sp× Sp(typically 3 × 3 or 5 × 5 window) centered at the pixel. A reliable watermark extraction is developed, in which the watermark bidiagonal singular values are extracted by considering the distortion caused by the attacks in neighboring bidiagonal singular values. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks and the superiority of the proposed method is carried out by the comparison which is made by us with the existing methods.

Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
Author(s):  
Mourad Kedadouche ◽  
Zhaoheng Liu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Support Vector ◽  
Rolling Bearings ◽  
The Matrix ◽  
Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

Performance Comparison of Dense Frequency Identifications for Empirical Mode Decomposition

2011 ◽  
Vol 378-379 ◽  
pp. 266-269
Author(s):  
Min Zheng ◽  
Fan Shen
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Wavelet Packet ◽  
Performance Comparison ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Value Decomposition

Empirical Mode Decomposition(EMD) suffers some difficulties in separating dense frequencies. The Wavelet Packet Transform (WPT) and Singular-Value Decomposition (SVD) as signal preprocessors were used to decompose a simulated signal with dense frequency components and the performances of two signal preprocess technologies were compared in this paper. The results show that Singular-Value Decomposition (SVD) as preprocessor was better in separating dense frequencies than Wavelet Packet Transform (WPT).

Envelope Signal of Partial Discharge Pattern Recognition Based on Wavelet Packet Transform

2013 ◽  
Vol 823 ◽  
pp. 536-540
Author(s):  
Dong Song Luo ◽  
Kun Peng Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Singular Value Decomposition ◽  
Bp Neural Network ◽  
High Frequency ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Envelope Signal ◽  
Value Decomposition

In order to achieve the GIS fault detection and defect type recognition, four typical defect models were designed and discharge tests are carried out aiming at insulation defect as well as discharge characteristics in the GIS .With a large number of ultra high frequency envelope signal ,a method of domain feature extraction was proposed based on wavelet packet transform with singular value decomposition .The envelope signal was decomposed through wavelet packet transform first in the method, then the coefficient matrix of wavelet packet transform was built in the scale ,after that feature vectors of matrix were extracted by means of singular value decomposition. On this basis, BP neural network was took advantage of for pattern recognition .The results show that the good recognition effect was obtained with that method . Keyword: Ultra high frequency; Envelope signal; Wavelet packet transform; Singular value decomposition; BP neural network

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

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