Signal Classification Method Based on Support Vector Machine and High-Order Cumulants

Zhou, Xin; Wu, Ying; Bin, Yang

doi:10.4236/wsn.2010.21007

Cited by 47 publications

(22 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other signals frequently used are those of digital modulation. For example, Zhou [78] used second-, fourth-and sixth-order cumulants as signal features, and also employed an RBF kernel combined with a method of cross-validation grid parameters selection to improve the SVM-based classification accuracy, reaching 92.2%.…”

Section: Other Applications Of Svm-based Classifiersmentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

View full text Add to dashboard Cite

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

show abstract

Section: Other Applications Of Svm-based Classifiersmentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The a priori information about the signal such as timing synchronization, phase jitter, phase offset, and frequency offset is considered for evaluation of correct classification. The first signal classification is done using 2nd, 4th, and 6th order cummulants and then kernel thought is used to map the feature to higher dimensional space and optimum hyper plane is constructed using SVM to classify the signals in [ 22 ]. The classifier based on back propagation neural network (BPNN) and trained by improved particle swarm optimization (PSO) which is used to optimal weights and threshold for BPNN is proposed in [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Modulation Classification Approach Using Gabor Filter Network

Ghauri

Qureshi

Cheema

et al. 2014

The Scientific World Journal

View full text Add to dashboard Cite

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

show abstract

“…In civil applications, it can be used for spectrum management, network traffic administration, signal confirmation, cognitive radio, software radios, and intelligent modems [1]. The early researches were concentrated on analog signals in [2] and have been recently extended to digital types of signals used in modern communication systems [3][4][5]. In this paper, we present an automatic digital signal type classifier for multi-user chirp signals in additive white Gaussian noise channels.…”

Section: Introductionmentioning

confidence: 99%

“…Higher order statistical (HOS) features have been recently proved to be very efficient in the classification of wideband communications, radar and biomedical signals with great accuracy [6][7][8][9]. For example, an automatic classifier of different digital modulation signals, in additive white Gaussian noise channels, was suggested using a combination of the higher order moments and higher order cumulants up to order eighth as features and using multilayer preceptor neural network (NN) in [3], and using a Hierarchical support vector machine (SVM) based Classifier in [4] and [5]. The bispectrum features were used as to classify mental tasks from EEG signals in [6] and to classify heart rate signals in [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

El‐Khamy¹,

Elsayed²

2012

IJCNS

View full text Add to dashboard Cite

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios

show abstract

Signal Classification Method Based on Support Vector Machine and High-Order Cumulants

Cited by 47 publications

References 6 publications

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

A Novel Modulation Classification Approach Using Gabor Filter Network

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Contact Info

Product

Resources

About