Higher Order Spectra Denoising Method of Phase Matching Noise Estimation

Liu, Jie; Wu, Peng

doi:10.1109/pcspa.2010.202

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The third order statistics namely, the bispectrum is used. The HOSF is also used in analysis of electro-enchepalogram signals [11] and de-noising method for noise estimation [12]. The bi-spectrum is defined by,…”

Section: A Hosf Feature Extractionmentioning

confidence: 99%

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

Mohanan¹

2017

International Journal of Advances in Signal and Image Sciences

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Video classification is one of the rising fields in the video analysis. A large number of videos are accessed by people's daily from television and internet. It is easy for humans to index the video from the collection of videos which contains news, cartoon, sports, comedy and drama. Among the categories, sports video plays a vital role due to their commercial demand. There is a similarity between the different sports video which makes the classification task difficult. In this study, the sports video categorization for five categories of sports like football, cricket, volleyball, tennis and basketball is presented. The sports video categorization system uses Higher Order Spectra Features (HOSF) for the feature extraction from video frames and multiclass Support Vector Machine (SVM) classifier for the classification of videos. The system gives average classification accuracy of 93.44% using HOSF and multiclass SVM classifier.

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Section: A Hosf Feature Extractionmentioning

confidence: 99%

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

Mohanan¹

2017

International Journal of Advances in Signal and Image Sciences

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“…High-order Matrix and higher-order cumulants If f (x) is the probability density function of a random variable x, the first characteristic function ( ) s Φ can be defined as [4] , ( )…”

Section: Time Series Ar Modelsmentioning

confidence: 99%

Fault Diagnosis of Elevator Based AR Bi-Spectrum

Huang

2012

AMR

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According to the characteristics of the elevator fault vibration signals, proposing a Based on analysis of time series AR bi-spectrum elevator fault diagnosis. When the zero-mean, non-Gaussian white noise elevator device, the vibration signal using sampling to establish time series autoregressive model (AR model), resulting in AR bi-spectrum. Bi-spectrum signal processing is a new, powerful signal processing technology, which can be described the nonlinear coupling, suppression Gaussian noise and retention of phase information, you can get the elevator working status of the different dynamic characteristics. The results show that bi-spectral analysis with AR elevator failure is feasible and effective.

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Phase Matching Denoising Algorithm and IP Design

Xia

Chen

2016

IJIIS

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At present, there are many kinds of denoising algorithms, but the hardware of these algorithms IP design is very lacked. In this paper, a phase matching denoising algorithm is proposed to remove the noise, and the IP design of this method is realized. Phase matching denoising algorithm is based on the signal phase matching method to obtain the actual signal and remove noise. Mathematical expression and mathematical proof of the method are given here. Using this method, the IP kernel design is given and the design method is given. Through the function simulation, it is proved that the IP kernel design can effectively remove the noise in the signal.

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Higher Order Spectra Denoising Method of Phase Matching Noise Estimation

Cited by 3 publications

References 9 publications

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

Fault Diagnosis of Elevator Based AR Bi-Spectrum

Phase Matching Denoising Algorithm and IP Design

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