2010Electronic Instrumentation CustomersRequires access

Denosing of signal based on wavelet analysis

Jiang Jing-tao

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Abstract

The paper analyses the features of wavelet analysis in the signal noise processing,and then researches the stable and unstable signals with one-dimensional wavelet,and it propose a method based on the theory of wavelet analysis,applies the wavelet transformation of binary system to factorize the signal jammed by the noise,selects an appropriate threshold value to estimate the coefficient of the wavelet factorization,then it re-constructs the coefficient of thehigh and low frequency.In this way,it realizes the separation of effective way to remove the noise of signal.The experiment proves it effective.

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What this paper is about

The paper analyses the features of wavelet analysis in the signal noise processing,and then researches the stable and unstable signals with one-dimensional wavelet,and it propose a method based on the theory of wavelet analysis,applies the wavelet transformation of binary system to factorize the signal jammed by the noise,selects an appropriate threshold value to estimate the coefficient of the wavelet factorization,then it re-constructs the coefficient of thehigh and low frequency.In this way,it realizes the separation of effective way to remove the noise of signal.The experiment proves it effective.

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Available abstract

The paper analyses the features of wavelet analysis in the signal noise processing,and then researches the stable and unstable signals with one-dimensional wavelet,and it propose a method based on the theory of wavelet analysis,applies the wavelet transformation of binary system to factorize the signal jammed by the noise,selects an appropriate threshold value to estimate the coefficient of the wavelet factorization,then it re-constructs the coefficient of thehigh and low frequency.In this way,it realizes the separation of effective way to remove the noise of signal.The experiment proves it effective.

Key concepts: Wavelet, Wavelet packet decomposition, Noise (video), Transformation (genetics), SIGNAL (programming language), Stationary wavelet transform, Wavelet transform, Second-generation wavelet transform

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