Earticle

초록

영어

A hybrid denoising method is presented as a combination of Empirical Mode Decomposition (EMD) and Higher Order Statistics (HOS). EMD, an adaptive data-driven method, is used for effective decomposition of a noisy signal into its functional components. Then Kurtosis and Bispectrum operate as Gaussianity estimators, supplemented by Bootstrap techniques, ensuring detection and removal of the signal’s Gaussian components. Thresholding techniques are used at the final step for maximum suppression of signal noise, where thresholds are set by estimating the long-term correlation of the corrupting colored noise in the form of the Hurst exponent. Experimental results prove the applicability of the method in signal denoising. Specifically, EMD-HOS outperforms similar denoising techniques based on Wavelets for the most types of test signals.

목차

Abstract
 1. Introduction
 2. Empirical Mode Decomposition
 3. Higher Order Statistics
 4. Gaussian Noise Model and EMD Signal Denoising
 5. EMD-HOS Method
 6. Experimental Results and Discussion
 7. Conclusions
 References

키워드

저자

• George Tsolis [ Aristotle University of Thessaloniki, Greece, Department of Electrical and Computer Engineering. ]
• Thomas D. Xenos [ Aristotle University of Thessaloniki, Greece, Department of Electrical and Computer Engineering. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605