DocumentCode :
226521
Title :
Statistical multirate high-resolution signal reconstruction using the empirical mode decomposition based denoising approach
Author :
Ukte, Adem ; Kizilkaya, Aydin ; Elbi, Mehmet Dogan
Author_Institution :
Dept. of Electr. & Electron. Eng., Pamukkale Univ., Denizli, Turkey
fYear :
2014
fDate :
9-10 Sept. 2014
Firstpage :
303
Lastpage :
306
Abstract :
High-resolution signal reconstruction from a set of its noisy low-resolution measurements is considered. As an alternative solution to this problem, a method employing the empirical mode decomposition (EMD) based denoising approach is proposed. In the framework of the proposed method, iterative EMD interval-thresholding based denoising procedure is applied to each noisy low-resolution measurement so as to filter the additive white Gaussian noise effect on it. We then synthesize the noise-reduced low-resolution signals to form the high-resolution signal. Unlike the method using the Wiener filter theory for high-resolution signal reconstruction, the proposed method does not require knowledge of any correlation information about the desired high-resolution signal and its low-resolution versions. The validity of the proposed method is demonstrated by an audio signal reconstruction application.
Keywords :
Gaussian noise; Wiener filters; iterative methods; signal reconstruction; EMD; Gaussian noise effect; Wiener filter theory; denoising approach; denoising procedure; empirical mode decomposition; iterative EMD interval thresholding; statistical multirate high resolution signal reconstruction; Filtering; Noise; Noise measurement; Signal reconstruction; Wiener filters; empirical mode decomposition; high-resolution signal reconstruction; multirate statistical signal processing; signal denoising;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Electronics (AE), 2014 International Conference on
Conference_Location :
Pilsen
ISSN :
1803-7232
Print_ISBN :
978-8-0261-0276-2
Type :
conf
DOI :
10.1109/AE.2014.7011725
Filename :
7011725
Link To Document :
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