Research on De-Noising of Ultrasonic Detection Signal via Multiwavelets with Different Preprocessing Methods

Article Preview

Abstract:

On the basis of introducing multi-wavelet theory, a new soft-threshold function is proposed, and the de-noising effect of Ultrasonic signal is investigated by adopting different preprocessing methods of various multi-wavelet. An improved matrix preprocessing method is proposed for GHM also. Simulation results indicate that the selection of a proper preprocessing method is important for multi-wavelet de-noising. Therefore, it is of great importance to choose a suitable preprocessing method and multi-wavelet in order to obtain a better de-noising effect.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

1823-1829

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Liu Ming CAI. Wavelet Analysis and Its Application [M]. Beijing: Publishing House of Tsinghai University, 2005. 9.

Google Scholar

[2] Cheng Zheng Xing, Yang Shou Zhi, Feng Xiao Xia. Development on Wavelet Analysis theory and its applications [M]. Beijing: National Defense Industry Press, 2007. 7.

Google Scholar

[3] Xia x-G, Gemmrno J S Hardin DP, Suter BW WHY AND HOW PREFILTERING FOR DISCRETE MULTIWAVELET TRANSFORMS [J] 0-7803-3 192-3/96 1996 IEEE.

DOI: 10.1109/icassp.1996.544103

Google Scholar

[4] Fang Shu Guang. The Application of Wavelet Based on Time-frequency Analysis Method in Ultrasonic Signal Processing, master's thesis of Shandong University, 2009. 4. 9.

Google Scholar

[5] Duan Shan, He Juan, Liu Shao ying. Application of Multiwavelet Transform in Signal De-Noising. Journal of South-Central University for Nationalities (N at. Sci. Edition) [J] Jun. 2009 Vo. l 28 No. 2.

Google Scholar

[6] David L. Donoho De-Noising by Soft Thresholding, IEEE transactions on information theory vol. 41, No. 3, May (1995).

DOI: 10.1109/18.382009

Google Scholar

[7] V. Strela and A.T. Walden. Signal and Image De-noising via Wavelet Thresholding: Orthogonal and Biorthogonal, Scalar and Multiple Wavelet Transforms, Imperial College, Statistics Section, Technical Report TR-98-01 (1998).

Google Scholar

[8] LebrunJ, Vetterli M. Balanced Multiwavelets Theory and Design [J]. IEEE Trans. on SP, 1998, 46(4): 1119-1124.

Google Scholar

[9] Geronimo J, Hardin D, Massopust P R. Fractal functions and wavelet expansions based on several functions [J]. J Approx Theory, 1994; 78: 373-401.

DOI: 10.1006/jath.1994.1085

Google Scholar

[10] L. -X. Shen, H. H. Tan, and J. Y. Tham, Symmetric antisymmetric orthonormal multiwavelets and related scalar wavelets, preprint (1997).

DOI: 10.1006/acha.1999.0288

Google Scholar

[11] I. Selesnick, Cardinal Multiwavelets and the Sampling Theorem, preprints on 1999 IEEE national Conference- Volume 03.

Google Scholar