A New Joint Algorithm Based on EMD-ICA to Reduce the Cross-Term in Wigner-Vill Distribution

Wei Liu; Zeng Li Liu; Xin Xin He; Hao Ran Yao

doi:10.4028/www.scientific.net/AMM.347-350.2407

Paper Titles

Kalman Filter Algorithm for Adaptive Digital Predistortion
p.2385

Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-Means Algorithm
p.2390

A Data Storage and Management Scheme in Cloud Storage Model
p.2395

A Computing Capability Allocation Algorithm for Cloud Computing Environment
p.2400

A New Joint Algorithm Based on EMD-ICA to Reduce the Cross-Term in Wigner-Vill Distribution
p.2407

Difference Curvature Driven Anisotropic Diffusion for Image Denoising Using Laplacian Kernel
p.2412

Research of Modern Information Technology on Tree Growth Management Decision Making Model
p.2418

Research and Application of Poplar Growth Model
p.2422

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm
p.2426

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350A New Joint Algorithm Based on EMD-ICA to Reduce...

A New Joint Algorithm Based on EMD-ICA to Reduce the Cross-Term in Wigner-Vill Distribution

Abstract:

Wigner-Vill distribution (WVD) will inevitably have cross-terms when it used for time-frequency representation in a multi-component signal. In order to suppress the cross-terms in Wigner-Vill distribution, this paper proposes a joint algorithm based on EMD-ICA. This algorithm resolves a multi-component signal into several IMF components used by EMD at first, and then each IMF component is used FastICA algorithm for processing, and next seeks the Wigner-Vill distribution of each component, finally, add up the results. This method effectively inhibited the emergence of cross-terms in Wigner-Vill distribution, and keeps the properties of time-frequency concentration higher.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

2407-2411

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.2407

Citation:

Cite this paper

Online since:

August 2013

Authors:

Wei Liu, Zeng Li Liu, Xin Xin He, Hao Ran Yao

Keywords:

Cross-Term, Empirical Mode Decomposition (EMD), ICA, Wigner-Vill Distribution

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Cohne L. Time-frequency analysis[M]. Englewood Clis: Prentice-Hall, (1995).

Google Scholar

[2] Brandoless C, Fornaciari W. An Instruction-level Functionality-based Energy Estimation Model for 32bits Microprocessors proceedings of 37th Annal Design automation conference[C] New York: ACM press，2000: 346—351.

DOI: 10.1145/337292.337437

Google Scholar

[3] Huang N E, Shen Z, Long S R. The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London, Series A, 1998, 454: 903-99.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[4] Huang N E, Wu M L, Shen S R. Applications of Hilbert-Huang transform to non-Stationary financial time series analysis. Applied Stochastic Models in Business and Industry, 2003, 19(3): 245-268I.

DOI: 10.1002/asmb.501

Google Scholar

[5] Hyvarinen A, oja E. Independent component analysis: algorithms and applications[J]. Neural Networks, 2000, 13(4—5): 411-430.

DOI: 10.1016/s0893-6080(00)00026-5

Google Scholar

[6] Cui H, Wen J R, Nie J Y, etal. Query expansion by mininguser logs [J] . IEEE Transactions on Knowledge and DataEngineering , 2003, 15( 4) : 1- 11.

Google Scholar

[7] Doan A, Madhavan J, Domingos P, etal. Learning to mapbetween ontologies on the semantic web [C]The WorldWide Web Conference. Hawaii: ACM Press, 2002: 662—673.

DOI: 10.1145/511446.511532

Google Scholar

[8] Zou Hongxing, Lu Xuguang, Dai Qionghai. Nonexistence of cross-term free time-frequency distribution with concentration of Wigner-Ville distribution[J]. Science In China, 2002, 06: 175-180.

Google Scholar