Residential College | false |
Status | 已發表Published |
Nonstationary signal analysis based on EMD and extremum points | |
JIAN-JIA PAN1; YUAN-YAN TANG2 | |
2012-10-29 | |
Conference Name | 2012 International Conference on Wavelet Analysis and Pattern Recognition |
Source Publication | Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition |
Pages | 260-265 |
Conference Date | 15-17 July 2012 |
Conference Place | Xian |
Country | China |
Abstract | Empirical mode decomposition (EMD) is a data driven processing algorithm, which has no predetermined filter. It is able to perfectly analyze the nonlinear and nonstationary signals. In EMD decomposition processing, the envelopes are computed by spline interpolation, which is time-consuming. In this work, firstly, we proposed a boundary extending method based on linear prediction and boundary extreme points adjusting, which reduce the end effects problem. And then, based on the straight line method, we proposed just using the extrema points to detect the extrema information about the signal, which is Extrema Points Empirical Mode Decomposition (EPEMD). By using the extrema points information, a fast and distinct frequency change detection method is proposed. |
Keyword | Boundary Extending Emd Extrema Points Time-frequency Analysis |
DOI | 10.1109/ICWAPR.2012.6294789 |
URL | View the original |
Language | 英語English |
Scopus ID | 2-s2.0-84867794995 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | University of Macau |
Affiliation | 1.Department of Computer Science, Hong Kong Baptist University, Hong Kong 2.Department of Computer Science, Hong Kong Baptist University, Hong Kong |
Recommended Citation GB/T 7714 | JIAN-JIA PAN,YUAN-YAN TANG. Nonstationary signal analysis based on EMD and extremum points[C], 2012, 260-265. |
APA | JIAN-JIA PAN., & YUAN-YAN TANG (2012). Nonstationary signal analysis based on EMD and extremum points. Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, 260-265. |
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