Name
Abstract | ||
|---|---|---|
Empirical mode decomposition (EMD) is extensively realised in its potential of non-parametric signal denoising. Ensemble EMD (EEMD) is an improved self-adapting signal decomposition approach that can produce signal components with no frequency aliasing. In this study, the interval thresholding and iteration operation of EMD-based denoising techniques are applied to the EEMD and found not entirely ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1049/iet-spr.2016.0147 | IET Signal Processing |
Keywords | Field | DocType |
computational complexity,iterative methods,signal denoising | Noise reduction,Pattern recognition,Signal-to-noise ratio,Aliasing,Sampling (statistics),Artificial intelligence,Thresholding,Time complexity,Mathematics,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
11 | 4 | 1751-9675 |
Citations | PageRank | References |
1 | 0.38 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongrui Wang | 1 | 2 | 4.78 |
| Zhigang Liu | 2 | 13 | 3.69 |
| Yang Song | 3 | 64 | 22.68 |
| Xiaobing Lu | 4 | 8 | 1.99 |