A complete ensemble empirical mode decomposition with adaptive noise

By María Eugenia Torres, Marcelo Alejandro Colominas, Gastón Schlotthauer, Patrick Flandrin. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011

2,336 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0222fa6e53ceb5fbcabfdcdfa79c23fd53b32e9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Mirko van der Baan
2. Jiajun Han
3. Jérôme Gilles
4. Ram Bilas Pachori
5. Abhijit Bhattacharyya
6. Konstantin Dragomiretskiy
7. Ebrahim Ghaderpour
8. Dominique Zosso

In Signal Processing

1. Jérôme Gilles
2. Mirko van der Baan
3. Jiajun Han

Citations in the corpus

• A Survey on ensemble learning under the era of deep learning (2021, methodology): “TFE17 EEMD (WU and HUANG 2009) / CEEMDAN (Torres et al. 2011) followed with PACF (Ramsey 1974)”
• Empirical Wavelet Transform (2013, methodology): “To deal with this problem, an Ensemble EMD (EEMD) was proposed in [17].”
• Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding (2015, methodology): “In this case, CEEMD does not obey this feature, it uses different decomposition process to obtain each IMF, which simply means that the IMFs energy after CEEMD may not follow equation 1.”