Filter Bank Property of Multivariate Empirical Mode Decomposition

By Naveed Urr Rehman, Danilo P. Mandic. IEEE Transactions on Signal Processing, 2011

428 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6e598cb6866785c3b926391f738586e65741d1a1

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. Naveed Urr Rehman
2. Danilo P. Mandic
3. Norden E. Huang
4. Wei Mo
5. Zhi Li
6. Heng Li
7. Zhaohua Wu
8. Hania Aftab

In Signal Processing

1. Naveed Urr Rehman
2. Danilo P. Mandic
3. Wei Mo
4. Zhi Li
5. Heng Li
6. Hania Aftab
7. Norden E. Huang
8. Zhaohua Wu

Citations in the corpus

• On the computational complexity of the empirical mode decomposition algorithm (2014, methodology): “To overcome this problem, the EEMD algorithm [2] and the noise-assisted MEMD [3] have been proposed.”
• A time varying filter approach for empirical mode decomposition (2017, methodology): “With EEMD and N-A MEMD, the decomposed results were AC CE PT ED M AN US CR IP T uniformly aligned according to the cut-off frequency bands.”
• Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, background): “This property also explains enhanced T-F localization of noise-aided EMD algorithms [15], [31].”
• Multivariate Variational Mode Decomposition (2019, methodology): “In [27], [35], EMD and MEMD had respectively been shown to exhibit quasi-dyadic filterbank structure for wGn, which is similar to the wavelet filterbank.”