Bivariate Empirical Mode Decomposition

By Gabriel Rilling, Patrick Flandrin, Paulo Gonçalves, Jonathan M. Lilly. IEEE Signal Processing Letters, 2007

578 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8c586bb7d697217fbc2b3bcefea76472aa7c2f16

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. Zhaohua Wu
5. Hania Aftab
6. Bingcheng Si
7. Wei Hu
8. Asim Biswas

In Signal Processing

1. Naveed Urr Rehman
2. Danilo P. Mandic
3. Usha R. Acharya
4. Hania Aftab
5. Ram Bilas Pachori
6. Norden E. Huang
7. Zhaohua Wu
8. Rajeev Sharma

Citations in the corpus

• Multivariate Variational Mode Decomposition (2019, methodology): “MEMD and other extensions of EMD, especially for bivariate [18] and trivariate [19] signals, have been utilized in wide ranging applications involving multivariate data e.”
• Filter Bank Property of Multivariate Empirical Mode Decomposition (2011, methodology): “On the other hand, recently developed multivariate extensions of EMD, including the complex and bivariate EMD [6], [7], trivariate EMD [8], and multivariate EMD (MEMD) [9] produce the same number of IMFs for all channels, facilitating direct multichannel modelling.”
• Application of multivariate empirical mode decomposition for revealing scale-and season-specific time stability of soil water storage (2014, background): “The decomposition is based on the assumption that at any given location, different simple oscillatory modes or frequencies (presenting different scales of underlying soil processes)may superimpose oneanother (Huanget al., 1998; Rilling et al., 2007).”
• Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “It separates the fast oscillations from the slow oscillations present in the signals [69].”
• Multivariate empirical mode decomposition (2010, methodology): “Similarly to bivariate (Rilling et al. 2007) and trivariate (Rehman & Mandic in press) extensions of EMD, we will now show that the proposed n -variate extension of EMD has the ability to align ‘common scales’ present within multivariate data.”
• Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, methodology): “Although both EMD and BEMD produced diagonally dominant correlograms of IMFs, since within BEMD same-index bivariate IMFs contain the same scale, the correlogram of bivariate IMFs [Figure 6(a)] exhibits a more pronounced diagonal dominance (reduced leakage), highlighting that ■ cross-correlation bet”