Multivariate empirical mode decomposition

By Naveed Urr Rehman, Danilo P. Mandic. Proceedings of the Royal Society A, 2010

1,069 citations recorded by the source corpus.

Subfields: Automotive Engineering, Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/78414ccd4334bd5cdf3ccbc6da5e91eae70af94f

How researchers see this paper

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

In Automotive Engineering

1. Naveed Urr Rehman
2. Hania Aftab
3. Bingcheng Si
4. Wei Hu
5. Danilo P. Mandic
6. Asim Biswas
7. Wei Mo
8. Zhi Li

In Civil and Structural Engineering

1. Naveed Urr Rehman
2. Danilo P. Mandic
3. Wei Mo
4. Zhi Li
5. Heng Li
6. Lean Yu
7. Ling Tang
8. Norden E. Huang

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

Citations in the corpus

• Filter Bank Property of Multivariate Empirical Mode Decomposition (2011, methodology): “AN INTRODUCTION TO MULTIVARIATE EMD The multivariate EMD algorithm has been recently proposed in [9] to process a general class of multivariate signals having an arbitrary number of channels.”
• Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach (2015, methodology): “EMD in the bivariate framework, which can generate much more accurate local means and thence more effective decomposition results (Rehman and Mandic, 2009). Therefore, the BEMD model by Rilling et al. (2007) is specially introduced as the multi-scale analysis method in this paper.”
• Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, methodology): “Figure 3(a) depicts a nonuniform sampling scheme based on spherical angles [29], while Figure 3(b) illustrates uniform sampling [28] for projection vector directions generated by low-discrepancy Hammersley sequences [30] (quasi-MonteCarlo sampling).”
• Multivariate Variational Mode Decomposition (2019, methodology): “A generic multivariate extension of EMD, termed as multivariate EMD (MEMD) [17] belongs to this class of methods that can operate on multichannel data with any number of input channels.”
• Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. (2014, background): “However, these time-domain transforms are typically applied to individual signals (see exceptions in [16]) and do not support spatial structures.”
• Application of multivariate empirical mode decomposition for revealing scale-and season-specific time stability of soil water storage (2014, methodology): “Rather it usesmethods like cubic spline interpolation between themaxima andminima to produce the local mean by taking an average of upper and lower envelopes (Rehman and Mandic, 2010).”
• 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.”