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Discrete Dynamics in Nature and Society
Volume 2011 (2011), Article ID 935034, 21 pages
http://dx.doi.org/10.1155/2011/935034
Research Article

Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability

1Geophysical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2G7
2Department of Computer Science and Engineering, The University of Rajshahi, Rajshahi 6205, Bangladesh
3Department of Information and Communication Engineering, The University of Tokyo, Tokyo 113-0033, Japan

Received 7 January 2011; Accepted 25 May 2011

Academic Editor: M. De la Sen

Copyright © 2011 Md. Khademul Islam Molla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Md. Ekramul Hamid, Md. Khademul Islam Molla, Xin Dang, and Takayoshi Nakai, “Single Channel Speech Enhancement Using Adaptive Soft-Thresholding with Bivariate EMD,” ISRN Signal Processing, vol. 2013, pp. 1–8, 2013. View at Publisher · View at Google Scholar
  • Pieter Hawinkel, Else Swinnen, Stef Lhermitte, Bruno Verbist, Jos Van Orshoven, and Bart Muys, “A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD),” Remote Sensing of Environment, vol. 169, pp. 375–389, 2015. View at Publisher · View at Google Scholar
  • P. Hawinkel, W. Thiery, S. Lhermitte, E. Swinnen, B. Verbist, J. Van Orshoven, and B. Muys, “Vegetation response to precipitation variability in East Africa controlled by biogeographical factors,” Journal of Geophysical Research: Biogeosciences, 2016. View at Publisher · View at Google Scholar