The Empirical Mode Decomposition and the Hilbert-Huang Transform

Call for Papers

Data from natural phenomena are usually nonstationary due to their transient nature; also, thespan of captured data may be shorter than the longest time scale that describes the phenomenon. In fact, since it is impossible or impractical to obtain infinite data points describing aphenomenon, all data are invariably short. To simplify processing and analysis, data stationarityis often assumed even though the condition may not be strictly satisfied. For instance, thestationarity assumption justifies traditional Fourier-based methods, which utilize a priori basissets to globally decompose a signal.

To directly address the processing of nonstationary and nonlinear signals, the Hilbert-Huangtransform (HHT) has recently been developed. The HHT comprises two steps: empirical modedecomposition (EMD) and Hilbert spectral analysis (HSA). Unlike Fourier-based methods, theEMD decomposes a signal into its components adaptively without using a priori basis. Thedecomposition is based on the local time scale of the data. The adaptive nature of the processsuccessfully decomposes nonlinear, nonstationary signals in the time domain. Moreover, thedecomposition components, referred to as intrinsic mode functions (IMF), are generally in goodagreement with intuitive and physical signal interpretations. Moreover, the IMFs have well-definedinstantaneous frequencies. Accordingly, the HSA Hilbert transforms the IMFs to generatea full energy-frequency-time plot (Hilbert spectrum), which gives the instantaneous energy andfrequency content of the signal. The bidimensional empirical mode decomposition (BEMD) hasrecently been introduced as a 2D extension to the EMD. Thus, the EMD and BEMD areincreasingly being employed to successfully address many contemporary signal processingapplications.

This special issue seeks to bring to the fore current advances in HHT, EMD, and BEMD theory, and applications. Topics of interest include, but are not limited to, the following:

  • Theoretical analysis and understanding of the EMD
  • Performance enhancements of the EMD
  • Single decomposition, monitoring, and analysis
  • Feature extraction
  • Fast and adaptive methods
  • Decomposition domain processing methods
  • Image analysis and segmentation
  • Texture representation and segmentation
  • Optimization
  • Signal fusion and interpolation
  • Signal processing applications in
    • Fluid dynamics
    • Acoustics
    • Seismic events
    • Biomedicine
    • Geophysics
    • Wind engineering
    • Ocean waves
    • Finance

Authors should follow the EURASIP Journal on Advances in Signal Processing manuscript format described at the journal site http://www.hindawi.com/journals/asp/. Prospective authors should submit an electronic copy of their complete manuscript through the EURASIP Journal on Advances in Signal Processing Manuscript Tracking System at http://mts.hindawi.com/, according to the following timetable:

Manuscript DueSeptember 1, 2007
First Round of ReviewsDecember 1, 2007
Publication DateMarch 1, 2008

Guest Editors

  • Nii O. Attoh-Okine, Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716-3120, USA
  • Kenneth E. Barner, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716-3130, USA
  • Daniel E. Bentil, Departments of Mathematics and Statistics and MolecularPhysiology Biophysics, The University of Vermont, Burlington, VT 05405, USA
  • Ray Ruichong Zhang, Civil Engineering Specialty, Division of Engineering, Colorado School of Mines, Golden, CO 80401, USA