Siu 2009 SİU 2009
Main Page Venue University of Kocaeli IEEE Contact Turkish türkçe
Paper Submission


The following seminars have been planned particularly oriented to both graduate and undergraduate students.

1- Wavelets and applications in signal and image processing
"Wavelets and applications in signal and image processing", Prof. Dr. A. Enis Çetin from Bilkent University.
2- Statistical methods for error measuring and comparison in classifiers
"Statistical methods for error measuring and comparison in classifiers" Prof. Dr. Ethem Alpaydın from Bogazici University.
3- Next Generation Wireless Sensor Networks
"Wireless sensor Networks" Assoc.Prof.Dr. Özgür B. Akan from Middle East Technical University
4- Multihop Mesh Networks And Cooperative Communications
"The New Wireless Radio Access Network Paradigm: Multihop Mesh Networks And Cooperative Communications" Assoc.Prof.Dr. Halim Yanikomeroglu from Carleton University
5- Empirical Mode Decomposition: an adaptive approach to analyze non-linear time series
Dr. Paulo Gonçalves
Empirical Mode Decomposition is a recent technique (N. E. Huang et al., 1998) introduced to analyze non-stationary and non-linear time series in a totally adaptive way. In contrast to standard kernel based approaches (e.g. wavelet decompositions), EMD is a fully data-driven method that recursively decomposes a complex signal into a variable but finite number of zero-mean with symmetric envelopes AM-FM components called Intrinsic Mode Functions (IMF). [To proceed, an iterative algorithm locally identifies in the signal the fastest oscillations and isolates them in the first IMF. Each successive IMF is then obtained iterating the same sifting process on the remaining lower trend.] This appealing analyzing tool is reversible by construction, and gives rise to a natural "scale" decomposition that goes beyond classic spectral analysis and its Fourier modes.

After a schematic presentation of the algorithm, we will address some of its technical issues and report on the major EMD weakness, that is the lack of a theoretical framework to support the method and to analytically characterize an IMF.

To finish, we will present an EMD application to satellite time series imagery for land cover classification. This simple study will not only illustrate the flexibility of this non parametric method but also show, by comparison with model-based identification procedures, the EMD ability at retrieving non-linear modes.