Empirical mode decomposition (EMD) was developed for analyzing non-linear and non-stationary data. EMD decomposition is based on the local characteristic time scale of data. EMD decomposes any data set into a finite and often small number of intrinsic mode functions (IMF). An IMF is defined as any function having the same numbers of zero crossings and extrema, and also having symmetric envelopes defined by the local maximal and minima, respectively. The IMF also admits well behaved Hilbert transform verified to be highly orthogonal. EMD is used in many applications such as signal enhancements and data analysis. In this paper, the EMD is presented using computer simulations. The complexity of classical EMD is calculated to determine the additive complexity to any system uses the EMD.
Elgamel, S. (2012). Empirical Mode Decomposition Complexity. The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), 1-9. doi: 10.21608/iceeng.2012.30659
MLA
Sherif Elgamel. "Empirical Mode Decomposition Complexity". The International Conference on Electrical Engineering, 8, 8th International Conference on Electrical Engineering ICEENG 2012, 2012, 1-9. doi: 10.21608/iceeng.2012.30659
HARVARD
Elgamel, S. (2012). 'Empirical Mode Decomposition Complexity', The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), pp. 1-9. doi: 10.21608/iceeng.2012.30659
VANCOUVER
Elgamel, S. Empirical Mode Decomposition Complexity. The International Conference on Electrical Engineering, 2012; 8(8th International Conference on Electrical Engineering ICEENG 2012): 1-9. doi: 10.21608/iceeng.2012.30659
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