Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Optimal Signal Reconstruction Using the Empirical Mode Decomposition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 845294 12 pages doi 2008 845294 Research Article Optimal Signal Reconstruction Using the Empirical Mode Decomposition Binwei Weng1 and Kenneth E. Barner2 1 Philips Medical Systems MS 455 Andover MA 01810 USA 2 Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA Correspondence should be addressed to Kenneth E. Barner barner@ Received 26 August 2007 Revised 12 February 2008 Accepted 20 July 2008 Recommended by Nii O. Attoh-Okine The empirical mode decomposition EMD was recently proposed as a new time-frequency analysis tool for nonstationary and nonlinear signals. Although the EMD is able to find the intrinsic modes of a signal and is completely self-adaptive it does not have any implication on reconstruction optimality. In some situations when a specified optimality is desired for signal reconstruction a more flexible scheme is required. We propose a modified method for signal reconstruction based on the EMD that enhances the capability of the EMD to meet a specified optimality criterion. The proposed reconstruction algorithm gives the best estimate of a given signal in the minimum mean square error sense. Two different formulations are proposed. The first formulation utilizes a linear weighting for the intrinsic mode functions IMF . The second algorithm adopts a bidirectional weighting namely it not only uses weighting for IMF modes but also exploits the correlations between samples in a specific window and carries out filtering of these samples. These two new EMD reconstruction methods enhance the capability of the traditional EMD reconstruction and are well suited for optimal signal recovery. Examples are given to show the applications of the proposed optimal EMD algorithms to simulated and real signals. Copyright 2008 B. Weng and K. E. Barner. This is an open access article distributed under the .
