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 Empirical Mode Decomposition Method Based on Wavelet with Translation Invariance | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 526038 6 pages doi 2008 526038 Research Article Empirical Mode Decomposition Method Based on Wavelet with Translation Invariance Qin Pinle 1 2 Lin Yan 1 2 and Chen Ming1 1 School of Electrical and Information Engineering Dalian University of Technology Dalian 116024 Liaoning China 2 Ship CAD Engineering Center Dalian University of Technology Dalian 116024 Liaoning China Correspondence should be addressed to Qin Pinle qpl001@ Received 20 August 2007 Revised 11 February 2008 Accepted 10 April 2008 Recommended by Nii Attoh-Okine For the mode mixing problem caused by intermittency signal in empirical mode decomposition EMD a novel filtering method is proposed in this paper. In this new method the original data is pretreated by using wavelet denoising method to avoid the mode mixture in the subsequent EMD procedure. Because traditional wavelet threshold denoising may exhibit pseudo-Gibbs phenomena in the neighborhood of discontinuities we make use of translation invariance algorithm to suppress the artifacts. Then the processed signal is decomposed into intrinsic mode functions IMFs by EMD. The numerical results show that the proposed method is able to effectively avoid the mode mixture and retain the useful information. Copyright 2008 Qin Pinle 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. 1. INTRODUCTION A new nonlinear technique empirical mode decomposition EMD has recently been more and more popular as a new tool for time-frequency analysis method 1 . The essence of EMD is to decompose time-varying data series into a finite set of functions named intrinsic mode functions IMFs . The extracted IMFs represent the local character of original data. Furthermore coupled with the Hilbert
