دانلود رایگان مقاله تقسیم حالت IMF در EMD برای تحلیل jitter

Abstract

We propose a novel noise cancellation method based on the scale-adaptive remixing and demixing of Intrinsic Mode Functions (IMFs) constructed using Empirical Mode Decomposition (EMD). The method addresses the problem of mode mixing in the EMD by performing IMF mode demixing using a heuristic algorithm that minimizes correlation between subsets of second order IMFs generated from partial sums of first order IMFs. An illustrative example using the proposed method for jitter analysis of a noisy random binary sequence is presented. The proposed approach allows achieving better denoising results (evaluated using correlation, Peak-to-Peak value and predictability with AR(4) model) than the classic first IMF discarding approach.

CONCLUSIONS

A novel method has been proposed to eliminate the phenomenon of mode mixing that exists in Empirical Mode Decomposition (EMD). The method remixes the adjacent Intrinsic Mode Functions (IMFs) and repeats the EMD procedure to derive IMFs that are more separated in terms of correlation (i.e., are less correlated). The proposed method has been verified to be effective for analyzing and denoising the simulated jitter signal. Using the proposed method, we can analyze jitter signal and decompose jitter into random jitter (RJ) (the first IMF – IMF1) and deterministic jitter (DJ) (the sum of the remaining IMFs) in the time domain. Furthermore, the proposed method can be used for deriving more physically meaningful IMFs. However more research still is needed in analyzing the results of the method when applied to different types of jitter. In future work, we intend to apply the proposed method for decomposition, analysis and denoising of complex noisy biological signals such as EEG, EMG, ECG and gaze tracking data.