Abstract In view of the nonlinear characteristics of chaotic signal, a threshold denoising method of noisy chaotic signal based on empirical mode decomposition (EMD) is presented. Firstly, noisy chaotic signal is decomposed into several intrinsic mode function (IMF) by empirical mode decomposition. Secondly, the intrinsic mode functions of high frequency are respectively processed using thresho...

Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and non-stationary signals. The mode-mixing problem is one of the most important topics for the improvement of the EMD algorithm. In this paper, we study the reasons of the mode mixing phenomenon. And then, we propose a new method to resolve this problem relying on the assumption th...

The empirical mode decomposition (EMD) is applied to violence data from Afghanistan between 2006 and 2012. Several key behaviours are identified at distinct time scales ranging from days, through weeks to months, through months to a year, and finally spanning multiple years. The identified behaviour was compared to the traditionally-used multiplicative seasonal decomposition. Unlike seasonal de...

This paper presents the implementation of face recognition system using JDL framework. Fusion of visible and thermal images enhances the recognition rate and efficiency under varying illumination conditions. In this system, registration of visible and thermal images is performed using Fourier based method and fusion is performed using Empirical Mode Decomposition (EMD). The feature extraction a...

In this paper, we propose some recent works on data analysis and synthesis based on Empirical Mode Decomposition (EMD). Firstly, a direct 2D extension of original Huang EMD algorithm with application to texture analysis, and fractional Brownian motion synthesis. Secondly, an analytical version of EMD based on PDE in 1D-space is presented. We proposed an extension in 2D-case of the so-called “si...

Abstract. Empirical Mode Decomposition (EMD) is widely used in biomedical field for biomedical signal processing and especially for electrocardiogram (ECG) processing. Removal of artifacts that corrupt ECG, is carried out by proper selection of Intrinsic Mode Functions (IMF) for the partial signal reconstruction. In this paper a study of the influence of White Gaussian Noise in synthetic electr...

The paper presents a novel method for reconstruction of missing data in time series. The method is based on the decomposition of known parts of time series into monocomponents (Intrinsic Mode Functions, IMF) using Empirical Mode Decomposition (EMD), construction of prediction models for each IMF using known parts of times series and their composition using weighted average. We demonstrate the e...

This paper proposes a new filter based on empirical mode decomposition that is based on different characteristics of signal with noise in different IMFS for suppressing speckle in SAR interferogram is proposed. At first empirical mode decomposition is used to divide signal and processed high-frequency IMF signals separately by adaptive filter. The denoising effect of the proposed method, usual ...

in this paper, empirical mode decomposition (eMd) is proposed as an alternative method in the framework of acoustic analysis of disordered speech for the purpose of clinical evaluation of voice. the empirical mode decomposition algorithm decomposes adaptively a given signal into oscillation modes extracted from the signal itself. the proposed approach for objective assessment of vocal dysperiod...