The Empirical Mode Decomposition (EMD) is a popular algorithm used for the processing of non-linear and non-stationary signals. In this paper we implemented the EMD algorithm and study the use of EMD in broad range of applications for extracting data from the signals. It decomposes the signal into highly varying Intrinsic Mode Functions (IMF) and slowly varying residues. Finally the results are...

A noiseless ECG identification technology is an emerging new biometric modality. Different techniques for de-noising of ECG signal are prevalent in recent literatures such as Particle Filter (PF), wavelet transforms (WT), Empirical Mode Decomposition (EMD) & Ensemble-EMD Method. In view of the fact that Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of...

The performance of a number of image processing methods depends on the output quality of a thresholding process. Typical thresholding methods are based on partitioning pixels in an image into two clusters. In this paper, a new thresholding method is presented. The main contribution of the proposed approach is the application of the empirical mode decomposition (EMD) on detecting an optimal thre...

Towards developing a rigorous mathematical theory for Empirical Mode Decomposition (EMD), we provide an overview of the algorithm and introduce a corresponding operator, attempting a preliminary study. We prove that the EMD is nonlinear, we identify the major reason of its nonlinearity, and we introduce the related concept of consistency, which we show the EMD does not satisfy either. & 2012 Pu...

Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary and non-linear processes. The method does not require any pre & post processing of signal and use of any specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. T...

A new pitch detection method is designed by the recurrence analysis in this paper, which is combined of Empirical Mode Decomposition (EMD) and Elliptic Filter (EF). The Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) is utilized tosolve the problem, and a noisy voice is first filtered on the elliptic band filter. The two Intrinsic Mode Functions (IMF) are synthesized by EMD ...

Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture ex...

This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using ...

Empirical Mode Decomposition (EMD) is a tool for the analysis of multi-component signals. The EMD algorithm decomposes adaptively a given oscillation modes namely the functions of intrinsic mode (IMFs) extracted from the signal itself signal. The analysis method is no need for a basic function fixed a priori as conventional analytical methods (eg Fourier transform and the wavelet transform). In...

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...