Empirical mode decomposition (EMD) is an algorithm for signal analysis recently introduced by Huang. It is a completely datadriven non-linear method for the decomposition of a signal into AM FM components. In this paper two new EMD-based methods for the analysis and classification of pathological voices are presented. They are applied to speech signals corresponding to real and simulated sustai...

Image fusion is a fundamental technique for integrating high-resolution panchromatic images and low-resolution multispectral (MS) images. Fused images may enhance image interpretation. Empirical mode decomposition (EMD) is an effective method of decomposing non-stationary signals into a set of intrinsic mode functions (IMFs). Hence, the characteristics of EMD may apply to image fusion technique...

‘Broadly speaking, spatial analysis can be defined as the formal quantitative study of phenomena that manifest themselves in space. This implies an attention to location, area, distance and interaction’ (Anselin et al., 1993). Spatial analysis has been a popular topic in geospatial sciences. Spatial analysis was viewed as being deeply rooted in spatial statistics, and quantitative geographers h...

The synchrosqueezed transform was proposed recently in [Daubechies et al. (2009)] as an alternative to the empirical mode decomposition (EMD) [Huang et al. (1998)], to decompose composite signals into a sum of “modes” that each have well-defined instantaneous frequencies. This paper presents, for synchrosqueezing, a study similar to that in [Rilling and Flandrin (2008)] for EMD, of how two sign...

In this paper, Empirical mode decomposition (EMD) and Hilbert vibration decomposition (HVD) are employed to process various different types of non-stationary and nonlinear vibration. The properties of these two methods are systematically analysis by comparing the efficacy in some numerically simulated signals. Meanwhile these two methods are used to analyze a Wenchuan earthquake strong ground m...

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is propos...

 Proposing a novel interval-valued electricity demand forecasting approach.  BEMD and SVR are integrated for interval forecasting of electricity demand.  The EMD-based modeling framework are extended to deal with interval forecasting  BEMD is used to decompose both the lower and upper bounds electricity demand series.  The proposed modeling framework is justified with real world data sets....

To improve the efficiency of facial expression recognition, this paper puts forward a kind of recognition algorithm based on local binary pattern (LBP) and empirical mode decomposition (EMD). First of all, process the empirical mode decomposition into the preprocessing facial image, and bring forward many a high frequency images instead of the original image; then, divide the sub domain of the ...

Image enhancement methods can be divided into two groups: the ones that use only one single image and the ones that rely on specific training set or use multiple images. In this paper an iterative algorithm, based on the quasi-Newton methods, is introduced with the objective to enhance resolution only by one single image. In the paper there will be compared results gained depending on the used ...

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power--50 Hz, EMG, and base line wander--were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering ...