Empirical mode decomposition (EMD) and Empirical wavelet transform (EWT) are recently developed adaptive signal processing tools. These techniques decompose a signal accordingly to its contained information. The main issue with EMD is its lack of theory and in case of EWT a prior knowledge of the signal is required. IMF’s obtained as a result of applying EMD are quasi-orthogonal .This paper sug...

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode ...

Previous work on Empirical Mode Decomposition in two dimensions typically generates a residue with many extrema points. In this paper we propose an improved method to decompose an image into a number of Intrinsic Mode Functions and a residue image with a minimum number of extrema points. We further propose a method for the variable sampling of the two-dimensional Empirical Mode Decomposition. S...

introduction automatic human emotion recognition is one of the most interesting topics in the field of affective computing. however, development of a reliable approach with a reasonable recognition rate is a challenging task. the main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (hrv). in t...

Empirical Mode Decomposition (EMD), recently proposed by Huang et al. [12], appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. This paper presents an...

ABSTRACT: For analyzing non-linear and non-stationary signals Empirical Mode Decomposition (EMD) is introduced as an adaptive method like wavelet packet best basis decomposition. Huang et. al introduced the empirical mode decomposition (EMD) in signal processing in 1998. In this communication we investigated the performance of video denoising using bi-dimensional EMD along with wavelet threshol...

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...

Image empirical mode decomposition (IEMD) is an empirical mode decomposition concept used in Hilbert–Huang transform (HHT) expanded into two dimensions for the use on images. IEMD provides a tool for image processing by its special ability to locally separate superposed spatial frequencies. The tendency is that the intrinsic mode functions (IMFs) other than the first are low-frequency images. I...

This paper presents the findings of an investigation into Partial Discharge signal denoising using techniques based on Empirical Mode Decomposition. The denoising techniques are based on thresholding the Intrinsic Mode Functions which result from the Empirical Mode Decomposition of a signal. The results of the tests carried out show clearly that these techniques can produce excellent results wh...

The empirical mode decomposition (EMD) was a method pioneered by Huang et al [8] as an alternative technique to the traditional Fourier and wavelet techniques for studying signals. It decomposes a signal into several components called intrinsic mode functions (IMF), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this paper we propose an alternative...