in this paper some new applications of empirical mode decomposition (emd) and hilbert spectrum in seismic ground-roll attenuation, random noise attenuation and spectral decomposition are introduced. hilbert spectrum is a time-frequency representation for hilbert-huang transform which is obtained by combination of instantaneous frequency (if) concept and intrinsic mode function of empirical mode...

ultrasoundelastographyisanon-invasivemethodwhichimagestheelasticityofsoft-tissues. tomakeanimage, preandafterasmallcompression, ultrasoundradio frequency(rf)signalsareacquiredandthetimedelaysbetweenthemareestimated. thefirstdifferentiationofdisplacementestimationsiscalledelastogram. inthisstudy, wearegoingtomakeanelastogramusingtheprocessingmethodnamed empirical mode decomposition (emd). emdisa...

Electrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal a weak bioelectrical and easily disturbed by baseline wander, powerline interference, muscle artefacts, which make detection of diseases more difficult. Therefore, it very important to denoise contaminated in practical application. In this article, effective segments denoising ...

A novel algorithm for 2D object orientation and scaling factor estimation, is proposed in this paper. The proposed method is accurate, effective, computationally efficient and fullyautomated. The object orientation is calculated by using object principal axes estimation. The main contribution of the proposed approach is the utilization of a 2D empirical mode like decomposition (EMD-like) as a n...

A new algorithm for pitch extraction based on the Ensemble Empirical Mode Decomposition (EEMD) is presented. Applications to normal and pathological voices are considered. EEMD is a completely data-driven method for signal decomposition into a sum of AM FM components, called Intrinsic Mode Functions (IMFs) or modes, which can be written as ( ) cos( ( )) A t t φ . The voice fundamental frequency...

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...