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...

Parkinson Disease is a neurological syndrome, characterized by resting tremor, reduced body movement and difficulty in maintaining proper posture. In this article I demonstrate a new method of parkinsonism diagnosis using Empirical Mode Decomposition (EMD) based on body movement synchronization analysis. EMD is a new method of signal decomposition into its monocomponents, so called Intrinsic Mo...

This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient when missing values are present. This paper proposes a modified EMD procedure ...

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

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...

There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demon...

The aim of this paper was to compare empirical mode decomposition (EMD) and two new extended methods of EMD named complex empirical mode decomposition (complex-EMD) and bivariate empirical mode decomposition (bivariate-EMD). All methods were used to analyze stabilogram center of pressure (COP) time series. The two new methods are suitable to be applied to complex time series to extract complex ...

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally ...

Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram  (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...

The empirical mode decomposition (EMD) is well known for predicting wind speed.However, but the joint application of relevance vector machine (RVM) and empirical mode decomposition in wind speed forecasting is seldom found in the field. This paper proposes a relevance vector machine model based on empirical mode decomposition to predict the wind speed. Before the wind speed forecasting with RVM...