Since late 1990s, Empirical Mode Decomposition (EMD) starts to emerge as a powerful tool for processing non-linear and non-stationary signals. Nonetheless, the research on exploring EMD-relevant techniques in the domain of geometric modeling and processing is extremely rare. Directly applying EMD to coordinate functions of 3D shape geometry will not take advantage of the attractive EMD properti...

We introduce a new adaptive method for analyzing nonlinear and nonstationary data. This method is inspired by the empirical mode decomposition (EMD) method and the recently developed compressed sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form {a(t) cos(θ(t))}, whe...

Local rub-impact is the common fault in rotating machinery and results in impact and friction between rotor and stator. The vibration signal due to impact and friction is always non-stationary which includes three components, namely, the rub-impact signal, the background signal and the noise signal. EMD (Empirical mode decomposition) is based upon the local characteristic time scale of signal a...

Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. First...

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the pr...

Sleep apnea is a growing sleep disorder issue and estimate to affect 7% of the adult population in Malaysia. In this study, the electrical activity of the brain is studied using Electroencephalogram (EEG). The data obtained was then decomposed using three methods; Empirical Mode Decomposition (EMD), Bivariate EMD and finally Ensemble EMD. The Index of Orthogonatility (IO) was obtained which sho...

The empirical mode decomposition (EMD) was a method pioneered by Huang et al, as an alternative technique to the traditional Fourier and wavelet techniques for nonlinear and non-stationary signals. It decomposes a signal into several components called intrinsic mode functions (IMFs), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this talk we prese...

The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities. The detection of epileptic seizures based on EEG signal is very useful in diagnostics. In this paper, we present a new method for discrimination between seizure and seizure-free EEG signals. The proposed method is based on empirical mode decomposition (EMD) process. We investigated that th...

Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomp...

Correlations for daily data of total ozone column are investigated by detrended fluctuation analysis (DFA). The removal of annual periodicity does not result in a background-free signal for the tropical station Mauna Loa. In order to identify the remaining quasiperiodic constituent, the relatively new method of empirical mode decomposition (EMD) is tested. We found that the so-called intrinsic ...