Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal compo...

in this paper, empirical mode decomposition (eMd) is proposed as an alternative method in the framework of acoustic analysis of disordered speech for the purpose of clinical evaluation of voice. the empirical mode decomposition algorithm decomposes adaptively a given signal into oscillation modes extracted from the signal itself. the proposed approach for objective assessment of vocal dysperiod...

The paper proposes an application of Empirical Mode Decomposition in technical analysis. The EMD-candlestick is designed to replace the traditional candlestick as the signal generators in technical trading strategies to improve the profitability. We investigate a representative set of technical trading strategies, including moving average, trading range break-out, relative strength index, and i...

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...

In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental...

The Hilbert transformation together with empirical mode decomposition (EMD) produces Hilbert spectrum (HS) which is a fine-resolution timefrequency (TF) representation of any nonlinear and non-stationary signal. A method of audio signal separation from stereo mixtures based on the spatial location of the sources is presented in this paper. The TF representation of the audio signal is obtained b...