removal of artifacts from bio-signals is a necessary step before automatic processing and obtaining clinical information. recently, many applications of empirical mode decomposition (emd) on biomedical researches have been presented that artifact reduction from bio-signals is one of them. emd separates a time series into finite numbers of its individual oscillations, which are called intrinsic ...

Long-term prediction of customer preferences is becoming essential for effective product portfolio design in broad industrial sectors such as automotive, aerospace, consumer electronics, where typical concept-to-release times are long (24-60 months). However, nonlinear and nonstationary evolutions of customer preferences hinder accurate prediction of the futures of customer preferences. This pa...

A data-driven method for extracting temporally persistent information, at different spatial scales, from rainfall data (as measured by radar/satellite) is described, which extends the Empirical Mode Decomposition (EMD) algorithm into two dimensions. The EMD technique is used here to decompose spatial rainfall data into a sequence of high through to low frequency components. This process is equi...

At present, in the feature extraction of non-stationary vibration signals, many timefrequency analytic methods have emerged to meet the further need of non-stationary signal analysis such as Wavelet Transform (WT), Short Time Fourier Transform (STFT), WignerVille Distribution (WVD), Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and so on. However, these time-f...

In this paper, we propose a general method to obtain Linearly Independent NonOrthogonal yet Energy (square of the norm) Preserving (LINOEP) vectors using iterative filtering operation and we refer it as Filter Mode Decomposition (FDM). We show that the general energy preserving theorem (EPT), which is valid for both linearly independent (orthogonal and nonorthogonal) and linearly dependent vect...

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

Brain responses to audio stimuli are analysed using data driven time-frequency analysis. This is achieved based on the electroencephalogram (EEG) recordings and with auditory chirps or music as the audio stimulus. The empirical mode decomposition (EMD) is applied to multichannel EEG recordings, and the insight into the brain responses is provided by the analysis of the dynamics of auditory stea...

the brain – computer interface (bci) provides a communicational channel between human and machine. most of these systems are based on brain activities. brain computer-interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. the success of this methodology depends on the selection of methods to process the brain signals in each pha...

Empirical Mode Decomposition (EMD) has been used effectively in the analysis of non-linear and non-stationary signals. As an application in Robust Signal Processing, in this paper we used this method to reduce noise from a corrupted signal which is obtained from a disaster environment. Conventional adaptive algorithms exhibit poor performance if we consider the signal from a real environment. I...

This paper is devoted to an analysis of environmental time series using the empirical mode decomposition (EMD) method. This is a new method, which was developed for the off-line analysis of non-stationary and stochastic signals. The purpose of the work was to modify the EMD algorithm for on-line analysis and to find out the states of ecosystems in real time. The practical realization of the alg...