In this article, the empirical mode decomposition (EMD) is introduced to the problem of signal detection in underwater sound. EMD is a new method pioneered by Huang et al. for non-linear and nonstationary signal analysis. Based on the EMD, any input data can be decomposed into a small number of intrinsic mode functions (IMFs) which can serve as the basis of non-stationary data for they are comp...

Considering the problem of extracting a trend from a time series, we propose a novel approach based on empirical mode decomposition (EMD), called EMD trend filtering. The rationale is that EMD is a completely data-driven technique, which offers the possibility of estimating a trend of arbitrary shape as a sum of low-frequency intrinsic mode functions produced by the EMD. Based on an empirical a...

This paper presents an empirical mode decomposition (EMD) based adaptive filter (AF) for channel estimation in OFDM system.  In this method, length of channel impulse response (CIR) is first approximated using Akaike information criterion (AIC). Then, CIR is estimated using adaptive filter with EMD decomposed IMF of the received OFDM symbol. The correlation and kurtosis measures are used to sel...

This paper describes a new technique, called the empirical mode decomposition (EMD), that allows the decomposition of one-dimensional signals into intrinsic oscillatory modes. The components, called intrinsic mode functions (IMFs), allow the calculation of a meaningful multicomponent instantaneous frequency. Applied to a seismic trace, the EMD allows us to study the di erent intrinsic oscillato...

The paper presents assessment of various power quality events based on Empirical Mode Decomposition (EMD) with Hilbert Transform (HT). EMD method decomposes the signal into waveforms modulated in both amplitude and frequency. The oscillatory modes embedded in the signal are extracted by employing sifting process. These oscillatory modes are called Intrinsic Mode Functions (IMFs). The magnitude ...

Empirical Mode Decomposition (EMD), introduced by Huang et al, in 1998 is a new and effective tool to analyze non-linear and non-stationary signals. With this method, a complicated and multiscale signal can be adaptively decomposed into a sum of finite number of zero mean oscillating components called as Intrinsic Mode Functions (IMF) whose instantaneous frequency computed by the analytic signa...

The concept of empirical mode decomposition (EMD) and the Hilbert spectrum (HS) has been developed rapidly in many disciplines of science and engineering since Huang et al. (1998) invented EMD. The key feature of EMD is to decompose a signal into so-called intrinsic mode function (IMF). Furthermore, the Hilbert spectral analysis of intrinsic mode functions provides frequency information evolvin...

A new modeling and classification method of ultrasonic signals based on empirical mode decomposition(EMD) and neural network is put forward in the paper. Firstly, the original ultrasonic flaw signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EMD, and the Fourier transformation of IMF is made. The next step is to find a set of classification values from...

The separation of chaos and signal is an important problem of chaos signal processing. In recent years, the time-frequency analysis method is more and more mature. This paper first introduces the basic theory of time-frequency methods. We compare wavelet method with empirical mode decomposition (EMD) method, according to the different noise situation of the performance analysis of harmonic sign...

Electroencephalography (EEG) based preliminary examination system has been proposed in the clinical brain death determination. This paper presents a novel data analysis algorithm based on multivariate empirical mode decomposition (MEMD) to calculate and evaluate the energy of EEG recorded from the comatose patients and brain deaths. MEMD is an extended approach of empirical mode decomposition (...