Hilbert Huang transform (HHT) is a relatively new method. It seems to be very promising for the different applications in signal processing because it could calculate instantaneous frequency and amplitude which is also important for the biomedical signals. HHT consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has ...

In order to distinguish the difference of non-stationary signals, the novel time-frequency analysis approach, i.e. Hilbert-Huang transform (HHT), is applied in this paper. Firstly, Hilbert-Huang transform is briefly introduced. Secondly, two different non-stationary signals are described in Empirical Mode Decomposition (EMD) and Hilbert spectra. With these results, the two signals that looked a...

An adaptive data analysis method, the Empirical Mode Decomposition and Hilbert Spectral Analysis, is introduced and reviewed briefly. The salient properties of the method is emphasized in this review; namely, physical meaningful adaptive basis, instantaneous frequency, and using intrawave frequency modulation to represent nonlinear waveform distortion. This method can perform and enhance most o...

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 ...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

-Support Vector Machines (SVMs) map inputs vectors nonlinearly into a high dimensional feature space and construct the optimum separating hyperplane in space to realize signal classification. Automatic classification of digital modulation signals plays an important role in communication applications such as an intelligent demodulator, interference identification and monitoring, so many investig...

Computing the Fourier transform and its inverse is important in many applications of mathematics, such as frequency analysis, signal modulation, and filtering. Two methods will be derived for numerically computing the inverse Fourier transforms, and they will be compared to the standard inverse discrete Fourier transform (IDFT) method. The first computes the inverse Fourier transform through di...