This paper describes a method of signal process applied on an end effects of Hilbert-Huang transform (HHT) to provide an improvement in the reality of spectrum. The method is based on back-propagation network (BPN). To improve the effect, the end extension of the original signal is obtained by back-propagation network. A full waveform including origin and its extension is decomposed by using em...

This paper presents a signal analysis technique for machine health monitoring based on the Hilbert-Huang Transform (HHT). The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of the HHT is introduced, based on ...

This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. T...

A method for non-linear and non-stationary characterisation of speech rhythm is presented using Hilbert Huang Transform (HHT) of ‘Speech Unit Intervals’ (SUI) signals. SUI signals are supported by intervals duration between given speech units such as vowel, consonant, or syllable. While HHT is based on the combination of the Empirical Mode Decomposition (EMD) and the Hilbert transform of the pr...

The success of health monitoring and condition assessment of power transformers based on winding current signature analysis lies on proper extraction of features. The extraction of features in turn depends on appropriate signal processing methods. Fourier based signal analysis provides only frequency information and also suitable only for stationary signals. In this paper we present a combined ...

ABSTRACT: Hilbert-Huang Transform (HHT), proposed by N. E. Huang in 1998, is a novel algorithm for nonlinear and non-stationary signal processing. The key part of this method is decomposition the signal into finite number of Intrinsic Mode Functions (IMF) which will meet the requirements of Hilbert Transform. In this part, the algorithm uses natural cubic spline to connect all local maxima and ...

The HHT (Hilbert-Huang transform) and wavelet transform are both signal processing methods. This paper is based on comparing HHT and Wavelet transform applied to Radar signals. HHT can be used for processing nonstationary and nonlinear signals. It is one of the time-frequency analysis techniques which consists of Empirical Mode Decomposition (EMD) and instantaneous frequency solution. EMD is a ...

Norden E. Huang et al. had proposed and published the Hilbert-Huang Transform (HHT) concept correspondently in 1996, 1998 [1]. The HHT is a novel method for adaptive spectral analysis of non-linear and non-stationary signals. The HHT comprises two components: – the Huang Empirical Mode Decomposition (EMD), resulting in an adaptive dataderived basis of Intrinsic Mode functions (IMFs), and the Hi...

Empirical mode decomposition (EMD) is a signal processing method that produces data-driven time-frequency representation suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic functions (IMF) are sequentially estimated from the of interest represent different oscilation modes produce an orthogonal original information. Different algorithms have been proposed for EMD esti...

This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features ...