A method of and a system for speech enhancement consists of Hilbert spectrum and wavelet packet analysis is studied. We implement ISA to separate speech and interfering signals from single mixture and wavelet packet based softthresholding algorithm to enhance the quality of target speech. The mixed signal is projected onto time-frequency (TF) space using empirical mode decomposition (EMD) based...

This study presents a novel method for the extraction and screening of knee joint vibroarthrographic (VAG) signals using an independent component analysis (ICA) technique. Compared to existing VAG methods, the proposed technique has the advantages of a lower computational complexity, a rapid convergence speed, and feature detection. A continuous wavelet transformation (CWT) technique is propose...

This paper proposes an analysis of high-impedance fault detection algorithms for medium voltage distribution lines based on the discrete wavelet transform (DWT) technique and a more advanced named independent component (ICA) independently. Three-phase line model two diodes high impedance model, which represents unsymmetrical current electric arc, simulated using MATLAB/Simulink. High (HIF) algo...

We examined the dopamine (DA) modulation of calcium currents (ICa) that could contribute to the plasticity of the pyloric network in the lobster stomatogastric ganglion. Pyloric somata were voltage-clamped under conditions designed to block voltage-gated Na+, K+, and H currents. Depolarizing steps from -60 mV generated voltage-dependent, inward currents that appeared to originate in electrotoni...

As a new approach of blind source separation (BSS), independent component analysis (ICA) has attracted extensive attention of researchers in the field of information processing. In this paper, the basic theory and algorithm of ICA are briefly introduced, and then ICA is used for the preprocessing of engine acoustic signals to identify the engine noise sources. The ICA decomposes the signals int...

The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is compare the automatic detection of EEG patterns using Discrete wavelet...

In recent years, as one of the biometric identification technology, palm-print identification has received many reseachers’ attention. To solve the key problem of palm-print recognition -feature extraction, we propose a new method, which based on wavelet transform and principal component analysis. In general, we use wavelet transform to deal with palm print images and extract high-dimensional w...

Next generation image compression system should be optimized the way human vision system (HVS) works. HVS has been evolved over millions of years for the images which exist in our environment. This idea is reinforced by the fact that sparse codes extracted from natural images resemble the primary visual cortex of HVS. We have introduced a novel technique in which basis functions trained by Inde...

Recent works have shown that artifact removal in biomedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both...