Electroencephalogram (EEG) signals are having very small amplitudes and because of that they can be easily contaminated by different Artifacts. The presence of artifacts makes the analysis of EEG difficult for clinical evaluation. The major types of artifacts that affect the EEG are Power Line noise, eye movements, Electromyogram (EMG), and Electrocardiogram (ECG). Out of these artifacts Power ...

The fetal electrocardiogram (fECG) monitoring yields important information about the fetus condition during pregnancy and it consists in collecting electrical signals by some sensors on the body of the mother. The Independent Component Analysis (ICA) has been widely exploited to isolate the fECG, while wavelet transform has been used as post-processing tool. Here we propose to fit the recently ...

A kind of image digital watermarking scheme is proposed in this paper. The scheme is based on Fast Independent Component Analysis (Fast ICA) and Discrete Wavelet Transform (DWT). In this scheme, a binary image is embedded into a wavelet approach sub-image. When extracting the watermarking, Fast ICA method is used. The experiment results show that the scheme is robust to many attacks. Keyword— B...

Along with the development of the watermark technique many new scheme were presented and most of them were proved efficient. Some researchers have presented extraction of audio watermark using ICA in spatial domain. In this paper, we present a wavelet audio watermark using ICA. We embedded the image watermark into the wavelet coefficient of the audio signal, and extracted the watermark image us...

In this paper, we present a technique by which highintensity feature vectors extracted from the Gabor wavelet transformation of frontal face images, is combined together with Independent Component Analysis (ICA) for enhanced face recognition. Firstly, the high-intensity feature vectors are automatically extracted using the local characteristics of each individual face from the Gabor transformed...

Method for image source separation based on Independent Component Analysis: ICA, Maximum Entropy Method: MEM, and Wavelet Based Method: WBM is proposed. Experimental results show that image separation can be done from the combined different images by using the proposed method with an acceptable residual error. Keywords-Blind separation; image separation; cucktail party effect; ICA; MEM; wavelet...

In this paper, a new process monitoring methodology is presented to detect fault occurrence. The proposed methodology incorporates a wavelet de-noising approach based on the fast wavelet transform (FWT) to extract the embodied fault dynamics from the noisy measured data. A level dependent soft thresholding technique using Daubechies 3 with three levels of decomposition is utilized. An appropria...

In this paper we propose a method to identify and remove artifacts, that have a relatively short duration, from complex EEG data. The method is based on the application of an ICA algorithm to three non-overlapping partitions of a given data, selection of sparse independent components, removal of the component, and the combination of three resultant signal reconstructions in one final reconstruc...

According to the problem of speech signal denoising, we propose a novel method in this paper, which combines empirical mode decomposition (EMD), wavelet threshold denoising and independent component analysis with reference (ICA-R). Because there is only one mixed recording, it is a single-channel independent component analysis (SCICA) problem in fact, which is hard to solve by traditional ICA m...

Aiming at the problem of noise suppression in power lines, traditional methods need to know prior knowledge and other defects. In this paper, blind source separation that do not are selected. case low signal-to-noise ratio, basic independent component analysis algorithm has poor denoising effect. Therefore, paper proposes a joint based on Wavelet Power (WD-PowerICA). work, firstly, pseudo obser...