This paper proposes three approaches to content-based watermarking for image authentication based on Independent Component Analysis (ICA). In this scheme, ICA is applied to blocks of the cover image and the resulting mixing matrix is used as the content-based feature. This is embedded in the mid-frequency DCT coefficient of the block in the first method. The watermark is embedded in the 3 rd le...

Remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes an image fusion algorithm based on extensions of independent component analysis (ICA) and multi-classifier system. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions ...

In mixtures of speech signals the energy content of the components of the mixture is important and determine the structure of the mixture. Energy contents of signals are better shown when time-frequency or time-scale planes are used. In this paper we present a comparison of wavelet transform (WT) and short time Fourier Transform (STFT) in spectral analysis of speech signals. We will show in wav...

In this study, we propose a brain-computer interface (BCI) system to analyze single-trial electroencephalogram (EEG) signals. After the automatic EOG-artifact elimination, wavelet-coherence features and support vector machine (SVM) are adopted for the classification of left and right motor imagery (MI) data. EOG artifacts are removed automatically via modified independent component analysis (IC...

In biomedical engineering, to extract the fetal electrocardiogram (FECG) exactly is a significant and challenging research topic, and so it has been a hot field in biomedical research. Now, a variety of different methods have been proposed to address this problem. From the perspective of blind signal processing, FECG extraction can be modeled as Blind source separation (BSS). In this paper, we ...

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The rem...

Important methods concerning artifact removal from EEG signals has been briefly described pertaining to its significance and its drawbacks. Some methods described herein range from conventional methods such as linear filtering, Linear combination and regression (LCR) to more contemporary methods such as blind source separation (BSS) with applications such as Principal component analysis (PCA) a...

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening ...

BACKGROUND The main ECG criteria for the diagnosis of long-QT syndrome (LQTS) include abnormal T-wave morphology as well as prolonged QT interval. The T wave in LQTS probably includes additional components of the myocardial repolarization process, which are derived from aberrant ion currents. We investigated whether independent component analysis (ICA) can extract such abnormal repolarization c...