Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is now a widely accepted tool for detection of artifacts in EEG data. One major challenge to artifact removal using ICA is the identification of the artifactual components. Although several strategies were proposed for...

The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform cod...

The Blind Source Separation (BSS) problem has become one of the main signal processing research areas, mainly due to its increasing application potential to many real world problems. A number of algorithms based on the Independent Component Analysis (ICA) theory have provided a solution to the BSS problems and have been successfully applied to a wide range of fields, covering diverse subjects s...

Fetal electrocardiogram (fECG) analysis, i.e., the analysis of the heart rate (HR) and the fECG morphology, offers clinicians vital information about fetal health condition. The fECG can be acquired non-invasively by applying electrodes on maternal abdomen, but the recorded abdominal signal (ADS) contains not only the signal of interest, fECG, but also some physiological (e.g., maternal electro...

It has been observed that denoising of ECG is done in stationary condition i.e. when the patient under scanner is lying on bed or in rest condition. This is very common case and the noise added in this condition can be removed by many different methodologies like FFT, wavelet, ICA etc. However, when the ECG is monitored in dynamic condition e.g., on tread mill testing, the noise inserted due to...

A construction paradigm is proposed to refine a class of wavelet bases such that the filter characteristics are enhanced. In particular, it is shown that the entire Mth order B-wavelet family belong to this class. Exploiting some recent work, a fast integral wavelet transform is found for the refined B-wavelet family. Moreover, this refined basis can be placed into the setting of a multiresolut...

We present a new speech enhancement method for robust speech recognition in a noisy car environment. The method is based on the combination of two important building blocks, namely blind source separation given two microphone signals and speech denoising using a hybrid Wavelet Independent Component Analysis (ICA) filterbank. The first block separates point sources such as the passenger’s voice ...

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait Analysis (i.e. recognizing people from the way they walk) is one of the r...

The forward and inverse wavelet transform using the continuous Morlet basis may be symmetrized by using an appropriate normalization factor. The loss of response due to wavelet truncation is addressed through a renormalization of the wavelet based on power. The spectral density has physical units which may be related to the squared amplitude of the signal, as do its margins the mean wavelet pow...

This paper presents a novel full reference (FR) image quality assessment algorithm by depicting the subband characteristics in the wavelet domain. The proposed image quality assesment method is based on the energy estimation in wavelet transformed image. Image quality assessment is achieved by applying a multi-level wavelet decomposition on the both original and enhanced image. Next, the wavele...