Vahid Abolghasemi

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Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current(More)
In this paper the problem of Compressive Sensing (CS) is addressed. The focus is on estimating a proper measurement matrix for compressive sampling of signals. The fact that a small mutual coherence between the measurement matrix and the representing matrix is a requirement for achieving a successful CS is now well known. Therefore, designing measurement(More)
A new ALE-based on singular spectrum analysis (SSA) is proposed here. In this approach in the reconstruction stage of SSA the eigentriples are adaptively selected using the delayed version of the data. Unlike for the conventional ALE where order statistics are taken into account, here full eigen-spectrum of the embedding matrix is exploited. Consequently,(More)
In this paper, we present an online signature verification system based on Dynamic Time Warping (DTW)-based segmentation technique combined with Multivariate Autoregressive (MVAR) modeling. We also use multilayer perceptron neural network architecture as data classifier. The input data that has been used is (x<inf>j</inf>,y<inf>j</inf>) coordinates of(More)
In this paper the problem of removing Ballistocardiogram (BCG) artifact from EEG signal is addressed. BCG removal is an important task in analysis of simultaneous EEG-fMRI data. We propose a new method by combining independent component analysis (ICA) and discrete Hermite transform (DHT) for this purpose. Discrete Hermite transform is a powerful technique(More)
In this paper the application of Nonnegative Matrix Factorization (NMF) to Functional Magnetic Resonance Images (fMRIs) is addressed. We attempt to blindly separate the sources of fMRI mixtures. However, our interest is to find only one particular source (task-related source), which indicates the active area in the brain. We utilize the prior knowledge(More)