functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

In the tandem feature extraction scheme a Multi-Layer Perceptron (MLP) with softmax output layer is discriminatively trained to estimate context independent phoneme posterior probabilities on a labeled database. The outputs of the MLP after nonlinear transformation and Principal ComponentAnalysis (PCA) are used as features in a Gaussian Mixture Model (GMM) based recognizer. The baseline tandem ...

introduction: the accuracy of analyzing functional mri (fmri) data is usually decreases in the presence of noise and artifact sources. a common solution in for analyzing fmri data having high noise is to use suitable preprocessing methods with the aim of data denoising. some effects of preprocessing methods on the parametric methods such as general linear model (glm) have previously been evalua...

Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...

The detection of transient events related to slow earthquakes in GNSS positional time series is key understanding seismogenic processes subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal spatial signals. PICCA based on an optimal combination principal (PCA) independent component analysis (ICA) network....

Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources, and unlike principal component analysis (PCA), which uncorrelates the d...

ICA decomposes a set of features into a basis whose components are statistically independent. It minimizes the statistical dependence between basis functions and searches for a linear transformation to express a set of features as a linear combination of statistically independent basis functions. Though ICA has found its application in face recognition, mostly spatial ICA was employed. Recently...

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result detailed inference underlying regulatory networks, but the diversity experimental platforms and protocols introduces critical biases that hinder scalable analysis existing data. Here, we show structure E . coli transcriptome, as determined by Independent Component Analysis (ICA...