We propose a method for the direct inversion of the permeability field of a porous medium from the analysis of the displacement of a passive tracer. By monitoring the displacement front at successive time intervals (for example, using a tomographic method), the permeability can be directly obtained from the solution of a nonlinear boundary-value problem. Well posedness requires knowledge of the...

A novel approach to reconstructing the principal directions of a diffusion tensor field directly from magnetic resonance imaging (MRI) data using a tensor tomography data acquisition approach was developed. If tensor eigenvalues are assumed to be known, the reconstruction of principal directions requires fewer measurements than the reconstruction of the full tensor field. The tensor tomography ...

We describe methods for taking into account unlabeled data in the training of a kernel-based classifier, such as a Support Vector Machines (SVM). We propose two approaches utilizing unlabeled points in the vicinity of labeled ones. Both of the approaches effectively modify the metric of the pattern space, either by using nonspherical Gaussian density estimates which are determined using EM, or ...

A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an al...

worldwide networks of seismic recording instruments mostly record only translational ground motion in two or three orthogonal directions. rotational component of earthquake is not commonly recorded. several theories are available to estimate a torsional component from recorded translational components of ground motion. in this paper detailed investigations are made to study the relative perform...

Abstract The thermoconvective instability of the parallel vertical flow in a fluid saturated porous layer bounded by open boundaries is studied. are assumed to be kept at constant uniform pressure while their temperatures and different, thus forcing horizontal temperature gradient across layer. anisotropic permeability accounted for assuming principal axes oriented along directions perpendicula...

Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, evolutiona...