In this paper, electroencephalography (EEG) measurements are used to assess cortical functional connectivity in response to perceived audio quality. Specifically, in the conducted experiment the brainwave response patterns of human subjects are directly recorded using a high resolution EEG while they listen to audio whose quality varies with time. A new causal bi-directional information (CBI) measure is proposed which quantifies the information flow between EEG electrodes by appropriately grouping them into specific regions of interest (ROIs) over the cortex. It is shown that CBI can be intuitively interpreted as a causal bi-directional modification of directed information applied to a generalized cortical network setting, and inherently calculates the divergence of the observed data from a multiple access channel with feedback. The proposed measure is used to analyze and compare the information flow between ROI pairs for the case when the subject listens to high quality audio compared to when the subject listens to low quality audio. The results indicate that CBI is a more robust measure for inferring connectivity when compared to using standard directed information measures.

In this paper, we use mutual information (MI) as a measure to quantify the subjective perception of audio quality by directly measuring the brainwave responses of human subjects using a high resolution electro-encephalogram (EEG). Specifically, we propose an information theoretic model to interpret the entire “transmission chain” comprising stimulus generation, brain processing by the human subject, and EEG measurements as a nonlinear, time-varying communication channel with memory. In the conducted experiment, subjects were presented with audio whose quality varies between two quality levels. The recorded EEG measurements can be modeled as a multidimensional Gaussian mixture model (GMM). In order to make the computation of the MI feasible, we present a novel approximation technique for the differential entropy of the multidimensional GMM. We find the proposed information theoretic approach to be successful in quantifying audio quality perception, with the results being consistent across different subjects and distortion types.

Imaging polarimetry has emerged as a powerful tool for application in the field of remote sensing. In this paper, we present a novel technique for estimating the surface normal angle of each of the individual facets of a target object using passive polarimetric data. The passive polarimetric imaging system described here uses multiple measurements of the output Stokes vectors along with the reflection Mueller matrix, to extract the surface normal angle corresponding to individual facets of the target object. The knowledge of this parameter is indispensable for determining the orientation and surface geometry of the target object and thus facilitates applications like object recognition, shape extraction and building scene geometry. The worst-case error is found to be less than 2%, based on Monte Carlo computer simulation results.

A glyph-based method for visualizing the nematic liquid crystal alignment tensor is introduced. Unlike previous approaches, the glyph is based upon physically-linked metrics, not offsets of the eigenvalues. These metrics, combined with a set of superellipsoid shapes, communicate both the strength of the crystal's uniaxial alignment and the amount of biaxiality. With small modifications, our approach can visualize any real symmetric traceless tensor.

A method for the semi-automatic detection and visualization of defects in models of nematic liquid crystals (NLCs) is introduced; this method is suitable for unstructured models, a previously unsolved problem. The detected defects-also known as disclinations-are regions were the alignment of the liquid crystal rapidly changes over space; these defects play a large role in the physical behavior of the NLC substrate. Defect detection is based upon a measure of total angular change of crystal orientation (the director) over a node neighborhood via the use of a nearest neighbor path. Visualizations based upon the detection algorithm clearly identifies complete defect regions as opposed to incomplete visual descriptions provided by cutting-plane and isosurface approaches. The introduced techniques are currently in use by scientists studying the dynamics of defect change.