Autism spectrum disorder (ASD) is a neurodevelopmental disorder emerging in the first few years of life. Currently, three characteristics are required for a diagnosis of ASD, impaired social interactions, impaired verbal communication and restricted and repetitive patterns of behaviour or interests. This last category can optionally include hyper- or hypo-reactivity to sensory input. Individuals with autism can also display superior performance on visual tasks where it may help to ignore global detail, behaviour sometimes described as ‘not seeing the forest for the trees’. At present, the exact mechanisms underlying the perceptual differences between autistic and neurotypical groups remain unknown, but they may reflect an imbalance in the contributions that bottom-up and top-down processing make in perceptual processing. Visual perception is thought to rely on interactions between the ‘bottom-up’ flow of ambiguous information from the retina and the ‘top-down’ flow of disambiguating information from higher cortical areas, via cortical circuits that have been shaped by a lifetime’s experience. These interactions lead to the activation of internal representations (of objects) which are necessary for the successful navigation of our environment. In order to investigate these perceptual differences, we employed three well-known experimental paradigms with a group of thirteen autistic participants and their matched controls. We investigated visual integration (involving bottom-up and top-down interactions) across low and intermediate stage neural mechanisms. A dim line (target) is easier to detect when flanked by two brighter collinear lines (flankers), an effect known as collinear facilitation, and we used two variations of this task to investigate low-level visual integration. In the first, we varied the orientation of the collinear flankers and found reduced integration for an autistic compared to a neurotypical group, a finding that conflicts with previous research. In a second collinear facilitation experiment with neurotypical participants, in which the target could be presented before, during or after flanker presentation, we were able to isolate facilitation that we believe was due to feedforward and feedback processing. However, in a subsequent study in which we compared autistic and neurotypical performance on this task, we found no significant difference. Moving onto intermediate level visual integration, we used a contour integration task consisting of open (lines) and closed (square) contours and found reduced integration for the autistic compared to the neurotypical groups when integrating closed contours. In our final study, we looked at global motion integration, and made use of a translating diamond. This is a bistable stimulus in which four lines can be perceived as independent line fragments moving vertically, or as a single integrated shape - a diamond moving horizontally. In this experiment, the autistic group showed an unexpected bias to perceiving the stimuli in its integrated form as a diamond. Perceptual processing of shapes based on squares or diamonds reflects visual integration at a global level, and so the differences we have found in shape processing between our experimental groups (reduced integration for the square and increased integration for the diamond in autism) are more likely to be the result of differences in top-down processing.

No abstract available / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / unrestricted

Quantile function modeling is a more robust, comprehensive, and flexible method of statistical analysis than the commonly used mean-based methods. More and more data are collected in the form of multivariate, functional, and multivariate functional data, for which many aspects of quantile analysis remain unexplored and challenging. This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions. Firstly, it proposes bivariate quantile analysis methods that can predict the joint distribution of bivariate response and improve on conventional univariate quantile regression. The proposed robust statistical techniques are applied to examine barley plants grown in saltwater and freshwater conditions providing interesting insights into barley’s responses, informing future crop decisions. Secondly, it proposes modeling and visualization of bivariate functional data to characterize the distribution and detect outliers. The proposed methods provide an informative visualization tool for bivariate functional data and can characterize non-Gaussian, skewed, and heavy-tailed distributions using directional quantile envelopes. The radiosonde wind data application illustrates our proposed quantile analysis methods for visualization, outlier detection, and prediction. However, the directional quantile envelopes are convex by definition. This feature is shared by most existing methods, which is not desirable in nonconvex and multimodal distributions. Thirdly, this challenge is addressed by modeling multivariate functional data for flexible quantile contour estimation and prediction. The estimated contours are flexible in the sense that they can characterize non-Gaussian and nonconvex marginal distributions. The proposed multivariate quantile function enjoys the theoretical properties of monotonicity, uniqueness, and the consistency of its contours. The proposed methods are applied to air pollution data. Finally, we perform quantile spatial prediction for non-Gaussian spatial data, which often emerges in environmental applications. We introduce a copula-based multiple indicator kriging model, which makes no distributional assumptions on the marginal distribution, thus offers more flexibility. The method performs better than the commonly used variogram approach and Gaussian kriging for spatial prediction in simulations and application to precipitation data.

No description available.

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties.

No description available.

<p> Prairie mounds from the general area of Foremost-Cypress Hills, Alberta, have been described. Various morphometric parameters, including relative and absolute altitude of the mounds, depth of their central depressions, orientation of breaches in their rims, angle of the bounding slopes, and distribution of surficial stones were measured. Several characteristics of the mounds were found to be widespread. Firstly, the mounds occur in elongate fields that correspond roughly to the 3,000 ft. contour interval. Secondly, surficial stones invariably have their greatest concentration on the rims of the mounds. Also the mounds are frequently separated by interrupted drainage channels. The bounding slopes of the mounds are asymmetrical, with west and north-west slopes being least steep. Interpretation of these morphological observations and a consideration of the various hypotheses of formation proposed in the literature leads to the conclusion that prairie mounds most likely originated by the differential melting of a stagnant ice mass in a manner proposed by Gravenor in 1955, and have subsequently been modified by mass-wasting processes. / Thesis / Master of Arts (MA)

No description available.

No description available.

No description available.