Vulkan is a modern, low-level, and low-overhead graphics library that allows for the distribution of work across CPU cores using multithreading. This multithreading is possible due to the near full control of the GPU that Vulkan allows. The additional control makes it possible to send multiple instructions to the GPU at the same time. There are a variety of techniques that can be used with Vulkan to effectively improve performance while multithreading instructions to the GPU. One of the challenges of multithreading is the lack of modern-day GPU hardware to support it, which leads to the purpose of this paper, to explore the practicality of multithreading techniques with Vulkan in today’s current computing environment.

Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.

This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data

This paper compares two approaches to implementing the Marching Cubes algorithm, a method of extracting a polygonal mesh from a 3D scalar field. One possible application of this algorithm is as a procedural terrain generation technique for use in video game development. The Marching Cubes algorithm is an easily parallelizable task, and as such benefits greatly from being executed on the GPU. The reason that the algorithm is so well suited for parallelization is that it breaks the problem of mesh generation into a large group of similar sub-problems that can be solved completely independently.

Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such as Perlin noise. However, interpreting noise in this manner has a number of challenges due to the pseudo-random nature of noise. We will discuss how to generate noise, how to render noise, and the challenges in interpreting noise.

While significant qualitative, user study-focused research has been done on augmented reality, relatively few studies have been conducted on multiple, co-located synchronously collaborating users in augmented reality. Recognizing the need for more collaborative user studies in augmented reality and the value such studies present, a user study is conducted of collaborative decision-making in augmented reality to investigate the following research question: “Does presenting data visualizations in augmented reality influence the collaborative decision-making behaviors of a team?” This user study evaluates how viewing data visualizations with augmented reality headsets impacts collaboration in small teams compared to viewing together on a single 2D desktop monitor as a baseline. Teams of two participants performed closed and open-ended evaluation tasks to collaboratively analyze data visualized in both augmented reality and on a desktop monitor. Multiple means of collecting and analyzing data were employed to develop a well-rounded context for results and conclusions, including software logging of participant interactions, qualitative analysis of video recordings of participant sessions, and pre- and post-study participant questionnaires. The results indicate that augmented reality doesn’t significantly change the quantity of team member communication but does impact the means and strategies participants use to collaborate.

In the last decade, the immense growth of computational power, enhanced data storage capabilities, and the increasing popularity of online learning systems has led to adaptive learning systems becoming more widely available. Parallel to infrastructure enhancements, more researchers have started to study the adaptive task selection systems, concluding that suggesting tasks appropriate to students' needs may increase students' learning gains.

This work built an adaptive task selection system for undergraduate organic chemistry students using a deep learning algorithm. The proposed model is based on a recursive neural network (RNN) architecture built with Long-Short Term Memory (LSTM) cells that recommends organic chemistry practice questions to students depending on their previous question selections.

For this study, educational data were collected from the Organic Chemistry Practice Environment (OPE) that is used in the Organic Chemistry course at Arizona State University. The OPE has more than three thousand questions. Each question is linked to one or more knowledge components (KCs) to enable recommendations that precisely address the knowledge that students need. Subject matter experts made the connection between questions and related KCs.

A linear model derived from students' exam results was used to identify skilled students. The neural network based recommendation system was trained using those skilled students' problem solving attempt sequences so that the trained system recommends questions that will likely improve learning gains the most. The model was evaluated by measuring the predicted questions' accuracy against learners' actual task selections. The proposed model not only accurately predicted the learners' actual task selection but also the correctness of their answers.

Mixed reality mobile platforms co-locate virtual objects with physical spaces, creating immersive user experiences. To create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic physical lighting. To this end, a system was designed that Generates Light Estimation Across Mixed-reality (GLEAM) devices to continually sense realistic lighting of a physical scene in all directions. GLEAM optionally operate across multiple mobile mixed-reality devices to leverage collaborative multi-viewpoint sensing for improved estimation. The system implements policies that prioritize resolution, coverage, or update interval of the illumination estimation depending on the situational needs of the virtual scene and physical environment.

To evaluate the runtime performance and perceptual efficacy of the system, GLEAM was implemented on the Unity 3D Game Engine. The implementation was deployed on Android and iOS devices. On these implementations, GLEAM can prioritize dynamic estimation with update intervals as low as 15 ms or prioritize high spatial quality with update intervals of 200 ms. User studies across 99 participants and 26 scene comparisons reported a preference towards GLEAM over other lighting techniques in 66.67% of the presented augmented scenes and indifference in 12.57% of the scenes. A controlled lighting user study on 18 participants revealed a general preference for policies that strike a balance between resolution and update rate.