Before there were airplanes, cars, trains, boats, or bicycles, the primary means of transportation was on foot. Unfortunately, many of the trails used by ancient travelers have long since been abandoned. We present a software tool which can help visualize and predict where these forgotten trails might lie through the use of a human-centered cost metric. By comparing the paths generated by our software with known historical trails, we demonstrate how the tool can indicate likely trails used by ancient travelers. In addition, this new tool provides novel visualizations to better help the user understand alternate paths, effect of terrain, and nearby areas of interest. Such a tool could be used by archaeologists and historians to better visualize and understand the terrain and paths around sites of historical interest. This thesis is a continuation of previous work, with emphasis on the ability to generate paths which traverse several thousand kilometers. To accomplish this, various graph simplification and path approximation algorithms are explored to construct a real-time path finding algorithm. To this end, we show that it is possible to restrict the search space for a path finding algorithm while not disrupting accuracy. Combined with a multi-threaded variant of Dijkstra's shortest path algorithm, we present a tool capable of traversing the contiguous US, a dataset containing over 19 billion datapoints, in under three hours on a 2.5 Ghz dual core processor. The tool is demonstrated on several examples which show the potential archaeological and historical applicability, and provide avenues for future improvements.

Networked virtual reality environments including virtual worlds devoted to entertainment, online socializing and remote collaboration have grown in popularity with the rise of commercially available consumer graphics hardware and the growing ubiquity of the Internet. These virtual worlds are typified by a persistent simulated three-dimensional space that communicates over a computer network, where users interact with the environment and each other through digital avatars. Development of these virtual worlds challenges the limits of the networking infrastructure, 3D streaming graphics techniques, and the distributed computing design of the virtual world systems that manages the simulation. In this dissertation, we explore solutions to different aspects of the overall problem of developing a general purpose, networked virtual environment, focusing on the networking and software system issues. Specifically, we show how to improve the networking infrastructure to better support the high packet-rate traffic that is typical of virtual worlds, efficiently stream terrain data for remote rendering, and construct a dynamically adaptive distributed systems framework suitable for virtual world simulations.

This project presents a virtual reality (VR) Interactive Narrative aiming to leave users reflecting on the perspectives one chooses to view life through. The narrative is driven by interactions designed using the concept of procedural rhetoric, which explores how rules and mechanics in games can persuade people about an idea, and Shin's cognitive model, which presents a dynamic view of immersion in VR. The persuasive nature of procedural rhetoric in combination with immersion techniques such as tangible interfaces and first-person elements of VR can effectively work together to immerse users into a compelling narrative experience with an intended emotional response output. The narrative is experienced through a young woman in a state between life and death, who wakes up as her subconscious-self in a limbo-like world consisting of core memories from her life, where the user is tasked with taking photos of the protagonist's memories for her to come back to life. Users primarily interact with and are integrated into the narrative through a photography mechanic, as they have the agency to select "perspective" filters to apply to the protagonist's camera from which to view a core memory through, ultimately choosing which perspectives of her memories become permanent when she comes back to life. This project hopes to provide an example of effectively applying procedural rhetoric to a VR interactive narrative so that future interactive narrative designers can further apply and explore how procedural rhetoric can work with immersion techniques to create compelling and immersive VR experiences.

Severe neuromuscular disorders can produce locked-in syndrome (LIS), a loss of nearly all voluntary muscle control. A brain-computer interface (BCI) using the P300 event-related potential provides communication that does not depend on neuromuscular activity and can be useful for those with LIS. Currently, there is no way of determining the effectiveness of P300-based BCIs without testing a person's performance multiple times. Additionally, P300 responses in BCI tasks may not resemble the typical P300 response. I sought to clarify the relationship between the P300 response and BCI task parameters and examine the possibility of a predictive relationship between traditional oddball tasks and BCI performance. Both waveform and component analysis have revealed several task-dependent aspects of brain activity that show significant correlation with the user's performance. These components may provide a fast and reliable metric to indicate whether the BCI system will work for a given individual.

Scientific visualization computer applications generate visual representations of large and complex sets of science data. These types of applications allow scientists to gain greater knowledge and insight into their data. For example, the visualization of environmental data is of particular interest to biologists when trying to understand how complex variables interact. Modern robotics and sensors have expanded the ability to collect environmental data, thus, the size and variety of these data-sets have likewise grown. Oftentimes, the collected data are deposited into files and databases where they sit in their separate and unique formats. Without easy to use visualization tools, it is difficult to understand and interpret the information within these data-sets. NORUS, the North America-Norway educational program, has a scientific focus on how climate-induced changes impact the living resources and ecosystems in the Arctic. In order to obtain the necessary science data, the NORUS program utilizes the Slocum Glider, a form of Autonomous Underwater Vehicle (AUV). This thesis aims to create a compelling, efficient, and easy to use interactive system for visualizing large sets of science data collected by the Slocum Glider. This goal is obtained through the implementation of various methods taken from scientific visualization, real time rendering, and scattered data interpolation. Methods include visualizations of the surrounding terrain, the ability to map various science data to glyphs, control over color mapping, scattered data interpolation and interactive camera control.

Traditional human-computer interaction (HCI) is based on 'cold' models of user cognition; that is, models of users as purely rational beings based on the information processing metaphor; however, an emerging perspective suggests that for the field of HCI to mature, its practitioners must adopt models of users that consider broader human needs and capabilities. Affective design is an umbrella term for research and practice being conducted in diverse domains, all with the common thread of integrating emotional aspects of use into the creation of information products. This thesis provides a review of the current state of the art in affective design research and practice to technical communicators and others involved in traditional HCI and usability enterprises. This paper is motivated by the developing technologies and the growing complexity of interaction that demand a more robust notion of HCI that incorporates affect in an augmented and holistic representation of the user and situated use.

The interaction of light in our world is immensely complex, but with mod- ern computers and advanced rendering algorithms, we are beginning to reach the point where photo-realistic renders are truly difficult to separate from real photographs. Achieving realistic or believable global illumination in scenes with participating media is exponentially more expensive compared to our traditional polygonal methods. Light interacts with the particles of a volume, creating com- plex radiance patterns. In this thesis, we introduce an extension to the commonly used point-based color bleeding (PCB) technique, implementing volume scatter contributions. With the addition of this PCB algorithm extension, we are able to render fast, be- lievable in- and out-scattering while building on existing data structures and paradigms. The proposed method achieves results comparable to that of existing Monte Carlo integration methods, obtaining render speeds between 10 and 36 times faster while keeping memory overhead under 5%.

Traditional global illumination lighting techniques like Radiosity and Monte Carlo sampling are computationally expensive. This has prompted the development of the Point-Based Color Bleeding (PBCB) algorithm by Pixar in order to approximate complex indirect illumination while meeting the demands of movie production; namely, reduced memory usage, surface shading independent run time, and faster renders than the aforementioned lighting techniques. The PBCB algorithm works by discretizing a scene’s directly illuminated geometry into a point cloud (surfel) representation. When computing the indirect illumination at a point, the surfels are rasterized onto cube faces surrounding that point, and the constituent pixels are combined into the final, approximate, indirect lighting value. In this thesis we present a performance enhancement to the Point-Based Color Bleeding algorithm through hardware acceleration; our contribution incorporates GPU-accelerated rasterization into the cube-face raster phase. The goal is to leverage the powerful rasterization capabilities of modern graphics processors in order to speed up the PBCB algorithm over standard software rasterization. Additionally, we contribute a preprocess that generates triangular surfels that are suited for fast rasterization by the GPU, and show that new heterogeneous architecture chips (e.g. Sandy Bridge from Intel) simplify the code required to leverage the power of the GPU. Our algorithm reproduces the output of the traditional Monte Carlo technique with a speedup of 41.65x, and additionally achieves a 3.12x speedup over software-rasterized PBCB.

Usability testing, or user experience (UX) testing, is increasingly recognized as an important part of the user interface design process. However, evaluating usability tests can be expensive in terms of time and resources and can lack consistency between human evaluators. This makes automation an appealing expansion or alternative to conventional usability techniques. Early usability automation focused on evaluating human behavior through quantitative metrics but the explosion of opinion mining and sentiment analysis applications in recent decades has led to exciting new possibilities for usability evaluation methods. This paper presents a survey of modern, open-source sentiment analyzers’ usefulness in extracting and correctly identifying moments of semantic significance in the context of recorded mock usability evaluations. Though our results did not find a text-based sentiment analyzer that could correctly parse moments as well as human evaluators, one analyzer was found to be able to parse positive moments found through audio-only cues as well as human evaluators. Further research into adjusting settings on current sentiment analyzers for usability evaluations and using multimodal tools instead of text-based analyzers could produce valuable tools for usability evaluations when used in conjunction with human evaluators.

While modern digital entertainment has seen a major shift toward photorealism in animation, there is still significant demand for stylized rendering tools. Stylized, or non-photorealistic rendering (NPR), applications generally sacrifice physical accuracy for artistic or functional visual output. Oftentimes, NPR applications focus on extracting specific features from a 3D environment and highlighting them in a unique manner. One application of interest involves recreating 2D hand-drawn art styles in a 3D-modeled environment. This task poses challenges in the form of spatial coherence, feature extraction, and stroke line rendering. Previous research on this topic has also struggled to overcome specific performance bottlenecks, which have limited use of this technology in real-time applications. Specifically, many stylized rendering techniques have difficulty operating on large-scale scenes, such as open-world terrain environments. In this paper, we describe various novel rendering techniques for mimicking hand-drawn art styles in a large-scale 3D environment, including modifications to existing methods for stroke rendering and hatch-line texturing. Our system focuses on providing various complex styles while maintaining real-time performance, to maximize user-interactability. Our results demonstrate improved performance over existing real-time methods, and offer a few unique style options for users, though the system still suffers from some visual inconsistencies.