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- Genre: Academic theses
Description
The video game graphics pipeline has traditionally rendered the scene using a polygonal approach. Advances in modern graphics hardware now allow the rendering of parametric methods. This thesis explores various smooth surface rendering methods that can be integrated into the video game graphics engine. Moving over to parametric or smooth surfaces from the polygonal domain has its share of issues and there is an inherent need to address various rendering bottlenecks that could hamper such a move. The game engine needs to choose an appropriate method based on in-game characteristics of the objects; character and animated objects need more sophisticated methods whereas static objects could use simpler techniques. Scaling the polygon count over various hardware platforms becomes an important factor. Much control is needed over the tessellation levels, either imposed by the hardware limitations or by the application, to be able to adaptively render the mesh without significant loss in performance. This thesis explores several methods that would help game engine developers in making correct design choices by optimally balancing the trade-offs while rendering the scene using smooth surfaces. It proposes a novel technique for adaptive tessellation of triangular meshes that vastly improves speed and tessellation count. It develops an approximate method for rendering Loop subdivision surfaces on tessellation enabled hardware. A taxonomy and evaluation of the methods is provided and a unified rendering system that provides automatic level of detail by switching between the methods is proposed.
ContributorsAmresh, Ashish (Author) / Farin, Gerlad (Thesis advisor) / Razdan, Anshuman (Thesis advisor) / Wonka, Peter (Committee member) / Hansford, Dianne (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this thesis study was to evaluate the nature of social anxiety in clinic-referred African American children versus their Caucasian counterparts. In particular, social anxiety symptom endorsement along the Social Phobia and Anxiety Inventory Scale for Children (SPAI-C; Beidel, Turner, & Morris, 1995) was examined in a sample of 107 African American and 364 Caucasian children (ages 7- to 17-years old) referred for anxiety. To evaluate symptom endorsement, simple descriptive analyses were conducted whereas measurement invariance tests were examined using confirmatory factor analyses. For the most commonly endorsed items, African American and Caucasian children shared seven of the top 10 most commonly identified social anxiety symptoms. Similar social fears across ethnicity focused on "assertiveness in situations perceived to be difficult" and ""speaking to large groups of peers they do not know." Findings also showed that African American children were more likely to report symptoms of "shaking when in social situations" than Caucasian children, and Caucasian children were more likely to report symptoms of "embarrassment when in front of adults" compared to African American children, but this was also on the basis of two items. When it came to the five factors of the SPAI-C, results showed measurement invariance across African American and Caucasian children. Overall, there were more similarities than differences between African American and Caucasian children in social anxiety symptoms based on the SPAI-C. Findings from this thesis study shed light on how to best accurately identify social anxiety among African American children compared to Caucasians, a contribution that can potentially impact assessment, treatment planning, and program response evaluation.
ContributorsWynne, Henry (Author) / Pina, Armando (Thesis advisor) / Gonzales, Nancy (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
Description
The present study utilized longitudinal data from a high-risk community sample (n= 377; 166 trauma-exposed; 54% males; 52% children of alcoholics; 73% non-Hispanic/Latino Caucasian; 22% Hispanic/Latino; 5% other ethnicity) to test a series of hypotheses that may help explain the risk pathways that link traumatic stress, posttraumatic stress disorder (PTSD) symptomatology, and problematic alcohol and drug use. Specifically, this study examined whether pre-trauma substance use problems increase risk for trauma exposure (the high-risk hypothesis) or PTSD symptoms (the susceptibility hypothesis), whether PTSD symptoms increase risk for later alcohol/drug problems (the self-medication hypothesis), and whether the association between PTSD symptoms and alcohol/drug problems is due to shared risk factors (the shared vulnerability hypothesis). This study also examined the roles of gender and ethnicity in these pathways. A series of logistic and negative binomial regressions were performed in a path analysis framework. A composite pre-trauma family adversity variable was formed from measures of family conflict, family life stress, parental alcoholism, and other parent psychopathology. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems among non-Hispanic/Latino Caucasian participants, over and above the influences of pre-trauma family adversity, pre-trauma substance use problems, trauma exposure, and demographic variables. Results partially supported the high-risk hypothesis, such that adolescent substance use problems had a marginally significant unique effect on risk for assaultive violence exposure but not on overall risk for trauma exposure. There was no support for the susceptibility hypothesis, as pre-trauma adolescent substance use problems did not significantly influence risk for PTSD diagnosis/symptoms over and above the influence of pre-trauma family adversity. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later substance use problems. These results add to a growing body of literature in support of the self-medication hypothesis. Findings extend previous research by showing that PTSD symptoms may influence the development of alcohol and drug problems over and above the influence of trauma exposure itself, preexisting family risk factors, and baseline levels of substance use.
ContributorsHaller, Moira (Author) / Chassin, Laurie (Thesis advisor) / Davis, Mary (Committee member) / Pina, Armando (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Night vision goggles (NVGs) are widely used by helicopter pilots for flight missions at night, but the equipment can present visually confusing images especially in urban areas. A simulation tool with realistic nighttime urban images would help pilots practice and train for flight with NVGs. However, there is a lack of tools for visualizing urban areas at night. This is mainly due to difficulties in gathering the light system data, placing the light systems at suitable locations, and rendering millions of lights with complex light intensity distributions (LID). Unlike daytime images, a city can have millions of light sources at night, including street lights, illuminated signs, and light shed from building interiors through windows. In this paper, a Procedural Lighting tool (PL), which predicts the positions and properties of street lights, is presented. The PL tool is used to accomplish three aims: (1) to generate vector data layers for geographic information systems (GIS) with statistically estimated information on lighting designs for streets, as well as the locations, orientations, and models for millions of streetlights; (2) to generate geo-referenced raster data to suitable for use as light maps that cover a large scale urban area so that the effect of millions of street light can be accurately rendered at real time, and (3) to extend existing 3D models by generating detailed light-maps that can be used as UV-mapped textures to render the model. An interactive graphical user interface (GUI) for configuring and previewing lights from a Light System Database (LDB) is also presented. The GUI includes physically accurate information about LID and also the lights' spectral power distributions (SPDs) so that a light-map can be generated for use with any sensor if the sensors luminosity function is known. Finally, for areas where more detail is required, a tool has been developed for editing and visualizing light effects over a 3D building from many light sources including area lights and windows. The above components are integrated in the PL tool to produce a night time urban view for not only a large-scale area but also a detail of a city building.
ContributorsChuang, Chia-Yuan (Author) / Femiani, John (Thesis advisor) / Razdan, Anshuman (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2011
Description
The moderating effects of five characteristics of peers--their effortful control, anger, sadness, aggression, and positive peer behavior--were investigated in two separate series of analyses of preschooler's social behavior: (a) the relation between children's own effortful control and social behavior, and (b) the relation between children's shyness and reticent behavior. Latent variable interactions were conducted in a structural equation framework. Peer context anger and effortful control, albeit with unexpected results, interacted with children's own characteristics to predict their behavior in both the EC and shy model series; these were the only significant interactions obtained for the EC model series. The relation between shyness and reticent behavior, however, showed the greatest impact of peer context and, conversely, the greatest susceptibility to environmental variations; significant interactions were obtained in all five models, despite the limited range of peer context sadness and aggression observed in this study.
ContributorsHuerta, Snježana (Author) / Eisenberg, Nancy (Thesis advisor) / Spinrad, Tracy (Committee member) / Pina, Armando (Committee member) / Geiser, Christian (Committee member) / Arizona State University (Publisher)
Created2012
Description
2019 coronavirus disease (COVID-19) remains a pressing health concern, especially with lagging youth vaccination rates despite its evident benefits. Given the significant role of vaccination in safeguarding individual and community health, this dissertation sought to explore how the use of serious games may offer hope for addressing the COVID-19 vaccine coverage gap among youths. This dissertation collected, appraised, and synthesized existing evidence on serious game-based vaccination interventions, finding increased youths’ vaccine knowledge but limited effectiveness in boosting their vaccination intentions and uptake. Using serious game in youth health education considered key attributes including gamification, game mechanics, educational, health-related content, and objective, tailored for youth and adaptability, real life relevance, engagement, interactivity, safe environment, feedback, and assessment. Stemming from technological advances and interdisciplinary collaborations, these games provided experiences that resonated with diverse populations. Outcomes from such educational games have shown improved health knowledge, attitudes, and behaviors, improved self-efficacy and reduced health disparities. The dissertation also presented a pilot study randomization control trial (RCT) on a COVID- 19 game-based intervention (vs. usual care) targeting unvaccinated youth, showing its feasibility, acceptability and positive influence on vaccine knowledge, vaccination intention and uptake. Partnering with key stakeholders and adapting game designs for ongoing relevance could contribute to intervention effectiveness in promoting youth vaccination, catering to diverse needs and preferences.
ContributorsOu, Lihong (Author) / Reifsnider, Elizabeth (Thesis advisor) / Chen, Angela Chia-Chen (Committee member) / Todd, Michael (Committee member) / Amresh, Ashish (Committee member) / Mun, Chung Jung (Committee member) / Arizona State University (Publisher)
Created2023
Description
Mobile health (mHealth) applications (apps) hold tremendous potential for addressing chronic health conditions. Smartphones are now the most popular form of computing, and the ubiquitous “always with us, always on” nature of mobile technology makes them amenable to interventions aimed and managing chronic disease. Several challenges exist, however, such as the difficulty in determining mHealth effects due to the rapidly changing nature of the technology and the challenges presented to existing methods of evaluation, and the ability to ensure end users consistently use the technology in order to achieve the desired effects. The latter challenge is in adherence, defined as the extent to which a patient conducts the activities defined in a clinical protocol (i.e. an intervention plan). Further, higher levels of adherence should lead to greater effects of the intervention (the greater fidelity to the protocol, the more benefit one should receive from the protocol). mHealth has limitations in these areas; the ability to have patients sustainably adhere to a protocol, and the ability to drive intervention effect sizes. My research considers personalized interventions, a new approach of study in the mHealth community, as a potential remedy to these limitations. Specifically, in the context of a pediatric preventative anxiety protocol, I introduce algorithms to drive greater levels of adherence and greater effect sizes by incorporating per-patient (personalized) information. These algorithms have been implemented within an existing mHealth app for middle school that has been successfully deployed in a school in the Phoenix Arizona metropolitan area. The number of users is small (n=3) so a case-by-case analysis of app usage is presented. In addition simulated user behaviors based on models of adherence and effects sizes over time are presented as a means to demonstrate the potential impact of personalized deployments on a larger scale.
ContributorsSingal, Vishakha (Author) / Gary, Kevin (Thesis advisor) / Pina, Armando (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2019
Description
The American Heart Association recommended in 1997 the data elements that should be collected from resuscitations in hospitals. (15) Currently, data documentation from resuscitation events in hospitals, termed ‘code blue’ events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of dynamic documentation of patient, event and outcome variables as the code blue event unfolds.
This thesis is based on the hypothesis that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. The primary goal of this thesis is to create an iOS based application, primarily designed for iPads, for code blue events at the Mayo Clinic Hospital. The secondary goal is to build an open-source software development framework for converting paper-based hospital protocols into digital format.
The tool created in this study enabled data documentation to be completed electronically rather than on paper for resuscitation outcomes. The tool was evaluated for usability with twenty nurses, the end-users, at Mayo Clinic in Phoenix, Arizona. The results showed the preference of users for the iPad application. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure.
This thesis is based on the hypothesis that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. The primary goal of this thesis is to create an iOS based application, primarily designed for iPads, for code blue events at the Mayo Clinic Hospital. The secondary goal is to build an open-source software development framework for converting paper-based hospital protocols into digital format.
The tool created in this study enabled data documentation to be completed electronically rather than on paper for resuscitation outcomes. The tool was evaluated for usability with twenty nurses, the end-users, at Mayo Clinic in Phoenix, Arizona. The results showed the preference of users for the iPad application. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure.
ContributorsBokhari, Wasif (Author) / Patel, Vimla L. (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Nelson, Brian (Committee member) / Sen, Ayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification. The benefits of automatic visible band VHR LULC classifications may include applications such as automatic change detection or mapping. Recent work has shown the potential of Deep Learning approaches for land use classification; however, this thesis improves on the state-of-the-art by applying additional dataset augmenting approaches that are well suited for geospatial data. Furthermore, the generalizability of the classifiers is tested by extensively evaluating the classifiers on unseen datasets and we present the accuracy levels of the classifier in order to show that the results actually generalize beyond the small benchmarks used in training. Deep networks have many parameters, and therefore they are often built with very large sets of labeled data. Suitably large datasets for LULC are not easy to come by, but techniques such as refinement learning allow networks trained for one task to be retrained to perform another recognition task. Contributions of this thesis include demonstrating that deep networks trained for image recognition in one task (ImageNet) can be efficiently transferred to remote sensing applications and perform as well or better than manually crafted classifiers without requiring massive training data sets. This is demonstrated on the UC Merced dataset, where 96% mean accuracy is achieved using a CNN (Convolutional Neural Network) and 5-fold cross validation. These results are further tested on unrelated VHR images at the same resolution as the training set.
ContributorsUba, Nagesh Kumar (Author) / Femiani, John (Thesis advisor) / Razdan, Anshuman (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016
Description
In the last decade, the number of people who own a mobile phone or portable electronic communication device has grown exponentially. Recent advances in smartphone technology have enabled mobile devices to provide applications (“mHealth apps”) to support delivering interventions, tracking health treatments, or involving a healthcare team into the treatment process and symptom monitoring. Although the popularity of mHealth apps is increasing, few lessons have been shared regarding user experience design and evaluation for such innovations as they relate to clinical outcomes. Studies assessing usability for mobile apps primarily rely on survey instruments. Though surveys are effective in determining user perception of usability and positive attitudes towards an app, they do not directly assess app feature usage, and whether feature usage and related aspects of app design are indicative of whether intended tasks are completed by users. This is significant in the area of mHealth apps, as proper utilization of the app determines compliance to a clinical study protocol. Therefore it is important to understand how design directly impacts compliance, specifically what design factors are prevalent in non-compliant users. This research studies the impact of usability features on clinical protocol compliance by applying a mixed methods approach to usability assessment, combining traditional surveys, log analysis, and clickstream analysis to determine the connection of design to outcomes. This research is novel in its construction of the mixed methods approach and in its attempt to tie usability results to impacts on clinical protocol compliance. The validation is a case study approach, applying the methods to an mHealth app developed for early prevention of anxiety in middle school students. The results of three empirical studies are shared that support the construction of the mixed methods approach.
ContributorsPatwardhan, Mandar (Author) / Gary, Kevin A (Thesis advisor) / Pina, Armando (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016