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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
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
Education of any skill based subject, such as mathematics or language, involves a significant amount of repetition and pratice. According to the National Survey of Student Engagements, students spend on average 17 hours per week reviewing and practicing material previously learned in a classroom, with higher performing students showing a tendency to spend more time practicing. As such, learning software has emerged in the past several decades focusing on providing a wide range of examples, practice problems, and situations for users to exercise their skills. Notably, math students have benefited from software that procedurally generates a virtually infinite number of practice problems and their corresponding solutions. This allows for instantaneous feedback and automatic generation of tests and quizzes. Of course, this is only possible because software is capable of generating and verifying a virtually endless supply of sample problems across a wide range of topics within mathematics. While English learning software has progressed in a similar manner, it faces a series of hurdles distinctly different from those of mathematics. In particular, there is a wide range of exception cases present in English grammar. Some words have unique spellings for their plural forms, some words have identical spelling for plural forms, and some words are conjugated differently for only one particular tense or person-of-speech. These issues combined make the problem of generating grammatically correct sentences complicated. To compound to this problem, the grammar rules in English are vast, and often depend on the context in which they are used. Verb-tense agreement (e.g. "I eat" vs "he eats"), and conjugation of irregular verbs (e.g. swim -> swam) are common examples. This thesis presents an algorithm designed to randomly generate a virtually infinite number of practice problems for students of English as a second language. This approach differs from other generation approaches by generating based on a context set by educators, so that problems can be generated in the context of what students are currently learning. The algorithm is validated through a study in which over 35 000 sentences generated by the algorithm are verified by multiple grammar checking algorithms, and a subset of the sentences are validated against 3 education standards by a subject matter expert in the field. The study found that this approach has a significantly reduced grammar error ratio compared to other generation algorithms, and shows potential where context specification is concerned.
ContributorsMoore, Zachary Christian (Author) / Amresh, Ashish (Thesis director) / Nelson, Brian (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Description
For this master's thesis, an open learner model is integrated with Quinn, a teachable robotic agent developed at Arizona State University. This system is represented as a feedback system, which aims to improve a student’s understanding of a subject. It also helps to understand the effect of the learner model when it is represented by performance of the teachable agent. The feedback system represents performance of the teachable agent, and not of a student. Data in the feedback system is thus updated according to a student's understanding of the subject. This provides students an opportunity to enhance their understanding of a subject by analyzing their performance. To test the effectiveness of the feedback system, student understanding in two different conditions is analyzed. In the first condition a feedback report is not provided to the students, while in the second condition the feedback report is provided in the form of the agent’s performance.
ContributorsUpadhyay, Abha (Author) / Walker, Erin (Thesis advisor) / Nelson, Brian (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016
Description
Tessellation and Screen-Space Ambient Occlusion are algorithms which have been widely-used in real-time rendering in the past decade. They aim to enhance the details of the mesh, cast better shadow effects and improve the quality of the rendered images in real time. WebGL is a web-based graphics library derived from OpenGL ES used for rendering in web applications. It is relatively new and has been rapidly evolving, this has resulted in it supporting a subset of rendering features normally supported by desktop applications. In this thesis, the research is focusing on evaluating Curved PN-Triangles tessellation with Screen Space Ambient Occlusion (SSAO), Horizon-Based Ambient Occlusion (HBAO) and Horizon-Based Ambient Occlusion Plus (HBAO+) in WebGL-based real-time application and comparing its performance to desktop based application and to discuss the capabilities, limitations and bottlenecks of WebGL 1.0.
ContributorsLi, Chenyang (Author) / Amresh, Ashish (Thesis advisor) / Wang, Yalin (Thesis advisor) / Kobayashi, Yoshihiro (Committee member) / Arizona State University (Publisher)
Created2017
Description
In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
ContributorsTadayon, Ramin (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Amresh, Ashish (Committee member) / Glenberg, Arthur (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Currently, educational games are designed with the educational content as the primary factor driving the design of the game. While this may seem to be the optimal approach, this design paradigm causes multiple issues. For one, the games themselves are often not engaging as game design principles were put aside in favor of increasing the educational value of the game. The other issue is that the code base of the game is mostly or completely unusable for any other games as the game mechanics are too strongly connected to the educational content being taught. This means that the mechanics are impossible to reuse in future projects without major revisions, and starting over is often more time and cost efficient.
This thesis presents the Content Agnostic Game Engineering (CAGE) model for designing educational games. CAGE is a way to separate the educational content from the game mechanics without compromising the educational value of the game. This is done by designing mechanics that can have multiple educational contents layered on top of them which can be switched out at any time. CAGE allows games to be designed with a game design first approach which allows them to maintain higher engagement levels. In addition, since the mechanics are not tied to the educational content several different educational topics can reuse the same set of mechanics without requiring major revisions to the existing code.
Results show that CAGE greatly reduces the amount of code needed to make additional versions of educational games, and speeds up the development process. The CAGE model is also shown to not induce high levels of cognitive load, allowing for more in depth topic work than was attempted in this thesis. However, engagement was low and switching the active content does interrupt the game flow considerably. Altering the difficulty of the game in real time in response to the affective state of the player was not shown to increase engagement. Potential causes of the issues with CAGE games and potential fixes are discussed.
This thesis presents the Content Agnostic Game Engineering (CAGE) model for designing educational games. CAGE is a way to separate the educational content from the game mechanics without compromising the educational value of the game. This is done by designing mechanics that can have multiple educational contents layered on top of them which can be switched out at any time. CAGE allows games to be designed with a game design first approach which allows them to maintain higher engagement levels. In addition, since the mechanics are not tied to the educational content several different educational topics can reuse the same set of mechanics without requiring major revisions to the existing code.
Results show that CAGE greatly reduces the amount of code needed to make additional versions of educational games, and speeds up the development process. The CAGE model is also shown to not induce high levels of cognitive load, allowing for more in depth topic work than was attempted in this thesis. However, engagement was low and switching the active content does interrupt the game flow considerably. Altering the difficulty of the game in real time in response to the affective state of the player was not shown to increase engagement. Potential causes of the issues with CAGE games and potential fixes are discussed.
ContributorsBaron, Tyler John (Author) / Amresh, Ashish (Thesis advisor) / Nelson, Brian C (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2017