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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
Assemblers and compilers provide feedback to a programmer in the form of error messages. These error messages become input to the debugging model of the programmer. For the programmer to fix an error, they should first locate the error in the program, understand what is causing that error, and finally resolve that error. Error messages play an important role in all three stages of fixing of errors. This thesis studies the effects of error messages in the context of teaching programming. Given an error message, this work investigates how it effects student’s way of 1) understanding the error, and 2) fixing the error. As part of the study, three error message types were developed – Default, Link and Example, to better understand the effects of error messages. The Default type provides an assembler-centric single line error message, the Link type provides a program-centric detailed error description with a hyperlink for more information, and the Example type provides a program centric detailed error description with a relevant example. All these error message types were developed for assembly language programming. A think aloud programming exercise was conducted as part of the study to capture the student programmer’s knowledge model. Different codes were developed to analyze the data collected as part of think aloud exercise. After transcribing, coding, and analyzing the data, it was found that the Link type of error message helped to fix the error in less time and with fewer steps. Among the three types, the Link type of error message also resulted in a significantly higher ratio of correct to incorrect steps taken by the programmer to fix the error.
ContributorsBeejady Murthy Kadekar, Harsha Kadekar (Author) / Sohoni, Sohum (Thesis advisor) / Craig, Scotty D. (Committee member) / Jordan, Shawn S (Committee member) / Gary, Kevin A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Distant is a Game Design Document describing an original game by the same name. The game was designed around the principle of core aesthetics, where the user experience is defined first and then the game is built from that experience. Distant is an action-exploration game set on a huge megastructure floating in the atmosphere of Saturn. Players take on the role of HUE, an artificial intelligence trapped in the body of a maintenance robot, as he explores this strange world and uncovers its secrets. Using acrobatic movement abilities, players will solve puzzles, evade enemies, and explore the world from top to bottom. The world, known as the Strobilus Megastructure, is conical in shape, with living quarters and environmental system in the upper sections and factories and resource mining in the lower sections. The game world is split up into 10 major areas and countless minor and connecting areas. Special movement abilities like wall running and anti-gravity allow players to progress further down in the world. These abilities also allow players to solve more complicated puzzles, and to find more difficult to reach items. The story revolves around six artificial intelligences that were created to maintain the station. Many centuries ago, these AI helped humankind maintain their day-to-day lives and helped researchers working on new scientific breakthroughs. This led to the discovery of faster-than-light travel, and humanity left the station and our solar system to explore the cosmos. HUE, the AI in charge of human relations, fell into depression and shut down. Awakening several hundred years in the future, HUE sets out to find the other AI. Along the way he helps them reconnect and discovers the history and secrets of the station. Distant is intended for players looking for three things: A fantastic world full of discovery, a rich, character driven narrative, and challenging acrobatic gameplay. Players of any age or background are recommended to give it a try, but it will require investment and a willingness to improve. Distant is intended to change players, to force them to confront difficulty and different perspectives. Most games involve upgrading a character; Distant is a game that upgrades the player.
ContributorsGarttmeier, Colin Reiser (Author) / Collins, Daniel (Thesis director) / Amresh, Ashish (Committee member) / School of Arts, Media and Engineering (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
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
Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
ContributorsXavier, Suhas (Author) / Gary, Kevin A (Thesis advisor) / Bansal, Srividya K (Committee member) / Sohoni, Sohum (Committee member) / Arizona State University (Publisher)
Created2016
Description
Lecture videos are a widely used resource for learning. A simple way to create
videos is to record live lectures, but these videos end up being lengthy, include long
pauses and repetitive words making the viewing experience time consuming. While
pauses are useful in live learning environments where students take notes, I question
the value of pauses in video lectures. Techniques and algorithms that can shorten such
videos can have a huge impact in saving students’ time and reducing storage space.
I study this problem of shortening videos by removing long pauses and adaptively
modifying the playback rate by emphasizing the most important sections of the video
and its effect on the student community. The playback rate is designed in such a
way to play uneventful sections faster and significant sections slower. Important and
unimportant sections of a video are identified using textual analysis. I use an existing
speech-to-text algorithm to extract the transcript and apply latent semantic analysis
and standard information retrieval techniques to identify the relevant segments of
the video. I compute relevance scores of different segments and propose a variable
playback rate for each of these segments. The aim is to reduce the amount of time
students spend on passive learning while watching videos without harming their ability
to follow the lecture. I validate the approach by conducting a user study among
computer science students and measuring their engagement. The results indicate
no significant difference in their engagement when this method is compared to the
original unedited video.
videos is to record live lectures, but these videos end up being lengthy, include long
pauses and repetitive words making the viewing experience time consuming. While
pauses are useful in live learning environments where students take notes, I question
the value of pauses in video lectures. Techniques and algorithms that can shorten such
videos can have a huge impact in saving students’ time and reducing storage space.
I study this problem of shortening videos by removing long pauses and adaptively
modifying the playback rate by emphasizing the most important sections of the video
and its effect on the student community. The playback rate is designed in such a
way to play uneventful sections faster and significant sections slower. Important and
unimportant sections of a video are identified using textual analysis. I use an existing
speech-to-text algorithm to extract the transcript and apply latent semantic analysis
and standard information retrieval techniques to identify the relevant segments of
the video. I compute relevance scores of different segments and propose a variable
playback rate for each of these segments. The aim is to reduce the amount of time
students spend on passive learning while watching videos without harming their ability
to follow the lecture. I validate the approach by conducting a user study among
computer science students and measuring their engagement. The results indicate
no significant difference in their engagement when this method is compared to the
original unedited video.
ContributorsPurushothama Shenoy, Sreenivas (Author) / Amresh, Ashish (Thesis advisor) / Femiani, John (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2016
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
One of the most common errors developers make is to provide incorrect string
identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a
significant percentage of defects observed in real-world codebases belong to this
category. Existing work focuses on semantic static analysis, while this thesis attempts to
tackle the challenges that can be solved using syntactic static analysis. This thesis
proposes a tool for quickly identifying defects at the time of injection due to
dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in
string identifiers. The proposed solution reduces the delta (time) between defect injection
and defect discovery with the use of a dedicated just-in-time syntactic string identifier
resolution tool. The solution focuses on modeling the nature of syntactic dependencies
across the stack, and providing a tool that helps developers discover such dependencies.
This thesis reports on an empirical study of the tool usage by developers in a realistic
scenario, with the focus on defect injection and defect discovery times of defects of this
nature (syntactic errors in string identifiers) with and without the use of the proposed
tool. Further, the tool was validated against a set of real-world codebases to analyze the
significance of these defects.
identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a
significant percentage of defects observed in real-world codebases belong to this
category. Existing work focuses on semantic static analysis, while this thesis attempts to
tackle the challenges that can be solved using syntactic static analysis. This thesis
proposes a tool for quickly identifying defects at the time of injection due to
dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in
string identifiers. The proposed solution reduces the delta (time) between defect injection
and defect discovery with the use of a dedicated just-in-time syntactic string identifier
resolution tool. The solution focuses on modeling the nature of syntactic dependencies
across the stack, and providing a tool that helps developers discover such dependencies.
This thesis reports on an empirical study of the tool usage by developers in a realistic
scenario, with the focus on defect injection and defect discovery times of defects of this
nature (syntactic errors in string identifiers) with and without the use of the proposed
tool. Further, the tool was validated against a set of real-world codebases to analyze the
significance of these defects.
ContributorsKalsi, Manit Singh (Author) / Gary, Kevin A (Thesis advisor) / Lindquist, Timothy E (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2016