Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"University of Arkansas" +contributor:("Tingxin Yan"). Showing records 1 – 3 of 3 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Arkansas

1. De Melo Aroxa, Rafael. Efficient Ray Tracing For Mobile Devices.

Degree: MS, 2014, University of Arkansas

The demand for mobile devices with higher graphics performance has increased substantially in the past few years. Most mobile applications that demand 3D graphics use commonly available frameworks, such as Unity and Unreal Engine. In mobile devices, these frameworks are built on top of OpenGL ES and use a graphics technique called rasterization, a simple concept that yields good performance without sacrificing graphic quality. However, rasterization cannot easily handle some physical phenomena of light (i.e. reflection and refraction). In order to support such effects, the graphics framework has to emulate them, thereby leading to suboptimal results in term of quality. Other techniques, such as ray tracing, do not require such emulation to be implemented, as the aforementioned phenomena of light are inherently considered. In this work, we first design and implement a 3D renderer that uses ray tracing to generate high quality graphics for mobile devices. Our implementation yielded high quality results but at a high computational cost, which impacted performance. To alleviate this problem, we then developed special algorithms and data structures to substantially improve the performance of the rendering engine. In our experiments, we achieved frame rates that were 7 to 15 times faster than the brute force approach. Being able to render high quality graphics with good performance can potentially revolutionize the mobile gaming industry. To the best of our knowledge, this has never before been implemented in commonly available devices, such as smartphones and tablets. Advisors/Committee Members: John M. Gauch, Craig Thompson, Tingxin Yan.

Subjects/Keywords: Applied sciences; Computer graphics; Mobile; Raytracing; Graphics and Human Computer Interfaces

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

De Melo Aroxa, R. (2014). Efficient Ray Tracing For Mobile Devices. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1055

Chicago Manual of Style (16th Edition):

De Melo Aroxa, Rafael. “Efficient Ray Tracing For Mobile Devices.” 2014. Masters Thesis, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/1055.

MLA Handbook (7th Edition):

De Melo Aroxa, Rafael. “Efficient Ray Tracing For Mobile Devices.” 2014. Web. 25 Oct 2020.

Vancouver:

De Melo Aroxa R. Efficient Ray Tracing For Mobile Devices. [Internet] [Masters thesis]. University of Arkansas; 2014. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/1055.

Council of Science Editors:

De Melo Aroxa R. Efficient Ray Tracing For Mobile Devices. [Masters Thesis]. University of Arkansas; 2014. Available from: https://scholarworks.uark.edu/etd/1055


University of Arkansas

2. Hammer, Jon C. Enabling Usage Pattern-based Logical Status Inference for Mobile Phones.

Degree: MS, 2016, University of Arkansas

Logical statuses of mobile users, such as isBusy and isAlone, are the key enabler for a plethora of context-aware mobile applications. While on-board hardware sensors (such as motion, proximity, and location sensors) have been extensively studied for logical status inference, continuous usage typically requires formidable energy consumption, which degrades the user experience. In this thesis, we argue that smartphone usage statistics can be used for logical status inference with negligible energy cost. To validate this argument, we present a continuous inference engine that (1) intercepts multiple operating system events, in particular foreground app, notifications, screen states, and connected networks; (2) extracts informative features from OS events; and (3) efficiently infers the logical status of mobile users. The proposed inference engine is implemented for unmodified Android phones, and an evaluation on a four-week trial has shown promising accuracy in identifying four logical statuses of mobile users with over 87% accuracy while the average energy impact on the battery life is less than 0.5%. Advisors/Committee Members: Tingxin Yan, Michael S. Gashler, John Gauch.

Subjects/Keywords: Applied sciences; Logical status inference; Mobile computing; Usage statistics; Graphics and Human Computer Interfaces

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hammer, J. C. (2016). Enabling Usage Pattern-based Logical Status Inference for Mobile Phones. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1552

Chicago Manual of Style (16th Edition):

Hammer, Jon C. “Enabling Usage Pattern-based Logical Status Inference for Mobile Phones.” 2016. Masters Thesis, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/1552.

MLA Handbook (7th Edition):

Hammer, Jon C. “Enabling Usage Pattern-based Logical Status Inference for Mobile Phones.” 2016. Web. 25 Oct 2020.

Vancouver:

Hammer JC. Enabling Usage Pattern-based Logical Status Inference for Mobile Phones. [Internet] [Masters thesis]. University of Arkansas; 2016. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/1552.

Council of Science Editors:

Hammer JC. Enabling Usage Pattern-based Logical Status Inference for Mobile Phones. [Masters Thesis]. University of Arkansas; 2016. Available from: https://scholarworks.uark.edu/etd/1552


University of Arkansas

3. Nugroho, Amin Rois Sinung. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.

Degree: MS, 2016, University of Arkansas

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile app? This thesis answers this question for two kinds of systems, a single mobile device and a mobile cloud computing system. First, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and explored in a mobile device and in a cloud computing environment. Experimental evaluations show that the type of app can be inferred with high probability. In particular, the attacks achieve up to 100% accuracy on a mobile device, and 66.7% accuracy in the mobile cloud computing environment. This study shows that resource usage patterns of mobile apps can be used to infer the type of apps being used, and thus can cause privacy leakage if not protected. Advisors/Committee Members: Qinghua Li, Xintao Wu, Tingxin Yan.

Subjects/Keywords: Applied sciences; CPU usage; Cloud; Machine learning; Mobile; Mobile app; Privacy; Information Security

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nugroho, A. R. S. (2016). Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1599

Chicago Manual of Style (16th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Masters Thesis, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/1599.

MLA Handbook (7th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Web. 25 Oct 2020.

Vancouver:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Internet] [Masters thesis]. University of Arkansas; 2016. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/1599.

Council of Science Editors:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Masters Thesis]. University of Arkansas; 2016. Available from: https://scholarworks.uark.edu/etd/1599

.