University of Arkansas
Hammer, Jon C.
Improving the Efficacy of Context-Aware Applications.
Degree: PhD, 2018, University of Arkansas
In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include proposed methods for fusing information from multiple logical sensors, localizing nearby objects using depth sensors, and building models to better understand the content of 2D images.
First, we propose a system called Unagi, designed to incorporate multiple logical sensors into a single framework that allows context-aware application developers to easily test new ideas and create novel experiences. Unagi is responsible for collecting data, extracting features, and building personalized models for each individual user. We demonstrate the utility of the system with two applications: adaptive notification filtering and a network content prefetcher. We also thoroughly evaluate the system with respect to predictive accuracy, temporal delay, and power consumption.
Next, we discuss a set of techniques that can be used to accurately determine the location of objects near a user in 3D space using a mobile device equipped with both depth and inertial sensors. Using a novel chaining approach, we are able to locate objects farther away than the standard range of the depth sensor without compromising localization accuracy. Empirical testing shows our method is capable of localizing objects 30m from the user with an error of less than 10cm.
Finally, we demonstrate a set of techniques that allow a multi-layer perceptron (MLP) to learn resolution-invariant representations of 2D images, including the proposal of an MCMC-based technique to improve the selection of pixels for mini-batches used for training. We also show that a deep convolutional encoder could be trained to output a resolution-independent representation in constant time, and we discuss several potential applications of this research, including image resampling, image compression, and security.
Advisors/Committee Members: Michael Gashler, John Gauch, Xintao Wu.
Subjects/Keywords: Computer Vision; Context-aware Computing; Depth-based Positioning; Logical Sensors; Machine Learning; Graphics and Human Computer Interfaces; OS and Networks
to Zotero / EndNote / Reference
APA (6th Edition):
Hammer, J. C. (2018). Improving the Efficacy of Context-Aware Applications. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2703
Chicago Manual of Style (16th Edition):
Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 25, 2020.
MLA Handbook (7th Edition):
Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Web. 25 Oct 2020.
Hammer JC. Improving the Efficacy of Context-Aware Applications. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 25].
Available from: https://scholarworks.uark.edu/etd/2703.
Council of Science Editors:
Hammer JC. Improving the Efficacy of Context-Aware Applications. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2703