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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Language
  • English
Status
  • Public
Subject
Date Range
2011 2020


Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, a novel problem of ranking images with respect to their aesthetic quality is formulated …

Contributors
Gattupalli, Jaya Vijetha R., Li, Baoxin, Davulcu, Hasan, et al.
Created Date
2016

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 …

Contributors
Tadayon, Ramin, Panchanathan, Sethuraman, McDaniel, Troy, et al.
Created Date
2017

In the sport of competitive water skiing, the skill of a human boat driver can affect athletic performance. Driver influence is not necessarily inhibitive to skiers, however, it reduces the fairness and credibility of the sport overall. In response to the stated problem, this thesis proposes a vision-based real-time control system designed specifically for tournament waterski boats. The challenges addressed in this thesis include: one, the segmentation of floating objects in frame sequences captured by a moving camera, two, the identification of segmented objects which fit a predefined model, and three, the accurate and fast estimation of camera position and …

Contributors
Walker, Collin Christopher, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2014

In this thesis, I propose a new technique of Aligning English sentence words with its Semantic Representation using Inductive Logic Programming(ILP). My work focusses on Abstract Meaning Representation(AMR). AMR is a semantic formalism to English natural language. It encodes meaning of a sentence in a rooted graph. This representation has gained attention for its simplicity and expressive power. An AMR Aligner aligns words in a sentence to nodes(concepts) in its AMR graph. As AMR annotation has no explicit alignment with words in English sentence, automatic alignment becomes a requirement for training AMR parsers. The aligner in this work comprises of …

Contributors
Agarwal, Shubham, Baral, Chitta, Li, Baoxin, et al.
Created Date
2017

Facial Expressions Recognition using the Convolution Neural Network has been actively researched upon in the last decade due to its high number of applications in the human-computer interaction domain. As Convolution Neural Networks have the exceptional ability to learn, they outperform the methods using handcrafted features. Though the state-of-the-art models achieve high accuracy on the lab-controlled images, they still struggle for the wild expressions. Wild expressions are captured in a real-world setting and have natural expressions. Wild databases have many challenges such as occlusion, variations in lighting conditions and head poses. In this work, I address these challenges and propose …

Contributors
Chhabra, Sachin, Li, Baoxin, Venkateswara, Hemanth, et al.
Created Date
2019

The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to …

Contributors
Manikonda, Lydia, Kambhampati, Subbarao, Liu, Huan, et al.
Created Date
2019

The burden of adaptation has been a major limiting factor in the adoption rates of new wearable assistive technologies. This burden has created a necessity for the exploration and combination of two key concepts in the development of upcoming wearables: anticipation and invisibility. The combination of these two topics has created the field of Anticipatory and Invisible Interfaces (AII) In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main …

Contributors
Tadayon, Arash, Panchanathan, Sethuraman, McDaniel, Troy, et al.
Created Date
2020

With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from …

Contributors
Nadella, Sravan, Davulcu, Hasan, Li, Baoxin, et al.
Created Date
2015

The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities …

Contributors
Chakraborty, Shayok, Panchanathan, Sethuraman, Balasubramanian, Vineeth N., et al.
Created Date
2013

In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of …

Contributors
Chattopadhyay, Rita, Panchanathan, Sethuraman, Ye, Jieping, et al.
Created Date
2013