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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.


Resource Type
  • Doctoral Dissertation
Date Range
2012 2019


Critical infrastructures in healthcare, power systems, and web services, incorporate cyber-physical systems (CPSes), where the software controlled computing systems interact with the physical environment through actuation and monitoring. Ensuring software safety in CPSes, to avoid hazards to property and human life as a result of un-controlled interactions, is essential and challenging. The principal hurdle in this regard is the characterization of the context driven interactions between software and the physical environment (cyber-physical interactions), which introduce multi-dimensional dynamics in space and time, complex non-linearities, and non-trivial aggregation of interaction in case of networked operations. Traditionally, CPS software is tested for safety …

Contributors
Banerjee, Ayan, Gupta, Sandeep K.S., Poovendran, Radha, et al.
Created Date
2012

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and …

Contributors
Li, Xun, Anselin, Luc, Koschinsky, Julia, et al.
Created Date
2012

With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors …

Contributors
Kondaveeti, Anirudh, Runger, George, Mirchandani, Pitu, et al.
Created Date
2012

Identifying important variation patterns is a key step to identifying root causes of process variability. This gives rise to a number of challenges. First, the variation patterns might be non-linear in the measured variables, while the existing research literature has focused on linear relationships. Second, it is important to remove noise from the dataset in order to visualize the true nature of the underlying patterns. Third, in addition to visualizing the pattern (preimage), it is also essential to understand the relevant features that define the process variation pattern. This dissertation considers these variation challenges. A base kernel principal component analysis …

Contributors
Sahu, Anshuman, Runger, George C., Wu, Teresa, et al.
Created Date
2013

This thesis focuses on generating and exploring design variations for architectural and urban layouts. I propose to study this general problem in three selected contexts. First, I introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for …

Contributors
Bao, Fan, Wonka, Peter, Maciejewski, Ross, et al.
Created Date
2014

Quad-dominant (QD) meshes, i.e., three-dimensional, 2-manifold polygonal meshes comprising mostly four-sided faces (i.e., quads), are a popular choice for many applications such as polygonal shape modeling, computer animation, base meshes for spline and subdivision surface, simulation, and architectural design. This thesis investigates the topic of connectivity control, i.e., exploring different choices of mesh connectivity to represent the same 3D shape or surface. One key concept of QD mesh connectivity is the distinction between regular and irregular elements: a vertex with valence 4 is regular; otherwise, it is irregular. In a similar sense, a face with four sides is regular; otherwise, …

Contributors
Peng, Chi-Han, Wonka, Peter, Maciejewski, Ross, et al.
Created Date
2014

Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media …

Contributors
Kumar, Shamanth, Liu, Huan, Davulcu, Hasan, et al.
Created Date
2015

Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, …

Contributors
Zhang, Yifan, Maciejewski, Ross, Mack, Elizabeth, et al.
Created Date
2016

Vectorization is an important process in the fields of graphics and image processing. In computer-aided design (CAD), drawings are scanned, vectorized and written as CAD files in a process called paper-to-CAD conversion or drawing conversion. In geographic information systems (GIS), satellite or aerial images are vectorized to create maps. In graphic design and photography, raster graphics can be vectorized for easier usage and resizing. Vector arts are popular as online contents. Vectorization takes raster images, point clouds, or a series of scattered data samples in space, outputs graphic elements of various types including points, lines, curves, polygons, parametric curves and …

Contributors
Yin, Xuetao, Razdan, Anshuman, Wonka, Peter, et al.
Created Date
2016

Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental …

Contributors
Lu, Yafeng, Maciejewski, Ross, Cooke, Nancy, et al.
Created Date
2017