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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
Resource Type
Subject
Date Range
2011 2020


The traditional access control system suffers from the problem of separation of data ownership and management. It poses data security issues in application scenarios such as cloud computing and blockchain where the data owners either do not trust the data storage provider or even do not know who would have access to their data once they are appended to the chain. In these scenarios, the data owner actually loses control of the data once they are uploaded to the outside storage. Encryption-before-uploading is the way to solve this issue, however traditional encryption schemes such as AES, RSA, ECC, bring about …

Contributors
Dong, Qiuxiang, Huang, Dijiang, Sen, Arunabha, et al.
Created Date
2020

Component simulation models, such as agent-based models, may depend on spatial data associated with geographic locations. Composition of such models can be achieved using a Geographic Knowledge Interchange Broker (GeoKIB) enabled with spatial-temporal data transformation functions, each of which is responsible for a set of interactions between two independent models. The use of autonomous interaction models allows model composition without alteration of the composed component models. An interaction model must handle differences in the spatial resolutions between models, in addition to differences in their temporal input/output data types and resolutions. A generalized GeoKIB was designed that regulates unidirectional spatially-based interactions …

Contributors
Boyd, William Angelo, Sarjoughian, Hessam S, Maciejewski, Ross, et al.
Created Date
2019

Deep neural networks (DNNs) have had tremendous success in a variety of statistical learning applications due to their vast expressive power. Most applications run DNNs on the cloud on parallelized architectures. There is a need for for efficient DNN inference on edge with low precision hardware and analog accelerators. To make trained models more robust for this setting, quantization and analog compute noise are modeled as weight space perturbations to DNNs and an information theoretic regularization scheme is used to penalize the KL-divergence between perturbed and unperturbed models. This regularizer has similarities to both natural gradient descent and knowledge distillation, …

Contributors
Kadambi, Pradyumna, Berisha, Visar, Dasarathy, Gautam, et al.
Created Date
2019

Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed using a Bernoulli-Gaussian (BG) prior distribution and is assumed to be the true generative model. The second is constructed using a Gamma-Normal (GN) prior distribution and is, therefore, a different (i.e. misspecified) model. To estimate the posterior distribution for the correctly specified scenario, an algorithm based on generalized approximated message …

Contributors
Alhowaish, Abdulhakim, Richmond, Christ D, Papandreou-Suppappola, Antonia, et al.
Created Date
2019

TolTEC is a three-color millimeter wavelength camera currently being developed for the Large Millimeter Telescope (LMT) in Mexico. Synthesizing data from previous astronomy cameras as well as knowledge of atmospheric physics, I have developed a simulation of the data collection of TolTEC on the LMT. The simulation was built off smaller sub-projects that informed the development with an understanding of the detector array, the time streams for astronomical mapping, and the science behind Lumped Element Kinetic Inductance Detectors (LEKIDs). Additionally, key aspects of software development processes were integrated into the scientific development process to streamline collaboration across multiple universities and …

Contributors
Horton, Paul, Mauskopf, Philip, Bansal, Ajay, et al.
Created Date
2019

Social networking sites like Twitter have provided people a platform to connect with each other, to discuss and share information and news or to entertain themselves. As the number of users continues to grow there has been explosive growth in the data generated by these users. Such a vast data source has provided researchers a way to study and monitor public health. Accurately analyzing tweets is a difficult task mainly because of their short length, the inventive spellings and creative language expressions. Instead of focusing at the topic level, identifying tweets that have personal health experience mentions would be more …

Contributors
Gondane, Shubham Bhagwan, Baral, Chitta, Anwar, Saadat, et al.
Created Date
2019

Machine learning (ML) and deep neural networks (DNNs) have achieved great success in a variety of application domains, however, despite significant effort to make these networks robust, they remain vulnerable to adversarial attacks in which input that is perceptually indistinguishable from natural data can be erroneously classified with high prediction confidence. Works on defending against adversarial examples can be broadly classified as correcting or detecting, which aim, respectively at negating the effects of the attack and correctly classifying the input, or detecting and rejecting the input as adversarial. In this work, a new approach for detecting adversarial examples is proposed. …

Contributors
Sun, Lin, Bazzi, Rida, Li, Baoxin, et al.
Created Date
2019

With the steady advancement of neural network research, new applications are continuously emerging. As a tool for test time reduction, neural networks provide a reliable method of identifying and applying correlations in datasets to speed data processing. By leveraging the power of a deep neural net, it is possible to record the motion of an accelerometer in response to an electrical stimulus and correlate the response with a trim code to reduce the total test time for such sensors. This reduction can be achieved by replacing traditional trimming methods such as physical shaking or mathematical models with a neural net …

Contributors
Debeurre, Nicholas, Ozev, Sule, Vrudhula, Sarma, et al.
Created Date
2019

Blockchain technology enables peer-to-peer transactions through the elimination of the need for a centralized entity governing consensus. Rather than having a centralized database, the data is distributed across multiple computers which enables crash fault tolerance as well as makes the system difficult to tamper with due to a distributed consensus algorithm. In this research, the potential of blockchain technology to manage energy transactions is examined. The energy production landscape is being reshaped by distributed energy resources (DERs): photo-voltaic panels, electric vehicles, smart appliances, and battery storage. Distributed energy sources such as microgrids, household solar installations, community solar installations, and plug-in …

Contributors
Sadaye, Raj Anil, Candan, Kasim S, Boscovic, Dragan, et al.
Created Date
2019

Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible, because the bots may get suspended or deleted, …

Contributors
Davis, Matthew William, Liu, Huan, Xue, Guoliang, et al.
Created Date
2019