Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper examines creative process and performance as a method of research for understanding self-in-context through the lens of my own artistic research for “Dress in Something Plain and Dark,” a project exploring my relationship as a woman to Mennonite religious and cultural identity, spirituality, and dance. Situating my artistic work in relationship to the fields of creative autoethnography, queer and transborder performance art, and somatic dance practice, I discuss the distinctions and commonalities of approach, methods, and practice of artists working in these fields, and the shared challenges of marginalization, translation, and contextualization. In response to these challenges, and the inadequacy of linear, Western, individualistic and mechanistic frameworks to address them, I draw from the ethnographic work of de la Garza, (formerly González, 2000) to seek a “creation-centered” ontological framework that the artist-researcher-performer may use to understand and contextualize their work. I offer the tree as an ontology to understand the organic, emergent nature of creative process, the stages of growth and seasonal cycles, and the structural parts that make up the creative and performative processes, and illustrate this model through a discussion of the growth of “Dress in Something Plain and Dark,” as it has emerged over two cyclical “seasons” of maturation.
Note: This work of creative scholarship is rooted in collaboration between three female artist-scholars: Carly Bates, Raji Ganesan, and Allyson Yoder. Working from a common intersectional, feminist framework, we served as artistic co-directors of each other’s solo pieces and co-producers of Negotiations, in which we share these pieces alongside each other. Negotiations is not a showcase of three individual works, but a conversation among three voices. As collaborators, we have been uncompromising in the pursuit of our own unique inquiries and voices and each of our works of creative scholarship stand alone. However, we believe that all of the parts are best understood in relationship to each other and to the whole. For this reason, we have chosen to cross-reference our thesis documents here, and we encourage readers to view the performance of Negotiations in its entirety.
Thesis documents cross-referenced:
French Vanilla: An Exploration of Biracial Identity Through Narrative Performance, by Carly Bates
Bhairavi: A Performance-Investigation of Belonging and Dis-Belonging in Diaspora Communities, by Raji Ganesan
Deep roots, shared fruits: Emergent creative process and the ecology of solo performance through “Dress in Something Plain and Dark,” by Allyson Yoder
Note: This work of creative scholarship is rooted in collaboration between three female artist-scholars: Carly Bates, Raji Ganesan, and Allyson Yoder. Working from a common intersectional, feminist framework, we served as artistic co-directors of each other’s solo pieces and co-producers of Negotiations, in which we share these pieces alongside each other. Negotiations is not a showcase of three individual works, but a conversation among three voices. As collaborators, we have been uncompromising in the pursuit of our own unique inquiries and voices and each of our works of creative scholarship stand alone. However, we believe that all of the parts are best understood in relationship to each other and to the whole. For this reason, we have chosen to cross-reference our thesis documents here, and we encourage readers to view the performance of Negotiations in its entirety.
Thesis documents cross-referenced:
French Vanilla: An Exploration of Biracial Identity Through Narrative Performance, by Carly Bates
Bhairavi: A Performance-Investigation of Belonging and Dis-Belonging in Diaspora Communities, by Raji Ganesan
Deep roots, shared fruits: Emergent creative process and the ecology of solo performance through “Dress in Something Plain and Dark,” by Allyson Yoder
ContributorsYoder, Allyson Joy (Author) / de la Garza, Sarah Amira (Thesis director) / Ellsworth, Angela (Committee member) / DeWitt, Inertia Q. E. D. (Committee member) / School of Film, Dance and Theatre (Contributor) / Herberger Institute for Design and the Arts (Contributor) / Barrett, The Honors College (Contributor) / Hugh Downs School of Human Communication (Contributor)
Created2016-05
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat transport, the primary goal of this endeavor is to reproduce the success shown in Meneghini et al. in using NNs for heat transport prediction in DIII-D. Being able to reproduce the results from is important because this in turn would provide scientists at PPPL with a quick and efficient toolset for reliably predicting heat transport profiles much faster than any existing computational methods allow; the progress towards this goal is outlined in this report, and potential additional applications of the NN framework are presented.
ContributorsLuna, Christopher Joseph (Author) / Tang, Wenbo (Thesis director) / Treacy, Michael (Committee member) / Orso, Meneghini (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
While the concept of healthcare is largely respected in Arab culture, the stigma underlying mental health is particularly startling. This study examined the differences in mental health treatment-seeking behaviors using data from Arabs living in Syria (12.9%) and Arabs (25.6%) and non-Arabs (61.5%) living in the United States of ages 18-60. A Web-based survey was developed to understand how factors like religiosity, acculturation, and positive attitudes towards psychological treatment increased help-seeking behaviors. This survey was also provided in Arabic to include non-English speaking participants. It was hypothesized that Arab-American individuals will be more open to pursuing professional psychological help when suffering from mental symptomology (i.e. anxiety) than individuals who identified as Syrian-Arabs. In contrast, both Syrian-Arabs and Arab-Americans would definitely pursue professional help when suffering from physical symptomology (i.e. ankle sprain). Striking differences were found based on Western acculturation. Findings suggested that Arab-Americans were less inclined towards treatment and more trusting of an in-group physician ("Dr. Ahmed") whereas Syrian-Arabs were more inclined to pursue psychological treatment and preferred to trust an out-group physician ("Dr. Smith"). The results of this study identify main concerns regarding Arab attitudes towards seeking mental health treatment, which can better inform future research and mental health services for this minority.
ContributorsRayes, Diana S (Author) / Brewer, Gene (Thesis director) / Cohen, Adam (Committee member) / Olive, Michael Foster (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2015-05