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
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (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
Throughout my experience in college, I learned many different techniques to communicate effectively. Most professors emphasized the importance of speaking clearly, and the ability to influence others. Dr. Kashiwagi piqued my interest when he explained his thoughts on how he wanted us to communicate to him. The criteria were simple, speak to him in a way that he could easily understand, without having to think. If thinking took place for him in the conversation, he determined that the person spoke too complexly and that his understanding of the student was low. After hearing this in class, I thought back to past conversations with my managers. I then wondered if I explained things clearer or simplified my wording, would things have gone better? I was also curious about simplicity in communication through writing, and how different presentations of information affected understanding. To further analyze these issues, I explored multiple research reports on verbal communication. Furthermore, I set up an experiment to test two common types of visual communication. The research concludes that Dr. Kashiwagi's theory was indeed correct, simplicity in conversation reduces miscommunication. The effectiveness of simplicity in written communication was partially proven by the survey results. The results indicated that the time required to fully understand a given topic dropped significantly if the information was depicted in a simplified format (list format). The more complex paragraph (textbook format) did have a higher level of understanding. However, the participants rated the textbook format job objectives as more complex, and stressful. After gathering the research, and running the experiment it can be concluded that by simplifying verbal communication, there are negligible differences in understanding of the topic, but the time of understanding decreases significantly.
ContributorsWilliams, Matthew Scott (Author) / Kashiwagi, Jacob (Thesis director) / Abraham, Seth (Committee member) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This Barrett, the Honors College senior thesis connects the experiences of cosplay with public speaking confidence. “Cosplay, abbreviated from the word ‘costume play,’ is a performance art in which the participant masquerades as a character from a selected film, television, video game, or comic book” (Gn, 2011, p. 583). The ability to “cosplay” in front of other relies on performing in front of an audience much like public speaking. When students speak with confidence, students will know their ideas are being expressed with conviction and assurance. Having the ability to speak professionally and publicly, is a highly valued skill in the workforce and key to success in all types of employment. Communication skills are frequently a top factor in determining whether a college student will obtain employment (Beebe & Beebe, 2006, p. 275-276). Despite their different definitions, there are multiple connections between cosplay and public speaking. This thesis explores the connection between peer support and belief in one’s self in both cosplay and public speaking. Now those who have direct support become self-reliant and confident as a result of these connections. This projects highlights Goffman’s identity theory, the Pygmalion effect, theories of fashion and identity, role-play, narrative paradigm, dramatism, and non-verbal communication, and explores how cosplay can contribute to the formation of one’s public speaking persona. The issue of anxiety is also included in the conversation as it is central to both cosplay and public speaking. Ultimately, this thesis explores the questions: Can cosplay help students become empowered public speakers?
ContributorsGallardo Rojas, Lizette (Author) / Ramsey, Ramsey Eric (Thesis director) / Wentzel, Bonnie (Committee member) / School of Social and Behavioral Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The foundations of legacy media, especially the news media, are not as strong as they once were. A digital revolution has changed the operation models for and journalistic organizations are trying to find their place in the new market. This project is intended to analyze the effects of new/emerging technologies on the journalism industry. Five different categories of technology will be explored. They are as follows: the semantic web, automation software, data analysis and aggregators, virtual reality and drone journalism. The potential of these technologies will be broken up according to four guidelines, ethical implications, effects on the reportorial process, business impacts and changes to the consumer experience. Upon my examination, it is apparent that no single technology will offer the journalism industry the remedy it has been searching for. Some combination of emerging technologies however, may form the basis for the next generation of news. Findings are presented on a website that features video, visuals, linked content, and original graphics. Website found at http://www.explorenewstech.com/
ContributorsMaxwell, Gavin William (Author) / McGuire, Tim (Thesis director) / Cornelius, David (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This project focuses on techniques contemporary American poets use in their work. Ten different poetry collections are analyzed for dominant writing styles and techniques, which I then apply to my own poems, concentrating on modeling that particular poet. I then reflect on those poems through an evaluation of my writing process, how those techniques were implemented, and how they affected the poem. In addition to these reviews and reflections, I also wrote three articles about the literary community and what I've learned from my interactions in that community. All these materials are organized into a website, which shows the connections between the different writings via links and menus. Creating this website brings all the materials together to demonstrate my growth as a poet, writer, and designer. This heavy focus on poetry and analysis has helped sharpen my critical thinking skills and has better prepared me for a career in design and journalism.
ContributorsHansen, Elizabeth Sarah (Author) / Murphy, Patricia (Thesis director) / Savard, Jeannine (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor) / Hugh Downs School of Human Communication (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2015-05
Description
The transition from high school to college is marked by many changes, one of the most significant being the increased accessibility of alcohol, putting college students at high risk for alcohol-related consequences. It is imperative to identify factors that can protect young adults against these risks during this critical period. Although peers become increasingly influential in college, extant literature has shown that parents still have an impact on their children's behavior during this time. While parents spend less time with their children after college matriculation, they may indirectly protect against risky drinking behaviors by instilling certain values into their children before they make this transition. Using data from a large sample of students during their senior year of high school and their freshman year of college, the current study sought to examine interactive effects of parental communication and parental knowledge and caring on drinking behavior, and the extent to which internalization of personal drinking values mediate these effects. The primary study hypotheses were tested using path analysis conducted in Mplus 7.0. Full information maximum likelihood (FIML) estimation was utilized to estimate missing data and bootstrapping was used to address non-normality in the data. Results showed that, for those whose parents were high in knowledge and caring, higher levels of communication were associated with lower risk for alcohol use and problems at wave 3 through less permissive drinking values at wave 1. This finding has important implications for prevention approaches designed to reduce risk for heavy drinking and related problems during the transition to college.
ContributorsHartman, Jessica Danielle (Author) / Corbin, William (Thesis director) / Knight, George (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2015-05