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 research looks at a group of students from Tumaini Children's Home in Nyeri, Kenya. The purpose of this paper is to explore why this particular group of students is so academically successful. Quantitative research was taken from the average 2013 test scores of Tumaini students who took the Kenyan Certificate of Primary Education (KCPE) exam in comparison to the scores of students who are not residing in the orphanage. Qualitative research involves interviews from those students who live in Tumaini and interviews from adults who are closely connected to the orphanage. The purpose is to understand why the students are performing so well academically and what support they have created for themselves that allows them to do so.
ContributorsTooker, Amy Elizabeth (Author) / Puckett, Kathleen (Thesis director) / Cocchiarella, Martha (Committee member) / Barrett, The Honors College (Contributor) / Division of Teacher Preparation (Contributor)
Created2014-12
Description
Today's prison industrial complex in the United States often dehumanizes inmates simply because they are criminals. Members of the free society are generally too far removed from the inside of prisons that most people do not see the harsh and cruel conditions for and treatment of prisoners. As a Dance and Justice Studies major at Arizona State University, I was curious about how to intertwine my interests in dance and justice. This paper chronicles my exploration of adding a human rights issue to my dance practice through choreographing a solo dance performance based on Cleve Foster's unusual experience on death row. Research on theories of prison and punishment in American society combined with physical research in the dance studio enabled me to create a solo performance that shed light on the inhumane conditions for and treatment of prison inmates in today's society. Through the process, I found that some elements of my dance practice stayed the same, while others changed. This informed me of what continuously remains important to me, while allowing me to expand my personal dance practice. I ultimately discovered a bridge between my two passions, dance and justice, and learned a meaningful way to convey a contemporary social justice issue to the general public.
ContributorsKerr, Elena Marie (Author) / Schupp, Karen (Thesis director) / Vissicaro, Pegge (Committee member) / Barrett, The Honors College (Contributor) / Herberger Institute for Design and the Arts (Contributor) / School of Social Transformation (Contributor) / School of Film, Dance and Theatre (Contributor)
Created2015-05
Description
This project explores the dimensions that affect the success of a nonprofit organizations' web presence by using a dance and health nonprofit website as the foundation of the research and redesign. This report includes literature and design research, analysis, recommendations and a journal of the web design process. Through research, three categories were identified as the primary dimensions affecting the success of a website: content, technical adequacy and appearance. Furthermore, website success is influenced by how the strength of individual categories relies on one another. To improve the web design of Dancers and Health Together Inc., content implementations and redesign elements were both research and personal preference-based. The redesigned website can be found at www.collaydennis.com and will become inactive after May 31, 2015.
ContributorsDennis, Collay Carole (Author) / Coleman, Grisha (Thesis director) / Hosmer, Anthony Ryan (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2015-05
Description
The Latino population is the fastest growing minority group in the United States (U.S Census Bureau, 2003). Such a rapidly changing demographic stresses the importance of implementing strategies into the community social framework to accommodate for cultural and language differences. This research paper seeks to answer: what factors influence the sense of community among Latino families in Phoenix? The following questions will help to assess the dynamic relationship between sense of community and literacy 1) what is the perceived importance of literacy among Latino families living in Phoenix? 2) How is language development reflected among the family dynamics within a predominantly collectivist culture? It is hypothesized that both collectivism and literacy are the main influences on sense of community among this population.
ContributorsBennett, Julie (Author) / Glenberg, Arthur (Thesis director) / Restrepo, Laida (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
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
This project, which consists of a review article and an applied creative project, proposes mirror neurons as being a physiological mechanism for motor imagery. The review article highlights similarities between motor imagery research and research on mirror neurons. The research is roughly divided into three types of studies: neuroimaging studies, transcranial magnetic stimulation (TMS) and electromyography (EMG) studies, and electroencephalography (EEG) studies. The review also discusses the associative hypothesis of mirror neuron origin as support for the hypothesis and concludes with an assessment of conflicting research and the limitations of the hypothesis. The applied creative project is an instructional brochure, aimed at anyone who teaches motor skills, such as dance teachers or sports coaches. The brochure takes the academic content of the review and presents it in a visually pleasing, reader-friendly fashion in an effort to educate the intended audience and make the research more accessible. The brochure also prescribes research-based suggestions for how to use motor imagery during teaching sessions and how to get the best benefits from it.
ContributorsNgai, Valerie Christina (Author) / Hoffner, Kristin (Thesis director) / Glenberg, Arthur (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor) / Department of Psychology (Contributor)
Created2015-05