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.
Displaying 1 - 10 of 199
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- All Subjects: Machine Learning
- All Subjects: Poetry
Description
Waltz, is a collection of poems written to play along the boundaries between sound, language, and meaning. As a vehicle for exploration, the poems in Waltz, commandeer themes of nostalgia, love, loss, and abstraction, all of which build up and break each other down to create something of a nonlinear narrative, and concomitant sketch of the poet.
ContributorsAieta, Joseph (Author) / Ball, Sally (Thesis director) / Liston, Chelsea (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Same Bed is a twelve-piece book of poetry that explores the theme of sexual violence. The speaker of the poems is processing the trauma surrounding her rape which leads her to explore her own family's dynamics regarding gender, power, and acknowledgment of sexuality. The speaker also observes the broader issue of how society reacts to rape and the effects that can have on a survivor of sexual violence. In the peak of the manuscript, the speaker pieces together part of her own police report, pinning her own voice and perspective against her rapists.
ContributorsPetersen, Gabrielle Nicole (Author) / Ball, Sally (Thesis director) / Kelsey, Meghan (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Poetry serves as a window through which we can convey emotions and experiences otherwise difficult to access and express. This chapbook addresses the moments in life that have dramatic transformational effects and those moments and events we wish to deny. Through my poetry, I reveal the honest revelations of hurt and pain, and the raw emotions evoked from the things that have occurred throughout my life. In doing so, I confront these painful experiences from a place of conscious awareness of the way in which they have impacted my life, and I allow others access to my hurt, self-hatred, and imperfection acknowledged throughout. This chapbook symbolizes the movement from a place of denial to a place of awareness and finally to a place of transformation and growth. As my poetry transformed from weak poems only accessible on an abstract level to powerful poems of honest and tangible pain and hurt, I experienced my own transformation. Allowing myself to candidly share my experiences with others has enabled me to grow from these experiences.
ContributorsLarson, Amanda Beth (Author) / Montesano, Mark (Thesis director) / Comeaux, Alexandra (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor) / Department of Psychology (Contributor)
Created2014-12
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
Poems for the Future President is a chapbook of poetry by Michael Bartelt. Rooted in the democratic idealism of Walt Whitman and the American poetic tradition, the collection is a reflection on Americas of the past, the America we live in now, and an America that could be. The poems encompass a thematic breadth that includes ecological examinations filtered through ancient Taoist and modern ecocritical philosophy, searches for political and ethical authenticity in an over-stimulated information age, and questions about the meaning of romance and tradition in a dystopian present. Included here is the manuscript's critical framework, which highlights the poetry's main influences. The manuscript itself is also included.
ContributorsBartelt, Michael Joseph (Author) / Dombrowski, Rosemarie (Thesis director) / Orion, Shawnte (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Department of English (Contributor)
Created2014-12