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
The current study investigated whether intermittent restraint stress (IRS) would impair fear extinction learning and lead to increased anxiety and depressive- like behaviors and then be attenuated when IRS ends and a post- stress rest period ensues for 6 weeks. Young adult, male Sprague Dawley rats underwent restraint stress using wire mesh (6hr/daily) for five days with two days off before restraint resumed for three weeks for a total of 23 restraint days. The groups consisted of control (CON) with no restraint other than food and water restriction yoked to the restrained groups, stress immediate (STR-IMM), which were restrained then fear conditioned soon after the end of the IRS paradigm, and stress given a rest for 6 weeks before fear conditioning commenced (STR-R6). Rats were fear conditioned by pairing a 20 second tone with a footshock, then given extinction training for two days (15 tone only on each day). On the first day of extinction, all groups discriminated well on the first trial, but then as trials progressed, STR-R6 discriminated between tone and context less than did CON. On the second day of extinction, STR- IMM froze more to context in the earlier trials than compared to STR-R6 and CON. As trials progressed STR-IMM and STR-R6 froze more to context than compared to CON. Together, CON discriminated between tone and context better than did STR-IMM and STR-R6. Sucrose preference, novelty suppressed feeding, and elevated plus maze was performed after fear extinction was completed. No statistical differences were observed among groups for sucrose preference or novelty suppressed feeding. For the elevated plus maze, STR-IMM entered the open arms and the sum of both open and closed arms fewer than did STR- R6 and CON. We interpret the findings to suggest that the stress groups displayed increased hypervigilance and anxiety with STR-R6 exhibiting a unique phenotype than that of STR-IMM and CON.
ContributorsShah, Vrishti Bimal (Author) / Conrad, Cheryl (Thesis director) / Newbern, Jason (Committee member) / Judd, Jessica (Committee member) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (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
Monoamine neurotransmitters (e.g., serotonin, norepinephrine, and dopamine) are powerful modulators of mood and cognitive function in health and disease. We have been investigating the modulation of monoamine clearance in select brain regions via organic cation transporters (OCTs), a family of nonselective monoamine transporters. OCTs are thought to complement the actions of selective monoamine transporters in the brain by helping to clear monoamines from the extracellular space; thus, assisting to terminate the monoamine signal. Of particular interest, stress hormones (corticosterone; CORT) inhibit OCT3-mediated transport of monoamine, to putatively lead to prolonged monoamine signaling. It has been demonstrated that stress levels of CORT block OCT3 transport in the rat hypothalamus, an effect that likely underlies the rapid, stress-induced increase in local monoamines. We examined the effect of chronic variable stress (CVS) on the development of mood disorders and OCT3 expression in limbic and hypothalamic regions of the rat brain. Animals subjected to CVS (14-days with random stressor exposure two times/day) showed reduced body weight gain, indicating that CVS was perceived as stressful. However, behavioral tests of anxiety and depressive-like behaviors in rats showed no group differences. Although there were no behavioral effects of stress, molecular analysis revealed that there were stress-related changes in OCT3 protein expression. In situ hybridization data confirmed that OCT3 mRNA is expressed in the hippocampus, amygdala, and hypothalamus. Analysis of Western blot data by two-way ANOVA revealed a significant treatment effect on OCT3 protein levels, with a significant decrease in OCT3 protein in the amygdala and hippocampus in CVS rats, compared to controls. These data suggest an important role for CORT sensitive OCT3 in the reduction of monoamine clearance during stress.
ContributorsBoyll, Piper Savannah (Author) / Orchinik, Miles (Thesis director) / Conrad, Cheryl (Committee member) / Talboom, Joshua (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of our study is to identify socio-economic risk factors for depressive disorder and poor mental health by statistically analyzing survey data from the CDC. The identification of risk groups in a particular demographic could aid in the development of targeted interventions to improve overall quality of mental health in the United States. In our analysis, we studied the influences and correlations of socioeconomic factors that regulate the risk of developing Depressive Disorders and overall poor mental health. Using the statistical software STATA, we ran a regression model of selected independent socio-economic variables with the dependent mental health variables. The independent variables of the statistical model include Income, Race, State, Age, Marital Status, Sex, Education, BMI, Smoker Status, and Alcohol Consumption. Once the regression coefficients were found, we illustrated the data in graphs and heat maps to qualitatively provide visuals of the prevalence of depression in the U.S. demography. Our study indicates that the low-income and under-educated populations who are everyday smokers, obese, and/or are in divorced or separated relationships should be of main concern. A suggestion for mental health organizations would be to support counseling and therapeutic efforts as secondary care for those in smoking cessation programs, weight management programs, marriage counseling, or divorce assistance group. General improvement in alleviating poverty and increasing education could additionally show progress in counter-acting the prevalence of depressive disorder and also improve overall mental health. The identification of these target groups and socio-economic risk factors are critical in developing future preventative measures.
ContributorsGrassel, Samuel (Co-author) / Choueiri, Alexi (Co-author) / Choueiri, Robert (Co-author) / Goegan, Brian (Thesis director) / Holter, Michael (Committee member) / Sandra Day O'Connor College of Law (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
This study examines the effectiveness of two modes of exercise on depression in adolescents with Down syndrome (DS). Thirty nine participants were randomly divided into a voluntary cycling group (VC) (i.e., self-selected cadence), an assisted cycling group (AC) (i.e., at least 30% faster than self-selected cadence accomplished by a motor), or a no exercise group (NC). In each cycling intervention the participant completed 30 minute cycling sessions, three times per week for a total of eight weeks. The Children's Depression Inventory II was administered prior to cycling (i.e., pretest) and after the eight week intervention (i.e., posttest). Although the data did not reach conventional levels of statistical significance, the results of the study demonstrated partial support for our hypothesis that adolescents with DS showed improvements in depression as measured by the Children's Depression Inventory II following assisted cycling, but not following eight weeks of voluntary cycling. In other words, eight weeks of moderate AC exercise demonstrated a trend for improved depression in adolescents with DS.
ContributorsMcgownd, Shana Leah (Author) / Ringenbach, Shannon (Thesis director) / Youngstedt, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (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