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
It is unknown which regions of the brain are most or least active for golfers during a peak performance state (Flow State or "The Zone") on the putting green. To address this issue, electroencephalographic (EEG) recordings were taken on 10 elite golfers while they performed a putting drill consisting of hitting nine putts spaced uniformly around a hole each five feet away. Data was collected at three time periods, before, during and after the putt. Galvanic Skin Response (GSR) measurements were also recorded on each subject. Three of the subjects performed a visualization of the same putting drill and their brain waves and GSR were recorded and then compared with their actual performance of the drill. EEG data in the Theta (4 \u2014 7 Hz) bandwidth and Alpha (7 \u2014 13 Hz) bandwidth in 11 different locations across the head were analyzed. Relative power spectrum was used to quantify the data. From the results, it was found that there is a higher magnitude of power in both the theta and alpha bandwidths for a missed putt in comparison to a made putt (p<0.05). It was also found that there is a higher average power in the right hemisphere for made putts. There was not a higher power in the occipital region of the brain nor was there a lower power level in the frontal cortical region during made putts. The hypothesis that there would be a difference between the means of the power level in performance compared to visualization techniques was also supported.
ContributorsCarpenter, Andrea (Co-author) / Hool, Nicholas (Co-author) / Muthuswamy, Jitendran (Thesis director) / Crews, Debbie (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Females are highly vulnerable to the effects of methamphetamine, and understanding the mechanisms of this is critical to addressing methamphetamine use as a public health issue. Hormones may play a role in methamphetamine sensitivity; thus, the fluctuation of various endogenous peptides during the postpartum experience is of interest. This honors thesis project explored the relation between anxiety-like behavior, as measured by activity in an open field, and conditioned place preference to methamphetamine in female versus male rats. The behavior of postpartum as well as virgin female rats was compared to that of male rats. There was not a significant difference between males and females in conditioned place preference to methamphetamine, yet females showed higher locomotor activity in response to the drug as well as increased anxiety-like behavior in open field testing as compared to males. Further study is vital to comprehending the complex mechanisms of sex differences in methamphetamine addiction.
ContributorsBaker, Allison Nicole (Author) / Olive, M. Foster (Thesis director) / Presson, Clark (Committee member) / Hansen, Whitney (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Environmental and genetic factors contribute to schizophrenia etiology, yet few studies have demonstrated how environmental stimuli impact genes associated with the disorder. Immediate early genes (IEGs) are of great interest to schizophrenia research because they are activated in response to physiological stress from the environment, and subsequently regulate the expression of downstream genes that are essential to neuropsychiatric function. An IEG, early growth response 3 (EGR3) has been identified as a main gene involved in a network of transcription factors implicated in schizophrenia susceptibility. The serotonin 2A receptor (5-HT2AR) seems to play an important role in schizophrenia and the dysfunction of the 5-HT2AR encoding gene, HTR2A, within the prefrontal cortex (PFC) contributes to multiple psychiatric illnesses including schizophrenia. EGR3's role as a transcription factor that is activated by environmental stimuli suggests it may regulate Htr2a transcription in response to physiological stress, thus affecting 5-HT2AR function in the prefrontal cortex (PFC). The aim of this study was to examine the relationship between Egr3 activation and Htr2a expression after an environmental stimulus. Sleep deprivation is an acute physiological stressor that activates Egr3. Therefore to examine the relationship between Egr3 and Htr2a expression after an acute stress, wild type and Egr3 knockout mice that express EGFP under the control of the Htr2a promoter were sleep deprived for 8 hours. We used immunohistochemistry to determine the location and density of Htr2a-EGFP expression after sleep deprivation and found that Htr2a-EGFP expression was not affected by sex or subregions of the PFC. Additionally, Htr2a-EGFP expression was not affected by the loss of Egr3 or sleep deprivation within the PFC. The LPFC subregions, layers V and VI showed significantly more Htr2a-EGFP expression than layers I-III in all animals for both sleep deprivation and control conditions. Possible explanations for the lack of significant effects in this study may be the limited sample size or possible biological abnormalities in the Htr2a-EGFP mice. Nonetheless, we did successfully visualize the anatomical distribution of Htr2a in the prefrontal cortex via immunohistochemical staining. This study and future studies will provide insight into how Egr3 activation affects Htr2a expression in the PFC and how physiological stress from the environment can alter candidate schizophrenia gene function.
ContributorsSabatino, Alissa Marie (Author) / Gallitano, Amelia (Thesis director) / Hruschka, Daniel (Thesis director) / Maple, Amanda (Committee member) / Barrett, The Honors College (Contributor)
Created2014-05
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
Scientists, lawyers, and bioethicists have pondered the impact of scientifically deterministic evidence on a judge or jury when deciding the sentence of a criminal. Though the impact may be one that relieves the amount of personal guilt on the part of the criminal, this evidence may also be the very reason that a judge or jury punishes more strongly, suggesting that this type of evidence may be a double-edged sword. 118 participants were shown three films of fictional sentencing hearings. All three films introduced scientifically deterministic evidence, and participants were asked to recommend a prison sentence. Each hearing portrayed a different criminal with different neurological conditions, a different crime, and a different extent of argumentation during closing arguments about the scientifically deterministic evidence. Though the argumentation from the prosecution and the defense did not affect sentencing, the interaction of type of crime and neurological condition did.
ContributorsMeschkow, Alisha Sadie (Author) / Schweitzer, Nicholas (Thesis director) / Robert, Jason (Committee member) / Patten, K. Jakob (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2014-05