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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

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.
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
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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.

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
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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

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
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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

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
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Description
Health and fast food are seemingly on two opposite ends of the spectrum, yet healthy fast food is quickly growing in popularity. As many fast food brands are adjusting their menu to accommodate to this trend, this study explores how health claims used in fast food advertising affect college students'

Health and fast food are seemingly on two opposite ends of the spectrum, yet healthy fast food is quickly growing in popularity. As many fast food brands are adjusting their menu to accommodate to this trend, this study explores how health claims used in fast food advertising affect college students' perceptions of health and their likelihood to purchase healthy fast food products. To test this, a survey gathered quantitative data to assess student's perceptions of health and fast food, as well as qualitative data of when eating healthy is appealing and unappealing. An ad manipulation was employed to test student's likelihood to purchase the product shown in the ad. Though the study did not yield significant results, the results collected indicate that health claims may not be enough to influence someone to purchase, but that taste is of student's highest priority when making food purchase decisions. Thus, the study opens the door for future research in this realm of health and fast food, and concludes with recommendations for both marketers and future researchers.
ContributorsMigray, Emilee Catherine (Author) / Gray, Nancy (Thesis director) / Samper, Adriana (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
This paper will review past unethical studies conducted in the last 100 years on humans, including studies such as the WWII Concentration Camp studies on hypothermia and sterilization, Tuskegee Syphilis Study, and the case of Henrietta Lacks; Analyze why they were deemed unethical, the laws that emerged from these studies,

This paper will review past unethical studies conducted in the last 100 years on humans, including studies such as the WWII Concentration Camp studies on hypothermia and sterilization, Tuskegee Syphilis Study, and the case of Henrietta Lacks; Analyze why they were deemed unethical, the laws that emerged from these studies, and how it relates to contemporary technology, with a focus on the issues surrounding the development of an electronic wearable pregnancy monitor. The studies will include details of how they were conducted as well as what deemed them unethical and an explanation of why the results are unusable. Following the studies will be an explanation of the laws that were set into place following the studies with a lead into current technologies and how these technologies created a new set of ethics. The Google Mini, the wearable biosensor onesies for infants, and the intensive care unit at Banner Baywood will be described and so will their role in the development of an electronic wearable pregnancy monitor. The mini-meta analysis includes possible features of the monitor as well as a description of what the ethical consent form will look like. To conclude the paper, the importance of analyzing past unethical studies will help create a new ethical device that will make a point to go above and beyond to ensure the physical health of unborn children, in a way that is both ethical and significant.
ContributorsWallace, Sydney Sarah (Author) / Hall, Rick (Thesis director) / Kamenca, Andrea (Committee member) / Human Systems Engineering (Contributor) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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Description
Forty collegiate gymnasts were recruited for a nutrition and health study. Participants must have been at least eighteen years old at Arizona State University (ASU) in the club or team gymnastics program. The Institutional Review Board (IRB) reviewed and accepted my survey in order to hand out to the gymnasts.

Forty collegiate gymnasts were recruited for a nutrition and health study. Participants must have been at least eighteen years old at Arizona State University (ASU) in the club or team gymnastics program. The Institutional Review Board (IRB) reviewed and accepted my survey in order to hand out to the gymnasts. The ASU club and team coach and the ASU study team also approved my survey. As soon as the survey was approved, it was emailed to all of the gymnasts. ASU gymnasts were surveyed on nutritional knowledge and personal health. Subjects answered a quiz on nutrient needs and serving sizes. Personal questions consisted of height, weight, injuries, body image, and typical meal plans. Gymnasts were given a $10 compensation to increase the participation. We found that only 16% of gymnasts surveyed scored a 70% or higher on their nutritional knowledge. Although these gymnasts do not have adequate knowledge, the majority consume a healthy diet. Diets included fruits, vegetables, protein-rich foods, and few high fat and sugary foods. Four of the gymnasts had one or fewer injuries in the past two years, although, four gymnasts also had three or more injuries. No correlation was found between diet and injuries. There was also no correlation between the gymnast's nutritional knowledge and their health.
ContributorsKugler, Natalie K. (Author) / Levinson, Simin (Thesis director) / Berger, Christopher (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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Description
According to the CDC, diabetes is the 7th leading cause of death in the U.S. and rates are continuing to rise nationally and internationally. Chronically elevated blood glucose levels can lead to type 2 diabetes and other complications. Medications can be used to treat diabetes, but often have side effects.

According to the CDC, diabetes is the 7th leading cause of death in the U.S. and rates are continuing to rise nationally and internationally. Chronically elevated blood glucose levels can lead to type 2 diabetes and other complications. Medications can be used to treat diabetes, but often have side effects. Lifestyle and diet modifications can be just as effective as medications in helping to improve glycemic control, and prevent diabetes or improve the condition in those who have it. Studies have demonstrated that consuming vinegar with carbohydrates can positively impact postprandial glycemia in diabetic and healthy individuals. Continuous vinegar intake with meals may even reduce fasting blood glucose levels. Since vinegar is a primary ingredient in mustard, the purpose of this study was to determine if mustard consumption with a carbohydrate-rich meal (bagel and fruit juice) had an effect on the postprandial blood glucose levels of subjects. The results showed that mustard improved glycemia by 17% when subjects consumed the meal with mustard as opposed to the control. A wide variety of vinegars exists. The defining ingredient in all vinegars is acetic acid, behind the improvement in glycemic response observed with vinegar ingestion. Vinegar-containing foods range from mustard, to vinaigrette dressings, to pickled foods. The benefits of vinegar ingestion with carbohydrates are dose-dependent, meaning that adding even small amounts to meals can help. Making a conscious effort to incorporate these foods into meals, in addition to an overall healthy lifestyle, could provide an additional tool for diabetics and nondiabetics alike to consume carbohydrates in a healthier manner.
ContributorsJimenez, Gabriela (Author) / Johnston, Carol (Thesis director) / Lespron, Christy (Committee member) / School of Nutrition and Health Promotion (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
Because children do not have the same decision-making powers as adults in matters affecting their health, their opinions have often been underrepresented in research (Bradding & Horstman, 1999). However, there is growing interest in the way that children view health because this knowledge elicits the development of more child-centered and

Because children do not have the same decision-making powers as adults in matters affecting their health, their opinions have often been underrepresented in research (Bradding & Horstman, 1999). However, there is growing interest in the way that children view health because this knowledge elicits the development of more child-centered and effective approaches to health education and intervention (Bradding & Horstman, 1999). Professionals have often utilized the write-and-draw technique in school settings to gain a better understanding of how to best implement health education programs. The "bottom-up" approach of the write-and-draw method encourages participation and has been shown to elicit thoughtful responses about how children conceptualize health (Pridmore & Bendelow, 1995). This study uses the write-and-draw method to perform a cross- cultural comparison of child perspectives of health in the United States and Guatemala, countries that represent contrasting paradigms for child health. The results of this study are consistent with previous research, especially the emergent health themes. Children from the United States and Guatemala predominantly depicted health in terms of food. Guatemalan students were more likely to refer to hygienic practices and environmental conditions, while US children mentioned vegetables, water, and exercise as being healthy. For the unhealthy category, themes of poor hygiene, chips, fat/grease, fruit, carbohydrates, and environment were mentioned more often in Guatemala, while U.S. students listed sweets and fast food more frequently. Results support claims made in other literature that children's concepts of health are shaped by life experience and social context. Potential applications of the research include exposing areas (themes) where children are less likely to understand health implications and developing educational curriculum to increase a more comprehensive understanding of health.
ContributorsRenslow, Jillian Marie (Author) / Maupin, Jonathan (Thesis director) / BurnSilver, Shauna (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
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Description
The purpose of this project was to establish a digital and social media presence to support a personal fitness trainer and d�TERRA essential oils wellness advocate in growing her health and wellness businesses. The first portion explores the role of digital and social media tools for health and wellness professionals.

The purpose of this project was to establish a digital and social media presence to support a personal fitness trainer and d�TERRA essential oils wellness advocate in growing her health and wellness businesses. The first portion explores the role of digital and social media tools for health and wellness professionals. It incorporates use of both secondary and primary research methods including focus groups and in-depth interviews. The second portion is a campaign proposal that serves as a creative response to the research and findings of the first portion. The proposal includes recommendations for strategic use of new brand building and social networking tools such as a personal website, Facebook, Twitter, LinkedIn and About.Me pages. It also offers collateral material for brand outreach, social media calendars and a 10-page social media guidebook offering suggestions on how to strategically implement the campaign elements.
ContributorsNichols, Emily Jaye (Author) / Wu, Xu (Thesis director) / Roschke, Kristy (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2015-05