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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
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
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
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, 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
Description
Due to artificial selection, dogs have high levels of phenotypic diversity, yet, there appears to be low genetic diversity within individual breeds. Through their domestication from wolves, dogs have gone through a series of population bottlenecks, which has resulted in a reduction in genetic diversity, with a large amount of linkage disequilibrium and the persistence of deleterious mutations. This has led to an increased susceptibility to a multitude of diseases, including cancer. To study the effects of artificial selection and life history characteristics on the risk of cancer mortality, we collected cancer mortality data from four studies as well as the percent of heterozygosity, body size, lifespan and breed group for 201 dog breeds. We also collected specific types of cancer breeds were susceptible to and compared the dog cancer mortality patterns to the patterns observed in other mammals. We found a relationship between cancer mortality rate and heterozygosity, body size, lifespan as well as breed group. Higher levels of heterozygosity were also associated with longer lifespan. These results indicate larger breeds, such as Irish Water Spaniels, Flat-coated Retrievers and Bernese Mountain Dogs, are more susceptible to cancer, with lower heterozygosity and lifespan. These breeds are also more susceptible to sarcomas, as opposed to carcinomas in smaller breeds, such as Miniature Pinschers, Chihuahuas, and Pekingese. Other mammals show that larger and long-lived animals have decreased cancer mortality, however, within dog breeds, the opposite relationship is observed. These relationships could be due to the trade-off between cellular maintenance and growing fast and large, with higher expression of growth factors, such as IGF-1. This study further demonstrates the relationships between cancer mortality, heterozygosity, and life history traits and exhibits dogs as an important model organism for understanding the relationship between genetics and health.
ContributorsBalsley, Cassandra Sierra (Author) / Maley, Carlo (Thesis director) / Wynne, Clive (Committee member) / Tollis, Marc (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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. 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
Description
Breast cancer is the leading cause of cancer-related deaths of women in the united states. Traditionally, Breast cancer is predominantly treated by a combination of surgery, chemotherapy, and radiation therapy. However, due to the significant negative side effects associated with these traditional treatments, there has been substantial efforts to develop alternative therapies to treat cancer. One such alternative therapy is a peptide-based therapeutic cancer vaccine. Therapeutic cancer vaccines enhance an individual's immune response to a specific tumor. They are capable of doing this through artificial activation of tumor specific CTLs (Cytotoxic T Lymphocytes). However, in order to artificially activate tumor specific CTLs, a patient must be treated with immunogenic epitopes derived from their specific cancer type. We have identified that the tumor associated antigen, TPD52, is an ideal target for a therapeutic cancer vaccine. This designation was due to the overexpression of TPD52 in a variety of different cancer types. In order to start the development of a therapeutic cancer vaccine for TPD52-related cancers, we have devised a two-step strategy. First, we plan to create a list of potential TPD52 epitopes by using epitope binding and processing prediction tools. Second, we plan to attempt to experimentally identify MHC class I TPD52 epitopes in vitro. We identified 942 potential 9 and 10 amino acid epitopes for the HLAs A1, A2, A3, A11, A24, B07, B27, B35, B44. These epitopes were predicted by using a combination of 3 binding prediction tools and 2 processing prediction tools. From these 942 potential epitopes, we selected the top 50 epitopes ranked by a combination of binding and processing scores. Due to the promiscuity of some predicted epitopes for multiple HLAs, we ordered 38 synthetic epitopes from the list of the top 50 epitope. We also performed a frequency analysis of the TPD52 protein sequence and identified 3 high volume regions of high epitope production. After the epitope predictions were completed, we proceeded to attempt to experimentally detected presented TPD52 epitopes. First, we successful transduced parental K562 cells with TPD52. After transduction, we started the optimization process for the immunoprecipitation protocol. The optimization of the immunoprecipitation protocol proved to be more difficult than originally believed and was the main reason that we were unable to progress past the transduction of the parental cells. However, we believe that we have identified the issues and will be able to complete the experiment in the coming months.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis director) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This purpose of this thesis study was to examine variables of the "War on Cancer" frame, loss-gain prime, and patient gender on treatment decision for advanced cancer patients. A total of 291 participants (141 females) participated in an online survey experiment and were randomly assigned to one of eight possible conditions, each of which were comprised of a combination of one of two levels for three total independent variables: war frame ("War on Cancer" frame or neutral frame), loss-gain prime (loss prime or gain prime), and patient gender (female or male). Each of the three variables were operationalized to determine whether or not the exposure to the war on cancer paradigm, loss-frame language, or male patient gender would increase the likelihood of a participant choosing a more aggressive cancer treatment. Participants read a patient scenario and were asked to respond to questions related to motivating factors. Participants were then asked to report preference for one of two treatment decisions. Participants were then asked to provide brief demographic information in addition to responding to questions about military history, war attitudes, and cancer history. The aforementioned manipulations sought to determine whether exposure to various factors would make a substantive difference in final treatment decision. Contrary to the predicted results, participants in the war frame condition (M = 3.85, SD = 1.48) were more likely to choose the pursuit of palliative care (as opposed to aggressive treatment) than participants in the neutral frame condition (M = 3.54, SD = 1.23). Ultimately, these significant findings suggest that there is practical information to be gained from treatment presentation manipulations. By arming healthcare providers with a more pointed understanding of the nuances of treatment presentation, we can hope to empower patients, their loved ones, and healthcare providers entrenched in the world of cancer treatment.
ContributorsKnowles, Madelyn Ann (Author) / Kwan, Virginia S. Y. (Thesis director) / Presson, Clark (Committee member) / Salamone, Damien (Committee member) / Department of Psychology (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05