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 39
Description
Preventive maintenance is a practice that has become popular in recent years, largely due to the increased dependency on electronics and other mechanical systems in modern technologies. The main idea of preventive maintenance is to take care of maintenance-type issues before they fully appear or cause disruption of processes and daily operations. One of the most important parts is being able to predict and foreshadow failures in the system, in order to make sure that those are fixed before they turn into large issues. One specific area where preventive maintenance is a very big part of daily activity is the automotive industry. Automobile owners are encouraged to take their cars in for maintenance on a routine schedule (based on mileage or time), or when their car signals that there is an issue (low oil levels for example). Although this level of maintenance is enough when people are in charge of cars, the rise of autonomous vehicles, specifically self-driving cars, changes that. Now instead of a human being able to look at a car and diagnose any issues, the car needs to be able to do this itself. The objective of this project was to create such a system. The Electronics Preventive Maintenance System is an internal system that is designed to meet all these criteria and more. The EPMS system is comprised of a central computer which monitors all major electronic components in an autonomous vehicle through the use of standard off-the-shelf sensors. The central computer compiles the sensor data, and is able to sort and analyze the readings. The filtered data is run through several mathematical models, each of which diagnoses issues in different parts of the vehicle. The data for each component in the vehicle is compared to pre-set operating conditions. These operating conditions are set in order to encompass all normal ranges of output. If the sensor data is outside the margins, the warning and deviation are recorded and a severity level is calculated. In addition to the individual focus, there's also a vehicle-wide model, which predicts how necessary maintenance is for the vehicle. All of these results are analyzed by a simple heuristic algorithm and a decision is made for the vehicle's health status, which is sent out to the Fleet Management System. This system allows for accurate, effortless monitoring of all parts of an autonomous vehicle as well as predictive modeling that allows the system to determine maintenance needs. With this system, human inspectors are no longer necessary for a fleet of autonomous vehicles. Instead, the Fleet Management System is able to oversee inspections, and the system operator is able to set parameters to decide when to send cars for maintenance. All the models used for the sensor and component analysis are tailored specifically to the vehicle. The models and operating margins are created using empirical data collected during normal testing operations. The system is modular and can be used in a variety of different vehicle platforms, including underwater autonomous vehicles and aerial vehicles.
ContributorsMian, Sami T. (Author) / Collofello, James (Thesis director) / Chen, Yinong (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Many psychology-rooted studies into the games industry seek to identify emotions players experience during gameplay. However, there is a need to extend this kind of research beyond the realm of emotion into more long-term concepts, like satisfaction. This experiment tested whether a specific game mechanic was enjoyable. Other literature has established a way to describe and quantify enjoyability. Using a survey based on that work, this study evaluated the addition of a 'gel gun' to a platforming game. The fun was found to significantly increase players' affective experiences, concentration, and sense of control, all being components of an enjoyable experience. It also exposed some conflicts within the survey that merit investigation. It was concluded that the 'gel gun' feature increased gameplay enjoyability without significantly diminishing any other enjoyable factors. Future work may explore the connections between this feature and specific elements of enjoyment.
ContributorsMints, John (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor)
Created2014-12
Description
Our goals in our project are to enable management of distributed systems from one central location, record system logs and audit system based on these logs, and to demonstrate feasibility of platform-independent management of distributed systems based on CIM schema. In order to achieve these goals, we will have to overcome research challenges such as identifying meaningful CIM classes and attributes that could help to achieve this goal, how to gather managed objects of these CIM classes to collect such attributes on a given platform, and to research whether a platform's implementation of CIM is complete or incomplete so as to decide which platform would be the best to implement our solution. Even if a platform's implementation of CIM is incomplete, would we be able to create our own solution to a missing attribute and perhaps provide our own extension of the implementation? One major practical accomplishment will include developing a tool to allow distributed systems management regardless of a target system's platform. However, our research accomplishments will include having found the CIM classes that would be advantageous for system management and determining which platform would be best to work with managed objects of these classes.
ContributorsTrang, Patrick D (Author) / Ahn, Gail-Joon (Thesis director) / Chen, Yinong (Committee member) / Wilson, Adrian (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
ContributorsMallya, Aatmik (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Recent advances in quantum computing have broadened the available techniques towards addressing existing computing problems. One area of interest is that of the emerging field of machine learning. The intersection of these fields, quantum machine learning, has the ability to perform high impact work such as that in the health industry. Use cases seen in previous research include that of the detection of illnesses in medical imaging through image classification. In this work, we explore the utilization of a hybrid quantum-classical approach for the classification of brain Magnetic Resonance Imaging (MRI) images for brain tumor detection utilizing public Kaggle datasets. More specifically, we aim to assess the performance and utility of a hybrid model, comprised of a classical pretrained portion and a quantum variational circuit. We will compare these results to purely classical approaches, one utilizing transfer learning and one without, for the stated datasets. While more research should be done for proving generalized quantum advantage, our work shows potential quantum advantages in validation accuracy and sensitivity for the specified task, particularly when training with limited data availability in a minimally skewed dataset under specific conditions. Utilizing the IBM’s Qiskit Runtime Estimator with built in error mitigation, our experiments on a physical quantum system confirmed some results generated through simulations.
ContributorsDiaz, Maryannette (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
ContributorsJoiner, Jae (Author) / Kim, Sujin (Thesis director) / Lawson, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Art (Contributor)
Created2023-05
ContributorsJoiner, Jae (Author) / Kim, Sujin (Thesis director) / Lawson, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Art (Contributor)
Created2023-05
ContributorsJoiner, Jae (Author) / Kim, Sujin (Thesis director) / Lawson, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Art (Contributor)
Created2023-05
ContributorsJoiner, Jae (Author) / Kim, Sujin (Thesis director) / Lawson, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Art (Contributor)
Created2023-05
ContributorsJoiner, Jae (Author) / Kim, Sujin (Thesis director) / Lawson, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Art (Contributor)
Created2023-05