Barrett, The Honors College Thesis/Creative Project Collection

This thesis proposes hardware and software security enhancements to the robotic explorer of a capstone team, in collaboration with the NASA Psyche Mission Student Collaborations program. The NASA Psyche Mission, launching in 2022 and reaching the metallic asteroid of the same name in 2026, will explore from orbit what is hypothesized to be remnant core material of an early planet, potentially providing key insights to planet formation. Following this initial mission, it is possible there would be scientists and engineers interested in proposing a mission to land an explorer on the surface of Psyche to further document various properties of the asteroid. As a proposal for a second mission, an interdisciplinary engineering and science capstone team at Arizona State University designed and constructed a robotic explorer for the hypothesized surfaces of Psyche, capable of semi-autonomously navigating simulated surfaces to collect scientific data from onboard sensors. A critical component of this explorer is the command and data handling subsystem, and as such, the security of this system, though outside the scope of the capstone project, remains a crucial consideration. This thesis proposes the pairing of Trusted Platform Module (TPM) technology for increased hardware security and the implementation of SELinux (Security Enhanced Linux) for increased software security for Earth-based testing as well as space-ready missions.

This thesis explores the potential for software to act as an educational experience for engineers who are learning system dynamics and controls. The specific focus is a spring-mass-damper system. First, a brief introduction of the spring-mass-damper system is given, followed by a review of the background and prior work concerning this topic. Then, the methodology and main approaches of the system are explained, as well as a more technical overview of the program. Lastly, a conclusion and discussion of potential future work is covered. The project was found to be useful by several engineers who tested it. While there is still plenty of functionality to add, it is a promising first attempt at teaching engineers through software development.

Automated vehicles are becoming more prevalent in the modern world. Using platoons of automated vehicles can have numerous benefits including increasing the safety of drivers as well as streamlining roadway operations. How individual automated vehicles within a platoon react to each other is essential to creating an efficient method of travel. This paper looks at two individual vehicles forming a platoon and tracks the time headway between the two. Several speed profiles are explored for the following vehicle including a triangular and trapezoidal speed profile. It is discovered that a safety violation occurs during platoon formation where the desired time headway between the vehicles is violated. The aim of this research is to explore if this violation can be eliminated or reduced through utilization of different speed profiles.

Food insecure populations suffer from the ability to access affordable and nutritious foods as a result of financial and transportation needs. Often these populations are concentrated in areas referenced as food deserts. A food desert is an area that does not have a supermarket or large grocery store within a mile and often is saturated with small non-traditional food stores and fast- food establishments. In this study, 21 food deserts along Grand Avenue in Downtown Phoenix were analyzed to better understand their access to food, population statistics and barriers to being food secure. The research question analyzed is the impact food insecurity has on communities in Phoenix, Arizona. The findings are presented in the form of a research paper, as well as 15 black and white film photographs accompanied by descriptions. There is primary qualitative data presented through photographs and observations, as well as secondary quantitative data analyzed from Census data. The food deserts studied consist of communities that are low-income and majority minority with little to no access to nutritious food in their area. The economics of food insecurity and grocery stores, racial discrimination, access to transportation, impacts on health and education and the sustainability of food deserts are all aspects of food insecurity discussed in the research. Possible solutions such as community gardens and subsidized grocery stores are also presented. The study revealed that food insecurity has several negative impacts on the affected populations and communities and disproportionately impacts low-income and minority communities.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Self-efficacy in engineering, engineering identity, and coping in engineering have been shown in previous studies to be highly important in the advancement of one’s development in the field of engineering. Through the creation and deployment of a 17 question survey, undergraduate and first year masters students were asked to provide information on their engagement at their university, their demographic information, and to rank their level of agreement with 22 statements relating to the aforementioned ideas. Using the results from the collected data, exploratory factor analysis was completed to identify the factors that existed and any correlations. No statistically significant correlations between the identified three factors and demographic or engagement information were found. There needs to be a significant increase in the data sample size for statistically significant results to be found. Additionally, there is future work needed in the creation of an engagement measure that successfully reflects the level and impact of participation in engineering activities beyond traditional coursework.

The colossal global counterfeit market and advances in cryptography including quantum computing supremacy have led the drive for a class of anti-counterfeit tags that are physically unclonable. Dendrites, previously considered an undesirable side effect of battery operation, have promise as an extremely versatile version of such tags, with their fundamental nature ensuring that no two dendrites are alike and that they can be read at multiple magnification scales. In this work, we first pursue a simulation for electrochemical dendrites that elucidates fundamental information about their growth mechanism. We then translate these results into physical dendrites and demonstrate methods of producing a hash from these dendrites that is damage-tolerant for real-world verification. Finally, we explore theoretical curiosities that arise from the fractal nature of dendrites. We find that uniquely ramified dendrites, which rely on lower ion mobility and conductive deposition, are particularly amenable to wavelet hashing, and demonstrate that these dendrites have strong commercial potential for securing supply chains at the highest level while maintaining a low price point.