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.

"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

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.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

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.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

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.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

Americans today face an age of information overload. With the evolution of Media 3.0, the internet, and the rise of Media 3.5—i.e., social media—relatively new communication technologies present pressing challenges for the First Amendment in American society. Twentieth century law defined freedom of expression, but in an information-limited world. By contrast, the twenty-first century is seeing the emergence of a world that is overloaded with information, largely shaped by an “unintentional press”—social media. Americans today rely on just a small concentration of private technology powerhouses exercising both economic and social influence over American society. This raises questions about censorship, access, and misinformation. While the First Amendment protects speech from government censorship only, First Amendment ideology is largely ingrained across American culture, including on social media. Technological advances arguably have made entry into the marketplace of ideas—a fundamental First Amendment doctrine—more accessible, but also more problematic for the average American, increasing his/her potential exposure to misinformation. <br/><br/>This thesis uses political and judicial frameworks to evaluate modern misinformation trends, social media platforms and current misinformation efforts, against the background of two misinformation accelerants in 2020, the COVID-19 pandemic and U.S. presidential election. Throughout history, times of hardship and intense fear have contributed to the shaping of First Amendment jurisprudence. Thus, this thesis looks at how fear can intensify the spread of misinformation and influence free speech values. Extensive research was conducted to provide the historical context behind relevant modern literature. This thesis then concludes with three solutions to misinformation that are supported by critical American free speech theory.