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.

Forensic entomology is an important field of forensic science that utilizes insect evidence in criminal investigations. Blow flies (Diptera: Calliphoridae) are among the first colonizers of remains and are therefore frequently used in determining the minimum postmortem interval (mPMI). Blow fly development, however, is influenced by a variety of factors including temperature and feeding substrate type. Unfortunately, dietary fat content remains an understudied factor on the development process, which is problematic given the relatively high rates of obesity in the United States. To study the effects of fat content on blow fly development we investigated the survivorship, adult weight and development of Lucilia sericata (Meigen; Diptera: Calliphoridae) and Phormia regina (Meigen; Diptera: Calliphoridae) on ground beef with a 10%, 20%, or 27% fat content. As fat content increased, survivorship decreased across both species with P. regina being significantly impacted. While P. regina adults were generally larger than L. sericata across all fat levels, only L. sericata demonstrated a significant (P < 0.05) difference in weight by sex. Average total development times for P. regina are comparable to averages published in other literature. Average total development times for L. sericata, however, were nearly 50 hours higher. These findings provide insight on the effect of fat content on blow fly development, a factor that should be considered when estimating a mPMI. By understanding how fat levels affect the survivorship and development of the species studied here, we can begin improving the practice of insect evidence analysis in casework.

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.

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.

Medicolegal forensic entomology is the study of insects to aid with legal investigations (Gemmellaro, 2017). Insect evidence can be used to provide information such as the post-mortem interval (PMI). Blow flies are especially useful as these insects are primary colonizers, quickly arriving at a corpse (Malainey & Anderson, 2020). The age of blow flies found at a scene is used to calculate the PMI. Blow fly age can be estimated using weather data as these insects are poikilothermic (Okpara, 2018). Morphological analysis also can be used to estimate age; however, it is more difficult with pupal samples as the pupae exterior does not change significantly as development progresses (Bala & Sharma, 2016). Gene regulation analysis can estimate the age of samples. MicroRNAs are short noncoding RNA that regulate gene expression (Cannell et al., 2008). Here, we aim to catalog miRNAs expressed during the development of three forensically relevant blow fly species preserved in several storage conditions. Results demonstrated that various miRNA sequences were differentially expressed across pupation. Expression of miR92b increased during mid pupation, aga-miR-92b expression increased during early pupation, and bantam, miR957, and dana-bantam-RA expression increased during late pupation. These results suggest that microRNA can be used to estimate the age of pupal samples as miRNA expression changes throughout pupation. Future work could develop a statistical model to accurately determine age using miRNA expression patterns.

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.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.