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.

Radiation hardening of electronic devices is generally necessary when designing for the space environment. Non-volatile memory technologies are of particular concern when designing for the mitigation of radiation effects. Among other radiation effects, single-event upsets can create bit flips in non-volatile memories, leading to data corruption. In this paper, a Verilog implementation of a Reed-Solomon error-correcting code is considered for its ability to mitigate the effects of single-event upsets on non-volatile memories. This implementation is compared with the simpler procedure of using triple modular redundancy.

Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.

Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.

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.

Soccer is bar none, the most popular sport in the entire world. It is played, followed, and loved by virtually every single country on Earth. Despite this massive support for the sport which houses some of the world’s biggest names in the world, its shortcomings when dealing with issues of racial injustice and incidents of racist behavior have become more pronounced in recent years. Although this open discussion regarding racism within the sport has recently begun to sprout, its roots can be tied back to decades ago while continuing to the present day, with players, referees, coaches, fans, commentators, and more all involved on both sides of the issue. <br/> We found this topic to be most prevalent in today’s society after witnessing multiple shameful racist incidents that have occurred to some of the world’s biggest players throughout European football in 2019, as well as the recent ongoing fight for racial reform and increased awareness regarding racial injustice in the United States. By doing comprehensive research and<br/>analysis on such incidents that have occurred throughout the years we hope to raise more<br/>awareness regarding this subject that has plagued the beautiful game. In addition, we hope to<br/>offer ways to remedy the problem one step at a time, all while answering the tough, but necessary questions regarding what specifically should be done in the sport, that others have been afraid to talk about for far too long. Specifically, we wanted to mainly highlight the experience of black players, with a further discussion on other minority groups, in English and Italian football as these two leagues have been a part of the largest debate between how club traditions, player-fan interactions, league policies, and staff management have all affected the way we view the game as the endemic of racism within the sport is exacerbated.

Soccer is bar none, the most popular sport in the entire world. It is played, followed, and loved by virtually every single country on Earth. Despite this massive support for the sport which houses some of the world’s biggest names in the world, its shortcomings when dealing with issues of racial injustice and incidents of racist behavior have become more pronounced in recent years. Although this open discussion regarding racism within the sport has recently begun to sprout, its roots can be tied back to decades ago while continuing to the present day, with players, referees, coaches, fans, commentators, and more all involved on both sides of the issue. <br/> We found this topic to be most prevalent in today’s society after witnessing multiple shameful racist incidents that have occurred to some of the world’s biggest players throughout European football in 2019, as well as the recent ongoing fight for racial reform and increased awareness regarding racial injustice in the United States. By doing comprehensive research and analysis on such incidents that have occurred throughout the years we hope to raise more awareness regarding this subject that has plagued the beautiful game. In addition, we hope to offer ways to remedy the problem one step at a time, all while answering the tough, but necessary questions regarding what specifically should be done in the sport, that others have been afraid to talk about for far too long. Specifically, we wanted to mainly highlight the experience of black players, with a further discussion on other minority groups, in English and Italian football as these two leagues have been a part of the largest debate between how club traditions, player-fan interactions, league policies, and staff management have all affected the way we view the game as the endemic of racism within the sport is exacerbated.

The 1980 Winter Olympic semi-final hockey game between the United States and Soviet Union changed the history of sporting events. The outcome took everyone by surprise, and it reinvigorated patriotism in the United States. It was and forever will be known as a miracle. The hockey game has only grown in popularity due to movies, books, and articles reminiscing on one of the greatest sports upsets in history. The United States-Russia rivalry has been a strong one since the very beginning of sports. The United States has always tried to keep up with the Russian talent, but in 1980 they took the Soviets by surprise, at height of the cold war. Not only were they rivals in sports, but enemies at a profound political level. Over the years, the tensions between the two countries have diminished, but recently they have increased again. Although these tensions continue to rise, these countries are able to battle for the top seed through sports. This has been shown throughout the years with one of the main competitions being the 1980 Winter Olympics semi-final hockey game.

In collaboration with Moog Broad Reach and Arizona State University, a<br/>team of five undergraduate students designed a hardware design solution for<br/>protecting flash memory data in a spaced-based radioactive environment. Team<br/>Aegis have been working on the research, design, and implementation of a<br/>Verilog- and Python-based error correction code using a Reed-Solomon method<br/>to identify bit changes of error code. For an additional senior design project, a<br/>Python code was implemented that runs statistical analysis to identify whether<br/>the error correction code is more effective than a triple-redundancy check as well<br/>as determining if the presence of errors can be modeled by a regression model.