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.

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show that “Moderate videogame play has been found to contribute to emotional stability” (Jones, 2014). This demonstrates that the stress of college can be mitigated by introducing the ability to interact with video games. This same concept has been applied in the workplace, where studies have shown that “Gaming principles such as challenges, competition, rewards and personalization keep employees engaged and learning” (Clark, 2020). This means that if we manage to gamify the college experience, students will be more engaged which will increase and stabilize the retention rate of colleges which utilize this type of experience. Gaming allows students to connect with their peers in a casual environment while also allowing them to find resources around campus and find new places to eat and relax. We plan to gamify the college experience by introducing augmented reality in the form of an app. Augmented reality is “. . . a technology that combines virtual information with the real world” (Chen, 2019). College students will be able to utilize the resources and amenities available to them on campus while completing quests that help them within the application. This demonstrates the ability for video games to engage students using artificial tasks but real actions and experiences which help them feel more connected to campus. Our Founders Lab team has developed and tested an AR application that can be used to connect students with their campus and the resources available to them.

The Constitution is a document that was made over 200 years ago by a population that could have never imagined the type of technology or social advances made in the 21st century. This creates a natural rift between governing ideals between then and now, that needs to be addressed. Rather than holding the values of the nation to a time when people were not considered citizens because of the color of their skin, there need to be updates made to the Constitution itself. The need for change and the mechanisms were both established by the Framers while creating and advancing the Constitution. The ideal process to go about these changes is split between the formal Article V amendment process and judicial activism. The amendment process has infinite scope for changes that can be done, but due to the challenge involved in trying to pass any form of the amendment through both State and Federal Congresses, that process should be reserved for only fundamental or structural changes. Judicial activism, by way of Supreme Court decisions, is a method best applied to the protection of people’s rights.

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.

In this study, I sought to determine which NFL Combine metrics are predictive of future NFL success among the quarterback, running back, and wide receiver positions, with the hope of providing meaningful information that can be utilized by NFL executives when making decisions about draft selections. I gathered samples spanning across the years 2010-2015 of all three of the aforementioned position groups. Among these samples, I used certain criteria which split them up within their position groups. The two groups of players were identified as: those who had successful careers and those who had unsuccessful careers. Given this information, I performed t-tests and ANOVA between successful and unsuccessful groups with the goal of identifying which combine metrics are predictive of future NFL success, and which are not. For quarterbacks, the 40-yard dash, broad jump, three-cone, and 10-yard shuttle all appear to be predictive of success. Notably, quarterback height does not appear to be predictive, despite the popular belief that a quarterback should be tall if they are to succeed. For running backs, player weight, 40-yard dash, and three-cone all appear to be predictive of success, with the broad jump and 10-yard shuttle seemingly predicting success as well, albeit to a lesser degree of strength. For wide receivers, all metrics do not appear to be predictive of success, with the exception of the 40-yard dash, which only appears to be slightly predictive. While there are likely many other factors that contribute to a player’s success than tests administered at the NFL combine, NFL general managers can look to these results when making draft selections.

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.

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 the early years of the National Football League, scouting and roster development resembled the wild west. Drafts were held in hotel ballrooms the day after the last game of regular season college football was played. There was no combine, limited scouting, and no salary cap. Over time, these aspects have changed dramatically, in part due to key figures from Pete Rozelle to Gil Brandt to Bill Belichick. The development and learning from this time period have laid the foundational infrastructure that modern roster construction is based upon. In this modern day, managing a team and putting together a roster involves numerous people, intense scouting, layers of technology, and, critically, the management of the salary cap. Since it was first put into place in 1994, managing the cap has become an essential element of building and sustaining a successful team. The New England Patriots’ mastery of the cap is a large part of what enabled their dynastic run over the past twenty years. While their model has undoubtedly proven to be successful, an opposing model has become increasingly popular and yielded results of its own. Both models center around different distributions of the salary cap, starting with the portion paid to the starting quarterback. The Patriots dynasty was, in part, made possible due to their use of both models over the course of their dominance. Drafting, organizational culture, and coaching are all among the numerous critical factors in determining a team’s success and it becomes difficult to pinpoint the true source of success for any given team. Ultimately, however, effective management of the cap proves to be a force multiplier; it does not guarantee that a team will be successful, but it helps teams that handle the other variables well sustain their success.

The field of behavioral economics explores the ways in which individuals make choices under uncertainty, in part, by examining the role that risk attitudes play in a person’s efforts to maximize their own utility. This thesis aims to contribute to the body of economic literature regarding risk attitudes by first evaluating the traditional economic method for discerning risk coefficients by examining whether students provide reasonable answers to lottery questions. Second, the answers of reasonable respondents are subject to our economic model using the CRRA utility function in which Python code is used to make predictions of the risk coefficients of respondents via a two-step regression procedure. Lastly, the degree to which the economic model provides a good fit for the lottery answers given by reasonable respondents is discerned. The most notable findings of the study are as follows. College students had extreme difficulty in understanding lottery questions of this sort, with Medical and Life Science majors struggling significantly more than both Business and Engineering majors. Additionally, gender was correlated with estimated risk coefficients, with females being more risk-loving relative to males. Lastly, in regards to the model’s goodness of fit when evaluating potential losses, the expected utility model involving choice under uncertainty was consistent with the behavior of progressives and moderates but inconsistent with the behavior of conservatives.