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.

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.

Globally, the incidental capture of non-target species in fisheries (bycatch) has been linked to declines of ecologically, economically, and culturally important marine species. Gillnet fisheries have especially high bycatch due to their non-selective nature, necessitating the development of new bycatch reduction technologies (BRTs). Net illumination is an emerging BRT that has shown promise in reducing bycatch of marine megafauna, including sea turtles, cetaceans, and seabirds. However, little research has been conducted to understand the effects of net illumination on fish assemblages, including bony fish and elasmobranchs (i.e. sharks, rays, and skates). Here, I assessed a 7-year dataset of paired net illumination trials using four different types of light (green LEDs, green chemical glowsticks, ultraviolet (UV) lights, and orange lights) to examine the effects of net illumination on fish catch and bycatch in a gillnet fishery at Baja California Sur, Mexico. Analysis revealed no significant effect on bony fish target catch or bycatch for any light type. There was a significant decrease in elasmobranch bycatch using UV and orange lights, with orange lights showing the most promise for decreasing elasmobranch bycatch, resulting in a 50% reduction in bycatch rates. Analysis of the effects of net illumination on elasmobranch target catch was limited due to insufficient data. These results indicate that the illumination of gillnets may offer a practical solution for reducing fish bycatch while maintaining target catch. More research should be conducted to understand the effects of net illumination in different fisheries, how net illumination affects fisher profit and efficiency, and how net illumination affects fish behavior. Further optimization of net illumination is also necessary before the technology can be recommended on a broader scale.

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.

As climate change continues to escalate natural hazards around the globe, certain communities feel the impacts of these disasters more so than others. After Hurricane Maria devastated communities in 2017, Puerto Rico struggled to respond to the needs of its citizens, particularly those in rural areas. Many of the regions affected did not have resilient community structures in place to be able to withstand the systemic ripple effects of the hurricane. However, various community endeavors have developed post-Hurricane Maria to foster community collaboration and resiliency, including the development of agricultural tourism, otherwise known as agritourism. <br/>Although agritourism has begun to develop in rural regions of Puerto Rico, including the municipalities of Utuado, Ciales, Florida, and Jayuya, a systems-understanding is lacking of the current agritourism situation in the region and its related capacities, limitations, and opportunities of agritourism. To address this gap, a spatially explicit understanding and map of the underlying tourism infrastructure is needed to support the development of sustainable agritourism in Utuado, Jayuya, Ciales, and Florida municipalities in Puerto Rico. <br/>This report spatially represents the current state of tourism opportunities in the region as a result of asking “What are the spatial networks of gastronomy, accommodations, farms, and attractions that support the development of agritourism in Utuado, Jayuya, Ciales and Florida municipalities in Puerto Rico?” Three steps lead to the spatial representation starting with developing a comprehensive inventory. Second, we visualize the spatial map through Google Maps. Lastly, we explore the larger context of the report through an ArcGIS Storymap. The inventory will help with better understanding the number and variety of tourism resources available. The spatial visualization will help with understanding the distribution of resources and explore potential connections between resources and what relationships could be fostered in the future. Lastly, the ArcGIS Storymap will serve as a framework for outlining the future development of the SARE project. Overall, this report outlines the spatial maps of tourism resources and provides a tool to be used by community partners, tourists, and project partners.

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.

Bioluminescent algae has long fascinated humans as a beautiful natural phenomenon. This creative project uses bioluminescent algae to push the limit of biomimicry, using the algae not as a model but as a technology. Through experimentation with algae samples and industrial design, two potential applications of bioluminescent algae as a sustainable lighting technology were generated. One design focuses on indoor, private lighting, while the other focuses on outdoor, public lighting. Both outcomes attempt to solve problems generated by nighttime lighting including light pollution, wasted electricity usage, and negative impacts on human and environmental health while retaining the benefits of safety and convenience.

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.

A large section of United States citizens live far away from supermarkets and do not have an easy way to get to one. This portion of the population lives in an area called a food desert. Food deserts are geographic areas in which access to affordable, healthy food, such as fresh produce, is limited or completely nonexistent due to the absence of convenient grocery stores. Individuals living in food deserts are left to rely on convenience store snacks and fast food for their meals because they do not have access to a grocery store with fresh produce in their area. Unhealthy foods also lead to health issues, as people living in food deserts are typically at a higher risk of diet-related conditions, such as obesity, diabetes, and cardiovascular disease. Harvest, a sustainable farming network, is a smartphone application that teaches and guides people living in small spaces through the process of growing fresh, nutritious produce in their own homes. The app will guide users through the entire process of gardening, from seed to harvest. Harvest would give individuals living in food deserts an opportunity to access fresh produce that they currently can’t access. An overwhelming response based on our user discussion and market analysis revealed that our platform was in demand. Development of a target market, brand guide, and full-lifecycle were beneficial during the second semester as Harvest moved forward. Through the development of a website, social media platform, and smartphone application, Harvest grew traction for our platform. Our social media accounts saw a 1700% growth rate, and this wider audience was able to provide helpful feedback.