Theses and Dissertations

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.

Human-environment interactions in aeolian (windblown) systems has focused research on<br/>human’s role in causing and aiding recovery from natural and anthropogenic disturbance. There<br/>is room for improvement in understanding the best methods and considerations for manual<br/>coastal foredune restoration. Furthermore, the extent to which humans play a role in changing the<br/>shape and surface textures of quartz sand grains is poorly understood. The goal of this thesis is<br/>two-fold: 1) quantify the geomorphic effectiveness of a multi-year manually rebuilt foredune and<br/>2) compare the shapes and microtextures on disturbed and undisturbed quartz sand grains. For<br/>the rebuilt foredune, uncrewed aerial systems (UAS) were used to survey the site, collecting<br/>photos to create digital surface models (DSMs). These DSMs were compared at discrete<br/>moments in time to create a sediment budget. Water levels and cross-shore modeling is also<br/>considered to predict the decadal evolution of the site. In the two years since rebuilding, the<br/>foredune has been stable, but not geomorphically resilient. Modeling shows landward foredune<br/>retreat and beach widening. For the quartz grains, t-testing of shape characteristics showed that<br/>there may be differences in the mean circularity between grains from off-highway vehicle and<br/>non-riding areas. Quartz grains from a variety of coastal and inland dunes were imaged using a<br/>scanning electron microscopy to search for evidence of anthropogenically-induced<br/>microtextures. On grains from Oceano Dunes in California, encouraging textures like parallel<br/>striations, grain fracturing, and linear conchoidal fractures provide exploratory evidence of<br/>anthropogenic microtextures. More focused research is recommended to confirm this exploratory<br/>work.

"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 includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based on this data, and c) an industry-standard pitch deck for a TV show created with data insights. In a larger sense, the aim of this study is to take the first steps in remedying information asymmetry between streaming services and content creators. If streaming services were more transparent with their data and communicated to their creators what has been proven to work in the past, showrunners and staff writers could have a new tool to increase the competitiveness of their series and aid in show renewal each year.

Fire is a naturally-occurring disruptive ecological force that is an essential part of certain ecosystems, and has historically been a tool used by indigenous fire stewards to maintain the health of the land. In the past century, fire has been severely suppressed throughout many areas of the Western United States as Western colonization and the suppression of native traditional ecological knowledge took place, causing a severe decline in ecosystem health and the accumulation of flammable vegetation, which has more recently contributed towards a frequency of catastrophic, high-intensity wildfires. Current fire management challenges include balancing social and ecological perspectives. In Colorado and other areas of the country, community wildfire protection plans (CWPP) are evolving as a means to involve a variety of community stakeholders in fire management decisions. Using Colorado CWPP boundaries as a social management unit and endangered species ranges as an ecological management unit, I analyzed the spatial overlap of these different factors. Since each CWPP has its own fire management policies, I drew implications from the results for which important factors different CWPPs should consider.

Surveys have shown that several hundred billion weather forecasts are obtained by the United States public each year, and that weather news is one of the most consumed topics in the media. This indicates that the forecast provides information that is significant to the public, and that the public utilizes details associated with it to inform aspects of their life. Phoenix, Arizona is a dry, desert region that experiences a monsoon season and extreme heat. How then, does the weather forecast influence the way Phoenix residents make decisions? This paper aims to draw connections between the weather forecast, decision making, and people who live in a desert environment. To do this, a ten-minute survey was deployed through Amazon Mechanical Turk (MTurk) in which 379 respondents were targeted. The survey asks 45 multiple choice and ranking questions categorized into four sections: obtainment of the forecast, forecast variables of interest, informed decision making based on unique weather variables, and demographics. This research illuminates how residents in the Phoenix metropolitan area use the local weather forecast for decision-making on daily activities, and the main meteorological factors that drive those decisions.

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.

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.