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.

Waste pickers are the victims of harsh economic and social factors that have hurt many<br/>developing countries and billions of people around the world. Due to the rise of industrialization<br/>since the 19th century, waste and disposable resources have been discarded around the world to<br/>provide more resources, products, and services to wealthy countries. This has put developing<br/>countries in a precarious position where people have had very few economic opportunities<br/>besides taking on the role of waste pickers, who not only face physical health consequences due<br/>to the work they do but also face exclusion from society due to the negative views of waste<br/>pickers. Many people view waste pickers as scavengers and people who survive off of doing<br/>dirty work, which creates tensions between waste pickers and others in society. This even leads<br/>to many countries outlawing waste picking and has led to the brutal treatment of waste pickers<br/>throughout the world and has even led to thousands of waste pickers being killed by anti-waste<br/>picker groups and law enforcement organizations in many countries.<br/>Waste pickers are often at the bottom of supply chains as they take resources that have<br/>been used and discarded, and provide them to recyclers, waste management organizations, and<br/>others who are able to turn these resources into usable materials again. Waste pickers do not have<br/>many opportunities to rise above the situation they are in as waste picking has become the only<br/>option for many people who need to provide for themselves and their families. They are not<br/>compensated very well for the work they do, which also contributes to the situation where waste<br/>pickers are forced into a position of severe health risks, backlash from society and governments,<br/>not being able to seek better opportunities due to a lack of earning potential, and not being<br/>connected with end-users. Now is the time to create new business models that solve these large<br/>problems in our global society and create a sustainable way to ensure that waste pickers are<br/>treated properly around the world.

Waste pickers are the victims of harsh economic and social factors that have hurt many developing countries and billions of people around the world. Due to the rise of industrialization since the 19th century, waste and disposable resources have been discarded around the world to provide more resources, products, and services to wealthy countries. This has put developing countries in a precarious position where people have had very few economic opportunities besides taking on the role of waste pickers, who not only face physical health consequences due to the work they do but also face exclusion from society due to the negative views of waste pickers. Many people view waste pickers as scavengers and people who survive off of doing dirty work, which creates tensions between waste pickers and others in society. This even leads to many countries outlawing waste picking and has led to the brutal treatment of waste pickers throughout the world and has even led to thousands of waste pickers being killed by anti-waste picker groups and law enforcement organizations in many countries. <br/> Waste pickers are often at the bottom of supply-chains as they take resources that have been used and discarded, and provide them to recyclers, waste management organizations, and others who are able to turn these resources into usable materials again. Waste pickers do not have many opportunities to rise above the situation they are in as waste picking has become the only option for many people who need to provide for themselves and their families. They are not compensated very well for the work they do, which also contributes to the situation where waste pickers are forced into a position of severe health risks, backlash from society and governments, not being able to seek better opportunities due to a lack of earning potential, and not being connected with end-users. Now is the time to create new business models that solve these large problems in our global society and create a sustainable way to ensure that waste pickers are treated properly around the world.

Though about 75 percent of American waste is recyclable, only 30 percent of it is actually recycled and less than ten percent of plastics disposed of in the United States in 2015 were recycled. A statistic like this demonstrates the immense need to increase recycling rates in order to move towards cultivating a circular economy and benefiting the environment. With Arizona State University’s (ASU) extensive population of on-campus students and faculty, our team was determined to create a solution that would increase recycling rates. After conducting initial market research, our team incentives or education. We conducted market research through student surveys to determine the level of knowledge of our target audience and barriers to entry for local recycling and composting resources. Further, we gained insight into the medium of recycling and sustainability programs they would be interested in participating in. Overall, the results of our surveys demonstrated that a majority of students were interested in participating in these programs, if they were not already involved, and most students on-campus already had access to these resources. Despite having access to these sustainable practices, we identified a knowledge gap between students and their information on how to properly execute sustainable practices such as composting and recycling. In order to address this audience, our team created Circulearning, an educational program that aims to bridge the gap of knowledge and address immediate concerns regarding circular economy topics. By engaging audiences through our quick, accessible educational modules and teaching them about circular practices, we aim to inspire everyone to implement these practices into their own lives. Though our team began the initiative with a focus on implementing these practices solely to ASU campus, we decided to expand our target audience to implement educational programs at all levels after discovering the interest and need for this resource in our community. Our team is extremely excited that our Circulearning educational modules have been shared with a broad audience including students at Mesa Skyline High School, ASU students, and additional connections outside of ASU. With Circulearning, we will educate and inspire people of all ages to live more sustainably and better the environment in which we live.

In this paper, our Founders Lab team members — Jacob Benevento, Sydney Evans, and Alec Whiteley — recount the year-long entrepreneurial journey that led to the creation and launch of our venture, Certified Circular. Certified Circular is a program that certifies on-campus events for implementing circular practices into their activities as well as off-campus businesses. The venture was formed in response to our group’s propelling question and industry selection, which called on us to create and market a venture within the ethical circular economy.

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.

In this paper, our Founders Lab team members — Jacob Benevento, Sydney Evans, and Alec Whiteley — recount the year-long entrepreneurial journey that led to the creation and launch of our venture, Certified Circular. Certified Circular is a program that certifies on-campus events for implementing circular practices into their activities as well as off-campus businesses. The venture was formed in response to our group’s propelling question and industry selection, which called on us to create and market a venture within the ethical circular economy.