Theses and Dissertations at Montana State University (MSU)
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Item Novel approach to fault tolerance in space computers leveraging the RISC-V architecture(Montana State University - Bozeman, College of Engineering, 2023) Major, Chris Michel; Chairperson, Graduate Committee: Brad WhitakerAs the aerospace industry continues to accelerate in growth and mission frequency, the demand for high-performance computers that can withstand radiation environments has become a critical need within the field. Traditional space computing systems rely on specialized and complex means of radiation resistance, but more modern systems seek to implement redundant components to mitigate radiation effects. This dissertation presents a novel approach to radiation-tolerant space computing, based on Montana State University's RadPC program, by developing a resilient architecture leveraging the open-source RISC- V processor. The architecture discussed is designed to withstand radiation environments and engage repairs for damaged sections using commercial, off-the-shelf components - without requiring radiation-hardened fabrication processes or specialized manufacturing. This dissertation discusses the design and performance of the components required to ensure radiation resilience in the system, reconfigure compromised processors in the event of damage, and provides a characterization of the system's overall performance in various space environments.Item Using instruction code obfuscation to defeat malware attacks(Montana State University - Bozeman, College of Engineering, 2023) Running Crane, Tristan Tanner; Chairperson, Graduate Committee: Brock LaMeresThis thesis investigates the use of obfuscated instruction codes within a redundant, RISC-V computer architecture to detect and defeat malware injected cyberattacks. The system abstracts the obfuscation from the user so that the system looks like a single processor edge computer. The system was tested using real-time sensor data coming from a camera while image processing algorithms were performed. The results of this thesis contribute to the body of knowledge on how to keep edge computers used in critical applications operational during a cyber attack.Item Hardware and software development for implementation of fast and safe charging of commercial lihtium-ion batteries(Montana State University - Bozeman, College of Engineering, 2023) Hedding, Noah Robert; Chairperson, Graduate Committee: Hongwei GaoFrom single cells in handheld electronics to enormous packs in battery electric vehicles (BEV), batteries govern modern life. Lithium ion batteries (LIB) present the best available commercially available products for these applications; they have the highest energy densities and can output currents many times their capacity. But safely charging LIBs requires a slow and detailed process which is typically unacceptable for use in BEV and other rugged handheld devices; therefore, decreasing the required charging time would be greatly beneficial. Fast charging methods do present dangers and concerns. Unmonitored fast charging of LIBs allows for the potential of lithium plating where the lithium ions within the cell are converted to metallic lithium at the battery anode. Lithium plating can remove these ions from the charging and discharging process causing reductions in battery capacity. The metallic lithium structures formed also present the dangers of short circuit and thermal runaway. In this thesis, a charging protocol is developed using equivalent circuit models and experimentation with the goal of the elimination of lithium plating. First, equivalent models of a test cell were determined and validated. Then, this test cell was used to find the fast charging protocol both experimentally and through the use the equivalent circuit elements. Custom power electronics and software were then developed to implement the proposed charging protocol on commercial LIBs for 350 cycles. The results of this experiment show that the charging protocol did not create noticeable lithium plating while decreasing the charging time required by a typical constant current - constant voltage (CC/CV) from 50 minutes to 29 minutes. The proposed charging protocol decreased the charging time without stressing the LIB beyond its set limitation.Item Injection attack immunity using redundant heterogeneous processing cores(Montana State University - Bozeman, College of Engineering, 2023) Barney, Colter Ross; Chairperson, Graduate Committee: Brock LaMeresTechnology is an integral part of modern society. Devices such as smart lights, locks and appliances are becoming more commonplace. This class of devices are called embedded systems. Embedded systems can be targeted by malicious cyber attacks just as a normal computer can. Unfortunately, many techniques used to secure and protect normal computers do not work on embedded systems. New security techniques must be developed and designed to protect embedded systems. This paper investigates using physically diverse processing cores to defeat cyber attacks in real time. Diverse processing cores were implemented using reconfigurable hardware devices called FPGAs. The use of FPGAs allows diverse cores to be utilized, without losing the benefits gained from standardized processors. The cores implemented were based on a commercial processor made by Texas Instruments (TI). Modeling the diverse cores after a commercial processor enables the cores to utilize development tools created for TI's processor. A complete system was built using diverse processors to prove the feasibility and usability of secure embedded systems. The cores were used to control a realistic embedded system application. While operating, the cores were subjected to a cyber attack, and they were able to nullify the attack. An identical setup was created using the commercially available processor. Attacking the commercial processor compromised the application and reinforced the need for secure systems. The techniques investigated and utilized in this paper can be expanded to increase security in the many embedded systems that have become an essential part of modern lifestyles.Item Fault injection system for FPGA-based space computers(Montana State University - Bozeman, College of Engineering, 2023) Austin, Hezekiah Ajax; Chairperson, Graduate Committee: Brock LaMeresAbstract: Simulation of radiation effects in aerospace computers is a key testing and verification component to space operations. Contemporary computer architectures utilizing Field Programmable Gate Arrays (FPGA) requires particular focus in testing the configuration memory of the device for faults that cannot be recovered using traditional strategies. Faults in the configuration memory propagate to the hardware settings of the FPGA, changing the implemented logic circuit functionality. The effects of faults in the configuration memory are unpredictable, limiting the effectiveness of computer simulation and analysis. Therefore, designers of FPGA-based aerospace computers prefer to physically induce faults in the configuration memory to measure their impact. This allows the results of configuration memory fault injection used to classify faults occurring during space operation. The process is difficult to implement as the FPGA configuration memory is large, often undocumented, and the injection process is tedious when done manually. This paper presents the results of the deployment of two FPGA-based aerospace computers payloads to the International Space Station and the subsequently developed process for configuration memory fault injection. The injections are designed to simulate errors caused by radiation strikes to the computer hardware. These injections were performed on duplicate hardware to the RadPC payloads that operated on the ISS and was bombardment by real radiation. This provided the ability to see if the ground-based injection was correlated to real flight data. The developed process is able to inject single bit faults, which represents the majority of faults observed in configuration memory for space applications, and continuous injection, which stress tests the aerospace computer's recovery capability. Depending on the effects of the injected fault, the error is marked as either repairable, nonrepairable and propagating, or nonrepairable and nonpropagating. The result of this testing illustrates the key components in the implemented computer architecture which are vulnerable to faults in the configuration memory. Vulnerable components include the softcores, voter components, and the input logic. The process allows these key components to be isolated for further testing and the comparison of payload results to configuration memory testing on the ground.Item Spectral processing for algae monitoring and mapping (SPAMM): remote sensing methodologies for river ecology(Montana State University - Bozeman, College of Engineering, 2024) Logan, Riley Donovan; Chairperson, Graduate Committee: Joseph A. Shaw; This is a manuscript style paper that includes co-authored chapters.Inland water quality is a growing concern to public health, riparian ecosystems, and recreational uses of our waterways. Many modern water quality programs include measures of the presence and abundance of harmful and nuisance algae. In southwestern Montana, large blooms of the nuisance algae, Cladophora glomerata, have become common in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through wastewater and anthropogenic activity along its banks. To improve understanding of bloom dynamics, such as algal biomass and percent algae cover, and their effects on water quality, a UAV-based hyperspectral imaging system was used to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during field sampling campaigns across the entirety of the project, beginning in 2019 and ending in 2023. In this dissertation, methodologies for monitoring water quality were developed. These methods include estimating benthic algal pigment abundance using spectral band ratios achieving R 2 values of up to 0.62 for chlorophyll alpha and 0.96 for phycocyanin; creating spatial algae distribution maps and estimating percent algae cover using machine learning classification algorithms with accuracies greater than 99%; combining spatial algae distribution maps and improved pigment estimation using machine learning regression algorithms for creating chlorophyll alpha abundance maps, achieving an R 2 of 0.873, while also comparing abundance values to Montana water quality thresholds; and identifying salient wavelengths for monitoring and mapping algae to inform the design of a low-cost and compact multispectral imager. Throughout all field campaigns, significant spatial variations in algal growth within each river reach and frequent violations of current water quality standards were observed, demonstrating the need for high-spatial resolution monitoring techniques to be incorporated in current water quality monitoring programs.Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item Automatic 2D material detection and quantum emission prediction using deep learning-based models(Montana State University - Bozeman, College of Engineering, 2023) Ramezani, Fereshteh; Chairperson, Graduate Committee: Brad WhitakerThe realm of quantum engineering holds immense promise for revolutionizing technological landscapes, particularly with the advent of 2D materials in quantum device applications. The fundamental properties of these materials make them pivotal in various quantum applications. However, the progress in quantum engineering faces significant roadblocks, primarily centered around two challenges: accurate 2D material detection and understanding the random nature of quantum fluctuations. In response to the first challenge, I have successfully implemented a new deep learning pipeline to identify 2D materials in microscopic images. I have used a state-of-the-art two-stage object detector and trained it on images containing flakes of varying thickness of hexagonal boron nitride (hBN, a 2D material). The trained model achieved a high detection accuracy for the rare category of thin flakes (< or = 50 atomic layers thick). My further analysis shows that this proposed pipeline is robust against changes in color or substrate background, and could be generalized to various microscope settings. As an achievement, I have integrated my proposed method to the 2D quantum material pipeline (2D-QMaP), that has been under development by the MonArk Quantum Foundry, to provide automated capabilities that unite and accelerate the primary stages of sample preparation and device fabrication for 2D quantum materials research. My proposed algorithm has given the 2D-QMaP fully automatic real-time 2D flake detection capabilities, which has never been done effectively before. To address the second challenge, I assessed the random nature of quantum fluctuations, and I developed time series forecasting deep learning models to analyze and predict quantum emission fluctuations for the first time. My trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. The automated 2D material identification, addressing the laborious process of flake detection, and the introduction of innovative quantum fluctuations analysis with predictive capabilities not only streamline research processes but also hold the promise of creating more stable and dependable quantum emission devices, thus significantly advancing the broader field of quantum engineering.Item Opto-mechanical design and analysis for coherent active imaging(Montana State University - Bozeman, College of Engineering, 2022) Neeley, Jaime Branson; Co-chairs, Graduate Committee: Wm. Randall Babbitt and Joseph A. ShawThe objective of this thesis project was to design a monostatic lidar transmit (Tx) and receive (Rx) opto-mechanical apparatus for remote sensing at a variable range of 50 m - 500 m. The scope of this project begins from the fiber output of a pre-designed Frequency-Modulated Continuous Wave (FMCW) lidar system. After design criteria for the lidar module are given, the optical and mechanical design is presented, opto-mechanical tolerancing is presented, and assembly, alignment, and testing procedures are covered as well. This thesis shows that the required design criteria of diffraction-limited optical performance was achieved while accounting for predictable manufacturing and assembly errors modeled using a Monte Carlo tolerance analysis. Furthermore, this thesis shows that the modeled and measured optical performance results were in good agreement and recommendations are given for improvements for the next-generation revision of the lidar Tx/Rx module.Item RadPC@Scale: an approach to mitigate single event upsets in the memory of space computers(Montana State University - Bozeman, College of Engineering, 2022) Williams, Justin Patrick; Chairperson, Graduate Committee: Brock LaMeresThis thesis presents the flight test results of a single event upset (SEU) mitigation strategy for computer data memory. This memory fault mitigation strategy is part of a larger effort to build a radiation tolerant computing system using commercial-off-the-shelf (COTS) field programmable gate arrays (FPGAs) called RadPC. While previous iterations of RadPC used FPGA block RAM (BRAM) for its data memory, the specific component of RadPC that is presented in this paper is a novel external memory scheme with accompanying systems that can detect, and correct faults that occur in the proposed data memory of the computer while allowing the computer to continue foreground operation. A prototype implementation of this memory protection scheme was flown on a Raven Aerostar Thunderhead high-altitude balloon system in July of 2021. This flight carried the experiment to an altitude of 75,000 feet for 50 hours allowing the memory in the experiment to be bombarded with ionizing radiation without being attenuated by the majority of Earth's atmosphere. This thesis discusses the details of the fault mitigation strategy, the design-of-experiments for the flight demonstration, and the results from the flight data. This thesis may be of interest to engineers that are designing flight computer systems that will be exposed to ionizing radiation and are looking for a lower cost SEU mitigation strategy compared to existing radiation- hardened solutions.