Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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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.Item Machine learning pipeline for rare-event detection in synthetic-aperture radar and LIDAR data(Montana State University - Bozeman, College of Engineering, 2021) Scofield, Trey Palmer; Chairperson, Graduate Committee: Brad WhitakerIn this work, we develop a machine learning pipeline to autonomously classify synthetic aperture radar (SAR) and lidar data in rare-event, remote sensing applications. Here, we are predicting the presence of volcanoes on the surface of Venus, fish in Yellowstone Lake, and select marine-life in the Gulf of Mexico. Given the efficiency of collecting SAR images in space and airborne lidar geographical surveys, the size of the datasets are immense. Immense training data is desirable for machine learning models; however, a large majority of the data we are using do not contain volcanoes or fish, respectively. Thus, the machine learning models must be formulated in such a way to place a high emphasis on the minority, target classes. The developed pipeline includes data preprocessing, unsupervised clustering, feature extraction, and classification. For each collection of data, sub-images are initially fed through the pipeline to capture fine detail characteristics until they are mapped back to their original image to identify overall region behavior and the location of the target class(es). For both sub-images and original images, results were quantified and the most effective algorithm combinations and parameters were assigned. In this analysis, we determined the classification results are not sufficient enough to propel a completely autonomous system, rather, some manual observing of the data will need to be performed. Nonetheless, the pipeline serves as an effective tool to reduce costs associated with electronic storage and transmission of the data, as well as human labor in manually inspecting the data. It does this by removing a majority of the unimportant, non-target data in some cases while successfully retaining a high percentage of the important images.Item Triplicated instruction set randomization in parallel heterogenous soft-core processors(Montana State University - Bozeman, College of Engineering, 2019) Gahl, Trevor James; Chairperson, Graduate Committee: Brock LaMeresToday's cyber landscape is as dangerous as ever, stemming from an ever increasing number of cybersecurity threats. A component of this danger comes from the execution of code-injection attacks that are hard to combat due to the monoculture environment fostered in today's society. One solution presented in the past, instruction set randomization, shows promise but requires large overhead both in timing and physical device space. To address this issue, a new processor architecture was developed to move instruction set randomization from software implementations to hardware. This new architecture consists of three functionally identical soft- core processors operating in parallel while utilizing individually generated random instruction sets. Successful hardware implementation and testing, using field programmable gate arrays, demonstrates the viability of the new architecture in small scale systems while also showing potential for expansion to larger systems.Item Development of a smart camera system using a system on module FPGA(Montana State University - Bozeman, College of Engineering, 2017) Dack, Connor Aquila; Chairperson, Graduate Committee: Ross K. SniderImaging systems can now produce more data than conventional PCs with frame grabbers can process in real-time. Moving real-time custom computation as close as possible to the image sensor will alleviate the bandwidth bottle-neck of moving data multiple times through buffers in conventional PC systems, which are also computation bottlenecks. An example of a high bandwidth, high computation application is the use of hyperspectral imagers for sorting applications. Hyperspectral imagers capture hundreds of colors ranging from the visible spectrum to the infrared. This masters thesis continues the development of the hyperspectral smart camera by integrating the image sensor with a field programmable gate array (FPGA) and by developing an object tracking algorithm for use during the sorting process, with the goal of creating a single compact embedded solution. An FPGA is a hardware programmable integrated circuit that can be reprogrammed depending on the application. The prototype integration involves the development of a custom printed circuit board to connect the data and control lines between the sensor, the FPGA, and the control code to read data from the sensor. The hyperspectral data is processed on the FPGA and is combined with the object edges to make a decision on the quality of the object. The object edges are determined using a line scan camera, which provides data via the Camera Link interface, and a custom object tracking algorithm. The object tracking algorithm determines the horizontal edges and center of the object while also tracking the vertical edges and center of the object. The object information is then passed to the air jet sorting subsystem which ejects bad objects. The solution is to integrate the hyperspectral image sensor, the two processing algorithms, and Camera Link interface into a single, compact unit by implementing the design on the Intel Arria 10 System on Module with custom printed circuit boards.Item Computer solutions of complex biological boundary-value problems(Montana State University - Bozeman, College of Engineering, 1984) Parker, Reed AllenItem A data acquisition and recording system(Montana State University - Bozeman, College of Engineering, 1977) Somppi, John EmilItem Development of Spring Creek data acquisition system(Montana State University - Bozeman, College of Engineering, 1974) Williams, Robert Matthew