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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.
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 Whitaker
Distribution 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.
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 Whitaker
The 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.
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. Shaw
The 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.
Fast charging of commercial lithium-ion battery without lithium plating
(Elsevier BV, 2023-12) Thapa, Arun; Hedding, Noah; Gao, Hongwei
Rapid charging of lithium-ion batteries (LIBs) enables the devices or systems powered by the batteries to provide services at faster rates or higher frequencies. However, fast charging of LIBs can cause lithium plating, resulting in rapid capacity degradation and even thermal runaway or fire in the batteries. Fast charging and lithium plating in a LIB are anode-centric events. Therefore, an anode-centric electrochemical model is critical for deriving a fast charging protocol for LIBs. In this work, we developed an electric circuit model for the negative electrode using tests conducted on laboratory three-electrode lithium-ion cells, used the model to estimate the fast charging current, and compared the fast charging current derived using the model to the fast charging current obtained from measurement. The fast charging current obtained using the model agrees well with the measured fast charging current. Furthermore, we implemented this fast charging protocol on commercial 18650 LIBs for 350 cycles using custom-built charging hardware and software and achieved an 80 % state of charge in 29 min with acceptable temperature rise. The cell aging analysis revealed no significant capacity degradation nor lithium plating on the anode surface, as the protocol explicitly imposes control to protect the battery from lithium plating.
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
(MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.
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 LaMeres
This 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.
Widefield micro-camera integrated into the objective lens of a reflectance confocal microscope for concurrent image registration
(Montana State University - Bozeman, College of Engineering, 2023) Aist, Joseph Nicholas; Chairperson, Graduate Committee: David L. Dickensheets
With millions of new skin disease cases reported annually, non-invasive imaging methods have been developed to diagnose skin disease accurately. Reflectance confocal microscopes (RCM) have led these new technologies with high sensitivity and specificity. However, current methods use multiple devices: a digital camera, a dermoscope, and an RCM, which are not co-registered. Therefore, locating small, microscopic RCM fields-of-view (0.5x0.5 mm) at specific suspicion sites within the larger dermoscopic field-of-view (10x10 mm) is extremely difficult. This 'blind' RCM imaging results in lower and more variable diagnostic accuracy, particularly sensitivity, where positive and negative predictive values can drop by up to 30%. Our team has designed a new objective lens with an integrated micro-camera to deliver a concurrent widefield image of the skin surface surrounding the location of microscopic RCM imaging. The widefield image can be used directly to provide context for RCM or can be registered to a previously stored high-resolution clinical image to show where RCM imaging is occurring. In this thesis, the micro-camera is characterized and tested in laboratory and clinical settings. In addition, this thesis investigates a co- and cross-polarized micro-camera and LED system. It compares them to the non-polarized system to explore whether the cross-polarized version enhances feature contrast and enables better dermoscopic imaging. Non-polarized, co-polarized, and cross-polarized mock-up probes of the objective lens with a micro-camera were designed and built for testing. Images of resolution targets, color charts, and skin were taken to obtain modulation transfer function (MTF) measurements, color analysis data, and representative skin images. The results showed improvement in the MTF for the cross- polarized probe when compared to the co- and non-polarized probes. It was also found that the polarization of the imaging system did not significantly affect the color quality of the images. When tested by scientists at Memorial Sloan Kettering Cancer Center, sub-surface features not seen with the co- and non-polarized probes were observed with the cross-polarized probe. The cross-polarized probe suppressed the surface reflections, allowing for sub-surface information to be captured.
Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization
(MDPI AG, 2023-06) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.
Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics
(Frontiers Media SA, 2023-04) Ledvina, Vincent; Brandt, Laura; MacDonald, Elizabeth; Frissell, Nathaniel; Anderson, Justin; Chen, Thomas Y.; French, Ryan J.; Mare, Francesca Di; Grover, Andrea; Sigsbee, Kristine; Gallardo-Lacourt, Bea; Lach, Donna; Shaw, Joseph A.; Hunnekuhl, Michael; Kosar, Burcu; Barkhouse, Wayne; Young, Tim; Kedhambadi, Chandresh; Ozturk, Dogacan S.; Claudepierre, Seth G.; Dong, Chuanfei; Witteman, Andy; Kuzub, Jeremy; Sinha, Gunjan
Citizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.

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