Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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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 Directed graph descriptors and distances for analyzing multivariate time series data(Montana State University - Bozeman, College of Letters & Science, 2022) Belton, Robin Lynne; Chairperson, Graduate Committee: Tomas GedeonLocal maxima and minima, or extremal events, in experimental time series can be used as a coarse summary to characterize data. However, the discrete sampling in recording experimental measurements suggests uncertainty in the true timing of extrema during the experiment. This in turn gives uncertainty in the timing order of extrema within the time series. Motivated by applications in genomic time series and biological network analysis, we construct a weighted directed acyclic graph (DAG) called an extremal event DAG using techniques from persistent homology that is robust to measurement noise. Furthermore, we define a distance between extremal event DAGs based on the edit distance between strings. We prove several properties including local stability for the extremal event DAG distance with respect to pairwise L1 distances between functions in the time series data. Lastly, we provide algorithms, publicly free software, and implementations on extremal event DAG construction and comparison.Item Time series analysis of irrigation return flow(Montana State University - Bozeman, College of Engineering, 1983) Nicklin, Michael E.Item The small sample properties of a nonstandard estimator in the context of first order autocorrelation(Montana State University - Bozeman, College of Agriculture, 1987) Siebrasse, Paul Benjamin; Chairperson, Graduate Committee: Jeffrey T. LaFrance.The purpose of this study is to compare the small sample properties of a nonstandard estimator for first order autocorrelated errors in a time series equation with those of the more widely used estimators by using Monte Carlo experiments. The estimation method of interest arises either from the assumption that the presample residuals are not generated from an autoregressive process or from fixing the estimates of the presample values of the residuals at their unconditional expectations. This method has several nice properties. First, the estimator that is obtained is asymptotically equivalent to the standard methods. Second, the initial observations in the sample are retained, which overcomes problems that can arise in small samples when the independent variables are trended. Third, the data transformation that is used to estimate the unknown parameters of the model can be generalized to any order autoregressive process without any substantial increase in complexity. The results indicate that this nonstandard estimator performs very well relative to the other estimators considered for most experimental designs. This implies that the costs of using this more convenient estimation technique in terms of accuracy of parameter estimates is low relative to the other techniques considered.