Theses and Dissertations
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Item Embargo High-energy, few-cycle laser beamline for relativistic interaction with aligned nanostructures(Colorado State University. Libraries, 2025) Meadows, Alexander, author; Rocca, Jorge, advisor; Menoni, Carmen, committee member; Wilson, Jesse, committee member; Yost, Dylan, committee memberUltra-high intensity lasers have been used to produce a variety of sources of intense radiation and energetic particles through the irradiation of nanostructured targets, including high-brightness x-ray sources, energetic collimated sources of ion and electron beams, and quasi-monoenergetic pulses of neutrons. However, these experiments have been constrained to the use of multi-cycle laser pulse drivers with duration of 30-50 fs or longer. This work presents results from the development and commissioning of a new relativistic-intensity laser beamline for solid target interaction experiments with pulses in the few-cycle regime. Application of these laser pulses to nanostructured targets will produce a unique and mostly unexplored plasma regime in which the driving pulse duration is shorter than the time scale of ion motions. The scaling of few-cycle pulse compression to the multi-terawatt regime is demonstrated here by the performance of a laser beamline based on the spectral broadening of Ti:sapphire pulses in a large-bore hollow-capillary fiber and subsequent recompression. The millimeter fiber waveguide presents a unique geometry for spectral broadening in the Ti:sapphire spectral range that results in an exceptionally high energy throughput and its performance has been characterized over a wide range of gas pressure conditions. The compressed output pulses of 15 mJ energy and 6.9 fs duration set a new record for the peak power of post-compressed pulses in the <10 fs regime. A new reverse pressure gradient operation mode has been introduced and applied to allow for operation of the hollow-capillary fiber beyond the usual peak power limit set by the onset of self focusing. The output of the beamline has been focused to a relativistic intensity of 6.5 · 1018 W/cm2 and relativistic electrons have been accelerated by the irradiation of solid flat and nanostructured targets and characterized by a custom-built magnetic spectrometer. This beamline will allow for relativistic laser-matter interactions with nanostructured targets in a new and unexplored few-cycle pulse duration regime.Item Open Access Precision bounds in localization microscopy(Colorado State University. Libraries, 2025) Varughese, Maxine X., author; Pezeshki, Ali, advisor; Bartels, Randy, advisor; Chong, Edwin, committee member; Peterson, Christopher, committee memberThis thesis presents two independent studies in theoretical and experimental optical imaging. The first part investigates the theoretical limits and simulation of Single-Pixel Localization Microscopy (SPLM), a computational imaging technique that employs spatio-temporally modulated (STM) illumination to enable sub-diffraction localization with a single-pixel detector. To quantitatively assess the performance of SPLM, we analyze the localization precision limit using the Cramér-Rao Lower Bound (CRLB) under shot-noise-limited conditions. To account for discrepancies between the assumed and actual imaging models —-- such as those caused by optical aberrations —-- we further introduce the Misspecified Cramér-Rao Bound (MCRB), which quantifies changes in estimation precision limit under model mismatch. These theoretical tools establish performance limits and characterize the robustness of SPLM to experimental imperfections. Following these analyses, we simulate photon detection from fluorescent emitters via Binomial point processes and perform localization on a discrete grid using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), with further refinement via a BFGS-based line search method, assuming an accurate forward model. The second part of the thesis reports the experimental development of Quantitative Scattering Microscopy (QSCAT), a label-free phase imaging technique designed for in situ materials characterization. The system employs a digital light processing (DLP) device and LED illumination to project half pupil patterns onto the back aperture of an objective, enabling differential phase contrast imaging. The recorded intensity measurements are inverted to recover the quantitative phase of the sample, providing optical susceptibility information. We demonstrate the utility of QSCAT by measuring the height of chromium features on a USAF resolution target. Additionally, we incorporate a convolutional neural network (CNN) for phase retrieval, representing a novel integration of learning-based reconstruction into scattering-based microscopy.Item Embargo Exposing data remanence in sanitized NOR flash: characterization and countermeasures(Colorado State University. Libraries, 2025) Murali, Anjali, author; Ray, Biswajit, advisor; Pasricha, Sudeep, committee member; Daily, Jeremy, committee memberThe rapid advancement of digital technology has led to the frequent decommissioning of electronic devices, many of which retain sensitive data in their non-volatile memory. NOR flash memory, widely used as a non-volatile storage medium in Internet of Things (IoT) devices, often stores proprietary firmware as well as sensitive user data collected by these systems. Although standard sanitization procedures are typically applied before disposing of such devices, residual data may persist in the storage medium, posing a significant risk of data leakage. In this work, we present a novel investigation into data remanence and recovery techniques in sanitized NOR flash memory chips. Our findings show that data can be partially or fully recovered from NOR flash arrays that have undergone an all-zero sanitization process. Given the widespread adoption of NOR flash in IoT devices, these results raise serious concerns regarding sensitive data leakage and the security of firmware updates in such systems.Item Open Access Bias correction of temperature and wind forecasts from the NOAA Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) using machine learning(Colorado State University. Libraries, 2025) Zhu, Qianya, author; Chen, Haonan, advisor; Jayasumana, Anura, committee member; Guo, Yanlin, committee memberNumerical Weather Prediction (NWP) models, such as the National Oceanic and Atmospheric Administration's (NOAA) Global Forecast System (GFS) and the Global Ensemble Forecast System (GEFS), are essential tools for modern weather forecasting. NWP models are the backbones of various applications in weather, climate, and water enterprises. However, due to model limitations, initialization errors, and discretizations of grids, large systematic biases still exist aside from advances in computing capabilities, spatial resolution, and physical parameterization of the models. This study presents a machine learning-based bias correction framework that is driven two models: Extreme Gradient Boosting (XGBoost) and U-Net. The target variables include 2-meter temperature (2m-T), 10-meter and 100-meter wind speed (10m-WS and 100m-WS). ERA5 reanalysis data are used as the reference for evaluating and correcting forecast biases. The models are trained on both seasonal (summer and winter) and all-season datasets to account for seasonal variability in forecast errors. The GFS-based experiments focus on the CONUS region (24.5°N–49.5°N, 125.0°W–66.75°W), while the GEFS-based experiments cover Germany, a climatically diverse region. Results show that U-Net significantly outperforms XGBoost in long-term forecasting, particularly beyond 120 hours, due to its capacity to learn complex spatial and temporal dependencies. In contrast, XGBoost exhibits superior performance in short-term forecasts (0–48 hours), especially when data are limited, offering efficient and interpretable bias correction. Seasonal training improves temperature correction across both regions and models—especially during summer—while all-season models enhance generalization for wind speed forecasts. Quantitative evaluation using root mean square error (RMSE) confirms that both models effectively reduce systematic forecast biases in GFS and GEFS outputs. This work has also indicated that without using sophisticated deep learning structures, a rather simple machine learning model may achieve decent performance when correcting weather forecast products.Item Embargo Deep learning for downscaling GOES-18 measurements for wildfire detection(Colorado State University. Libraries, 2025) Taulbee, Luke, author; Chen, Haonan, advisor; Simske, Steve, committee member; Venkatachalem, Chandrasekar, committee memberThis thesis aims to address the challenge of accurate wildfire detection using satellite imagery. Despite the availability of various satellite-based fire products, real-time detection of fire perimeters remain difficult due to limitations in the spatio-temporal resolution of current satellite imagery. For example, the Geostationary Operational Environmental Satellites (GOES-R) series containing the Advanced Baseline Imager (ABI) offers high temporal resolution for frequent observations but suffers from low spatial resolution. In contrast, low Earth orbit (LEO) satellites like Suomi-NPP, NOAA-20, and NOAA-21 with the Visible Infrared Imaging Radiometer Suite (VIIRS) imager provide high spatial resolution but with limited temporal coverage. To overcome these limitations, this research proposes a deep learning framework for wildfire detection that leverages GOES ABI observations, which are downscaled to a spatial resolution of 375 meters using a Generative Adversarial Network (GAN). High-resolution VIIRS images are used as ground truth labels during the training phase. Experimental results demonstrate that the proposed framework successfully enhances the spatial resolution of GOES ABI data while preserving its high temporal frequency, allowing more precise and timely wildfire detection.Item Open Access Optimizing machine learning models for autonomous vehicles(Colorado State University. Libraries, 2025) Balasubramaniam, Abhishek, author; Pasricha, Sudeep, advisor; Chong, Edwin, committee member; Pouchet, Louis-Noël, committee memberObject detectors (ODs) stand as a cornerstone of modern computer vision tasks, increasingly essential in a wide array of consumer applications. Its utility spans enhancing surveillance and security systems, enabling mobile text recognition for digital document accessibility, and facilitating the diagnosis of diseases through advanced imaging techniques like MRI and CT scans. This multifaceted technology is pivotal across various domains, with one of its most critical applications being autonomous driving. Autonomous vehicles (AVs) rely extensively on their ability to perceive and interpret their surroundings, a capability fundamental to ensuring safe and reliable driving performance. Sophisticated perception systems in these vehicles utilize state-of-the-art object detection algorithms, both 2D and 3D, to accurately identify and localize various objects within the vehicle's operational vicinity. 2D ODs are designed to detect and localize objects in images or video frames, providing information in the form of bounding boxes on a 2-dimensional plane. They are less complex and computationally demanding compared to 3D detectors and are commonly used in applications like image recognition, face detection, and pedestrian detection in surveillance systems. Models such as YOLO, SSD, and Faster R-CNN are widely used examples of 2D ODs. Conversely, 3D ODs incorporate depth information to detect and localize objects in a three-dimensional space, utilizing data from 3D sensors like LiDAR, stereo cameras, or depth cameras. These detectors are essential for applications requiring a precise understanding of the environment, such as autonomous driving, robotics, and augmented reality. Popular models include PointNet, VoxelNet, and Frustum PointNet. The data provided by these ODs, especially when combining 2D and 3D capabilities, is indispensable for informing crucial driving decisions and enabling the vehicle to navigate complex environments with enhanced safety and efficiency. However, these advanced ODs come with high memory and computational overheads, which pose significant challenges. To address this challenge, ongoing research and development efforts are dedicated to optimizing these models. The primary goal is to reduce their memory footprint and computational requirements while maintaining or even improving their performance. This ensures that these sophisticated algorithms can be efficiently deployed on resource-constrained embedded platforms, often used in AVs, without compromising their effectiveness. Such advancements are pivotal in maintaining the efficiency and reliability of AVs, further solidifying the indispensable role of ODs in modern technology. This thesis introduces two novel OD optimization algorithms, which can reduce model footprint and computation cost while decreasing the inference time of the model. The first contribution, R-TOSS, is a novel semi-structured pruning framework for 2D ODs. R-TOSS outperforms various state-of-the-art model optimization techniques while also improving performance on embedded resource-constrained platforms. For accelerating 3D ODs, we propose UPAQ, which uses a combination of pruning and quantization to improve model accuracy and reduce model footprint. We also showcase how UPAQ outperforms other state-of-the-art models in terms of performance.Item Embargo Novel time resolved optical and machine learning methods for label free biomedical imaging(Colorado State University. Libraries, 2025) Mugdha, Arya Chowdhury, author; Wilson, Jesse W., advisor; Bartels, Randy, committee member; Lear, Kevin, committee member; Tobet, Stuart, committee memberRecent advances in multiphoton microscopy are paving the way for better visualization and understanding of intracellular organisms, leading to powerful non-invasive methods for cell and tissue diagnosis. Mitochondrial disorder is a type of disorder that affects the cell's mitochondria and impacts the cell's functionality as a whole. Roughly, these mitochondrial disorders affect between 1 in 6000 and 1 in 8000 live births. These disorders are hard to diagnose as each individual is affected differently by these disorders. Current tools for diagnosis of mitochondrial disorders are either invasive or time consuming in nature. The focus of this dissertation is to develop novel nonlinear optical imaging techniques and algorithms for studying mitochondrial heteroplasmy, one of the markers of mitochondrial disorder where the cells or tissues contain a mixture of healthy and diseased mitochondria. The defects in Electron Transport Chain (ETC) are often the causes behind various mitochondrial disorders. This dissertation aims to develop non-invasive optical methods for studying redox states of Cytochromes in living human cells such as Fibroblasts. Cytochromes are a particular type of Hemeprotein that are comprised of iron porphyrin and are primarily responsible for electron transport within the ETC of Mitochondria. We utilize a two-color visible wavelength pump-probe technique that can roughly detect up to 10-6 absorption changes of the probe beam arising from the approximately 10-15 μm thick live human Fibroblast samples. We detail the technical challenges faced in getting the optical instrument sensitivity to the required level so that Fibroblasts can be imaged with sufficient Signal-to-Noise Ratio (SNR). The major technical challenges that had to be overcome to obtain reasonable pump-probe signal levels from fibroblasts are: (1) minimizing the noise floor of the entire optical instrument so that we are performing shot-noise limited detection using a photodiode; (2) correcting for the focal plane mismatch arising due to chromatic aberration in the axial direction. Additionally, we explore machine learning based algorithms for better data interpretability and visualization of Transient Absorption data. We demonstrate that Hyperspectral Autoencoders, a specific type of Convolutional Neural Networks (CNNs), are able to unmix spectral signatures arising from different molecules in Transient Absorption images of reduced and oxidized muscle fibers. We also devise a novel loss function that takes into account the correlation between different channels of the input image to train the neural network so that the inherent randomness of gradient descent algorithms does not impact the network predictions each time. Furthermore, we explore image-to-image translation neural network algorithms to investigate the possibility of translating Reflectance Confocal Microscopy (RCM) images to Second Harmonic Generation (SHG) images. For this study, we collect co-registered RCM and SHG images from canine oral mucosa muscle tissue with the same Field of View (FOV) and excitation objective. We explore various state-of-the-art deep learning algorithms and evaluate their performance in transforming the RCM modality to SHG modality.Item Embargo Development of cryogenic liquid sensors to monitor flow and pressure(Colorado State University. Libraries, 2025) Oloriz Calvo, Sergio, author; Rocca, Jorge, advisor; Menoni, Carmen, committee member; Yalin, Azer, committee memberThis thesis presents the development and implementation of a bearing-less propeller flow meter designed for reliable cryogenic fluid flow measurement, particularly in applications requiring precise flow control and protection of liquid nitrogen cooled high-power laser systems. Traditional flow meters, such as differential pressure, turbine, variable area, and thermal flow meters, present significant limitations in cryogenic environments, including susceptibility to freezing, pressure drop-induced cavitation, and inaccuracy in two-phase flow conditions. To address these challenges, a custom flow meter was developed based on a flat propeller design with a magnetic pickup sensor, ensuring minimal friction and improved sensitivity at low flow rates. The system operates by detecting the rotational frequency of the propeller, which is converted into a proportional voltage signal. The final design includes real-time monitoring using an Arduino and a programmable display, providing flow rate readings and an interlock signal for system safety. A safety interlock mechanism was integrated into the control system of a high power laser system to protect it by automatically shutting it down if the flow rate falls below the required threshold to prevent overheating and damage of the laser gain medium, in this case Yb:YAG. Additionally, a diaphragm cryogenic pressure sensor was developed and used in combination with the flowmeter to characterize a liquid nitrogen-based cooling system. This approach ensures a reliable cryogenic flow measurement system with minimal pressure loss. The bearing-less propeller flow meter offers a robust alternative to conventional metering techniques, making it an optimal solution for applications requiring precise cryogenic fluid management. The results demonstrate that this design successfully addresses the challenges associated with cryogenic flow measurement while ensuring the protection and optimal operation of sensitive laser systems and other applications.Item Open Access Investigation on hafnium oxide mixtures for UV coatings for fusion energy applications(Colorado State University. Libraries, 2025) Weiss, Maxwell, author; Menoni, Carmen, advisor; Wilson, Jesse, committee member; Sambur, Justin, committee memberMetal oxide thin films play a crucial role in optical coatings for high power lasers. High laser damage resistance optical coatings find use in vacuum windows, mirrors, laser crystals, and harmonic generation crystals in high average and peak power lasers. The laser damage of multi-layer dielectric optical coatings is limited by that of the high index of refraction material, which is typically hafnium dioxide. Therefore, this work is focused on improving the laser damage performance of hafnium oxide by modifying the material's optical and structural properties through doping or mixing with other metal cations for applications in ultraviolet interference coatings for λ=355 nm wavelength. UV coatings capable of enduring billions of laser shots unscathed are pivotal to drivers for inertial confined fusion energy (IFE). The laser-induced damage threshold (LIDT) of metal oxide thin films is constrained by various intrinsic and extrinsic factors, including crystallinity, fabrication method, and defect incorporation during processing. The implementation of high LIDT optical coatings is primarily limited by the ability to engineer amorphous oxide layers with controlled structural and optical properties. For UV lasers, amorphous dielectric coatings are multilayer stacks of alternating layers of HfO2 and SiO2. HfO2 has a high index of refraction and a high band gap at λ=355 nn while SiO2 has a low index of refraction, offering sufficient index contrast to engineer high reflector and anti-reflection coatings. To optimize LIDT, mixed amorphous oxide alloys offer a promising research direction for UV high energy lasers for IFE. This thesis investigates a variety of metal oxide mixture thin films as the high index material in anti-reflection coatings for λ=355 nm. Primarily, the optical properties, laser damage performance, amorphous morphology, and electronic state analysis of hafnium oxide mixtures with SiO2 and Al2O3 are investigated. These mixtures are fabricated with the biased target deposition (BTD) technique which is entirely unexplored in the context of high LIDT interference coatings. Two-layer anti-reflection (AR) coatings were designed and fabricated by reactive biased target deposition (BTD) using mixtures of HfO2 and SiO2, HfO2 and Al2O3, as the high index layer and SiO2 as the low index layer. The laser damage response was assessed from 1-on-1 and S-on-1 tests from which the laser induced damage threshold (LIDT) fluence was determined. It is shown that in BTD 2-layer ARs using mixtures of Hf1-ySiyOx and Hf1-yAlyOx the 1-on-1 LIDT increases with respect to the HfO2/SiO2 AR. The BTD ARs have a density higher than electron beam evaporated (EBE) HfO2/SiO2 ARs but lower than ion beam sputtered (IBS) HfO2/SiO2 ARs. The 1-on-1 LIDT of the BTD ARs is slightly lower than that of electron beam evaporated (EBE) HfO2/SiO2 ARs and ion beam sputtered (IBS) HfO2/SiO2 ARs. While the 104-on-1 LIDT of EBE ARs was higher than that of either sputtering method. Substrate etching prior to deposition and UV conditioning increase the 1-on-1 LIDT of sputtered ARs. The S-on-1 LIDT of BTD ARs decreases by ~25% for S=10 and remains unchanged to S=104 laser shots, indicating no accumulation fatigue.Item Open Access Full-wave and asymptotic computational electromagnetics methods: on their use and implementation in received signal strength, radar-cross-section, and uncertainty quantification predictions(Colorado State University. Libraries, 2024) Kasdorf, Stephen, author; Notaroš, Branislav M., advisor; Ilić, Milan, committee member; Wilson, Jesse, committee member; Venayagamoorthy, Karan, committee memberWe propose and evaluate several improvements to the accuracy of the shooting and bouncing rays (SBR) method for ray-tracing (RT) electromagnetic modeling. A per-ray cone angle calculation is introduced, with the maximum separation angle determined for each individual ray based on local neighbors, allowing the smallest theoretical error in SBR. This enables adaptive ray spawning and provides a unique analysis of the effect of ray cone sizes on accuracy. For conventional uniform angular distribution, we derive an optimal cone angle to further enhance accuracy. Both approaches are integrated with icosahedral ray spawning geometry and a double-counted ray removal technique, which avoids complex ray path searches. The results demonstrate that the advanced SBR method can perform wireless propagation modeling of tunnel environments with accuracy comparable to the image theory RT method, but with much greater efficiency. To further advance the efficiency of the SBR method, we propose a unified parallelization framework leveraging NVIDIA OptiX Prime programming interfaces on graphics processing units (GPUs). The framework achieves comprehensive parallelization of all components of the SBR algorithm, including traditionally sequential tasks like electric field computation and postprocessing. Through optimization of memory usage and GPU resources, the new SBR method achieves upwards of 99% parallelism under Amdahl's scaling law. This innovative parallelization yields dramatic speedups without sacrificing the previously enhanced accuracy of the SBR method, demonstrating an unparalleled level of computational efficiency for large-scale electromagnetic propagation simulations. Finally, we implement and validate several advanced Kriging methodologies for uncertainty quantification (UQ) in computational electromagnetics (CEM). The universal Kriging, Taylor Kriging, and gradient-enhanced Kriging methods are applied to reconstruct probability density functions, offering efficient alternatives to Monte Carlo simulations. We further propose the novel gradient-enhanced Taylor Kriging (GETK) method, which combines the advantages of gradient information and basis functions, yielding superior surrogate function accuracy and faster convergence. Numerical results using higher-order finite-element scattering modeling show that GETK dramatically outperforms other Kriging and non-Kriging methods in UQ problems, accurately predicting the impact of stochastic input parameters, such as material uncertainties, on quantities of interest like radar cross-section.Item Embargo IMSIS: an instrumented microphysiological system with integrated sensors for monitoring cellular metabolic activities(Colorado State University. Libraries, 2024) Cheng, Ming-Hao, author; Chen, Thomas W., advisor; Lear, Kevin, committee member; Wilson, Jesse W., committee member; Carnevale, Elaine, committee member; Chicco, Adam J., committee memberWell plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology. Their popularity stems from their versatility to support a variety of fluorescent markers for high throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has its own challenges, namely, they can be potentially toxic to cells, and thus, may perturb their biological functions; and it is difficult to monitor multiple analytes concurrently and in real-time inside each well. In this dissertation, an Instrumented Microphyiological System with Integrated Sensors (IMSIS) platform is presented. The IMSIS platform is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The IMSIS platform currently incorporates O2, H2O2, and pH sensors inside each well, allowing up to six wells to perform concurrent non-destructive and label-free measurements in real-time. The system has integrated microfluidics to maintain its microphysiological environment within each well. The miniaturized design ensures portability, suitable for small offices and field applications. The IMSIS platform is equipped with a 14-bit ADC and read channel bioelectronics with the signal-to-noise ratio (SNR) of 79 dB, 112 dB, and 48 dB for measuring oxygen consumption rate (OCR), hydrogen peroxide production rate (HPR), and extracellular acidification rate (ECAR), respectively. Furthermore, the scalable design of the architecture allows easy expansion to accommodate a higher throughput in the future. A graphical user interface was developed to provide a dashboard control by users for system operation. The versatile platform supports electrochemical sensing techniques such as amperometry, chronoamperometry, and potentiometry, with a reference electrode voltage range of ±1 V. The IMSIS platform has been used to monitor the real-time metabolic activities of various biological samples, including bovine, equine, and human oocytes, bovine and equine embryos, as well as isolated mouse cardiac mitochondria. The IMSIS platform has successfully shown its capability to simultaneously measure OCR, ECAR, and HPR both in the sample's basal state and in response to external stimuli, such as oligomycin. The design of the IMSIS platform and the experimental results underscore its significant potential for diverse clinical and research applications. These include embryo quality assessment for assisted reproductive technology (ART), investigation of the effects of obesity-induced mitochondrial dysfunction, and analysis of cancer tumors and their metabolic response to therapeutics.Item Open Access Improvements to the tracking process(Colorado State University. Libraries, 2024) Lewis, Codie T., author; Cheney, Margaret, advisor; Chandrasekaran, Venkatacha, advisor; Crouse, David, committee member; Kirby, Michael, committee memberAccurate target tracking is a fundamental requirement of modern automated systems. An accurate tracker must correctly associate new observations to existing tracks and update those tracks to reflect the new information. An accurate tracker is one which predicts assignments and measurement distributions closely matching the ground truth. This work will show that aspects of the GNP algorithm and IMM filter require amendments and renewed investigation. To aid the framing of the solutions in the context of tracking, some general background will be presented first. More specific background will be given prior to the corresponding contributions. Modern sensor networks require the alignment of track pictures from multiple sensors (sometimes called sensor registration). This issue was described in the 1990s and termed the global nearest pattern problem in the early 2000s. The following work presents a correction and extension of the solution to the global nearest pattern problem with a heuristic error estimation algorithm. Its use for sensor calibration is demonstrated. Once measurements have been associated to tracks, there still remain several choices that define the tracking algorithm, one being the filtering algorithm which updates the track state. One common solution for filtering is the interacting multiple model filter which was originally developed in the 1980s. This is essentially a bank of Kalman filters which are weighted and mixed based on a predefined Markov chain. The validity of the assumptions on that Markov chain will be discussed and recommendation for replacing those assumptions with neural networks will be proposed and assessed. Finally, following association of two tracks for a single target, it is necessary to combine their information while respecting the lack of knowledge about correlations between the tracks. Covariance intersection was developed in the 1990s and 2000s for track-to-track fusion when tracks are assumed Gaussian. A generalization of covariance intersection, Chernoff fusion, was developed in the 2000s for handling general track states. A connection made in the literature which allows for direct analysis of the error of Chernoff fusion is used to evaluate the effectiveness of Fibonacci lattices for quasi-Monte Carlo integration solutions required by Chernoff fusion.Item Open Access Design and optimization of efficient, fault-tolerant and secure 2.5D chiplet systems(Colorado State University. Libraries, 2024) Taheri, Ebad, author; Nikdast, Mahdi, advisor; Pasricha, Sudeep, advisor; Malaiya, Yashwant K., committee member; Jayasumana, Anura P., committee memberIn response to the burgeoning demand for high-performance computing systems, this Ph.D. dissertation investigates the pivotal challenges surrounding Networks-on-Chip (NoCs) within the framework of 2.5D and 3D integration technologies, with a primary objective of enhancing the efficiency, fault tolerance, and security of forthcoming computing system architectures. The inherent limitations in bandwidth and reliability at the boundary of chiplets in 2.5D chiplet systems engender significant challenges in traffic management, latency, and energy efficiency. Furthermore, the interconnected global network on an interposer, linking multiple chiplets, necessitates high-bandwidth, low-latency communication to accommodate the substantial traffic generated by numerous cores across diverse chiplets. This Ph.D. dissertation emphasizes various design aspects of NoCs, such as latency, energy efficiency, fault tolerance, and security. It explores the design of 3D NoCs leveraging Through-Silicon Vias (TSVs) for vertical communication. To address reliability concerns and fabrication costs associated with high TSV density, Partially Connected 3D NoC (PC-3DNoC) has been proposed. An adaptive congestion-aware TSV link selection algorithm is introduced to manage traffic load and optimize communication, resulting in reduced latency and improved energy efficiency. For 2.5D chiplet systems, a novel deadlock-free and fault-tolerant routing algorithm is presented. The fault-tolerant algorithm enhances redundancy in vertical link selection and offers improved network reachability with reduced latency compared to existing solutions, even in the presence of faults. Furthermore, to address the energy consumption concerns of silicon-photonic-based 2.5D networks, a reconfigurable power-efficient and congestion-aware silicon-photonic-based 2.5D Interposer network is proposed. The proposed photonic interposer utilizes phase change materials (PCMs) for dynamic reconfiguration and power gating of the photonic network, leading to lower latency and improved energy efficiency. Additionally, the research investigates the integration of optical computation and communication into 2.5D chiplet platforms for domain-specific machine learning (ML) processing. This approach aims to overcome limitations in computation density and communication speeds faced by traditional accelerators, paving the way for sustainable and scalable ML hardware. Furthermore, this dissertation proposes a 2.5D chiplet-based architecture utilizing a silicon-photonic-based interposer, which tackles the limitations of conventional bus-based communication by employing a novel switch-based network, achieving significant energy efficiency improvements for high-bandwidth, low-latency data movement in machine learning accelerators. The switch-based network employs our proposed optical switch based on Mach--Zehnder Interferometer (MZI) devices with a dividing state to facilitate broadcast and optimize communication for ML workloads. Finally, the dissertation explores security considerations in 2.5D chiplet systems with diverse, potentially untrusted chiplets. To address this, a secure routing framework for Network-on-Interposer is presented. The proposed secure framework protects the system against distributed denial-of-service (DDoS) attacks by concealing predictable routing paths. It leverages multi-objective optimization to balance efficiency and reliability for the NoI. The proposed contributions in this dissertation help advance the field of chip-scale interconnection networks by proposing novel techniques for improved performance, reliability, and power efficiency in 3D and 2.5D NoC architectures. These advancements hold promise for the design of future high-performance computing systems, particularly in the areas of machine learning and other computationally intensive applications.Item Open Access Design exploration and optimization of silicon photonic integrated circuits under fabrication-process variations(Colorado State University. Libraries, 2024) Mirza, Asif Anwar Baig, author; Nikdast, Mahdi, advisor; Pasricha, Sudeep, advisor; Wilson, Jesse, committee member; Brewer, Samuel, committee memberSilicon photonic integrated circuits (PICs) have become a key solution to handle the growing demands of large data transmission in emerging applications by consuming less power and low heat dissipation while offering ultra-high data bandwidth than electronic circuits. With Moore's Law slowing down and the end of Dennard scaling, PICs offer a logical step to improve data movement and processing performance in future computing systems. On PICs, light is processed and routed by means of optical waveguides. Silicon has a unique feature of high refractive index contrast in the silicon-on-insulator (SOI) platform which allows for tight confinement of light in nanometer waveguide cores and bends with a radius of only a few microns. PICs comprise of a diverse set of elements such as waveguide splitters, combiners, crossings, and couplers which help with distribution, routing, and computation of optical signals. Optical signals are converted to electrical signals with the help of photodiodes which in silicon photonics are implemented using Germanium. To enable PICs for wavelength-division multiplexing (WDM), there is a need for efficient wavelength filters consisting of optical delay lines or resonators. Optical delay lines are usually built using Mach-Zehnder Interferometers (MZIs) which consists of a splitter, two waveguides with a given group delay, and a combiner. Other devices such as microring resonators (MRRs) can be used as wavelength filters when the input wavelength matches a whole multiple times in the circumference of the ring. Other components such as grating coupler help couple the light into and out of a PIC. PICs can be fabricated on the infrastructure developed for complimentary metal–oxide–semiconductor (CMOS) electronics. This technology now enables deep submicron features with unprecedented accuracy in large volumes along with close integration of photonics and electronic circuits. The use of silicon as a base material makes reuse of these manufacturing tools possible, but photonics imposes different demands on the processes. Although silicon photonics offers data transmission and computation at light speed with high bandwidth and low power consumption, the fundamental building blocks in PICs (e.g., optical waveguides) are extremely sensitive to nanometer-scale fabrication-process variations (FPVs) caused due to slight randomness in optical lithography processes. Active compensation by means of electronic circuits (a.k.a. tuning) is necessary to compensate for FPVs. Tunable microheaters can be used for active compensation which affect the material properties of silicon to improve PIC's performance under FPVs. However, the total power consumed due to tuning in a working PIC can be drastically high. For example, variations as small as 1 nm in an MRR can deviate the optical frequency response of the device by 2 nm that leads to approximately 25% increase in the tuning power consumption to compensate for variations of a single MRR. Additionally, a system can have thousands of such MRRs that can easily add up the total power consumption of the system. In order to address FPVs we need to observe the reliability not just at a system level but down to the device level by enabling reliable, FPV-aware devices to enable FPV-resilient PICs and photonic systems. Designing more reliable and FPV-tolerant photonic devices should not only help us with reducing the total power consumption but also build more reliable circuits with fault-free operational behavior for data transmission and computation in future computing systems. This PhD thesis covers the impact of process variations on photonic devices primarily MRRs. We take a bottom-up approach in improving the reliability of an MRR towards FPVs. We propose an improved and optimized MRR designs which can be used in any PIC to reduce the overall shift in resonant wavelength of the device due to FPVs, further reducing the total power consumption required to tune the device. We confirmed our findings by further fabricating such MRRs and comparing the improved and optimized designs against conventional MRRs. Furthermore, we study the impact these improved MRRs have in photonic artificial intelligence (AI) accelerators and how they can further improve the network accuracy and overall power consumption. Finally, we also compile our work into a device exploration tool that allows photonic designer to set design parameters in an MRR and study its behavior under different FPV profiles. With this tool we aim to give the designer the ability to determine desired MRR designs based on desired design and performance requirements and budget constraints set on a photonic system.Item Open Access Engineering a silicon- photonic bimodal biosensor(Colorado State University. Libraries, 2024) Mohammad, Ahmed, author; Nikdast, Mahdi, advisor; Lear, Kevin, advisor; Kipper, Matthew, committee memberBiosensors are powerful analytical devices that integrate biological sensing elements with physicochemical transducers to detect and quantify specific analytes, offering wide-ranging applications in fields such as medical diagnostics, environmental monitoring, food safety, and drug discovery. Bimodal waveguide (BiMW) biosensors, an interferometric optical biosensor, proven to be one of the best optical biosensors based on their high sensitivity, real time detection and compact design. During its early development stages, early 2010's, the height of the bimodal waveguide was increased to induce interference between the fundamental and first-order modes. Later, in late 2010's, change in the width of the bimodal waveguide were introduced to induce this interference. Our novel design builds upon these advancements, focusing on optimizing some parameters, mainly the width of the bimodal biosensor, to enhance performance and sensitivity. Many attempts were simulated to get a high fringe visibility and to determine the reduction in the transmission monitor was due to reduce the input power or the change in the effective index in the sensing region. Then, we came out with a design with one input, to maximize the fringe visibility, and two output, to determine the source power fluctuation. Multiple changes in the parameters, such as the width and the offset of the input waveguide, were investigated. In addition, change in the width of the bimodal waveguide was also included in this experiment. Finally, we varied the gap between the two output bends. All these parameters were varied to get a higher fringe visibility and lead to better sensitivity. Moreover, we discovered that this design requires the sample to be placed on top of the bimodal waveguide, rather than on the sides. We concluded that the best design we can extract is the one with 120 rad/RIU cm.Item Open Access Deep learning for short-term prediction of wildfire using geostationary satellite observations(Colorado State University. Libraries, 2024) Saqer, Yousef, author; Chen, Haonan, advisor; Azimi-Sadjadi, Mahmood R., committee member; Wei, Yu, committee memberThe aim of this thesis is to utilize the Geostationary Operational Environmental Satellite (GOES) data for predictions regarding the intensity and potential path of wildfires. Using GOES to identify wildfires and extracting data from those events to help train a deep learning model. Three fires were selected for training the deep learning model: the Sequoia, Calwood, and Maui fires. The GOES data of the fires was obtained from band 7 which operates in the Shortwave Window or 3.9μm wavelength, band 7 is able to capture hotspots which is beneficial for wildfire prediction. The radiance data from band 7 is pulled from an Amazon Web Service (AWS) and becomes part of a dataset of 2513 samples. The data is then stacked to form a time series of approximately two hours and converted into a compressed h5 file. The pipeline distributes the dataset by taking in twenty five minutes of input data and feeding four different models to predict seventy five minutes, one hundred minutes, and one hundred and twenty five minutes of data. The data is then fed into a deep learning model utilizing a model known as Self Attention Gated Recurrent Unit (SaGRU). The SaGRU is tested four times, once for predicting seventy five minutes, once for predicting one hundred minutes, and twice for one hundred and twenty five minutes. The models were then compared against each other regarding Mean Squared Error (MSE) and Mean Absolute Error (MAE) along with the Normalized Mean Squared Error (NME) and the Normalized Mean Absolute Error (NMAE). Each metric was taken along multiple thresholds comparing the performance when hotspots are present and when hotspots are absent. The resultant showed that regardless of the sequence length, there was minimal negative impact on early predictions, but as the predicted sequence increased significant loss could be seen on the later predicted frames.Item Embargo Analysis of LEAC biosensor for scalable manufacturing using BPM and FDTD simulation methods(Colorado State University. Libraries, 2024) Holmes, Cameron Dane, author; Lear, Kevin L., advisor; Nikdast, Mahdi, committee member; Kipper, Matt, committee memberThe increasing demand for rapid, scalable, and accurate diagnostic tools has driven the development of optical biosensing technologies. LEAC (Local Evanescent Array-Coupled) biosensors, which leverage the evanescent field generated by optical waveguides, are particularly well-suited for applications in biomedical diagnostics, environmental monitoring, and point-of-care testing. LEAC biosensors have previously been fabricated in incomplete and unoptimized near-commercial CMOS processes and fully custom processes in a university cleanroom but have not been implemented in suitable high-volume processes such as commercial silicon photonics. A primary motivation for the research presented in this thesis is to evaluate the ability to fabricate LEAC biosensors operating at 1550 nm wavelengths in the commercial AIM Photonics' active silicon photonics process. This thesis presents a comprehensive tolerance analysis of LEAC sensors for both bulk sample layers (400 nm thick) and protein monolayers (10 nm thick) in AIM's process, focusing on the impact of variations in key design parameters—specifically waveguide core thickness, cladding layers, and photodetector placement—on sensor sensitivity. Beam Propagation Method (BPM) and Finite-Difference Time-Domain (FDTD) simulation techniques are employed to assess how these tolerances affect optical field propagation, power dissipation, and flux into the photodetector, serving as proxies for sensor performance. Additionally, the study examines crosstalk between multiple sensing regions, evaluating how refractive index variations in one region influence adjacent regions—an important consideration for multi-region sensors. Results show that sensor sensitivity increases with cladding thickness and decreases with waveguide core thickness. A 25 nm manufacturing error in core thickness resulted in less than a 10% sensitivity shift, and a 300 nm cladding thickness error had a similarly small effect. Resonant absorption between the core and photodetector was observed across both bulk and monolayer samples. Sensitivity depends heavily on proximity to resonance; a 10% error in photodetector thickness at resonance caused a 600% change in sensitivity, while off-resonance, the same error had minimal impact. Coupled Mode Theory (CMT) explained these energy transfers and power fluctuations. ANOVA analysis of full-device FDTD simulations quantified forward crosstalk due to modulated absorption from sample regions closer to the optical source (upstream). Forward crosstalk was found to be negligible for protein monolayer samples but could be significant in bulk samples. However, even in bulk samples, forward crosstalk was largely mitigated using photocurrent ratios with a reference region. A crosstalk ration was used as a metric to determine the influence of each refractive index (n1, n3) on the photocurrent ratio. In the forward crosstalk direction, the use of photocurrent ratios decreased the magnitude of the forward crosstalk ratio; however, the use of photocurrents inherently introduce dependance on downstream indices (reverse crosstalk). Reverse crosstalk, caused by reflections at the dielectric boundary between sensing regions, was found to be negligible using photocurrent ratios with bulk analytes; however, with monolayers, the use of photocurrent ratios introduced a slight dependence on the downstream region, indicating minor backward crosstalk. This can be mitigated by using raw current values rather than current ratios. Raw currents eliminate backward crosstalk in region 1, while photocurrent ratios effectively eliminate forward crosstalk in region 3.Item Open Access High-rate GNSS satellite clock estimation: implications for radio occultation bending angle precision(Colorado State University. Libraries, 2024) Ko, Yao-Chun, author; Chen, Haonan, advisor; Yao, Jian, advisor; Chiu, Christine, committee memberThe Global Navigation Satellite System (GNSS) radio occultation (RO) technique plays a vital role in collecting data for meteorological and space weather prediction. It is exemplified by the COSMIC-2 low-Earth-orbit (LEO) satellite constellation, which collects the GNSS signals from an elevation angle of 90° to below the horizon. Those GNSS observation data above 5° elevation angle are used for the precise orbit determination of satellites, while those GNSS observation data below 5° are used for the RO processing. A key part of the RO processing is to estimate the bending angle due to the atmospheric refraction, which requires an accurate information of the positions and clock offsets of both the transmitter (i.e., GNSS satellite) and the receiver (i.e., COSMIC-2 satellite). Previous research at University Corporation for Atmospheric Research (UCAR) [1] indicates a notable reduction in the intrinsic uncertainty of GLONASS radio occultation when employing higher-rate GNSS satellite clock products (e.g., from 30-second sampling interval to 2-second sampling interval). However, that work only analyzed one day of dataset. To analyze multiple days of dataset, I have developed a software program that can automatically generate high-rate GNSS clock products by using a GNSS toolkit called GINAN [2]. This program is also important to the future UCAR's RO postprocessing and near-real-time processing. To be specific, it first downloads, merges, and decimates 1-second GNSS-receiver data from 50 worldwide ground stations, and then runs the GINAN software to generate clock products. I have validated the clock products generated by the program by comparing to International GNSS Service (IGS) analysis centers' clock products – the standard deviation of the time difference between our clock products and the clock products published by the Center for Orbit Determination in Europe is as small as ~ 0.1 nanoseconds. Using one week of 2-sec clock products generated by the program, I have run the standard RO processing and found that the bending-angle uncertainty of the GLONASS RO has been reduced by ~ 34%, as compared to if using the existing 30-sec clock products. Admittedly, there is no obvious improvement for the GPS RO because the GPS satellite clocks are stable at a short term of <= 30 seconds. By pushing down the noise of the RO technique, we can possibly observe the atmosphere at an unprecedented precision which could benefit the research of atmosphere modelling, the operation of weather monitoring and forecast, and even the study of space weather.Item Embargo Fusion of observations from C-band polarimetric radar and S-band profiler radar during a convective storm(Colorado State University. Libraries, 2024) Adubi, Tunde Habibullah, author; Venkatachalam, Chandrasekar, advisor; Cheney, Margaret, committee member; Popat, Ketul, committee memberThis study discusses a procedure to measure and correct attenuation of the radar signals caused by the presence of partially melted ice hydrometeors (graupels and hails) in convective storms by utilizing simultaneous observations from a C-Band dual-polarization scanning radar and a vertically pointing S-Band profiler radar. The C-Band radar, used in this study, is known as the Atmospheric Radar for Meteorological and Operational Research (ARMOR) radar, situated in Huntsville, Alabama. Also, the S-Band profiler radar is maintained by the NOAA Physical Sciences Laboratory (PSL) and is about 50 kilometers west of the ARMOR radar site. A convective storm event characterized by squall lines is investigated. Within the squall line region, the presence of partially melted ice particles led to significant attenuation of the radar signal at C-Band, resulting in reduced reflectivity (Z). To address this issue, a conventional attenuation correction approach based on differential propagation phase measurements for rain medium was applied and compared with measurements from the S-Band profiler. The analysis revealed that correcting for rain attenuation alone was insufficient to address the heightened attenuation caused by melting ice hydrometeors. Consequently, a new attenuation correction methodology was developed, accounting for melting ice hydrometeors. Initially, profiles of specific differential propagation phase (Kdp) were studied to identify the exact location (range gates) containing melting ice particles. An attenuation correction coefficient for melting ice hydrometeors was estimated, and a piecewise attenuation correction procedure was implemented to address regions of rain and melting ice hydrometeors separately. Validation of the new attenuation correction technique involved simultaneous comparison of vertical reflectivity profiles obtained from the C-Band and S-Band profiler radar. Both instruments were matched spatially and temporally due to different viewing geometry. The results demonstrate that the new approach significantly enhanced the correlation between profiler measurements and attenuation-corrected radar reflectivity from the C-Band radar. Overall, this thesis determines experimentally, the attenuation coefficient in melting ice that is not available much in the literature today.Item Open Access Rotor position synchronization control methods in central-converter multi-machine architectures with application to aerospace electrification(Colorado State University. Libraries, 2024) Lima, Cláudio de Andrade, author; Cale, James, advisor; Chong, Edwin, committee member; Herber, Daniel, committee member; Kirby, Michael, committee memberWith the continuous advancement of the aerospace industry, there has been a significant shift towards More Electric Aircraft (MEA). Some of the advantages of the electrification of some actuation systems in an aircraft include lower weight --- hence, lower fuel consumption, --- robustness, flexibility, ease of integration, and higher availability of sensors to achieve better diagnostics of the system. One cannot ignore the challenges of the electrification process, which encompasses finding appropriate hardware architectures, and control schemes, and obtaining at least the same reliability as traditional drives. The thrust reverser actuation system (TRAS), which acts during landing to reduce the necessary runway for the aircraft to fully decelerate, has a big potential to be replaced by an electromechanical version, the so-called EM-TRAS. Among the different hardware architectures, the central-converter multi-machine (CCMM) stands out for employing a single power converter that drives multiple machines in parallel, saving weight and room usage inside the aircraft. This solution comes with its challenges related to the requirement of ensuring position synchronization among all the machines, even under potentially unbalanced mechanical loads. Since there is only one central converter, all the machines are subject to its common output, limiting the control independence of each machine. Moreover, the lack of position synchronization among the machines can cause harmful stresses to the mechanical structure of the EM-TRAS. This work proposes a solution for position synchronization under CCMM architectures, for aerospace applications. The proposed method utilizes three-phase external and variable resistors connected in series with each of the machines, which increases the degrees of freedom (DOF) to control independently each machine under different demands. Mathematical modeling for the different components of the system is presented, from which the proposed solution is derived. Numerical simulations are used to show the working capabilities of the external resistor method. The performance of the position synchronization is enhanced via H-infinity control design methods. Hardware experiments are also presented, obtained from an experimental testbed that was partially designed and constructed during this work. Both numerical and experimental results are in agreement. Initial findings show that the method is promising and works well under some operating conditions. However, some limitations of the method are presented, such as the unstable operation under negative loads. An alternative position synchronization method for CCMM systems is proposed at the end of this work. The method is based on independently controlled induced voltages on each machine's power cables through low-power auxiliary converters and three-phase compact transformers, resulting in independent terminal voltages applied to each machine. This work describes the method and validates it through numerical simulations. Initial findings show that the method overcomes some of the limitations of the external resistors method, while keeping -- and, in some cases, improving -- the overall performance in terms of convergence time and peak position error.