Digital Commons Network^™

Investigating The Dynamic Rating Method: Effects Of Flood Wave Geometry On Discharge Estimation, Daniel James Read

Masters Theses

Rating curves are a vital tool to convert from observed stage to discharge in streamflow monitoring. Most gaging sites utilize a simple rating curve, which assumes a monotonic relationship between stage and discharge. In most cases, this assumption is valid; however, dynamic effects of flood waves often cause significant error in discharge estimation for mildly sloped streams. The dynamic rating method utilizes a numerical solution of the St. Venant Equations applied to time series stage data to compute discharge. Within this method, there is a flood wave parameter called the flood wave factor. The researchers designed a formal computational model …

Validating A Low-Cost Radio Frequency Characterization Framework For Development-Grade Software-Defined Radios In Short-Duration Cubesat Missions, Thomas Wayne Francois

Masters Theses

Software-defined radios (SDRs) and CubeSat platforms have reduced the cost and complexity of space-based communication systems, enabling broader participation in satellite missions. While low-cost radio hardware is increasingly accessible, the ability to characterize and validate its performance remains constrained by the high cost and limited access to traditional RF test equipment. This disparity creates a challenge for small satellite development teams, which must characterize communication-system technical performance with limited access to laboratory-grade instrumentation.

This thesis presents a low-cost RF characterization framework for assessing key radio-frequency performance metrics using readily available hardware and measurement techniques. The approach integrates frequency translation, SDR-based …

Transformer-Based Language Models For Bitcoin Market Prediction And Interpretation, Erich Gozebina

Masters Theses

"In the context of a Master's thesis in applied mathematics, this work investigates compact transformer-based language models as an instrument for Bitcoin-related predictions and decision support. The work connects three topics: the structure of the Bitcoin system and its data, the mathematical and algorithmic foundations of deep autoregressive transformers, and the design of practical training pipelines for financial applications. On this foundation, a reproducible ETL pipeline for Bitcoin data is developed and two forecasting experiments are conducted with the compact language model nanochat. The first experiment approaches the prediction of next-day price movements through autoregressive next-token generation based on structured …

Characterizing A Delay-Programmable Two-Color Femtosecond Light Source For Coherent Control Of Atomic Multi-Photon Ionization Processes, Mason Tanner Toombs

Masters Theses

"In this work a delay-programmable two-color femtosecond laser based on a chirped-seed noncollinear optical parametric amplifier was characterized. This design utilizes a titanium-sapphire crystal oscillator to generate broadband pulses of light spanning approximately 600-1200 nm, with a pulse duration of ~5 fs, repetition rate of 80 MHz, and pulse energy of ~2.5 nJ. An infrared portion of the light, spanning approximately 1020-1060 nm, is split off from the pulse and used to generate a pump beam. By dispersing the remaining seed beam and controlling the relative timing delay between the seed and the pump, tunable frequencies within the seed can …

Structured Decomposition And Efficient Adaptation For Scientific Data Movement: From Transfer Concurrency To Genomic Data Acquisition, Rasman Mubtasim Swargo

Masters Theses

"The end-to-end performance of scientific data movement is increasingly limited not by raw network capacity but by whichever resource along the path is momentarily slowest, a bottleneck that no single static configuration can track. This thesis argues that the right response is to decompose each problem along the resource or workflow axis where bottlenecks vary, adapt only the genuinely dynamic control variables at runtime, and make that adaptation efficient through utility-guided or offline-learned control rather than expensive online search. The thesis develops this principle through four systems of widening scope. AutoMDT decomposes a transfer by operation, assigning separate concurrency levels …

Exploring New Poly-Anion Based Materials For Lithium – Ion Battery Cathodes, Sutapa Bhattacharya

Doctoral Dissertations

The escalating global demand for sustainable energy storage solutions has driven intensive research into novel electrode materials. Polyanion-based cathode materials have emerged as promising candidates due to their structural stability, safety, and voltage tunability enabled by the inductive effect of polyanionic groups. This research explores the synthesis, crystal structure, and electrochemical performance of new polyanion-based cathode materials featuring vanadium, molybdenum, and iron within phosphate and selenite frameworks.

Novel selenite-based materials, such as LiFe(SeO₃)₂ and Li₀.₂₅V₂O₃(SeO₃)₂, were synthesized and characterized, revealing stable electrochemical cycling associated with Fe2+/Fe3+ and V⁴⁺/V⁵⁺ transitions. Additionally, a systematic investigation was conducted on molybdenum phosphate compounds, including …

Theoretical Study Of Long-Range Molecular Interactions, Adrian Luis Batista-Planas

Doctoral Dissertations

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions—with electrostatic, induction, and dispersion as main components—play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well-established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed Long-Range-Fit (LRF), an interactive and user-friendly software package designed to automate …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

Security And Energy-Efficiency In Federated Learning, Priyesh Ranjan

Doctoral Dissertations

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, the participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the participants' identities, FL may attract adversaries aiming to hamper the underlying model. These adversaries aim to submit malicious weight updates that corrupt the performance of the server model. Further, these models when communicated to the participating clients extend the behavior which is undesirable. Additionally, FL suffers from increased energy consumption at the edge device level due to …

Zirconium Carbide Based Materials For Extreme Aerospace Environments, Nathaniel Hyman Blatt

Doctoral Dissertations

This research focuses on the processing and properties of zirconium carbide-based materials to promote their use in extreme environment aerospace applications, including nuclear thermal propulsion and hyper sonics. Several carbide systems including ZrC, ZrC-Mo cermets, (Zr, Nb)C, and a high entropy carbide were developed. The ZrC-Mo cermet was studied extensively to understand the effect of starting carbide grain size on the final microstructure, composition, elastic moduli, hardness, fracture toughness, room and elevated temperature flexural strength, thermal diffusivity, electrical resistivity, thermal expansion coefficient, and thermal conductivity. It was shown that heat transport in the cermets was dominated by the ZrC phase …

Investigations In Hydrogen Ironmaking, Joseph William Govro

Doctoral Dissertations

The purpose of this research is to contribute to the Grid Interactive Steelmaking with Hydrogen (GISH) project. This research investigates the viability of both producing and melting Direct-Reduced Iron (DRI) utilizing hydrogen. Conventional CO reduced DRI will be referred to as “C-DRI” and DRI produced using hydrogen gas will be referred to as “H-DRI”.

An H-DRI pilot plant was constructed in Golden Colorado. The pilot plant was commissioned and successfully operated four campaigns. Process improvements were made throughout the campaigns and the process was optimized. In addition to running the pilot plant in a pure hydrogen condition, the pilot plant …

Strontium Titanate For Capacitor And Energy Storage Applications At Cryogenic Temperatures, Hung Trinh

Doctoral Dissertations

This study investigates the dielectric properties of single crystal and ceramic strontium titanate (SrTiO₃) for cryogenic capacitor applications from room temperature to 4 K. Permittivity (k) and loss tangent are dependent on temperature, frequency, mechanical stress, and applied DC electric field. Accordingly, the dielectric constant and loss tangent were measured at various frequencies and DC bias levels. Loss tangent data are also presented as equivalent series resistance (ESR). For single crystal SrTiO₃, an impurity level of ≈500 ppm barium resulted in an increase of the maximum permittivity to approximately 50,000 at 6 K. This relatively high …

Essays On Accelerated Failure Time Models For Recurrent Event Data, Emmanuel Masavo Djegou

Doctoral Dissertations

Recurrent event data arise in many fields such as medicine, reliability, insurance, and economics, where the same event may occur repeatedly for a subject. Accelerated Failure Time (AFT) models provide an intuitive framework for relating covariates to event times and offer a useful alternative to proportional hazards models, allowing direct prediction of event timing under right censoring. However, existing AFT extensions for recurrent events, such as accelerated gap time (AGT) models, often fail to account for interventions between events and may not capture complex temporal patterns.

In this work, we first propose a class of semiparametric AGT models incorporating an …

Machine Learning Based Automation Of Pcb Pdn Design And Optimization, Haran Manoharan

Doctoral Dissertations

The rapid increase in power density and stringent power-integrity requirements in modern System-on-Chip (SoC) platforms have made Power Delivery Network (PDN) design an increasingly complex, multi-stage challenge. Critical decisions must be made both during pre-layout planning, such as stackup configuration, power-plane geometry, and early decoupling capacitor (decap) budgeting, and during post-layout refinements. Traditional heuristic and evolutionary optimization techniques struggle with scalability, require extensive manual iteration, leading to long runtimes and limited adaptability across varying board configurations. To address these challenges, this work proposes a unified reinforcement-learning-driven framework for automated PDN synthesis and decap optimization that spans both pre-layout and post-layout …

Advancing Coal Rib Support Design Through The Integration Of Field Studies And Numerical Simulations, Alper Kirmaci

Doctoral Dissertations

Coal rib stability remains a major safety concern in U.S. underground coal mines, with rib failure-related injuries and fatalities still occurring. A key challenge is the lack of a standardized methodology for designing rib support systems that can address varying geological conditions. As a result, many mines rely on trial-and-error or traditional practices, leading to inconsistent designs. This research aims to develop a systematic methodology for rib support design to improve coal rib stability in U.S. mining operations.

The study consists of: i) field monitoring in active room-and-pillar coal mines, ii) in-situ pull-out tests on coal ribs, iii) numerical model …

Critical Parameters Controlling Oxide Scale Formation And Hydro-Descaling Efficiency During Steelmaking, Tochukwu Princewill Ojiako

Doctoral Dissertations

In modern steelmaking, cast slabs are exposed to high-temperature oxidizing environments during secondary cooling, reheating, and hot rolling, resulting in the formation of multilayer oxide scales on the steel surface. These scales interact with mold-flux residues originating from the casting process (CC). The morphology, chemistry, and adhesion of oxide scale strongly influence its removability during high-pressure hydraulic descaling and ultimately determine the surface quality of hot-rolled products. However, the mechanistic relationship between oxide scale evolution, scale-steel interfacial structure, and hydraulic descaling performance remains poorly understood.

This dissertation investigates oxide scale formation, modification, and removal in low-carbon thin-slab steels produced by …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Fully Differential Studies On Dissociative Capture In P + D2 Collisions And On Ionization In P + He Collisions, Shruti Majumdar

Doctoral Dissertations

Advancing our understanding of few-body dynamics in simple atomic systems is a fundamental objective in atomic scattering research. The underlying problem is that the Schrödinger equation is not analytically solvable for more than two mutually interacting particles. This involves a comprehensive exploration of various channels, such as ionization, capture, and excitation. A common theoretical approach to describe ion-atom collisions is based on perturbation theory, where the scattering amplitude is expanded in powers of the interaction potential. Here, understanding the few-body problem means accurately describing the relative importance of the higher- vs the first order terms.

In the case of ionization, …

Milankovitch Paleoclimatic Signals In Upper Permian–Lower Triassic Fluvial-Lacustrine Records, Bogda Mountains, Greater Turpan-Junggar Intracontinental Rift Basin, Nw China, Wentao Zhang

Doctoral Dissertations

"The objective of this study is to understand whether Milankovitch climatic and/or tectonic processes influenced the sedimentation of the upper Permian–Lower Triassic fluvial-lacustrine cyclic deposits in Bogda Mountains, greater Turpan-Junggar intracontinental rift basin, Xinjiang Uygur Autonomous Region, NW China. This study carried out gamma analysis, gamma and astronomical tuning, and spectral analysis on the Wutonggou, Jiucaiyuan, and Shaofanggou low-order cycles of the South and Central Taodonggou, South and North Tarlong, Dalongkou, and Zhaobishan sections. Stable and positive gamma values support the assumption of facies-dependent sedimentation rates for non-marine facies. The sedimentation rates range from 0.04 to 2.43 m/kyr, and 0.21 …

Accelerating Circular Economy Transition For A Sustainable Built Environment: A Systems-Based Framework, Radwa Walid Yassin Eissa

Doctoral Dissertations

"The Circular Economy (CE) has gained momentum as a transformative framework for steering the construction value chain towards greater sustainability. However, this transition requires shifts in production and consumption patterns, adoption of circular business models, and the replacement of linear practices. Despite growing interest, CE adoption in construction faces persistent challenges, including sectoral fragmentation, lack of integrated governance frameworks, and limited coordination among stakeholders. This dissertation aims to accelerate the CE transition in the built environment through a multi-scale, interdisciplinary approach spanning five modules: (1) Module 1 develops a construction value chain-structured portfolio of CE strategies and assesses their implementation; …

Bond Graph And Extended Generalized Average Method With Applications In Cyber-Physical Systems And Power Electronics, Arnold Anthony Fernandes

Doctoral Dissertations

"This research examines two applications of control theory. The first application considers the bond graph (BG) modeling technique, which is used to develop the MATLAB structural analysis toolbox (MATSAT), an open-source toolbox for sensor placement and qualitative system analysis that considers the observability and fault-detection capabilities of multi-domain cyberphysical systems. The toolbox provides information on redundant sensors, guiding the system designer in cost and security trade-offs. The toolbox uses traditional BG causality assignment procedures. Additionally, MATSAT provides optimal causality assignment methods that perform significantly better at assigning causality to BGs with increased junctions, sources, and simple meshes, without encountering causality …

Integrating Stakeholder Priorities Into Resilience Planning And Investment Decision-Making, Victoria Jeaneen Kraemer

Doctoral Dissertations

"Resilience is a system’s ability to bounce back from a disruption. However, resilience needs may vary across different stakeholders. To better integrate stakeholder input into resilience planning and investment decision-making, existing frameworks were combined to develop the Integrated Stakeholder-Centered Resilience Framework. This broad framework is tailorable across contexts and stakeholder types. Research results demonstrate the use of this framework to evaluate the effects of disruptions and represent stakeholder preferences for investments. In the first study, participants were randomly assigned to power outage scenarios, which did not affect their criteria priorities. An exploratory factor analysis revealed two main priorities, (1) Absorption …

An Energy-Stable Mixed Cg-Dg Scheme For A Full Shliomis Phase Field Model Of Two-Phase Ferrofluid Flows, Mahdi Gharehbaygloo

Doctoral Dissertations

"Ferrofluids are magnetic nanoparticle suspensions whose motion couples surface tension, flow field, magnetostatics, and magnetization dynamics. This dissertation develops, analyzes, and validates an energy-stable finite element method for a two-phase ferrofluid model that couples the Cahn-Hilliard equations with the full Shliomis model of single-phase ferrofluids, retaining its damping torque term, magnetic torque term, and magnetic stress term.

The spatial discretization is a mixed continuous Galerkin (CG) and discontinuous Galerkin (DG) formulation. It uses continuous ��2 elements for the phase field, chemical potential, velocity, and magnetostatic potential, discontinuous ��2 elements for the magnetization, and discontinuous ��1 elements for the pressure. The …

Sapphire Optical Fiber Bragg Grating Sensors Based On Dispersive Microwave-Photonic Frequency-Time Domain Analysis, Ruimin Jie, Chen Zhu, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Sapphire fiber Bragg gratings (SFBGs) have attracted growing interest for high temperature sensing in harsh environments, yet their interrogation typically relies on optical spectrum measurements, demanding a high-resolution optical spectrum analyzer (OSA) that is bulky, expensive, and constrained in acquisition speed. Moreover, the inherently multimode nature of sapphire fiber further complicates spectrum-based demodulation, thereby limiting the achievable sensing resolution. In this paper, we propose and experimentally demonstrate a microwave-photonic interrogation approach for SFBG sensors. Instead of measuring the optical reflection spectrum, the complex frequency response in the microwave domain of an SFBG is acquired using a vector network analyzer (VNA) …

Resolving Stiffness Trade-Offs In Simultaneous Pressure And Vibration Sensing Using A Corrugated-Tube Fiber-Optic Sensor, Yizheng Chen, Yan Tang, Jie Huang, Qi Zhang, Biyao Shi, Zewei Wu

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes and experimentally demonstrates a corrugated-tube-based fiber-optic sensor capable of measuring pressure, vibration, or both simultaneously. To address the limited sensitivity of conventional diaphragm-based designs, the sensor incorporates an optimized corrugated tube that balances the conflicting stiffness requirements for pressure and vibration measurements. The corrugated tube, acting as a mechanical transducer, is integrated with an extrinsic fiber-optic Fabry–Perot interferometer (EFPI). The EFPI cavity is formed between a reflective surface at the sealed end of the corrugated tube and the cleaved end face of an optical fiber fixed within a mounting assembly. In this configuration, displacement of the corrugated …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Deep Learning Approach For Microwave Imaging Based On Deep Convolutional Asymmetric Encoder-Decoder Structure And Physics-Induced Loss, He Ming Yao, Shiji Song, Michael Kwok Po Ng, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we introduce an innovative deep learning (DL) methodology designed for real-time quantitative microwave imaging (MWI). Our approach is centered around the utilization of a deep convolutional asymmetric encoder-decoder structure (DCAEDS), which requires only a single-frequency far-field measurement of the electromagnetic (EM) scattered field as input and subsequently predicts the contrasts (permittivities) of the target materials. During the offline training process, we incorporate an EM forward solver specifically crafted to compute the EM scattered field generated by the predicted target contrasts (permittivities) produced by the DCAEDS. The DCAEDS is seamlessly integrated with this EM forward solver to optimize …

Femtosecond-Laser-Inscribed Cladding Fiber Bragg Gratings: Architectures, Design Trade-Offs And Future Sensing Opportunities, Koustav Dey, Rex E. Gerald, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Femtosecond (fs) laser inscription enables fiber Bragg gratings (FBGs) to be positioned beyond the fiber core, including within the cladding, thereby expanding control over modal interactions and directional sensing. These cladding fiber Bragg gratings (CLFBGs) extend FBG functionality through interactions with core, cladding, and evanescent fields. Their operation does not involve fundamentally different grating physics; rather, core-mode, cladding-mode, and coupled-mode responses depend on power distribution, modal overlap with the laser-inscribed refractive-index modulation (RIM), grating position, fiber geometry, modal excitation, and interrogation configuration. This Perspective reviews advances in CLFBG fabrication, optical characteristics, sensing mechanisms, and applications. Cladding-waveguide-assisted, evanescent-field-coupled, and direct cladding-mode-excitation …

Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg

Computer Science Faculty Research & Creative Works

The Antarctic Demonstrator for the Advanced Particle-astrophysics Telescope (ADAPT) gamma-ray/cosmic-ray instrument serves as a precursor to the proposed APT mission. The APT mission is designed to improve sensitivity in the MeV-TeV gamma-ray range by an order of magnitude compared to current missions and is optimized for dark-matter and multimessenger research. The ADAPT instrument uses scintillating fibers for particle tracking and sodium-doped cesium iodide (CsI:Na) tiles read out with wavelength shifting (WLS) fibers for imaging, with solid-state silicon photomultipliers (SiPMs) for calorimetry. It includes four layers of imaging calorimeter detectors and scintillating-fiber trackers, functioning both as a Compton and Pair telescope …

Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to read out sensor data from front-end electronics. To support continuous data streams or high trigger rates, FPGA logic may be employed to process raw sensor readout values, reducing the volume of data transmitted, processed, and stored by downstream CPU-based computational platforms. Across instruments, these FPGA-based processing pipelines often have similar semantics and share common stages. However, diverse telescope designs require unique implementations of the constituent algorithms, and the logic is often rewritten from scratch for a new instrument. Writing, simulating, and debugging firmware is difficult and time consuming. However, High-Level Synthesis …