Digital Commons Network^™

Machine-Checked Proofs For Correctness Guarantees In Containment Architectures, Sophie Russ

Master's Theses

Modern computing systems are increasingly susceptible to attacks at the hardware and software levels. Formal methods offer promising guarantees as to the correctness and security of systems. However, these methods tend to scale poorly and are thus insufficient to protect complex systems. Leveraging minimal amounts of trusted hardware and software to ensure the security of whole systems is a promising approach to gain the benefits of formal methods without having to overcome the scaling problem. TrustGuard realizes this approach: a containment architecture model that requires all outgoing communication from the host computer to be validated by a small, external hardware …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Exploiting Compiler-Introduced Vulnerabilities In C: A Cross-Compiler And Cross-Architecture Analysis Of Undefined Behavior, Erik Mccutchen

Master's Theses

Compilers are a critical component in generating secure software across engineering disciplines. However, languages like C that permit undefined behavior introduce a fundamental tension between the compiler’s interpretation of undefined behavior and the security of the generated code. This tension can result in security vulnerabilities that, from the programmer's perspective, are ``created'' by the compiler. The widespread use of these languages, combined with the complexity of modern optimizations and limited developer visibility into compiler behavior, makes these vulnerabilities both pervasive and difficult to detect.

Building on prior work, this thesis refines a dataset of C code snippets that exhibit Compiler-Introduced …

Frequent Itemset Mining With Tidyclust In R, Andrew D. Kerr

Master's Theses

Unsupervised learning is closely associated with clustering, however other methods fall under this umbrella such as data mining. In R, the tidyclust package provides a unified interface for clustering models, yet lacks support for data mining. This thesis addresses this gap by introducing the Apriori and ECLAT algorithms into tidyclust, with a focus on frequent itemset mining. Unlike traditional clustering models, frequent itemsets produce groupings of column variables, rather than cluster labels or partitions of observations. To address this, a novel clustering approach is proposed: items (columns) are grouped based on their ”dominant” frequent itemset. A key contribution is a …

Stability Insights From Modeling Chronic Myelogenous Leukemia, Giovani Thai

Master's Theses

This thesis centers around a model for chronic myelogenous leukemia (CML) as it behaves under imatinib treatment, a common medication for CML patients, and the anti-leukemia immune response. The dynamics are represented with a system of nonlinear delay-differential equations first constructed by Kim et al. in 2008, capturing population changes of T-cells and various CML growth stages. We investigate stability in both the clinical and mathematical sense. Through numerical simulations, we computationally incorporate a supplementary treatment plan to determine its effectiveness in aiding immune response and medication in achieving remission and full elimination. The primary goal is to conduct a …

Structural Color Pigments For Waterborne Coatings, Kyle William Liston

Master's Theses

Structural colors offer unique alternatives to conventional pigments, utilizing their microstructures to produce vibrant colors. These pigments offer an alternative source for colorants without the need for less environmentally friendly conventional pigments. In keeping with the sustainability approach, the use of waterborne coatings was chosen over solvent-based coatings. Here we report the synthesis of brushite platelet, silica supraball, and inverse opal pigments. Brushite platelets were produced through a condensation reaction, while silica supraballs were synthesized via the Stöber process and assembly was attempted using an oil-in-water emulsion. Inverse opal was produced through the templating of silica on poly(methyl methacrylate) (PMMA) …

Langlands Reciprocity And The Splitting Behavior Of Primes In Number Fields, Skip E. Moses

Master's Theses

This thesis explores the evolution of reciprocity laws in number theory in order to provide a conceptual bridge between the classical ideas of quadratic reciprocity and the modern framework of the Langlands program. We develop the necessary algebraic background to understand how the splitting behavior of primes in number fields reflects deep arithmetic structure in ℚ. Starting with quadratic fields and cyclotomic extensions, we motivate the development of the Kronecker–Weber theorem and the characterization of abelian extensions of ℚ. We then introduce Artin reciprocity and show how it generalizes quadratic reciprocity through the formalism of Frobenius elements and Artin L-functions. …

A Case Study Of Leveraging Auxiliary Data To Improve Treatment Effect Estimation Precision In An Hiv Clinical Trial, Lana Mai Huynh

Master's Theses

Randomized controlled trials (RCTs) are typically the gold standard for evaluating treatment efficacy in the medical field, yet they can have small sample sizes due to logistical, ethical, and financial constraints. This limitation can result in imprecise treatment effect estimates. Recent methods have sought to enhance the precision of RCT estimates by incorporating information from large, observational, “auxiliary” datasets. An auxiliary dataset includes units that were not randomized in the trial itself, but may be similar to the RCT sample. By leveraging predictive models trained on these auxiliary data, researchers can adjust for potentially more powerful covariates, thereby reducing variance …

Catalyzed Chemical Recycling Of Poly(Ethylene Terephthalate) For Enhanced Sustainability, Gilberto J. Hernandez-Leypon Jr.

Master's Theses

Plastics are ubiquitous in the production of consumer and industrial products; their chemical stability, low cost of production, and performance versatility make them the best option for many industries. Their inherent benefits, specifically durability, is the basis for the negative environmental impact of polymers. Improper containment of endof-life polymer products causes unintentional migration into the environment. The monomers for many common polymer types are also fossil fuel based further increasing the environmental impact of the plastic lifecycle. Recycling is a possible method of closing the loop of the plastic lifecycle. Currently most recycling is physical, thermally reprocessing used plastics into …

Data Driven Analysis Of Samara Seed Kinematics And Dynamics, Shashwat Sparsh

Master's Theses

Samara Seeds are a class of fruit most famously belonging to the Acer species and are characterized by their single-bladed geometry and their auto-rotation response during descent. This steady-state auto-rotation response is the subject of aerodynamic analysis which aim to quantify the performance. The period prior to the beginning of steady-state auto-rotation is classified as the transition regime and has not been the subject of intense scrutiny.

This thesis employs a data-driven approach to analyzing the kinematic and dynamic response of these seeds during both the transition and auto-rotation stages of flight to quantify the performance with respect to the …

Assessing Students’ Attitudes And Self-Efficacy In Introductory Statistics Courses, Kendall Hipes

Master's Theses

This study investigates the cognitive factors that shape students’ experiences in introductory statistics courses, with particular emphasis on the role of self-efficacy sources on student attitudes (mastery experiences, verbal persuasion, vicarious experiences, and physiological and emotional states) measured across multiple time points. Using linear multilevel models and qualitative interview data, the research examines patterns in students’ perceptions of interest, difficulty, effort, value, cognitive competence, and affect throughout an academic quarter. Findings indicate that higher levels of mastery experiences consistently predict more positive outcomes across all attitude dimensions, underscoring the importance of early and ongoing opportunities for success in fostering engagement …

Spatial And Functional Metrics For Analyzing Seedling Roots, Irina Ivchenko

Master's Theses

To study the complicated structure of a maestro pea seedling root, we investigate three spatial metrics and one functional metric. The spatial metrics are fractal dimension which measures the complexity, convex hull area which measures the occupied space, and Euler characteristic which topologically describes the shape of the root. We discuss the successes of various metrics and the possible limitations they bring to analysis. As a functional metric, we study the rhizosphere: the volume radially around the root branches from which the plant accesses nutrients and water. We investigate the relationship of the rhizosphere to the root, and propose how …

Fault Tolerant Dynamic Task Allocation For Heterogeneous Multi-Robot Systems, Jack R. Cline

Master's Theses

This research presents a novel approach to dynamic task allocation in heterogeneous multi-robot systems with integrated fault detection capabilities. As multiple industries are becoming more reliant on multi-robot systems for tasks, maintaining operational efficiency despite robot failures becomes critical. We propose a framework that combines optimization-based task allocation with a Kalman filter that estimates task progress for anomaly detection to identify unreliable agents and dynamically redistribute tasks. Observing values such as the normalized innovation squared (NIS), covariance, and progress rate, the algorithm can designate a robot as faulty. Embedding information about which robots are faulty in the task algorithm allows …

Understanding The Habitat Associations Of Nearshore Rockfishes Inside And Outside Of Marine Protected Areas Using Hook-And-Line Data, Kaila Raeanne Fritch

Master's Theses

Habitat is an important driver in shaping the distribution of a range of species. For demersal fish species, benthic habitat characteristics can drive distribution and community structure. Understanding the relationship between habitat and species distribution in important, especially for exploited species such as rockfishes (Sebastes spp.). Increased data on the relationship between rockfish and benthic habitat characteristics could enhance marine protected areas (MPAs) and management practices which are used to protect rockfishes along the U.S. West Coast. To address this, we used hook-and-line, catch-and-release data that were collected inside two MPAs on the central coast of California, and …

Banach Algebras And The Gelfand Theory Of Group Algebras On Locally Compact Abelian Groups, James Gabriel Bonvanie

Master's Theses

A Banach algebra is a complex algebra that is simultaneously a Banach space in which the norm is submultiplicative. Notably, $L^1(\mathbb{R})$ with the convolutional product is an Abelian, non-unital Banach algebra that admits an approximate identity. We rectify $L^1(\mathbb{R})$ lacking a unit via the unitization $L^1(\mathbb{R})\times\mathbb{C}$ with identity $(0,1)$. Unitization opens the discussion to the spectrum $\sigma(x)$ of a Banach algebra element, in which the spectrum is a nonempty, compact subset of the complex plane. The spectrum of an Abelian Banach algebra is fully characterized with multiplicative linear functionals, and we prove that the Fourier transform is the unique multiplicative …

Spos: An Attestation Solution For The Detection And Mitigation Of Point Of Sale Malware, Damian Singh Dhesi

Master's Theses

Securing 95% of card present transactions, accounting for billions of transactions a year, has made EMV the premier protocol for card-based payment. Created by and named after Europay, Mastercard, and Visa, the EMV protocol provides multiple solutions to resolve security concerns with the outdated, swipe-based, magnetic stripe payment. Such solutions are Chip and PIN which provides a more secure transaction at a significant time cost and EMV contactless which provides improved security to Chip and PIN at greater ease of use with its quick, tap-to-pay based payment. However, regardless of how secure the EMV protocol makes the card side of …

On Diffeomorphism Groups Of Surfaces, Madeleine Goertz

Master's Theses

Let $M$ be a closed, connected, smooth manifold. What are the symmetries of $M$? From a geometric viewpoint, the symmetries of $M$ are precisely its isometries, the self maps which preserve lengths and angles. In the smooth category, the symmetries of $M$ are its diffeomorphisms, the self maps which are smooth and have a smooth inverse. Does expanding our notion of symmetries to include diffeomorphisms result in ``more'' symmetries in a meaningful sense? As a formal conjecture, the claim is that the isometry group of $M$ is a deformation retract of the diffeomorphism group of $M$. If $M$ is the …

Investigating Social Presence In Collaborative Keys For Asynchronous Courses, Lily P. Cook

Master's Theses

Asynchronous virtual courses have become increasingly popular in the years following the global Covid-19 pandemic. These courses, with no set meeting schedule, offer flexibility to both students and instructors, but pose challenges for developing a collaborative learning environment. The Community of Inquiry framework (Rourke et al., 1999) identifies three essential components in an online course necessary to foster a collaborative environment- teaching presence, cognitive presence, and social presence. Social presence is especially impacted in asynchronous learning settings, which presents challenges for the students to display their personalities and connect with the community. This study investigates Collaborative Keys (CKs), structured collaborative …

Minifying Deep Denoising Networks With Knowledge Distillation, Antonio L. Rozzi

Master's Theses

Hearing loss is a prevalent condition, affecting hundreds of millions globally, with a higher incidence among older adults. While hearing aids are the standard treatment, the majority of those who could benefit from hearing aids choose not to wear them, attributing this decision in large part to their inability to perform well in conversations in large groups and in noisy situations. To date, no denoising systems on commercial hearing aids are able to improve speech intelligibility. Recent advances in artificial intelligence research have shown that large deep-learning models can in fact improve speech intelligibility by removing background noise from audio. …

Analog Hardware Implementation Of A Linearly Constrained Quadratic Program Real-Time Solver, Claire E. Tylutki, Claire Tylutki

Master's Theses

This thesis presents the design, implementation, and analysis of a hardware system for solving Linearly Constrained Quadratic Programs (LCQPs) in real time. The architecture follows a generalized feedback structure composed of three key elements: gradient descent on the quadratic cost function, saturation-based nonlinearity to enforce inequality constraints, and an integral controller with an anti-windup mechanism to regulate dynamic behavior and determine steady-state error. This majority analog system converges with equilibria that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. Using a representative LCQP, this work presents simulation of the circuit in PLECS and LT Spice to confirm the feasibility of the novel …

Filling Gaps In Scientific Data Sets Using Physics Informed Neural Networks: A Case Study In Velocity Fields, Ellen Saunders

Master's Theses

Gaps in scientific data sets are a persistent issue for researchers in a variety of fields, and while nothing makes up for missing out on real data, well-simulated synthetic data can be a useful tool. In the world of image processing, machine learning techniques have become quite sophisticated at taking an image with a missing component and filling in that space with something believable. The aim of this thesis is to take machine learning techniques similar to what gets used in image processing and repurpose them to infill gaps in scientific data sets in a realistic manner. This thesis compares …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Flair: A Fog-Native Workload Placement Framework For Qos-Aware Scheduling In Smart Manufacturing, Daiwik N. Swaminathan

Master's Theses

Smart manufacturing environments utilize a variety of heterogeneous IoT sensors that continuously stream large volumes of data. To detect and mitigate critical events such as fires or accidents in real time, this data must be promptly analyzed by machine learning (ML) image classifiers. These classifiers are typically deployed on fog computing nodes located within the manufacturing warehouse facilities to ensure low-latency responses and to protect data privacy. Although fog nodes offer a better alternative to remote cloud data centers for such latency-sensitive tasks, they are often resource-constrained and highly sensitive to the composition of workloads, particularly in heterogeneous environments where …

Distributed Formation Control Of Nonholonomic Mobile Robots: Safety-Critical Leader-Follower Approach With Obstacle Avoidance And Dynamic Reconfiguration, Kelvin C. Villago

Master's Theses

Networked control systems for multi-agent robotics have emerged as a critical paradigm for executing complex coordinated tasks in diverse environments. While formation control serves as the backbone of such systems, real-world deployment introduces significant challenges including communication constraints, environmental obstacles, and the need for adaptive reconfiguration. This research addresses these challenges by developing a novel unified framework that seamlessly integrates obstacle avoidance algorithms with dynamic formation reconfiguration capabilities, specifically designed for communication-limited networked control architectures. The proposed framework represents a significant advancement over existing approaches by simultaneously handling both static and dynamic obstacles while maintaining system cohesion under communication constraints. …

Opening The Black Box With Regal: A Novel Explainable Ai Approach To Uncover Key Predictors In Search And Rescue Success, Brandon Hyunjun Kim

Master's Theses

The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL …

Sentry V3: Extending Context Switches On A Trusted Secure Coprocessor, Mark Kong

Master's Theses

Software correctness and integrity is only ensured through trust in the un- derlying hardware. However, modern computer systems are complex to design and secure. Thus, given the choice between performance and security, companies will often prioritize performance, resulting in vulnerable systems. This creates exploitable systems that must be patched retroactively because business value performance over security. One approach to this issue is to separate the root of security from the rest of the system to create a minimal trusted computing base. Trustguard is one instance of this. Trustguard implements a Containment Architecture with Ver- ified Output (CAVO) model which shows …

Enhancement Of A Python Integral Boundary Layer Method, Jeffrey T. Azuma

Master's Theses

This thesis presents a series of additions to a Python based integral boundary layer software library known as PyBL. This library provides the user the means to calculate boundary layer properties using different models for boundary layer behavior. The additions feature the integration of a set of new laminar and turbulent integral boundary layer models originally developed for and currently found in the program XFOIL. These models were modified to fit the ODE solver method at the foundation of PyBL. A new method for PyBL that stitches a laminar and turbulent solution together has been developed to improve the ability …

Efficient Gan-Based Adversarial Example Generation Against Ml-Based Network Intrusion Detection Systems, Darren D. Hartono

Master's Theses

In the realm of network security, Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating malicious activities targeting networked devices. Traditionally, these systems have relied on signature-based and anomaly-based detection techniques. However, the increasing complexity and adapt- ability of cyber threats have driven the adoption of Machine Learning (ML) ap- proaches in modern NIDS, significantly improving their ability to detect a wider range of attack vectors. Despite these advancements, ML-based NIDS remain vulnerable to adversarial examples—deliberately crafted inputs designed to mislead models and trigger incorrect classifications. Originally identified in the field of computer vision, adversarial examples now pose …

Data-Driven Product Recommendations: A Decision Support Framework Utilizing Customer Reviews, Tyler A. Lopez

Master's Theses

With the considerable presence of e-commerce in society, vast number of purchasable goods, and increasing brand variety, consumers are faced with the challenge of buying products that they perceive to be of greatest value to them. To assist consumers with making better informed decisions, e-commerce websites allow individuals to post their own experiences and score the products that they purchase. Despite this information, the variety of experiences and feedback that consumers share do not always lead to clarity on whether a product is best suited for the purchaser. To help guide customers through a simplified purchasing process from the perspective …

Design And Development Of A Laboratory Star Tracker System, Kevin D. Iverson

Master's Theses

This thesis presents the design, calibration, and validation of a low-cost star tracker system and hardware-in-the-loop testbed intended for small satellite applications. The system is composed entirely of commercial off-the-shelf components and integrates both a custom star tracker and a screen-based star field simulator for controlled, repeatable testing. An analytical modeling approach was used in the early design phase to predict system accuracy based on key optical and geometric parameters, informing the selection and configuration of components and guiding the development of calibration routines.

A simulation environment was developed to validate the star tracker software and assess the impact of …