Digital Commons Network^™

Information Capacity For Time-Varying Adversarial Networks, Raphael Thorp

Theses

This thesis investigates the capacity of time-varying adversarial networks where intelligent adversaries can corrupt transmissions on restricted subsets of network edges that change availability over time. Building upon the work of Beemer et al. on adversarial network coding with restricted adversaries, we analyze networks whose topology evolves temporally, motivated by applications in space networking and delay-tolerant systems. We introduce different adversarial modes that capture different temporal aspects of corruption behavior and establish their hierarchical relationship with respect to network capacity. The analysis employs time-expanded graph representations and develops recursive counting methods for computing expected network capacity under random time assignments, …

Evaluating Sequential Inference Via Poisson And Change-Point Frameworks, Samuel Johnson

Theses

Sequential testing reduces sample size and costs compared to fixed-sample approaches by allowing decision making during data collection. This thesis investigates sequential methods for Poisson processes and change-point detection. Wald’s sequential probability ratio test (SPRT) and efficiency measures are developed. Practical applications of the SPRT are studied, from detecting noisy sources to classification algorithms. The MaxSPRT is derived, and its critical value algorithm is optimized. The MaxSPRT, a Bayesian SPRT, Wilk’s generalized likelihood ratio test, and the Cash statistic are examined. Various change-point algorithms are surveyed, including Shewhart control charts, cumulative sum methods, homogeneity tests, and multiple change-point approaches. These …

Simplified Soil-Based Approach For Maximum Pressure Prediction In Horizontal Directional Drilling Operations, India Clark

Theses

Horizontal Directional Drilling (HDD) is a trenchless pipeline installation method used in areas where traditional trenching is not feasible due to environmental concerns, existing infrastructure, or other constraints. However, HDD presents significant engineering challenges, with design failures often resulting from excessive installation stresses and Hydro Fracture. Hydro Fracture occurs when the pressure of drilling fluid exceeds the soil’s bearing capacity, leading to unintended subsurface fractures. To mitigate this risk, researchers have relied on the Delft Equation to estimate the maximum allowable mud pressure for borehole stability. However, this equation does have notable limitations and assumptions, which may not accurately reflect …

A Deep Learning Multi-Channel Framework For Indoor Image Retrieval, Vidhi M. Bhatia

Theses

Processing and analyzing multi-source image data is vital across a wide range of fields, including artificial intelligence (AI), deep learning, computer vision, geolocation, autonomous navigation, remote sensing, and forensic investigations. Although conventional image matching methods such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) have proven effective in controlled setting, they often face challenges when applied to diverse datasets that exhibit substantial variations in viewpoint, scale, lighting conditions, and modality. Recent developments in deep learning, including convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), and self-supervised learning, have led to the creation of more efficient and precise …

Multiscale Finite Element Analysis Of Cylindrical Jellyroll Based Energy Storage Devices, Wesley James Knee

Theses

Lithium batteries are an ever-expanding solution for energy storage; however, their internal chemistry is highly prone to combustion. Any severe mechanical failure can lead to this scenario, which is problematic as their use in electric vehicles increases. As such, finite element modeling efforts need to improve in order to properly define the failure mechanics of these batteries. Efforts have already been made to create an accurate model of the battery as a whole, but this generalized model does not capture the short-circuiting phenomena within the internal components. To solve this problem, a representative volume model that generates input data and …

Near-Field Analysis Of Horn Antennas For Electromagnetic Compatibility Testing, Landon Riley Carroll

Theses

Standard gain horn antennas are widely used in electromagnetic compatibility (EMC) testing and antenna measurements. In EMC testing, it is often required to generate high electric field intensities across a wide frequency range within the near-field region. However, the focusing properties of antennas are deteriorated in the radiating near-field region. In order to increase engineering confidence in EMC testing, it is desirable to understand the root causes of the aforementioned radiation degradation. In this thesis, the near-field characteristics of three different pyramidal standard gain horns are investigated using full-wave analysis, analytical methods, and measurement. The causes of near-field gain reduction …

Active Learning Methodologies And Applications, Pradeep Bajracharya

Theses

Active learning (AL) is a Machine Learning (ML) strategy that offers a promising solution to challenges posed by the data-intensive tasks by selectively querying the most informative data, reducing the need for extensive labeled datasets. This approach is particularly valuable for fields like cardiac research and scientific simulations, where data labeling becomes expensive and often impractical. Deep Active Learning (DAL) extends this concept to deep learning models, which are typically data hungry, where the focus is more to optimize data selection in discrete search spaces. Conversely, Explorative Active Learning (EAL), is tailored for exploration of continuous search spaces, employing machine …

A Study Of Neural Compression Techniques For Image Compression Using A Novel Algorithmic Lossless-Ness Criteria, Abhishek Prasanna Simha

Theses

Neural compression methods claim near-lossless reconstruction capabilities based on traditional metrics such as PSNR and MS-SSIM. However, their impact on downstream machine learning tasks remains largely unexplored. This thesis evaluates three state-of-the-art CompressAI models (Cheng2020-anchor, bmshj2018-hyperprior and mbt2018 ) on high-resolution satellite imagery using semantic segmentation as a downstream task benchmark. We introduce the idea of algorithmic losslessness with respect to a downstream machine learning task and use it to benchmark existing state-of-the-art neural compression algorithms. We also compare the results with non-neural compression techniques. We conducted experiments on 11 urban satellite images using two segmentation approaches: MMSegmentation (7 classes) …

A Comparative Study Of Classical And Deep Feature Learning Methods For Prediction Of User Request Patterns For Cloud Resources, Swaroopa Ammenadka

Theses

Large-scale cloud storage systems managing petabyte-scale data face cost challenges due to sparse, irregular file access patterns. Traditional attribute-based methods often fail to capture dynamic temporal and event-driven behaviors. This thesis compares classical statistical and deep learning approaches for predicting file access patterns using five months of real-world access logs from Tsinghua University’s FTP server (2.9 million events). Five forecasting models—ARIMA, SARIMA, Exponential Smoothing, Prophet, and 1D CNNs—are evaluated across 1,161 files. A novel hybrid clustering method combines time series similarity with forecasting accuracy metrics. Results show ARIMA significantly outperforms deep learning (2.3× better accuracy: 0.0188 vs. 0.036 MAE) for …

Concentrated Benefits And Dispersed Costs: The Contributions Of Pareto, Olson, And Ostrom, Nathan John Cuthbertson

Theses

Concentrated benefits and dispersed costs (CBDC) is a central concept in the field of public choice as well as economics more broadly. The phrase concentrated benefits and dispersed costs refers to a situation in which economic benefits are concentrated and the associated costs are borne by a larger group. This thesis argues that the elements of CBDC are common in the literature without the term being used directly. This study is an exercise in the history of economic thought. The study is structured into three case studies, with each dedicated to the work of an influential economist: Vilfredo Pareto (1848-1923); …

A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia

Theses

Understanding complex three-dimensional systems and spatial relationships is a recurring difficulty in healthcare education, where students are often expected to reason about internal structures and multi-system processes from 2D diagrams, textbook figures, and static mannequins. This thesis presents the design, implementation, and mixed-methods evaluation of Systems Simulation, a reusable mixed reality (MR) application intended to help undergraduate nursing students explore human anatomy and pathophysiology using immersive 3D visualization.

Built in C# with the StereoKit framework for Microsoft HoloLens 2, Systems Simulation organizes nine anatomical body systems within a shared application. Learners can select a system, anchor the model in their …

Vision Or Exemplification? Augustine And Aquinas On Divine Illumination, Matthew Richard Cain

Theses

Many interpreters of Thomas Aquinas since the second half of the twentieth-century emphasise the Platonist dimensions of his thought, particularly his metaphysics. This contrasts with, and is a shift away from, the dominant Aristotelian interpretations of previous generations. Aquinas’s theory of knowledge, however, tends still to be interpreted in Aristotelian terms, according to which Augustine’s Platonist illuminationism is replaced by a Peripatetic emphasis on sensation and abstraction in the soul.

This thesis reexamines the role of illumination in Augustine and Aquinas’ theories of knowledge. It asks whether scholarship today ought to reconceive the relationship between these two major thinkers in …

The Wellbeing Experiences Of Newly Employed Teachers At High-Fee-Paying, Independent, Pre-Kindergarten To Year 12 Schools In Perth, Western Austalia, Helen Mary Aguiar

Theses

The purpose of this research was to explore the wellbeing experiences of newly employed teachers at high-fee-paying, independent, Pre-Kindergarten to Year 12 schools in Perth, Western Australia. The research explored two factors: experiences that enhance teacher wellbeing and experiences that diminish the wellbeing of newly employed teachers in these schools.

The review of literature highlighted four areas that formed the conceptual framework for the thesis. These four areas were the role of the teacher, wellbeing defined, the factors enhancing teacher wellbeing and the factors diminishing teacher wellbeing. These areas led to the development of the primary research question and the …

Bayesian Networks For Safety-Critical Systems, Joseph Mietkiewicz

Theses

This thesis addresses a operational challenge in modern industrial operations: the increasing complexity of systems and the consequent cognitive burden on operators. As industrial technologies advance, the human-computer interface has become the primary conduit for information flow, playing a pivotal role in operational decision-making. However, the proliferation of data often leads to information overload, potentially compromising rather than enhancing operator performance. This research explores an approach to this pressing issue through the application of Bayesian networks as decision support systems in safety- critical scenarios. Our study employs a multi-faceted approach, combining theoretical modeling with empirical testing. Through collaboration with industry …

Dual-Mode Linear Phased Array Antennas For Monopulse Radars, Jonathan Waters

Theses

Phased array antennas are widely used around the world. Much research is focused on extending capabilities of modern wireless systems and radars. Dual-mode elements are researched for use in beamforming, monopulse radar and multi-frequency applications. Beamforming in itself is an extensively researched topic, aimed at canceling interference and/or finding signal angle-of-arrivals. The goals of this thesis are two-fold. Firstly, to take the monopulse capabilities of a dual-mode antenna element to allow for a linear phased array antenna to generate the sum, delta-azimuth, and delta-elevation channels whereas normal linear phased array antennas can only generate a single difference channel. Secondly, to …

Heat Transfer Of Additively Manufactured Turbine Alloy Blade Tips With Different Surface Enhancement Post-Processing Methods, Morgan Tatge

Theses

The investigation results presented here consider the heat transfer characteristics of seven different turbine blade tips. Six of the blades are additive manufactured with GRX-810, followed by different surface enhancement post processing techniques. Those techniques include as built, abrasive flow machining, chemical polishing with chemical mechanical polishing, micromachining, chemical polishing, and electropolishing. The seventh blade is machined with 1016 steel. These procedures give a different surface texture roughness for each blade. A transonic linear cascade within a transonic/supersonic wind tunnel is utilized. The impulse response method is employed to determine heat transfer coefficient distributions for the blades. The Tad/T0i temperature …

Multi-Factor Mathematical Optimization For Healthcare Referral And Facility Placement, Mitchell Moody

Theses

This thesis addresses the challenge of optimizing patient referrals in healthcare systems by integrating clinical needs, geographic proximity, and dynamic infection risk. Healthcare referral networks (HRNs), which capture patient transfers between hospitals, serve as the foundation of the study. First, a recommendation algorithm is developed to assign patients by jointly considering clinical compatibility and logistical considerations like travel distance, striking a balance between quality and accessibility. Building on this, a reinforcement learning framework is proposed to dynamically adjust referral strategies for vulnerable patients by incorporating the evolving risk of hospital-acquired infections. Finally, long-term planning is explored through methods that recommend …

Generation Of True Random Numbers With Actively Stabilized, Low Dimensional Chaos, Aidan Barton

Theses

True Random Number Generators (TRNGs) are heavily used and their design often relies on empirical validation from statistical test suites. Reliance solely on empirical observations to estimate entropy rates for cryptographic applications introduces risk, as accurately inferring long-term correlations demands prohibitively large datasets. Empirical validation of TRNGs must be supplemented by strong theoretical backing to justify estimated information theoretic quantities. We present an electronic, hardware TRNG scheme that produces a maximally random output guaranteed from first principles. This TRNG uses a pulse-width based hardware realization of an iterated map that produces physical entropy via low-dimensional chaotic dynamics. The simple dynamics …

Chaos-Based Thumbnail-Preserving Image Encryption, Sara Mog

Theses

As the landscape of image data usage continues to change, cloud services need to store data securely, reliably, and at times maintain a reasonable level of human usability. Thumbnail-preserving encryption (TPE) is a niche area of image encryption research that provides a balance between security and privacy. A TPE scheme encrypts an image in such a way that the thumbnail of the encrypted image is the same as or similar to the thumbnail of the original image. This work presented a chaos-based TPE scheme and explored potential pitfalls encountered when using chaos for encryption. The TPE scheme used a chaos-based …

Development Of A Constitutive Model Free Method To Determine Full-Field Stresses Based Upon Fundamental Physics Principles, Joel David Barnes

Theses

Advances in imaging techniques allow for the determination of full-field displacements and strains during materials testing. However, most analysis techniques rely on strain gages and virtual extensometers instead of utilizing all available full-field data. Those methods that do utilize the full-field strain and displacement measurements, such as Finite Element Model Updating and the Virtual Fields Method, must also rely on a user-defined constitutive material models to relate the stresses and strains. While these methods can provide accurate full-field stresses and insights into complex material behavior in some cases, they are constrained by the fact that they require a pre-selected constitutive …

Predicting Bus Ridership In Dubai Using Advanced Machine Learning Models, Fatma Almheiri

Theses

In rapidly urbanizing cities, the public transport systems have to be efficient and reliable with proper demand forecasting to guarantee customer satisfaction. This paper explores how evidence-based machine learning tools can be used to forecast weekly bus ridership in Dubai, using a dataset of more than 10 million trip-level boarding observations between June 2013 and July 2014. It was aggregated to 25,195 route-week observations, and preprocessed with temporal feature engineering, lag variables and rolling statistics. Three predictive models have been tested: Linear Regression (as a baseline), Random Forest and XGBoost (as an ensemble method). Mean Absolute Error (MAE), Root Mean …

Determination Of Material Deformation At Truly Constant Strain Rates Utilizing Full-Field Stress And Strain Measurements, Derryk Lee Daignault

Theses

The rate sensitivity of materials is an important aspect to consider when designing components; such as in manufacturing processes, aerospace vehicle impacts/propulsion systems, and automobile collisions. Characterizing a material’s rate sensitivity can be expensive and time consuming due to multiple tests being necessary at each given strain-rate to fully characterize the response. This project proposes a methodology to determine the material response at multiple truly constant true strain rates from a single test using non-uniform geometry and the strain localization phenomenon. Finite element simulations using Ansys LS-DYNA are used to design potential new test specimen geometries to induce constant and …

Fault Tolerance In Krylov Subspace Methods, Sandesh Pandit

Theses

The increasing complexity of modern high-performance computing (HPC) systems, with their vast number of processing units and extensive memory hierarchies, introduces significant challenges to system resilience, particularly in the face of soft errors. Krylov subspace methods, which play a pivotal role in solving large-scale sparse linear systems and eigenvalue problems, are iterative in nature and thus susceptible to the propagation of soft errors. In this paper, we examine the fault tolerance properties of two widely-used Krylov subspace methods: the Lanczos method and the Bi- Conjugate Gradient (BiCG) method. Specifically, we analyze the impact of soft errors introduced during the critical …

Neural Acceleration Of Graph Partitioning, Vishvam Patel

Theses

Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more. Spectral methods are known to produce quality partitions while minimizing edge cuts for a wide range of problems. However, the computational cost associated with the calculation of the Fiedler vector, an eigenvector associated with the second smallest eigenvalue of the graph Laplacian, remains a significant bottleneck. In this paper, we present an neural acceleration approach to spectral bisection partitioning by replacing the traditional eigenvalue calculation with a simple artificial neural network model to approximate the fiedler vector. We demonstrate …

Nonlinear Dynamics And Data Driven Modeling, Mattan Tseng

Theses

The research areas of dynamics and data-driven modeling overlap in existing literature. Some research uses dynamical systems as a test bed while developing new modeling methods. Other papers attempt to use data-driven modeling as a tool to better understand, or potentially predict the behavior of a dynamical system. The primary goal of this thesis is to further the symbiotic relationship between these two research areas. Firstly, this paper provides a concrete example of how a basis function decomposition of the Lorenz system can provide a quantitative performance metric for data-driven modeling methods. Secondly, a novel application of an existing modeling …

Application Of Machine Learning To Predict Ultrasound Wave Propagation In Biphasic Fluid–Solid Media, Jahnavi Dandamudi

Theses

The objective of this thesis is to investigate the application of Machine Learning (ML) algorithms to predict ultrasound wave propagation in the cartilage tissue of the knee joint. The learning data necessary for the ML algorithm has been generated through finite-element method (FEM)-based simulations for solving the Biot theory equations governing the propagation of continuous ultrasound through the cartilage. Specifically, we computed the ultrasound-induced dilatations and displacements in the microscale cartilage that is represented as consisting of four zones, namely the chondrocyte cell and its nucleus, the pericellular matrix (PCM) that forms a layer around the chondrocyte, and the extracellular …

Efficient Lossless Compression Of Malaria Infected Image Datasets Using Vision Transformers And Deep Autoencoders, Md Firoz Mahmud

Theses

In the field of medical imaging, lossless compression techniques play a critical role in maintaining the fidelity of medical data, while improving the efficiency of data storage and transmission, especially in telemedicine applications. This thesis addresses the development of a novel machine learning-based method for lossless compression of images of erythrocytes infected with malaria. The proposed method has two stages. In the first stage, a Vision Transformer, a computer vision model that can learn long-range dependence, is employed to separate erythrocyte images into two categories: infected and uninfected. This classification stage is essential as it allows the subsequent compression approaches …

An Evaluation Of The Idm Car-Following Model : Simulation And A Dynamical Systems Perspective, Trevor Mcclain

Theses

The Intelligent Driver Model (IDM) is a car-following model that has existed since the early 2000s, existing as one of the most widely used follow-the-leader models to simulate safe and realistic driver behavior. In this work, the IDM is used to evaluate the suitability of ROS 2-Gazebo for vehicle simulation, showing that significant non-determinism is present within the simulation. Additionally, we evaluate the effects of delay and an inserted Followerstopper car following model on the traffic dynamics of the system. Specifically, the generation of stop-and-go waves in idealized traffic from a dynamical systems perspective is examined. We also show that …

A Mutually Exclusive 3d Convolution Based Time Dilation Framework For Future Frame Prediction In Video Streams Of Natural Events, Benjamin Robinson

Theses

In this thesis, a framework for predicting future frames from videos of naturally occurring processes will be presented. This is achieved by learning the underlying physical laws of the natural process through the exploitation of spatiotemporal features from the frames of the video. Our framework defines windows containing frames at varying time intervals, which we call dilations, and uses them to predict future frames using a 3D convolutional neural network (CNN). The network architecture accepts six time-dilated windows of input video frames and computes spatiotemporal features for each window maintaining the exclusion of the resulting features across three phases of …

Evaluating The Accuracy Of Johnson-Cook Strain Rate Sensitivity For Fcc Aerospace Structural Materials, Sydney Carlota Resnick

Theses

Understanding aerospace materials’ strain rate sensitivity is crucial for accurate designs and analysis for structural performance in dynamic loading conditions, such as rocket launches, airplane collisions, and debris impacts. Three common aerospace face-centered cubic (FCC) metals are evaluated through various strain rate tests and FEA and simulation modeling. Quasi-static and intermediate tests are performed on a hydraulic load frame, and dynamic tests are run on a split-Hopkinson pressure bar, all in tensile motion. This covers the range of strain rates from 1x10-3 s-1 to 1x103 s-1. Results reveal little strain rate sensitivity at room temperature for A286 steel and AL7075 …