Digital Commons Network^™

An Exploration And Evaluation Of Biologically Informed Variational Autoencoders For Scrna-Seq Data Analysis, Marjorie R. Principato

Graduate Thesis and Dissertation post-2024

Deep learning has been widely applied to the analysis of high-dimensional biological omics data, especially single-cell RNA sequencing (scRNA-seq). Incorporating biological information into the architecture of variational autoencoders (VAEs) has been shown to enhance model interpretability, allowing the model’s behavior to reflect the underlying mechanisms of the biological system used for its architecture. In recent years, several studies have employed these biologically informed VAEs to model large-scale single-cell transcriptomics data, with models correctly differentiating between cell states and identifying active pathways. However, systematic benchmarking and comparison of different biologically informed VAE architectures remain limited.

In this study, I evaluated and …

When Sediment Meets Strategy: Linking Shoaling Dynamics, Vessel Behavior, And Dredging Optimization, Matthew P. Davies

Graduate Thesis and Dissertation post-2024

Navigation channels are the economic arteries of U.S. maritime trade, yet their efficiency and safety are continually threatened by sedimentation and the escalating costs of dredging. Traditional tools used by the U.S. Army Corps of Engineers (USACE) to forecast shoaling and estimate dredging needs often fall short of capturing the complex interplay between channel condition, vessel behavior, and maintenance decisions. This dissertation develops and integrates three complementary innovations to address this challenge. First, a Parametric Linear Regression Methodology (PLRM) is introduced to improve shoaling predictions by blending long-term and short-term sedimentation rates with tunable weighting factors. This method achieves forecasting …

Pbwt-Based Methods For Biobank-Scale Haplotype Data Analysis, Pramesh Shakya

Graduate Thesis and Dissertation post-2024

Haplotype matching is the task of finding identical matching segments given a group of aligned genetic sequences. Haplotype matches play an important role as they represent biological phenomena. Long haplotype matching segments could indicate identity-by-descent segments showing some degree of genealogical relatedness between the individuals that the haplotypes belong to. Similarly, long segment matches that are adjacent to each other may indicate recombination events. Advanced genotyping technology has made it feasible for large numbers of individuals to be genotyped resulting in many biobanks across the world. This requires efficient algorithms that can analyze large amounts of genetic data.

In this …

Cross-Layer Optimization In C-V2x Towards Level-5 Vehicular Automation, Mahdi Zaman

Graduate Thesis and Dissertation post-2024

Connected and autonomous vehicles (CAVs) necessitate highly reliable, scalable, and responsive Vehicle-to-Everything (V2X) communication systems. Despite recent advancements, Cellular-V2X (C-V2X) in its state-of-the-art form exhibits limitations in managing resource allocation and communication robustness, particularly in dense vehicular scenarios. Additionally, C-V2X standards have been tailored to particularly support periodic broadcasts; whereas true vehicular autonomy demands service-focused communication which differs in transmission behavior compared to periodic safety messages. This document addresses critical bottlenecks within C-V2X by developing cross-layer optimizations across physical, MAC, and application layers. This thesis proposes enhancements on C-V2X resource scheduling protocol with proposing novel adaptive and selective power control …

Examining Information Flows Within Transformer Models Using Transfer Entropy, Clayton Barham

Graduate Thesis and Dissertation post-2024

The rapid growth of artificial intelligence, in terms of both power and prevalence, motivates the need for new ways to study explainability in deep neural networks. This is especially true for transformer architectures and large language models, which are the driving force behind the current surge in artificial intelligence. The complexity of transformer architecture foils many pre-existing methods for studying explainability in deep neural networks. To address this gap in knowledge, this dissertation proposes a new method of studying explainability in transformer architectures that leverages insights from neuroscience, employing transfer entropy to map information flows between components of the transformer …

On Transient Dynamics And Persistent Organization Of Landscape Evolution, Aysan Hassanzadeh Bavojdan

Graduate Thesis and Dissertation post-2024

The evolution and organization of landscapes result from the competition between tectonic uplift, fluvial incision, and hillslope diffusion. Understanding how these processes interact under varying external conditions (i.e., extreme climatic events) is critical for predicting landscape dynamics and long-term geomorphic adjustment. Using a physically based landscape evolution model, we first investigate how variations in the fluvial incision coefficient (K) and soil diffusion coefficient (D) mimic different climatic conditions and control the transient and steady-state organization of landscapes. Results indicate that landscapes with the same non-dimensional index (defined as the ratio of the timescales of advective (fluvial) to diffusive (hillslope) processes) …

Study Of Injector Performance From An Accumulator-Based Plumbing System, Michael R. Benedict

Graduate Thesis and Dissertation post-2024

Accumulators are an alternative solution to pumps as they do not require energy in flight. This means power generated does not have to be diverted to an accumulator, boosting overall efficiency. Additionally, accumulators do not have pressure oscillations or cavitation risks typically associated with pump-driven systems. Further, they are lightweight systems with lower points of failure than traditional pumps. This makes them an interesting candidate for use in hypersonic flight. However, prior to flight data, accumulator-based systems should be tested statically on the ground to determine its feasibility. In this study, a piston accumulator is used in a plumbing system …

Exploring Segmentation, Detection And Tracking In Videos, Jyoti Kini

Graduate Thesis and Dissertation post-2024

Perception is the cornerstone of autonomy, enabling agents to interpret complex environments through segmentation, detection, and tracking. These perception capabilities are fundamental to intelligent systems, whether guiding self-driving vehicles along urban roads, aerial robots mapping diverse terrains, or embodied AI agents interacting naturally with people and objects. Despite sustained research efforts, current perception models remain constrained by fragmented architectures, heavy annotation dependence, and poor generalization to unseen viewpoints, which hinder scalability and reliability. This dissertation advances scene understanding through innovations in segmentation, detection, and tracking, with contributions spanning four key directions that emphasize scalability through end-to-end formulations in 2D and …

Informative Path Planning Algorithms For Anomaly Detection, Samuel Matloob

Graduate Thesis and Dissertation post-2024

Informative path planning (IPP) algorithms are widely used to control the movement of drones or ground robots when the objective is to efficiently collect information from an environment of interest. For instance, in agriculture, drones might be used for the timely detection of outbreaks of plant diseases. Two distinct classes of IPP algorithms are the system2atic algorithms, where the robot moves in a regular planned pattern (such as lawnmower) aiming for uniform sampling and random algorithms (such as random waypoint) that aims to collect a random sample of the environment. In this dissertation, we present several novel IPP algorithms, that …

Thermal Characterization Of Cmc Materials Subjected To Deflagration And Detonation Process, Luis C. Longas

Graduate Thesis and Dissertation post-2024

Transition to greener fuels such as hydrogen is a process that requires developing several technologies. One of those is being able to develop materials that can withstand the higher temperatures of hydrogen combustion. One of the most promising materials are the ceramic matrix composites which could potentially replace the superalloys with thermal barrier coatings which are used in gas turbine engines. This project aims to characterize a ceramic matrix composite manufactured by Polymer Infiltration and Pyrolysis process. The characterization of the material would be done under deflagration and detonation operating conditions. This study would pave the way for the development …

A Predictive Model Of Adsorption Of Cryogenic Liquids In Aerogel-Based Materials, Julie E. Foroosh

Graduate Thesis and Dissertation post-2024

Commodities that are gaseous at standard temperature and pressure are generally stored in thick-walled pressure vessels, or as cryogenic liquids in vacuum-jacketed tanks. An advantage of cryogenic storage is that more mass can be stored per unit volume. However, cryogenic tanks are often bulky and cannot be made into conformal geometries. Adsorption storage in porous and flexible materials, such as aerogel blankets, is a promising method for storing these commodities. Previous studies have shown that when the commodity is adsorbed from the liquid cryogen, instead of from a gaseous state, the mass adsorbed per unit volume of aerogel blanket is …

Modeling And Predicting The Thermal Behavior Of Si/Cnt Nano Composites Using Pinns, Olamide Osigbemeh

Graduate Thesis and Dissertation post-2024

Silicon/Carbon Nanotube (Si/CNT) composites have shown great performance as Thermal Interface Materials (TIMs) for cooling in advanced electronics. The heat transfer in these materials is highly influenced by the interfacial thermal resistance (ITR) at the interfaces between the CNT filler and the silicon matrix. The ITR poses a critical challenge that hinders the design and performance of the TIM. This research designs a Physics Informed Neural Network (PINN) that predicts the thermal behavior of Si/CNT through a forward and inverse solver since they are known networks in solving complex partial and ordinary differential equations. However, despite the advantages of PINNs, …

Fuel Injection For Hypersonic Air-Breathing Propulsion Systems, Justin Sprunger

Graduate Thesis and Dissertation post-2024

Hypersonic air-breathing propulsion systems face unique challenges related to fuel injection due to the supersonic flow in the combustor. For example, residence times within supersonic combustors are often orders of magnitude shorter than the timescales required for quality delivery of fuel. Additionally, because of the high incoming momentum of the free stream, fuel penetration is difficult to achieve without intrusive fueling structures. Intrusive fueling structures, however, are not desired as they induce high efficiency losses and require advanced cooling systems or thermal protection solutions. Therefore, an understanding of the trajectory of fuel injected from the sidewall of a combustor is …

Autonomous Navigation And Real-Time 3d Reconstruction Of Interior Spaces Using A Quadruped Robot, Steve Wilhelm

Graduate Thesis and Dissertation post-2024

This thesis develops and evaluates a ROS2-based application that enables the quadruped robot Unitree Go2 to simultaneously perform autonomous navigation, real-time mapping, and 3D reconstruction in indoor environments using Simultaneous Localization and Mapping (SLAM). The robot system, equipped with onboard sensors, aims to generate high-resolution point clouds that provide detailed 3D scans. The assessment focuses on the platform’s practical feasibility, operational reliability, and accuracy. The hardware setup combines the Unitree Go2 robot with its built-in sensors, a high-resolution 3D LiDAR, and an RGB-D camera. Software components include ROS2, the Unitree SDK, a navigation framework, and SLAM approaches. The application was …

Towards Robust Deep Learning: Label Noise Modeling, Algorithms, And Applications, Diego Fernando Linares Gonzalez

Graduate Thesis and Dissertation post-2024

This thesis investigates robust and hybrid deep learning with emphasis on improving accuracy and generalization in challenging real-world settings, including learning under noisy supervision and applications to complex scientific data.

In the first part, we address instance-dependent label noise by using a tensor factorization–based methodology that models the noise transition process as an instance-dependent decomposition. We also utilize low-rank block-term structures to capture class-specific and instance-specific correlations. Moreover, we include a robust loss function and rank regularization to achieve stable optimization during model training. We validated this with experiments on benchmark datasets, like Fashion-MNIST, CIFAR-10, and CIFAR-10N, demonstrating higher accuracy …

Reconfigurable Antenna Arrays For Mm-Wave Directional Software-Defined Radio Communications, Ectis Velazquez Jr

Graduate Thesis and Dissertation post-2024

This dissertation explores design challenges and promising solutions for mm-Wave directional software-defined radio (DSDR) reconfigurable antenna arrays. Current state-of-the-art solutions for DSDR antenna arrays at mm-Wave frequencies are typically limited to one beam, which does not take advantage of the directivity and spatial reusability of mm-Wave bands. Additionally, mm-Wave phased array antennas typically incur large amounts of loss from both commercial and state-of-the-art beam steering methods. In this dissertation, both challenges are addressed and so- lutions are proposed through several novel antenna array designs. The first challenge, designing an array for mm-Wave DSDR communications is achieved through a switched-beam array, …

A Framework For Machine Learning Surrogate Modeling In Physics-Based Reacting Flow Simulations, Jose O. Bobren-Diaz

Graduate Thesis and Dissertation post-2024

The design of next-generation combustion systems relies on computer simulations that capture the multidimensional and multiscale physics of turbulent reacting flows. These simulations are computationally intensive, requiring trade-offs to produce results within allowable time and cost constraints. These trade-offs often consist of general assumptions and modeling simplifications that approximate complex physical phenomena. Consequently, these models can limit both the accuracy and the regime of applicability to real systems since these approximations are often heuristic and inconsistent with the governing physics. Moreover, in today’s rapid design-to-prototype development phase, making timely and informed design decisions is important. This requires fast and reliable …

Advancing Heterogeneous Collaborative Perception For Autonomous Vehicles, Babak Ebrahimi Soorchaei

Graduate Thesis and Dissertation post-2024

Recent advances in computational and communication technologies have enabled deep neural networks (DNNs) and high‐speed vehicular wireless links, paving the way for cooperative perception and cognition in autonomous systems. By exchanging complementary observations, these methods overcome inherent sensor limitations, such as occlusions and restricted range, and thereby improve the detection of partially hidden targets. However, real‐world deployments face challenges from noisy measurements (e.g., GPS inaccuracies) and heterogeneous perception stacks: vehicles often rely heavily on visual detectors, yet there is no standardization ensuring uniform object‐detector architectures across a fleet.

In this dissertation, we first explore the design of a lightweight detector …

Communication-Aware Trajectory Prediction And Tracking For Connected And Autonomous Vehicles, Arash Raftari

Graduate Thesis and Dissertation post-2024

This dissertation presents a unified framework for communication-aware trajectory prediction and vehicle state tracking in connected and autonomous vehicle (CAV) systems. Unlike most deep learning–based forecasting models that assume full observability of surrounding agents, the proposed approach explicitly accounts for limited communication range, packet loss, and bandwidth constraints that characterize real vehicular networks.

A model-based, error-driven information-passing policy is introduced to extend situational awareness beyond one hop. Rather than relaying messages based on distance or freshness, each vehicle evaluates the utility of received content in reducing system-wide position tracking error (PTE). Using compact Gaussian Mixture Model (GMM) representations of motion …

A Multivariable Feedback Approach To Individual Pitch Control For Fatigue Load Reduction In Wind Turbines, Kazi Ishtiak Mohsin Mr.

Graduate Thesis and Dissertation post-2024

This dissertation investigates advanced control strategies to improve performance and reduce fatigue loads in both land-based and floating offshore wind turbines. The proposed approaches target fatigue load mitigation at critical component frequencies while ensuring rotor speed regulation and efficient power generation. The control strategies are implemented and validated using both our in-house modeling and simulation platform, CRAFTS (Control Oriented Reconfigurable Acausal Floating Turbine Simulator), and the industry-standard OpenFAST model.

For land-based turbines, torque actuation and collective blade pitch control are incorporated into a nonlinear controller for rotor speed regulation and power generation. To address fatigue load reduction, individual pitch control …

Attention-Based 3d-Convolutional Neural Network Model For Mechanical Property Predictions Using, Daniel E. Traczyk

Graduate Thesis and Dissertation post-2024

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process …

Load Modulated Balanced Amplifier (Lmba) With Antenna-Vswr Resilience For Energy- And Spectrum-Efficient Communications, Jiachen Guo

Graduate Thesis and Dissertation post-2024

The rapid evolution of 5G and forthcoming 6G networks demands higher data rates and lower latency within increasingly congested spectrum resources. Advanced modulation schemes such as high-order quadrature amplitude modulation (QAM) and orthogonal frequency-division multiplexing (OFDM) have been widely adopted to improve spectral efficiency; however, these techniques substantially increase the peak-to-average-power ratio (PAPR) of transmitted signals. To enable power amplifiers (PAs) to operate efficiently under this conditions, load-modulated balanced amplifiers (LMBAs) have been developed as an effective solution while inherently supporting broadband operation. On the other hand, the deployment of massive multiple-input multiple-output (MIMO) technology and highly integrated active-antenna systems …

Enhancing Social Participation: The Role Of Communication Accommodations On Young Children With Ccn, Carli C. Brent

Graduate Thesis and Dissertation post-2024

BACKGROUND: Children with complex communication needs (CCN) have difficulty using and/or understanding speech to communicate, thus facing barriers for successful social participation in family-leisure activities due to limited access to communication supports in these environments. Social participation includes an individual’s active involvement in activities that consist of a social element to produce opportunities for meaningful interactions with others. Children with CCN benefit from communication supports, like Augmentative and Alternative Communication (AAC) devices. AAC supports children with CCN to effectively communicate with themselves, others, and their environment. While communication supports have been recently implemented into private and public spaces, the …

Everglades Restoration: Interdisciplinary Analysis And Pluralist Approaches, Sarah Swiersz

Graduate Thesis and Dissertation post-2024

This study uses interdisciplinary methods to analyze ecological restoration of the Florida Everglades (Everglades Restoration). Everglades Restoration is considered one of the largest ecosystem restoration projects in the world, relying on complex intergovernmental and sociopolitical relationships. Framing Everglades Restoration as a quintessential “wicked problem”, a problem that is inherently complex and unsolvable, provides grounds for interdisciplinary analysis. This study analyzes two epistemological approaches to Everglades Restoration: Western-scientific knowledge, the epistemological foundation of Everglades Restoration, and Indigenous Traditional Ecological Knowledge, which challenges the foundations of Everglades Restoration. Interdisciplinary analysis within a decolonial framework identifies limits to integration of knowledge, a core …

Characterization, Imaging, And Modeling Of Thermal Dynamics And Failure Mechanisms Of Quantum Cascade Lasers, Alejandro M. Villalobos Meza

Graduate Thesis and Dissertation post-2024

As advancements in Quantum Cascade Laser (QCL) technology continue to push the upper limits of continuous-wave performance, effective thermal management remains at the forefront of design considerations. The compact and highly tunable active region that has made QCL technology so attractive also imposes challenges to heat dissipation during continuous wave operation. To address this ongoing challenge, this work demonstrates a set of characterization and modeling techniques which can be used to interrogate different aspects of the thermal and structural properties of QCLs. Emphasis is placed on a suite of electron microscopy techniques and their role in providing a window into …

Holographic Optical Elements For Complex Beam Transformations, Shaghayegh Yaraghi

Graduate Thesis and Dissertation post-2024

In this thesis, we explore the design, fabrication, and application of volume holographic phase elements in photo-thermo-refractive (PTR) glass for two key purposes: expanding the eye-box in augmented reality (AR) displays and controlling optical angular momentum (OAM) modes in laser cavities.

Encoding complex phase structures in PTR glass enables efficient beam shaping, multiplexing, and wavefront engineering, offering strong potential in AR displays, optical communications, and structured light. Holographic optical elements (HOEs) recorded in PTR glass exhibit high diffraction efficiency (over 99%), thermal stability, and chemical durability. Combined with low absorption and scattering in the visible and near-IR, these features make …

Wavefront Sensing Using Nonlocal Metasurfaces, Arturo Martin Jimenez

Graduate Thesis and Dissertation post-2024

Many laser applications require wavefront control to compensate for aberrations caused by system misalignment, component fabrication errors, and propagation through inhomogeneous atmospheres. Aberrations caused by atmospheric turbulence remain particularly challenging to measure due to the temporally fluctuating wavefront distortions and large transverse phase variations in the beam. Conventional wavefront sensors, like the Schack-Hartmann (SHWS) are effective at low turbulence levels, but require relatively bulky optical elements. This thesis investigates the use of metasurface optical elements for use in wavefront sensors both in the low-turbulence limit and in deep turbulence conditions. Such metasurface-based wavefront sensors can achieve the functionality of complex …

Specialty Optical Fibers: Design, Fabrication, And Characterization, Md Abu Sufian

Graduate Thesis and Dissertation post-2024

As conventional silica fibers approach their performance limits in the face of exponentially growing demands for high data throughput in optical communication systems and constraints on power handling capability stemming from material limitations, several alternative technologies have been explored over the past decade. Despite the implementation of advanced multiplexing schemes and progress in digital signal processing capabilities, conventional fibers remain limited by their inherent physical constraints. In this advent, space division multiplexing, where the fiber cross-section comprises multiple simultaneous data channels, has been proposed as a potential solution to satisfy the bandwidth requirements of hyper-scale data centers, Artificial Intelligence infrastructures, …

Metasurface-Refractive Hybrid Lens Design, Ko-Han Shih

Graduate Thesis and Dissertation post-2024

Refractive optics are widely used in imaging systems while optical aberrations can limit their imaging performance and the typical solution to correct is cascading additional refractive optics with varying materials and shapes. Still, this scheme can result in bulky and costly lenses. Metasurfaces (MSs), with their compactness and ability to locally manipulate wavefronts, offer additional degrees of freedom in aberration correction. Integrating MSs with refractive optics creates MS-refractive hybrid lenses, enabling advanced optical performance while maintaining a compact design. Various methods have been proposed for designing aberration-correcting MSs in hybrid lenses, which often rely on predefined target phase profiles or …

Utilizing Programmable Spatial Light Modulators For Beam Shaping And Spectral Synthesis, Shree Ram Thapa

Graduate Thesis and Dissertation post-2024

Programmable spatial light modulators (SLMs) built on Liquid Crystal on Silicon (LCoS) technology enable precise control of multiple degrees of freedom of light, such as amplitude, phase, frequency, polarization, and time. We demonstrate applications of SLM technology that go beyond the conventional 4-f ultrafast pulse shaping approach. By developing a dual-oscillator power amplifier (DOPA), we show that nonlinear wave interactions inside optical fibers can be managed through the spectral phase adjustment of optical frequency combs produced by non-degenerate, cascaded four-wave mixing (CFWM). Our approach utilizes a programmable spectral phase profile, which allows precise regulation of an optical comb’s spectral content. …