Engineering Commons^™

The Impact Of Urbanization On Water Scarcity And Waterborne Diseases In Eastern Africa: A Case Study Of Nairobi, Lucy N. Kamau

Open Access Master's Theses

Urbanization in Eastern African cities has rapidly accelerated, placing immense strain on existing water infrastructure and sanitation systems. In Nairobi, this has resulted in spatial disparities in access to clean water and heightened vulnerability to waterborne disease risks. The study aimed to (1) assess the impact of urbanization on water scarcity and waterborne diseases in Nairobi using geospatial analysis and remote sensing (2) quantify urbanization trends 1999–2024 (3) identify spatio-temporal water scarcity hotspots for the years 2019 and 2024 (4) model waterborne disease risk maps for 2019 and 2024 (5) Overlay disease risk map with hospitals. To quantify urbanization for …

The Coral Carousel: A Device And Method For In-Situ Propagation Of Deep-Sea Corals, Gregory Bales

Open Access Master's Theses

The research described herein covers the development of a tool station for performing in-situ propagation of corals using a work class ROV. This includes a system for manipulating coral fragments and affixing them to a cement base plug. To validate this tool station, testing was performed both in the lab as well as at depth in the Gulf of Mexico. As restoration of shallow-water corals has grown in popularity, many techniques have been developed for propagation. However, it is difficult or inappropriate to directly apply these techniques to deep-sea corals. While some forms of diving are capable of approaching the …

Numeric Simulation Of A Realistic Hydrokinetic Turbine In Exposed Ocean Environments, Anneke Neber

Open Access Master's Theses

As renewable energies become increasingly more important, it becomes necessary to tap into new resources, such as energy from tidal or ocean currents. A recent development is the use of hydrokinetic turbines, deployed on floating plat- forms. Numerical modeling can be an important tool to determine the forces acting on the floating platform and the turbine.

In this thesis, a realistic floating hydrokinetic turbine was simulated in OpenFAST. Experimental data and hand calculations, were used to partially verify the simulation. Environmental data at a site was analyzed to determine conditions to run the simulations for different tidal current speeds and …

Minimalistic Communication-Based Swarm Slam, Felix B. Koch

Open Access Master's Theses

This study presents and validates Range of Communication Swarm SLAM(RCS-SLAM), a novel SLAM approach designed for multi-robot systems using minimal hardware. Unlike traditional methods that rely on range or bearing measurements, RCS-SLAM only uses the identifiers of nearby robots within communication range. In the front-end, each robot constructs an individual pose graph based on its own odometry, while recording communication events with neighboring robots. The key novelty lies in the use of one-hop communication, where robots not only detect nearby peers but also relay received identifiers, effectively acting as transmitters themselves. This mechanism extends the communication graph and enables the …

Fiber Optic Distributed Acoustic Sensing For Surface Wave Inversion In Terrestrial And Marine Environments, Constantine Coclin

Open Access Master's Theses

The use of distributed acoustic sensing (DAS) has gained popularity for a variety of seismic sensing applications due to its capability to sense broadband frequency ranges on spatial scales unattainable with conventional point sensors. The ability to sense and characterize vibrational surface waves in the near-surface can provide valuable information about the subsurface structure of an environment. The objective of this study is to evaluate the use of DAS to sense surface wave propagation for the multi-channel analysis of surface waves (MASW) methodology, such that a sub-surface shear wave velocity profile can be obtained in both a terrestrial and near-shore …

Toward Precise Long-Range Underwater Acoustic Geo-Positioning: Utilizing Vehicle Data And Deepening The Gnss Analogy Through Uncertainty Modeling, Isaac B. Salazar

Open Access Master's Theses

Electromagnetic signals, such as those used by Global Navigation Satellite Systems (GNSS), attenuate dramatically underwater, but acoustic signals can travel hundreds of kilometers, and can be used for positioning in much the same way. Concepts from GNSS can be applied to the subsurface context, as at a basic level, the principles of geo-positioning are identical. Key challenges in translating satellite positioning models to the underwater acoustic domain include differences in signal type as well as instrumentation and propagation environment. Acoustic signals travel at much slower speeds and are subject to significant environmental variability due to complex ocean dynamics. Here, the …

Impacts Of A Multi-Layered Acousto-Elastic Bottom On Sound Propagation In A Seamount Environment, Haley H. Green

Open Access Master's Theses

The New England Seamounts Acoustics (NEMSA) experiment is a comprehensive oceanographic field study aimed at investigating underwater acoustic propagation and scattering effects within a complex marine environment. The primary objective of this research is to assess the influence of seafloor and sub-bottom geoacoustic properties on acoustic propagation, signal reflections, and transmission loss at Atlantis II Seamount. Two acoustic models, BOUNCE and BELLHOP, were employed to incorporate elastic wave effects within the sub-bottom layers and facilitate the analysis of range-dependent bathymetric features. Two model scenarios were analyzed and compared, one with a layered elastic bottom and another with a fluid bottom …

Design And Control Of A Stroke Therapy Device, Hugh Elliott

Open Access Master's Theses

Stroke is a leading cause of physical disability around the world, and the likelihood of stroke increases as people live longer. The current number of physiotherapists is insufficient to meet the increasing demand for their services. As a result, there has been a focus on developing robotic devices that function similarly to traditional therapy, enabling multiple patients to be seen simultaneously. While many devices have been created and tested, most are expensive, complex, and require trained personnel for supervision, thereby limiting their outreach. This thesis presents the design and control of a low-cost stroke therapy device designed to promote upper …

Dynamic Beamforming And Array Shape Estimation, Nicholas Ryan Costick

Open Access Master's Theses

The accurate estimation of towed sonar array shapes during complex maneuvers is a critical challenge affecting beamforming and target localization performance. When underwater arrays experience sharp turns or rapid movements, sensor positions become difficult to track precisely, negatively impacting the reliability of beamforming methods. This thesis addresses the issue of dynamic array shape uncertainty, motivated by operational challenges faced by the Navy.

A maximum-likelihood estimation (MLE) method is developed to simultaneously estimate the array shape and field directionality (spatial spectrum) during maneuvers. The proposed solution expands upon previous research, specifically the dynamic spatial spectrum estimation techniques described by Rogers and …

Heart Rate And Electrodermal Activity And Their Relationship With Performance In High Fatigue Environments, Brian Dunbar

Open Access Master's Theses

Confidential material has been removed.

Develop A Cross-Platform Toolset For In Situ Deep-Sea Coral Propagation, Joe Bevilacqua

Open Access Master's Theses

Mesophotic and deep-sea gorgonian octocorals in the Gulf of Mexico were significantly damaged following the Deepwater Horizon oil spill, prompting a major federal investment in developing restoration techniques for these fragile, slow-growing communities. Asexual, in-situ coral propagation offers a biologically viable path for restoration at depth. However, current ROV systems are not optimized for the delicate, fine-scale interactions required to perform this process reliably. The objective of this research was to design, prototype, and evaluate a ROV-mountable toolset capable of preparing octocoral fragments for potting in epoxy substrate, with the additional goal of integrating the toolset into URI’s Coral Carousel …

Exploring Area And Performance Benefits Of Programmable Logic Integrated Cpu Core, Jonathan Pollard

Open Access Master's Theses

This work proposes replacing fully static ASIC functional units with programmable functional units that leverage tightly integrated programmable logic within CPU cores. The programmable units can be configured to match the execution behavior of a specific workload while still allowing the CPU to remain general-purpose across all workloads. This approach reduces the total area required for functional units without sacrificing performance. We evaluate two configurations: an Ideal implementation, where programmable and ASIC functional units have equal area, and an Area-Larger implementation, where programmable units occupy twice the area of their ASIC counterparts. To establish feasibility, we first conduct binary instrumentation …

Optimal Subspace Estimation For Linear Nested Arrays: Applications And Performance Metrics, Brendan Dunn

Open Access Master's Theses

OSE (Optimal Subspace Estimation) is an algorithm that obtains an estimated subspace from structured data observed in noise. While OSE is able to obtain an accurate subspace estimate with a small number of snapshots, it has not been demonstrated how this affects the performance of many applications that leverage OSE. One such application is beamforming; Using an OSE-based beamformer, this thesis will apply common performance metrics to measure how much of an advantage an accurate subspace estimate provides. This algorithm will be compared against other widely used beamformers such as MPDR and DMR to benchmark its efficacy. Mismatch is then …

Experimental Validation Of Model Predictive Control For Roll Attenuation Of Scale Model Barge In Beam Sea Conditions, Callum Robbins

Open Access Master's Theses

Floating offshore wind turbines (FOWTs) are the next great frontier in the offshore energy world. However, for these machines to be safe, effective, and reliable, they must be stable. In this thesis, an active control strategy is developed that uses future wave information and model predictive control to attenuate wave induced motions of a floating platform, such as a FOWT. The future wave information is provided by a wave reconstruction and prediction (WRP) algorithm which allows for wave events to be calculated faster than real-time. This wave information is provided to a nonlinear model predictive controller (NMPC) that uses a …

Experimental Design Considerations Of Gravity-Capillary Wave - Current Interaction In A Lab, Erin Hub

Open Access Master's Theses

An experiment was designed to create a dataset of wave-current interaction of gravity-capillary waves and currents of similar magnitude to understand how the wave elevation field is modulated by the current. The data product from the experiments will be spatial plots of the wave elevation field of the refracted waves by the current for a range of speeds, headings, and wave periods. The field of view in these spatial plots includes portions of the undisturbed wavefield, a current region of finite width where the waves pass through, and the refracted wavefield on the opposite side of the current.

To verify …

Sensitivity Analysis Of The 197 Mhz Prototype Crab Cavity For Eic, Subashini De Silva, Jean R. Delayen, B. Xiao, E. Drachuk, I. H. Senevirathne, A. Castilla, N. Huque, Z. Li

Physics Faculty Publications

The Electron-Ion Collider at BNL requires several crabbing systems that will be operating at 197 MHz and 394 MHz to compensate for the loss of luminosity due to the large crossing angle of the colliding beams. Two 197 MHz crab cavity cryomodules containing two cavities each will be installed in the Hadron Storage Ring (HSR) at the IP6 interaction region. Due to its large size compared to previously developed crabbing cavities, the 197 MHz crabbing cavity system was identified as one of the critical rf systems in the EIC. Therefore, a cavity has been designed including the ancillaries, and is …

Investigation Of New Superconducting Materials For The Next Generation High-Performance Rf Superconducting Cavities For Particle Accelerators, Alex Gurevich, Jean Delayen, Chang-Beom Eom, Gianluigi Ciovati

Physics Faculty Publications

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

Effect Of Activation Temperature On Quantum Efficiency And Lifetime Of Nea Truncated Nanocone Array Gaas Photocathode, Md. Aziz Arroyo Rahman, Md Abdullah Mamun, Shukui Zhang, Hani E. Elsayed-Ali

Physics Faculty Publications

This study investigates the quantum efficiency (QE) and operational lifetime of a negative electron affinity GaAs truncated nanocone array (TNCA) photocathode benchmarked against a conventional flat GaAs photocathode under varying activation temperatures. The TNCA structure demonstrated a QE of up to 13.6% at 590 nm with room temperature (RT) activation—approximately 1.5 times higher than its flat counterpart. This enhancement is due to Mie resonance effects within the nanostructure, as confirmed by finite-difference time-domain simulations. Moreover, the TNCA photocathode exhibits significantly extended charge lifetime, with enhancement factors of ∼6.1 and ∼19.8 under RT and 50 °C activations, respectively. These gains are …

Modeling Strain And Quantum Confinement In Gaas/GaXIn1-XP Superlattices For Spin-Polarized Electron Sources, A. Kachwala, G. Blume, S. Marsillac, J. Grames, M. Grau

Physics Faculty Publications

In this study, we systematically design and simulate a series of GaAs-based superlattice configurations aimed at enhancing heavy-hole–light-hole band splitting while simultaneously optimizing band alignment to reduce the conduction band barrier, thereby facilitating efficient electron transport. These combined effects are crucial for achieving high electron spin polarization and high quantum efficiency, the two key performance metrics of next-generation spin-polarized electron sources. We investigated three types of superlattice architectures: (1) compressively strained GaAs wells on GaInP barriers, yielding a maximum band splitting of 140 meV, (2) lattice-matched GaAs/GaInP structures, resulting in the maximum band splitting of 75 meV, and (3) tensile …

Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Abstract: In remanufacturing, a vital segment of the sustainable, low-carbon circular economy, existing versions of the traditional unequal-areas facility layout problem (UA-FLP) model face significant limitations in designing layouts. To be specific, in the process of minimizing the material-handling cost (MHC), these models also alter departmental dimensions, often diverging from construction specifications. This poses a difficulty, as critical equipment required for remanufacturing, e.g., sorting and cleaning machines, have unalterable dimensions, which implies that departmental dimensions cannot be changed from specifications provided. To address this, a novel Flexible Envelope UA-FLP (FE-UA-FLP) model is proposed in this work for designing layouts wherein …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Homomorphically Encrypted Faceted Values, Tanmay Singal

Master's Projects

Faceted values prevent the implicit flow of sensitive information by controlling the visibility of program data. They achieve this by maintaining two facets for each variable: a public facet, which is observable, and a private facet, which remains hidden. Although this method secures the flow of sensitive data, it can be leaked if the server storing the faceted values is compromised. While faceted values may be encrypted on the server, doing so would necessitate that the private facets be briefly decrypted during execution to allow arithmetic operations to be performed on them, creating an attack vector for information to be …

Bot Detection In Social Media Using Graphsage And Bert, Abhishek Deshmukh

Master's Projects

This project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.

The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

Machine Learning Based Network Traffic Classification With Cosine-Similarity Based Out-Of-Distribution Detection, Prabhat Edupuganti

Master's Projects

The changes occurring in the amount of encrypted network traffic is growing at an alarming rate. This development has created intricate problems in traffic classification which is vital for effective cybersecurity. Moreover, most frameworks seem to ignore OOD detection, model calibration and novel pattern detection as cornerstone problem areas. The due analysis is presented as a machine learning approach aimed at resolving encrypted traffic classification issues and focuses on novel OOD detection and calibration issues. Primary contributions comprise detection of out-of-distribution states using softmax scaled cosine similarity, advanced variance-based feature elimination, and lowering ECE using stringent NNs. This work demonstrates …

Mitigating Cold Start Problem Through Metadata Integration And User Preference Analysis, Prabaljit Walia

Master's Projects

Recommendation systems power the most popular platforms in the world: from content catalogs on Netflix to custom feeds on TikTok – the importance of recommendation systems is significant. Collaborative filtering, the most popular recommendation technique, is essentially based on the idea of leveraging collective user intelligence i.e., creating recommendations by finding similar users. But this technique suffers when there is not enough data in the profiles of users, formally termed as the cold start problem. This research focuses on this problem by introducing an approach that integrates metadata-driven similarity measures with profile expansion techniques. Our approach combines traditional collaborative filtering …

Real-Time Adaptive Framework For Topic Modeling In Social Engineering Attacks, Manav Bhasin

Master's Projects

Detecting social engineering attempts is crucial for security, as these threats are becoming more frequent and increasingly exploit human vulnerabilities. This research focuses on topic modeling using conversational data from Kevin Mitnick’s ”The Art of Deception” with dialogues that illustrate various social engineering strategies. The dataset comprises manually extracted and synthetically augmented conversations to ensure natural dialogue flow. Two methodologies are presented for utterance-level and global topic extraction: prompt engineering leveraging OpenAI’s GPT-4o-mini, characterized by few-shot learning and chain-of-thought prompting, and Quantized Low Rank Adaptation (QLoRA) utilizing Mistral’s 7B instruct model for efficient fine-tuning. Through experimentation and evaluation, this study …