Engineering Commons^™

Angular Scanning Method For Efficient Maximum Power Point Tracking, Saikot Hossain Dadon

Student Theses and Dissertations

The Photovoltaic System (PV) is one of the widely used sources of renewable energy. There are various control mechanisms to manipulate the PV system. However, one of the major control mechanisms is Maximum Power Point Tracking (MPPT) to extract the maximum power from the solar panel. The solar power is intermittent and stochastic in nature due to the unpredictable sunlight exposure to the solar panel and temperature variance throughout the day and year. Consequently, the PV module does not run at Maximum Power Point (MPP) naturally; therefore, an MPPT controller is utilized so that the solar panel runs in the …

Methods For Radiation-Sparing Navigation In Endovascular And Spine Surgery, William Ross Warner

Dartmouth College Ph.D Dissertations

Modern surgical navigation systems improve procedural accuracy, limit complications, and can reduce reliance on intraoperative ionizing radiation, yet their adoption remains uneven across surgical domains. Informed by clinical immersion, this thesis identifies and addresses two unmet needs revealed through contrasting image-guidance practices in endovascular and spine surgery, where existing guidance techniques are costly, intermittent, or fundamentally limited. Specifically, these needs include (1) the absence of radiation-sparing navigation techniques for endovascular procedures, which rely heavily on 2D x-ray angiography, and (2) the need for dynamic registration methods in open spine surgery, where navigation accuracy can degrade after initial registration and intraoperative …

Volumetric Wear Analysis Of Polyethylene Orthopedic Liners Using Point Clouds, Gabriel Landi

Dartmouth College Master’s Theses

In vivo wear of ultra-high molecular weight polyethylene (UHMWPE) orthopedic bearings represents a major problem in shoulder and knee arthroplasty, with significant amounts of devices failing due to the complication itself, and even more due to associated reasons for retrieval, including aseptic loosening. However, quantification of UHMWPE wear has largely been limited to linear and in vitro volumetric analyses, obscuring the understanding of the amount of clinically relevant material loss. This study proposes new methods to measure articular, rim, and backside surfaces of retrieved shoulder and knee UHMWPE liners, centered around the coordinate measuring machine (CMM)-based collection of high density …

Applications For The Quasi-Atomic Orbital And Constrained Density Functional Theory Methods On Catalytic Reactions, Alvaro David Loaiza Orduz

LSU Doctoral Dissertations

Selective activation of C–O, C–H, and C–C bonds underpins biomass upgrading, alkane functionalization, and CO₂ conversion. Despite their importance, the electronic factors governing catalytic performance remain incompletely understood, and many industrial processes still rely on empirical trends or material-specific observations. This dissertation addresses this gap by applying density functional theory (DFT), thermodynamic decomposition, and electronic-structure analysis to identify unifying principles of reactivity across transition-metal phosphides, vanadate oxides, copper-based electrocatalysts, and mixed IrO₂–RuO₂ layers. This work examines how charge transfer, orbital localization, and ligand-induced perturbations control reaction pathways in diverse catalytic systems. C–O bond scission in 2-methyltetrahydrofuran (MTHF) and methanol was …

Trip Generation Model For Mixed-Use Development In Klang Valley, Malaysia., Goh Boon Hoe

Student Works (2020-2029)

Mixed-Use Developments (MXDs) are single development projects that integrate and interact with different land uses, such as offices, retail, restaurants, entertainment venues, hotels, and/or residential areas. MXDs are internally connected, allowing users of different land uses to share internal streets, walkways, driveways, and parking spaces. Traffic is estimated to be reduced with such developments. Currently, in Malaysia, there is no standard procedure to estimate the MXD trip generation rates. The Malaysian Trip Generation Manual (MTGM) 2010 is the guideline used to forecast future trips for a single land use and hence, by summing the trip generation rates for multiple land …

Cross-Layer Performance Analysis Of P-Edca, Claude Davis Robertson V

Computer Science and Engineering Master's Theses

Modern Wi-Fi applications require tight latency bounds and predictable throughput, and they rely on a mix of transport protocols (e.g., TCP Cubic, BBR, New Reno, and UDP) whose performance is highly sensitive to MAC-layer behavior. The forthcoming 802.11bn amendment introduces Prioritized EDCA (P-EDCA) to reduce latency for high-priority traffic, yet its cross-layer impact on transport performance remains insufficiently understood. This thesis evaluates the performance of TCP and UDP traffic operating over both EDCA and P-EDCA. Our results show that (i) both TCP and UDP traffic exhibit strong sensitivity to P-EDCA configuration, with uplink tail latency varying widely; (ii) by reducing …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Data-Driven Prediction Of Concrete Bridge Deck Corrosion Using Ground Penetrating Radar (Gpr), Nafisa Shafiullah

Civil Engineering Theses

Corrosion detection remains a crucial factor in the proper maintenance of Reinforced Concrete (RC) structures. While several studies have used Ground Penetrating Radar (GPR) for corrosion detection, quantitative models for prediction are still limited. This study presents an approach for the quantitative prediction of rebar corrosion in concrete using GPR data obtained from a prior experimental program involving accelerated corrosion. A total of 36 RC samples were cast, varying in cover depth, rebar diameter, and concrete porosity. The samples were subjected to an impressed current of 0.65 A in a 5% NaCl solution over three phases: 10, 20, and 30 …

Teleoperated Robotic Arm With Vision-Based Imitation Learning, Favour E. Emmanuel, Hunter J. Donahue

Williams Honors College, Honors Research Projects

This report presents the design, development, and deployment of a teleoperated robotic arm system capable of vision-based imitation learning. The system uses a leader-follower architecture with myArm C650 and myArm M750 robots manufactured by Elephant Robotics. The leader arm movement is controlled by the operator, whereas the follower performs actions acquired from demonstrations. Data collection included synchronized joint-angle recording and multi- camera video streams capturing human demonstrations.

These demonstrations were processed with a deep learning imitation algorithm called Action Chunking with Transformers (ACT). The main capability of this algorithm is its ability to make future action predictions based solely on …

Areosense, Josephine Turney

Williams Honors College, Honors Research Projects

The AeroSense growing system is designed to make indoor aeroponic gardening easy and accessible for everyone. By combining sensors, automation, and AI, the system can monitor and adjust humidity, lighting, and nutrient levels to help plants thrive without requiring expert knowledge. The goal is to create a self-regulating garden that takes the guesswork out of growing fresh herbs and vegetables at home. The project involves building a working aeroponic prototype equipped with misters, pumps, and environmental sensors, all managed by a Raspberry Pi. Using computer vision and machine learning, AeroSense will be able to assess plant health and respond automatically …

Dynamic Thrust Testbed For Uav Electric Motors, Colin M. Flowers, Robert C. Johnson Jr., Carson P. Holt

Williams Honors College, Honors Research Projects

This project seeks to bring new testing capability to the Zips Aero Design Team through the design, manufacture, integration, and testing of a dynamic thrust testbed for UAV electric motors. The project centers upon the revival and modification of a past prototype aircraft for use as a testbed for in-flight thrust measuring and recording. The team currently lacks the ability to test dynamic thrust under the operating conditions and flight speeds that are encountered in competition. Project technical areas include component and product design, electrical and electronics, and fluid mechanics, including the use of SolidWorks, MATLAB, and ArduPilot. Aside from …

Parking Information And Supervision System, Joshua A. Thum, Alex J. Kinch, Jacob A. Dye

Williams Honors College, Honors Research Projects

In densely populated areas, finding parking can be an arduous and time-consuming struggle, especially in large and tall parking decks. Drivers would benefit from a convenient way to find an open parking spot without having to scour the entire lot first. The goal of this project is to sense available parking spots in a parking garage or parking lot using physical object detection and visual detection with computer vision verification, and display the open spots to drivers entering the lot. This information will be displayed locally at the lot and in an app, with the latter allowing someone to see …

High-Speed Non-Pneumatic Tire (Npt) Design For Speeds Above 60 Mph, Jacob Grams, Brady Maurer, Jake Groewa

Williams Honors College, Honors Research Projects

This project focuses on developing a high-speed non-pneumatic tire (NPT) capable of safely operating at speeds greater than 60 mph, a performance range where most existing airless tire designs fail due to structural instability and performance limitations. The objective of this work is to design, analyze, and prototype a structurally optimized honeycomb-based tire structure with improved load distribution, deformation control, and durability. Multiple geometries were evaluated using finite element analysis (FEA) to assess stress and maximum resultant (total) displacement behavior under loading conditions, and a material selection study was performed to identify a suitable material for the design. The final …

Robotic Air Hockey Table, William Forcey, Andrew Piunno, Kaden Carpenter, Xander Zavatchen

Williams Honors College, Honors Research Projects

Air hockey, a popular arcade game, is traditionally designed for two players. This limits the game’s accessibility for individuals who wish to practice or enjoy it as a single player. To solve this problem, a robotic system was implemented to play air hockey against a human player. The speed and acceleration of the puck and mallet were measured from a game played between humans to inform the required movement capabilities of the robot. The robotic opponent implemented observes the location of the puck on the table using a camera and predicts where it will be in the future. A Cartesian …

Nissan Leaf Disassembly Tool And Test Stand Design, Mariah Z. Hodge

Williams Honors College, Honors Research Projects

The Akron Engineering Tribology Laboratory has developed a water-based lubricant and aims to test and observe if the water-based lubricant could work in an electric vehicle environment. As parameters for creating a test stand to mimic an electric motor environment are not readily available online, the goal of this senior design project is to take apart a used Nissan Leaf engine to define the parameters a test stand must meet to accurately recreate an electric motor environment. Knowing these parameters, a test stand will then be designed for testing the water-based lubricants. The purpose of building a test stand to …

Effect Of Snow Melt On Slope Stability, Gustav G. Gothberg

Williams Honors College, Honors Research Projects

This project explores the effect of snow melt on slope stability. The different types of slope failure are examined to determine which are most affected by snow melt. Then, the report examines the equations that geotechnical engineers use to determine the exact moment when a slope fails. Using GeoStudio modeling software, Slope/W, landslides caused by snow melt were modeled. GeoStudio was also used to explore methods of mitigating the effects of snow melt. This project also explores how to take advantage of these models to minimize the damage done by landslides.

The Effects Of Low-Frequency, Low-Intensity Ultrasound On Gene Expression In Macrophage-Endothelial Cell Co-Culture, Evelyn Hergenhan

Honors Theses

Introduction

Low-frequency, low-intensity ultrasound (LFLI US) has been shown to be a promising treatment for noninvasive accelerated chronic wound healing. However, the biological mechanisms of this ultrasound-assisted healing are not yet understood, limiting clinical adoption. The objective of this study is to characterize the effects of LFLI US on gene expression in a simulated chronic wound environment to gain a better understanding of the biological mechanisms underlying this therapeutic.

Methods

A simulated chronic wound environment consisting of endothelial cells and inflammatory M1 macrophages co-cultured in 3D was subject to LFLI US at 50, 100, or 150 mW/cm² Spatial-Peak, Temporal-Peak …

Comparative Evaluation Of Deep Learning Models: Resnet18, Minivgg, And Yolov8 For Five-Class Blood Cell Classification, Keita Sakurai

Master's Theses or Doctor of Nursing Practice

Accurate classification of blood cell types is a critical task in automated hematological analysis. This study presents a comparative evaluation of three deep learning architectures, ResNet18, MiniVGG, and YOLOv8, for five-class blood cell image classification. To ensure a fair comparison, all models were trained under standardized conditions, including a consistent 90:10 training–validation split, controlled dataset size, and fixed training epochs. ResNet18 was trained to establish a baseline using residual learning. MiniVGG employed a compact VGG-inspired design with regularization to balance efficiency and accuracy, while YOLOv8 leveraged a lightweight, pretrained classification backbone with integrated data augmentation. Experimental results demonstrate a clear …

Eletric Charging Arm Mechanism - E-Charm, Jacob A. Lewis, Gabriel A. Greer

Williams Honors College, Honors Research Projects

As demand for electric and hybrid vehicles continues to rise, many EV owners rely on home charging in their garages. Gabriel Greer and I’s Senior Design Project, the Electric Charging Arm Mechanism (ECHARM), addresses this by creating a robotic system that autonomously detects a vehicle’s charging port, connects to it, and returns to a rest position when charging is complete. ECHARM is designed as a six-axis robotic arm with wrist and shoulder joints driven by stepper motors through custom cycloidal drive gearboxes. A closed-loop control system using rotary potentiometers ensures precise motion control. At the end of the arm is …

Numerical And Machine Learning Based Recreation Of Damage Morphologies Of Barely Visible Impact Damage, Oscar A. Valdez

Mechanical and Aerospace Engineering Dissertations

Composite laminates are highly sought after in the aerospace industry as they provide strength without dramatically increasing the weight of manufactured structures. However, composite laminates are susceptible to barely visible impact damage caused by routine activities. This type of damage can easily go unnoticed while significantly reducing the load-carrying capability of the laminate. Current non-destructive evaluation techniques, such as ultrasonic scanning, can reveal the damage footprint but provide no insight into delamination through-the-thickness due to the shadowing effect. Micro-computed tomography offers ply-by-ply damage resolution but is unsuitable for field inspections and is constrained by specimen size. This study aims to …

Set-Theoretic Reachability-Informed Model Predictive Control For Mechanical And Aerospace Systems, Jinaykumar Nitinkumar Patel

Mechanical and Aerospace Engineering Dissertations

Modern mechanical and aerospace systems increasingly operate autonomously in environments characterized by nonlinear dynamics, uncertainty, and safety constraints. In these settings, estimation and control methods based on nominal models and single-point trajectory predictions are usually insufficient to ensure safe and reliable operation. This dissertation uses a set-theoretic perspective, in which the system state, uncertainty, and admissible behavior are described by sets instead of point estimates. The key question is not only what the state is, but what set of states remains consistent with the dynamics, disturbances, control limits, and available measurements. This provides bounded descriptions of uncertainty and supports control …

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

Navigating The Intersection Of Faa Grant Assurances And Environmental Regulations: Assessing Impacts On Airport Sustainability Efforts, Chad D. Richards

Senior Honors Theses and Projects

Airports in the United States operate within a very complicated regulatory environment that is shaped by federal aviation policy, environmental and state laws, and promises made to the federal government through sponsor assurances, also known as grant assurances. As environmental concerns surrounding aviation continuously grow, airports have been increasingly implementing sustainability initiatives aimed at reducing emissions and improving energy efficiency. Implementing these initiatives is often influenced by regulatory frameworks governing airport operations and infrastructure development. The goal is to examine how federal regulatory structures influence sustainability initiatives at U.S. airports by using a qualitative case study approach to examine sustainability …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Stentrx, Emma Barna, Minuthi Cooray, Bruce Newlands, Joseph A. Sabetta

Williams Honors College, Honors Research Projects

This project aims to develop a biodegradable, drug-eluting biliary stent for the treatment of biliary cancers. Current metal stents that restore bile flow bit are non-degradable, prone to migration, and unable to deliver drugs locally. The proposed stent will be biocompatible, biodegradable, and capable of controlled drug release to both maintain bile flow and treat tumors. The end goal is to produce a functional prototype that can be deployed via a minimally invasive catheter and maintain bile duct patency for at least three months. The project is expected to be involved in the NEOMED Bench-to-Bedside competition and provisionally patented.

Goal-Driven Shared Control In Eeg-Based Brain Machine Interface For Freewill Reaching And Grasping With Movement Intention Detection And Goal Position Decoding, Bhoj Raj Thapa

Theses and Dissertations--Electrical and Computer Engineering

Upper limb motor impairments can severely limit a person’s ability to perform everyday reaching and grasping tasks. Electroencephalogram (EEG)-based brain machine interfaces (BMIs) offer a non-invasive approach for translating neural activity into control signals for assistive devices such as robotic arms. However, traditional EEG-based BMI studies have generally focused on externally cued paradigms, where both movement timing and target selection are specified by the experimenter rather than freely chosen by the user. In addition, shared control offers a practical framework for assistive BMI operation by dividing responsibility between the user and the intelligent robotic system. However, in many EEG-based shared …

Ai-Driven Network Orchestration: Adaptive Routing For Federated Learning Over Software-Defined Hybrid Wans, Osama Abu Hamdan

Computer Science and Engineering Dissertations - Archive

Modern wide-area networks increasingly adopt hybrid architectures that combine high-capacity wired backbones with flexible wireless links to extend connectivity to remote and underserved locations. However, the bandwidth variability inherent in wireless segments creates routing challenges that traditional protocols, designed for static link capacities, cannot adequately address. Simultaneously, Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm in which geographically dispersed clients collaboratively train shared models without exchanging raw data. When deployed over wide-area networks, FL training is severely bottlenecked by communication overhead, particularly in cross-silo settings where model payloads reach hundreds of megabytes and synchronous aggregation protocols …

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

Masters Theses

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

Optimal Slotting In Hybrid Warehousing For Industry 4.0, Teng Yang

Masters Theses

In the era of Industry 4.0, the warehouse management system (WMS) employed by many firms prescribes hybrid storage, i.e., products with high turnover, called fast movers, are kept in random storage for a short time duration before being shifted to a dedicated storage area, while products with low turnover, called slow movers, remain in random storage. From dedicated storage, the products are dispatched to the customer. The challenge for managers is selecting the slot in dedicated storage to assign to each product while demand data change because of fluctuating market conditions; this problem is referred to as slotting in the …

Developing Discharge Estimation Algorithm Using Low-Cost Velocity Sensor And Machine Learning, Barkha Gautam

Masters Theses

Accurate river discharge estimation is essential for flood forecasting, water resources management, and hydraulic decision-making; however, continuous discharge records are unavailable at many river locations. Traditional stage-discharge rating curves are widely used but their reliability may decrease when channel conditions change or flow conditions vary rapidly. This study develops and evaluates Long Short-Term Memory (LSTM) models for discharge prediction using 15-minute time-series data from river monitoring stations in Missouri. Two model configurations, a baseline stage-only model and an enhanced stage-plus-velocity model, are developed and evaluated independently at two river sites to determine whether the inclusion of surface velocity improves discharge …