Engineering Commons^™

Non-Catalytic Direct Partial Oxidation Of Methane To Methanol In A Wall-Coated Microreactor, Kelly Cohen

LSU Doctoral Dissertations

This project used an integrated mixer-reactor-heat exchanger microtube scalable module for the non-catalytic direct partial oxidation of methane in natural gas to methanol at elevated pressures (70-95 bar), reaction temperatures of 380-460C, reactant methane:air ratios of 0.5-4.4 and reaction zone residence times on the order of 1 min. Currently, methane in remote locations is often flared in large quantities rather than transported or converted to the more easily transportable methanol. However, if this gaseous methane were converted to liquid methanol at the wellhead, it could be transported in ways other than pipeline or used onsite.

Additionally, methane is currently converted …

Integration Of Agile Approach Into The Implementation Of The Iso/Sae 21434 On Top Of The V-Model To Enable Continuous Secure-By-Design Automotive Cybersecurity Development, Pooja Patil

Master's Theses and Doctoral Dissertations

The rapid evolution of technology is revolutionizing the automotive industry, with connected and autonomous vehicles at the forefront. These vehicles rely on complex digital ecosystems to enhance safety and efficiency but are increasingly vulnerable to cybersecurity threats. Addressing these challenges requires following robust development methodologies, while complying with cybersecurity standards. This study introduces a framework that merges the widely used agile methodology practices with the ISO/SAE 21434 standard to support secure-by-design automotive product development. Traditional development approaches like the V-model provide structured and linear project phases, but they often lack the flexibility and the ability to adapt to evolving security …

Very Large Scale Robotics Path Planning With Centroidal Voronoi Tessellation, Xu (James) Gao

Theses, Dissertations and Capstones

Swarm robotics, also referred to as very large-scale robotics (VLSR), has emerged as a transformative approach for addressing complex tasks that are infeasible for single-robot systems. Applications range from environmental monitoring and disaster response to large-scale agricultural and industrial operations. However, as the number of robots in a swarm increases, so do the challenges associated with motion control, energy efficiency, and scalability. These challenges necessitate innovative solutions that balance microscopic robot behaviors with macroscopic system-level objectives.

In this thesis, we address these challenges by building upon existing research [40], which introduced novel methods for optimizing swarm robotics systems using macroscopic …

Lagrange Multipliers As A Metric For Early-Stage Shipboard Power System Design, Magdalen A. Barnes

Theses and Dissertations--Electrical and Computer Engineering

Integrated power systems are essential in the operation of electric ships and must function under a diverse range of conditions. In addition to routine operation, ships should be prepared to respond to challenging events that require significant amounts of power. Demanding scenarios, coupled with complex and interconnected power systems, present challenging issues. The power and energy ratings of equipment are crucial factors in the operational success of the ship; however, to meet economic constraints, balance between performance and cost must be upheld. Details regarding equipment models and specifications are typically unavailable during early-stage design. Power system modeling and simulation can …

Taking The Leap: A Qualitative Study Exploring The Entanglement Of The Vertical Transfer Process And Engineering Identity Development Among Community College Students, Janice Edwards

Dissertations of Practice

Taking the Leap: A Qualitative Study Exploring the Entanglement of the Vertical Transfer Process and Engineering Identity Development among Community College Students

Community colleges provide an open access, cost effective pathway to earning a baccalaureate degree in engineering. However, the transfer and degree completion rates remain low. Improved persistence has been linked to a variety of factors including a strong engineering identity. The purpose of this qualitative case study was to expand the understanding of engineering identity development during the vertical transfer process. Each milestone in the process, framed within transfer student capital, was considered to determine what, if any, …

In Vitro Hydrogel Blood Clots Phantoms, Ethan Evans

Bioengineering Master's Theses

This thesis presents a mechanical and microstructural characterization of agarose-based hydrogels as synthetic thrombus analogs for balloon catheter testing. Agarose gels ranging from 0.25% to 1.0% (w/v) were fabricated and analyzed using dynamic shear rheometry, capillary flow porometry, and gas pycnometry. Rheological data showed a nonlinear increase in storage modulus (G′), from 110 ± 66 Pa at 0.25% agarose to 4,900 ± 1,600 Pa at 1.0%, effectively mimicking the viscoelastic spectrum of acute to chronic thrombi. Gas pycnometry measurements indicated that true density (P_true) increased modestly from 1.03 g/mL to 1.13 g/mL over the same concentration range. This …

Feasibility Study Of A Smart Insole With Triboelectric Energy Harvesters For Early Flatfoot Detection, Mohammad Alghamaz

Mechanical Engineering Theses

This study introduces a self-powered smart insole system designed for real-time monitoring of foot health, with a specific focus on detecting flatfoot conditions. The insole integrates multiple triboelectric energy harvesters strategically positioned to capture electrical signals generated from ground reaction forces during daily activities such as walking, jogging, and running. Proof-of-concept testing was conducted on a single participant under two conditions: a healthy foot and a simulated flatfoot created by reducing the medial arch height by approximately 70\. In the healthy foot trials, the system demonstrated consistent and reliable performance, with negligible electrical output from the medial arch sensor, as …

A Computational Fluid Dynamics Approach To Analyze The Virtual Impactor In Pneumatic Aerosol Jet Printing, Akashita Sareen

Theses, Dissertations and Capstones

Aerosol jet printing (AJP) is a 3D printing, advanced manufacturing process that generates an aerosol mist appropriate for fine printing small, low-volume electronic parts. The pneumatic aerosol jet printing technology’s virtual impactor is the focus of this study. In this technology, high velocity nitrogen gas aerosolizes various inks in the atomizer. The aerosolized stream of ink is then transported to a virtual impactor (VI) to become dense and concentrated as it begins to enter the deposition head for high precision electronics printing. AJP faces challenges in large-scale adoption due to challenges related to overspray, instability, ink clogging etc. There is …

Developing A Guideline And Feasibility Tool For Highway Infrastructure Readiness For Autonomous Vehicles, Soujanya Pillala

Master's Theses and Doctoral Dissertations

Autonomous Vehicles (AVs) are one of the advanced technologies that is being developed, and infrastructure modifications supporting AVs will be the next major research that is needed. A systematic literature review methodology is used to explore key parameters needed for implementation of AVs. The key parameters are analyzed against economic implications, costs and projected benefits of the integration of Level 4 and Level 5 AVs into the highway system, offering a nuanced understanding of deployment of AV technologies. The research aims at developing a guideline and framework for highway infrastructure readiness for AVs, a toolbox for policy makers and government …

Smart Homes, Grids, And Electric Vehicles Large-Scale Integration Studies Employing Machine Learning And Optimization Techniques, Rosemary E. Alden

Theses and Dissertations--Electrical and Computer Engineering

Residential digital twins are fundamental to the smart grid transition, and thus, must be both accurate and representative of existing homes and scalable for large distribution systems. Within this dissertation, new machine learning (ML) and physics-based methodologies are applied to the major individual residential loads, energy storage devices, and resources in the US to develop computationally efficient digital twins and new optimal control strategies for the virtual power plant (VPP) concept. Big data from experimental field demonstrations with dedicated metering, thousands of residential smart meter profiles, and large national human behavior surveys are employed to develop ultra-fast scalable residential load …

Multiobjective Optimal Design Of Hierarchical Control Architectures For Quadcopter Systems: Linear And Hybrid Approaches, Xinhuang Wu

Theses, Dissertations and Capstones

This thesis investigates the design of many-objective optimal controllers for quadcopter unmanned aerial vehicles (UAVs), focusing on both linear and hybrid control structures. In Chapter 2, a multi-input–multi-output (MIMO) optimal control system is developed for a six-degree-of-freedom UAV actuated by four brushless DC motors. The UAV dynamics are first derived and linearized around an operating point, and then organized into a three-loop nested control structure. The outer loop computes the roll and pitch angles required to maneuver the vehicle in the global X and Y directions, the middle loop regulates attitude and altitude, and the inner loop controls the angular …

Nonconvex Optimization Methods Under Inexact Information, Dat Ba Tran

Wayne State University Dissertations

This thesis focuses on the design and convergence analysis of algorithms for solving nonconvex optimization problems under inexact first-order information. We introduce Inexact Reduced Gradient (IRG) methods for general smooth functions and Inexact Gradient Descent (IGD) methods for $\mathcal{C}^{1,1}_L$ functions with relative and absolute errors. Additionally, we develop Inexact Proximal Point and Inexact Proximal Gradient methods for weakly convex functions. Our methods improve the performance of standard inexact proximal point methods, inexact proximal gradient methods, and inexact augmented Lagrangian methods by approximately 2.5 to 10 times in terms of iteration complexity for image processing tasks. Moreover, we propose new derivative-free …

Virtual Fixtures For Teleoperated Robots For The Visually Impaired, Vishwaak Chandran Thamaraiselvan

Computer Science and Engineering Theses - Archive

This paper presents our preliminary study on enabling individuals with visual impairments to safely operate mobile robots and vehicles. To achieve this, we developed a teleoperation with accessibility at its core. The system incorporates features that enhance usability and situational awareness, including assistive control based on artificial potential fields to prevent collisions and ensure smooth navigation. It also provides multimodal feedback through (a) haptic vibrations on the gamepad controller, which convey the proximity of nearby objects detected by the robot’s laser sensor, and (b) color-coded overlays that differentiate paths, obstacles, and people through semantic segmentation performed by a deep neural …

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Exploring Instruction Generation For Uavs: Dataset Adaptation, Model Behavior, And Diagnostic Insights, Seyedarman Vaziri Bozorg

Computer Science and Engineering Theses - Archive

This thesis explores the development of an answering agent capable of generating natural language instructions for unmanned aerial vehicles (UAVs), grounded in a limited, real-world dialogue dataset. The objective is to adapt a static dataset into a training pipeline that can support instruction generation and serve as a foundation for future interactive systems involving question-asking agents and internal dialogue. A hybrid architecture is implemented using a semantic teacher model (MPNet) and a T5-base encoder-decoder trained with contrastive and supervised objectives. The adapted training process yields statistically acceptable performance across standard evaluation metrics. However, qualitative analysis reveals a mismatch between metric …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Methods Of Optimizing Storage And Retrieval Of Structured Data, Neelim Haider

Computer Science and Engineering Theses - Archive

Storing and retrieving large amounts of data reliably is becoming more and more important as time goes on. There are high demands to store highly personal information such as social security numbers, bank account information, and residence information to rapidly changing data such as employee information, inventory information, and stock information. Therefore, the ability of a system to store, remove, and update such information efficiently and correctly is critical. There are different types of data that database systems can potentially hold: structured, unstructured, and semistructured data. Various database models have been developed to provide a framework that allows designers to …

Transformer And Recurrent Architectures For Dynamics Prediction And Policy Learning On Long-Horizon Tasks, Vinal Jitendrabhai Gadhiya

Computer Science and Engineering Theses - Archive

Model-based reinforcement learning promises improved sample efficiency by learning environment dynamics and using them for planning or policy improvement. However, the choice of neural architecture for dynamics prediction significantly impacts the model's ability to capture temporal dependencies and maintain long-term context, capabilities crucial for complex, open-world environments.

This thesis investigates three neural architectures for learning world models: Transformer-based, GRU-based, and a hybrid Transformer+GRU approach. We evaluate these architectures on Crafter, a 2D open-world survival environment that requires long-horizon planning and sequential task completion. In Crafter, agents must perform hierarchical sequences of actions, such as collecting wood, placing a table, and …

Diversity-Driven Xor Secret Sharing: Reliable And Secure Multi-Path Transmission, Richard M. Olu Jordan

Computer Science and Engineering Theses - Archive

The growing reliance on distributed storage and multipath communication sys- tems has intensified the need for security mechanisms that remain robust even when individual nodes or channels are compromised. Secret sharing provides an information- theoretic approach to achieving both confidentiality and availability, and XOR-based constructions in particular offer lightweight and highly structured designs. This thesis develops a unified analytical framework for understanding and evalu- ating XOR-based secret sharing schemes across multiple operational settings, includ- ing plaintext storage, encrypted-data scenarios, and noisy binary symmetric chan- nels (BSCs). Building on a general (t, n) system model, we examine five threshold configurations—(2, 3), …

Crime Theory Informed Agent-Based Modeling For Crime Prediction And Patrolling Route Optimization, Shohreh Moradi

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Crime reduction remains a global priority, demanding both accurate modeling of criminal dynamics and efficient allocation of scarce policing resources. To address these needs, this study presents a two‐fold framework that (1) simulates street‐level crime patterns using an agent‐based model (ABM) grounded in Routine Activity Theory (RAT), Rational Choice Theory (RCT), and Crime Pattern Theory (CPT), and (2) optimizes patrol routing through a time-dependent, multi‐visit mixed‐integer linear programming (MILP) formulation.

In the first component, we integrate real‐world crime, environmental, and census data to reproduce realistic offender, citizen, and Police behaviors, capturing where and when robbery, burglary, and larceny occur across …

Guided-Mode Resonant Nanopatterns For Raman Generation And Photonic Devices, Renjie Chen

Electrical Engineering Dissertations - Archive

This dissertation explores advanced strategies for enhancing Raman amplification in silicon photonic devices, focusing on guided-mode resonance engineering and resonant mode manipulation. Silicon, despite its indirect bandgap, exhibits a strong Raman scattering coefficient, enabling it to function as a viable gain medium for integrated photonic systems. However, the realization of efficient, compact, and low-threshold silicon Raman amplifiers and lasers necessitates innovative design approaches that overcome inherent material and structural limitations.

The first chapter provides a fundamental overview of optics, including physical principles, spectral characteristics, guided-mode resonance, simulation methods, and nanopattern fabrication methods.

The second chapter delves into silicon-based Raman amplification …

Data-Driven Forecasting Of Power Demand Via Convex Optimization, Mersedeh Ashraphijuo

Electrical Engineering Dissertations - Archive

This dissertation develops interpretable, data-driven frameworks for short-term power demand forecasting using convex optimization and advanced feature engineering. The models combine historical load data, calendar structures, and meteorological variables to deliver accurate point, quantile, and probabilistic forecasts. By leveraging multi-periodic Fourier features, temperature-based regressors, and autoregressive memory, the proposed approach balances predictive performance with interpretability and scalability. Evaluations on multi-year datasets across US regions show consistent accuracy gains over benchmarks, while preserving transparency critical for real-world deployment. Beyond power systems, the framework generalizes to other time series applications in data science and AI, offering a robust, explainable alternative to black-box …

Characterization And Modeling Of Polyphase Machines And Power Conditioning Components, Nathan Lockhart

Electrical Engineering Dissertations - Archive

Microgrid power configurations have become increasingly prevalent in recent power systems due to the rise of power electronic energy generation, energy storage, and the many diverse electrical demands. Microgrids offer numerous advantages over traditional power electronic networks, which rely on large rotating generators to supply power over extensive distances to multiple users. Remote power grids are particularly beneficial for smaller networks that may be isolated or have unique power requirements, often incorporating energy storage to enhance operational flexibility. Advances in power electronics, such as medium voltage DC distribution, are enhancing the reliability, redundancy, and integration capabilities of isolated microgrids.

To …

Multi-Modal Model-Based Optical Flow Estimation For Event-Based Vision, Pritam Karmokar

Computer Science and Engineering Dissertations - Archive

Event cameras offer a fundamentally different sensing paradigm by asynchronously capturing brightness changes at high temporal resolution, directly encoding motion in the scene. However, their sparse and non-traditional data format poses significant challenges for dense motion estimation, particularly in the context of optical flow. Contrast Maximization (CM) has emerged as a powerful model-based framework for estimating optical flow from event data by optimizing the sharpness of motion-compensated event representations. This dissertation builds upon and significantly advances the CM framework through two complementary contributions.

First, we propose Edge-Informed Contrast Maximization (EINCM), a hybrid approach that augments the traditional events-only CM framework …

An Slo-Aware, Multi-Pronged Approach To Enhancing Resource And Energy Efficiency In Server Applications, Ning Li

Computer Science and Engineering Dissertations - Archive

Server applications operating in oversubscribed cloud environments face the dual challenges of meeting strict Quality-of-Service (QoS) requirements and improving resource and energy efficiency. As the number of user connections and workload diversity continue to grow, existing scheduling mechanisms struggle to balance QoS guarantees, fairness, resource efficiency, and power consumption. This dissertation presents a unified, cross-layer framework to address these challenges through three key contributions: AppleS, UTSLO, and REEF.

First, we propose AppleS, a user-space QoS-aware fine-grained I/O scheduling framework that delivers fair and efficient service to concurrent client connections. AppleS introduces a QoS-centric metric that guides admission control and scheduling …

Synthesis And Application Of Cu-Based Ultrasmall Nanoparticles For Targeted Glioblastoma Treatment, Ryan T. Hart

Material Science and Engineering Dissertations - Archive

Glioblastoma (GBM) is a highly malignant form of brain cancer with a bleak prognosis. Current maximal treatment involves surgical resection followed by chemo-or radiotherapy. Two critical barriers make treating GBM a formidable challenge. First, the tumor's tendril-like proliferation throughout healthy brain tissue often makes complete surgical removal and conventional radiation insufficient. Second, the blood-brain barrier (BBB), which protects the brain, unfortunately also shields GBM from life-saving anti-cancer drugs. Here, we develop a radioactive nanoparticle-based therapy that directly confronts these limitations, offering a new strategy for GBM treatment.

Prostate-specific membrane antigen (PSMA) is a promising target for glioblastoma (GBM) treatment because …

Phase-Field Modeling Of A Protective Layer For The Suppression Of Dendrites In Metallic Anode-Based Rechargeable Batteries, Bharat R. Pant

Material Science and Engineering Dissertations - Archive

Metallic anodes, such as Lithium (Li) and Zinc (Zn), offer promise for the development of high-capacity rechargeable batteries. However, metallic anode-based batteries suffer from severe dendrite growth during the plating process, which poses safety hazards and accelerates capacity degradation. Experimental studies suggest that using a protective coating on the anode surface could potentially mitigate dendrite growth and prolong the battery’s life. However, there are limited theoretical studies on the inhibition effect of a protective layer on dendrite growth. Herein, we developed a phase-field model to simulate the impact of a protective layer on the plating and stripping cycle for Li …

Fabrication Of Microscale Oxide Architectures On Silicon Via A Cmos-Compatible, Deposition-Last Process, Jamal A. Brown

Material Science and Engineering Theses - Archive

This work explores the feasibility of integrating complex oxide devices on silicon using a CMOS-compatible, deposition-last approach. While previous demonstrations of this method have succeeded at larger scales, this study focuses on extending the process to microscale features, with lateral dimensions as small as two microns. The fabrication sequence begins with photolithographic patterning and reactive ion etching of the silicon substrate, followed by the deposition of a silicon nitride mask to delineate device regions. We then epitaxially grew SrTiO3 and La-doped SrTiO3 layers on top of the nitride mask via molecular beam epitaxy. Electrical transport measurements of the La:STO layer …

Improving The Reliability Of Parts Produced Via Laser Powder Bed Fusion Through A Data-Driven Geometry Optimization Methodology, Federico Venturi

Mechanical and Aerospace Engineering Dissertations - Archive

This research aims to qualify the effect of design geometry on quality metrics of additively manufactured (AM) components that define reliability. Through the use of a novel methodology to characterize these quality metrics, AM components are inspected for the presence of defects such as surface roughness notches and porosity. These directly hinder the high-cycle fatigue characteristics demonstrated through the Kitagawa-Takahashi diagram and the El-Haddad model. By demonstrating the effect of geometry through a designed experiment and the adoption of the Murakami square root area parameter and Arola-Ramulu model, the improvement to fatigue life can be quantified. Incorporating this data into …

Stress Analysis Of Anisotropic Inclusion Problems Using Complex Variables, Liming Chen

Mechanical and Aerospace Engineering Dissertations - Archive

This research presents an analytical framework for determining stress fields in an elastic medium containing a circular anisotropic inclusion embedded in an infinitely extended isotropic matrix subjected to far-field uniform stresses. Stress distributions within both the inclusion and the matrix are described using classical stress functions, widely employed in two-dimensional elasticity theory.

To manage the complexity of the mathematical derivations, symbolic computation software (Mathematica) is used to streamline the analysis and obtain closed-form solutions. This approach overcomes traditional computational barriers that have limited the practical application of complex variable methods (CVM) in elasticity.

The methodology builds upon the foundational work …