Engineering Commons^™

Assessment Of The Imx7ulp Heterogeneous Soc For Use In A Next Generation Student Cubesat Obc, Lorenzo A. Pedroza

Master's Theses

CubeSats represent a rapidly evolving platform for space research, industry, and education, demanding increasingly sophisticated onboard computing solutions that balance performance, fault tolerance, size, weight, and power constraints. Although the design of the Cal Poly's CubeSat Lab's (PolySat) current On-Board Computer (OBC) keeps up with many of these factors, the demands of modern workloads like fine attitude determination will start to outpace available compute. While the selection of a more capable processor to address this would have traditionally resulted in increased energy usage, modern system-on-chip (SoC) architectures offer novel ways to trade available compute for power savings on-the-fly. With this …

Evaluating Nanobubble-Enriched Water Treatments For Cleaning Stainless Steel Winery Tanks At The Gallo Winery Research Station In Livingston, California, Edgar Godoy-Garcia

Master's Theses

ABSTRACT

Evaluating nanobubble-enriched water treatments for cleaning stainless steel winery tanks at the Gallo winery research station in Livingston, California.

Edgar A. Godoy-Garcia

Sanitation of stainless steel wine storage tanks is a critical component of quality control in commercial winemaking operations. Ineffective cleaning can lead to microbial contamination, product spoilage, and safety concerns. Traditionally, wineries rely on manual scrubbing, chemical cleaning agents, and extensive water rinsing cycles to maintain sanitary tank conditions. However, these methods are resource-intensive and may pose risks to both workers and the environment due to chemical exposure and wastewater generation. In response to growing industry interest …

Leveraging Machine-Learning Algorithms In Two Car Crash Detection Systems On A Custom Dataset, Addison Jacob Sandvik

Master's Theses

Traffic accidents pose a significant threat to public safety, causing millions of deaths and injuries worldwide each year. While efforts to reduce accidents have seen limited progress in recent years, improving emergency response times through automated detection systems is a promising avenue for saving lives. This thesis describes the development of machine learning-based traffic accident detection systems, exploring both video classification and image detection models. The models are trained on a new dataset deemed the Cal Poly Traffic Accident Dataset, an extension of the existing Car Accident Detection and Prediction (CADP) dataset with a precise collision annotations. Two systems were …

Nonlinear Integral Control Schemes For A Cadence-Heartrate Process: A Matlab Exploration, Alexander G. Elliott

Master's Theses

Keeping one’s heart-rate within a specific range during a cardiovascular workout can be difficult due to many factors including variations in the exercise environment and changes in energy level. One’s heart-rate can be controlled by tuning the intensity level of the activity over time. This study focuses on the relationship between a runner’s cadence and their heart-rate and explores ways to control the heart-rate by adjusting the cadence. Previous work in this area modeled the cadence-heartrate plant as a first-order linear system, but this has been shown to be insufficient. This work improves upon previous research in this area by …

Griddle: A Novel Hardware Based Matrix Multiplier Architecture, Seth Kiefer

Master's Theses

Matrix multiplication is a computational cornerstone in modern artificial intelligence and scientific computing, yet general-purpose processors struggle to perform these operations efficiently at scale. This thesis presents Griddle, a novel hardware architecture for matrix multiplication implemented on a Xilinx Artix-7 FPGA. Griddle focuses on flexibility and scalability by adopting a purely iterative approach that supports arbitrarily shaped input matrices without requiring padding or strict dimensional constraints. The architecture uses computational pipelines to execute a multiplication operation. Each pipe consists of a multiplication core and accumulation buffer that compute matrix products in parallel. The multiplication core contains a set of multiplier …

Streamlined Intelligence: Resource-Efficient Machine Learning For 5g Nr V2v Channel Equalization, Jacqueline G. Radding

Master's Theses

As autonomous vehicles continue to evolve, reliable and efficient real-time communication between vehicles is essential for safety and performance. This thesis explores Streamlined Intelligence: Resource Efficient machine learning for 5G NR V2V Channel Equalization, focusing on lightweight random forest decision tree models to address the challenges of channel equalization in 5G New Radio (NR) vehicle to vehicle (V2V) systems. Using orthogonal frequency division multiplexing (OFDM) with QPSK modulation, the study simulates data transmission in nonlinear channels characterized by obstructions, Doppler shifts, and fading. Decision trees are proposed as a computationally efficient alternative to other machine learning methods while being compared …

Experimental Characterization And Finite Element Analysis Of The Effects Of Pitting Corrosion On The Mechanical Properties Of Stainless Steel, Duncan Jay Fure, Long Wang

Master's Theses

Many structures do not fail from poor design, but instead from environmental degradation. Corrosion is one mode whose effects can be diminished, but not eliminated, by employing corrosion resistant materials, such as 304 stainless steel, which are still subject to pitting corrosion. Pitting corrosion is a chemical process of the localized removal of material leaving cavities. Despite the prevalence of pitting corrosion, there has been limited testing conducted of the effect of stress during corrosion. This study conducted accelerated controlled corrosion experiments with and without mechanical loading and various corrosion times. Pit morphology of different conditions was analyzed with statistical …

Low Earth Orbit Drag Sail Degradation Due To Atomic Oxygen And Ultraviolet Radiation, Hannah Rost

Master's Theses

As the orbital debris population increases, so does the risk of collisions with operational spacecraft. In response, the Federal Communications Commission has shortened the deorbit requirement from 25 years to five years. While natural atmospheric decay was sufficient under the previous guideline, many objects with low area to mass ratios can no longer passively comply. Smaller, non-propellant-carrying space objects such as CubeSats lack the ability to maneuver and must rely on alternative deorbit methods. One proposed solution is to deploy a drag sail at end of life to increase atmospheric drag and accelerate orbital decay. However, exposure to atomic oxygen …

In-Situ Defect Detection In Selective Laser Melting Using Convolutional Neural Networks, Ethan Gaigalas

Master's Theses

This thesis develops and trains a convolutional neural network (CNN) to classify defects in metal parts produced by selective laser melting (SLM) using acoustic emission (AE) data. The model successfully identified porosity and standard build layers with high accuracy but consistently struggled to classify lack of fusion (LOF) defects, regardless of input type, normalization, or class count. These results suggest that while AE signals contain distinguishable features for certain defect types, LOF signals may be too subtle or inconsistent for reliable detection. The study addresses the ongoing challenge of in-situ quality monitoring in metal additive manufacturing, where defects like cracks, …

Design And Development Of A Half-Bridge Llc Resonant Converter Laboratory Module, Alyssa Lam

Master's Theses

Resonant DC-DC converters have become a well-known solution in power supplies due to their ability to improve efficiency by utilizing soft-switching techniques. This further has caused a growing interest in industry to use these converters for high-switching-frequency, high-efficiency, and high-power applications such as EV charging stations, photovoltaic systems, battery chargers, etc. One particular resonant topology that has gained popularity in recent years is the LLC resonant converter. Due to the prevalent use of the LLC resonant converter, it is therefore crucial for students preparing for a career path in power electronics to become well-versed in the concept and implementation of …

Deeppanorf: Deep Prior For Neural 3d Reconstruction From Sparse Panoramas, Edward Du

Master's Theses

Advances in neural field representations have led to a significant improvement in view synthesis quality. However, many current novel view synthesis methods rely on a dense set of input views, which can be impractical and inefficient in real-world applications. We propose DeepPanoRF, a novel method for 360◦ scene reconstruction from a sparse set of input equirectangular panoramas. Built upon K-Planes, a radiance field representation that encodes explicit features on orthogonal feature planes, our method does not directly learn feature grids. Instead, we parameterize the feature grids to enable sparse view reconstruction without pretraining or additional regularization. We implement a custom …

Coherent Synchronization For Distributed Digital Phased Arrays, Zachary C. Numa

Master's Theses

Distributed digital phased arrays are rising technologies that help enable applications such as search and rescue operations, wireless communication, radar navigation, and military operations, among many others. Due to their improved angular resolution, digital phased arrays offer superior direction-finding capabilities compared to traditional analog phased array systems. However, this improvement comes at the cost of increased complexity—specifically, the need for precise synchronization of phase, time, and frequency across physically separated nodes. Without synchronization, the distributed phased array's gain and direction-of-arrival (DoA) estimations deteriorate significantly.

There are multiple aspects to implementing and synchronizing a non-stationary distributed digital phased array. This research …

Open Source Microgrid Integration, Zachary J. Kwast

Master's Theses

Microgrids are small-scale electrical systems that operate in tandem with a centralized grid to provide power to a smaller group of target devices. These microgrids are used for a wide range of different purposes, which invites the need for a generalized model that can be tweaked for a variety of uses. This document goes over the theory used to design an open source AC and DC microgrid meant for use by anyone. The microgrid is created using a Raspberry Pi that controls two different power converters so that a wide range of voltage and current characteristics can be achieved. These …

Investigation Into Silicone-Silicate Conversion Due To Atomic Oxygen In The Low Earth Orbit Environment, Justin T. Self

Master's Theses

Silicones have been widely used over the years in spacecraft for a variety of functions, but atomic oxygen (AO) exposure in Low Earth Orbit causes a silicate (SiO₂) surface layer to rapidly develop on the silicone which results in a permanent change in its thermal and optical properties. Although this silicone to silicate conversion is known to occur, statistical predictions of layer formation as a function of time on orbit are not found in literature. This thesis utilized optical and scanning electron microscopy, Fourier-Transform Infrared Spectroscopy, diffuse reflectance spectroscopy, and nonlinear regression statistical techniques after RF plasma asher …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Analog Hardware Implementation Of A Linearly Constrained Quadratic Program Real-Time Solver, Claire E. Tylutki, Claire Tylutki

Master's Theses

This thesis presents the design, implementation, and analysis of a hardware system for solving Linearly Constrained Quadratic Programs (LCQPs) in real time. The architecture follows a generalized feedback structure composed of three key elements: gradient descent on the quadratic cost function, saturation-based nonlinearity to enforce inequality constraints, and an integral controller with an anti-windup mechanism to regulate dynamic behavior and determine steady-state error. This majority analog system converges with equilibria that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. Using a representative LCQP, this work presents simulation of the circuit in PLECS and LT Spice to confirm the feasibility of the novel …

Sentry V3: Extending Context Switches On A Trusted Secure Coprocessor, Mark Kong

Master's Theses

Software correctness and integrity is only ensured through trust in the un- derlying hardware. However, modern computer systems are complex to design and secure. Thus, given the choice between performance and security, companies will often prioritize performance, resulting in vulnerable systems. This creates exploitable systems that must be patched retroactively because business value performance over security. One approach to this issue is to separate the root of security from the rest of the system to create a minimal trusted computing base. Trustguard is one instance of this. Trustguard implements a Containment Architecture with Ver- ified Output (CAVO) model which shows …

Using Hydrologic And Hydraulic Simulations To Predict Near-Term Effects Of Beaver Habitat Restoration On Flood Flows, Luke S. Stewart

Master's Theses

There is a growing need to predict the complex hydrologic and hydraulic effects of beaver-maintained wetlands. This study uses relatively simple inputs and a replicable methodology to predict flood flow impacts of beaver habitat restoration at the site design scale using the engineering industry’s customary hydrology and hydraulic modeling approach. This study focuses on Toro Creek, the primary drainage for a 15.2 square mile watershed on the California Central Coast.

A hydrologic model of Toro Creek watershed was developed in HEC-HMS to approximate saturated soil conditions typical of late-spring flooding events observed in the region. The hydrologic model was calibrated …

A Parallel Fuzzy Logic Framework For Surgical Skill Evaluation Via Instance Segmentation And Deepsort Tracking, Mohsen M. Mohaidat

Dissertations

Manual evaluation of suturing skills during laparoscopic training is often subjective and labor-intensive, resulting in the lack of scalable and consistent feedback for trainees. This study proposes an automated framework that not only significantly reduces the need for in-person assessment by experts but also ensures scalability, thereby addressing the objectivity and cost-effectiveness limitations. While low-cost laparoscopic box trainers have become increasingly popular for residency training, performance assessment still depends on expert supervision. The proposed system aims to alleviate these limitations.

This study introduces a novel automated framework incorporating an optimized DeepSORT algorithm for classifying, localizing, and tracking surgical tools using …

Developing Machine Learning Models And Graphene-Based Flexible Humidity And Temperature Sensors For Machine-Learning-Assisted Sensing, Seth Hajian

Dissertations

Flexible sensor technology has recently gained tremendous momentum in both academic research and industrial applications, transitioning from conceptual frameworks to practical implementations across diverse fields. This remarkable advancement can be attributed to several converging factors, including the maturation of nanomaterial science, the advancements of machine learning algorithms, and the critical demand for intelligent sensing solutions in healthcare, environmental monitoring, and industrial automation. The growing emphasis on personalized medicine and real-time health monitoring, accelerated by global health challenges, has further highlighted the necessity for accurate, cost-effective, and adaptable sensing platforms. This dissertation presents the fulfillment of three interconnected research projects focused …

Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith

Theses and Dissertations

This thesis explores the requirements on nuclear data uncertainties needed for the use of the ¹⁹F(α, n)²²Na reaction for nuclear non-proliferation applications. An overview of how neutrons are produced from alpha decays in a UF₆ medium is discussed. Calculation demonstrate the role nuclear data uncertainties effect the neutron yield and energy spectra as a function of enrichment.

Isotope Production Modeling In Sodium-Cooled Fast Reactors, Aaron W. Burkhardt

Theses and Dissertations

The accurate prediction of isotopic compositions in Sodium-Cooled Fast Reactors (SFRs) is essential for nuclear forensic analyses and international nuclear treaty monitoring, particularly with the increased global deployment of Generation-IV reactors. This research developed and validated a detailed computational model tailored specifically to the Prototype Fast Breeder Reactor (PFBR), employing advanced Monte Carlo neutron transport methods, sophisticated burnup modeling, and variance reduction techniques. Validation against empirical data from the Experimental Breeder Reactor-II confirmed the model’s accuracy, producing a comprehensive database of isotopic compositions across 301 assembly locations and 580 isotopes through the reactor’s initial operation and equilibrium cycle. The model …

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Contract Quality Feature Extraction Using Llm, Aaron C. Washington

Theses and Dissertations

This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.

Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames

Theses and Dissertations

Modern defense systems continue to grow in complexity, placing increasing pressure on engineering workflows to be faster and more adaptable. While Model-Based Systems Engineering (MBSE) with the emerging SysML v2 standard provides a framework for capturing system behavior, its practical use is often limited by the expertise and time required for manual modeling. This research investigates whether large language models (LLMs) can help overcome that barrier by automatically generating SysML v2 state machines from Guidance, Navigation, and Control (GNC) textual inputs. Three LLM Flowise-based models were developed and evaluated: the Structured Transformation Model (STM), which uses a structured extraction and …

Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling

Theses and Dissertations

Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …

Development Of A Health And Wellness Assessment For Kytc Personnel, Gabriel Dadi, Farshid Taherpour, Caitlin Pope

Kentucky Transportation Center Research Report

Highway construction workers operate in high-stress, hazardous settings where they have to perform physically taxing labor in environments where exposure to variable weather conditions, heavy equipment, high volumes of vehicle traffic, and other conditions that induce stress and anxiety are the norm. Up to 70% of workers in this industry have reported experiencing mental health issues, including secondary traumatic stress, post-traumatic stress disorder, depression, anxiety, and substance abuse disorders. Few state departments of transportation, however, offer adequate resources staff can draw on to effectively manage their psychological well-being. To help Kentucky Transportation Cabinet (KYTC) roadway maintenance and construction workers develop …

Aggregator Zone Selection For Ev Smart Controls Based-On Ml Clustering Of Grid Strength, Distance, And Charging Homogeneity, Rosemary E. Alden, Sam H. Lowe Ii, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Smart electric vehicle (EV) charging control methods from a central utility hub often require communication infrastructure over a large service area of electric power distribution systems with a large number of nodes. Industry standards such as Open Charge Point Protocol (OCPP) 2.1 have evolved to include topologies for local controllers to the individual chargers, i.e. EV aggregator zones. A machine learning (ML) application of k-means clustering is proposed to establish zones for coordination of EV charging based on grid strength and EV owner decision-making to charge per day. Very large-scale distribution networks including the IEEE 123 and 8500 benchmark feeders …

Cluster-Based Volt/Var Optimization On A Utility Distribution Feeder With Forecasted Ev Penetration, Steven B. Poore, Grant M. Fischer, Rosemary E. Alden, Evan S. Jones, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

With significant increase in EV adoption expected in the near future and the associated impacts on power systems, the effect of volt/var optimization (VVO) as the next step to conservation voltage reduction (CVR), requires reevaluation. Implementation of a cluster-based VVO control strategy employs a novel approach with machine learning (ML) load forecasting to reduce device adjustments through k-means clustering of contiguous time steps with similar active power load in which a single adjustment would be sufficient. The cluster-based VVO method is tested on a complex real world utility distribution feeder with 2,018 nodes, 8.65MW peak load, 9 capacitor banks (CBs), …

Hybrid Fea And Meta-Modeling For De Optimization Of A Pm Stator-Excited Motor With A Reluctance Rotor, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for designing high-performance electric motors by integrating machine learning (ML) based meta-modeling with a differential evolution (DE) optimization algorithm. The approach utilizes finite element analysis (FEA) data to train the ML meta-model, allowing for efficient optimization of high-power-density machines, such as the reluctance rotor and permanent magnet (PM) stator combined excitation motor, which is characterized by nonlinearities. The meta-modeling process employs an Artificial Neural Network (ANN) with 3 hidden layers and uses the motor’s geometrical variables as inputs. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters, core losses, and …