Electrical and Computer Engineering Commons^™

Testing And Qualification Of Low-Voltage Power Supplies For The Atlas Tile Hadronic Calorimeter Phase-Ii Upgrade, Justice A. Jones

2026 Spring Honors Capstones Projects

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) places increased thermal and operational demands on detector electronics, requiring highly reliable power systems. The ATLAS Tile Hadronic Calorimeter (TileCal) uses low-voltage power supply (LVPS) bricks to power front-end electronics, but these units operate in inaccessible regions, making failures difficult to repair. Therefore, rigorous qualification procedures are essential. This work focuses on improving LVPS reliability through a structured burn-in process. Each unit undergoes pre-burn-in electrical verification using a Single Test Stand (STS), followed by sustained operation under load and elevated temperature, and post-burn-in requalification. Standard cooling conditions limit temperatures to …

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 …

Array Signal Processing And Machine Learning In 5g/6g Networks, Roopesh Kumar Polaganga

Electrical Engineering Dissertations - Archive

This dissertation investigates advanced methodologies in Array Signal Processing (ASP) and Machine Learning (ML) to enhance the performance, efficiency, and intelligence of next-generation wireless networks, with a primary focus on 5G and emerging 6G systems. As wireless networks face rapid traffic growth, increasingly heterogeneous service requirements, and more complex propagation environments, conventional design and optimization approaches become insufficient to meet evolving demands in reliability, capacity, spectral efficiency, and energy efficiency. On the network intelligence side, this work develops data-driven frameworks for causal discovery, scheduler enhancement, session-duration prediction, and Radio Resource Control (RRC) state optimization using real-world telecommunication network data. On …

Deep Learning For Wireless Communications, Swarada Ajit Kulkarni

Electrical Engineering Dissertations

The rapid evolution of wireless communication imposes stringent requirements for ultra-reliable, low-latency transmission in dynamic, interference-prone environments. Traditional model-driven signal processing struggles to adapt to nonlinear hardware effects, time-varying channels, and complex interference patterns. Deep learning (DL) offers a transformative, data-driven alternative, enabling end-to-end optimization and robust adaptation under uncertain propagation conditions.

This dissertation investigates deep learning architectures for intelligent and resilient wireless communication through three complementary contributions. The first introduces a Vision Transformer (ViT)-based modulation classification framework that leverages self-attention to capture local and global dependencies in spectrogram representations of Quadrature Amplitude Modulation (QAM) signals. The ViT achieves superior …

Application Of Matlab Simulation For Quantum Wells And Absorption Modelling, Mohamed Nur

Electrical Engineering Theses

The Quantum-Well User Entered Simulation Tool (QUEST), originally developed at the University of Texas at Arlington in 2005, is a simulation program built in MATLAB for computing energy eigenvalues and wavefunctions in user-defined semiconductor quantum well structures. This thesis presents a new revision and extension of QUEST with three primary contributions: compatibility updates to the existing MATLAB codebase, intersubband absorption modelling, and a redesigned graphical user interface for ease-of-use in testing.

The modernization effort for this program addresses incompatibilities introduced by changes to the MATLAB runtime environment since QUEST’s original release in 2005, including corrections to the self-consistent Schrödinger-Poisson solver …

Ai Data Center Dynamic Load Effects On Current Transformer Saturation, Sergio A. Hernandez

Electrical Engineering Theses

AI data centers can produce rapid changes in electrical demand that may influence current transformer performance during faults. This study evaluates the effect of an AI data center transient on CT saturation during single line-to-ground faults using a 400 V, 60 Hz grid connected inverter model in MATLAB/Simulink. The normal condition transient produced a maximum RMS current rate of approximately 211 A/ms, which was used along with the maximum power condition to define fault inception cases. A MATLAB time-domain CT model then swept the fault current DC offset coefficient to determine the minimum offset required for CT saturation. The calculated …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Power Solutions For Large Loads: Mastering Electrical Supply For New Industrial Facilities, Chris Boyer, William Bourgeois, Skyler Bryant

Mavs Open Press Open Educational Resources - Archive

The next 25 years will see unprecedented growth in U.S. electricity demand, driven primarily by data centers for AI and industrial electrification. This surge presents unique challenges for power generation, especially for large loads requiring hundreds of megawatts to gigawatts from a single location. Conventional grid expansion is inadequate, prompting the need for innovative, flexible, and sustainable solutions. This book explores the opportunities, challenges, and hybrid generation strategies to support the evolving landscape of large-scale electric loads.

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

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 …

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 …

Novel Electrophysiological Biomarkers In Pediatric Drug Resistant Epilepsy And Genetic Epilepsy Syndromes, Sakar Rijal

Bioengineering Dissertations - Archive

Pediatric epilepsies, particularly those that are drug-resistant or genetically driven, represent some of the most complex neurological disorders encountered in childhood. Central to their pathophysiology is a disruption in the delicate balance between cortical excitation and inhibition (E/I), often resulting from impaired GABAergic interneuron function. This imbalance manifests as aberrant network dynamics and altered neural oscillations, giving rise to seizures and long-term cognitive impairments. In this thesis, we developed a translational framework to identify electrophysiological biomarkers that (i) assess cortical E/I imbalance and (ii) map epileptogenic zones, with the aim of enhancing diagnosis, guiding surgical planning, and informing therapeutic monitoring …

Photonic Crystal Devices For Chip Scale Sensing Systems, Yudong Chen

Electrical Engineering Dissertations - Archive

This thesis investigates the design and integration of photonic crystal (PC) structures for compact, high-performance optical platforms, with a focus on applications in gas sensing, on-chip lasers, and flat optics. Chapter 1 introduces the fundamental principles of PC design and simulation, highlighting their potential to replace bulky components in micro-gas chromatography (µGC) systems through miniaturization and integration. Chapter 2 explores PC-based nanobeam lasers, including the Lambda-Scale Embedded Active-Region Photonic Crystal (LEAP) laser, which demonstrates strong optical confinement and energy-efficient operation, with energy consumption as low as 8 fJ/bit. These laser designs are evaluated for their suitability in low-power, high-speed on-chip …

Medical Ai: Solving Healthcare Challenges And Inspiring Ai Innovation, Xiaowei Yu

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence (AI) is transforming healthcare by enabling large-scale analysis of medical data and integrating multimodal information for more comprehensive diagnostics. I present my work addressing fundamental and challenging problems in developing state-of-the-art AI models for medical data analysis, including multimodal brain data and other medical datasets. Additionally, I design brain-inspired AI models by integrating insights from organizational principles of brain networks. Specifically, my research tackles three critical aspects: (1) AI in Computational Neuroscience, where I design deep learning models for brain network analysis to uncover the organizational principles of brain networks; (2) Brain-Inspired AI, where I integrate superior brain …

3d Perception, Mapping, And Navigation For Mobile Cobot, Tuan T. Dang

Computer Science and Engineering Dissertations - Archive

Service robots are migrating from tightly controlled factory lines into offices, hospitals, and homes, where they must perceive, remember, and act amid people, clutter, and perpetual change. Humans solve this daily by forming compact, task-relevant “cognitive maps”: we sample just enough sensory detail to guide the moment, stitch those snapshots into a sparse topological scaffold, and continuously refine it as we move. Guided by that insight, this dissertation proposes a biologically inspired mapping framework that turns partial RGB-D observations into a hybrid temporal-spatial memory—locally metric for centimeter-scale navigation yet globally topological for room-to-building navigation. The system first distills raw depth …

Characterization And Profile Considerations Of Nickel-Zinc Energy Storage Under High-Power And Pulsed Loads, Benjamin P. Schmitz

Electrical Engineering Theses - Archive

There are many types of energy storage devices that are available for driving high-power electrical loads. Choosing the right chemistry is difficult and factors such as energy density, power density, safety, cycle life, and recharge rate are among the many that must be considered. Lithium-ion batteries (LIB) possess the highest combined power and energy density, making them an attractive option for many applications. Previous studies at the Pulsed Power and Energy Lab (PPEL) characterized lithium-iron-phosphate (LFP) and lithium-titanate-oxide (LTO) battery chemistries. LFP’s and LTO’s have modest power density, modest energy density and modest cycle life. However, there is still potential …

Evaluation Of Electric Vehicle Batteries For Enabling High Power Loads, Maxton K. Manker

Electrical Engineering Theses - Archive

The growing demand for high-power energy storage systems in applications such as artificial intelligence (AI) data centers, industrial backup systems, and grid-level stabilization efforts presents new challenges in technology selection. These loads have a uniquely high continuous or transient power demand that can impact the stability of the electric grid. To mitigate these challenges, energy storage in the form of batteries or supercapacitors has been proposed either as stand-alone or as intelligently controlled grid buffering sources. These same types of energy storage are commonly found in electric vehicles (EV) where they must respond to abrupt throttle and braking behavior, similar …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

Characterization Of Dc Arc Flash Events Generated By Electrochemical Energy Storage, Nicolaus E. Jennings

Electrical Engineering Dissertations - Archive

The increasing rise in the use of electrochemical energy storage (ECES) like in the form of valve regulated lead acid (VRLA) batteries, lithium-ion (Li-ion) batteries, electric double layer capacitors (EDLC), and metalized film, oil filled capacitors prompt new challenges concerning electric worker safety. The primary safety hazards associated with ECES are electric shock and arc flash. The electric shock hazard is well understood to the extent where it is known what potential and exposure duration will cause levels of pain and ultimately fatality. Various personal protective equipment (PPE) like insulating gloves allow electric workers to perform maintenance with sufficient protection …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

Study Of Deep Neural Network Trained With Salient, Compressed Medical Video Data For Enhanced Predication, Aileen Sengupta

Electrical Engineering Dissertations - Archive

The rapid growth of surgical video analysis presents a need for efficient deep learning models for surgical training, while reducing the need for excessive image and video image storage. Traditional training approaches typically rely on uniformly compressed video data, instead of selectively preserving the most surgically relevant regions. This dissertation investigates the impact of training deep neural networks (DNNs), both convolutional and transformer-based architectures, on saliency-guided, differentially compressed surgical video sequences. The study systematically evaluates how such compression influences prediction accuracy, computational efficiency and storage requirements. Experimental results demonstrate that models trained on ROI-focused compressed data combined with motion vectors …

Correlation Of Internal Pressure To The Fundamental Aging Mechanism Of Li-Ion Batteries, Kayla Garcia

Electrical Engineering Theses - Archive

As technology continues to evolve, the demand for more reliable and longer-lasting batteries is growing rapidly in the electronics industry. Batteries are vital for powering a wide range of technologies, from smartphones and consumer electronics to automotive systems and high-end tools used across multiple sectors. Lithium-ion batteries (LIBs) have helped bridge the power and energy gap that these different technologies require while achieving sufficient efficiency and reliability to make carrying them around feasible and long lasting. To further enhance the performance and longevity of a these devices, a deeper understanding of the degradation mechanisms occurring inside LIBs is crucial. The …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Exploring Smart Thermostat, Don P. Dang

2024 Fall Honors Capstone Projects - Archive

This study examines the security, privacy, and compatibility challenges associated with smart thermostats in smart home systems. Smart thermostats, as part of the growing Internet of Things (IoT) ecosystem, face vulnerabilities such as unauthorized access, data breaches, and inconsistent security protocols. Using a mixed-methods approach, this research evaluates encryption techniques, communication protocols (Zigbee, Z-Wave, Wi Fi), and user behaviors that impact system security and efficiency. Key findings indicate that 35% of users are concerned about hacking risks, while 25% express data privacy concerns. Many users lack awareness of security measures, such as firmware updates and password management, which increases vulnerabilities. …

Building A Wireless Electronic Control Unit For An Electric Vehicle, Hector S. Sosa

2024 Spring Honors Capstone Projects - Archive

Modern vehicles have a variety of features such remote start systems, advance drive assist systems, smart suspension systems, and a feature rich infotainment system. With all this technology in the car, there exists a complex network of computers working together in conjunction to deliver the modern driving experience that many people have today. To be competitive in the market, auto manufacturers are tasked to add additional features to vehicles by adding additional or modifying electronic control units (ECUs). In this study, an ECU will be designed for a mock electric vehicle which contains an already established network of ECU’s. The …

A Low-Noise, Low-Power Cmos Readout Ic For Amperometric Electrochemical Sensing System, Manu Chilukuri

Electrical Engineering Dissertations - Archive

Electrochemical sensing systems have become essential tools in numerous fields due to their exceptional sensitivity, selectivity, and versatility. They play a critical role in modern research, industry, environmental monitoring, and healthcare applications. In environmental monitoring, these systems are utilized to detect pollutants, heavy metals, and toxic gases, enabling real-time monitoring and ensuring environmental protection. In the biomedical sector, electrochemical sensing systems are vital for diagnosing diseases, monitoring biomarkers, and transforming healthcare practices.

Affordable wearable electrochemical sensing devices are crucial for making healthcare more accessible and improving public health outcomes. These devices allow for continuous monitoring of physiological parameters, supporting early …

Unsupervised Machine Learning In Wireless Sensor Measurements And Hyperspectral Imaging, Abrar U. Alam

Electrical Engineering Dissertations - Archive

The increasing demand for real-time analysis of sensor data in dynamic environments necessitates innovative approaches to data clustering. This work introduces a novel Online Kernel Clustering (OKC) framework that efficiently determines time-varying clustering configurations without requiring training data. The proposed method employs sparse kernel factorization, guided by a time-dependent metric to quantify the closeness of kernel similarity matrices to a block diagonal structure. By processing data sequentially, the OKC framework is tailored for non-stationary settings. The optimization process integrates block coordinate descent, difference-of-convex functions minimization, and projected sub-gradient descent to iteratively update kernel covariance matrices and cluster memberships online. Numerical …