Electrical and Electronics Commons^™

Design And Parametric Testing Of A Transimpedance Amplifier For Low-Power Biomedical Applications, Stanlon Tan, William Chung, Brandon Wu

Electrical Engineering

This project developed and evaluated an optical sensing system for detecting changes associated with glucose concentration. The system combined a laser-diode, cuvette sample holder, photodiode, resistive-feedback transimpedance amplifier, high-resolution analog-to-digital converter, and microcontroller. Parametric testing evaluated the effects of input current and feedback resistance on transimpedance gain, output range, and linearity. Firmware was developed to configure the ADC, average repeated conversions, monitor measurement variation, convert raw digital counts into voltage using a source-meter calibration equation, and compare sample measurements with a water reference. A cuvette enclosure maintained alignment between the laser-diode, sample, and photodiode while reducing external optical interference. Testing …

Portable Food Pasteurization Using Pulsed Electric Fields (Pef), Thomas E. Mcmillan Iii

Electrical Engineering

Foodborne illnesses remain a significant public health concern, motivating continued development of alternative food pasteurization technologies. Pulsed electric field (PEF) treatment is a non-thermal pasteurization method that inactivates microorganisms through electroporation while minimizing thermal degradation of food products. The long-term objective of this project was development of a portable PEF food pasteurization system.

An iterative design approach was used to investigate generation of the high-voltage pulses required for PEF treatment. Multiple pulse forming network topologies were evaluated through simulation, leading to selection of a solid-state Marx generator architecture. A low-voltage five-stage prototype was designed, constructed, and tested to validate the …

Lora Networking Hardware Reference Designs For Terrestrial And Low-Earth-Orbit Communications, Kevin Nottberg

Master's Theses

The work presented here demonstrates a highly robust and tested LoRa networking hardware reference design to meet the needs of the embedded networking company OWL Integrations. The hardware presented integrated a unique combination of features and design choices. The detailing of its unique design is potentially of use to others for use in fielded battery powered terrestrial sensor networking hardware utilizing LoRa. Also, the terrestrial hardware presented is shown to have the capability to support command and control (C2) LoRa links in low-earth orbit beyond terrestrial systems. The design presented has a more U.S. centric component bill-of-materials (BOM), with the …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …

Model Based Control And Hil Verification Of An Integrated Battery Management System, Catalin Sabou

UNLV Theses, Dissertations, Professional Papers, and Capstones

The rapid advancement of electric vehicle technologies necessitates highly reliable Battery Management Systems (BMS); however, validating embedded supervisory logic presents a notable challenge. While physical pack testing is accurate, it is costly and hazardous for early stage software evaluation. This thesis presents the design, implementation, and rigorous validation of an integrated BMS developed for the Battery Workforce Challenge, bridging the gap between model based design and safe hardware execution. The core of this work is a model based supervisory controller, developed in MATLAB/Simulink and executed on an STM32G4 embedded target. To facilitate embedded validation while preserving a representative battery environment, …

A Near Linear-Phase Analog Frequency Sampling Filter Design Framework Using A Second-Order Trust-Region Optimization Technique, Edreese Basharyar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) provide an efficient means of realizing finite impulse response (FIR)-like behavior in continuous-time systems, but their practical implementation is constrained by the requirement for perfect pole-zero cancellation along the imaginary axis. Because exact cancellation is physically unattainable due to component variations, ideal linear-phase Type 1 analog FSFs exhibit uncancelled poles that result in system instability. To address this limitation, this thesis introduces a near-linear-phase design framework for Type 1 analog FSFs that achieves both stability and design flexibility through the inclusion of a damping constant, ρ, which shifts the poles into the left half of …

Radio Frequency Resonate And Fire (Rf-Raf) Neurons Supporting Device Classification, David L. Weathers, Michael A. Temple, Brett J. Borghetti

Faculty Publications

Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability is demonstrated here using recently introduced Radio Frequency Resonate-and-Fire (RF-RAF) neurons and eight WirelessHART devices. Performance is evaluated for RF-RAF-generated fingerprints against the established Gabor Transform (GTX) baseline using three classifier architectures: Random Forest (RndF), Convolutional Neural Network (CNN), and a Time-Incremented Spiking Neural Network (TI-SNN). The results show that RF-RAF fingerprints achieve an average classification accuracy …

Riki&Dolphin: Real Time Data Transmission From The Bottom Of A Cave To A Website, Luca Tringali, Giacomo Canciani Dr., Tecla Tripari, Alexander Debenjak, Caterina Bearzotti

International Journal of Speleology

Coming from over 10 years of experience in cave monitoring in northeast Italy, Gruppo Speleologico Talpe del Carso, has designed Riki and Dolphin: customizable, low cost, and easy to assemble tools for getting real time data transmission from the bottom of a cave, even underwater, to a webserver. Their use has been tested to monitor air temperature inside the Abisso Bonetti Cave (Classical Karst, Italy), proving for the first time that a cave in Gorizian Karst can systematically be colder than the outdoor temperature even in winter, recording an internal temperature even lower than 0°C. The Dolphin device can be …

Data-Driven Physical-Layer Optimization For Rf And Optical Communications: Transmitarray Metasurfaces, Contextual-Bandit Mud, And Coherent Optical Links, Chengtao M. Xu

Doctoral Dissertations and Master's Theses

This dissertation studies three physical-layer optimization problems that share a common difficulty: the forward model, whether an electromagnetic solver or a statistical channel model, is either too expensive to evaluate at design scale or too unstable to hold across a single processing block. The first two problems are treated in their own right, as problems in antenna design and in multi-user detection over time-variant channels; the third carries the methodology onto a coherent optical link, where size, weight, and power constraints make a small-satellite deployment the natural target.

The antenna work targets a tri-layer pixelated unit cell fabricated on Avient …

Woodward Cuk Converter, Aditi Venkatesh, Jennifer Pereira

Honors Capstones

The purpose of this senior design project is to design and build a Cuk converter, a type of buck–boost converter. The project involves developing an analytical model and validating it through hardware testing. Cuk converters are important because they are widely used in everyday electronics, particularly in renewable energy systems such as solar power, power supplies, and electric vehicles. Their ability to increase, decrease, and reverse the polarity of voltage makes them flexible. The continuous input and output currents result in low ripple, reducing component stress and improving efficiency. To achieve these goals, simulations will be conducted using LTspice to …

Data-Driven Multimodal Mri Representation Learning For Subtype Discovery And Disease Progression Modeling In Parkinson’S Disease, Zihan Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

Parkinson’s disease (PD) exhibits substantial clinical and neuroanatomical heterogeneity, limiting robust patient stratification and clinically meaningful progression modeling from MRI. We propose a unified multimodal 3D generative representation-learning framework that learns an interpretable latent space from co-registered baseline T1/T2 MRI with an edge-aware channel. Confound-corrected latent embeddings support unsupervised subtype discovery, while disease duration provides weak supervision to orient a continuous progression axis. On an independent external cohort (PPMI, N=171), the discovery-trained subtype structure shows significant partial replication (ARI=0.35; permutation test p=0.001) and enables longitudinal clinical stratification: mixed-effects modeling reveals subtype-dependent MDS-UPDRS III progression, with the strongest effect in subtype …

Small-Scale Analog Spiking Neural Network: Design And Simulation Of An Analog Deep Neural Network, Lucas Hogue

Electrical Engineering and Computer Science Undergraduate Honors Theses

A small-scale analog schematic of a spiking neural network (SNN) is designed and demonstrated through the analog design environment (ADE) within Cadence Virtuoso. An SNN is a type of neuromorphic system that utilizes components mimicking the function of the neurons and synapses found in biological neural networks. The SNN designed in this project is composed of four layers: one input layer, two hidden layers, and one output layer. The inclusion of more than one hidden layer classifies the network as “deep” and increases the network’s efficiency at handling data with increased complexity. The SNN categorizes inputs by producing a set …

Design And Reliability Analysis Of A Radiation-Tolerant On-Board Computer System For Martian Surface Missions, Jack Ryan

Master's Theses

Space environments present complex challenges for electronic devices, perhaps most notably in the form of radiation effects; the natural protections provided by Earth’s atmosphere and magnetosphere are largely absent in deep space and extraterrestrial environments, making single-event effects (SEE) a critical concern. Although radiation-hardened components offer near-immunity to SEE, they possess tremendous drawbacks in both cost and performance. To circumvent such issues, this thesis investigates the feasibility of leveraging a commercial-off-the-shelf (COTS) device, the AMD KRIA K24 system-on-module (SOM), for use in Martian surface missions.

Detailed models were used to predict SEE rates in the system, and system-level fault tree …

Transformer Vulnerability To Geomagnetically Induced Currents Using Layered Soil Conductivity Models, Junior A. Sterling

Masters Theses

Geomagnetically induced currents (GICs) pose a significant threat to modern power transformers connected to long high-voltage transmission systems, particularly in regions with low subsurface electrical conductivity. During geomagnetic disturbances (GMDs), time-varying magnetic fields induce surface electric fields that drive quasi-DC currents through grounded transformer neutrals. These currents can bias transformer cores, leading to saturation, waveform distortion, and potential system instability. Despite extensive research, the role of regional Earth conductivity in shaping electric fields and transformer response remains an area requiring deeper investigation.

This study analyzes how contrasting Earth conductivity profiles influence GIC magnitude and transformer saturation risk using two representative …

Central Pattern Generator Test Bench Report, Stephanie Wing-Yee Lee, Dustin Wong, Joseph Hernandez

Electrical Engineering

The project presents the design, fabrication, and implementation of a PCB test bench that interfaces with a custom VLSI chip designed to emulate the neural activity of a Central Pattern Generator (CPG). The test bench provides a precise and reliable platform for supporting signal operations and validating neuromorphic functionality. The design integrates both analog and digital circuitry to provide stable biasing, signal routing, and measurement access for chip characterization. The incorporation of op-amp buffers and adjustable potentiometers enables precise voltage tuning, resulting in controlled neuron-like behavior within the custom IC. The two-layer PCB supports modular interfacing with standard laboratory instruments …

Optimization-Based Power Management For Hybrid Towing Vessels: A Comparative Framework Toward Mission-Aware Control, Rachel K. Burchill

LSU New Orleans Theses and Dissertations

This thesis presents an optimization-based framework for power management in hybrid towing vessels operating under dynamic mission conditions. A physically consistent modeling approach is developed, incorporating battery state-of-charge dynamics (SOC), unified power flow, and operational constraints. The framework evaluates rule-based control using fixed heuristic thresholds, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and multi-objective Non-Dominated Sorting Genetic Algorithm II (NSGA-II) under a representative harbor-assist mission profile. Results show that optimization-based methods significantly outperform rule-based control. PSO achieves the lowest fuel consumption and near-perfect tracking with minimal battery use, while GA provides balanced performance with moderate battery utilization. NSGA-II reveals a …

Laser-Induced Acoustic Signal Generation, Resonance Characterization, And Optical Sensing In Metallic Plates, Xinghang Zhao

All Theses

This thesis investigates laser-induced acoustic signal generation, resonance characterization, and optical sensing in metallic plates as a foundational study for damage-sensitive inspection. Rather than presenting a complete defect-detection system, the work focuses on how measurable acoustic responses can be generated, amplified, and interpreted in a controlled laboratory setting. Experiments showed that pulsed-laser excitation can produce strong and repeatable acoustic responses in metallic structures. Repetition-rate sweeping identified narrow resonant responses, including a cantilever resonance at 57.862 kHz and a maximum circular-disk response at an FFT frequency of 48.8 kHz with a laser repetition rate of about 49.1 kHz. High-Q resonances exceeding …

Neural-Network-Based Modeling Of Grid-Forming Inverters, Jacob D. Levesque

All Theses

Renewable energy production has grown significantly in recent years and continues to expand, resulting in an increasing number of inverter-based resources (IBRs) in the grid. As the number of IBRs in the grid continues to grow, grid-forming (GFM) inverters are becoming increasingly popular due to their ability to regulate voltage and frequency, providing increased grid stability and enabling islanding. As GFM inverters become more widely used in power systems, accurate and efficient models of their behavior are needed for system design purposes.

Emerging advances in neural networks have led to research on computationally efficient neural-network-based (NN-based) modeling approaches for inverters. …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Enhancing Reliable Performance Of Microgrids With Autonomous Control Of Distributed Energy Resources, Sahand Liasi

All Dissertations

This dissertation presents a comprehensive investigation into enhancing the reliability, efficiency, and autonomous operation of modern power networks through advanced microgrid integration and control strategies. The research addresses critical challenges across three interconnected domains: microgrid control, thermodynamic modeling of heat recovery system (HRS), and optimal distribution network reliability.

In the realm of microgrid control, this work introduces novel methodologies for grid forming inverter-based resources (IBRs). A significant contribution is the development and validation of an "Auto Frequency Change" controller, which drastically reduces microgrid synchronization time with the main grid from several minutes to approximately four seconds, with theoretical potential for …

The Pid-Based Three Quadcopter Uavs Formation Control Under External Disturbance, Vo Van An, Trinh Luong Mien

Makara Journal of Technology

This paper presents the design and evaluation of a formation control strategy for three quadcopter UAVs, based on a PID controller in a leader–follower structure, under the influence of external disturbances. Each UAV employs a six-degree of-freedom dynamic model and utilises a cascade PID control architecture, in which the inner control loop stabilises the attitude. In contrast, the outer control loop regulates position and maintains the formation. The PID parameters are tuned using the Ziegler–Nichols method to ensure simple implementation and low computational cost. The performance of the control system is evaluated through simulations in the MATLAB environment for two …

Piano Aid, Christopher G. Sayers, Tyler Baugus, Gabby Taunton

Create@State

Pianos provide recreational and educational value and is a cornerstone of the culture experienced worldwide today. Despite the many benefits and enrichment music brings these benefits are often inaccessible or difficult to learn by most people but especially to those of the Deaf and Hard of Hearing (DHH) community due to its innate auditory nature. While adaptive instruments have been proposed to address this gap, many remain conceptual or fail to reach production because of high development costs and limited commercial markets. This project presents an economically feasible alternative in the design of an adaptive digital piano that enables both …

Implantable, Sensor-Embedded Vascular Graft Towards Wireless Monitoring Of Stenosis, Nnamdi Dike

LSU Master's Theses

Arteriovenous (AV) grafts are commonly used to provide vascular access for hemodialysis in patients with end-stage renal disease. Despite their widespread use, AV grafts are prone to complications such as stenosis and thrombosis. Early detection of these conditions remains challenging with current monitoring methods too costly or insufficient. This work presents the design, fabrication, and validation of an LC pressure sensor embedded within a model AV graft to enable real-time monitoring. The proposed system integrates a parallel-plate capacitive pressure sensor with a spiral inductor to form an LC circuit embedded within an elastomeric graft wall. The Ecoflex 00-30 dielectric layer …

Control And Stability Analysis Of Three-Phase Grid-Connected Inverters In Renewable Energy Systems, Yasser Ayeva

Electrical Engineering and Computer Science Faculty Publications and Presentations

The increase in demand for renewable energy sources such as solar and wind systems has led to widespread use and integration of a three-phase grid connected inverters in electric modern electric power systems. The inverters are essential to convert DC power into AC power and to control the delivered power to the grid. However, the use of the inverters in a renewable energy system has some challenges related to stability and control due to the presence of power electronic interfaces and filter dynamics. This paper analyzes the control and stability of three phase grid connected inverter through an LCL filter. …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Tuning And Performance Of Pid Controlled Low Complexity Systems, Timothy Evans

Honors Theses

Proportional integral derivative (PID) controllers are used for precise position and orientation control in systems such as autonomous underwater vehicles (AUVs). This project supports the University of Southern Mississippi’s (USM) Robotics Club’s RoboSub AUV effort by developing, troubleshooting, and manually tuning PID controllers to characterize tracking performance and settling time across systems of increasing complexity. Initially, the project hypothesized that tracking performance would be reduced and settling times would increase as system complexity advanced from one degree-of-freedom (DOF) to two DOF. However, prior research was found that suggests that for small disturbances around an equilibrium state, separate PID-controlled DOFs can …

Condition Monitoring Of Induction Motor Using Motor Current Signature Analysis, Francis Ikechukwu Obianke, Joseph Okhaifoh, Benjamin Akinloye

Al-Bahir

Induction machines serve as the cornerstone and driving force of modern manufacturing and production systems. This research aims to monitor and analyse the operating performance of induction motors using motor current signature analysis (MCSA). MCSA is employed to process the current signal of a motor into a frequency spectrum, known as the current signature by applying the Fast Fourier Transform (FFT) algorithm. The underlying principle is that vibration generated in a motor is closely related to the changes of the magnetic field density, and the induced voltage varies with the stator current.

A simulation model replicating the behaviour of an …

An Interpretable Hybrid Deep And Reinforcement Learning Paradigm For Glioma Prognosis, Renuga Devi M Ms

Theses and Dissertations

Brain tumors are highly aggressive and lethal types of cancer, particularly gliomas. These cancerous growths show complicated pathophysiological behaviours and with poor prognosis despite therapeutic advances. Due to their biological differences and infiltrating growth, as well as overlapping radiological characteristics, they pose great difficulty in diagnosis, grading, and survival prediction. Artificial intelligence technology, which includes machine learning , deep learning, and reinforcement learning has developed into a new paradigm for the automation of brain tumor diagnostics and personalized treatment. The main goal of this study is to create an integrated AI-based framework that can perform brain tumor segmentation, grading and …

Enhanced Cuckoo Search-Based Optimization For Single Distributed Generation Placement And Sizing In Radial Distribution Systems, Samson Oladayo Ayanlade, Abdulrasaq Jimoh, Richard Oladayo Olarewaju, Ignatius Kema Okakwu, Israel O. Adejumobi, Joseph B. Samson, Oluwadare A. Adebisi, Oluwadare O. Akinrogunde

Al-Bahir

This paper presents an Enhanced Cuckoo Search Algorithm (ECSA) to optimally place and size Distributed Generation (DG) in radial distribution systems to minimize real power loss within operating constraints. The proposed ECSA has exponentially decaying adaptive Lévy flights, constraint-aware solution repair with dynamic penalty coefficients, and diversity-directed stochastic replacement to enhance search robustness and convergence speed. It was tested with 30 independent runs on the IEEE 33-bus, IEEE 69-bus, and a practical Nigerian 32-bus distribution network. The simulations show that the ECSA lowers the active power loss of the IEEE 33-bus system from 201.58 kW to 102.75 kW (49.03%), and …

Voltage-Mode Driver With Sar-Based Termination Calibration, Moustafa M. Elsayed, Abeer T. Khalil, Sameh A. Ibrahim, Mohy Eldin A. Abo-Elsoud

Mansoura Engineering Journal

This paper introduces a DAC-based four-level pulse-amplitude modulation (PAM-4) driver capable of operating at data rates up to 80 Gb/s in 65-nm CMOS technology. A replica-based termination calibration loop is proposed to preserve driver linearity across process, voltage, and temperature (PVT) variations. The proposed driver achieves a relative level mismatch (RLM) of 99.3%. In addition, the transmitter demonstrates a vertical eye opening of 134.8 mV and a horizontal eye opening of 0.44 UI under worst-case conditions.