Digital Commons Network^™

Experimental Validation Of Optical Wireless Communication And Power Transfer For Uav Applications, Fnu Dhruv

UNF Graduate Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) have revolutionized emergency response, disaster assessment, and search-and-rescue operations. However, their operational efficacy is fundamentally constrained by limited battery endurance and the susceptibility of traditional radio-frequency communication to disruption in adverse weather. To address these limitations, this thesis proposes and experimentally validates a novel architecture integrating Free-Space Optical (FSO) communication with Simultaneous Lightweight Information and Power Transfer (SLIPT). This system utilizes a split-beam configuration to concurrently enable high-bandwidth data transmission and optical energy harvesting to replenish the UAV's battery pack. The research was conducted in three progressive phases. Initially, system feasibility was established through rigorous optical …

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 …

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Open Source Tools For Ecological Research, Alex P. Riebe

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of an open source wireless sensor network for hibernacula manipulation with an emphasis on accessibility and reproducibility. It addresses the design of the electrical hardware, guidance on antennas and RF implementation, and design of an application-specific communication protocol, all with the explicit goal of enabling ecologists and other conservationists to be able to manufacture, deploy, operate, and maintain the system for their research. The resulting sensor network designed in this thesis is to control the temperature inside bat hibernacula during the winter to study the relationship between temperature and bat mortality rate due to White …

Design And Validation Of A Low-Cost Wearable Electromyography (Emg) System For Monitoring Exercise-Induced Changes In Muscle Activity, Ingrid E. Halverson

Dissertations, Master's Theses and Master's Reports

Wearable technologies have expanded opportunities for monitoring athletic performance, but many existing systems remain costly and confined to laboratory or medical settings. This thesis presents the design, development, and evaluation of a low-cost, wearable EMG platform for monitoring neuromuscular activity during exercise. A wireless device incorporating a surface EMG sensor, an ESP32 microcontroller, and Wi-Fi transmission was developed to acquire muscle activation data. Signal processing techniques, including filtering, root-mean-square (RMS), mean frequency (MNF), and median frequency (MDF) analyses, were used to evaluate changes in muscle activation. Experimental testing demonstrated reliable wireless data acquisition and successful capture of physiological changes before …

High-Performance Circuit Manufacturing And Testing Exercises, Benjamin S. Keppers

Dissertations, Master's Theses and Master's Reports

An advanced demonstrator Printed Circuit Board (PCB) has been designed and implemented providing a framework for advancing students’ knowledge in hands-on PCB design and manufacturing process through industry recognized test coupons, stack ups, and transmission lines. Students are guided through several key aspects of design and simulation relating to manufacturing and qualifications. Manufacturing allows students to refine process development and analyze performance data with respect to qualification tests specified by Global Electronics Association standards. Results are then used to build a stackup model, and complete a design activity for calculating expected test results for a series of controlled impedance electrical …

Antenna-Based Sensors For Dielectric Characterization Of Materials, Hilary Scott Nkimbeng Cho

Dissertations, Master's Theses and Master's Reports

This research introduces antenna-based sensors for dielectric characterization aimed at overcoming the limitations of conventional microwave sensors. Although traditional microwave sensors are widely used for their noncontact operation, high sensitivity, and ability to penetrate various materials, they often face challenges such as large size and high-power consumption. To address these issues, antenna-based sensors are explored for their compactness, ease of fabrication, and flexible design adaptability across diverse sensing applications. An in-depth analysis of multiple antenna sensor configurations is conducted, followed by the design and development of high-performance sensing structures. A saw-tooth slot antenna sensor is developed for highly sensitive liquid …

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Dynamic Direct Voltage Control Under Maximum Torque Per Ampere For Interior Pmsms, Mohamad Alzayed, Hicham Chaoui, Alaref Elhaj

Electrical & Computer Engineering Faculty Publications

A novel method for controlling the speed of interior permanent magnet synchronous motors (IPMSMs), known as the current-sensing-based dynamic direct voltage control method under the maximum torque per ampere (MTPA) concept, is introduced. This technique achieves precise tracking of machine velocity by determining the optimal combination of voltage amplitude and angle for each specific motor velocity and current/load condition. Unlike previous studies, this approach takes into account the transient model of the machine, resulting in improved accuracy during dynamic operating conditions compared with existing methods in the literature. Moreover, a comparative analysis is conducted involving different direct voltage MTPA speed …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Modern Potentiostat Architectures For Electrochemical Sensing: Design, Integration, And Future Directions, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined …

Bridging Physics-Based Modeling And Machine Learning To Predict Material Behavior: Applications In Fatigue Crack Growth And Dielectric Property Characterization, Ansan Pokharel

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation integrates physics-based modeling with machine learning (ML) to predict how materials behave under complex thermal and mechanical conditions. A key innovation of this work is the use of finite element analysis (FEA) to supplement experimental data. This approach creates more diverse and representative synthetic datasets, helping to reduce the limitations and biases that arise when training ML models solely on experimental measurements. The research focuses on two applications: improving the prediction of fatigue properties in superalloys and estimating temperature-dependent, high-frequency dielectric properties relevant to microwave-based chemical processing.

In the first study, low-cycle fatigue experiments were performed on the …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

Hyperglycemia Detection From Sigle - Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract
Hyperglycemia Detection from Single-Lead ECG using a Hybrid CNN & Transformer Model
Adam Ogunjembola

Diabetes Mellitus is known as high blood glucose. This high blood glucose level happens when the body has a problem with producing or using insulin. Insulin is a very important hormone that the pancreas makes to control how much glucose gets into the bloodstream and cells. Diabetes Mellitus has an effect on the body if it is not treated, such as damaging the blood vessels and nerves which can lead to stroke, kidney failure, heart attack and permanent loss of vision. Since people with diabetes …

Bi-Level Optimization Of Peer-To-Peer Trading In A Decentralized Energy Market, Marshal Miezah

Graduate Theses, Dissertations, and Problem Reports (ETD)

Distributed power generation based on rooftop photovoltaic (PV) systems integrated with battery storage emerges as a promising pathway for reducing greenhouse gas emissions and im- proving flexibility in modern power systems. This study develops a bi-level optimization model to examine how prosumers maximize profit through peer-to-peer (P2P) energy trading with consumers and the grid, and how consumers minimize cost by leveraging P2P trading. The bi-level problem is reformulated as a single-level mixed-integer programming (MIP) model using Karush- Kuhn-Tucker (KKT) conditions to improve tractability and preserve market-clearing behavior. For the model validation and case study development, prosumer and consumer data are …

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 …

Artificial Sense-Making Dataset, Jason A. Bengtson, John Sandstrom, Nathan Camp

NMSU Library: Datasets

No abstract provided.

Delay-Doppler Integrated Sensing And Communications (Dd-Isac) With Predictive Beamforming, Mohammad Abdul Mobin, Yanjun Pan, Jingxian Wu

Electrical Engineering and Computer Science Faculty Publications and Presentations

A new delay-Doppler (DD) integrated sensing and communications (ISAC) framework is proposed for unmanned aerial vehicle (UAV) systems. In the DD-ISAC framework, both sensing and communications are performed by using the orthogonal delay Doppler division multiplexing (ODDM) waveforms, which unify sensing and communication designs through the unique ODDM waveform properties, such as local DD-domain bi-orthogonality and dual-resolution. Specifically, the dual-resolution property enables the generation of a range-Doppler map for accurate and low complexity sensing, and the bi-orthogonality minimizes interference for both sensing and communications. The ODDM waveforms are used in combination with the phase comparison monopulse technique and a scaled …

Control System Emendation And Ai-Driven Optimization For Enhancing A Smart Residential Microgrid, Anthony Nyoyoko

Master’s Theses

This thesis began with a simple idea: to restore the control system of a smart residential microgrid to respond intelligently to electricity prices while remaining safe, reliable, and practical on low-cost hardware. At the start, the goal was to design an economically aware microgrid that could look at electricity prices from the PJM market and make better operational decisions than traditional rule-based control. The motivation was straightforward. As residential renewable energy adoption increases, microgrids are expected to do more than just supply power. They are expected to respond to price volatility, integrate renewable generation, and operate reliably using embedded controllers …

Printer For Music Box, Chad Lewis, Caleb Murawski, Zion Smith, Bryan Tibbs

Williams Honors College, Honors Research Projects

The traditional method of creating music box sheet music involves manually punching holes into a paper strip using a hand-operated hole punch. This process involves precise knowledge of each note’s location and the ability to achieve perfect accuracy for hours.

The goal of this system is to automate this process, significantly reducing the time required while greatly improving the accuracy of the resulting music box playback. The user simply uploads a MIDI file of their choice into a user-friendly application. Here, the file is modified based on the user’s needs and sent to an automated hole-punching system to punch the …

Adaptive Lighting System, Jacob Samerdak, Ethan Schwartz, Brandon Breen, Matthew Carozza

Williams Honors College, Honors Research Projects

The Adaptive Lighting System is a modular network of components that requires minimal installation in homes. Additionally, all components of the system communicate wirelessly and interface with a mobile application. The system utilizes occupancy and daylight sensors to detect the environment of a room and adjust the lighting accordingly. The system detects ambient lighting as well as occupancy within a room. Using the data collected from the sensors, the system adjusts lighting temperature, color, and brightness. The user can also manually control these settings. The Adaptive Lighting System aims to help regulate circadian rhythm, so there is a configuration in …

Dashboard And Racing Telemetry, Cole Barach, Jacob Koshel, Ethan Zifzal, Matthew Sullivan

Williams Honors College, Honors Research Projects

The main goal of the project is to design and manufacture a combined dashboard and data logger for the vehicles produced by the Zips Racing design team. The dashboard will intuitively display real-time information to the driver and record all received information while driving. This information may be pulled off the device later for performing data analysis. This project will incorporate custom PCB design, surface mount soldering, embedded software development, and the CAN communication protocol.

Aquaflow Pro, Ashton Henley, Jeannie Fritz, Khalin Rubbo

Williams Honors College, Honors Research Projects

Changing aquarium water is a hassle, and when done incorrectly, it will result in changes in water quality and potential harm to aquatic life. Aquarium water changes are crucial in removing toxins such as ammonia, nitrites, and nitrates. There is a need for an automated, stressless fish tank water-changing solution that ensures aquatic safety. To this end, an automatic temperature-controlled fish tank water changing system was designed, carrying out a water change for a designated main tank in which water heated within an inbound reservoir is pumped into the tank to replace the outbound water, responding to both the tank’s …

Optically-Pumped Semiconductor Optical Amplifiers, Dhruvkumar Desai

Graduate Studies Theses and Dissertations 2026

Semiconductor optical amplifiers (SOAs) offer a low-cost, compact solution for power amplification needs in an optical communication system compared with the dominant erbium-doped fiber amplifiers (EDFAs). They can also provide a wide gain bandwidth. However, conventional electrically-pumped SOAs suffer from larger noise figures, low saturation power, and polarization dependence, in comparison with EDFAs. We propose an optically-pumped SOA (OP-SOA) that will maintain the benefits of conventional SOAs while closing the gaps in other amplifier performance metrics. The underlying reasons for optical pumping are twofold. First, optical pumping allows higher carrier injection and thus "population inversion." Second, optical pumping decouples carrier …

Connecting The Existing Fiber Infrastructure To The Future With Antiresonant Hollow Core Fibers, Timothy Bate

Graduate Studies Theses and Dissertations 2026

Optical fiber systems based on solid-core silica waveguides underpin modern telecommunications, high-power laser delivery, precision sensing, and coherent optical systems. However, nonlinear effects, material absorption, and thermal limitations within silica increasingly constrain further scaling in both optical power and transmission performance. Antiresonant hollow-core fibers provide a promising alternative by guiding light predominantly in air, substantially reducing nonlinear interactions, latency, and optical damage while enabling transmission regimes inaccessible to conventional solid-core fibers. Despite rapid advances in antiresonant hollow-core fiber attenuation and power handling, one of the largest remaining barriers to widespread adoption is reliable integration with the existing solid-core fiber ecosystem. …

Epistemic Edge: Subjective Logic Guardrails For Llm-Driven Iot Actuation, Muntaser Syed, Marius Silaghi

Electrical Engineering and Computer Science Student Publications

Deploying large language models (LLMs) as decision-making agents in safety-critical Internet of Things (IoT) systems introduces risks that purely behavioral guardrails, such as action whitelists, cannot fully address. This paper presents Epistemic Edge, a four-tier neuro-symbolic pipeline that augments LLM-driven actuation with subjective logic (SL) uncertainty quantification, temporal decay, and dual guardrails combining epistemic threshold checks with behavioral whitelists. We evaluate seven locally-deployed models: three PrismML Bonsai 1-bit models (1.7B, 4B, 8B), three 4-bit quantized models (Qwen3-8B, Llama 3.2-3B, Phi-3.5-mini), and the DeepSeek-R18B reasoning model, across eight ablation conditions and five IoT actuation scenarios (2,800 controlled trials). We further validate …