Electrical and Computer Engineering Commons^™

Machine Learning Based Three-Limb Core-Type Transformer Core Aspect Ratios Identification, Ananta Bijoy Bhadra

College of Graduate Studies: Theses & Dissertations

Power transformers are considered one of the key elements of electric grids. Transient studies include transformer transient analysis which is required for the continuous power supply. However, to perform the transient analysis, the details of the internal structure of the transformer are required which are unobtainable and considered as confidential information. Therefore, the application of topological-based transformer models is limited although the models can accurately represent the transformers. To address this concern, a novel approach utilizing Machine Learning (ML) to identify the core aspect ratios of the three-limb core-type transformer is introduced. The proposed approach, using only the voltage and …

Applications Of Plasmonic Biosensors In Chiral And Achiral Sensing, Aritra Biswas

Graduate Thesis and Dissertation 2023-2024

Monitoring biological systems is crucial in healthcare, driving the need for reliable and noninvasive solutions. The proliferation of unverified drugs in the market necessitates reliable methods for their detection and identification, especially amidst advancements in pharmaceuticals. Plasmonic biosensors emerge as a great platform for ultra-sensitive detection, identification, and manipulation of biomolecular systems. This dissertation report addresses the critical need for precise detection and monitoring of biomolecules and drugs, presenting innovative solutions through the design of a plasmonic biosensor to tackle challenges in sensitivity, selectivity, and label-free detection and identification. We introduce a robust and tunable, cavity-integrated plasmonic nanopatterned sensor that …

Exploratory Prompting Of Large Language Models To Act As Co-Pilots For Augmenting Business Process Work In Document Classification, Jose Ramon Ilagan, Joseph Benjamin R. Ilagan, Claire Louisse Basallo, Zachary Matthew Alabastro

Quantitative Methods and Information Technology Faculty Publications

Businesses deal with different types of documents containing unstructured documents. The data in these documents must be converted into digital forms other automated systems could only process. One generic use case is document classification, which usually involves manual transformation due to human understanding needed in the process. These documents go beyond those generated through regular business transactions and operations and also include web-based content such as online news, blogs, e-mails, and various digital libraries. Recent developments in robotic process automation (RPA) and artificial intelligence (AI) aim to automate the otherwise expensive, time-consuming, and repetitive manual steps. Through more powerful natural …

Evaluating The Performance Of Egemaps Features In Depression Detection Using E-Daic Subsets, Joshua Turnipseed

Graduate Research Theses & Dissertations

This paper investigates the performance of the eGeMAPS (extended Geneva Minimalistic Acoustic Parameter Set) feature set in detecting depression from audio samples using subsets of the E-DAIC (Extended Distress Analysis Interview Corpus) database. With depression affecting a significant portion of the U.S. adult population, efficient detection methods are critical for timely diagnosis and treatment. Various classifiers in the WEKA machine learning toolbox are used to evaluate the performance of eGeMAPS features in distinguishing between depressed and nondepressed (D&ND) individuals. Our methodology involves creating balanced subsets of E-DAIC, extracting eGeMAPS features using openSMILE, and testing different machine learning models. This study …

Tunable Metasurface For Efficient Harmonic Generation And Amplitude Modulation, Steve Mares

Masters Theses

The manipulation of microwave signals is critical in the information and communication technology industry. This research investigates the application of time-coding techniques on metasurfaces to achieve tunable control of electromagnetic signal transmission. The integration of mounted PIN diodes facilitates dynamic modulation of signal behavior. Both passive (without PIN diodes) and active (with PIN diodes) metasurfaces were analyzed through simulations using Ansys HFSS and validated with experimental measurements. A major contribution of this work is the design and testing of active metasurfaces that leverage PIN diodes for harmonic generation and amplitude modulation. The study reveals a clear relationship between modulating signal …

Active Uncertainty Representation Learning: Toward More Label Efficiency In Deep Learning, Salman Mohamadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary goal of this dissertation is to investigate and improve the efficiency of deep learning algorithms, especially within computer vision problem domains, from the perspective of label-efficiency. This investigation showed that deep learning algorithms are mostly notorious for the lack of uncertainty representation. Accordingly, we aimed to develop an array of deep learning frameworks rich with uncertainty representation. These frameworks are mainly within two current pillars of machine learning, deep active learning and self-supervised learning. These frameworks include deep active ensemble sampling for efficient sample selection within deep active learning, a two-stage ensemble-based general self-training approach for existing visual …

Analyzing Viability Of Blue Indium Gallium Nitride Leds For Use In Space Missions Using A Low Earth Orbit Cubesa, Bertrand Edward Wieliczko

Graduate Theses, Dissertations, and Problem Reports (ETD)

The payload capacity of spacecraft is constrained by the weight of the craft itself, including fuel and electronic systems. The protective measures used to shield onboard electronics from the harsh space environment, characterized by high-energy particles and significant temperature fluctuations, can further diminish the available payload capacity. This thesis explores the potential of naturally radiation-hard alternatives to commonly used electronic materials, such as Silicon, to reduce the need for shielding and other protective measures, thereby decreasing the weight and cost of space missions.

III-V semiconductor materials, such as Gallium Nitride (GaN), are known for their inherent resilience to temperature swings …

Enhancing 5g Fixed Wireless Access In Rural Settings Via Machine Learning-Driven Resource Optimization, Maryam Amini

Graduate Theses, Dissertations, and Problem Reports (ETD)

Providing broadband access to rural communities continues to be an important societal problem whose solution would help to break down the digital divide. While 5G wireless networks may be used for rural broadband, a key challenge is the placement of base stations, which is exacerbated by the use of high frequencies in the millimeter-wave band. Such technology requires an unobstructed line of sight, demanding meticulous planning of the number, height, and location of base stations for optimal coverage. Conventional methods, such as ray-tracing to simulate signal propagation across varied terrain, are computational costly and not feasible for vast coverage areas. …

Spin Wave Devices For Embedded Applications And Information Processing, Raisa Fabiha

Theses and Dissertations

Device miniaturization is a prerequisite for modern day electronics. Spintronics offers immense potential in energy efficient data storage, solid states devices, ultrafast computing and other electronic applications because of their low power consumption, non-volatile nature and unique activation mechanism. The conventional nano-antennas used for wireless communication, biomedical and wearable devices and IoT applications are limited by the traditional Harrington limit. As we attempt to miniaturize the antenna size beyond its emitted wavelength (also called “subwavelength” antenna), its gain and efficiency plummet. To overcome this challenge, the researchers have proposed magnetoelectric antennas. The ultra-thin film based magnetoelastic antennas suffer from eddy …

Simulation Of A Pick And Place System For Electronic Cards Using A Yumi Cobot, Derrick Sze, Rosula Sj Reyes, Patricia Angela R. Abu

Electronics, Computer, and Communications Engineering Faculty Publications

Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing repetitive work and requiring highly skilled workers. The goal of the research is to be able to simulate a pick and place environment with electronic cards using a YuMi cobot and mobile platforms in Coppeliasim. The mobile robot is responsible for transporting the electronic cards to the target location through path planning implemented using the OMPL plug-in. After arriving at the target location, YuMi will then perform the …

Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li

Electrical and Computer Engineering Faculty Publications

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Transient Simulations Of Power Systems With Inverter Interfaced Resources, Gaurish Shreedhar Gokhale

Dissertations, Master's Theses and Master's Reports

Renewable energy sources are interfaced with the electrical grid using power electronic inverters. These inverter-interfaced resources have been deployed for nearly 20 years. Still, NERC only recently highlighted the vast gap between the actual behavior of these inverters during power system transients and those observed in simulations. Simulation models need significant improvements, mainly for developing accurate inverter current controls, phase-locked loops, and fault response during different power priority modes. Additionally, only time-domain electromagnetic transient simulation tools can fully represent the fault response of the inverter-interfaced resources.

The developed simulation model of the inverter-interfaced resource is based on the recommendations made …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Developing Robust Autonomous Vehicles With Ros, Dylan J. Kangas

Dissertations, Master's Theses and Master's Reports

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets.

The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping …

Modeling Of Inverter-Based Resources For Hardware In The Loop Testing Of Protection And Control Schemes, Md Aamir Rahmani

Dissertations, Master's Theses and Master's Reports

High penetration of inverter-based resources (IBR) may cause the misoperation of protective relays due to the dynamic nature of fault currents fed by the IBR during short-circuit faults. Misoperation may cause damage to power system equipment and affect the reliability of the power system. This dissertation presents an in-depth investigation into the modeling of IBR in the context of transmission line fault scenarios to understand and analyze the characteristics of fault currents fed by the IBR. The research objectives encompass the development of reliable IBR models capable of accurately replicating fault current behaviors, designing IBR operation control schemes, and implementing …

Real-Time Cyber-Power Testbed Enhancement And Synchrophasor Data Generation For Anomaly Detection Using Physics-Informed Machine Learning, Vasavi Sivaramakrishnan

Graduate Theses, Dissertations, and Problem Reports (ETD)

Advanced sensing and automation are essential to managing the evolving electric power system with tight coupling of information and power system layer. However, this increasing number of cyber-physical devices brings vulnerabilities from cyber threats and extreme weather events can endanger the power system on a physical level as well. Advanced monitoring and control algorithms with human operators in the loop are needed to enable power system resiliency despite these increasing threats. These advanced algorithms require validation using a realistic test system that mimics real-world scenarios.

This work focuses on a) developing a realistic real-time cyber-power testbed with hardware-in-the-loop to generate …

Magnetotransport Properties Of Dirac Semimetal Taco2te2 And Ferromagnetic Weyl Semimetal Co3sn2s2, Samuel Pate

Graduate Research Theses & Dissertations

This dissertation investigates the magnetotransport properties of topological semimetals, specifically focusing on the Dirac semimetal TaCo2Te2 and the Weyl semimetal Co3Sn2S2. In TaCo2Te2, I observed extremely large magnetoresistance that violates Kohler’s rule. Extended Kohler’s rule can be applied with a calculation of Hall factor at low temperatures. I also explored the applicability of the two-band and four-band models of carrier analysis and correlate the region where Kohler’s rule is obeyed to the four-band model. In Co3Sn2S2, I explored the angle-dependent anomalous Hall effect (AHE) near the Kagome plane, revealing a tunable AHE under applied fields and an abrupt disappearance of …

Estimation Of Methyl Orange Dye's Molar Absorptivity Using A Photoresistor-Based Photometer, Patricia M. Ludovice, Ramon M. Delos Santos

Physics Faculty Publications

The need to further develop solar cell technology, particularly on dye-sensitized solar cells (DSSCs), drives absorption studies of various chemical species. In this study, absorbance analysis of methyl orange (MO) dye was performed using the adapted and modified photoresistor-based photometer of Adams-McNichol et al. [1]. The research aims to improve the stability of the reference setup, while maintaining the accuracy of absorbance results it yields. The methodology includes photometer fabrication, MO dye sample preparation, and the evaluation of MO dye's molar absorptivity in an aqueous solution. Results show that the estimated molar absorptivity of MO using the voltage readings from …

Passive Wireless Corrosion And Temperature Detection In High-Temperature Environments, Noah Lane Strader

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work focuses on the theory and development of LC sensors for high temperature and corrosion measurement for stainless steel and copper surfaces with power industry and general corrosion detection applications. The LC resonators were fabricated via screen printing an Ag inductor on an alumina substrate. The LC design was modeled using the ANSYS HFSS modeling package. The LC passive wireless sensors operate with resonant frequencies centered at 85-110 MHz. The wireless response of the LC sensor was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50-800 °C for copper ground planes and …

Design And Fabrication Of Quantum Cascade Laser Tree Arrays, Luke Milbocker

Graduate Thesis and Dissertation 2023-2024

Quantum cascade lasers (QCLs) are semiconductor lasers that can be designed to emit over a very broad wavelength range from the mid-wave infrared (MWIR) to terahertz frequencies. Their compact size and ability to output several watts of MWIR or long-wave infrared (LWIR) radiation makes them ideal sources for directional infrared counter measures (DIRCM). This application is fueling demand for ever more powerful QCLs, but power gains from single QCLs have largely stagnated in recent years. Novel waveguide geometries such as tree-arrays seek to increase output power delivered in a single high-quality beam. InGaAs/AlInAs tree array QCLs based on ridge waveguides …

Structural Construction And Surface Modification Of Copper Current Collectors For Lithium Metal Batteries, Yaohua Liang

Electronic Theses and Dissertations

Graphene, a prevalent anode material in commercial lithium-ion batteries, has reached its theoretical capacity limit. The imperative is to develop high-capacity anode materials to meet the growing demand for energy density. Lithium metal, renowned for its exceptionally high theoretical specific capacity density (3680 mAh g-1) and low reduction potential (-3.04 V, relative to the standard hydrogen electrode), is commonly dubbed the "Holy Grail" for negative electrode materials in high-energy-density batteries. However, practical advancements in lithium metal anodes face obstacles like low Coulombic efficiency, limited cycle life, and heightened reactivity to the electrolyte and internal short circuits resulting from lithium dendrite …

Evaluating The Impact Of Perceptual Loss In Generative Adversarial Models And Diffusion Models For Document Image Enhancement, Farzaneh Karimpour

Electronic Theses and Dissertations

Documents often suffer from various types of degradation which make them difficult to read and restrict OCR performance. This study investigates the effectiveness of perceptual loss in enhancing document image cleanup by comparing a GAN-based model and a diffusion model. In our experiments, we utilized the DE-GAN model as a GAN-based model and the NAF-DPM model as a diffusion model, both enhanced by incorporating perceptual loss. We then compared the results of both models and evaluated them by using the DIBCO 2013, DIBCO 2017, and H-DIBCO 2018 datasets revealed that our approach consistently outperforms existing state-of-the-art methods. Results showed that …

Comparative Analysis Of Deep Learning-Based Anomaly Detection Models For Gps Spoofing Detection, Hasan Mirzakhaninafchi

Electronic Theses and Dissertations

As autonomous vehicles (AVs) become integral to modern transportation, their susceptibility to cyber-attacks, particularly GPS spoofing, presents a serious security threat. This study addresses these challenges by applying a suite of deep learning models to enhance the detection of anomalous GPS signals. Focusing on autoencoder-based architectures, the proposed models such as long short-term memory-based variational autoencoder (LSTM-VAE), LSTM-based autoencoder (LSTM-AE), multilayer perceptron-based variational autoencoder (MLP-VAE), MLP-based Autoencoder (MLPAE), Stacked LSTM-based variational autoencoder (Stacked-LSTM-VAE), stacked LSTM-based autoencoder (Stacked-LSTM-AE), memory-augmented-LSTM-VAE (Mem-LSTM-VAE), and time-series-anomaly-detection-generative-adversarial-networks (TadGAN) were trained exclusively on authentic GPS data. This unsupervised learning approach which used for the above-mentioned models enables …

An Integrated Hybrid P2p-Dr Networks For A Transactive Energy Market Platform Considering Electricity Network Constraints, Sheroze Liaquat

Electronic Theses and Dissertations

No abstract provided.

Anomaly Detection Approaches Of Energy Storage Systems Using Kalman Filter And Machine Learning Techniques, Phadungsak Tubuntoeng

UNF Graduate Theses and Dissertations

The increasing prevalence of cyber-attacks poses a significant concern, particularly within critical infrastructures like the power system. Such attacks have the potential to cause substantial impacts on essential services, economic stability, and national security. Energy storage systems (ESS) are integral components of the power grid and are particularly vulnerable to cyber threats. These vulnerabilities can be exploited through various means, including two-way communication, web portals, and remote access.

Given the critical nature of ESS, the ability to detect and mitigate malicious cyber-attacks is imperative. Various methods can be employed for cyber-attack detection, including signature-based, anomaly-based, and behavior-based approaches. In this …

Integrating Therapy Into Play: Stand-On Ride-On For A Child With Cerebral Palsy, Kira Flanagan

UNF Graduate Theses and Dissertations

This study focuses on the development of an adaptive ride-on toy specifically designed for a 2.5-year-old child with spastic diplegic cerebral palsy. As a feasibility study, the primary objective of this innovative device is to enhance motor function, foster autonomy, and improve the overall quality of life for the child. The Power Mobility Device (PMD) integrates actuation and steering modifications, and a harness mechanism tailored to meet the child's unique needs. Comprehensive assessments of the child's spatiotemporal gait characteristics were conducted before and after a three-month usage period. The results reveal significant improvements in the child's ability to control dynamic …

Photoluminescence Switching In Quantum Dots Connected With Fluorinated And Hydrogenated Photochromic Molecules, Ephraiem S. Sarabamoun, Jonathan M. Bietsch, Pramod Aryal, Amelia G. Reid, Maurice Curran, Grayson Johnson, Esther H. R. Tsai, Charles W. Machan, Guijun Wang, Joshua J. Choi

Chemistry & Biochemistry Faculty Publications

We investigate switching of photoluminescence (PL) from PbS quantum dots (QDs) crosslinked with two different types of photochromic diarylethene molecules, 4,4'-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (1H) and 4,4'-(1-perfluorocyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (2F). Our results show that the QDs crosslinked with the hydrogenated molecule (1H) exhibit a greater amount of switching in photoluminescence intensity compared to QDs crosslinked with the fluorinated molecule (2F). With a combination of differential pulse voltammetry and density functional theory, we attribute the different amount of PL switching to the different energy levels between 1H and 2F molecules which result in different potential barrier …

Modeling And Analysis Methods For Esd And Emi Problems, Xin Yan

Doctoral Dissertations

"Electrostatic discharge (ESD) failures and Electromagnetic interference (EMI) problems are becoming more critical in electronic devices and large systems. In this work, four studies are presented to model and analyze ESD and EMI problems.

First, a simplified physical-based model for deep-snapback transient voltage suppressors (TVS) is developed. While based on physics, the number of parameters and components is minimized. Results show that the proposed model captures the most important behaviors of the TVS response using a limited number of parameters, allowing the model to be tuned relatively easily using data obtained only from package-level transient and quasi-static measurements. Second, a …

Low Power Remote Sensing System For Ceramic Thermocouples, Syed Khaleduzzaman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The ceramic thermocouple sensor is a promising alternative to traditional thermocouple due to their corrosion resistance and cost-effective manufacturing. Currently, no electronic Interface or Integrated Circuit solution is commercially available which can be used to interface these thermocouples at low-cost and low power for accurate temperature measurement. An electronic system is needed to use these ceramic sensors which consumes less amount of power for an extensive period of time with remote monitoring option.

This thesis proposes a low-power electronic remote sensing system that works for ceramic thermocouples. The system consists of three parts. They are battery-operated sensor node electronic circuit, …