Digital Commons Network^™

A Study On The Propagation And Exploitation Of Structured Light In Underwater Turbulence, Jaxon P. Wiley

All Dissertations

The development and optimization of optical systems will play a pivotal role in the continued exploration and exploitation of the world’s underwater environments. These systems offer advantages in many sectors, and includes applications in areas such as high-speed communication, advanced sensing and imaging, and environmental characterization and monitoring. Underwater environments offer a plethora of challenges, however, and mitigating these obstacles remains an arduous task. In this work, the inherent advantages of structured light are leveraged to optimize optical system performance through non-ideal underwater conditions. Additionally, fundamental relationships between the generation of specified structured modes and their interactions with complex environments …

An Ant-Colony Approach To Scheduling Charging Sessions Of Electric Vehicle Fleets With Heterogeneous Scheduling Constraints, Derek D. Redmond

All Graduate Theses and Dissertations, Fall 2023 to Present

Electric vehicles and electric-vehicle fleets are gaining widespread usage. A major challenge of managing an electric-vehicle fleet is scheduling when the vehicles can charge and when the vehicles can complete their tasks. Finding smart ways to schedule electric-vehicle charging reduces both the cost of charging vehicles and increases the health of the grid and the environment. This best-of-both-worlds outcome is because of the pricing structure that electric utility companies use. When minimizing the cost of charging the vehicles, it is important to consider that vehicle fleets have constraints on when they can charge. Vehicles are usually limited by the tasks …

Robust Online Inertia Estimation In Power Systems Using Ambient Data In The Presence Of Inverter-Based Resources, Narges Ghiasi

All Dissertations

The increasing penetration of Inverter-Based Resources (IBRs) in power systems has significantly altered system dynamics, reducing the system's effective rotational inertia and challenging frequency stability. Accurate online inertia estimation is essential for maintaining system reliability under these evolving conditions. This paper introduces a robust methodology for online inertia estimation using ambient data collected during normal system operation. The proposed method employs a state-space model to represent system dynamics and introduces synthetic step changes to simulate disturbances. By analyzing the frequency response and applying advanced signal processing techniques, the methodology estimates system inertia without requiring real large-scale disturbances. The approach is …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

Optimization And Acceleration Of Puf Design Through Reduced Order Standard Cell Modeling, Ian Z. Wilcox

Electrical and Computer Engineering ETDs

Application Specific Integrated Circuit (ASIC) designs continue to scale with ever increasing complexity and device counts in the billions. Demand for scalable high-fidelity simulations of these systems drives the need for the development of novel modeling capabilities. This research formulates a non-intrusive model order reduc-tion (MOR) framework, called PUF-ROMS, to accelerate and optimize the design and analysis of physical unclonable functions (PUFs) on ASICs. The primary goals of PUF-ROMS are to estimate entropy and temperature-voltage noise (TV-noise) of circuit structures used in the design in an accelerated evaluation environment to enable designers to explore architecture options with the goal of …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Research Progress On Thermal Management Of Lithium-Ion Batteries, Hong-Da Li, Qiu-Wan Shen, Zhao-Yang Zhang, Xin-Yue Zhao, Yuan Wei, Shi-An Li

Journal of Electrochemistry

Nowadays, new energy technologies are developing rapidly, energy storage systems are widely used, and lithium-ion batteries occupy a dominant position among them. Therefore, it is also very important to ensure their performance, safety and service life through thermal management technology. In this paper, the causes of thermal runaway of lithium batteries are reviewed firstly, and three commonly used thermal management technologies, namely, air cooling, liquid cooling and phase change material cooling, are compared according to relevant literature in recent years. Air cooling technology has been widely studied because of its simple structure and low cost, but its temperature control effect …

Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan

Turkish Journal of Electrical Engineering and Computer Sciences

The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. The point process …

Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …

Adaptive Backstepping Control With Real-Time Fuzzy Logic Parameter Selection Of A Field- Oriented Control-Based Permanent Magnet Synchronous Motor Driver, Fati̇h Bayir, Erkan Zergeroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an adaptive backstepping control approach integrated with a real-time fuzzy logic parameter selection algorithm to enhance the robustness and stability of a permanent magnet synchronous motor (PMSM) controller under parametric uncertainties and external disturbances. Although backstepping control performs well under varying disturbances, it must be supported by an adaptive control algorithm to effectively handle both variable disturbances and parameter uncertainties. Moreover, because the fixed parameters of the adaptive backstepping controller limit the dynamic performance of the velocity tracking loop, this study incorporates fuzzy logic control—a soft computing algorithm capable of real-time parameter adjustment—to achieve more robust outcomes. …

Excitation Of Synchronous Machine By Contactless Power Transfer - Review From The Perspective Of Electric Vehicles, Erhan Tuncel, Emi̇n Yildiriz

Turkish Journal of Electrical Engineering and Computer Sciences

Electrically excited synchronous machines (EESMs) are one of the best choices for propulsion motor appli cation in electric vehicles (EVs) due to their wide torque-speed characteristics. Moreover, the air gap flux density can be easily controlled by varying the excitation current. Despite these advantages, it is difficult to transfer the current required by the rotating excitation winding into the motor under conventional methods, so it is not widely used in EVs. In this study, the emerging literature on contactless power transfer methods is reviewed for applicability to an EESM that can operate as an EV propulsion motor. Design criteria such …

Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel

Turkish Journal of Electrical Engineering and Computer Sciences

Real-world super-resolution is a highly challenging problem in the field of computer vision. Besides enhancing image resolution and improving visual details, information loss due to complex real-world degradations is desired to be restored. One of the primary hardness of this problem is finding sufficiently large paired datasets for training. Researchers have developed techniques that generate synthetic low-resolution pairs using high-resolution images with a generative adversarial network-based degradation generator to address this issue. In these approaches, the degradation generator is trained by utilizing real-world low-resolution images as the target domain, generating a degraded low-resolution counterpart of the high-resolution input. However, in …

Regulating The Amount Of Graphene Oxide For Enhanced Capacitive Energy Storage Of Mof Derived Materials, Yong-Ji Qin, Jing-Quan Yang, Hao Wang, Mei-Ling Lian, Pei-Pei Jia, Jun Luo, Xi-Jun Liu, Jun-Feng Liu

Journal of Electrochemistry

In pursuit of more efficient and stable electrochemical energy storage materials, composite materials consisting of metal oxides and graphene oxide have garnered significant attention due to their unique structures and exceptional properties. Graphene oxide (GO), a two-dimensional material with an extremely high specific surface area and excellent conductivity, offers new possibilities for enhancing the electrochemical performance of metal oxides. In this work, we synthesized metal- organic framework (MOF) and GO composites by regulating the amount of GO, and successfully prepared composites of metal oxides supported by nitrogen-doped carbon frameworks and GO through a simple one-step calcination process. Based on the …

Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal

Turkish Journal of Electrical Engineering and Computer Sciences

Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …

Enhanced Energy Harvesting From Nf-Pvdf Piezoelectric Material For Wearable Electronics: I– V Characterization And Charge-Discharge Performance, Ahmed Emara, Afaf Farag, Gehad Ali, Mohamed Mamdouh, Sameh O. Abdellatif, Khaled Nassar, Tamer Hamoud

Electrical Engineering

This study explores the utilization of fabricated piezoelectric polyvinylidene fluoride nanofiber (NF-PVDF) materials in wearable electronic sensing applications by investigating their current-voltage ( I − V ) characteristics under controlled ultra-low-frequency excitation forces. The results demonstrate a significant power harvesting capability, achieving an output power of 0.12 µW/mm2 at an operating point of 5.04 V and 7.7 µA. Additionally, the piezoelectric harvester was integrated into a charging-discharge circuit alongside a rectifier capacitor and a typical IoT wearable sensor, leveraging the advantages of a flexible substrate. Experimental measurements of the charging and discharging curves confirm the effective energy management of the …

A Deep-Learning-Based Dehazing Framework For Non-Homogenous Scenes, Shimaa Mohammed Abd Elghany, Doaa A. Altantawy, Hossam El-Din Moustafa Moustafa

Mansoura Engineering Journal

The advancement of single-image dehazing techniques has been rapid in recent years. Several existing algorithms that are based on deep learning have shown remarkable efficiency for dealing with homogeneous hazing-free issues, but convolutional neural networks (CNNS) frequently fail on non-homogeneous dehazing datasets. Meanwhile, dehaze results from dense haze regions are often blurry because the information of these regions is typically unknown and difficult to estimate. To address these issues, an efficient image enhancement dehazing algorithm that utilizes deep learning techniques, and a non-uniform atmospheric scattering model had been proposed. Unlike the majority of existing dehazing methods, the medium transmission function …

Algorithmic Optimization Of Filter Bank: Harnessing Rapid Convergence And Its Applications, Keerthana B Ms

Theses and Dissertations

A novel optimization framework is proposed for the design of both uniform and non-uniform filter banks, aimed at improving the accuracy and computational efficiency of biomedical signal classification tasks, with a particular emphasis on the detection of sleep disorders and Alzheimer’s disease.The proposed algorithm is grounded in multirate signal processing theory and aims to achieve Near Perfect Reconstruction (NPR) with minimal computational overhead. The process begins with the optimization of a uniform cosine-modulated filter bank (CMFB), achieved through iterative frequency-domain analysis and parameter tuning. This is followed by the derivation of a non-uniform filter bank via selective merging of bandpass …

Algorithmic Optimization Of Filter Bank: Harnessing Rapid Convergence And Its Applications, Keerthana B

Theses and Dissertations

A novel optimization framework is proposed for the design of both uniform and non-uniform filter banks, aimed at improving the accuracy and computational efficiency of biomedical signal classification tasks, with a particular emphasis on the detection of sleep disorders and Alzheimer’s disease. The proposed algorithm is grounded in multirate signal processing theory and aims to achieve Near Perfect Reconstruction (NPR) with minimal computational overhead. The process begins with the optimization of a uniform cosine-modulated filter bank (CMFB), achieved through iterative frequency-domain analysis and parameter tuning.

This is followed by the derivation of a non-uniform filter bank via selective merging of …

Mode Engineering And Functional Enhancement In Piezoelectric-On-Silicon Mems Resonators With Magnetic Field Sensing Applications, Ugur Guneroglu

USF Tampa Graduate Theses and Dissertations

This dissertation significantly advances the field of micro-electro-mechanical systems (MEMS) resonators by exploring novel post-fabrication tuning techniques, electrode configuration effects, and innovative sensor applications for thin-film piezoelectric-on-silicon (TPoS) resonator devices. Driven by the increasing demand for tunable, high-precision and robust MEMS resonators in radio frequency (RF) applications such as sensing, timing, and filtering, this research aim to provide foundational knowledge and practical solutions for overcoming existing technological limitations. The dissertation is organized into three main research thrusts, each addressing critical challenges and opportunities in the design, fabrication, and application of TPoS MEMS resonators.

The first research thrust investigates an innovative …

The Rise Of Foss In India: Empirical Evidence And Insights From Cross-Sectoral Case Studies, Arul George Scaria, Suryaprakash Mishra, Shubham Shinde, Rashi Singhal

Research Reports

The report analyses the adoption of FOSS in India, primarily through case studies across four sectors (healthcare, education, finance, and software and IT services) and different types of organisations (start-ups, non-profits, medium, large, and public sector organisations). The study highlights both the benefits and challenges experienced by organisations using FOSS. The study illustrates that while organisations benefit from increased innovations, cost/ time savings, flexibility, and enhanced security, they also face challenges such as lack of enough skilled personnel and limited community support. Organisations are also seen taking a cautious approach to licensing, favouring permissive licenses over restrictive ones. Based on …

In The Shadow Of Prompts: Adversarial Attacks And Model Cloning In Large Language Models, Kanchon Gharami

Doctoral Dissertations and Master's Theses

Large-language models (LLMs) already power mission critical tasks such as command-and-control chat, satellite ground-station automation, military analytics, and cyber-defense. Since most of these services are offered through application programming interfaces (APIs) that still expose full or top-k logits and lack mature safeguards, they present a serious, often overlooked attack surface. Earlier work has shown how to rebuild the output projection layer or distill surface behavior, but no attack has produced a deployable clone within a tight query budget. In this thesis, we address this problem by presenting a practical pipeline for cloning LLMs under constrained settings. The approach first estimates …

Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications, Mahmoud Mansour, Hebatalla H. Mohammad Dr, Khalaf S. Sultan Prof.

Basic Science Engineering

This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, …

Non-Invasive Near-Field Microwave Sensors For Industrial Internet Of Things Applications, Abirami K

Theses and Dissertations

The industrial sector has been revolutionized by Industrial Revolution 4.0 with the advent of the Internet of Things (IoT) which integrates the physical environment using smart sensors and Artificial Intelligence (AI) mechanisms. This demands the design of smart sensors and actuators to improve the quality of services in Industrial IoT (IIoT). The sensor design using the microwave resonating principle has attracted the research and industrial community for portable, non-invasive, non-destructive and hygienic means to evaluate the quality of substances in the food and farming industry. The microwave planar sensor has advantages in terms of low footprint, low cost, and ease …

Studying Effect Of Temperature On Photovoltaic Characteristics Of Photovoltaic Modules Based On Cigs Solar Cells, Farrux Anvar O‘G‘Li Akbarov, Anvar Akbarovich Alimov

Technical science and innovation

The article presents the experimental results of the temperature dependence of the load current-voltage characteristics of a solar photovoltaic (PV) module based on a polycrystalline semiconductor binary compound Cu(In,Ga)Se₂ under normal sunlight P_rad=800±5 W/m², in a temperature range of (32÷60) ^оС. It was found that the temperature dependence of the photocurrent has two slopes, which is explained by a change in the generation-recombination mechanism. An increase in the short-circuit current with an increase in the temperature of the photovoltaic module is explained by an increase in the drift length of minority charge carriers, due …

Algorithms For Combined Regular Synthesis Of Controller Parameters In Control Systems For Dynamic Objects, Khusan Zakirovich Igamberdiev, Latafat Abbas Gizi Gardashova, Yulduz Mukhtarkhodjayevna Abdurakhmanova, Uktam Farkhodovich Mamirov

Technical science and innovation

Nonlinear control system that includes m-dimensional input control signal and extended (n+s) - dimensional state vector, the last s components of which form a vector of unknown parameters θ satisfying a general difference equation is being considered. The quality criterion is determined by the loss function. The optimal control must satisfy the Bellman equation with respect to the optimal loss function. To be defined an approximate solution that preserves an active use of information. For this purpose, the system is linearized in accordance to the nominal trajectory. This problem is seen as incorrectly stated. The values of the preliminary data …

Training Stages And Algorithms Of Multilayer Neural Networks For The Control System, Abdishukurov Maqsudovich Shavkat, Xuecheng Li

Technical science and innovation

The application of neural network technologies in control systems consists of several stages. In technological object control systems, each layer of the multilayer perceptron neural networks is represented using separate transfer functions. The result of synthesizing neural network technology into control systems allows for the rapid solution of many problems. Using neural network technology, it is possible to perform many calculations. Chain rules of neural networks are added this paper. Lots of training algorithms are compared to choose better to train network. Backpropagation neural networks is seen deeply in the last page. Problems of interpolation and extrapolation are solved. Algorithms …

Comparative Analysis Of Gas And Mechanical Emissions From Electric Cars And Internal Combustion Engine Vehicles, Bakhtiyor Akhmatjonovich Kosimov

Technical science and innovation

This article analyzes the environmental impact of modern cars, in particular the amount of harmful substances emitted into the environment by electric cars and internal combustion engine cars, and provides scientific information on the substances that are released from cars in the form of not only gaseous emissions, but also dust particles generated by the mechanical wear of the brake system and tires. At the same time, this article also takes a scientific approach to the wear rates of brake pads and tires, their impact on environmental safety, and the level of dispersion of the microdust generated. Comparative tables have …