Digital Commons Network^™

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 …

Heat-Pipe-Based Thermal Management System Design For A 250-Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Design Of Directive Wideband Microstrip Antenna, Hassan Ali Ragheb

Electrical Engineering

The study under consideration demonstrates design, simulation, and optimization of a compact Ultra-Wideband (UWB) antenna employing a coplanar waveguide (CPW) feeding structure and a circular patch geometry. The design is targeting a 7 GHz operating frequency, offering a balance between compact size, wide bandwidth, and directional radiation characteristics. An initial antenna configuration was developed and analyzed using HFSS, showing satisfactory performance in terms of impedance matching and radiation efficiency. However, asymmetries in the radiation pattern prompted further enhancement. An improved design employees strategically director elements placed above the circular patch to improve forward gain and radiation beam directivity. The optimized …

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, …

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 …

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 …

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 …

Flysurf: A Flying Robotic Surface With Shape Morphing And Motion Control, Kevin Aubert De Luchi Lomellini

Electrical and Computer Engineering ETDs

This thesis introduces FlySurf, a novel flying robotic surface capable of simultaneous shape morphing and motion control. FlySurf is modeled as a mesh-based dynamic structure actuated by multiple unmanned aerial vehicles, enabling complex deformations during flight. The proposed control architecture comprises three main components. First, a state estimator to infer the robotic surface state from limited observations. Second, a shape trajectory planner that incorporates an affine deformation term to preserve surface integrity during shape transitions. Third, a surface controller to minimize the error between the desired and actual surface configurations.

The methodology is validated through realistic simulations and hardware experiments, …

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 …

Participation Of Battery Energy Storage Systems In Load Frequency Control Of Power Systems, Zakaria Afsharbakeshloo

LSU Doctoral Dissertations

In this research, functionalities and roles of Battery Energy Storage Systems (BESSs) in power systems are extended beyond primary frequency control (PFC). The BESSs, while participating in PFC, are controlled through charge controllers to first maintain their state-of-charge (SOC) within an acceptable range through a primary charge controller, and to recharge the BESS to its maximum SOC through a secondary charge controller. This forms a hierarchical frequency and SOC control of power grids with BESSs. In this regard, multiple BESS case is considered as well, and by employing the SOC balancing principle, it is shown that desired power sharing among …

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, …

Chromatography In Analysis And Quality Control Of Oil And Gas Products: Modern Approaches And Prospects, Muborak Akramovna Mirshomilova, Natalia Evgenievna Sheina, Lala Bekirova

Technical science and innovation

Chromatography plays a key role in the analysis and quality control of oil and gas products. Modern chromatographic methods, including gas and liquid chromatography, provide high accuracy, sensitivity and speed of analysis. They allow you to identify impurities, control the composition and compliance of products with standards. The article considers innovative approaches, such as multidimensional chromatography and the use of automated systems. Development prospects are discussed, including integration with AI and digital technologies, which opens up new opportunities to improve the efficiency of quality control in the oil and gas industry. This article considers the role of chromatographic methods in …

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 …

Statistical Monitoring Of Hard Faults In Digital Systems, Dany Akshay Deep Isukapalli

Electrical Engineering Theses and Dissertations

Achieving a high test coverage is crucial for helping to ensure that integrated circuits are working correctly and are non-defective. Although scan-based structural tests are used throughout the industry, high-level functional tests may be needed to detect some defects— especially those that are environmentally sensitive. Unfortunately, the character of functional test makes it difficult to obtain high coverage, and it is even hard to estimate coverage because fault simulation times of large circuits are long. As a result, some method is required for predicting the ability of a functional test that has not been fault simulated to detect defects. In …

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

Distributed Monitoring Of Moving Thermal Targets Using Unmanned Aerial Vehicles And Gaussian Mixture Models, Gustavo Vejarano, Yuanji Huang, Pavithra Sripathanallur Murali

Electrical and Computer Engineering Faculty Works

This paper contributes a two-step approach to monitor clusters of thermal targets on the ground using unmanned aerial vehicles (UAVs) and Gaussian mixture models (GMMs) in a distributed manner. The approach is tailored to networks of UAVs that establish a flying ad hoc network (FANET) and operate without central command. The first step is a monitoring algorithm that determines if the GMM corresponds to the current spatial distribution of clusters of thermal targets on the ground. UAVs make this determination using local data and a sequence of data exchanges with UAVs that are one-hop neighbors in the FANET. The second …

Seconds From Impact: Anticipatory Vehicular Crash Prediction Using Video Vision Transormers, Ryan P. Geisen

Master's Theses

Vehicular collisions represent a significant public health concern, necessitating re search into advanced emergency notification systems. While deep learning has shown promise in accident detection, a research gap persists in applying state-of-the-art transformer architectures to the task of anticipatory, real-time crash prediction from video. This thesis addresses this gap by developing and evaluating a Video Vision Transformer (ViViT) for the binary classification of imminent vehicular collisions. Utilizing a curated dataset of 1,493 unique collision sequences, this study systemati cally investigates the impact of temporal context by comparing the ViViT against a single-frame Vision Transformer (ViT) baseline and conducting comprehensive exper …

Optical And Thermal Characterization Of Vertically Conducting Β-Ga₂O₃ Diodes Grown On 4h-Sic Substrates For Short-Wavelength (<254 Nm) Detection, Muhammad Hassan Tahir

Theses and Dissertations

The advancement of compact, thermally stable, and efficient devices for deepultraviolet (DUV) detection below 254 nm is critical for solar-blind sensing, radiation monitoring, and high-power electronics. This thesis presents the growth, fabrication, and detailed optical and thermal characterization of vertically conducting β-gallium oxide (βGa₂O₃) Schottky barrier diodes (SBDs) grown on n-type 4H-Silicon Carbide (4H-SiC) substrates using metal-organic chemical vapor deposition (MOCVD).

β-Ga₂O₃ is an ultra-wide bandgap (UWBG) semiconductor (~4.8 eV) with intrinsic DUV transparency and high breakdown electric field, making it an ideal candidate for short-wavelength photodetection. However, its low thermal conductivity limits its vertical device performance. To overcome this, …

Effect Of Growth Conditions On The Performance Of Vertically Conducting Beta-Ga2o3 Diodes On 4h-Sic Substrate By Mocvd, Nifat Jahan Nipa

Theses and Dissertations

This thesis highlights the critical influence of doping strategies, specifically co-delta doping with silicon (Si) and indium (In), on improving the structural quality and device performance of β-Ga₂O₃-based Schottky barrier diodes (SBDs). To achieve better thermal management, β-Ga₂O₃ thin film was grown on bulk 4H-SiC substrates using the Metal-Organic Chemical Vapor Deposition (MOCVD) technique, renowned for its precise control over composition, thickness and doping profile. The ultra-wide bandgap (UWBG), high critical breakdown field and low turn-on resistance of β-Ga₂O₃ make it a strong candidate for next-generation power electronics and short-wavelength detection in extreme environments. However, the presence of structural defects …

Decentralised Consensus Control Of Hybrid Synchronous Condenser And Grid-Forming Inverter Systems In Renewable-Dominated Low-Inertia Grids, Hamid Soleimani, Asma Aziz, S. M.Muslem Uddin, Mehrdad Ghahramani, Daryoush Habibi

Research outputs 2022 to 2026

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. …

Optimisation Of Astatic Controllers In Automatic Control Systems, Isamiddin Xakimovich Siddikov, Dilnoza Maxamadjanovna Umurzakova

Chemical Technology, Control and Management

This article examines the influence of various factors on the efficiency of optimizing station controllers in automatic control systems. The main focus is on minimizing static errors and improving the reliability of controllers. Optimization methods for PID and PI²D controllers are discussed, including the use of modern approaches such as genetic algorithms. The necessity of compromise solutions to achieve high-quality system responses to various impacts is investigated. It was also suggested that there is a need for an integrated approach to setting up controllers in order to increase their reliability and flexibility in conditions of dynamically changing processes. …

The Importance Of The Analog-To-Digital Converter In The Measurement System, Aliev Ravshan, Anvar Djalilov

Chemical Technology, Control and Management

At the moment, many scientific researches are being conducted all over the world on the economical use of water and energy resources. Most of the scientific research works are aimed at improving measurement techniques and technologies, that is, increasing their accuracy. With this in mind, a high-precision analog-to-digital converter due to its unique metrological and technical characteristics was studied in this research paper. As a result of the study, it became clear that the use of a small-sized, high-precision sigma-delta analog-to-digital converter in modern measuring technology has a positive effect on its accurate and efficient operation.

Mitigation Of Voltage Disturbances In Industrial Power Distribution Networks Using Dynamic Voltage Restorers, Aviti T. Mushi

Tanzania Journal of Engineering and Technology (TJET)

Most electrical and electronic equipment in industries require high-quality power to function efficiently. Nonetheless, voltage sags and swells are pressing concerns and prone to directly impact the economy of industrial customers. One such customer embattled with these problems is Mbeya Cement Company Limited (MCC) located in Mbeya, Tanzania. These issues mainly are caused by upstream faults and switching operations. One way to address these is by utilizing the voltage injection method, which employs a power device known as a dynamic voltage restorer (DVR). In this paper, the voltage sags and swells of balanced three-phase, unbalanced double-line and single-line to ground …

The Impacts Of Renewable Energy Sources: A Review On Grid Inertia And Frequency Regulation, Sospeter Gabriel

Tanzania Journal of Engineering and Technology (TJET)

The power system is gradually transitioning into low inertia due to integrating substantial intermittency quantity of converter-based renewable energy sources, such as wind and photovoltaic power, into the current power grid network. This integration presents significant inertia and frequency control challenges to the network as a result of a decrease in the percentage of synchronous generators. Moreover, faster frequency deviations are posed by the mismatch between the supply and demand during contingencies, which creates difficulties in preserving the frequency stability of the power system. This research explores the impacts of renewable energy sources (RESs) on grid inertia and frequency management …

Redox-Active Monolayers On Ito Prepared By Post-Amidation And Direct Esterification And Their Spectroelectrochemical Characterization, Takamasa Sagara, Sae Nakai, Ryusuke Yofu, Shota Kojin

Journal of Electrochemistry

A redox-active monolayer on an optically transparent electrode constitutes a typical platform for spectroelectrochemical sensing. The necessity for its sophistication arises from the availability of multi-dimensional sensing signals. Simultaneous monitoring of the redox current and color change synchronized with the oxidation state change significantly enhances sensitivity and selectivity. This study aimed to elucidate the modification of an indium tin oxide (ITO) electrode with a viologen monolayer with an ordered orientation. Novel methods were developed to immobilize a viologen molecule bearing a carboxyl group to form assembled monolayers through a condensation reaction using 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide with N-hydroxy-succinimide (EDC/NHS). In the two methods …

A Yolo-Based Deep Learning Approach For Vibration-Based Rotating Shaft Imbalance Detection, Abdelhameed Zayed Dr., Ali Siam Dr.

Journal of Engineering Research

One of the prevailing causes of vibrations in machines is rotor imbalance. Rotor balancing can be used to fix the majority of rotating machinery issues. When it comes to high-speed running equipment, even a slight imbalance can lead to serious issues and decrease the operational efficiency of rotating machinery. This work proposed a deep learning approach for the detection of binary and multiclass imbalance in rotating shafts. A YOLOv11 model-based approach is developed to detect imbalance and identify unbalanced rotor positions. To precisely identify unbalanced positions, this method trains the YOLOv11 model using numerous sets of measured response data and …

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Mutual Coupling Impedance Effect On Ris-Assisted Terahertz Communication, Radwa A. Roshdy Dr., Hossam M. Kasem Prof., Mohammed A. Salem Dr.

Journal of Engineering Research

Terahertz (THz) communication based on reconfigurable intelligent surfaces (RIS) is a promising technology in future wireless networks, with the capabilities of ultra-high data rates and energy-efficient beam communicating. Nevertheless, the mutual coupling impedance of the closely spaced RIS reveals the inefficacy in existing channel estimation schemes, while it is usually ignored in the literature. In this paper, we fill this gap by developing an EM-compliant channel model that integrates mutual coupling explicitly for RIS-assisted THz systems. We evaluate the performance of mutual impedance-induced influence to the accuracy of the root mean square error (RMSE), which is considered as a pivotal …

Performance Enhancement Of Ev Drive System Under Open-Phase Fault Using Reinforcement Learning With Mpc, Ahmed M. Hassan, Mohammed E. Metwally Dr.

Journal of Engineering Research

Improving the operation of electric vehicles (EVs) under fault is a very important subject because it increases their ability to operate in case of an emergency. This paper proposes a fault-tolerant control methodology for an EV drive system under open phase fault (OPF). The 5-ph interior permanent magnet synchronous motor (IPMSM) is employed in the drive system because it has several merits, such as high efficiency and reliability. The proposed technique is based on utilizing a reinforcement learning (RL) control algorithm, based twin-delayed deep deterministic policy gradient (TD3) algorithm, to operate the motor at maximum torque per ampere under OPF. …