Digital Commons Network^™

Exploratory Study Of Semiconductor Nanomembranes In Em Applications, Grant D. Heileman

Electrical and Computer Engineering ETDs

Antenna systems are a cornerstone of modern technologies, playing an increasingly vital role in their advancement. As demand for compact, high-performance, and adaptable communication platforms grows reconfigurable antenna technologies are becoming essential. This research explores a novel front-end reconfigurable antenna system (FERAS) architecture that leverages the mechanical flexibility and photoconductive behavior of semiconductor nanomembrane (SNM) devices. By exploiting the emergent properties of ultra-thin silicon (Si) or gallium arsenide (GaAs) nanomaterials and optically exciting these samples using vertical-cavity surface-emitting laser (VCSEL) arrays, this study develops lightweight, low-cost, deployable antenna structures for satellite communications, remote sensing, GPS, and radar. Despite their significant …

Television Guidebook, Ujwal S. Thirunagari

STEM Month

In this television guidebook, we work to clearly explain television technology by including detailed illustrations and introducing fundamental concepts. This guidebook first explains the Cathode Ray Tube by laying out the basics of circuits, thermionic emission, the electron beam gun, and the deflection yoke. Then, the guidebook tackles the modernly adopted LED panel technology used in most phones and computers by explaining the fundamentals of light and liquid crystal. In future iterations of the guidebook, we hope to delve into more detail on color CRTs, thin film transistors, OLED panels, and Micro-LED technology.

A Fully Automated Drilling Machine For Printed Circuit Boards With Superior Path Optimization, Mohamed Mammdouh, Ahmed Khaled, Reem Mahmoud, Osama Desouki, Sameh O. Abdellatif

Electrical Engineering

This paper addresses a critical challenge in Printed Circuit Board (PCB) manufacturing by proposing an AI-driven, fully automated drilling machine that employs sophisticated path-planning techniques. Current methodologies often fail to adequately assess designs with varying hole sizes, diverse component placements, and complex geometries, leading to compromised precision and increased manufacturing times. Our innovative approach leverages advanced algorithms to intelligently analyze PCB designs and optimize drilling paths, significantly reducing production time and minimizing errors. By automating the drilling process, we enhance overall productivity while ensuring precise hole placement, essential for maintaining high-quality circuit boards. Utilizing KiCAD EDA software, we automate the …

“How Can Ai Data Centers In The U.S. Meet Projected Electricity Demands By 2030?, Aiden M. Matano

Sustainable Supply Chain Management

The rapid expansion of artificial intelligence is driving a sharp increase in U.S. electricity demand, with AI-driven data centers emerging as a central contributor through 2030. This paper asks how U.S. AI data centers can meet projected electricity needs while simultaneously reducing carbon emissions. Using a supply-chain and systems perspective, it analyzes the full energy chain of AI data centers: upstream electricity supply and grid deliverability, midstream facility design and grid interaction, and downstream operational practices, waste management, and disclosure. Drawing on recent projections from the International Energy Agency, Lawrence Berkeley National Laboratory, and U.S. federal guidance, the paper shows …

Harnessing Electrochemical Hydrogen Evolution In Α-Fe2o3/2h–Mos2 Heterojunction: A Theoretical And Experimental Study On Electronic Modulation And Basal Plane Activation, Mohamed Sayed, Ossama Metwalli, Sameh O. Abdellatif, Kai Yin, Mohamed Saber, Ahmed Khalil, Hossam Abdelwahan

Electrical Engineering

Hydrogen production through water electrolysis is a promising method for storing renewable energy and reducing greenhouse gas emissions. However, the efficiency and stability of hydrogen production electrodes remain challenging issues. In this study, we present a rationally designed α-Fe2O3/2H–MoS2 heterojunction that unlocks the electrocatalytic potential of the inert basal plane of the semiconducting 2H–MoS2 phase. The heterostructure, synthesized via a facile hydrothermal method, features α-Fe2O3 nanocubes anchored onto flowerlike 2H–MoS2 nanosheets. The electrochemical performance of the α-Fe2O3/2H–MoS2 heterojunction electrode was evaluated by linear sweep voltammetry, Tafel polarization, and electrochemical impedance spectroscopy. The results showed that the α-Fe2O3/2H–MoS2 electrode exhibited lower …

Modern Significance And Development Trends In The Production Of Vegetable Oils, Umidjon Ruziev, F.O. Qosimov, M.K. Shodiev

Chemical Technology, Control and Management

This article provides a comprehensive analytical review of the current state of the global vegetable oil production market. It describes the diversity of raw materials supplied, which includes both traditional and emerging fat sources. The paper also describes the technological stages of production, emphasizing modern innovations that contribute to more efficient, high-quality, and safe production. The article also covers global trends by comparing production dynamics between countries, highlighting regions with the highest rates of production growth and explaining the reasons for their competitive advantages. Particular attention is paid to environmental and socio-economic aspects, including sustainable land use, certification, carbon footprint, …

Algorithms For The Synthesis Of A Temperature Control System For The Inner Tube Heat Exchanger With A Steam Jacket, H.Z. Igamberdiyev, Jasur Sevinov, U.F. Mamirov, Sh.M. Abdishukurov

Chemical Technology, Control and Management

The synthesis of a feedback propagation control law for an inner tube heat exchanger with a steam jacket is addressed in this text. A controller has been developed that, based on temperature measurements taken at four points. The maintains the output temperature at a specified level by acting on the steam jacket temperature. To determine the parameters of the plant, a linear quadratic optimal (LQ-optimal) algorithm is employed. In the considered case, the optimal controller includes a proportional–integral (PI) component, as well as an additional term that requires storing the control input over the current interval for its computation. The …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

Intelligent Method Of Dynamic Control For A Class Of Stochastic Nonlinear Systems, Isamidin Khakimovich Siddikov, Davronbek Abdalimovich Khalmatov, Gulchekhra Rakhimjanovna Alimova, Dilnoza Rakhmanovna Khushnazarova

Chemical Technology, Control and Management

The paper considered the problems of researching the stabilisation system and backstepping control of stochastic nonlinear systems. The characteristics of stochastic nonlinear dynamic control systems are random signals with normal lawful distribution, which significantly complicates task control. In stochastic control, it is necessary to determine the trajectories of the control variables in order to achieve the desired control objective at minimum cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this work, a neuro-fuzzy network is used to evaluate the parameters of the control system with backstepping, …

باستخدام مجموعة بيانات متعددة من الطائرات بدون طيار Yolo البحث والإنقاذ البحري القائم على في ظروف الطقس الصعبة, Aysha Ali Alshebli

Theses

Object detection models, powered by deep learning and computer vision, are revolutionizing marine search and rescue (SAR). By analyzing aerial imagery and live drone footage, these systems automatically identify critical targets like survivors, life rafts, and debris across vast and treacherous ocean areas. This capability enhances operational efficiency by reducing human workload and accelerating response times, even in challenging conditions such as poor light, high seas, or cluttered backgrounds. The result is continuous monitoring, faster decision-making, and a significantly improved probability of successful rescue.

Departing from prior methodologies, YOLO introduced a paradigm shift through its single-shot architecture, which concurrently predicts …

Teaching Machine Learning To Undergraduate Electrical Engineering Students, Gerald L. Fudge, Anika Rimu, William Zorn, July Ringle, Cody Barnet

Faculty Publications

Proficiency in machine learning (ML) and the associated computational math foundations have become critical skills for engineers. Required areas of proficiency include the ability to use available ML tools and the ability to develop new tools to solve engineering problems. Engineers also need to be proficient in using generative artificial intelligence (AI) tools in a variety of contexts, including as an aid to learning, research, writing, and code generation. Using these tools properly requires a solid understanding of the associated computational math foundation. Without this foundation, engineers will struggle with developing new tools and can easily misuse available ML/AI tools, …

A Radiation-Hardened 4-Bit Flash Adc With Compact Fault-Tolerant Logic For Seu Mitigation, Fnu Naveed, Jeff Dix

Electrical Engineering and Computer Science Faculty Publications and Presentations

This paper presents a radiation-hardened 4-bit flash analog-to-digital converter (ADC) implemented in a 22 nm fully depleted silicon-on-insulator (FD-SOI) process for high-reliability applications in radiation environments. To improve single-event upsets (SEU) tolerance, the design introduces a compact fault-tolerant logic scheme based on Dual Modular Redundancy (DMR), offering reliability comparable to Triple Modular Redundancy (TMR) while using two storage nodes instead of three, and a simple XOR-based check in place of a majority voter. A distributed sampling architecture mitigates SEU vulnerabilities in the input path, while thin-oxide devices are used in analog-critical circuits to enhance total ionizing dose (TID) resilience. Post-layout …

Design And Implementation Of A Cross-Platform, Modular Electronic-Nose System, Kaushika Rudraraju

USF Tampa Graduate Theses and Dissertations

Electronic noses (e-noses) are a subject of great research. They are useful in multiple platforms and serve a great purpose in both studies and safety. This thesis presents a portable, modular e-nose platform that spans the full pipeline from sensing to visualization, with emphasis on scalability, repeatability, and straightforward integration. The hardware is organized as swappable sensor modules each hosting multiple commercial sensors and local signal-conditioning networked to a master microcontroller via structured communication protocols. High-resolution acquisition, time alignment, and efficient streaming to a host enable reliable logging and analysis. A controlled exposure chamber and a repeatable sampling protocol (baseline-exposure-purge …

Measures To Improve The Structure And Operating Principle Of Perovsite Light-Emitting Devices, Latofat Utkir Qizi Shuhratova, Rustam Rashidovich Kabulov, Farrux Anvar Ugli Akbarov, Anvar Akbarovich Alimov

Technical science and innovation

Since the discovery of the unique properties of perovskite materials, these materials have been used as active layers in various optoelectronic devices. In particular, perovskite-based light-emitting diodes (LEDs) have attracted the attention of many researchers as a highly efficient and versatile technology for lighting and display devices. Perovskite-based LEDs allow for tunable light emission and can be manufactured at low cost and with flexible fabrication technology. These advantages make them suitable for use in devices ranging from modern displays to wearable electronics. Despite their potential, solving problems such as material stability and industrial scale is one of the current challenges …

Methodology For Creating An Intelligent Mechatron Module Based On A Synergistic Approach, Nurbek Rustamovich Matyokubov, Temurbek Omonboyevich Rakhimov

Technical science and innovation

This article is devoted to the methodology of creating an intelligent mechatron module based on a synergistic approach. Creating a multi-coordinate intelligent mechatron module and expanding the functional capabilities of the controlled object based on them, allows to optimize the dynamic characteristics. Also, the methodology for creating an intelligent mechatronic module based on a synergistic approach allows for the integration of electrical, magnetic, and mechanical components into a single circuit, the integration of technical parameter modification, and the integration of processing functions into a single information-measuring module. In addition, the article proposes a Eurorhythm scheme for expressing how …

Climate Change Projections Through Optimized Machine Learning And Deep Learning Models: A Comprehensive Analysis, Hala H. Youssef, Salma S. Mohammed, Hanan M. Amer, Abeer T. Khalil

Mansoura Engineering Journal

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), …

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …

Towards Autonomous Energy Management: Machine Learning For Effective Auditing And Optimization, Sameh O. Abdellatif, Sherif Ashraf, Mira Mohsen

Electrical Engineering

This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Analysis And Evaluation Of Backtracking Settings For Energy Yield Optimization At The Cal Poly Solar Farm, Kayla Go-Oco

College of Engineering Summer Undergraduate Research Program

The Cal Poly Solar farm has been built as a single axis tracking facility with two different types of panels. Both conventional single cell solar panels and twin cell solar cells have been used in its construction. Twin Cell panels typically perform better than their conventional counterparts when shaded by other panels in the row in front of them in a fixed tilt system. However neither module performs well when even a small portion is shaded. This project will access the system API to access data for the field and process to determine the energy yield improvements that can be …

Foundations For Multi-Bit-Per-Cell Phase Change Memory Modeling Gst Crossbar Arrays, Sashah Wilson-Thompson

Holster Scholar Projects

This project builds a simulation foundation for selective cell heating in a phase-change memory (PCM) crossbar using Ge2Sb2Te5 (GST) as the active material. Using COMSOL Multiphysics® a 3D modeling software, couples Electric Currents, Electric Circuits, Heat Transfer in Solids, and Electromagnetic Heating for the simulation. A parameterized Tungsten (W)/GST-Amorphous/GST-Crystalline(phases) /W embedded in Silica Dioxide (SiO2) and surrounded in Silica Nitride (Si3N4) is validated at the single-cell level and scaled to small GST crossbars A terminal voltage (V_active/V_inactive, or 0 V if unselected) is applied through MOSFET and diode selector elements at the ends of each word line and bit line. …

Spectral Modeling Of Electromagnetic Radiation From Power Converters, Regan A. Varner

Theses and Dissertations

Switching power converters are increasingly utilized in contemporary electronics. They are becoming more compact and operate at higher frequencies, which contributes to radiated electromagnetic interference (EMI). This EMI is important to quantify for electromagnetic compatibility of the converters with other nearby systems. In this thesis, analytical models have been developed for both ideal and non-ideal switching waveforms, employing piecewise functions to describe time-domain waveforms, with their corresponding frequency spectra derived and compared to empirical spectra. The results demonstrate that the comparisons with measured frequency data exhibit agreement with the analytical models up to approximately 25 MHz. Additionally, our analysis indicates …

Towards Robust Bmocz Schemes For Non-Coherent Wireless Communication, Anthony Joseph Perre

Theses and Dissertations

Non-coherent communication has emerged as a promising approach to address several challenges at the physical layer. A particular scheme called binary modulation on conjugate-reciprocal zeros (BMOCZ) uses the zeros (i.e, roots) of a polynomial to convey information bits. In this thesis, we strive to improve the reliability of BMOCZ in various scenarios. In particular, we utilize machine learning (ML) methods to determine the parameters for BMOCZ and improve upon the decoding performance. Moreover, we introduce a smooshed BMOCZ variant to combat typical impairments encountered within wireless communications, including timing offsets (TOs) for noncoherent orthogonal frequency division multiplexing (OFDM). Through numerical …

Design Of A Robust Adaptive Cascade Fractional-Order Proportional–Integral–Derivative Controller Enhanced By Reinforcement Learning Algorithm For Speed Regulation Of Brushless Dc Motor In Electric Vehicles, Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better …

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (2/4): Report 2: High-Performance Water Splitting Electrocatalysts, Ling Zhang, Wang-Yang Wu, Qiu-Yue Hu, Shi-Dan Yang, Li Li, Rui-Jin Liao, Zi-Dong Wei

Journal of Electrochemistry

The unavailability of high-performance and cost-effective electrocatalysts has impeded the large-scale deployment of alkaline water electrolyzers. Professor Zidong Wei’s group has focused on resolving critical challenges in industrial alkaline electrolysis, particularly elucidating hydrogen and oxygen evolution reaction (HER/OER) mechanisms while addressing the persistent activity-stability trade-off. This review summarizes their decade-long progress in developing advanced electrodes, analyzing the origins of sluggish alkaline HER kinetics and OER stability limitations. Professor Wei proposes a unifying “12345 Principle” as an optimization framework. For HER electrocatalysts, they have identified that metal/metal oxide interfaces create synergistic “chimney effect” and “local electric field enhancement effect”, enhancing selective …

Local Electric Fields Coupled With Cl− Fixation Strategy For Improving Seawater Oxygen Reduction Reaction Performance, Yu-Rong Liu, Miao Zhang, Yan-Hui Yu, Ya-Lin Liu, Jing Li, Xiao-Dong Shi, Zhen-Ye Kang, Dao-Xiong Wu, Peng Rao, Ying Liang, Xin-Long Tian

Journal of Electrochemistry

Development of robust electrocatalyst for oxygen reduction reaction (ORR) in a seawater electrolyte is the key to realize seawater electrolyte-based zinc-air batteries (SZABs). Herein, constructing a local electric field coupled with chloride ions (Cl⁻) fixation strategy in dual single-atom catalysts (DSACs) was proposed, and the resultant catalyst delivered considerable ORR performance in a seawater electrolyte, with a high half-wave potential (E_1/2) of 0.868 V and a good maximum power density (Pmax) of 182 mW·cm⁻² in the assembled SZABs, much higher than those of the Pt/C catalyst (E_1/2: 0.846 V; Pmax: 150 mW·cm^{−2 …}

Pendant Micro-Droplet Evaporation Fabricates Fiber-Optic Mof Gas Sensor In Seconds, Abhishek Prakash Hungund, Bohong Zhang, Narasimman Subramaniyam, Thomas Spudich, Ryan O'Malley, Farhan Mumtaz, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The development of photonic-based gas sensors using metal–organic frameworks (MOFs) and other microporous solids is often a multistep, complex process, typically involving MOF synthesis, purification, and attachment of microcrystals to an optical fiber end face. This study introduces a one-step method that integrates MOF synthesis and sensor head fabrication directly onto the fiber end face, forming an extrinsic Fabry–Perot interferometer (EFPI) with a thin film of MOF microcrystals. The resulting film, only 3–10-μm-thick, enhances sensor response by enabling rapid gas detection within seconds. Utilizing a pendant micro-droplet evaporation technique, this method forms a microporous MOF layer in situ, allowing unreacted …

Robust Fault Detection And Classification In Power Systems Via Physics-Informed And Data-Driven Learning, Biswash Basnet, Varsha Sen

Graduate Student Scholarship

Electrical faults in power transmission systems can severely affect grid stability, equipment safety, and operational reliability. Traditional protection schemes, particularly distance relays, depend on apparent impedance computation that changes with error, creating a risk of misclassification. The results from relay overreach, underreach, or complete maloperation due to CT/PT saturation lead to developing problems in high impedance situations. These limitations highlight the need for adaptive, data-driven alternatives. This paper proposes an intelligent fault detection and classification model based on supervised machine learning techniques that overcome these challenges. The system’s robustness was validated under different training sizes and Gaussian noise levels, demonstrating …

An Event-Based Time-Incremented Snn Architecture Supporting Energy-Efficient Device Classification, David L. Weathers, Michael A. Temple, Brett J. Borghetti

Faculty Publications

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF …