Engineering Commons^™

Realization Of Deterministic Quantum Circuits For Non-Deterministic Or Incompletely Specified Quantum State Machines, Manjith Kumar, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

In classical logic design, there are machine learning methods based on converting a set of input-output traces to non-deterministic automata that are then converted to deterministic automata and synthesized using logic gates. This approach has not yet been extended to quantum automata. In this paper, we present a method to convert a set of input-output traces to a non-deterministic automaton, which is then converted to an incompletely specified multi-output Boolean function. The existing logic synthesis approaches for designing quantum circuits are insufficient to handle incompletely specified functions. So, we present a novel algorithm to synthesize logic functions with don’t cares …

Optimizing Pv Solar Array Design By Analysis Of The Industrial And Training Assessment Center (Itac) Database, Angel Samuel Fernandez

Theses and Dissertations

Since 2017, the Industrial Training and Assessment Center (ITAC) at UTRGV has included photovoltaic (PV) system evaluations in its industrial energy assessments. A review of these reports showed inconsistencies in PV system design, including variations in sizing methods, performance assumptions, and application of NEC requirements. To address these issues, this thesis develops a standardized and NEC-compliant methodology for designing PV systems, incorporating demand-based sizing, solar-geometry principles, and electrical calculations guided by NEC Articles 690 and 705.

This thesis focuses on demand-based PV system sizing and incorporates NEC-guided electrical design to ensure technical compliance and safety. System performance is evaluated using …

Lstm Network-Based Scheme For Automatic Characterization Of Power Quality Disturbances, Akram Elmitwally, Mohamed Nader

Mansoura Engineering Journal

Recognition of power quality (PQ) troubles is a critical task in the electrical power industry. Most previous works solve the classification problem using separate feature extraction phase and classification phase. Each phase has its own techniques, and consumes a computation time. This study proposes to utilize the long short-term memory (LSTM) network as a deep learning model to classify the PQ events in one shot. The LSTM network uses its particular processing to classify a PQ event signal directly by reading its time-sequence data. Then, a dedicated post-classification algorithm (PCA) extracts start time, end time, duration, amplitude, and total harmonic …

Bio-Inspired Computational Intelligence Metaheuristic-Based Optimization And Sensitivity Analysis Approach To Determine Techno-Economic Feasibility Of Hydrogen Refueling Stations For Fuel Cell Vehicles, Paul C. Okonkwo, Samuel Chukwujindu Nwokolo, Saad S. Alarifi, Stephen E. Ekwok, Rita Orji, Sunday O. Udo, Ahmed M. Eldosouky, El Manaa Barhoumi, Barun Kumar Das, David Gomez-Ortiz, Kamal Abdelrahman, Anthony E. Akpan

Research outputs 2022 to 2026

This study presents a comprehensive economic and technological evaluation of renewable hybrid power systems for hydrogen refueling stations (HRS) in Nizwa, Oman, leveraging cutting-edge optimization algorithms to determine the most cost-effective and efficient hybrid energy system configurations. Three hybrid energy systems of photovoltaic-wind turbine-battery (PV-WT-B), photovoltaic-wind-fuel cell-battery (PV-WT-FC-B), and wind turbine-battery (WT-B) were evaluated based on net present cost (NPC), levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The study employs advanced optimization techniques, including the Mayfly Algorithm, Genetic Algorithm, CUKO Search, Gray Wolf Optimizer (GWO), Constrained Particle Swarm Optimization (CPSO), Harmony Search (HS), and Flower Pollination …

The Equity Implications Of Pecuniary Externalities On An Electric Grid, Charles Sims, Gasser G. Ali, J Scott Holladay, Tim Roberson, Chien-Fei Chen, Islam H. El-Haddad

Civil Engineering Faculty Publications

The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of rooftop solar adoption, we show how this pecuniary externality between low- and high-income customers increases low-income electricity bills by 10% in an area with some of the highest poverty rates in the United …

Hallucination Techniques For Self-Supervised Synthetic Datasets For Mobile Robots, Wyatt D. Colburn

Master's Theses

Classical techniques in autonomous navigation struggle in tightly constrained spaces. Machine learning has been shown to perform better in these difficult environments but most techniques require large amounts of navigation experience for training. Using a new machine learning paradigm learning from hallucination (LfH), training data can be collected in a safe environment and not require supervision. Data is collected in real time while an agent performs a random walk in free space, supervision is not required as there are no obstacles for the robot to run into. After a random walk a post processing pipeline will hallucinate a safety corridor …

Enhancing Grid Integration Of High-Power Loads Through Modular Unfolding-Based Power Conversion With Decentralized Control, Sanat R. Poddar

All Graduate Theses and Dissertations, Fall 2023 to Present

The transition to electric vehicles (EVs) is a critical step toward reducing greenhouse gas emissions and creating a cleaner, more sustainable transportation sector. However, many drivers — particularly those operating larger vehicles such as trucks and buses — face significant challenges, including limited driving range and lengthy charging times. High-power, fast-charging stations, which can rapidly replenish EV batteries, are therefore essential for mitigating these barriers and making electric transportation practical across all vehicle classes.

Despite the promise of electric vehicles, deploying high-power fast-charging stations presents significant challenges. They require large amounts of electricity, making them expensive to install, operate, and …

Reconstruction And Texturing Of 3d Surfaces From Fused Low-Cost Aerial Lidar And Optical Imagery, Samuel Kiguthi

All Graduate Theses and Dissertations, Fall 2023 to Present

Drones equipped with laser scanners (LiDAR) and cameras capture detailed 3D scenes. Combining the laser points with photos builds realistic, photo-textured 3D maps used in surveying, agriculture, and infrastructure planning. Conventional methods to create these maps often misrepresent inward shapes such as doorways, overhangs, and outcrops. These shapes are misrepresented because drones mainly view top surfaces and do not collect significant data on vertical or hidden areas.

This study introduces a surface-reconstruction method that groups LiDAR points into clusters, reconstructs smooth surfaces for each cluster, and uses information from recorded camera poses to stitch clusters together. Tests on real drone …

Design, Control, And Optimization Of Unfolding-Based Ac-Dc Topologies With Three-Port Resonant Converters For Electric Vehicle Battery Charging Applications, Aditya Zade

All Graduate Theses and Dissertations, Fall 2023 to Present

The global effort to reduce greenhouse gas emissions and reliance on fossil fuels has made electric mobility a cornerstone of sustainable transportation. This transition is driving demand for advanced charging infrastructure and more efficient power conversion systems. Power converters play a central role, not only in enabling reliable EV charging but also in integrating renewable energy sources with the grid. Because of the large amount of power involved, these converters must operate efficiently, reliably, and at low cost. Conventional high-power chargers often use two stages of conversion, which are effective but limited by size and energy losses. To overcome these …

Lace Network Firmware: A Polarfire Fpga Network For Data Routing And Command Interfacing In Space Applications, Kade C. Howes

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern small spacecraft rely on powerful yet efficient onboard compute devices to process data from sensors in real-time due to tight power and volume constraints. This work explores a new compute device system built from PolarFire Field-Programmable Gate Arrays (FPGAs), which are power-efficient, reprogrammable chips well-suited for space applications. The system connects via a central controller FPGA with one or multiple companion processing FPGAs, allowing sensor data to be quickly received and shared across the network. Standardized data formats and interfaces increase compatibility with spacecraft computers. By simplifying data handling and using high-speed communication links, this architecture makes it easier …

Optimization Of Size And Siting Of Distributed Generation In Unbalanced Distribution Systems: A Literature Review, Pema Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

Renewable energy sources (RES) are essential for meeting the rising global electricity demand while reducing greenhouse gas emissions from conventional generation. As traditional systems approach capacity saturation, the integration of RES into power grids becomes increasingly vital. However, the intermittent and variable nature of RES introduces significant technical, economic, and operational challenges. This review focuses on the optimal planning and integration of distributed generation in unbalanced distribution systems, which more accurately reflect real-world power network conditions. Emphasis is placed on siting and sizing strategies aimed at enhancing voltage stability, minimizing power losses, and reducing system costs and emissions. The review …

Design Of A Novel Robust Adaptive Fractional-Order Model Predictive Controller For Boost Converter Using Grey Wolf Optimization Algorithm, Chao Peng, Seyyed Morteza [email protected] Ghamari, Hasan Mollaee, Omid Rezaei

Research outputs 2022 to 2026

Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise …

Machine Learning Assisted Quality Control In Metal Additive Manufacturing: A Review, Zeqi Hu, Changlin Huang, Lechun Xie, Lin Hua, Yujie Yuan, Lai Chang Zhang

Research outputs 2022 to 2026

Additive manufacturing (AM) promotes the production of metallic parts with significant design flexibility, yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance. Process variability often leads to defects, insufficient geometric accuracy and inadequate material properties, which are difficult to effectively manage due to limitations of traditional quality control methods in modeling high-dimensional nonlinear relationships and enabling adaptive control. Machine learning (ML) offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM. The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM. First, …

Next-Generation Underwater Localization: Artificial Intelligence-Based And Energy-Aware Approaches, Mainul Islam Chowdhury, Quoc Viet Phung, Iftekhar Ahmed, Walid K. Hasan, Daryoush Habibi

Research outputs 2022 to 2026

Designing accurate, reliable, and energy-efficient localization techniques for underwater acoustic networks is highly challenging due to factors such as large propagation delays, the absence of Global Positioning System (GPS), node mobility, and limited acoustic link capacity. In any underwater sensor network (UWSN) monitoring application, data collected by underwater nodes becomes more meaningful when accompanied by location information. However, traditional localization methods often rely on geometric models and statistical filters that are highly sensitive to sensor noise and communication constraints. Energy consumption is another primary concern in UWSNs, not only because replacing and recharging underwater batteries are challenging, but also due …

Investigation Of Insertion Loss In Inkjet-Printed Coplanar Waveguide Based On Drying Temperature, Jun Ho Yu, Sung Min Sim, Jin Woo Choi, Sang Ho Lee, Jung Mu Kim

Michigan Tech Publications

In this study, we propose an optimized inkjet printing process to improve the insertion loss of inkjet-printed coplanar waveguide (CPW) transmission lines. The process involves varying the drying temperature and adjusting the number of printing steps to investigate their effects on the electrical characteristics of the printed CPW. The relationships between surface roughness, surface cavities, morphological changes, and insertion loss are studied by conducting atomic force microscopy analysis and by examining the insertion loss up to 3 GHz. The printed CPW that underwent low-temperature drying after the first printing and high-temperature drying after the second printing before sintering showed improved …

Regulating Lithium Metal Nucleation And Growth For Dendrite Suppression: From Liquid-Electrolyte To Solid-State Batteries, Ao Du, Juan Zhang, Pan Xu, Ya-Jie Li, Kang-Yu Yi, Zhen-Zhen Shen, Hui-Lin Ge, Guang-Wen Zhang, Chao-Hui Zhang, Yu-Hao Wang, Chen-Zi Zhao, Meng-Yang Xu, Yu-Lin Jie, Rui Wen, Shu-Hong Jiao, Si-Qi Shi, Qiang Zhang, Chun-Peng Yang, Yu-Guo Guo

Journal of Electrochemistry

Lithium metal anodes, with a theoretical capacity of up to 3860 mAh·g⁻¹, are regarded as the cornerstone for developing next-generation high-energy-density batteries. However, several key challenges hinder their practical applications, including dendrite formation, unstable solid electrolyte interphase (SEI), side reactions with electrolytes, and associated safety risks. This review systematically explores the mechanisms of lithium nucleation, growth, and stripping in both liquid and solid-state battery systems, analyzing critical theoretical concepts like heterogeneous nucleation thermodynamics, surface diffusion kinetics, space charge effects, and SEI-induced nucleation, which are crucial for understanding the genesis of dendrite growth. Additionally, the review discusses the electrochemical-mechanical …

Evolving Towards Efficient Technologies In Power Systems: The Paradigm Of Energy Internet Survey, Dina Emad, Omar Abdel-Rahim, Tanemasa Asano, Sobhy M. Abdelkader

Mansoura Engineering Journal

The Energy Internet represents a significant advancement in the modernization and automation of electricity systems, offering a framework for future smart grids where all electrical devices are interconnected through energy routers. This system integrates advanced power electronics, information technology, and smart control technologies, enabling the accommodation of diverse energy sources and storage devices. With its bidirectional energy flow and sharing capabilities, the Energy Internet holds great promise for the future. However, several challenges must be addressed to fully realize this potential. This review provides an overview of the Energy Internet framework and explores key challenges, particularly those related to energy …

Blockchain In Smart Cities, Shamma Alnuaimi

Thesis/ Dissertation Defenses

This thesis explores the integration of blockchain technology with smart cities to enhance efficiency, transparency, and reliability. The basic principles of blockchain, its layered architecture, consensus protocols, and decentralized security are first reviewed. The thesis then highlights how blockchain features can be integrated with urban and energy systems. The research focuses on how token economy can be used with smart cities to promote individuals to engage in desired behaviors by employing blockchain-based incentive mechanisms that can encourage responsible energy consumption.

The FairChain test system was developed and tested using blockchain technology and the Internet of Things (IoT) to demonstrate the …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …

Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the implementation of a fuzzy logic–based control system on a field-programmable gate array (FPGA) for a quadrotor autonomous aerial vehicle (UAV). The objective is to design and integrate six Takagi–Sugeno fuzzy controllers to regulate roll, pitch, and yaw angles, along with longitudinal, latitudinal, and altitude movements, thereby stabilizing the UAV and enabling it to follow a desired trajectory. Due to the computational complexity of the six controllers, achieving the desired performance requires considerable processing time, which can adversely affect the quadrotor’s mission. Owing to their high processing power and operating frequency, FPGAs enable the control algorithm to …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Field Canals Improvement Projects Duration Prediction: A Comparative Analysis Of Machine Learning Models, Hania Ghouse, Ukaegbu Chinonso Ishmael, Edgar Dario Obando-Paredes, Hashem Shafik Shakir, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field …

Yolo-Based Marine Search And Rescue Using Uav Multi-Dataset In Challenging Weather Conditions, Aysha Alshebli

Thesis/ Dissertation Defenses

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 …

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Rail Anomalies Dataset For Semantic Segmentation Analysis, Saarah Majid, Arian Izadi, Paul Stanik

Undergraduate Research Symposium Posters

Rail maintenance is a core necessity of upkeep, maintenance, deterioration, and safety implications of large rail systems. Doing so in an orderly and uniform way, however, has proven difficult with the thousands of miles that railways span. Use of computer vision on locomotives during normal operation can allow for a more streamlined process that would eliminate the need for costly specialized equipment and disruptions of normal operations. This project aims to efficiently detect vegetation overgrowth, mud-pumping, and standing water on railways. With the use of machine learning algorithms and dataset curation, models can be trained, allowing them to gain a …