Digital Commons Network^™

Development Of Si-Based Germanium-Tin Infrared Photodiode, Sylvester Amoah

Graduate Theses and Dissertations

Short-wave infrared (SWIR) and mid-wave infrared (MWIR) photodetectors have attracted increasing attention for the past few decades. SWIR and MWIR detectors are used for a wide range of applications in military and civilian defense, such as future automotive night vision, video surveillance security cameras, and integrated night vision in mobile/wearable electronics. The current market-dominating IR photodetector technology is mainly developed with the costly III-V or II-V material systems, such as InGaAs, InSb, and HgCdTe. While many efforts have been made towards the hybrid integration of III-Vs or II-VIs on a Si substrate, it is highly desirable to develop an alternative …

Grid Forming Inverters For Microgrid Operation Enhancement, Jan Westman

All Dissertations

Modern electric power systems are confronted with several major challenges including the increasing frequency and severity of weather events, aging infrastructure, high material costs, cyber-attacks, and substantial delays in global supply chains. Microgrids provide a solution to these challenges by insulating customers from the power outages occurring in their local distribution system. A microgrid is a small independent power system with its own power generation and energy storage resources that can supply customers’ electricity needs. When an outage occurs, a circuit breaker will disconnect the microgrid from the distribution system. Successful operation of the microgrid requires one or more devices …

Robust Distributed Learning Against Both Distributional Shifts And Byzantine Attacks, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

In distributed learning systems, robustness threat may arise from two major sources. On the one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to Byzantine attacks, which could invalidate the learning result. In this article, we propose a new research direction that jointly considers distributional shifts and Byzantine attacks. We illuminate the major challenges in addressing these two issues simultaneously. Accordingly, we design a new algorithm that equips distributed learning with both distributional robustness and Byzantine robustness. …

Advanced Day-Ahead Scheduling Of Hvac Demand Response Control Using Novel Strategy Of Q-Learning, Model Predictive Control, And Input Convex Neural Networks, Rahman Heidarykiany, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, …

Optimization-Based Distributed Controller For Multi-Agents System In Microgrid Secondary Control, Fahad S. Alshammari, Ayman El-Refaie, Saleh Alyahya, Sheroz Khan

Electrical and Computer Engineering Faculty Research and Publications

Micro-grids function to connect to power system power produced by the renewable energy resources. In islanded micro-grids, grid-forming units collaborate to maintain the micro-grids voltage and frequency by utilizing droop control technique that includes primary, secondary and tertiary levels. Secondary control intervenes to improve power sharing and restore voltage and frequency to their nominal levels. However, the conventional droop control applied to a grid with mismatched line parameters experiences a trade-off between reactive power sharing and voltage regulations. This paper applies real-time trajectory tracking convex optimization to ensure by communicating power sharing between units in a consensus topology. The optimization …

Application Of Quantum Algorithms In Optimization Of Cognitive Decision-Making Systems, Noilakhon Yakubova

Chemical Technology, Control and Management

The article analyzes the potential of quantum algorithms for optimizing cognitive decision-making systems in production processes in the energy sector. Compared with traditional algorithms, quantum algorithms allow high-speed processing of large amounts of variable data in real time and solving multiparameter problems based on an integrated approach. The study considers uncertainties and issues of increasing efficiency in the processes of production and distribution of electricity using a cognitive model. The integration of quantum algorithms into this model allows developing optimal strategies for using resources in production. A heating boiler was taken as a control object, and taking into account its …

Improving Image Quality In Electrical Capacitance Tomography Using Otsu Thresholding, Josiah Nombo

Tanzania Journal of Engineering and Technology (TJET)

Electrical Capacitance Tomography (ECT) is an imaging technique used in industrial process monitoring, particularly for monitoring and measuring the composition of multiphase flows. Despite its widespread application, the commonly used Linear Back Projection (LBP) algorithm often produces low-quality images due to its limited ability to handle high permittivity contrasts and nonlinearities. This study investigates the use of Otsu thresholding as a post-processing technique to enhance ECT image quality. By maximizing inter-class variance in the image histogram, Otsu thresholding improves contrast, clarity, and structural definition, enabling more effective segmentation of oil and gas components in multiphase flows. The proposed Otsu-based reconstruction …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Development Of A Microcontroller-Based Intelligent Traffic Light Control System For Vehicular Movement In T-Junctions, Frederick O. Ehiagwina

Tanzania Journal of Engineering and Technology (TJET)

This research is devoted to the issue of regulating traffic congestion in major cities using light-dependent resistors coupled with the PIC16F877A microcontroller. This study proposes an intelligent traffic control system for T-Junctions, utilizing sensing and control to optimize traffic flow through dynamic phase adjustments and congestion reduction, enabled by a microcontroller-based decision-making system. The proposed system reduces traffic congestion, automates control, and enhances safety, minimizing accidents and lowering infrastructure costs. Under simulated environment, it demonstrates an average response time of 50 ms and achieves 99% accuracy in displaying the correct countdown. Finally, the number of state transitions handled per minute …

Clustering Mini-Grids To Enhance Power Systems Reliability In Distributed Generation, Santos Kihwele

Tanzania Journal of Engineering and Technology (TJET)

This study investigates the effectiveness of clustering mini-grids as a method to improve the reliability and operational stability of distributed generation systems in remote areas, particularly where renewable energy sources are predominant. As renewable resources like solar and wind are increasingly integrated into mini-grids, these systems face various challenges, particularly in maintaining stable voltage and frequency across interconnected networks. Such issues are compounded by the intermittent nature of renewable energy sources and the lack of extensive communication infrastructure in rural or resource-constrained settings. To address these control challenges, this study applies droop control techniques to two mini-grids in Uganda, utilizing …

Algorithms For Feature Extraction And Optimisation Of Object Recognition Operator, Hudayberdiev Xakkulmirzayevich Mirzaakbar, Bobomurod Mamitjonovich Tojiboev, Feruza Komiljonovna Samadova

Chemical Technology, Control and Management

This paper deals with the development and analysis of feature extraction and optimisation algorithms for object recognition operators. Different algorithms are used to improve the efficiency of recognition operators in the automatic analysis of remote sensing images. The information model of objects and methods for selecting, extracting and optimising their features have been studied. The issue of extracting important features of objects using spectral, textural and statistical features and constructing optimal operators based on them has also been studied. Effective approaches based on the theory of convex hulls and multidimensional analysis methods have been proposed, taking into account the mutual …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Detection Of Http Flood Attacks Based On Machine Learning Algorithms, Norbek Karimov, Furkat Rakhmatov, Oybek Xolmuminov

Chemical Technology, Control and Management

This paper analyzes the effectiveness of Random Forest and SVM models for detecting HTTP Flood attacks. Experimental results demonstrate that both models achieve high accuracy. Evaluation was conducted using Precision, Recall, and F1 Score metrics. Additionally, key features of network traffic were extracted through correlation analysis to enable real-time application of the models in attack detection. The findings provide important insights into detecting DDoS attacks using machine learning and improving model performance.

Security In Biometric Access Control Systems Based On Fingerprints, Elmurad Jamuradovich Qilichev, Baxodir Saydullaevich Achilov, Ismoil Ergash OʻGʻLi Isroilov, Mirshod Mirkomil O'G'Li Ahmadov

Chemical Technology, Control and Management

This article analyzes biometric access control systems using fingerprints. The article considers the advantages of using biometric technologies, including solutions aimed at ensuring security, protecting users' personal data, and increasing the efficiency of systems. A detailed explanation of the principles of operation of fingerprint biometric systems, their underlying algorithms, and technological advances is provided. Problems that arise when assessing the level of security, ease of use, and technological and social aspects of biometric access systems and methods for combating them are also covered. At the end of the article, opinions are expressed about the future of fingerprint-based biometric systems and …

Deep Learning-Based Multi-Class Classification Of Breast Cancer Ultrasound Images Using Convolutional Neural Networks, Andres E. Dewendt Urdaneta

ATU Scholars Symposium

The National Cancer Institute forecasts 2,001,140 cancer diagnoses in 2024, with approximately 600,000 expected deaths. Breast cancer is projected to be the most prevalent, with about 310,000 cases. Early diagnosis is critical to improving outcomes, and various diagnostic technologies, including imaging, biopsies, and blood tests, play a vital role. Image testing methods include X-rays, ultrasounds, magnetic resonance imaging (MRI), and PET scans. Artificial intelligence (AI) has recently significantly improved cancer detection, improving speed, accuracy, and effectiveness. This research project uses a convolution neural network (CNN) to analyze ultrasound breast images, classifying them as benign, malignant, or normal. Our CNN model …

Arkansas Tech University Modular Robot Training System, Bryson C. Hicks, Dominic Snow, Harrison Taylor, Spencer Barnett, Leland Bosley

ATU Scholars Symposium

The Arkansas Tech University Modular Robotics Training System (ATUM RTS) project was started two semesters ago by a team of six engineering students at Arkansas Tech. Their goal was to combine the two existing systems of the Georgia Tech Robotarium and the Micromouse maze solving competition. This was done to bring new challenges and excitement to maze-solving robotics while incorporating the cloud-based learning system of the Georgia Tech Robotarium. By creating randomized mazes using tiles and remote walls, ATUM RTS will provide an additional resource to students that will allow them to utilize their knowledge in real world applications. This …

Designing A Control System With Automatic Transition Between Flight Modes For A Vtol Airplane, Asher Cunningham

Honors College Theses

Vertical Take-off and Landing (VTOL) airplanes provide a great degree of flexibility in mission type, as they combine the landing ability of a helicopter with the range of an airplane. This paper describes the design, implementation, and testing of the control system for just such a vehicle. Due to its non-typical motor configuration, there is an unbalanced torque on the frame. Two control methods are suggested and tested to balance this torque, and a PID controller with a high I gain is selected. Furthermore, the distance of the pods from the centerline is calculated and set at 14 inches. A …

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

Scalable Cyber-Physical Testbed For Cybersecurity Evaluation Of Synchrophasors In Power Systems, Shuvangkar Chandra Das, Tuyen Vu, Hebert L. Ginn Iii

Faculty Publications

This paper presents a synchrophasor-based real-time cyber-physical power system testbed with a novel security evaluation tool, pySynphasor, that can emulate different real attack scenarios on the phasor measurement unit (PMU). The testbed focuses on real-time cyber-security emulation using different components, including a real-time digital simulator, virtual machines (VM), a communication network emulator, and a packet manipulation tool. The script-based VM deployment and software-defined network emulation facilitate a highly scalable cyber-physical testbed, which enables emulations of a real power system under different attack scenarios such as address resolution protocol (ARP) poisoning attack, man-in-the-middle (MITM) attack, false data injection attack (FDIA), and …

Rf Emission From Partial Breakdown Events And Their Classification, Torin Sammeth

Electrical and Computer Engineering ETDs

Electrical breakdown is a core component of Pulse Power both in its utilization and as a failure point in systems. This research is investigating the feasibility of D-dot antennas to detect and potentially identify different forms of partial electrical breakdown in a point-to-plane gap geometry. In this work, the RF emission detected by a D-dot probe during a partial breakdown event is shown to correlate with the presence of light and current using a fast-current transformer and photomultiplier tube. Additionally, the frequency components of the RF signal were measured with the use of a fast Fourier transform. Identifying these frequencies …

Navajo Speech Recognition Using Low-Resource Language Models, Emery M. Sutherland

Electrical and Computer Engineering ETDs

This thesis describes the development of a speech recognition system to classify Navajo (Dine) words using Low Resource Language (LRL) datasets. Presently there are no recognized high-quality open-sourced datasets for the Dine language needed to train models for speech recognition. A small balanced dataset was designed to train several models. To overcome the scarcity of the LRL dataset, the audio recordings were augmented to account for time-stretching, amplitude variations, time shifts, small amounts of white Gaussian noise, and SpecAugmentation. The models included a Recurrent Neural Network (RNN), a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM) model with …

Antenna Analysis Using Mixed Computer Platforms (Aacp), Keith H. Bova

Electrical and Computer Engineering ETDs

This thesis reviews the design, implementation, and benchmarking of commercially available, previous generation (CAPG) computer systems, and how they can be used with computational electromagnetics. The thesis focuses on their architecture, performance, and practical applications. It begins with an overview of CAPG system components, including different node types such as login/controllers, compute nodes, block storage, and object storage. The process of assembling these systems is outlined. The conjugate gradient method and direct linear solvers were benchmarked. The performance of CAPG systems is compared with modern computing architectures. A detailed analysis provides recommendations for designing CAPG systems around the performance of …

A Dynamically Efficient Predictive Controller For Voltage Stability In A Dc To Dc Boost Converter, Giovanni Reyna

Electrical and Computer Engineering ETDs

Reliable voltage regulation has become a critical issue with increase demand in modern power electronic applications. This study presents a Model Predictive Controller (MPC) for voltage regulation in DC-to-DC boost converters. A closed loop analytical MPC framework is created with the purpose of computing optimal duty cycles to maintain a stable output voltage under varying load conditions, input changes, and disturbances.

To evaluate the performance of the MPC we set the controller against a comparative analyzing with a traditional cascading proportional integral (PI) controller. The proposed controller design is specifically targeted to improve dynamic efficiency during the converter’s transient events. …

Single-Entity Protein Electrochemistry Of A Diffusion-Limited Enzyme, Ziwen Zhao, Nikolaos Kostopoulos, Sagar Ganguli, Paul Bergstrom, Alina Sekretareva

Michigan Tech Publications

In this work, we present single-entity protein electrochemistry (SEPE) experiments on catalase, along with a theoretical model to simulate its enzymatic activity and predict the experimentally observed current responses. We perform SEPE measurements at various enzyme concentrations and use protein film voltammetry to investigate the origin of the observed electrochemical signals in SEPE. The modeling approach we develop combines finite element simulations in COMSOL Multiphysics with random walk simulations in MATLAB. The enzyme is represented as a partially active sphere, with an arc on the surface corresponding to the enzyme’s diffusion channel leading to the active site. Notably, the model …

Enhancing Frequency Event Detection In Power Systems Using Two Optimization Methods With Variable Weighted Metrics, Hussain A. Alghamdi, Midrar A. Adham, Umar Farooq, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This research presents a novel technique that refines the performance of a frequency event detection algorithm with four adjustable parameters based on signal processing and statistical methods. The algorithm parameters were optimized using two well-established optimization techniques: Grey Wolf Optimization and Particle Swarm Optimization. Unlike conventional approaches that apply equally weighted metrics within the objective function, this work implements variable weighted metrics that prioritize specificity, thereby strengthening detection accuracy by minimizing false-positive events. Realistic small- and large-scale frequency datasets were processed and analyzed, incorporating various events, quasi-events, and non-events obtained from a phasor measurement unit in the Western Interconnection. An …

Utilizing Machine Learning To Predict The Charge Storage Capability Of Lithium-Ion Battery Materials, Manoj Chhetri, Karen S. Martirosyan

Physics & Astronomy Faculty Publications

With the increasing demand for high-performance batteries in applications such as electric vehicles and portable electronics, accurately predicting the charge storage capacity of battery materials is crucial for developing more efficient and reliable energy storage systems. Machine Learning (ML) and data-driven approaches, plays a vital role in enhancing our understanding of Li-ion battery performance, guiding materials design, optimizing system efficiency, and accelerating innovation in energy storage technologies. In this study, an ML-based approach was applied to a dataset of 2345 rechargeable Li-ion battery materials, obtained from the Materials Project online portal, to predict gravimetric charge storage capacity ─ a key …

Sustainable Poultry Farming: A Concept Of Iot-Based Poultry Management System For Small-Scale Farmers, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Olufemi Peter Alao, Nurudeen Samuel. Lawal, Ayoola Abiola Babalola, Abisola Olayiwola

Al-Bahir

Conventional poultry management techniques are failing to meet increased demand for poultry products as the population continues to grow. As a result, this issue has become a major concern for small-scale farmers, particularly those in low-income areas, in terms of food security. One of the main reasons for this is that the farmers rely on intensive farming methods which are inefficient for automating daily poultry operations. However, intensive farming methods pose major environmental concerns to ecosystems and poultry health. Also, the environmental conditions, welfare, and productivity of poultry operations may be harmed by the global climate crisis and poultry waste …

Cyber Physical Emulation Of Power Grids: Co-Simulation Of Exata Cps And Hypersim, Mckayla Snow

Electrical and Computer Engineering ETDs

This thesis explores the use of cyber-physical emulation of power grids to analyze the impact of different types of cyberattacks. It outlines the co-simulation process of HYPERSIM and EXata CPS within the OPAL-RT real-time digital simulator, demonstrating various cyberattacks on two power system models. The first model is a simple communication-focused testbed for easy demonstration of co-simulation setup, while the second is a power system model of a secondary network used to assess the cybersecurity of network protector units in low-voltage networks through a hardware-in-the-loop (HIL) testbed. Using this testbed, the cybersecurity of the direct transfer trip (DTT) scheme is …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …