Digital Commons Network^™

Lightlink: Visible Light Communication For Batteryless Devices With Variable Computational Load, Charles Phillips

All Theses

Visible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables.

Blockchain-Integrated Version Control For Secure And Transparent Software Supply Chains, Iwinosa W. Aideyan

All Theses

The software supply chain encompasses all stages of software development and delivery from initial coding and version control to integration and deployment. As development environments become increasingly distributed and reliant on external dependencies, ensuring the integrity, auditability, and consistency of code changes has become a pressing challenge. Traditional version control systems like Git, while effective for collaboration and tracking revisions, do not inherently provide tamper-evident commit histories. Features such as history rewriting (e.g., git rebase, git push --force) can be exploited to manipulate commit logs without detection, posing risks in security-sensitive domains. This thesis proposes a blockchain-integrated version control framework …

Fairness And Robustness In Decentralized Federated Learning, Kaichuang Zhang

Theses and Dissertations

Federated Learning (FL) has emerged as a privacy-preserving paradigm that allows multiple clients to collaboratively train a machine learning model without sharing raw data. However, traditional FL relies on a central server for model aggregation, which introduces a single point of failure and makes the system vulnerable to server-side attacks or breakdowns. To address these limitations, Decentralized Federated Learning (DFL) has been proposed, eliminating the need for a central server and enhancing system resilience. Despite these advantages, DFL faces critical challenges related to fairness and robustness, especially under non-i.i.d. data distributions and adversarial conditions. In this thesis, we propose a …

Spin Coater Design With Pid Algorithm Using Polynomial Regression Approach And Bias Tuning For Tio2 Deposition Process, Geo Surya Andika, Nofrijon Sofyan, Donanta Dhaneswara, Akhmad Herman Yuwono

Journal of Materials Exploration and Findings

The thin-film deposition technique using spin coating offers a cost-effective alternative to Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). The spin-coating process requires precise control of the motor drive system to ensure that the rotational speed, measured in rotations per minute (RPM), aligns with the set point and remains stable. This study presents the design and development of a spin coater prototype to achieve uniform thin-film deposition. The control method employed utilizes a Proportional-Integral-Derivative (PID) algorithm, incorporating a polynomial approach with bias tuning. The PID control was chosen to achieve stable operation in a non-linear system. The performance …

Adaptive Delay Compensation Frameworks For Distributed Real-Time Co-Simulation In Power Systems, Elutunji Buraimoh

All Dissertations

This dissertation presents a model-free, adaptive delay prediction and compensation framework for geographically distributed real-time power system co-simulation environments. Communication delays—both constant and real-time-varying—significantly degrade the accuracy, fidelity, and stability of co-simulated systems, particularly in dynamic and transient analyses of partitioned power systems. To address this, a predictor-based framework is developed that compensates for delays without requiring system models, computationally intensive signal transformations, or manual intervention.

The proposed solution leverages a Damping Impedance Method as the interface algorithm, combined with a sliding-mode control-inspired predictor system. Both single-parameter and multi-parameter predictor configurations are implemented, with the multi-parameter design providing an additional …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Inference Per Joule: A Performance Metric For Artificial Intelligence In Space Applications, Eduardo Macias Zugasti

Open Access Theses & Dissertations

The use of artificial intelligence (AI) has grown exponentially in recent years. This growth is driven in part by the significant advancements in computing capabilities, which have also increased exponentially. Computers have not only become more powerful but also smaller in size, thanks to the evolution of transistor technology. These developments have enabled AI to become a widely accessible tool, even in recreational activities such as image creation and entertainment videos.

More recently, the use of AI has extended to space applications, where it can enhance and optimize various tasks. However, space conditions pose significant challenges for conventional computers due …

Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman

Open Access Theses & Dissertations

Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network …

Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez

Open Access Theses & Dissertations

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …

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 …

Real-Time Active Thermal Management And Degradation Forecasting Of Power Electronics Building Blocks In All-Electric Ships, Ali Moghassemi

All Dissertations

With advancements in semiconductor technology, power electronic devices play a critical role in modern power systems, enabling efficient energy conversion in applications such as renewable energy integration, electric vehicle drives, industrial automation, and Navy ship power systems. However, the increasing use of power converters presents challenges such as harmonic reduction, cost optimization, reliability improvement, and thermal management, especially in weight- and space-constrained environments like wind turbines and shipboard systems. High-frequency switching, while reducing passive filter size, increases thermal stress on semiconductor devices, impacting efficiency and longevity. Active thermal control, which regulates junction temperature through power loss management, and degradation forecasting, …

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. …

Networked Multi-Agent Deep Reinforcement Learning Framework For The Provision Of Ancillary Services In Hybrid Power Plants, Muhammad Ikram, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) …

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 …

A Study Of The Impact Of Balancing, Geometric Transformation, Generative Networks, And Roi Techniques In Eye Diseases Classification, Sghaira Hareb Alnuaimi

Theses

Automatic detection of ocular diseases helps medical professionals efficiently identify eye disorders, reduce diagnostic errors, and accelerate diagnoses to prevent blindness. Deep learning has been successfully utilized in various fields, including medical image classification. However, in spite of these advancements, challenges remain in ocular disease classification.

The objective of this work is to address these challenges using data processing, data augmentation in combination with Region of Interest (ROI) techniques. Medical datasets often suffer from scarcity, imbalance, and low-quality images, leading to inaccurate classification. To mitigate these issues, we utilize the ODIR dataset, which contains 7,000 labelled training images for both …

Blind Construction Of A Party Check Matrix For Arbitrary Linear Block Codes, Aaron J. Womack

All Graduate Theses and Dissertations, Fall 2023 to Present

Communication systems are systems in which a transmitter sends data across a channel to a receiver. In any modern communication system, Forward Error Correction (FEC) is used to detect and correct errors that occur during transmission. As communication systems have developed over the years, many FEC algorithms have been proposed. Each FEC algorithm has parameters that can be selected based on the needs of the communication system. Typically, the transmitter and receiver are both aware of the FEC parameters, but in some cases the receiver may not know how the transmitter encoded its message. In this case, the receiver must …

C-Band Air To Ground Communication System For Uav, Mubark Alghamdi

Theses and Dissertations

C band spectrum 5030-5091 MHz is allocated for command-and-control communication services with unmanned aircraft systems. This document evaluates the possibility of using 3GPP 5G standards for provisioning of such services. Channelization of the spectrum and major system parameters are proposed. The performance of the proposed system in channel fading environment is evaluated using MATLAB based simulations. The evaluations examine SINR, and average throughput of the proposed system at different altitudes of the unmanned aircraft system. MATLAB simulations are used to evaluate system performance in free-space environments. Signal-to-Interference-plus-Noise Ratio (SINR) and Reference Signal Received Power (RSRP) are predicted at different UAS …

Characterizing Human Mobility Patterns In Saudi Arabia Using Cellular Data, Meshal Alnefaie

Theses and Dissertations

The study analyzes human mobility in Saudi Arabia. Using crowd-source data, Riyadh mobility is analyzed to find trends and highlight mobility patterns of individuals in Riyadh. Then, the mobility of Riyadh is compared with that of Jeddah and Dammam in a similar data collection and analysis. Four mobility metrics are utilized: Number of Visited Locations (NLOC), Number of Unique Locations (NULOC), Radius of Gyration (RGYR), and Distance Traveled (DTRV). The results show interesting outcomes about individuals in the three cities. Although these cities are far from each other, they observe the same mobility patterns. These findings have the potential to …

Using Rf Hardware Technologies To Counteract Interference Within Dsrc Caused By Adjacent Unlicensed Bands, Grayson Hatcher, Billy Kihei, Nathan Kirkwood, Marco Tello

Symposium of Student Scholars

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia Department of Transportation. Past research con- firmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On- Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs …

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 …

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 …

Investigation Of The Electrical Capacitance Tomography System For Determining The Spatial Distribution Of Moisture In Grain Storage, Shamte J. Kawambwa

Tanzania Journal of Engineering and Technology (TJET)

Excessive moisture content (MC) in stored grains promotes the growth of fungi and molds, which affects the quality of the grains. Effective grain monitoring methods are essential for detecting and managing grain spoilage. Electromagnetic imaging (EMI) techniques provide a promising alternative to traditional techniques by visualizing the spatial distribution of moisture within grains rather than merely average MC values offered by traditional techniques. However, existing EMI approaches often use hard-field waves, which are expensive and fragile. This study introduces an Electrical Capacitance Tomography (ECT) system that employs soft-field waves to monitor the moisture content of the grains. The system was …

Design And Performance Analysis Of Fiber Bragg Grating Temperature Sensor For Industrial Processes Sensing Applications, Paul Stone Stone Brown Macheso S.B.

Tanzania Journal of Engineering and Technology (TJET)

The Fiber Bragg Grating (FBG) sensor has become a widespread sensing device because of its small size, passive design, immunity to electromagnetic interference, and direct ability to measure physical properties like temperature and strain. Recently, femtosecond infrared laser processing and regeneration techniques have resulted in the development of stable high-temperature gratings, which are a powerful tool in smart factories, an aspect of the fourth Industrial Revolution (4IR), and show promise for application in harsh environments like high pressure, high temperature, or ionizing radiation. The development of stable high-temperature gratings that can withstand harsh environmental factors like high temperatures, pressures, and …

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 …

Research Of The Dynamic Characteristics Of A Time-Pulse Ultrasonic Sensor, Aliev Ravshan, A.U. Djalilov

Chemical Technology, Control and Management

This article investigates the dynamic characteristics of a time-pulsed ultrasonic sensor used to measure water flow in open channels. The operating principle of the sensor, its response time in various hydrodynamic conditions, speed, and factors affecting measurement accuracy are analyzed. In the process of research, the improved ultrasonic sensor was tested and its effectiveness in measuring water flow in real time was evaluated. The results obtained showed that the sensor adapts to the velocity, temperature and turbulence level of the water flow. Based on the results of the research, the possibilities of working of time-impulse ultrasound sensors in open channels …

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 …

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.

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 …