Digital Commons Network^™

Development Of Power Management On Unmanned Surface Vehicles To Measure Battery Voltage And Power, Diana Alia, Henna Nurdiansari, Akhmad Kasan Gupron, Amelia Greacy Talenta Siregar

Journal of Mechanical Engineering Science and Technology (JMEST)

This research focuses on designing an electrical system for unmanned surface vehicles (USV) to ensure optimal performance during survey operations. This USV ship is a catamaran-type ship with a Sonar Deeper depth sensor to know the depth of the water and is equipped with long range (LoRa) as a data transmitter. The USV electrical system design incorporates the use of a 4050 mAh 11.1 V LiPo battery and an iMAX B6AC charger, with evaluations covering battery charging, power consumption, and voltage stability. The trials showed that the battery supported the operation of the USV for approximately 47.8 minutes at a …

Boiling Heat Transfer Coefficient And Critical Heat Flux On Conical Cylindrical Copper Under Surface Modification, Muhammad Dimyati Nashrullah, Andi Sanata, Imam Solahuddin, Nasrul Ilminnafik, Indro Pranoto, Adhika Widyaparaga

Journal of Mechanical Engineering Science and Technology (JMEST)

This study examined how surface roughness and nanoceramic coating, influences the boiling heat transfer coefficient (BHTC) and critical heat flux (CHF) of a conical cylindrical copper test specimen. Three test specimens exhibiting surface roughness of 0.22 µm, 0.14 µm, and 0.04 µm were analyzed for comparison. Two additional test specimens were treated with nanoceramic coatings utilizing silicon carbide as the base material: one specimen received a single-layer coating and the second was applied with a double-layer coating. The behavior of the bubbles was closely observed with a high-speed camera to deepen the analysis. The experimental results showed that the test …

Wireless Sensor Networks In Renewable Energy Monitoring: A Comprehensive Survey Of Protocols And Applications, Atheer Yousif Oudah, Lina M. Shaker

AUIQ Technical Engineering Science

The global shift to sustainable energy has heightened the demand for smart, robust, and efficient monitoring systems. Wireless Sensor Networks (WSNs) play a key role by enabling real-time data collection, decentralized control, and flexible communication in renewable energy applications. This review presents a comprehensive analysis of WSN architectures, routing protocols, and deployment strategies tailored to photovoltaic (PV) arrays, wind energy systems, hydroelectric plants, and smart microgrids. Core operational challenges are examined through a critical evaluation of energy-aware routing protocols including LEACH, PEGASIS, TEEN, ZRP, and advanced variants like EE-OLEACH and PT-Hybrid. Real-world applications, such as offshore wind farms, rooftop PV …

Retracted: Dynamics And Stability Analysis Of 8-Dimensional Hyperchaotic Systems: Study Of Lyapunov Exponents, Abdulsattar Abdullah Hamad, Nida Muhsin Ali, Muayyad Mahmood Khalil

Iraqi Journal for Computer Science and Mathematics

This research explores the complex dynamics of an 8D hyperchaotic system, focusing on its trajectory stability and behavior under various control parameters. By using numerical simulations, we investigate the relationship between the Lyapunov components and the stability of the system. It provides a quantitative measure of the chaos within the model of the matrix, whose derivation consists of the system's sensitivity to perturbation, simulating chaotic systems in high dimensions faces challenges such as high computational resource demands. Difficulty in ensuring numerical convergence and stability. The results highlight the profound impact of control parameters on the dynamic behavior of hyperchaotic systems. …

Exploring Immune System Through Computational Modeling: A Comprehensive Study Of Lymph Nodes And Immune Response Scaling, Vaccine Efficacy, And Large-Scale Extreme First Passage Time, Jannatul Ferdous

Computer Science ETDs

The adaptive immune response is a complex defense mechanism that develops over time to recognize and eliminate pathogens with remarkable precision and durability. This dissertation investigates the dynamics, scaling, and efficiency of the adaptive immune response through a synthesis of computational modeling, mathematical analysis, and agent-based simulations. First, we analyze the topology of the lymphatic network and investigate the T cell search time to find the lymph node that is containing the matching dendritic cell. Second we show how the scaling of lymph node number and volume with body mass, leads to scale-invariant search times for T cells locating antigen-bearing …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Networked Systems: Synchronization, Decomposition And Transient Dynamics, Amirhossein Nazerian

Mechanical Engineering ETDs

This dissertation investigates synchronization, transient dynamics, control, and signal amplification in networked dynamical systems. First, it examines complete and cluster synchronization, including cases with large parametric mismatches, in both natural (e.g., brain networks, fireflies) and technological systems (e.g., power grids, robotics). Second, it studies transient dynamics in consensus processes, designing synchronization strategies that link reactivity, contraction theory, and Lyapunov exponents. Third, it addresses the computational limits of Model Predictive Control (MPC) for large networks by introducing an algebraic decomposition method that enables parallel online computation of smaller subproblems, enhancing MPC performance under hardware constraints. Finally, it analyzes the structural role …

Phase Nanoscopy With Correlated Frequency Combs, Xiaobing Zhu

Optical Science and Engineering ETDs

In this dissertation a sensing method applying to any physical quantity that modifies optical phase is developed. Two pulses are produced inside a synchronously pumped Optical Parametric Oscillator, generating two identical, undistinguishable frequency combs. The physical quantity to be measured applies a small phase shift/round trip to one of the pulses, resulting in a frequency shift of the corresponding comb. The latter frequency is measured as a beat by interfering the two combs on a detector. A world record resolution, close to the quantum limit, of 0.033 nanoradian (corresponding to 0.006 fm in displacement) is achieved. A detailed analysis of …

Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan

Turkish Journal of Electrical Engineering and Computer Sciences

The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. The point process …

Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …

Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel

Turkish Journal of Electrical Engineering and Computer Sciences

Real-world super-resolution is a highly challenging problem in the field of computer vision. Besides enhancing image resolution and improving visual details, information loss due to complex real-world degradations is desired to be restored. One of the primary hardness of this problem is finding sufficiently large paired datasets for training. Researchers have developed techniques that generate synthetic low-resolution pairs using high-resolution images with a generative adversarial network-based degradation generator to address this issue. In these approaches, the degradation generator is trained by utilizing real-world low-resolution images as the target domain, generating a degraded low-resolution counterpart of the high-resolution input. However, in …

Flight Instructor Enhanced Qualification Training Program, Brendan Aug

Aviation Student Publications

Due to December 2024 regulatory changes made to 14 CFR Part 61.195(h)— Qualifications to provide ground or flight training to initial flight instructor applicants—a new training program, titled the Flight Instructor Enhanced Qualification Training Program (FIEQTP), must be developed under 61.195(h)(3).

The purpose of this packet is, in response to regulatory changes, to conduct a thorough training needs analysis, and utilizing its conclusions, develop a new 14 CFR Part 141 course and assessment plan that meets the requirements of Part 61.195(h)(3) Flight instructor enhanced qualification training program.

Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal

Turkish Journal of Electrical Engineering and Computer Sciences

Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …

Regulating The Amount Of Graphene Oxide For Enhanced Capacitive Energy Storage Of Mof Derived Materials, Yong-Ji Qin, Jing-Quan Yang, Hao Wang, Mei-Ling Lian, Pei-Pei Jia, Jun Luo, Xi-Jun Liu, Jun-Feng Liu

Journal of Electrochemistry

In pursuit of more efficient and stable electrochemical energy storage materials, composite materials consisting of metal oxides and graphene oxide have garnered significant attention due to their unique structures and exceptional properties. Graphene oxide (GO), a two-dimensional material with an extremely high specific surface area and excellent conductivity, offers new possibilities for enhancing the electrochemical performance of metal oxides. In this work, we synthesized metal- organic framework (MOF) and GO composites by regulating the amount of GO, and successfully prepared composites of metal oxides supported by nitrogen-doped carbon frameworks and GO through a simple one-step calcination process. Based on the …

Ai-Based Object Detection And Risk Identification For Enhanced Construction Site Safety, Mahdi Bonyaniakbarabadi

LSU Master's Theses

Recent advancements in computer vision for construction site safety have encountered significant hurdles, especially in the nuanced tasks of object detection and the identification of unsafe worker behaviors. These challenges are often exacerbated by complex and cluttered backgrounds, wide variations in object scale, and inconsistent image quality. While existing methodologies have utilized attention mechanisms to analyze spatial and temporal features, they frequently neglect the benefits of adaptive sampling and channel-wise feature adjustments, thereby failing to exploit potential spatiotemporal redundancies. This thesis introduces a two-pronged approach to address these limitations. First, we propose the Optimized-Position Network (OP-Net), a novel architecture for …

The Operational Performance Of Buildings In A Humid Subtropical Climate: Insights From Empirical Data And Assessment Models, Oluwafemi Awolesi

LSU Master's Theses

This thesis evaluates the operational performance of residential and non-residential buildings in a humid subtropical climate, emphasizing Indoor Environmental Quality (IEQ) and energy performance through empirical data and assessment models. Motivated by the increasing need for integrated sustainability evaluations, this study combines a systematic review with multi-method field investigations to advance understanding in this domain. The systematic review critically analyzed 99 case study articles, identifying dominant IEQ assessment methodologies and highlighting methodological inconsistencies that hinder cross-study comparability. Emerging computational approaches, although promising, remain underutilized. The review advocates for standardized, climate-responsive, and feedback-oriented evaluation frameworks. Field investigations assessed IEQ and energy …

Adaptive Multi-Agent Reinforcement Learning For Electric Vehicle Charging Optimization Under Dynamic Traffic Conditions, Shaghayegh Rabbanian

LSU Master's Theses

Electric vehicle (EV) charging optimization is a critical challenge in sustainable transportation. This study focuses on three fundamental questions: (1) when is the best time to charge an EV, (2) where is the optimal charging location, and (3) how should charging be planned considering navigation and routing decisions. Our primary objective is to determine the optimal time and location for EV charging while accounting for key factors such as real-time traffic conditions, spatial distribution of charging stations, and EV-specific attributes such as state of charge (SOC), driving range, and efficiency. To develop a robust and adaptive EV charging recommendation system, …

Research On Monitoring And Early Warning Systems For Mine Water: Progress And Prospects, Yin Shangxian, Ding Yingying, Lian Huiqing, Dong Donglin, Du Tong, Yin Huichao, Zhao Peng, Zhang Yi’An, Wang Xiong

Coal Geology & Exploration

Background Water disasters in mines are occasional accidents occurring due to the influence or triggered by mining-induced intense changes in geological bodies. The complexity, heterogeneity, and dynamics of geological bodies, coupled with the random and instantaneous nature of mining influence, lead to the uncertainty, instability, occasionality, and transience of water disasters in mines. Given that mine water monitoring and early warning serve as a prerequisite for advanced disaster prevention and control, their research and system construction hold great theoretical significance and practical value. Progress and Prospects Fighting against water disasters in mines is conducted throughout the development and utilization of …

Multi-Scale Color Correction And Contrast Enhancement Via Leaf In Wind Optimization For Improved Weld Defect Detection In Non- Destructive Testing, Senthil Anand N Mr

Theses and Dissertations

Image enhancement is an essential process in numerous fields, including industrial inspection, medical imaging, remote sensing, and photography, as it improves image quality for accurate analysis and interpretation. Among the advanced image enhancement techniques, Focused Super Resolution (FSR) with Self-Attention Single Candidate Optimizer-based Generative Adversarial Networks (GANs) is specifically designed for weld defect detection, while Advanced Image Enhancement through Multi-scale Color Correction and Contrast Stretching using Leaf in Wind Optimization focuses on enhancing the overall visual quality of images. Although both approaches aim to improve image quality, they differ significantly in their objectives and application areas. The FSR method concentrates …

Efficient Small Tool Detection In Construction Via Lightweight Deep Neural Networks, Maryam Soleymani

LSU Master's Theses

Construction sites are dynamic and inherently hazardous environments, where small hand tools—although essential—pose serious safety risks due to their frequent use, portability, and tendency to be misplaced or dropped. This study introduces a novel and lightweight deep learning-based architecture, Lightweight Small Tool Detection (LSTD), specifically designed for fast detection of small tools in unstructured and challenging construction environments. Recognizing that small object detection remains a persistent limitation in existing computer vision models, particularly under poor lighting or cluttered backgrounds, LSTD integrates advanced modules for enhanced feature extraction, fusion, and classification. It achieves notable improvements in accuracy, recall, and computational efficiency …

Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia

Master's Theses

Billions of people today rely on traffic predictions to optimize their travels. Digital mapping services deliver accurate predictions by learning from vast troves of historical data. Impressive as these systems are, their assumptions do not always apply. They depend on an endless flow of sensitive user data to a central authority, a stable Internet connection, and trustworthiness on both sides of the traditional client-server model. This thesis explores a novel architecture which bucks those assumptions. In the proposed model, traffic data remains on edge devices which individually train models via federated learning. Beyond the obvious privacy benefits, this architecture enables …

Structural Differential Privacy In Graph Neural Networks, Bibek Giri

Master’s Dissertations

Graph Neural Networks (GNNs) have demonstrated impressive performance across a range of graph-based learning tasks. However, their application to domains with sensitive relational data raises serious privacy concerns, as the graph structure itself may leak confidential information. This thesis investigates a decentralized framework for enforcing edge-level local di!erential privacy (LDP) in graph-structured data. We introduce two mechanisms that perturb a node’s neighborhood in a privacy-preserving yet utility-aware manner. The first approach replaces randomly selected neighbors with feature-similar nodes from the 2-hop neighborhood, ensuring structural realism while preserving degree. The second approach eliminates the need for explicit 2-hop propagation and dummy …

Influence Of Zinc Oxide Nanoparticles On The Efficiency Of Oxytetracycline Removal From Wastewater Using Continuous Catalytic Ozonation, Sarmad Al-Anssari, Hassanain A. Hassan, Maha K. Mohsin, Ahmed A. Mohammed

Research outputs 2022 to 2026

Antibiotics must be fully eliminated before they are released into the environment. In most cases, conventional wastewater treatment systems are not built to handle polar microcontaminants such as antibiotics. Oxytetracycline (OTC) is one of these antibiotics, an environmental hazard contaminant in aqueous solutions. Therefore, an advanced treatment method is needed for wastewater contaminated with antibiotics. In this study, we employed zinc oxide nanoparticles (ZnO), and the catalytic ozonation procedure was employed to increase the ozonation efficiency. A continuous experiment was carried out to compare the effectiveness of catalytic and single ozonation in degrading OTC in a continuous reactor. The flow …

Sbv_Dps: A Stacking-Bagging-Voting Nested Ensemble Based Diabetes Prediction System Using K-Fold Cross Validation, Sourabh Shastri, Sachin Kumar, Paramjit Kour, Vibhakar Mansotra

Mansoura Engineering Journal

With the advancement of machine learning techniques, the introduction of the most accurate model has become a necessity. In real-world scenarios, every model has some constraints and assimilates errors, so their performance is not always highly efficient; this sparked the development of ensemble learning. The ensemble approach aims to consolidate the strengths of existing approaches and minimize their weaknesses or decision-making risks. The proposed diabetes prediction system encases a resampling filter, applied to balance the dataset and model builder method, i.e., without the SBV ensemble and with the SBV ensemble method. The model is initially built without using the SBV …

Investigating The Impact Of Agent Openness On Planning In Multi-Agent Systems, Bala Subramanyam Duggirala

School of Computing: Dissertations, Theses, and Student Research

Multi-agent systems (MAS) possess significant potential for modeling real-world scenarios requiring coordinated actions (like wildfire fighting or ridesharing) among autonomous entities or agents (e.g., wildfire fighting agents) in complex, dynamic environments. Effective decision-theoretic planning (where each agent must carefully consider both the immediate and the future situations or states, and coordinate with the other agents (neighbors) to evaluate what needs to be done at present) within MAS, especially multiagent planning, where the planning agent directly models its neighbors in order to estimate their optimal actions, is critical, yet challenged by factors like partial observability, openness, and diverse agent types with …

Dynamic Sparsification In Secure Gradient Aggregation For Federated Learning, Bikash Samanta

Master’s Dissertations

Secure aggregation is a critical component of privacy-preserving federated learning. However, existing fixed-sparsity approaches often incur unnecessary communication overhead. We present DynamicSecAgg, a novel framework that introduces dynamic sparsity while preserving coordinate-level privacy. Our method achieves significant improvements in communication efficiency while maintaining — and in some cases improving — model accuracy across both IID and non-IID user distributions. The framework maintains information-theoretic privacy guarantees via adaptive gradient thresholding and polynomial-based aggregation, proving particularly effective under heterogeneous data settings. These results establish dynamic sparsity as a key optimization for efficient and privacy-preserving federated learning.

Cloud-Assisted Multi-Channel Data Broadcasting, Debabrata Maji

Master’s Dissertations

The convergence of Internet of Things (IoT) and cloud computing has transformed technology, impacting commerce, industrial production, data management, etc. Multi- Channel Broadcast Encryption (MCBE), first introduced by Phan et al. (ASIACCS 2013), is a cryptographic encryption primitive used for both IoT and Cloud that permits a sender to e!ciently and securely encrypt several messages for di”erent groups of receivers. After thoroughly exploring the existing literature, we observe that none achieves the robust provable security within the standard model. This paper addresses this gap, aiming to achieve adaptive INDistinguishable under full-IDentity Chosen-Ciphertext Attack (IND-ID-CCA) security by constructing an e!cient identity-based …

From Devices To The Cloud: Digital Forensics In The Changing Social Media Landscape, Joseph Brown

LSU Master's Theses

This thesis presents a comprehensive digital forensic analysis of emerging and alternative social media platforms, including Truth Social, Threads, Bluesky, Nextdoor, and Neighbors. These platforms, which range from politically aligned alt-tech networks to hyperlocal neighborhood apps, present unique forensic challenges and security vulnerabilities. Across all case studies, established forensic techniques were applied using a hybrid methodology combining mobile device analysis, network traffic monitoring, and API interrogation. Findings include the discovery of plaintext credentials, session tokens, and other sensitive artifacts, particularly in platforms with weaker security postures such as Truth Social, Bluesky, Nextdoor, and Neighbors. Threads, by contrast, demonstrated greater resilience …

Multi-Party Key Establishment For Resource-Constrained Devices, Supriyo Banerjee

Master’s Dissertations

As the number of IoT (Internet of Things) devices continues to grow, ensuring secure communication among them has become increasingly important. Traditional pairing schemes rely on centralized architectures, which are vulnerable to temporary or permanent failures due to operational malfunctions of their central hubs or gateways. To address these challenges, decentralized communication is essential. However, existing decentralized pairing schemes suffer from high pairing times and significant computational overhead. Given the diverse capabilities of IoT devices, ranging from high-performance edge devices to resource-constrained sensors, many of these schemes become impractical in real-world scenarios. Therefore, we require a lightweight pairing scheme. Our …

A Descriptive Case Study On How Suburban Texas Police Department Drone Use Conforms To Public Opinion, Terrill Brett Spell

Doctoral Dissertations and Projects

The purpose of this qualitative case study was to describe the use of drones by suburban police departments in Texas, and its central research question asked how that drone use aligns with what prior research found the public supports. The research design used a holistic multiple-case approach that involved interviewing police officers who use drones as part of their law enforcement responsibilities. Relevant theoretical frameworks were social contract theory, Packer’s theory on law enforcement, and evidence-based policing theory. Data collection involved semistructured interviews of 13 officers representing the same number of Texas suburban police departments. The data collected and analyzed …