Computational Engineering Commons^™

Replicating The Functionality Of Ghost Knifefish Cerebellar Feedback Using Synthetic Nervous Systems, Sheldon Paul Cj Johnson

Graduate Theses, Dissertations, and Problem Reports (ETD)

Sensory inputs allow animals to perceive, react, and adapt to an environment. However, the sensory information received by the body, such as visual, auditory, and proprioceptive information, could become overwhelming, thus overloading the brain. Yet, animals can process all this information by canceling redundant signals from their surroundings, allowing them to be more sensitive to novel or unexpected signals in their environment. Each species (i.e., birds, fish, mammals) has its own way of using and filtering sensory information, from auditory to locomotion adaptivity. Cerebellar circuits contribute to sensory filtering in a variety of systems. In particular, research on Ghost knifefish …

Efficient And Test-Time Adaptive Visual Object Tracking In The Wild, Ram J. Zaveri

Graduate Theses, Dissertations, and Problem Reports (ETD)

Tracking a single object, given the location at the first frame, has been an ongoing challenge in the vision community for decades. Most recent approaches provide reasonably good performance, especially when benchmarked on in-distribution (ID) datasets, i.e., on the testing portion of the same datasets used for training. However, they incur high computational costs and hardware constraints, making their deployment in the wild for mobile, autonomous, and IoT applications still challenging. Efficient visual trackers address the efficiency aspect of such bottlenecks; however, they tend to overfit to their training distributions and lack generalization abilities, resulting in them performing well on …

Virtual Environment Creation And Camera Calibration For Soft Target Identification And Assistance In Crowded Spaces With A Sensor Network And A Robotic Dog, Eltan Samoylov

Dissertations and Theses

Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.

The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

Quantum-Resilient Architectures For Enterprise And Cloud Information Systems, Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

The emergence of quantum computing presents unprecedented challenges to contemporary enterprise cybersecurity frameworks. Current cryptographic systems that protect sensitive data and secure communications will become vulnerable to quantum attacks within the next decade. This research examines the implications of quantum computing advancement for enterprise and cloud information systems, proposing quantum-resilient architectural frameworks that can withstand both classical and quantum threats. We analyze the timeline of quantum computing development, assess vulnerabilities in existing enterprise security infrastructures, and evaluate post-quantum cryptographic approaches suitable for organizational implementation. Through comparative analysis of quantum-resistant algorithms and architectural patterns, this study demonstrates that enterprises must begin …

Blockchain-Enabled Trust Frameworks For Enterprise Information Systems, Establishing Verifiable Trust In Distributed Organizational Environments, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise information systems increasingly operate in distributed environments where traditional trust mechanisms based on centralized authority prove insufficient. This research develops a comprehensive blockchain-enabled trust framework that establishes verifiable, decentralized trust mechanisms for enterprise systems operating across organizational boundaries. The study addresses critical gaps in current enterprise architectures where trust depends on centralized intermediaries, creating single points of failure and limiting inter-organizational collaboration. Through examination of existing trust models and blockchain capabilities, we propose an integrated framework that combines cryptographic verification, distributed consensus, and smart contract automation to establish trust without centralized control. Our framework enables organizations to verify data …

Mitigating Information Overload In Aviation Safety: Ai-Driven Hierarchical Tagging And Summarization Of Notam For Pre-Flight Information Bulletin, Rutong Gu, Yefeng Qu, Dongling Chen, Yu Tang, Ailin Zhou

International Journal of Aviation, Aeronautics, and Aerospace

To address the challenges of aviation safety information overload in Pre-flight Information Bulletin (PIB) systems, this study proposes an intelligent classification framework (ERNIE-DPCNN) that integrates knowledge-enhanced semantic representation with a Deep Pyramid Convolutional Network. Traditional systems relying on rule-based filtering mechanisms suffer from inefficiencies in critical information identification and high risks of human misjudgment. The proposed framework achieves breakthroughs through three technical innovations: (1) An aviation domain-adapted ERNIE model is constructed, leveraging phrase-level masking strategies to enhance semantic representation of compound identifiers; (2) A Deep Pyramid Convolutional Network (DPCNN) is designed to extract multi-granularity features via hierarchical convolution-pooling architecture, optimized …

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Hybrid Rans-Les Analysis Of Turbulent Flame Under Near Blow-Off Condition In A Multi-Stage Swirl Combustor, Brandon O'Brien

College of Graduate Studies: Theses & Dissertations

Advances in computational resources are making computational fluid dynamics (CFD) increasingly accessible for a variety of engineering applications. However, certain complex problems, such as simulating detailed flame kinetics, remain computationally prohibitive for many users. Developing cost-effective methods to simulate these challenges would greatly benefit applications involving flame dynamics, such as turbine engines or industrial burners. This study investigates the effect of main stage swirler intensity on near lean blow-off characteristics in a multi-staged swirl combustor using Ansys Fluent. The hybrid RANS-LES turbulence model, Stress Blended Eddy Simulation (SBES) coupled with Flamelet Generated Manifold (FGM) combustion model was selected to model …

Enhancing Risk And Crisis Communication With Computational Methods: A Systematic Literature Review, Madison H. Munro, Ross J. Gore, Christopher J. Lynch, Yvette D. Hastings, Ann Marie Reinhold

VMASC Publications

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational …

Methods To Improve The Computational Efficiency And Accuracy Of Second-Order Elastic Steel Frame Analyses, Nadine Faramawi

Electronic Theses and Dissertations 2020 - Present

This paper investigates the derivation and performance of new stiffness coefficients. The estimated coefficients aimed to improve the geometric stiffness matrix representation and their application in elastic second-order analysis for steel frames. The newly developed (C1-C4) coefficients incorporate non-linear effects and reduce computational efforts to efficiently enhance the accuracy of second-order analysis. These coefficients are particularly beneficial for braced structures where they allow more refined analysis using fewer elements per member, especially as the load applied to the frame approaches the critical buckling load. However, for cases of unbraced frames, using these approximated coefficients showed no significant advantages in comparison …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Fast-Sparse-Spanner: A Practical Algorithm For Constructing Low-Stretch Sparse Geometric Graphs, Fnu Shariful

UNF Graduate Theses and Dissertations

When constructing geometric graphs (vertices are points and edges are line segments connecting point pairs) on pointsets, stretch-factor (worst-case detour between any point pair) is often considered a quality metric. A low stretch-factor (a quantity that is usually > 1) guarantees short paths between all vertex pairs. A geometric graph having a stretch-factor of t is known as a t-spanner. Creating low stretch-factor geometric graphs for large pointsets with a low number of edges is an open problem in computational geometry.

In this work, we have designed and engineered a new simple and practical (fast and memory-efficient) algorithm named Fast-Sparse-Spanner algorithm …

Solar Energy Prediction Using Advanced Hybrid Machine Learning Models, Jelawi A. Alqhtani

UNF Graduate Theses and Dissertations

Accurate short-term forecasting of solar power generation is critical for the reliable and cost-effective operation of renewable-based microgrids, where sudden weather-induced variability can compromise grid stability, battery scheduling, and energy trading decisions. Traditional physical and statistical models struggle to capture the complex non-linear relationships and localized weather effects, while individual deep learning architectures often exhibit systematic biases such as chronic under-prediction of peak generation. This thesis proposes a novel Cross-Feedback Ensemble framework that combines the complementary strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1D-CNN) models through an iterative cross-feedback mechanism and a …

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Real-Time Congestion Control And Load Optimization In Cloud-Manets Using Predictive Algorithms, Preeti Rani, Mohammed Hussien Falaah

NJF Intelligent Engineering Journal

Cloud-MANET environments require a system to balance load and control congestion. As a result of integrating real-time network metrics with predictive traffic algorithms, the proposed model optimizes the management of dynamic topologies, network bandwidth constraints, and fluctuating traffic loads. In addition to energy-aware multi-path routing, the framework incorporates adaptive congestion control mechanisms to ensure data transmission is efficient and stable. This algorithm provides higher packet delivery ratios, reduces end-to-end delays, and increases throughput over existing algorithms, according to the evaluation results. Hybrid Cloud-MANET systems can benefit from this approach by optimizing resource utilization and network performance.

A Comparative Study Of Conventional And Statistically Active Corrosion Methods For Corrosion Growth Assessment Of A 24-Inch Gas Pipeline, Rudi Rinaldi, Jaka Fajar Fatriansyah

Journal of Materials Exploration and Findings

Component failures in oil and gas pipelines can have fatal consequences, leading to operational downtimes and environmental damage. Knowledge of the corrosion growth rate is fundamental to pipeline integrity management, as it is essential for risk assessment and decisions related to asset management. This article aimed to compare two approaches for the corrosion growth estimation of the 24-inch offshore gas pipeline: the conventional method versus the Statistically Active Corrosion (SAC) method. This article is based on the in-line inspection (ILI) results of two consecutive assessments from 2020 to 2023 of the entire 73 km of the pipeline. The results show …

Quantitative Analysis Of Machine Learning Model Performance And The Need To Consider Explainability, Vishnu S. Pendyala

Open Educational Resources

This presentation, titled "Quantitative analysis of Machine Learning model performance and the need to consider explainability," delves into various metrics used for evaluating machine learning models. It thoroughly examines fundamental classification metrics like accuracy, precision, recall, and F-score, while also discussing more advanced measures such as the Kappa Statistic and Matthews Correlation Coefficient (MCC), particularly highlighting their relevance in scenarios with imbalanced datasets. The presentation underscores the importance of model accuracy in real-world applications and briefly introduces regression metrics like R-squared and F-statistic. Additionally, it addresses challenges related to data imbalance and fairness in ML models, stressing the critical need …

Hidden Markov Model For Identifying Local Variants In Human Genomes Using Simulated Data, Scott Mccallum

Electronic Theses and Dissertations

Identifying adaptive mutations in genetic data is challenging due to the low frequency of occurrence of such events, and because signatures of selection are intertwined with the footprints of various other evolutionary forces that shape our genomes. Even when a larger region appears to be under selection, genomic sites that are linked to adaptive mutations have similar statistical signals, and thus can obfuscate the identification of the actual adaptive mutation. The new method described here uses a Hidden Markov Model that allows for classification of neutral, linked, and sweep (adaptive mutation) genomic sites. This model is general and can be …