Computer Engineering Commons^™

Deep Learning In Eye-Tracking Biomarkers For Anomaly Detection, Jay Rajesh

College of Engineering Summer Undergraduate Research Program

This research project proposes the development of a novel, data-driven framework for detecting concussions using machine learning (ML) and deep learning (DL) models applied to high-resolution eye-tracking data. Unlike traditional concussion assessments that rely on subjective evaluations, this work seeks to identify objective, quantifiable biomarkers derived from ocular dynamics—such as saccadic velocity, smooth pursuit accuracy, and pupillary response—captured through advanced eye-tracking technology. The project will explore state-of-the-art feature extraction techniques and predictive modeling approaches to uncover subtle neuro-ocular signatures associated with mild traumatic brain injury. By combining principles from biomedical signal processing, artificial intelligence, and neurophysiology, this work advances current …

Relational Algebra Interpreter, Sydney Lynch

College of Engineering Summer Undergraduate Research Program

We would like to build a compiler for relational algebra that converts it to SQL. This compiler will be used in database labs to give the students hands-on experience to write relational algebra code. Current relational algebra compilers that are open source are not very good and are hard for the students to use.

Hpc Configuration And Automated System Administration, Matty Witt, Deep Singh, Christopher Imirian

College of Engineering Summer Undergraduate Research Program

With the exponential growth in the use of computing, there is a growing need for undergraduate students to enter the workforce with experience with more complicated computing architectures. The CFD research group in the Aerospace Engineering Department received an HPC system and related computing hardware through the Air Force Research Lab. This system consists of three components: (1) a cluster compute engine with 256 CPU cores, 3.2 TB of RAM, 4 Tesla A100 GPUs, and 200 Gbps InfiniBand network backplane; (2) a high performance storage platform with 540 TB of raw storage, 200 Gbps InfiniBand network, and BeeGFS parallel cluster …

Hand-Tracking And Extended Reality Interfa, Alexander Bloomer, Alberto Cornejo

College of Engineering Summer Undergraduate Research Program

This project explores the use of Extended Reality (XR) technologies to enhance human- robot interaction in industrial contexts. Building upon prior research in affective and cognitive state recognition during human-cobot collaboration, this study investigates how natural hand and head gestures, captured through Meta Quest passthrough mode, can be used to communicate human intent to a Universal Robotics e-Series collaborative robot. The XR system provides users with an immersive, real-world visual interface while tracking motion and position in real time. The captured gestures are interpreted through a custom software pipeline that integrates machine learning models and rule-based logic to trigger adaptive …

Romance In Games, Camila Yermin

College of Engineering Summer Undergraduate Research Program

“Romance” is a common genre of books, especially among women, but in games it has a complex reputation: often relegated to visual novels or side mechanics in life simulations or role-playing games. These games rely on heavily scripted interactions, pre-written scenes, or represent relationships as a simple binary in code. This project aims to use cutting-edge embodied agent research and drama management research to build more inclusive, robust, satisfying, and interactive romance game mechanics. The project will likely be in 2D and use the Godot game engine, though the students involved will have the freedom to influence the design decisions …

Ai Fact-Checking Claims In Videos, Jake Altieri

College of Engineering Summer Undergraduate Research Program

This project investigates the use of acoustic signals captured during Fused Deposition Modeling (FDM) 3D printing to predict part quality and detect process anomalies. Traditional quality monitoring in FDM often relies on visual inspection or post-process evaluation, which can be slow and inconsistent. This research explores a low-cost, non-contact alternative using microphones and accelerometers to capture real-time audio and vibration signatures of the printing process. By applying signal processing and machine learning techniques to these acoustic signals, the project aims to classify part quality and identify defects such as under-extrusion, layer misalignment, or nozzle clogging. The outcomes have potential applications …

Computational Investigation Of The Role Of 3d Genome Architecture In The Lifecycle Of The Malaria Parasite, Srish Maulik

College of Engineering Summer Undergraduate Research Program

Malaria, a mosquito-borne infectious disease caused by the Plasmodium parasite, is responsible for more than a half a million deaths per year, the vast majority of which occur in central Africa. The parasite undergoes an incredibly complex cell molecular transformation as it transitions from living in mosquitoes to living in humans with different sets of genes being activated or silenced in order to evade the immune system of the host. Understanding how its genome guides this transition is critical for developing adequate treatments. In this project, we aim to develop a computational framework for investigating the role of the three …

Deterministic Motion Planning For Highly Articulated Multi-Link Robots, Matthew Flynn

College of Engineering Summer Undergraduate Research Program

Slender, multi-link, highly articulated, and extensible robots designed for minimally invasive surgeries have the potential to significantly transform the performance of common medical procedures. These advanced robots can reduce uncertainties and risks associated with surgeries, leading to shorter patient recovery times, accelerated healing, and minimized scarring. Made possible by their numerous mechanical linkages and concentric mechanisms, these multi-link articulated robots can navigate along non-linear paths, a capability that traditional straight probes lack. This flexibility allows surgeons to perform minimally invasive procedures on clinically significant targets that were previously difficult or impossible to access while avoiding vital anatomical structures. Beyond their …

Math+Cs Integrated Curriculum For K-12 Computer Science Education, Amogh Arora

College of Engineering Summer Undergraduate Research Program

This is a proposal for an activity to initiate an effort to create a series of X+CS integrated curricula for learning Computer Science (CS) in K-12. As a start, we examine Mathematics and CS standards, to find the cross-cutting concepts between the two fields. By leveraging these concepts, we bring to the foreground the ways CS can be used in the mathematical context. The goal for this research is to create a 15-week teacher training curriculum that will expose teachers to the CS concepts of Abstraction, Data Representation, Problem Comprehension and Decomposition, Control Structures, Functions and Generalization. Historically, Mathematics and …

Analysis Of Student Inclusivity In Computing Education: Eeg-Based Prediction Of Student Belonging, Alec Odell

College of Engineering Summer Undergraduate Research Program

This project focuses on the use of Natural Language Processing (NLP) techniques to analyze text stimuli used in cognitive research. Specifically, the project involves analyzing text that presents different types of mindsets, such as growth and fixed mindsets, to understand their impact on cognitive state. Students will apply various NLP methods, such as tokenization, text classification, and sentiment analysis, to analyze the language used in different types of mindset stimuli. The goal is to understand how text-based stimuli can influence cognitive responses and to extract meaningful features from the text that can be used to predict outcomes like engagement or …

Analyzing Privacy And Usability Tradeoffs In Multi-Party Relay Systems, Jess Alencaster, Leticia Leon-Rodriguez

College of Engineering Summer Undergraduate Research Program

Nearly everything we do on the Internet leaves a trace, and in recent decades the value of user data has proven to be highly profitable and become a fundamental business strategy of the Internet. The only recourse users have in this situation is seeking increased privacy, yet privacy is uniquely challenging on the Internet because we inherently rely on others (e.g., ISPs, content providers, CDNs) to carry and serve our traffic. Recent systems have sought to enhance user privacy without sacrificing performance by adopting Multi-Party Relay (MPR) architectures, including Apple's iCloud Private Relay. These architectures mask user IP addresses by …

A Study In Object Detection And Classification Performance By Sensing Modality For Autonomous Surface Vessels, Daniel Lane

Doctoral Dissertations and Master's Theses

This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the …

Exploration Of Physics-Informed Grid Generation Technique For Wall-Modeled Les Using Eagle3d, Dominic Schneider

Doctoral Dissertations and Master's Theses

Wall-Modeled Large Eddy Simulation (WMLES) is an area of interest due to its ability to lower computational costs of LES. Even with the application of wall models, LES still proves to have practicality issues when it comes to use in industry, due to the expertise, time, and computational resources required. A novel technique for generating a lean, physics based WMLES grid is described.

The technique utilizes a RANS solution to extract turbulence information, user-specified values related to resolution of turbulent energy levels, acoustics waves, and shock waves, to generate a point cloud for producing a lean WMLES grid with in-house …

Threshold Symmetric-Key Encryption For Tabular Data, Subhendu Pramanick

Master’s Dissertations

Abstract Through the distribution of secret key information among several parties, threshold cryptography improves the security of cryptographic systems by preventing any one entity from possessing the entire secret key and requiring a threshold number of participants to carry out cryptographic operations. This paradigm not only mitigates single points of failure but also ensures fault tolerance in the presence of compromised or unavailable parties. The Distributed Symmetric-key Encryption (DiSE) framework, introduced by Agrawal et al., realizes Threshold Symmetric-key Encryption (TSE) by requiring interactive participation from a threshold subset of servers for each encryption or decryption operation. While DiSE and similar …

2025 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2025.

Deep Learning-Based Change Detection In High-Resolution Remote Sensing Imagery, Hazem Badawy

Theses and Dissertations

Remote sensing has become a key tool for monitoring Earth’s surface over time, offering valuable insights into both natural and human-driven changes. Among its many applications, change detection focuses on analyzing multi-temporal imagery to reveal how specific areas evolve across different time periods. It plays a pivotal role in Earth observation applications, including urban development monitoring, environmental degradation assessment, and disaster response. However, existing approaches often struggle with limited contextual awareness, high sensitivity to noise, and imprecise localization of change boundaries, especially with high-resolution imagery. This thesis investigates the complex problem of change detection in remote sensing imagery by proposing …

Impacto De La Inteligencia Artificial En La Educación Superior. Guía Reflexiva, Jairo Eduardo Márquez Díaz

Ingeniería

La inteligencia artificial (IA) está revolucionando la educación superior en diversas formas como, por ejemplo, la personalización del aprendizaje, la creación de tutorías inteligentes y el análisis de aprendizaje. Este libro se presenta como una herramienta valiosa para todos aquellos interesados en comprender y aprovechar las oportunidades que la ia ofrece en el campo de la educación superior. Con un enfoque equilibrado y exhaustivo, esta publicación pretende servir como una guía integral para profesores y estudiantes que buscan entender cómo la ia está transformando la enseñanza y el aprendizaje en la actualidad. A lo largo de sus páginas, aborda diversos …

Use Matters: How Different Ways Of Using Chatgpt Drive Ai Acceptance And Solutionism, Florian Golo Flaßhoff, Fabian Anicker, Frank Marcinkowski

Human-Machine Communication

Artificial intelligence is central to solutionism—the vision of a world where all major problems are solved through technology. This study theorizes about how human–AI communication shapes attitudes toward AI and influences the formation of public opinion, sparking solutionist imaginaries. We empirically examine the attitude formation resulting from the non-simulated use of an unmanipulated conversational model in a controlled laboratory experiment. Using a between-subjects design, participants engaged in three semi-structured 20-minute sessions with ChatGPT, providing a novel perspective on the effects of its use. The findings reveal that mere use of ChatGPT causally increases AI acceptance; however, its impact significantly depends …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Retracted: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Abbas M. Ahmed, Tarik A. Rashid

Iraqi Journal for Computer Science and Mathematics

This study proposes a more effective concept of Learner Performance-based Behavior (LPB). It is a new metaheuristic algorithm based on how the university admission process is done for high school students in various departments. The adaptive crossover and mutation methods were incorporated into the LPB algorithm as part of an investigation. The goal is to enhance convergence and significantly improve the quality of the solutions. The aLPB (adaptive-learner performance-based Behaviour) method stands out because it sets the crossover and mutation parameters based on the performance of the parent solutions. Thus, the proposed technique achieves a balance between exploration and exploitation …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Multi Features Data Clustering Using Novel Statistical Method With Application To Color Images, Husham Y. A. Alameen, Ali Karah Bash

Iraqi Journal for Computer Science and Mathematics

The efficiency and performance of the color image clustering algorithms are determined by various factors, including accuracy, data size, speed, and reliability (the absence of randomness in the results). Some applications, like microscopes analyzing images of biological objects or telescopes observing planetary motion prioritize accuracy over execution time. In contrast, surveillance cameras and moving object tracking prioritize speed and reliability over accuracy. This study introduces a novel algorithm that balances these four factors by clustering data with multiple features linked through specific relationships. The proposed algorithm has been practically applied to RGB color images. Traditional clustering methods, such as K-means, …

A New Approach For Multiprocessor System-On-Chip Application Scheduling In Multi-Objective Flow Shops, Tahani Jabbar Khraibet, Bayda Atiya Kalaf, Ahmed Abbas Jasim

Iraqi Journal for Computer Science and Mathematics

The flow shop scheduling problem in Multiprocessor-System-on-Chip (MPSoC) architectures presents challenges for traditional optimization algorithms, especially when addressing multiple conflicting objectives. Hence, advanced optimization approaches are required to tackle these objectives simultaneously. Therefore, this research aims to propose and evaluate a new optimization approach based on the integration of the Fire Hawk Optimizer with the Smart Battery Scheduling Algorithm (FHO-SBSA) to address the multi-objective (make span, CPU time, global average delay, network throughput, and total energy consumption) flow shop scheduling problem in MPSoC systems. To evaluate the performance of the FHO-SBSA optimization approach, two benchmark applications were selected, with ten …

Face Off: Evaluating Virtual Human Expressions And Non-Tracking Control Methods In Vr, J K Sangeeth Chandran, Marisa Llorens Salvador, Cathy Ennis

Conference papers

Social virtual reality (VR) applications have become more ubiquitous in recent years; central to this is the communication pipeline, how users perceive virtual human facial expressions, and how they control them in real time, especially when using VR devices without face-tracking. We investigated both aspects in a set of experiments. Firstly, we compared the perception of virtual human emotions on a traditional 2D screen and in VR. In a second experiment, we used a validated set of stimuli to compare three different control methods for manipulating an avatar’s facial expressions in VR. These control methods utilize non-tracking control techniques, which …

Cyberbullying Defensive Strategy In Social Media Sessions Via Machine Learning And Cyber Deception, Mohammad Shafiqul Islam

Masters Theses

Cyberbullying poses a significant challenge on social media, where traditional detection systems struggle with the nuanced and dynamic nature of online abuse. This thesis proposes an integrated framework that combines large language model (LLM)-based detection with generative decoy responses to enable real-time protection for victims on platforms like Instagram and WhatsApp. Using models such as Mistral 7B, GPT-3.5 Turbo, and Phi-3 Mini, prompt-based one-shot learning achieved 87% detection accuracy and a 5% F1-score improvement over zero-shot approaches, demonstrating robust identification of text-based bullying. A novel synthetic dataset of 98 multi-turn conversations, designed with diverse subtypes and evaluated for realism, addressed …

Optimization Of Multi-Target Interception Scheme Based On Performance Simulation Modeling, Hanwen Liu, Zhimin Zhuo, Xue Yang

Journal of System Simulation

Abstract: The air attack scenarios faced by air defense weapons and equipment show the trend of saturation, diversification and intelligence. It is very important to establish multi-target interception efficiency model and optimize interception scheme according to simulation. The current intercepting efficiency index mainly considers the whole operation process, and can not guide the optimization of the intercepting scheme of specific intercepting rounds. The generation of interception schemes mainly relies on experience and simple mathematical model, which is difficult to cope with the increasingly complex and changeable battlefield environment. Therefore, an interception scheme advantage index that comprehensively considers interception probability and …

Algorithm Simulation Of Multi-Targets Track Correlation Based On Spectral Feature, Zhenping Ding, Huidong Guo

Journal of System Simulation

Abstract: In order to solve the problems of multi-targets track correlation in dense scenes, a method of track sequential real-time processing for multi-source track correlation system modeling is proposed. By calculating the absolute and relative position of the spectral features, the unified correlation matrix can be defined based on fuzzy decision theory, and the multi-target track correlation can be realized. Numerical simulations have shown the effectiveness of the track correlation algorithm on the basis of spectral features. Especially, the accuracy of correlation is much larger than that of the nearest-neighbor distance algorithm under the condition of dense target environment …

Second-Order Cone Optimization Modeling And Simulation For Three-Phase Unbalanced Active Distribution Networks, Yiran Zhao, Yong Xue, Haoxin Tian, Ruixin Zhang, Zhi Zhang, Yanbo Chen

Journal of System Simulation

Abstract: Guided by the carbon peaking and carbon neutrality goals, and propelled by the development of new type power systems, the significance of distribution networks as key energy infrastructure has been increasingly underscored. Amidst the burgeoning rise of distributed photovoltaics, electric vehicles, and novel energy storage technologies, distribution networks are transitioning from passive entities to active systems capable of bidirectional interaction, heralding the advent of active distribution networks with a critical mission. This research tackles the optimal power flow issue in three-phase unbalanced active distribution networks, incorporating inter-phase coupling relationships. By employing dimensionality lifting and rank relaxation, along with the …

Design And Prediction Of Deep Fuzzy Neural Network, Chengbiao Wei, Taoyan Zhao, Jiangtao Cao, Ping Li

Journal of System Simulation

Abstract: A deep fuzzy neural network (DFNN) is proposed to solve the problem that the deep neural network has poor interpretability and the correction of the model is not targeted when dealing with the big data regression prediction problem. The proposed deep fuzzy neural network adopts an adaptive fuzzy Cmeans (AFCM) clustering algorithm in structural learning. The structure of the model, namely the number of rules and the antecedent parameters of the rules, is determined by calculating the introduced validity function. The identification of consequent parameters uses an improved grey wolf optimization (IGWO) algorithm. By replacing the linear decreasing strategy …

Research On Real-Time Cgf Maneuvering State Generation Method Based On Random Finite Set, Xiaoyan Zhang, Ge Li, Peng Wang

Journal of System Simulation

Abstract: With the rapid development of sensor networks and other technologies, the acquisition of measurement data in the real physical space has become easier. How to utilize the measurement data from the real battlefield space to improve the accuracy and credibility of CGF simulation is the key issue to realize the CGF simulation combining virtual and real. The method is studied of using real measurement data to generate CGF model maneuvering state data in real time, in order to realize the virtual-real synchronization and real-time mapping between the real battlefield and CGF simulation system, and to provide environmental inputs for …