Computer Engineering Commons^™

Ultragps: A Low-Cost, Open-Source Ultrasonic Positioning System, Scott Roelker Murillo, Nnamdi Jesse Onwuzurike

Posters - 2026

We want to raise the bar in high school robotics. In Texas, and likely in many other states as well, high school robotics has reached a roadblock when it comes to autonomous navigation. In many competitions, the autonomous portion sees few, teams successfully completing tasks that require positioning and guidance. In modern robotics, it is no longer sufficient for a robot merely be “remote controlled.” They need to be able to navigate independently and adapt to the environment around them. To achieve this goal a positioning system is needed to develop the foundational algorithms for autonomous controls. However, these systems …

Machine Learning For Real-Time Body Movement Classification Using Eeg And Vr Technologies, Aiden H. Behler, Robin Ghosh

Undergraduate Research

This project investigates the feasibility of real-time full-body movement classification using electroencephalography (EEG) integrated with virtual reality (VR) technologies. The primary objective is to develop and evaluate machine learning models for predicting human body movements using EEG data alone, with the long-term goal of reducing or eliminating reliance on wearable motion trackers. Currently, several machine learning algorithms have been tested, but classification accuracy remains modest, indicating the complexity of the task. Ongoing work focuses on optimizing preprocessing, feature selection, and model architectures to improve performance. The system architecture combines synchronized neural and motion data collected within a VR environment. EEG …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Behavioral-Centric Team Evaluation Via Consistent Rewards, Clement Kudakwashe Nyanhongo

Dartmouth College Ph.D Dissertations

Across human domains ranging from sports to business and organizational settings, complex tasks are often solved by teams rather than individuals, leveraging benefits such as interaction, mutual support, complementary skills, cohesion, and task allocation. Evaluating team effectiveness, however, is inherently challenging due to the subjectivity of many existing techniques and the limitations of outcome-driven metrics that primarily focus on performance scores while overlooking the team processes that generated the scores. To address these challenges, this dissertation proposes a behavioral-centric, end-to-end framework for team evaluation grounded in reward functions that model sequential team behavior. Reward functions offer compact and interpretable representations …

Tuning And Performance Of Pid Controlled Low Complexity Systems, Timothy Evans

Honors Theses

Proportional integral derivative (PID) controllers are used for precise position and orientation control in systems such as autonomous underwater vehicles (AUVs). This project supports the University of Southern Mississippi’s (USM) Robotics Club’s RoboSub AUV effort by developing, troubleshooting, and manually tuning PID controllers to characterize tracking performance and settling time across systems of increasing complexity. Initially, the project hypothesized that tracking performance would be reduced and settling times would increase as system complexity advanced from one degree-of-freedom (DOF) to two DOF. However, prior research was found that suggests that for small disturbances around an equilibrium state, separate PID-controlled DOFs can …

Research Days: Case Study: Creation Of A Student-Driven Radio Talk Show To Answer Cybersecurity Questions And Concerns To Boost Power Skills, Joel Leiva, Anthony Bayate, David Abiandu, Keith Fernandez

Center for Cybersecurity

Power skills are essential in any professional career. Oftentimes, college students don’t feel prepared enough to enter the workforce. Having good power skills in a group can greatly increase production and efficiency. This case study aims to develop these power skills in a group of college students through the creation of a student-driven radio talk show answering cybersecurity questions and concerns. A qualitative approach was used via the creation of the C.Y.B.E.R. radio show. This show enhanced the participants’ power skills such as collaboration, teamwork, and communication skills. The findings from this case study prove the alternate hypothesis of boosting …

Adopting Zero Trust Security In Cloud: A Comparative Study, Landy Jimenez

Center for Cybersecurity

Adopting Zero Trust Security in Cloud: A Comparative StudyLandy Jimenez, Dr. Jiaxin LeiDepartment of Computer Science & Technology, Kean UniversityAbstract:As organizations transition to cloud-native environments, ensuring security across distributed systems has become more difficult. Modern cyberthreats like insider breaches and lateral movement attacks have shown that traditional perimeter-based security strategies, which rely on implicit trust within internal networks, are inadequate. Zero Trust Architecture (ZTA) addresses these challenges by requiring continuous authentication, authorization, and encryption for every access request, regardless of network location. However, cloud native Zero Trust presents concerns about scalability, latency, and resource overhead.This study evaluates Zero Trust at …

The Impact Of Ai Ethics Education On Student Engagement And Ethical Perspectives, Diana Medina

Center for Cybersecurity

Artificial Intelligence (AI) has become a cornerstone of technological innovation. The world has come to see the many advancements AI has to offer and the impact it has on everyday life. The benefits of AI are promising, and institutions are learning how to implement AI to further advance productivity and efficiency. However, AI-based products may produce harmful or unjust consequences, especially when ethical considerations are not deliberated during the developmental stages. This study investigates student engagement and examines the impact in infusing ethical reasoning in AI education. With five participating computer science professors and two historians, ethics modules were introduced …

Stamp-V: Steganographic Traceability For ​ Ai-Generated Images With Multimodal Verification, Xinlei Guan, David Arosema, Tejaswi Dhandu, Meng Xu, Kuan Huang, Tida Umamheswara Rao, Bingya Shen

Center for Cybersecurity

The rapid growth of generative AI has intensified challenges in content moderation and digital forensics, particularly when benign AI-generated images are paired with harmful or misleading text. This contextual misuse undermines traditional moderation systems and complicates attribution, as synthetic images typically lack persistent metadata or device signatures. We introduce STAMP-V, a steganography-enabled provenance framework that embeds cryptographically signed identifiers into images at creation time and verifies provenance through multimodal harmful content detection. Our system evaluates five watermarking methods across spatial, frequency, and wavelet domains, and integrates a CLIP-based fusion model that performs multimodal harmful-content detection as part of the provenance …

A Software-Defined Radio Testbed For Mimo-Ofdm Communications, Chaz G. Maschman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we implement a testbed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems via GNU Radio. Specifically, we implement a configurable framework for the construction of MIMO-OFDM software-defined radio (SDR) systems as a GNU Radio module. The GNU Radio MIMO-OFDM module consists of multiple algorithmic blocks necessary for implementation of a MIMO-OFDM system. This includes a library for the generation of orthogonal or pseudo-random pilot sequences, amendments to the Schmidl-Cox protocol for MIMO synchronization, and the creation of click-and-drag GNU Radio blocks implementing the conversion of arbitrary data sent via external programs to MIMO-OFDM frames, the initial …

Using Ai To Predict Energy Expenditure In Lower Limb Prosthesis Users, Nelly Diaz, Siem Hadish

Posters - 2026

• Computer vision has evolved from simple image classification and object detection to analyzing human motion and biomechanics (1). • CNN’s are usually focused on image classification, but, in this case, we are not asking the model if a person is walking. • Many real-world problems require regression: Predicting a continuous number like energy expenditure of walking is a complex task. • It is essential for Prosthetists to understand energy expenditure of their prosthetic patients (2). • An amputee may use 20-30% more energy to walk. • In this project, we developed an AI model to analyze human motion and …

Bio-Payload Senior Design Report, Connor Bishop, Oliver Goodall, Marcus Kirk, Gabby Robles, Anish Shanmuganathan

Interdisciplinary Design Senior Theses

This project focuses on the design and development of an autonomous experimental platform capable of conducting cellular biology experiments in a well plate within a CubeSat environment. The system integrates microfluidics, robotics, and onboard sensors to remotely initiate experiments, monitor them, and collect data without human intervention. The objective is to create a platform for automated biological experimentation in microgravity, while reducing reliance on ground-based control and increasing mission efficiency and reproducibility. The team used Saccharomyces cerevisiae to assess the biocompatibility of the well plate and monitor changes in cell culture, including optical density and cell viability.

Active Listening And Reassurance In Text-Based Virtual Health Coaches, Ghulam Hussain

Dissertations

Conversational agents (CAs) have strong potential to support health and physical wellbeing through text-based coaching, but they remain limited in their ability to demonstrate supportive social behaviours that are important in human coaching interactions. In particular, relatively little is known about how Active Listening and Reassurance are perceived, modelled, and evaluated in text-based virtual healthcare coaching, or how such behaviours should be adapted to individual users.

This dissertation investigates how supportive interaction behaviours can enhance text based virtual health coaching, with an initial focus on Active Listening and Reassurance and a later theoretical emphasis on Active Listening. Across five unique …

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Drones Detection Via Skeletonization And Small-Object-Aware Detr, Gissell Torres, Delio Rincon

Center for Cybersecurity

Detecting drones in video streaming environments remains challenging in computer vision due to scale variation and background complexity. Building up from prior work in real-time detection and skeletonization for streaming environments, this study aims to improve small object detection through Skeletonization and a Small-Object-Aware Detection Transformer framework, which uses DETR technology as a foundational step toward reliable motion prediction in dynamic aerial scenes. A transformer-based detection model was trained on a drone dataset converted to COCO format and evaluated using standard COCO metrics, including AP, AP50, and AP_small. Initial testing revealed low-confidence predictions, suggesting limitations in backbone freezing and training …

An Integrated Bayesian Network-Based Zero Trust Model To Quantify Cyber Risk In Small-Medium Businesses, Ahmed Abdelmagid

Engineering Management & Systems Engineering Theses & Dissertations

Small-medium businesses (SMBs) play a pivotal role in the worldwide economy as they constitute the most considerable portion of businesses in developed countries like the UK and the US. As such, SMBs are likely targets of cybercrimes by malicious agents because of their vulnerable IT systems. The digital infrastructure of SMBs is more likely to be hit by cyberattacks than large businesses due to many factors that facilitate hackers’ missions. These factors include a limited financial budget devoted to cybersecurity, a lack of knowledge, an underrating of how dangerous cyber threats are, and a shortage of IT expertise. The enormous …

Optimizing Auv Perception For Competitive Underwater Robotics: Analyzing Performance And Simplicity Of Machine Vision Hardware, Joseph King

Honors Theses

Underwater robotics is a growing field with many practical applications, such as pipeline and offshore structure monitoring, deep sea mining, mine reconnaissance/removal, and marine environmental monitoring. USM’s Robotics Club will participate in the international RoboSub competition, where student-led teams build autonomous underwater vehicles (AUVs) that perform a variety of tasks within a pool requiring camera-based object detection. This project analyzes affordable hardware and software options for providing machine vision to USM’s future RoboSub AUV. The performance of an ESP32-CAM microcontroller, Raspberry Pi 5, and Jetson Orin Nano single-board computers running FOMO and YOLO object detection models was compared. These models …

2026 (Spring) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the spring of 2026.

How Do Social Network Models Compare To All-To-All Models For Forecasting Tuberculosis Epidemics? A Mathematical Modeling Study, Masabho Peter Milali, Hae-Young Kim, George Corliss, Anna Bershteyn

Electrical and Computer Engineering Faculty Research and Publications

Background. Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions.

Methods. We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention.

Results. Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed …

On The Extreme Complexity Of Certain Nearly Regular Graphs, Gregory P. Constantine, Gregory C. Magda

Theory & Applications of Graphs

The complexity of a graph is the number of its labeled spanning trees. It is demonstrated that the seven known triangle-free strongly regular graphs are graphs of maximal complexity among all graphs of the same order and degree; their complements are shown to be of minimal complexity. A generalization to nearly regular graphs with two distinct eigenvalues of the Laplacian is presented. Conjectures and applications of these results to biological problems on neuronal activity are described.

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan

LSU Master's Theses

Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Virtual Humans In Virtual Reality: A Scoping Review On Sociability, Fidelity, And Expression, J K Sangeeth Chandran, Marisa Llorens Salvador, Cathy Ennis

Articles

Introduction:

Virtual reality (VR) systems have evolved significantly over the past decade, enabling immersive experiences with enhanced realism and interactivity. This has motivated an interest in socially oriented applications. As user proxies, Virtual Humans (VHs) play essential roles in such applications. However, despite technological advancements, achieving realistic, expressive, and socially responsive VHs continues to present design and implementation challenges. In this scoping review, we present the state-of-the-art of VR VHs, examining the impact of VHs on the user experience.

Methodology:

We reviewed 59 papers retrieved from five databases across three core themes: the implementation and impact of VH facial expressions, …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Influence Of Gender-Specific Data Imbalance On Scgpt Fine-Tuning For Single-Cell Genomics, Mohammad Aman Ullah Al Amin, Daniil Filienko, Hong Qin

Knowledge and Creativity Expo

The transformer-based foundation model scGPT has demonstrated strong capabilities in analyzing high-dimensional single-cell RNA sequencing data. However, the impact of demographic factors, particularly gender, on model performance remains insufficiently understood. Gender is known to influence cell-type compositions in the immune system. Here, using the gender-sensitive cell-type composition in immune system, we comprehensively evaluated how the gender-sensitive imbalance of training data influences the performance of scGPT in cell-type predictions. We fine-tuned scGPT on male-only, female-only, and mixed-gender subsets from two large-scale datasets containing immune cells. We used a logit difference to measure the confidence gap between the true label and the …

The Writing On The Wall: The Rise Of Applied Ai And The Life-Or-Death Choice Every Ceo Must Make Now, Gary Sheng, Ron Roberts

Digital Laboratory: Publisher of Internet Journal

Applied AI is putting AI to its highest and best use: running your organization as autonomously as possible so you can deliver value for humanity while maximizing the scale of the value. The economy is splitting. Organizations that adopt applied AI are expanding their capacity, accelerating their impact, and pulling ahead. Those that don't are quietly becoming irrelevant, not because they're doing bad work, but because the gap between what they can do and what the moment requires is widening every day. This is not a technology question. It is a leadership question. This paper is written for organizational leaders, …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …