Computer Engineering Commons^™

Mozgus, Damian Cerda, Madison Lopez

Computer Science and Software Engineering

The indie game market is flooded with genre experiments, yet few successfully combine fast-paced action with meaningful strategic decision-making. Our project aims to fill this gap by creating a game that fuses top-down action combat with resource-management tycoon mechanics. We found that in many games, the management phases lack mechanical stakes. Our goal was to intertwine these systems so that choices made in one phase meaningfully impact the other.

Reinforcement Learning Based Security Schemes For Distributed Ai Systems, Ashan Chamath Gunawardena

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Distributed machine learning (DML) is a component of modern intelligent systems, enabling collaborative training across devices such as mobile clients, vehicles, and edge networks. However, the decentralized nature of these systems introduces vulnerabilities, particularly data poisoning attacks that compromise model integrity and degrade performance. Traditional defenses, such as statistical filtering, robust aggregation, and privacy-preserving techniques, often struggle to adapt to overwhelming adversaries or operate under strict privacy and real-time constraints. This dissertation proposes the use of reinforcement learning (RL) and deep reinforcement learning (DRL) based misbehavior detection schemes that dynamically identify poisoning attempts in distributed AI systems, including federated learning, …

Advancement Of Wearable Hardware & Possible Cross-Sector Applications, Yassine Chahid, Patrick Slattery

Publications and Research

This study examines recent advances in wearable technologies including smart glasses, watches, rings, and related devices. It evaluates their applications across healthcare, manufacturing, education, and logistics. As real-time data collection, hands-free interaction, and continuous health monitoring become more prevalent, wearables are emerging as pivotal tools for both personal and professional contexts. The methodology combines a comparative analysis of current-generation devices, a review of technical specifications from manufacturer documentation, and case studies of specialized deployments, particularly in medical diagnostics, remote monitoring, and workplace efficiency. Project findings will indicate the rate at which wearable devices are moving beyond consumer fitness tracking and …

Design And Testing Of A Vr Escape Room Game For Philippine Martial Law History, Eric Cesar Vidal, Jr., Johanna Marion R. Torres, Jesus Alvaro Pato, Kenneth King L. Ko

Department of Information Systems & Computer Science Faculty Publications

This paper presents a Virtual Reality Educational Escape Room game where players learn about the highly divisive Martial Law period in Philippine history. We describe the game’s general design and the results of a user test to evaluate the game in terms of VR presence, immersion, and overall usability.

User Evaluation Of A Virtual Patient For Philippine Medical Education, Ma. Mercedes T. Rodrigo, Samantha Castaneda, James Alvir Maclin V. Alaan, Paolo Santino P. Caoile

Department of Information Systems & Computer Science Faculty Publications

Caladrius is a virtual patient system designed for use in Philippine medical schools. It responds to the need for medical interview training, for greater variety in teaching-learning strategies, and for culturally appropriate technologies. It has two versions: a text-only version whose interface is similar to a chat interface, and an audio version that accepts speech input and responds with speech output. In a prior test of Caladrius, users requested improved audio response time and the inclusion of different patient personalities. A subsequent version of Caladrius was created to comply with these requests. As much of the lag was attributable to …

Property Management System, Abbas Kurnool

Electronic Theses, Projects, and Dissertations

The real estate industry generates and manages large amounts of data, including tenant information, lease agreements, property maintenance schedules, and financial transactions. Reliance on traditional manual methods often results in inefficiencies, fragmented data, and delays in decision-making. To overcome these challenges, this project presents the design and implementation of a Real Estate Property Management System (REMS) for Future Properties, a company aiming to optimize its operations through digital transformation.

The proposed system is developed on Microsoft Dynamics 365 as the core platform, integrated with the Microsoft Power Platform tools (Power Apps, Power Automate, and Power Pages). This integrated framework provides …

Multi-Modal Data-Efficient Learning For 3d Machine Vision, Zhimin Chen

All Dissertations

The rapid progress of 3D computer vision has enabled a wide range of applications in autonomous driving, robotics, and augmented reality. Despite this growth, training robust 3D perception models remains challenging due to limited labeled data, the complexity of integrating multiple modalities, and the inherently imbalanced and long-tailed nature of 3D datasets. This dissertation addresses these challenges by proposing data-efficient, multi-modal learning frameworks that improve the accuracy, generalization, and scalability of 3D scene understanding.

In the semi-supervised setting, this work presents novel approaches that combine limited annotations with large amounts of unlabeled data to enhance 3D object classification and retrieval. …

Expanderizing Higher-Order Random Walks, Vedat Levi Alev, Shravas Rao

Computer Science Faculty Publications and Presentations

We study a variant of the down-up (also known as the Glauber dynamics) and up-down walks over an 𝑛-partite simplicial complex, which we call expanderized higher-order random walks—where the sequence of updated coordinates corresponds to the sequence of vertices visited by a random walk over an auxiliary expander graph 𝐻. When 𝐻 is the clique with self-loops on [𝑛], this random walk reduces to the usual down-up walk, and when 𝐻 is the directed cycle on [𝑛], this random walk reduces to the well-known systematic scan Glauber dynamics. We show that whenever the usual higher-order random walks satisfy a log-Sobolev …

3d Face Modeling From 2d Images Using Deep Neural Networks, Mario Alberto De La Cruz Armendariz

Open Access Theses & Dissertations

Applications of 3D face reconstruction include biometric authentication, personalized avatars and digital identity, medical visualization, forensic analysis, and broader human-computer interaction. We propose an approach to 3D face reconstruction that can generate a fully textured 3D facial model using only two grayscale images: a front view and a profile view of the subject. Once trained, the system can perform the reconstruction autonomously without manual intervention. Unlike traditional methods requiring multi-camera setups, depth sensors, or cloud-based processing, the proposed approach runs fully offline on a standard CPU, supporting dynamic execution across CPU cores and eliminating the need for a dedicated GPU. …

Efficient Adaptive Spline-Based Path Planning For In-Space Servicing, Assembly, And Manufacturing Applications, Christian Lozoya

Open Access Theses & Dissertations

Autonomous robotic systems operating in cluttered and partially observed environments require trajectory generation methods that produce smooth and dynamically feasible motion while reacting to locally sensed obstacles. This requirement is especially pronounced for free-flyer and in-space servicing, assembly, and manufacturing (ISAM) platforms, where onboard sensing is sparse, global environmental information is unavailable, and communication or computational resources are constrained. In such settings, motion plans must be updated online using incomplete and rapidly changing local observations, while avoiding excessive replanning that can lead to oscillatory or unstable behavior. Many existing approaches either rely on dense optimization over extended horizons, which is …

Further Insights Into The Network Link Outlier Factor's (Nlof) Light-Load Penalty, Sunday Oluwaleke Ogundele

Open Access Theses & Dissertations

This research investigates the performance of the Network Link Outlier Factor with Most Likely Links (NLOF:MLL), under varying network load conditions. Earlier studies reported that the NLOF:MLL algorithm experienced a noticeable drop in fault-localization accuracy when operating in lightly loaded networks. To further examine this limitation, 240 experiments were carried out to observe how the algorithm responds as overall network load increases. The evaluation focused on the classification performance metrics: precision, recall, and F1-score. The results show that NLOF:MLL’s effectiveness improves as network load increases but that the rate of improvement slows progressively, eventually stabilizing in a pattern consistent with …

Gpu-Based Electromagnetic Microwave Tomography For Brain Imaging And Stroke Detection, Pablo Sotelo Torres

Open Access Theses & Dissertations

Each year, an estimated 795,000 people in the U.S. suffer a stroke, with approximately 610,000 being first-time cases. Of these, 87% are ischemic strokes, while the remaining 13% are hemorrhagic. Current imaging methods, such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI), provide useful information into brain tissue properties; while each technique has its advantages, they remain expensive, non-portable, and often too slow for emergency bedside or in-ambulance use. Electromagnetic Microwave Tomography (EMT) offers a promising alternative: an affordable, portable, rapid, and safe method for stroke detection. By contrasting dielectric properties between healthy and affected …

Securing Connected And Autonomous Vehicles, Owana Marzia Moushi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. In this dissertation, we focused on detecting various insider attacks in vehicular networks to enhance the security of the network.

Our first contribution in this dissertation is the detection of both binary and multi-class data replay and data replay Sybil attacks in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

The Future Is Now: Empowering Society Through Ai Literacy, Jason S. Wrench, Sanae Elmoudden

Milne Open Textbooks

Artificial Intelligence (AI) is no longer a futuristic concept—it is the reality of the present. From the algorithms shaping our social media feeds to the generative tools transforming our workplaces, AI has permeated every aspect of modern life. The Future is Now moves beyond the hype to provide a comprehensive roadmap for understanding, navigating, and shaping this technological revolution.

Demystifying the Machine

This textbook serves as a user-friendly guide to the “black box” of AI. It breaks down complex technical concepts—from machine learning and neural networks to large language models—making them accessible to students across all disciplines. By establishing a …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Qualitative Stereo Vision Using Distributed Extended Waltz Filtering, Ben Mathew

Theses and Dissertations

Stereo vision is a fundamental problem in computer vision, aimed at reconstructing three-dimensional scene structure from two or more two-dimensional images. Traditional stereo algorithms rely on quantitative disparity estimation, often constrained by calibration precision, lighting variations, and surface texture. In contrast, our proposed Qualitative Stereo Vision seeks to understand depth relationships and spatial configurations from multiple planar views through symbolic reasoning and constraint satisfaction, offering a more flexible and cognitively plausible approach to scene interpretation.

This dissertation presents a novel framework called Distributed Extended Waltz Filtering, designed to provide qualitative stereo vision, particularly in the presence of occlusions—a persistent challenge …

Real-Time, Co-Regulated Design For Cyber-Physical, Multi-Rotor Uas Swarms, Grant Simon Phillips

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Uncrewed Aerial Systems (UAS) have been integrated into a wide range of research and industrial applications, with growing interest in extending mission duration and spatial coverage through coordinated multi-UAS systems, or swarms. While swarming offers the potential for extended mission endurance and robustness through advanced path-planning, control, and estimation algorithms, significant challenges arise when implementing these methods on decentralized platforms composed of size, weight, and power-constrained (SWaP) vehicles. Limitations in onboard computational capacity and congested communication channels can break critical design-time assumptions, which at best, will degrade application quality of service, and at worst, destabilize the fleet through excessive delays …

Making Deep Neural Networks Trustworthy: Intelligibility And Safety Through Symbolic Methods, Eleanor Catherine Quint

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The rapid adoption of deep learning has come at the cost of properties long valued in artificial intelligence: intelligibility and safety. This dissertation develops methods that restore these properties by coupling neural networks with symbolic structure.

First, for supervised classification, I propose a differentiable decision tree integrated with a supervised variational autoencoder. The resulting model maintains competitive accuracy and generative performance while exposing clear macro-features in its latent space, improving interpretability.

Second, for reinforcement learning, I extend constrained Markov decision processes by specifying constraints in formal languages. This formal language constrained MDP enables the use of automata for state augmentation, …