Computer Engineering Commons^™

Deep Spiking Neural Network Autoencoders For Efficient Temporal Data Compression, Shruti Bhandari

ATU Scholars Symposium

High dimensional temporal data processing, such as that required for neuroprosthetics and remote physiological monitoring presents significant challenges for real time deployment because transmitting and storing raw signals is computationally demanding and energy intensive. Effective data compression is essential to act as a "biological zip file," reducing transmission bandwidth while preserving the critical temporal features required for accurate signal reconstruction and analysis. This study proposes a deep Spiking Neural Network (SNN) Autoencoder designed for high-fidelity data compression by utilizing the event-driven firing behavior of Leaky Integrate-and-Fire (LIF) neurons, which ensures extreme computational efficiency compared to traditional models. The model is …

Approach To Physics Education Using Local Ai With Rag For Open Educational Materials Generation, Dmitriy Beznosko

All Things Open

The new tool that starts to enter all parts of our life is AI – and it enters education as well. There are two standing large concerns with using AI for education – the safety of students’ data and the AI missing specific knowledge about the given class. The approach of using the Retrieval Augmented Generation (RAG) provides the user data to the locally run LLM model (using Ollama framework, a free and open-source tool that allows you to run large language models locally on your system) as a context for the generation of the OER materials. As this AI …

Multiple-Valued Quantum Automata For Robotics, Yuchen Huang

Dissertations and Theses

This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches.

The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce …

Examining The Use And Perceived Benefits Of Artificial Intelligence Tools In Higher Education: Student Perspectives, Musa Pinar, Haydar Cukurtepe, Aysegul Yayimli, Faruk Guder

Atlantic Marketing Association Proceedings

No abstract provided.

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

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 …

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 …

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 …

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 …

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.

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.