Computer Engineering Commons^™

Safe Data-Enabled Control Of Human-In-The-Loop Robotic Manipulator Systems, Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan

Publications

Safe control of human-in-the-loop (HIL) robotic manipulators is critical for applications such as assistive robotics, teleoperation in hazardous environments, and collaborative manufacturing. However, this remains challenging due to the lack of a unified framework that simultaneously addresses safety constraints, external disturbances, unmodeled dynamics, and dynamic role switching in the HIL setting. In this paper, we propose a novel NN-driven HIL control framework in which human–robot dyadic interaction occurs through the haptic channel. Using Lyapunov stability analysis, we theoretically show that the proposed NN-based controller ensures accurate joint trajectory tracking, compensates for system uncertainties, and adapts to human inputs modeled as …

Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor

Masters

The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

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 …

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

Exploring Rust’S Performance In A Serverless Environment, Saoirse Mullen, Gary Clynch

Academic Poster Collection

Exploring Rust’s Performance in a Serverless Environment

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations

Power Saving In Open Ran By Using Advanced Cpu Scheduling Algorithm, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN is an emerging wireless technology that is gaining significant attention for its potential to enable flexi- ble, cost-efficient, and interoperable networks. Reducing power utilization in Open RAN, particularly for 5G base stations (gNodeBs) deployed in remote areas, remains a critical challenge due to limited power availability. In our previous work, we developed a CPU scheduling algorithm that optimized core allocation based on load conditions, reducing power utilization for gNodeB in a virtualized Open RAN environment. Extending our previous work, this paper introduces an advanced CPU scheduling for Open RAN designed to reduce power utilization in real hardware Open …

Demonstrating The Impact Of Cpu Scheduling On Power Consumption In Virtualized Open Ran, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN (Open Radio Access Network) is a next- generation wireless network gaining significant research interest globally due to its potential to provide a cost-efficient and scalable solution for growing network demands. Energy efficiency is an important area of focus in Open RAN deployments, as reducing power consumption while maintaining network performance is essential for sustainable wireless communication. This paper demonstrates the impact of CPU (Central Processing Unit) scheduling process priorities on power consumption and network performance in an Open RAN NodeB deployed on a testbed in the USA. The experimental results are demonstrated using two scenarios: (1) CPU Priority-Based …

A Comparison Of Aks And K3s On Vmss, Craig Dillon, Omar Portillo

Academic Poster Collection

A Comparison of AKS and K3s on VMSS

Nomad Vs. Kubernetes, Alexandru Constantin Cardas, Omar Portillo

Academic Poster Collection

Nomad vs. Kubernetes

Investigation Into Finops Techniques To Optimise Cost In Aws Cloud Deployments, Denis Parker, Kevin Bayliss

Academic Poster Collection

Investigation Into FinOps Techniques To Optimise Cost in AWS Cloud Deployments

An Evaluation Of Zero Trust Principles In Modern Software Development, Cezar Vararu, David White

Academic Poster Collection

An evaluation of Zero Trust Principles in modern software development

Cost Optimization In Open Telemetry, Niksa Jadric, Cormac Keogh

Academic Poster Collection

Cost Optimization in Open Telemetry

The Digital Loophole: Evaluating The Effectiveness Of Child Age Verification Methods On Social Media, Fatmaelzahraa Eltaher Ph.D, Rahul Gajula, Luis Miralles-Pechuán, Christina Thorpe, Susan Mckeever

Conference papers

Social media platforms are an integral part of daily life for nearly five billion people worldwide. However, the growing presence of underage users on these platforms raises significant concerns regarding children's exposure to harmful content and its impact on their mental health. This paper examines the effectiveness of age verification measures implemented on leading platforms Facebook, YouTube, Instagram, TikTok, Snapchat, and X. We evaluate the age verification processes required for account creation by simulating the registration steps for minors on these platforms. We also compare these methods to best practices in online age assurance in finance, betting and public transportation …

Agency, Index & Process: Investigating The Role Of The Artist’S Body In Digital Sculpture Production, Alan Magee

Doctoral

Contemporary Digital Sculpture has emerged out of recent developments in digital art, 3d modelling and virtual modes of production. These advances range from the creation of more powerful software and hardware systems to the to nascent XR sectors, and the potential for materialisation through technologies such as 3d printing, laser-cutting, or CNC1 machining. However, critical discourse remains predominantly focused on the end product, often overlooking the embodied labour processes inherent in its creation. As revealed through indexical traces of the artist’s body, these processes encapsulate the gestures, actions and subjective agency of artistic activity. Consequently, with the limitations of digital …

Funkcjonowanie Doręczeń Elektronicznych W Ujęciu Technicznym, Michał Tabor

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

The article provides a legal-technical and market analysis of electronic delivery in Poland, concluding that while the system complies with the basic requirements of the eIDAS Regulation, it needs significant organizational and technical improvements. The author reviews the National Electronic Delivery System, the role of the designated operator and qualified trust service providers, and highlights issues with interoperability, address registration and portability, delivery mailboxes, and the hybrid delivery service. Recommended legal reforms include granting the public delivery service qualified status, enabling multiple delivery addresses for public and complex organizations, partly opening the market to commercial qualified providers, and moving supervision …

Artificial Intelligence For Digital Deception: A Study On Detection, Generation, And Evaluation, Tasnim Akter Onisha

College of Graduate Studies: Theses & Dissertations

The rapid advancement of artificial intelligence has significantly influenced digital media, enabling both the detection and generation of synthetic content. This thesis, titled Artificial Intelligence for digital deception: A Study on Detection, Generation, and Evaluation, explores AI’s role in digital deception through three distinct studies focused on facial expression analysis for deepfake detection, machine learning-based spam classification on cloud platforms, and the evaluation of generative AI state-of-the-art text to video models. The first study investigates the effectiveness of facial expression analysis in distinguishing between deepfake and genuine videos. Using Noldus FaceReader 7, participant’s emotional responses were analyzed while viewing deep-fake …

Exploring The Benefits, Barriers, And Sustainability Of Community Stem Partnerships: A Qualitative Case Study On Stem Collaboration In Elementary Education, Katherine M. Blagden

Open Access Dissertations

In an era of increasing emphasis on science, technology, engineering, and mathematics (STEM) education, elementary schools often face significant challenges in implementing high-quality, integrated STEM instruction. These challenges include limited resources, inconsistent professional development, and a lack of access to real-world, community-based learning experiences (Dorph et al., 2018; National Science Board, 2024). This dissertation explores how collaborative partnerships among elementary schools, community organizations, and industry professionals can overcome these barriers and foster meaningful, sustainable STEM learning opportunities for young students. Drawing on the Asset-Based Community Development (ABCD) model (Kretzmann & McKnight, 1993) and informed by a constructivist theoretical framework (Vygotsky, …

Sharing The Stage With The Future: Humans And Robots Together At Last, Donna L. Clevinger

Honors in Practice Online Archive

This essay presents a co-curricular collaboration bringing ancient comedy to a modern audience. Students and faculty at a large, public R1 university combine art and engineering to create a STEAM-based approach to theatrical production. The author describes how integrating classical text, creative expression, and transformational technologies demonstrates that collaboration between disciplines can produce gains for each, fostering advancements and human understanding that would be unattainable independently. Script writing, casting, stage production, and outcomes are presented.

Automatic Cat Caretaker, Connor Mcclenathan, Ryan Anderson, Andrew Tate

Williams Honors College, Honors Research Projects

This project will involve developing and constructing a self-cleaning litter box with feeding and watering functions attached. The machine will have a user interface for setting both the feeding and watering times, as well as how much to fill the food and water bowls at the same time. The project will involve the use of motors for the cleaning, feeding, and watering functions, sensors to detect when to perform those functions, and a microcontroller to process all the data and to tell the motors when to perform their respective functions. This project's goal is to make the task of caring …

Autonomous Landscape Exploration Rover Using Imaging Surface Navigation (Alexis), Noah Jones, Dominic Salupo

Williams Honors College, Honors Research Projects

The objective is to develop a small-form-factor rover prototype that can be used to prove out a novel traversal method for use on extraterrestrial surfaces. The novel traversal method being proposed is LIDAR/CV-enhanced navigation, provided by a detachable flight vehicle that can communicate with the rover. On planets with thin atmospheres, cold gas thrusters or similar may be needed, but for the scope of this project more traditional flight/propulsion methods will be used.

Detecting Wireless Security Threats Through Ieee 802.11 Frame Field Anomalies, Aria Young

Williams Honors College, Honors Research Projects

It is not uncommon for most public spaces to offer Wi-Fi, while it is convenient and affordable, there are significant security risks due to its open nature. Rogue access points pose a security threat to many networks because they have the potential to bypass security measures and intercept traffic containing sensitive information. The attempt to formulate a method that one hundred percent guarantees the detection of a rogue access point has proven to be an intricate and complex problem for many to tackle, as there are numerous ways a rogue access point can be configured. This project aims to demonstrate …

Deep Learning For Computer Vision Applications In Medical Diagnostics And Wildlife Monitoring, Mostapha Al Saidi

Electronic Theses and Dissertations 2020 - Present

This dissertation explores innovative applications of deep learning and computer vision techniques across three distinct domains: medical imaging, dermatological diagnostics, and wildlife monitoring. The research addresses critical challenges in each field through the development and optimization of convolutional neural networks and other deep learning architectures.

The first study examines COVID-19 classification from X-ray images, comparing one-shot versus two-stage classification approaches using transfer learning with pre-trained models such as VGG16 and VGG19. The initial hypothesis was that breaking down the classification task into two optimized tasks would yield better results than one-shot classification. Results demonstrated that the single-stage approach achieved superior …

Movie Genre Classification Using Script Texts, Michael Roman Cuomo

Electronic Theses and Dissertations 2020 - Present

Genres are used to classify movies so that they can be grouped with others that have similar themes and structures. These classifications are categories created by humans. In the process of creating a movie, a script is often the first creation to write and share ideas about a topic. The script contains large amounts of text that is used to describe the dialog, setting and direction of the film. Although the script contains important information for the film, the amount of text can present a challenge for machine learning algorithms. Often in studies on film classification, if text is used, …

Utilizing Information Technology And Hands-On Learning Practices To Improve Student Learning Outcomes In A High Failure Rate Introductory Programming Course, Todd Edward Thomas

Theses and Dissertations

The purpose of this study was to introduce and examine the impact that two interventions have on a high failure rate introductory programing course: a pedagogical approach of introducing Live Coding instruction technique to the in-person lecture portion, and a technological approach of introducing remote collaboration software (VS Code Liveshare) to the online lab portion. This study used convergent parallel mixed methods approach for both data collection and analysis; utilizing four data collection methods: 1) online survey questionnaires 2) in-depth interviews 3) in class observations and 4) quantifiable data collection (ie., student demographic, IT experience, GPA data). The Live Coding …

Ai-Driven Traffic Scene Understanding Using Static Lidar Sensors, Elham Binshaflout, Chaima Zaghouani, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Sameh Najeh, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

Traffic congestion and road safety remain critical challenges in urban environments, driving the need for more effective traffic monitoring solutions. While recent advancements in computer vision have enhanced traffic perception, the dynamic viewpoint of autonomous vehicles is often insufficient for comprehensive traffic management. To address this gap, we propose an AI-driven framework for enhanced traffic scene understanding using static LiDAR sensors at road intersections. The system collects 3D point clouds from roadside static LiDAR sensors, providing a complete view of vehicles and pedestrians. We integrate state-of-the-art 3D object detection (i.e., PV-RCNN) and instance segmentation models (i.e., PointGroup3heads) to accurately identify …

Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono

Electrical and Computer Engineering Faculty Research & Creative Works

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO₂ concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …

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 …

Précis Of The Edge Of Sentience: Risk And Precaution In Humans, Other Animals, And Ai, Jonathan Birch

Animal Sentience

We often face grave practical decisions that seem to hinge on whether a system is sentient. This family of cases includes invertebrate animals, people who are unresponsive after brain injury, fetuses, neural organoids, and now AI technologies. We must decide what to do despite ongoing disagreement about the nature of sentience. In our state of uncertainty, we should pragmatically transform the question from “Is it sentient?” to “Is it a sentience candidate, an investigation priority, or neither?”. When a system is a sentience candidate, it is negligent to fail to consider precautions. We should instead evaluate precautions for their proportionality …