Engineering Commons^™

Cost Optimization In Open Telemetry, Niksa Jadric, Cormac Keogh

Academic Poster Collection

Cost Optimization in Open Telemetry

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

Academic Poster Collection

Exploring Rust’s Performance in a Serverless Environment

Performance Evaluation Of Zabbix And Azure Monitor In Hybrid It Infrastructure, Ivan Godoy, Cormac Keogh

Academic Poster Collection

Performance Evaluation of Zabbix and Azure Monitor in Hybrid IT Infrastructure

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

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations

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.

Design And Evaluation Of A Thai Speech Emotion Recognition Corpus With Ambiguous Annotations, Chompakorn Chaksangchaichot

Chulalongkorn University Theses and Dissertations (Chula ETD)

THAI-SER is the first large-scale Thai speech emotion recognition corpus, comprising 41.6 hours (27,854 utterances) from 100 recordings across diverse environments (Zoom and studio). The data includes both scripted and improvised speech by 200 professional actors (112 females, 88 males, aged 18–55), covering five emotions: neutral, angry, happy, sad, and frustrated. Utterances were labeled via crowdsourcing, with rigorous quality control ensuring a majority agreement score above 0.71. Annotation reliability, measured by Krippendorff’s alpha, reached 0.692 (above the 0.667 threshold), and human emotion recognition accuracy reached 0.772 after filtering. We also report benchmark results from models trained and evaluated on both …

Nobocap: Unlocking Mdr/Ivdr Regulations For Innovators In Europe, Graham Gavin, Claire Brougham

Conference Papers

The NoBoCap project (nobocap.eu) is aimed at addressing some of the challenges encountered by both SMEs and Notified Bodies across the EU. It is a multi-organizational consortium including universities, a Notified Body, and Bio-health and Innovation Hubs and Clusters. The NoBoCap project has several work packages focussed on:• Design and delivery of funded short-term courses.• Creating a dedicated NB job board.• Design and delivery of funded university accredited modules.• Design and development of e-tools to support manufacturers.• Develop a community platform to act as a voice for start-ups and SMEs

Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz

Master's Theses and Doctoral Dissertations

The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …

Immersive Executive Functions Assessment System (Iexec): Integrating Embodied Cognition And Virtual Reality, Hamza Reza Pavel

Computer Science and Engineering Dissertations - Archive

Executive functions (EFs) are higher-order cognitive processes that include working memory, inhibitory control, and cognitive flexibility. These higher-order processes facilitate the achievement of goal-directed behavior and enable both adaptive decision-making and emotional regulation. Traditional EF assessment tools depend on static pen-and-paper tasks or basic computer-based tasks, which fail to capture real-world cognitive complexity and dynamics. Some of these assessment tools are specifically geared towards children or older adults, while others are more generic and designed to be used for people of all ages. This dissertation addresses these limitations by introducing iExec: The Immersive Executive Functions Assessment System, which functions as …

Enabling Energy And Water Sustainability Through Out-Of-Band Emi Sensing And Infrastructure Modeling, Pranjol Sen Gupta

Computer Science and Engineering Dissertations - Archive

As demand for Internet and cloud services surges, data centers have emerged as critical infrastructure—but they are also among theworld’s most energy- andwater-intensive facilities. Effective power management, particularly at the server level, is essential for improving efficiency, reliability, and sustainability. However, server-level power monitoring remains uncommon due to the high cost of hardware instrumentation and the intrusiveness of software-based solutions, especially in shared colocation environments. My research introduces a novel, low-cost, and non-intrusive method for server-level power monitoring using conducted electromagnetic interference (EMI). By analyzing EMI signals captured from higher levels in the power distribution network, this approach estimates individual …

Optimizing Indoor Localization Using Rssi And Iq Data With Machine Learning, Gokdeniz Tingur

Computer Science Theses

This paper explores implementing and evaluating a Bluetooth Low Energy (BLE)-based indoor localization system using Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) data via machine learning. A survey of localization technologies (RFID, GPS, ZigBee, and BLE) provides context on capabilities and limitations in indoor positioning. IQ data and phase-based angle estimation show how BLE 5.1’s direction-finding features enable sub-meter accuracy. A multi-phase experiment in a three-story academic building examines model performance with different tag distributions, movement patterns, and environmental constraints. Machine learning models such as Support Vector Machines and Deep Neural Networks are trained and evaluated across …

Improving The Operator-Swarm Dynamic Under Mental Fatigue Constraints In Search And Rescue Operations, Jordan Morrow

Masters Theses

"Human-robot applications that allow for work to be done remotely are largely dependent on the lassitude of the operators. The exhaustion of these operators is a result of work completion and duration. Previous research attempts to evaluate the impact on reaction by quantiying human weariness. This paper examines how human weariness affects the human-robot dynamic in UAV-assisted search and rescue missions. An explanation of the connection between mental exhaustion and operator responsiveness over prolonged durations is provided by the search and rescue operations using UAV swarms (SAROUS) model. Through the use of artificial intelligence, SAROUS is modernized. This allows the …

Advanced 3d Lidar-Based Systems For Urban Traffic And Pedestrian Monitoring: Integrating Elevated Lidar, Data Collection, And Deep Learning For Precise Detection And Activity Classification, Nawfal Guefrachi

Masters Theses

"Accurate and real-time monitoring of urban traffic and pedestrian activities is crucial for intelligent transportation systems (ITS) and smart cities. Traditional camera-based methods struggle with issues like lighting and privacy. This research leverages advanced three-dimension light detection and ranging (3D LiDAR) technology and computational frameworks to address these challenges, providing transformative solutions for urban traffic management and pedestrian safety. By strategically deploying elevated LiDAR sensors, detailed 3D point cloud data is captured, enabling precise monitoring of urban environments. Enhancements to LiDAR-based frameworks, such as fine-tuning the Point Voxel Region-Based Convolutional Neural Network (PV-RCNN), improve the detection of vehicles and pedestrians …

Lidar From The Skies: A Uav-Based Approach For Efficient Object Detection And Tracking, Baya Cherif

Masters Theses

"Recently, there has been a growing interest in deploying the Light Detection and Ranging (LiDAR) technology to gain traction in the autonomous vehicle industry, its applications are expanding into areas like smart cities, agriculture, and renewable energy. This work proposes an advanced approach to enhance aerial traffic monitoring using Li- DAR. We aim to provide accurate, real-time object detection and tracking from an aerial perspective by integrating Unmanned Aerial Vehicle (UAV) with LiDAR, culminating in a smart UAV-integrated LiDAR (A-LiD) sensor for traffic surveillance. We introduce an adapted version of one of the newest methods of the cutting-edge 3D object …

On Optimizing Sensor Data Collection, Processing, And Storage For Industrial Additive Manufacturing, Steven Thompson

Masters Theses

The widespread adoption of digital data management methods for transformative technologies, such as additive manufacturing (AM), within the aerospace industry is impeded by poor interoperability between AM component manufacturing processes. Moreover, data quality may be compromised due to sensor failures or other corruptions. Additionally, massive amounts of data are collected during these processes, often needing to remain accessible for decades. These storage costs can place a significant financial burden on smaller suppliers. This work aims to make digital data management methods more affordable and, therefore, approachable for smaller suppliers.

First, the design and initial implementation of an affordable and adaptable …

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 …

Focused Feature Extraction For Driver Drowsiness Detection Using An Enhanced Attention-Based Resnet Model, Nada Ayman Atia, Rawan Sameh

The Undergraduate Research Journal

In the context of increasing road safety concerns, particularly in Egypt, this paper addresses the critical issue of driver drowsiness, a significant contributor to road accidents worldwide. With alarming statistics from the World Health Organization citing human error, chiefly drowsiness, as the cause for a majority of road accidents in Egypt, there is a compelling need for an effective drowsiness detection system. This research introduces a novel, vision-based driver drowsiness detection system leveraging a multi-dimensional approach with a Residual Neural Network (ResNet) architecture and attention layers. This system aims to accurately identify drowsiness by analyzing key facial features. The paper …

Comparing Funders' Altruism Versus Self-Interest: Leveraging The Context Of Crisis, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

While reward-based crowdfunding has widespread popularity, the motivations driving funders, balancing self-interest and altruism, have remained puzzling. Prior research has been constrained by examination methods and produced mixed findings regarding the weight of altruism versus self-interest among funders. Our study takes a fresh perspective, delving into funder behavior amid a major crisis—the tumultuous backdrop of the COVID-19 pandemic. Our findings reveal that funders not only display an increased willingness to contribute but also significantly amplify their contributions, particularly to projects in crisis-affected regions, irrespective of external incentives like rewards. This underscores the prevalence of altruistic motives among funders in challenging …

Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch

Journal of International Technology and Information Management

Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …

Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment, Jinhee Yoo, Eugene A. Ohu, Radosław Mącik

Journal of International Technology and Information Management

This study (N = 170), involving participants from Nigeria and the U.S., investigated how different technologies (TV and VR) affect users' empathy (α = .93), engagement (α = .93), enjoyment (α = .93), preferences, and likelihood of technology use. Participants watched an animated documentary titled “Is Anna OK?” at two different time points, utilizing VR (Oculus Rift S) and TV, following which they completed measuring empathy, engagement, enjoyment, device preference, and usage likelihood. Analysis via one-way ANOVA and chi-square tests revealed that VR users reported significantly higher empathy and enjoyment compared to TV viewers, particularly on second viewing. Combining both …

Introducing Catalizer: A Framework For Prototyping Models Of Biological Systems, Andy M. Day

Honors Theses

Modeling the light response system of Nannochloropsis oceanica brings

a set of challenges that make modeling difficult. Notably, potential models

may contain a large number of chemical species. A large number of

species creates a quadratic explosion in the number of potential pathways.

In addition, mathematically defining these pathways is error prone, yet

follows a surprising simple set of rules. We seek to create a domain specific

language which can precisely define these chemical reaction networks.

Once the networks have been defined, they can be exported as procedures

defined in popular programming languages for further analysis.

Mastering Enterprise Networks (2nd Ed), Mathew J. Heath Van Horn

OER Main

Mastering Enterprise Networks, is a comprehensive guide to building, defending, and attacking enterprise networks. It covers a wide range of topics, from network fundamentals to advanced security concepts. The book is well-organized and easy to follow, making it a valuable resource for both beginners and experienced network professionals.

One of the strengths of this book is its focus on hands-on learning. The book includes 50 chapters of labs that allow readers to practice the concepts they have learned. These labs are a great way to reinforce your understanding of enterprise networks while developing practical skills.

This book covers security from …

Insights In Cybersecurity Of A Smart Campus - A Review, Mircea Ţălu

Journal of Cybersecurity Education, Research and Practice

The profound impact of the Internet of Things (IoT) on various fronts, is driven by technological advancements, the ubiquitous spread of information, and the emergence of transformative events. IoT presents a diverse array of possibilities within university environments, fostering a more connected and enhanced educational experience. This research undertakes a comprehensive review of existing literature to provide context to the IoT and underscore its crucial significance in the realm of smart campuses. Additionally, the paper explores the intricate connections between IoT and key concepts such as cybersecurity and wireless sensor networks to present a holistic perspective. It delves into the …

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Cca Analysis Using Computer Vision Techniques, Rahul Thakur

Master's Projects

Coral reefs are an essential part of the marine ecosystem. They perform a wide variety of tasks, some directly and others indirectly. They can produce oxygen, absorb carbon dioxide, along with supporting ocean habitat. Crustose Coralline Algae (“CCA”) plays an important role in helping provide structural support to Coral Reef ecosystems. However, global warming is causing ocean water to become more acidic resulting in coral bleaching. This is leading to changes in coral environments and causing coral deaths at alarming rates. Object detection using computer vision techniques, specifically deep learning, can help to monitor coral reef health and identify CCA …

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Smart: Semantic Mapping And Analysis For Regional Terrain Using Multi-Scale U-Net And Topsis, Rashmi Sonth

Master's Projects

Accurate land use classification is the backbone for urban planning. But with poor quality satellite images, varied landscapes and structures which are changing faster than ever, it becomes a challenge to define clear boundaries and hence to urban planning. This research explores the application of deep-learning model for land use classification and asses the suitability of the land. The proposed model combines a multi-scale U-Net architecture with Transformer blocks applied on a multi-spectral satellite images that improves the semantic segmentation greatly across the urban and rural regions. Additionally, a patch-wise segmentation is applied to overcome the common problem of feature …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy

Master's Projects

The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …