Computer Engineering Commons^™

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Modeling Student Depression With Decision Trees: Predictive Insights From Data, Ahloe Feomaia, David Montoya, Israel De Leon, Oluwabunmi V. Sanusi

Posters - 2025

• Student depression is a growing public health concern that adversely affects academic performance and general well-being.

• According to Ibrahim et al. (2013), the prevalence of depression among university students ranges from 10% to 85%, with an overall weighted mean of 30.6%.

• Factors such as financial challenges, academic stress, and social adjustments significantly contribute to higher depression rates among students compared to the general population.

• Studies have shown that female students are more prone to depression due to hormonal, psychological, and social factors (Altemus et al., 2014). Depression impacts academic performance, reducing cognitive function and increasing dropout …

Rattler Notehub, Emily Medlin

Posters - 2025

Students lack diverse and comprehensive study materials that help develop effective learning. More specifically, students at St. Mary’s may find that study material outside of class are not as effective or not relevant to what was taught in class. This leads to prolonged study sessions or completely missing information that was taught when the student was absent. Rattler NoteHub tries to accomplish giving access to students to collaborate, find supplement resources and promote efficient studying.

Rattler NoteHub is a full-stack website that was initially developed as a Software Engineering project. Since then, the website has expanded its functionality to better …

Aiops–Driven Adaptive Anomaly Detection In Evolving Cloud Environments Using Transfer Learning, Mayur Shivakumar

Master's Theses

As cloud-based microservice architectures have become the foundation of contempo- rary enterprise solutions, performance interference, wherein co-located services com- pete for shared resources, remains a significant challenge. This phenomenon, often referred to as the noisy neighbor problem, manifests when one workload unexpect- edly increases the CPU, memory, disk I/O, or network consumption, resulting in latency spikes or throughput degradation for other services. While existing isolation mechanisms (e.g., cgroups and QoS policies) provide some mitigation, they rarely prevent contention entirely, particularly in dynamic, rapidly evolving environments with frequent code deployments.

This thesis proposes an AIOps-driven adaptive anomaly detection framework that integrates …

Understanding The Breadth And Impact Of The Ias [Presidents Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Heat-Pipe-Based Thermal Management System Design For A 250 Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Cross-Layer Design And Optimization Of Analog In-Memory Computing Systems, Md Hasibul Amin

Theses and Dissertations

There has been a rapid growth in the computational demands of machine learning (ML) workloads in recent days. Conventional von Neumann architectures are not capable of keeping up with the high cost of data movement between the processor and memory, well-known as memory wall problem. In-memory computing (IMC) has been focused as a solution by the researchers, where the computation is performed inside the memory devices such as SRAM, MRAM, RRAM etc. Most commonly, the memory devices are arranged in a crossbar setting where the matrixvector multiplication (MVM) operation is performed through intrinsic parallelism of analog computations. The conventional IMC …

Ai, Blockchain, And Autonomous Innovation : Charting The Future Of Intelligent Enterprises, Shubham Gupta

Harrisburg University Other Works

In today’s digital economy, artificial intelligence (AI) and blockchain are twin forces driving transformative change. AI and blockchain each rose to prominence on their own, but together they hold the promise of revolutionizing how businesses operate and create value. AI systems can analyze massive datasets, automate complex decisions, and even mimic human learning and reasoning. Blockchain technology, on the other hand, enables secure and tamper-proof transactions by distributing records across a network, ensuring transparency and trust without relying on a central authority. The convergence of these technologies is ushering in new possibilities for automation, smarter decision-making, and secure digital transactions …

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

Comparative Performance Analysis Of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study On Faas And Iaas Platforms Dataset, Jeremiah Webb

Doctoral Dissertations and Master's Theses

Cloud computing has become a relatively new paradigm for the delivery of compute resources, with key management services (KMS) playing a crucial role in securely handling cryptographic operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, including SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryption/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction with KMS offerings from Ama- zon Web Services (AWS) and Microsoft Azure to conduct a comparative performance analysis. The methodology involves the AWS Cloud Development Kit (CDK) and the Bicep language to deploy AWS Lambda Functions and Azure Functions, respectively, to …

S.P.R.O.U.T (Seedling Planting And Robotic Observation With Unsupervised Tracking), Katharine Ott, Joshua Spagnola, Brayden Ristow, Elliot Clyde, Anshuman Sahu, Zen Yamao

Computer Science and Engineering Senior Theses

The current commercial agriculture market reveals two significant gaps: one for small-scale farmers needing automation, and another for the efficient planting of seedlings. Acquiring large-scale automated equipment is often impractical for small-scale farmers due to high costs and the mismatch between commercial-scale solutions and the needs of smaller operations, creating a need for accessible robotic solutions. The Seedling Planting and Robotic Observation with Unsupervised Tracking (SPROUT) design addresses this gap by developing an autonomous seedling planting attachment for the Agriculture robot (Agbot) platform - a modular agricultural robot developed by Santa Clara University (SCU). This year’s work involved designing and …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

The Impact Of Tariffs On Auto Parts Trade With China, Canada And Mexico: Ai-Driven Strategies For Supply Chain Optimization, Katie Cerda, Layla Dickerson, Riley Gibson, Oluwabunmi Sanusi

Posters - 2025

U.S. tariffs (7.5-25%) on auto parts from China, Canada, and Mexico are severely disrupting the automotive industry, a key global economic driver. These tariffs dramatically increase production costs and vehicle prices, potentially by up to $12,200 per vehicle (CBS News, 2025; MarketWatch, 2025). These tariffs necessitate major supply chain adjustments, leading to inefficiencies (MIT Sloan, 2024). Supplier diversification, while intended to mitigate tariff impact, extends lead times and shrinks profit margins (XenonStack, 2025). The industry's complex supplier network is now highly vulnerable, compelling companies to seek more adaptable strategies. AI-driven technologies like predictive analytics and route optimization offer potential solutions …

Cult: Virtual Tourian, Ian Poll

Posters - 2025

Introduction: The Blank Shepherd Building is an important place at St. Mary’s University where students research, invent, and work together. But not everyone can visit it. This virtual tour solves that problem by using games and technology to bring the building to life.

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Towards The Advancement Of Violence Recognition In Security Footage With Explainable Neural Networks, Paris Her

Dissertations (1934 -)

This dissertation investigates the problem of violence recognition in surveillance footage using computer vision and machine learning techniques. More specifically, our goal is to achieve interpretable and explainable deep learning models because violence recognition is a sensitive task. We first propose to perform violence recognition using a 3D convolutional neural network through intuitive hyperparameter tuning and transfer learning. We utilize a state-of-the-art 3D model used for general activity recognition that is lightweight and adjustable. Along with that, we introduce a data augmentation technique called "resize-within" which uses interpolation, rather than cropping, to resize the original input video to a new …

Every Relevant Detail, Salomé Viljoen

Michigan Law Review

A review of The Ordinal Society. By Marion Fourcade and Kieran Healy.

A Formal Simulation Model For Discrete Rate Simulation, Thomas J. Tracey

Electrical & Computer Engineering Theses & Dissertations

Simulation is an essential tool for virtualizing systems by creating a representative model of real or hypothetical systems and observing how they change over time. Two predominant simulation paradigms include Discrete Event Simulation (DES) and Continuous Simulation, which both have their strengths and weaknesses. DES does not handle continuous state variables, while continuous simulation handles continuous state variables but encounters errors where these variables have discrete changes in their behavior. This difficulty between the two predominant simulation paradigms prompted the creation of a new simulation paradigm to cover this gap: Discrete Rate Simulation (DRS). DRS as a simulation paradigm focuses …

Peerproxy: A Webrtc Proxy For Http, Nathan Li-En Lee

Master's Theses

Advances in networking technologies have empowered individuals to easily self-host digital services such as websites and smart home systems. However, accessing these services externally often requires port forwarding, which requires manual router configuration, technical expertise in networking, and is sometimes restricted by internet service providers. Proxy-based services such as Ngrok and Cloudflare Tunnels simplify external access by using publicly hosted proxy servers, but introduce increased infrastructure costs and privacy concerns due to reliance on third-party servers that can inspect or store traffic.

This thesis presents PeerProxy, a novel framework that simplifies access to self-hosted web services without manual network configuration, …

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Aimr-Brainstorm: Ai-Enhanced Interactive Mixed Reality For Collaborative Ideation, Yuchuan Ma

Dartmouth College Master’s Theses

This study investigates the potential of an AI-enhanced Mixed Reality (MR) brainstorming system, named AIMR-Brainstorm, in comparison to traditional sticky notes for creative ideation. By integrating real-time idea extraction through ChatGPT with immersive, physics-based visualizations, the system aims to transform analog brainstorming workflows into dynamic, interactive digital experiences. Using a within-subject experimental design, 30 participants engaged in paired brainstorming sessions with both AIMR and sticky notes. Quantitative measures of efficiency, engagement, creativity, and user satisfaction were collected through between-session and post-study surveys, while qualitative feedback provided additional insights into user experiences. Conclusively, while traditional sticky notes were generally preferred for …

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

High Gain Defected Slots 3d Antenna Structure For Millimetre Applications, Arkan Mousa Majeed, Fatma Taher, Taha A. Elwi, Zaid A. Abdul Hassain, Sherif K. El-Diasty, Mohamed Fathy Abo Sree, Sara Yehia Abdel Fatah, Umi Aisah Asli

All Works

The antenna is structured in three dimensions, employing a conductive cylindrical cone as its base. This cone configuration is achieved through the etching of an elliptical slot array onto the antenna. To enhance its performance, a conductive circular reflector is situated beneath the cone, thereby augmenting its gain. The antenna demonstrates operational bandwidth across various frequencies: Ultra-Wideband (UWB) operates at approximately 5 GHz, extending to about 15 GHz; Wideband (WB) is cantered at roughly 20 GHz, while narrowband operates at approximately 27 GHz. Within the frequency range of interest, the antenna's gain varies between 3dBi and 15dBi. Geometric specifications of …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Performance Comparison Of Iot-Powered Indoor Hydroponic Systems And Outdoor Traditional Environment, Saleha Alharthi

Theses

Traditional agriculture faces challenges, including high water consumption, greenhouse gas emissions, and fluctuations in environmental conditions. The research aims to develop alternative sustainable solutions to address issues related to traditional farming. Hydroponics provides opportunities to grow different types of vegetables indoors, where conventional agriculture is challenging. This thesis presents a comparative study of soil-based and hydroponic arugula cultivation using the nutrient film technique (NFT). The proposed methodology focuses on maintaining high similarity in the implementation of components across systems to ensure fair comparison. This thesis utilized distinct approaches. The first approach compared an outdoor soil-based system with an indoor hydroponic …

From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose: The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program. Design/methodology/approach: This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former …

Llm-Enhanced Multiple Instance Learning For Joint Rumor And Stance Detection With Social Context Information, Ruichao Yang, Jing Ma, Wei Gao, Hongzhan Lin

Research Collection School Of Computing and Information Systems

The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. Rumors can be identified by cross-referencing stances in related posts, and stances are influenced by the nature of the rumor. However, existing stance detection methods often require post-level stance annotations, which are costly to obtain. We propose a novel LLM-enhanced Multiple Instance Learning (MIL) approach to jointly predict post stance and claim class labels, supervised solely by claim labels, using an undirected microblog propagation model. …

Unveiling The Transformative Power Of Unsupervised Machine Learning Through Clustering, Vishnu S. Pendyala

Open Educational Resources

Clustering methods demonstrated their transformative potential across various industries through image segmentation, anomaly detection, bioinformatics, and customer segmentation. The presentation explores these techniques in unsupervised machine learning, focusing on foundational clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN. Through an in-depth analysis of their underlying principles and computational intricacies, the presentation highlights how these methods have evolved to address complex, high-dimensional data problems. The presentation provides insights into how K-means remains a versatile tool for partitioning data in linear spaces. It delves into Hierarchical Clustering's unique approach to building dendrograms and capturing multi-scale data relationships, and how DBSCAN's density-based …