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 …

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 …

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 …

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 …

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 …

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 …

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. …

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 …

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, …

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 …

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 …

Accurate And Scalable Control-Flow Differential Analysis On System Traces, Yuta Nakamura

College of Computing and Digital Media Dissertations

Debugging and understanding system behavior pose technical challenges, often necessitating the comparison of two audited execution traces. Although provenance systems execution traces, the audited traces at most enable causal analysis within a single known execution. As a result, utilizing provenance systems differential analysis thus for debugging and reasoning is a challenging task. This thesis addresses the challenge of using provenance in debugging by developing accurate and scalable methods for differential analysis of system provenance. Our approach emphasizes the importance of knowing the application’s provenance graph structure and embedding this graph structure information within traces to conduct a precise differential analysis …

Automated Data Analysis For Concussion Patient Records: A Flutter-Based Desktop Application, Fhaheem Tadamarry

USF Tampa Graduate Theses and Dissertations

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The …

Autonomous Underwater Vehicle Planning Using Hybrid D* Lite With Ppo And Td3: Experimental Design And Performance Analysis, Matthew J. Rice

Undergraduate Theses

Autonomous Underwater Vehicles (AUVs) face significant challenges in underwater navigation, including generating smooth paths, avoiding obstacles, and adapting to complex conditions. This paper introduces a hybrid path-planning algorithm, D-RL*, that integrates the D* Lite algorithm for efficient initial pathfinding with Deep Reinforcement Learning methods to refine paths for smoother trajectories. The proposed approach addresses D* Lite's inability to produce continuous, smooth paths and baseline Reinforcement Learnings’ failures in environments requiring significant detours. Experimental results in four progressively complex environments highlight D-RL*’s ability to plan smoother paths than D* Lite while training in a shorter amount of time and generating shorter …

Web Application For Simulation Of An Agent-Based Model In Netlogo3d, Chris Davis Perumal, Abraham Nofal, Benedict J. Kolber, Rachael Miller Neilan

Spora: A Journal of Biomathematics

Agent-based models (ABMs) are computer simulation models for studying systems of autonomous agents. Modelers often use specialized software like NetLogo to develop ABMs, but this software poses barriers to researchers in other disciplines with no prior programming experience. To address this issue, we developed a web application that allows users to simulate our ABM via a web browser, eliminating the need for the user to download and use specialized software. While presented in the context of a specific ABM, our approach can be applied to other ABMs to enhance accessibility. The ABM presented here was developed in NetLogo3D. The model …

Retracted: Image Denoising: Smooth Total Variation Minimization For 5g Enhanced Mobile Broadband Transmission System, Shehab Ahmed Ibrahem, Walled Khalid Khalid Abdulwahab, Moceheb Lazam Lazam Shuwandy

Iraqi Journal for Computer Science and Mathematics

Image denoising is an important area of computer vision. Rudin-Osher-Fatemi model based on a gradient is one of the simplest models used in image denoising to solve the problem of restoring the clear image. The challenge in solving this model is the non-differentiability of Total Variation function (TV-function) minimization. Image transmission is widespread over wireless systems, including the fifth generation (5G) cellular network. Transmission impairment can affect transmitted images, including noise, attenuation, and distortion. This study proposed a new smoothing technique to make the TV-function differentiable and smooth. The new smoothed function was used for de-noising images with the help …

War Strategy Algorithm- Based Hybrid Optimization For Accurate And Rapid Speech Recognition, Shahad Thamear Abd Al-Latief, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, Ahmed Alkhayyat

Iraqi Journal for Computer Science and Mathematics

Speech recognition-based applications increased and developed as a result of artificial intelligence's rapid growth, particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related to human-computer interaction, and natural language processing. The complexity and diversity of speech signals provides challenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuning is a crucial step in machine learning that has a significant role in optimizing the performance and generalization by determining the optimal values for the model's hyperparameters. This paper employed the recently developed WAR Strategy optimization algorithm for …

Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Iraqi Journal for Computer Science and Mathematics

The face morphing process blends two or more facial images to produce a singular morphed facial image that shows the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a significant risk to national security. This research presents a hybrid approach for feature extraction from facial images. The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, …

Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Mariem Hassan Lafta, Zahir Abdul Haddi Hassan

Iraqi Journal for Computer Science and Mathematics

Most real-world technological systems are highly complex, making it challenging to examine their reliability. Many systems can be represented as Complex Parallel-Series Networks (CPSN). The large number of components and subnetworks, along with their intricate connection, complicates the identification, evaluation, and potential failure of the CPSN. A minimal path set is a minimal set of components whose proper functioning (success) guarantees the success (operability) of the system. The set is minimal in the sense that removing any component from it means it no longer guarantees system success. The primary research problem is to identify these minimal path sets, both for …

A Beginner’S Guide To Artificial Intelligence (Ai) And Generative Ai (Gen Ai) For Small Businesses, Stanley Mierzwa, Iassen Christov

Center for Cybersecurity

Generative Artificial Intelligence (GenAI) guide for small businesses.

Support and funding for this effort was received from the United States Small Business Administration (Contract 73351023C0016) for this activity and research. Formulated and ultimately created as a result of this grant was the New Jersey Cybersecurity Regional Cluster (NJCRC), which included the partners of NTouch-BCT Strategies, Covenant Business Concepts, and the Kean University Center for Cybersecurity. As part of the free cybersecurity risk assessments offered to small businesses in the community, organizations were introduced to GenAI through a demonstration, with the key information provided in this booklet.

Generalized Plant Disease Detection Using Residual Networks With Eca And Senet Integration, Asmaa Aly Hagar, Marwa Reda Bastwesy, Reda Elbasiony, Mohamed Talaat Faheem

Journal of Engineering Research

Early and accurate detection of plant leaf diseases is crucial for safeguarding agricultural productivity and ensuring food security. Traditional methods of plant disease detection, which often rely on manual inspections and specialized models, encounter challenges such as limited scalability, data annotation difficulties, and task-spe-cific constraints. This paper introduces two innovative and general-ized approaches for plant disease detection by combining Residual Networks with channel attention modules. The first approach inte-grates ResNet-101, the Efficient Channel Attention (ECA) mecha-nism, and the Squeeze-and-Excitation Network (SENet), while the second combines ResNetRS-101 with the ECA mechanism. These models leverage the strengths of ResNets, the dynamic channel …

A Generalized Plant Disease Detection Technique Based On Residual Network With Efficient Channel Attention, Asmaa Aly Hagar, Marwa Reda Bastwesy, Reda Elbasiony, Mohamed Talaat Saidahmed

Journal of Engineering Research

The detection of plant diseases a vital task for ensuring crop health and optimizing agricultural productivity. Machine learning and computer vision have significantly advanced the automation of plant disease detection. However, traditional methods face several challenges, particularly with data annotation and the limitations of task-specific models, which often fail to generalize across different plant diseases. These methods are hindered by their reliance on models tailored to individual crops and the ongoing need for manual inspections, leading to inefficiencies and restricted scalability. This study proposes a method designed to enable the efficient and accurate identification of a broad range of plant …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …

A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …

Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …