Computer Engineering Commons^™

Mastering Undergraduate Algorithms: Improving Problem-Solving Skills And Fluency Through Scaffolding Learning Modules, Anissa Soungpanya

College of Engineering Summer Undergraduate Research Program

The primary objective of the undergraduate algorithms course is to equip students with the ability to design and analyze algorithms, prove theorems about computation, and effectively communicate these algorithms and proofs to a human audience. In addition, the course aims to help students foster their fluency in the process of formulating and solving computational problems. Scaffolding exercises and activities that focus on these aspects could serve as useful learning tools in providing targeted feedback and helping students develop their confidence and fluency. To address the challenges that students encounter when solving algorithmic problems -- particularly in regard to problem decomposition …

Augmented Reality Application For Real-Time Coastal Data Visualization, Nithyasri Palanisamy

College of Engineering Summer Undergraduate Research Program

This project aims to develop an Augmented Reality (AR) application that overlays real-time coastal environmental data onto physical landscapes when viewed through AR devices such as smartphones and mixed reality headsets. By integrating data from machine learning models, computer vision-based event detection, coastal sensors, and geospatial mapping technologies, the application will provide users with an immersive and interactive experience, enhancing their understanding of coastal dynamics and environmental changes. The application will focus on observing coastal phenomena such as rip currents, tracking endangered coastal species and marine mammals, monitoring crowd levels on beaches, etc. Data sources will include NOAA's National Data …

Machine Unlearning: The Right To Be Forgotten, Colin Ngo

College of Engineering Summer Undergraduate Research Program

The objective of this SURP proposal is to investigate machine unlearning as a viable approach to support the right to be forgotten in artificial intelligence (AI) systems, many of which rely heavily on personal data. The capability to selectively remove user-specific information upon request—without necessitating full model retraining—is critical for safeguarding individual privacy and enhancing computational and energy efficiency. This project will undertake a systematic review of state-of-the-art unlearning techniques, evaluate their performance using standard benchmark datasets, and assess their feasibility in real-world applications. In addition, the project will provide participating students with practical experience in implementing and analyzing machine …

Relational Algebra Interpreter, Sydney Lynch

College of Engineering Summer Undergraduate Research Program

We would like to build a compiler for relational algebra that converts it to SQL. This compiler will be used in database labs to give the students hands-on experience to write relational algebra code. Current relational algebra compilers that are open source are not very good and are hard for the students to use.

Hand-Tracking And Extended Reality Interfa, Alexander Bloomer, Alberto Cornejo

College of Engineering Summer Undergraduate Research Program

This project explores the use of Extended Reality (XR) technologies to enhance human- robot interaction in industrial contexts. Building upon prior research in affective and cognitive state recognition during human-cobot collaboration, this study investigates how natural hand and head gestures, captured through Meta Quest passthrough mode, can be used to communicate human intent to a Universal Robotics e-Series collaborative robot. The XR system provides users with an immersive, real-world visual interface while tracking motion and position in real time. The captured gestures are interpreted through a custom software pipeline that integrates machine learning models and rule-based logic to trigger adaptive …

Ai Fact-Checking Claims In Videos, Jake Altieri

College of Engineering Summer Undergraduate Research Program

This project investigates the use of acoustic signals captured during Fused Deposition Modeling (FDM) 3D printing to predict part quality and detect process anomalies. Traditional quality monitoring in FDM often relies on visual inspection or post-process evaluation, which can be slow and inconsistent. This research explores a low-cost, non-contact alternative using microphones and accelerometers to capture real-time audio and vibration signatures of the printing process. By applying signal processing and machine learning techniques to these acoustic signals, the project aims to classify part quality and identify defects such as under-extrusion, layer misalignment, or nozzle clogging. The outcomes have potential applications …

Deterministic Motion Planning For Highly Articulated Multi-Link Robots, Matthew Flynn

College of Engineering Summer Undergraduate Research Program

Slender, multi-link, highly articulated, and extensible robots designed for minimally invasive surgeries have the potential to significantly transform the performance of common medical procedures. These advanced robots can reduce uncertainties and risks associated with surgeries, leading to shorter patient recovery times, accelerated healing, and minimized scarring. Made possible by their numerous mechanical linkages and concentric mechanisms, these multi-link articulated robots can navigate along non-linear paths, a capability that traditional straight probes lack. This flexibility allows surgeons to perform minimally invasive procedures on clinically significant targets that were previously difficult or impossible to access while avoiding vital anatomical structures. Beyond their …

Math+Cs Integrated Curriculum For K-12 Computer Science Education, Amogh Arora

College of Engineering Summer Undergraduate Research Program

This is a proposal for an activity to initiate an effort to create a series of X+CS integrated curricula for learning Computer Science (CS) in K-12. As a start, we examine Mathematics and CS standards, to find the cross-cutting concepts between the two fields. By leveraging these concepts, we bring to the foreground the ways CS can be used in the mathematical context. The goal for this research is to create a 15-week teacher training curriculum that will expose teachers to the CS concepts of Abstraction, Data Representation, Problem Comprehension and Decomposition, Control Structures, Functions and Generalization. Historically, Mathematics and …

Analysis Of Student Inclusivity In Computing Education: Eeg-Based Prediction Of Student Belonging, Alec Odell

College of Engineering Summer Undergraduate Research Program

This project focuses on the use of Natural Language Processing (NLP) techniques to analyze text stimuli used in cognitive research. Specifically, the project involves analyzing text that presents different types of mindsets, such as growth and fixed mindsets, to understand their impact on cognitive state. Students will apply various NLP methods, such as tokenization, text classification, and sentiment analysis, to analyze the language used in different types of mindset stimuli. The goal is to understand how text-based stimuli can influence cognitive responses and to extract meaningful features from the text that can be used to predict outcomes like engagement or …

Analyzing Privacy And Usability Tradeoffs In Multi-Party Relay Systems, Jess Alencaster, Leticia Leon-Rodriguez

College of Engineering Summer Undergraduate Research Program

Nearly everything we do on the Internet leaves a trace, and in recent decades the value of user data has proven to be highly profitable and become a fundamental business strategy of the Internet. The only recourse users have in this situation is seeking increased privacy, yet privacy is uniquely challenging on the Internet because we inherently rely on others (e.g., ISPs, content providers, CDNs) to carry and serve our traffic. Recent systems have sought to enhance user privacy without sacrificing performance by adopting Multi-Party Relay (MPR) architectures, including Apple's iCloud Private Relay. These architectures mask user IP addresses by …

Exploration Of Physics-Informed Grid Generation Technique For Wall-Modeled Les Using Eagle3d, Dominic Schneider

Doctoral Dissertations and Master's Theses

Wall-Modeled Large Eddy Simulation (WMLES) is an area of interest due to its ability to lower computational costs of LES. Even with the application of wall models, LES still proves to have practicality issues when it comes to use in industry, due to the expertise, time, and computational resources required. A novel technique for generating a lean, physics based WMLES grid is described.

The technique utilizes a RANS solution to extract turbulence information, user-specified values related to resolution of turbulent energy levels, acoustics waves, and shock waves, to generate a point cloud for producing a lean WMLES grid with in-house …

Deep Learning-Based Change Detection In High-Resolution Remote Sensing Imagery, Hazem Badawy

Theses and Dissertations

Remote sensing has become a key tool for monitoring Earth’s surface over time, offering valuable insights into both natural and human-driven changes. Among its many applications, change detection focuses on analyzing multi-temporal imagery to reveal how specific areas evolve across different time periods. It plays a pivotal role in Earth observation applications, including urban development monitoring, environmental degradation assessment, and disaster response. However, existing approaches often struggle with limited contextual awareness, high sensitivity to noise, and imprecise localization of change boundaries, especially with high-resolution imagery. This thesis investigates the complex problem of change detection in remote sensing imagery by proposing …

Impacto De La Inteligencia Artificial En La Educación Superior. Guía Reflexiva, Jairo Eduardo Márquez Díaz

Ingeniería

La inteligencia artificial (IA) está revolucionando la educación superior en diversas formas como, por ejemplo, la personalización del aprendizaje, la creación de tutorías inteligentes y el análisis de aprendizaje. Este libro se presenta como una herramienta valiosa para todos aquellos interesados en comprender y aprovechar las oportunidades que la ia ofrece en el campo de la educación superior. Con un enfoque equilibrado y exhaustivo, esta publicación pretende servir como una guía integral para profesores y estudiantes que buscan entender cómo la ia está transformando la enseñanza y el aprendizaje en la actualidad. A lo largo de sus páginas, aborda diversos …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Enhancing Non-Player Character Dialogue In Video Gages: An Evaluation Of Large Language Model-Generated Responses, Lam P. Quach

Master's Theses

As video games increasingly emphasize narrative depth and player immersion, the quality of Non-Player Character (NPC) dialogue has become crucial for creating engaging gaming experiences. This thesis investigates the potential of Large Language Models (LLMs) to generate high-quality NPC dialogue by comprehensively evaluating four state-of-the-art models: Gemma 3 27B, Mistral 7B, QWEN 2.5, and LLAMA 3.1. The study employs a mixed-methods approach, combining human evaluation (N=50 participants) with AI-based assessment across five key benchmarks: coherence, personality expression, engagement, style/tone appropriateness, and overall quality. Participants evaluated 32 dialogue samples (8 per model) generated for a fantasy game context featuring two distinct …

Low-Resource Ecoacoustic Audio Classification, Enis Berk Coban

Dissertations, Theses, and Capstone Projects

Ecoacoustic monitoring via machine learning enables scalable analysis but is often constrained by labeled data scarcity, particularly in remote regions like the Arctic. This thesis confronts low-resource ecoacoustic audio classification by developing and evaluating complementary machine learning methodologies. We introduce EDANSA, the first publicly available, expert- labeled Arctic dataset of its kind, curated via novel active learning, alongside a baseline CNN. We systematically evaluate transfer learning, showing general audio embeddings effectively bootstrap classifiers for challenging Arctic sounds, significantly outperforming direct label mapping. Optimizing label utility, we investigate standard data augmentation and introduce novel audio data valuation via Shapley values, revealing …

Toward A Generalizable Perceptual Hashing Framework For Image Manipulation Detection, Priyanka Samanta

Dissertations, Theses, and Capstone Projects

This thesis contributes to research in adversarial image manipulation detection. The primary motivation is the increasing need to verify digital images, especially for legal evidence, journalistic proof, or social media content—where manipulated or fabricated images can mislead, defame, or distort reality. A key application and contribution of this work is the development of eWitness, a blockchain application that generates and registers image provenance at capture time to enable independent verification of authenticity. The secret sauce behind the system is SmartHash, a novel and efficient perceptual hashing algorithm designed for real-world deployment in systems like eWitness. Unlike existing algorithms, SmartHash targets …

Multimodal Learning In Real-World Application: Enhancing Feature Representation And Training Strategies, Nana Lin

Graduate Doctoral Dissertations

Multimodal learning has emerged as a critical paradigm for developing intelligent systems that can understand and reason across diverse inputs such as images, text, and audio data. Despite significant advances, effective deployment of multimodal models in practice remains a challenging task. This dissertation explores how multimodal learning can be effectively applied to high-stakes, real-world scenarios, with a focus on enhancing feature representation and training efficiency. Specifically, this research investigates multimodal learning strategies in two key domains: healthcare and surveillance.

In the healthcare domain, we explored the data fusion and alignment approaches for cognitive decline diagnoses. First, we propose the LOVEMA …

Human-Machine Communication: Complete Volume. Volume 10

Human-Machine Communication

This is the complete volume of HMC Volume 10.

Smart Mobility Technologies In Urban Areas Of Emerging Economies: A Bibliometric Analysis, Peter Mugisha, Rose Luke, Joash Mageto, Hossana Twinomurinzi

African Conference on Information Systems and Technology

Despite the adoption of smart mobility solutions in emerging economies, challenges such as traffic congestion, pollution and inadequate infrastructure still persist. This study analyses 540 scholarly articles published between 2003 and 2024 to evaluate how smart mobility technologies – such as Intelligent Transportation Systems (ITS), Internet of Things (IoT) and Artificial Intelligence (AI) – have been implemented in these regions. Data was retrieved from Scopus and Web of Science and analysed using Biblioshiny for bibliometric mapping and Atlas.ti for thematic analysis. The review identifies research trends and gaps, showing how ITS has improved transport management in cities like Nairobi, and …

Intelligent System Designs For Hvac Energy Reduction In Buildings: Ai-Based Forecasting And Hybrid Active/Passive Approaches, Leena N. Alam, Rim M. Obaid, Thoraya Musa, Wegdan O. Alshateri, Passent M. Elkafrawy Prof

Effat Undergraduate Research Journal

The majority of building energy utilization worldwide is related to HVAC (Heating, Ventilation, and Air-Conditioning) systems. Eighty percent of the energy produced in Saudi Arabia is used by buildings, and since 70\% of that energy is used for ventilation, air conditioning accounts for roughly 50\% of the nation’s electrical use. This study reviewed and compared much research that used various AI-based forecasting algorithms. Specifically, the study explored the potential of passive and active cooling methods and intelligent system designs and used this analysis to develop a hybrid model that combined AI-based forecasting with active/passive approaches for optimal energy savings. The …

Early Detection Of Oak Wilt Using Unmanned Aerial Vehicles (Uav) & Computer Vision, Muttaki I. Bismoy

Masters Theses

Forests are critical ecosystems, delivering services such as biodiversity conservation, climate regulation, timber production, and recreation. However, they face increasing threats from pathogens like Bretziella fagacearum, which causes Oak Wilt, a lethal disease that disrupts water transport in oak trees, leading to canopy dieback and eventual death. Traditional detection methods rely on manual ground surveys, which are labor-intensive, time-consuming, and prone to error, particularly in early disease stages.

This research presents an automated, scalable, high-precision Oak Wilt detection system using Unmanned Aerial Vehicles (UAVs) combined with deep learning-based computer vision. Expanding on earlier work with a lightweight CNN achieving an …

Design And Development Of Deep Learning Based Generic Platform For Promoting Precision Agriculture, Srilakshmi A

Theses and Dissertations

Precision agriculture also referred as precision farming or smart farming, is an innovative approach to agricultural management that leverages technology and data to optimize various aspects of the farming process. This approach aims to make farming more effective, sustainable, and profitable by affording farmers with the application tools and information they need to make more informed decisions.

Precision agriculture combines elements of agriculture, technology, and data science to enhance crop production, and resource utilization. Precision agriculture techniques can be highly effective in leaf disease detection within crop fields. Machine learning has been developed incredibly across multiple domains and shown it …

Encountering And Mitigating Selfish Mining In Bitcoin Mining Pools, Jeyasheela Rakkini M J

Theses and Dissertations

Blockchain, an innovative decentralized distributed, disrupting programming paradigm embodies key principles such as decentralization, data provenance, immutability, and transparency. At its core blockchain begins with the genesis block and progresses with each subsequent block containing the hash of the previous block, Merkle root, timestamp, a coin base transaction address, and a nonce. Miners compete to discover a target hash value (hash value of the previous block and nonce) for the current block, that is less than or equal to the difficulty value set by the system, a process known as mining.

This work encounters selfish mining attacks in bitcoin mining …

Ai Based Early Detection Of Hormonal Imbalance And Poly-Cystic Ovary Syndrome In Young Women, Reka S

Theses and Dissertations

A hormonal disorder, Poly-Cystic Ovary Syndrome (PCOS) usually affects women during the reproductive age. It is characterised by imbalances in hormones, particularly a rise in the female body's androgen level (male hormone) and enlarged ovaries with small cysts. PCOS can cause ovarian cysts, weight gain, acne, excessive hair growth, insulin resistance, and irregular menstrual cycles along with other health problems. While the exact origin of PCOS is uncertain and its symptoms are unclear, diagnosing PCOS in real-world conditions is a difficult task. Therefore, prompt and precise PCOS diagnosis is essential for efficient treatment and for averting long-term issues.

Clinicians typically …

Investigation Of Dependency Parsing Techniques For Digital Document Analysis Through Deep Learning Approach, Rekah D Ms

Theses and Dissertations

Digital document dependency parsing is a significant task in natural language processing. Dependency parsing supports verification of the grammatical correctness of a sentence besides enabling extraction of relevant documents. Inappropriate extraction of features may result in falsely parsing a document, leading to decreased accuracy. Machine learning methods have been employed to perform feature extraction. However, selecting pertinent features was never achieved which minimizes the time consumption and overhead.

Hence, novel machine learning and deep learning techniques have been designed in our work for accurate and computationally efficient digital document analytics through dependency parsing. Four different contributions have been proposed for …

Cyclone Intensity Prediction In The Bay Of Bengal Using Deep Learning Methods, Senthil Kumar J

Theses and Dissertations

The Bay of Bengal region's coastlines have been badly devastated by tropical cyclones, as the region experiences an average of five to six cyclones per year, with about two to three of these intensifying into tropical storms or severe cyclones. Thus it necessitates to study the accurate and efficient forecasting of their intensity to improve preparedness and response to natural disasters. The present study compares and examines three distinct approaches to cyclone intensity prediction using historical datasets from 1998 to 2020: hybrid optimisation, deep learning-based, and empirical approaches.The predicted accuracy, computational effectiveness, and feasibility for real-time scenarios of each model …

Investigations Of Secure Memory For Vlsi Based Crypto System, Vijay Sai R Mr

Theses and Dissertations

Semiconductor technology is growing very rapidly in their architectural developments, involving the usage of processor and memory. Presence of memory, in general is a vital commodity in various devices which are almost embedded into human activity, from robust work stations to handy mobile phones. Security of data stored in memory is very important, and hence, observation must be made that these valuable data should not be thwarted by malicious means. Security in cache memory is a major issue in memory related applications such as smart cards and bio-metric implementations.

Cache, is a small and limited memory located between central processing …