Engineering Commons^™

Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave

Browse all Theses and Dissertations

The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance …

Ai-Enabled Hardware Security Approach For Aging Classification And Manufacturer Identification Of Sram Pufs, Harshdeep Singh

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Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly …

Ml-Assisted Side Channel Security Approaches For Hardware Trojan Detection And Puf Modeling Attacks, Niraj Prasad Bhatta

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Hardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further …

Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart

Browse all Theses and Dissertations

Deep neural networks have great representational power. However, most deep neural nets today optimize directly for performance on a single task defined only by labeled training data. This excludes potential sources of knowledge and ways of learning which could improve their performance, and address challenges, such as explainability, which are pressing to the field. We propose a framework for neural network architecture which generalizes it to a graph of many semantically-meaningful variables. We call it the Multi-Semantic-Stage Neural Network (MSSNN). An MSSNN models its domain as a web of conditional probabilities, i.e. a collection of inter-related tasks which can learn …

Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki

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The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable …

Test-Time Backdoor Attack Using Universal Perturbation, Jesse Alexander Smith

Browse all Theses and Dissertations

The rapid growth and widespread reliance on machine learning (ML) systems across critical applications such as healthcare, autonomous driving, and cybersecurity have un- derscored their transformative potential and heightened their susceptibility to adversarial attacks and vulnerabilities. This thesis investigates vulnerabilities in ML models, focusing on backdoor attacks, including naive backdoor attack, feature collision backdoor attack, hidden trigger backdoor attack, and test-time backdoor attack using universal perturbation technique. These methodologies demonstrate how adversaries can automate and conceal malicious behaviors to achieve specific objectives, posing significant challenges to ML model integrity and trustworthiness. The research provides a comprehensive analysis of the theoretical …

Semantics-Aware Text-Guided Aerial Image Synthesis By Feature Augmented Diffusion Models, Douglas J. Townsell

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Aerial imagery provides crucial insights for various fields, including remote monitoring, environmental assessment, and autonomous navigation. However, the availability of aerial image datasets is limited due to privacy concerns and imbalanced data distribution, impeding the development of robust deep learning models. While recent text-guided generative models have shown promise in synthesizing high-quality images, they fall short in handling the unique challenges of aerial imagery, including densely packed objects, intricate spatial relationships, and the absence of paired text-aerial image datasets. To tackle these limitations, we propose STARS, a groundbreaking framework for Semantic-aware Text-guided Aerial image Refinement and Synthesis. STARS introduces a …

Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore

Browse all Theses and Dissertations

The digital landscape is ever-evolving. In recent years the amount of bot traffic, traffic generated by autonomous applications over the internet has increased significantly. Many bots perform useful and needed functions, however, malicious bots are known sources of both common and emerging security threats. Denial-of-Services (DoS), information theft, and credential stuffing have all been conducted by malicious software running on unknowingly infected machines. The dichotomy of useful bots operating in the same networks as malicious bots combined with novel bot attacks and an ever-increasing number of personal devices connecting to the Internet drives the need for continued advancement of malicious …

A Trusted Adversarial Ml Countermeasure Approach For Secure And Resilient Ai-Driven Hardware Trojan Detection, Mohammed G M Alkurdi

Browse all Theses and Dissertations

Semiconductor microelectronics Integrated Circuits (ICs) are increasingly integrated into critical life applications including medical, aerospace, and Internet of things. Their increasing importance as a technology gave rise to critical concerns regarding their security. This has led to the focus of the research community on hardware Trojans, which are malicious modifications to the ICs with undesirable outcomes. Their detection is becoming increasingly critical, with many researchers proposing methods to do so such as reverse engineering, logic testing, and side-channel analysis. Many of these proposals utilize machine learning methods to detect these malicious modifications with high accuracy and confidence. However, machine learning …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai

Theses and Dissertations--Electrical and Computer Engineering

Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Advancing Household Robotics: Deep Interactive Reinforcement Learning For Efficient Training And Enhanced Performance, Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla

Engineering Management & Systems Engineering Faculty Publications

The market for domestic robots—made to perform household chore, is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labour, in contrast to industrial robots, which are frequently criticised for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in a number of minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviours. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to …

Improving Neuropathological Analysis With Aβgan: Addressing Morphology Imbalance For Efficient Alzheimer's Disease Diagnosis, Sujatha Alla, Prasanthi Chidipudi, Nagesh Bheesetty, Vedvikash Reddy Velur, Joshit Mohanty, Puneeth Bheesetty, Marisha Jmukhadze, Narendra Lakshmana Gowda, Sai Gireesh Komaragiri

Engineering Management & Systems Engineering Faculty Publications

Histopathologists are experiencing a digital revolution in their field thanks to the digitization of Whole Slide Images (WSIs), which are microscope slides of tissue that can measure gigapixels in size. With so much high resolution data at their disposal, computer vision techniques can now be used to automate laboratory processes, create visual standards, and increase analysis throughput, all of which reduce the workload of pathologists [1]. The "gold" standard in neuropathology, particularly for Alzheimer's Disease- is pathological diagnosis made by looking at White Matter Inclusions (WSIs) in brain tissue. Semi-quantitative scoring in accordance with the standards established by the Consortium …

Context-Free Grammar Framework For Automatic Shooting Game Enemy Pattern Generation, Nitit Kaweeratanakit

Chulalongkorn University Theses and Dissertations (Chula ETD)

This research proposes a framework for generating enemy patterns for SHMUPs game. It is directly based on a grammar derived from the enemy behavior of existing commercial SHMUPs, and implemented using a new description language called "Enemy Pattern Description Language" (EPDL). EPDL contains all information required to construct the enemy, with no requirement of external data content. The language is human-readable and can be connected to any game engine of choice using an EPDL interpreter. The interpreter itself consists of lexer and recursive descent parser. The results shown in this research is implemented in. "rdnh", a private fork of Touhou …

การพยากรณ์การจ่ายยาของโรงพยาบาลโดยการเรียนรู้ของเครื่องและวิธีการวิเคราะห์เชิงสถิติ, วริศ ปุณณะหิตานนท์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบันการพยากรณ์การจ่ายยาของโรงพยาบาลถือเป็นหัวใจสำคัญต่อการจัดการคลังยาและการสั่งซื้อยาเป็นอย่างมาก เนื่องจากการพยากรณ์ที่น้อยเกินไปทำให้ยาไม่เพียงพอส่งผลให้เกิดความล่าช้าภายในโรงพยาบาล ในขณะที่การพยากรณ์ที่มากเกินไปทำให้เปลืองพื้นที่ใช้สอยและอาจทำให้ยาเสื่อมสภาพหรือหมดอายุซึ่งส่งผลให้โรงพยาบาลสูญเสียรายได้ การมีแบบจำลองที่สามารถพยากรณ์ปริมาณการจ่ายยาให้ใกล้เคียงกับค่าจริงจะสามารถลดปัญหาการขาดแคลนยาในแต่ละห้องจ่ายยาหรือการที่ห้องจ่ายยามีการกักตุนตัวยาเกินความจำเป็น จากปัญหาที่กล่าวมาข้างต้น โครงงานมหาบัณฑิตนี้จึงถูกจัดทำขึ้นเพื่อนำเสนอแบบจำลองที่จะมาแทนค่าเฉลี่ยเคลื่อนที่แบบทั่วไปซึ่งจะช่วยให้โรงพยาบาลสามารถพยากรณ์ปริมาณการจ่ายยาแต่ละวันได้แม่นยำมากขึ้น โดยจะนำเทคนิคสำหรับพยากรณ์ข้อมูลที่อยู่ในรูปแบบของอนุกรมเวลามาประยุกต์ใช้กับข้อมูลการจ่ายยาย้อนหลังและข้อมูลการนัดหมายแพทย์ย้อนหลัง หลังจากนั้นจะนำผลลัพธ์ที่ได้มาคำนวณค่าเคลาดเคลื่อนด้วยค่าเฉลี่ยของเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์และค่าเฉลี่ยสมมาตรของเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์และนำผลที่ได้มาใช้ในการเลือกว่าแบบจำลองไหนให้ค่าคลาดเคลื่อนต่ำที่สุด ผลการทดลองพบว่าแบบจำลองซัพพอร์ตเวกเตอร์รีเกรสชันให้ค่าความคลาดเคลื่อนที่ต่ำกว่าค่าเฉลี่ยเคลื่อนที่แบบทั่วไป แบบจำลองที่ผู้จัดทำโครงงานนำเสนอสามารถนำไปประยุกต์ใช้กับการพยากรณ์การจ่ายยาเพื่อให้แต่ละห้องจ่ายยามียาสำหรับให้บริการในปริมาณที่เพียงพอต่อความต้องการ

Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu

Engineering Technology Faculty Publications

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia

Research outputs 2022 to 2026

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection …

Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach, Hongyu Guo, Mehdi Keyvan-Ekbatani, Kun Xie

Civil & Environmental Engineering Faculty Publications

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with …

Implications Of Alternative Communications And Sensing Technologies For Implementing Variable Speed Limit Control Through Connected Vehicles: Sag Curve As A Case Study, Reza Vatani Nezafat, Mecit Cetin, Elizabeth Williams, George F. List

Civil & Environmental Engineering Faculty Publications

Connected vehicles (CVs) will enable various applications to improve traffic flow. This paper's focus is to investigate how the potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs makes it possible to mitigate congestion at a sag curve bottleneck. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) options for CVs are investigated when implementing the VSL control strategy in a simulation environment. Also, for measuring traffic density …

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Transformer-Enabled Deep Reinforcement Learning For Coverage Path Planning, Daniel B. Tiu

UNF Graduate Theses and Dissertations

Coverage path planning (CPP) is the problem of covering all points in an environment and is a well-researched topic in robotics due to its sheer practical relevance. This paper investigates such an offline CPP problem where the primary objective is to minimize the path length to achieve complete coverage. Furthermore, the literature suggests that taking turns leads to a higher energy use than going straight. To this end, we design a novel objective function that aims to minimize the number of turns as well. We have proposed a deep reinforcement learning (DRL)-based framework that uses a Transformer model. Unlike state-of-the-art …

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Ai-Based Defect Detection In Aerospace Ultrasonic Signals, Rami Issac Lake

Graduate Research Theses & Dissertations

Ensuring the safety and integrity of materials and structures throughout the manufacturing cycle is a critical concern across various industries, including aerospace, automotive, oiland gas, and civil engineering. Non-Destructive Inspection (NDI) techniques allow for the examination of materials without causing damage or alteration, enabling the early detection of potential issues before materials are utilized in the field. The inspection of fuselage composites presents a particular challenge due to their complex structures, diverse materials, and differences in thickness, making defect detection a challenging yet crucial task. Moreover, defects of various types and causes can emerge across all depths of the material …

Simulation Of A Pick And Place System For Electronic Cards Using A Yumi Cobot, Derrick Sze, Rosula Sj Reyes, Patricia Angela R. Abu

Electronics, Computer, and Communications Engineering Faculty Publications

Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing repetitive work and requiring highly skilled workers. The goal of the research is to be able to simulate a pick and place environment with electronic cards using a YuMi cobot and mobile platforms in Coppeliasim. The mobile robot is responsible for transporting the electronic cards to the target location through path planning implemented using the OMPL plug-in. After arriving at the target location, YuMi will then perform the …

Machine Learning Assisted Optimization For Calculation And Automated Tuning Of Antennas, Lauren Linkous

Theses and Dissertations

The Antenna Calculation and Autotuning Tool (AntennaCAT) software suite represents a significant advancement in the field of antenna design by automating the entire design, CAD, simulation, and optimization process compatible with several EM simulation software suites. It is the first comprehensive implementation of machine learning in this context. In particular, this work includes the capability to create and export structured datasets from the aforementioned EM software for iterative improvement and includes an expandable selection of optimizers.