Digital Commons Network^™

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Succinctness Meets Transparency A Linking Framework Combining Hyrax And Kzg, And Its Applications, Argha Sardar

Master’s Dissertations

The thesis begins by introducing the concept of zero knowledge and exploring its various paradigms. It then focuses on a interesting problem: the construction of linking proofs. Chap- ter 2 addresses the challenge of binding two distinct polynomial commitment schemes so that they are consistent at a common evaluation point. The chapter further introduces a method for equating two different polynomials with different domains using an affine line construction. By applying certain optimizations to the bulletproofs protocol, the work achieves a reduction in both the prover’s time and the number of rounds required for proving a large committed inner product. …

Multiple View Neural Regression Of A Facial Shape Model, Xiang Li

All Dissertations

Creating re-topologized 3D facial meshes is a critical step in high-quality facial animation pipelines, yet it remains a labor-intensive and time-consuming task. Traditional approaches typically rely on multiview stereo reconstruction and specialized photometric environments to acquire accurate geometric and reflectance data under controlled conditions. This dissertation presents work toward more efficient capture of production-ready meshes including (1) developmental aspects of VarIS, a custom-designed light sphere capable of capturing high-resolution stereo geometry and reflectance maps—including diffuse, specular, and normal components under programmable illumination; (2) a study of the effects of camera parameters on automatic 2D and 3D landmarking methods, (3) methods …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Low-Level Memory Attacks On Edge Assisted Robotic Applications, William Arnold

Master of Engineering Theses

This thesis investigates how low-level memory faults can undermine edge-assisted robotic systems that rely on memory optimization. As robots are utilized in real world applications, the ability to operate safely and successfully in mission critical deployment becomes important. To help achieve these goals, developers are increasingly starting to place computation nodes at network edges to meet latency and reliability requirements. Edge nodes, however, are resource-constrained and resources conservation techniques such as Kernel Same-page Merging (KSM) are enabled to deduplicate identical pages across processes or virtual machines. This thesis shows that this optimization technique quietly widens the attack surface and can …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Retracted: Dynamics And Stability Analysis Of 8-Dimensional Hyperchaotic Systems: Study Of Lyapunov Exponents, Abdulsattar Abdullah Hamad, Nida Muhsin Ali, Muayyad Mahmood Khalil

Iraqi Journal for Computer Science and Mathematics

This research explores the complex dynamics of an 8D hyperchaotic system, focusing on its trajectory stability and behavior under various control parameters. By using numerical simulations, we investigate the relationship between the Lyapunov components and the stability of the system. It provides a quantitative measure of the chaos within the model of the matrix, whose derivation consists of the system's sensitivity to perturbation, simulating chaotic systems in high dimensions faces challenges such as high computational resource demands. Difficulty in ensuring numerical convergence and stability. The results highlight the profound impact of control parameters on the dynamic behavior of hyperchaotic systems. …

Corneal Elevation Maps Patterns Classification Using Correlation Method, Sura M. Ahmed, Nebras H. Ghaeb, Salman Yussof, Noor T. Al-Sharify, Husam Yahya Nser, Zainab T. Al-Sharify, Ong Hang See, Leong Yeng Weng

Iraqi Journal for Computer Science and Mathematics

The ophthalmologist uses various techniques to diagnose corneal abnormalities, including corneal topography and tomography devices. Currently generated color corneal elevation surface maps from topographic imaging devices are essential for detecting ocular diseases, while accurately classifying these maps to differentiate between different shapes remains an issue. This study aims to assess and compare parameters of the front and back corneal surface elevation map patterns of normal/abnormal corneas. Two hundred cases were randomly taken (100 normal and 100 abnormal) with a single normal reference image, and then an additional 25 cases were added later for the optimization process. The preprocessing of all …

Retracted: Comparative Study Based On Continuous Analysis Of Autism Spectrum Disorder Using Advanced Deep Learning With Model Interpretability Insights, Ayan Sar, Hussain Falih Mahdi, Sumit Aich, Pranav Singh, Tanupriya Choudhury

Iraqi Journal for Computer Science and Mathematics

Hysterical conversion has similar cognition and behaviours to those in the case of ASD; it is, therefore, complex when diagnosing and classifying the condition. The majority of employed diagnostic tests are cross-sectional and fail to describe the developmental and clinical features of ASD; for this reason, they are pretty inaccurate in the diagnosis of ASD and thus cause disparities in the efficiency of the therapeutic interventions used. The present study's research contribution is a new application of deep learning that aims to analyse the spectrum of ASD with gradient-based classifications. In this case, we use a DL model trained on …

Exploring Immune System Through Computational Modeling: A Comprehensive Study Of Lymph Nodes And Immune Response Scaling, Vaccine Efficacy, And Large-Scale Extreme First Passage Time, Jannatul Ferdous

Computer Science ETDs

The adaptive immune response is a complex defense mechanism that develops over time to recognize and eliminate pathogens with remarkable precision and durability. This dissertation investigates the dynamics, scaling, and efficiency of the adaptive immune response through a synthesis of computational modeling, mathematical analysis, and agent-based simulations. First, we analyze the topology of the lymphatic network and investigate the T cell search time to find the lymph node that is containing the matching dendritic cell. Second we show how the scaling of lymph node number and volume with body mass, leads to scale-invariant search times for T cells locating antigen-bearing …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan

Turkish Journal of Electrical Engineering and Computer Sciences

The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. The point process …

Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …

Adaptive Backstepping Control With Real-Time Fuzzy Logic Parameter Selection Of A Field- Oriented Control-Based Permanent Magnet Synchronous Motor Driver, Fati̇h Bayir, Erkan Zergeroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an adaptive backstepping control approach integrated with a real-time fuzzy logic parameter selection algorithm to enhance the robustness and stability of a permanent magnet synchronous motor (PMSM) controller under parametric uncertainties and external disturbances. Although backstepping control performs well under varying disturbances, it must be supported by an adaptive control algorithm to effectively handle both variable disturbances and parameter uncertainties. Moreover, because the fixed parameters of the adaptive backstepping controller limit the dynamic performance of the velocity tracking loop, this study incorporates fuzzy logic control—a soft computing algorithm capable of real-time parameter adjustment—to achieve more robust outcomes. …

Excitation Of Synchronous Machine By Contactless Power Transfer - Review From The Perspective Of Electric Vehicles, Erhan Tuncel, Emi̇n Yildiriz

Turkish Journal of Electrical Engineering and Computer Sciences

Electrically excited synchronous machines (EESMs) are one of the best choices for propulsion motor appli cation in electric vehicles (EVs) due to their wide torque-speed characteristics. Moreover, the air gap flux density can be easily controlled by varying the excitation current. Despite these advantages, it is difficult to transfer the current required by the rotating excitation winding into the motor under conventional methods, so it is not widely used in EVs. In this study, the emerging literature on contactless power transfer methods is reviewed for applicability to an EESM that can operate as an EV propulsion motor. Design criteria such …

Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel

Turkish Journal of Electrical Engineering and Computer Sciences

Real-world super-resolution is a highly challenging problem in the field of computer vision. Besides enhancing image resolution and improving visual details, information loss due to complex real-world degradations is desired to be restored. One of the primary hardness of this problem is finding sufficiently large paired datasets for training. Researchers have developed techniques that generate synthetic low-resolution pairs using high-resolution images with a generative adversarial network-based degradation generator to address this issue. In these approaches, the degradation generator is trained by utilizing real-world low-resolution images as the target domain, generating a degraded low-resolution counterpart of the high-resolution input. However, in …

Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal

Turkish Journal of Electrical Engineering and Computer Sciences

Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …

Adaptive Multi-Agent Reinforcement Learning For Electric Vehicle Charging Optimization Under Dynamic Traffic Conditions, Shaghayegh Rabbanian

LSU Master's Theses

Electric vehicle (EV) charging optimization is a critical challenge in sustainable transportation. This study focuses on three fundamental questions: (1) when is the best time to charge an EV, (2) where is the optimal charging location, and (3) how should charging be planned considering navigation and routing decisions. Our primary objective is to determine the optimal time and location for EV charging while accounting for key factors such as real-time traffic conditions, spatial distribution of charging stations, and EV-specific attributes such as state of charge (SOC), driving range, and efficiency. To develop a robust and adaptive EV charging recommendation system, …

Quantization On Graph Neural Networks For Image Classification, Rithik Reddy Katpally

Master's Theses

Quantization has become a key approach for reducing storage and computational demands of deep neural networks while maintaining high accuracy. Although 8-bit quantization is well-established for convolutional architectures such as ResNet50 and MobileNetV2, its application to graph-based vision models remains underexplored. In this work, we extend quantization-aware training to Vision Graph Neural Networks (ViGs) and conduct comparisons with quantized CNNs on the CIFAR-100 dataset. To ensure parity, all models have same training hyperparameters such as learning rate, batch size, optimizer, number of epochs. We used numerous techniques to preserve performance for low-bit precision. First, Pauta Quantization clips activation outliers based …

Quantum Algorithm Emulation Using Fpgas, Samuel Petruescu

Master's Theses

Field Programmable Gate Arrays (FPGAs) have been used in most of the physics sub-fields for various unique purposes. This includes particle physics, quantum optics, and, more recently, quantum computing. FPGAs boast many benefits over previous experimental and computational setups. They are versatile, easy to program, and cost-effective, leading to an understandable desire to incorporate them into the new field of quantum computing. While FPGAs have been used to help control the readout and control of physical qubits, they can also be a good tool for improving algorithm simulations, which is the focus of this paper. Different algorithms have different computational …

Using Facial Recognition For Selective Pose Detection, William J. Parker

Master's Theses

Pose detection involves locating and identifying key body points for all individuals within a frame. This enables the ability to convert the pose into a digital format, which can then be recorded and analyzed for a variety of purposes. Advancements in the field have already opened applications in areas such as digital fitness coaches, fall detection, and virtual reality. Existing approaches primarily focus on tracking all detected individuals, which limits the practical applications when attempting to analyze a single or specific subject when there are other people in frame. Previous work has discussed integrating identification, but these approaches use identification …

Investigating The Impact Of Agent Openness On Planning In Multi-Agent Systems, Bala Subramanyam Duggirala

School of Computing: Dissertations, Theses, and Student Research

Multi-agent systems (MAS) possess significant potential for modeling real-world scenarios requiring coordinated actions (like wildfire fighting or ridesharing) among autonomous entities or agents (e.g., wildfire fighting agents) in complex, dynamic environments. Effective decision-theoretic planning (where each agent must carefully consider both the immediate and the future situations or states, and coordinate with the other agents (neighbors) to evaluate what needs to be done at present) within MAS, especially multiagent planning, where the planning agent directly models its neighbors in order to estimate their optimal actions, is critical, yet challenged by factors like partial observability, openness, and diverse agent types with …

Sbv_Dps: A Stacking-Bagging-Voting Nested Ensemble Based Diabetes Prediction System Using K-Fold Cross Validation, Sourabh Shastri, Sachin Kumar, Paramjit Kour, Vibhakar Mansotra

Mansoura Engineering Journal

With the advancement of machine learning techniques, the introduction of the most accurate model has become a necessity. In real-world scenarios, every model has some constraints and assimilates errors, so their performance is not always highly efficient; this sparked the development of ensemble learning. The ensemble approach aims to consolidate the strengths of existing approaches and minimize their weaknesses or decision-making risks. The proposed diabetes prediction system encases a resampling filter, applied to balance the dataset and model builder method, i.e., without the SBV ensemble and with the SBV ensemble method. The model is initially built without using the SBV …

Turning Data Into Guardrails-Decoding Financial Vulnerability Through Behavioural Signs, Debanwita Hajra

Master’s Dissertations

This report presents the work I did during my internship at Hongkong and Shanghai Banking Corporation (HSBC), Kolkata. As a financial institution, the strength of the bank is fundamentally rooted in the behavior and reliability of its customers. Understanding this behavior is not only desirable; it is essential for the security, risk mitigation and future strategic planning of the bank. To do this, banks must invest in a thorough analysis of the financial behavior of their customers to detect early signs of risk and act accordingly. I worked in the Finance Support Team within the Data and Analytics division, where …

Research On Policy Representation In Deep Reinforcement Learning, Zhen Chen, Zhuoyi Wu, Lin Zhang

Journal of System Simulation

Abstract: Deep reinforcement learning (DRL) has achieved remarkable success in various domains. Nevertheless, existing policy networks in DRL still face significant challenges in areas such as generalizability, multi-task adaptability, and sample efficiency. Policy representation, as a crucial research direction for enhancing DRL capabilities, aims to improve an agent's adaptability to environmental changes and novel tasks by constructing more efficient and generalizable forms of policy expression. This paper provided a concise overview of key research advances in the field of policy representation. It introduced diverse policy architectures, ranging from traditional multi-layer perceptron (MLP) -based policies to those based on pointer networks, …