Digital Commons Network^™

Ferroelectric Field-Effect Transistors In Analog Applications: Demonstrated In Time-Domain Neuromorphic Computing Cells, Jacob O'Donnell

Theses

In response to diminishing improvements from transistor scaling, the semiconductor field has shifted its focus to architectural innovation and emerging device technologies. Ferroelectric field-effect transistors (FeFETs) are non-volatile memory elements which offer new opportunities for energy-efficient computing, particularly in compute-in-memory (CIM) architectures. This thesis presents a novel FeFET-based computing cell integrated into a time-domain (TD) computing architecture. The proposed design is evaluated using a device model derived from experimental data and compared with competing non-volatile memory implementations. Results demonstrate improvements in area efficiency and performance, highlighting the potential of the FeFET cell for next-generation TD-CIM systems.

A Microgravity Investigation Of The Subsurface At The United Arab Emirates University Campus, Fidha Farzana Ismail

Theses

This thesis investigates subsurface structures within the United Arab Emirates University (UAEU) campus using microgravity survey. Gravity methods are widely used in geophysical exploration to detect density variations that may indicate subsurface anomalies. The primary objective of this research is to generate the Bouguer anomaly map of the UAEU campus to map subsurface structures and to assess potential geological hazards such as cavities. A total of 76 gravity stations were measured across the UAEU campus using the Scintrex CG-6 gravimeter. The acquired gravity data underwent standard processing to obtain the Bouguer anomaly, including tidal, instrumental drift, latitude, free air, Bouguer, …

Detection And Quantitation Of A Drug And Pesticides In Food Products Of The Uae (Dates And Camel Milk) By Liquid Chromatographic Techniques, Afnan Wael Alanqar

Theses

In this thesis the detection and quantification of the traces of multi-residue pesticides in 26 date fruits samples as well as the presence of the anthelmintic drug thiabendazole in pasteurized commercial camel milk consumed in the United Arab Emirates is examined. The main objective of this thesis is two folds; first is to determine whether a selected 12 pesticides of 8 different chemical groups- in the case of date fruits- and the drug thiabendazole-in case of camel milk- exists in the randomly collected samples of corresponding matrices. Second, is to quantify these compounds and compare their amount to the established …

Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez

Theses

Virtual reality (VR) is commonly used as a tool for enhancing immersion within virtual environments, with one of its primary goals being to approximate real-world perception as closely as possible. In everyday life, variations in lighting conditions significantly influence how colors are perceived across different times and settings. However, the human visual system compensates for these changes, maintaining relatively stable color perception; a phenomenon known as color constancy. This process is driven by chromatic adaptation, the visual system’s adjustment to changes in illumination. Despite the growing use of VR, little research has explored how color constancy operates in virtual environments …

Construction Of Stock Portfolios By Machine Learning Methods, Alfan Gehad Abulehia

Theses

We study the theory and application of two machine learning (ML) algorithms: Long-Short Term Memory Network (LSTM) and Random Forest. The study begins by providing an overview of mathematical foundation, highlighting the significance of mathematics in machine learning. We study and explain the mathematical details involved in these two algorithms. We also study closely related subjects which include but not limited to gradient descent, automatic differentiation, and recurrent neural network. The main ideas behind these topics form the foundation of any ML algorithms.

As an application of the ML algorithms, we focus on the U.S. stock market. We build stock …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Theses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.

The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

Investigating Teachers' Perceptions On The Effectiveness Of School Inspection In Public Schools In The United Arab Emirates, Ruqaia Adel Mohammed

Theses

School inspection plays a significant role in ensuring quality education in UAE public schools. This paper investigates public school teachers’ perceptions of the effectiveness of school inspections in the United Arab Emirates (UAE) and examines the impact of teachers’ demographic factors on these perceptions. A total of 218 teachers across the UAE participated in the study. The research focused on four key domains within the School Inspection Perception Framework (SIPF): Perceived Effectiveness of School Inspections (PESI), Perceived Effectiveness of School Inspection Training (PESIT), Perceived Credibility of School Inspections (PCSI), and Perceived Usefulness of School Inspection Feedback (PUSIF). Data were collected …

Study Of Falāj Pathways And Their Unknown Extension In Al Ain City Using Geophysical Methods, Saud Mohammed Alsenaani

Theses

Historically, the city of Al Ain has relied on its Falāj systems as historic water resources for freshwater, which is vital for drinking and irrigating its oases and essential for supporting life in the region for centuries, providing the foundation for communities and farming. The remarkable rainfall in 2024 uncovered several hidden ancient Falāj systems and newly enlarged versions of previously recorded ones, highlighting the importance of ongoing research for their preservation. This research project aims to examine the current Falāj systems in Al Ain, particularly Falāj Mezyad, using geophysical methods to identify uncharted extensions and to examine current Falāj …

Study Of The Extension Of Cavities In Jebel Hafeet By Implementing Geophysical Methods, Mai Rashed Alkaabi

Theses

Subsurface cavities pose challenges to the buildings and their stability, leading to ground instability and subsidence. In Al Ain City, UAE, particularly in Jebel Hafeet, cavities within carbonate formations and sedimentary rocks present risks to infrastructure and urban development. This study employs Electrical Resistivity Tomography (ERT) and Gravity Surveys to detect, locate, and determine the depth of these cavities, enhancing geological understanding and geohazard assessment. The ERT survey, using a pole-dipole array, was conducted along eight lines to identify subsurface anomalies. It revealed high-resistivity anomalies (> 45 Ω·m), indicative of air-filled cavities, and low-resistivity zones (< 6 Ω·m), corresponding to water-saturated or clay-rich deposits. The ERT survey detected resistivity variations, highlighting anomalies ranging from 45 to 120 Ω·m at depths between 1 m and 17 m. The gravity survey, conducted at 28 stations, detected low-density anomalies, which aligned with high-resistivity ERT zones, confirming subsurface cavities at depths between 10 m and 220 m (below ground level). Five 3D gravity inversion models visualized density variations, revealing cavities at different depths for the models from 0 m to 195 m (a.s.l). The integration of ERT and gravity methods significantly improved cavity detection accuracy, demonstrating a strong correlation between low-density gravity anomalies and high-resistivity features. This study recommends increasing gravity station density, and avoiding heavy infrastructure in cavity-prone areas. The findings contribute to the geological understanding of Jebel Hafeet, providing insights for sustainable urban planning, risk mitigation, and infrastructure safety in Al Ain City.

Implementation Of Machine Learning To Predict Cable Failures In Electrical Networks, Sultan Bader Albahri

Theses

Electrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution. This research focuses on developing an ML-based predictive model to forecast cable failures in Dubai …

Network-On-Chip Packet Obfuscation And Encoding For Hardware Trojan Mitigation, Sydale John Ayi

Theses

Networks-on-Chip (NoCs) represent a transformative advancement in System-on- Chip (SoC) communication architectures, addressing the scalability and performance limitations of traditional bus-based systems. The increasing reliance on NoCs in modern SoCs has raised security concerns, particularly in untrusted fabrication environments. This thesis investigates the vulnerabilities posed by malicious hardware Trojans and untrusted IP cores, such as reverse engineering, misdirection attacks, and brute-force decryption. To counter these threats, the study introduces a robust security framework leveraging encoding and obfuscation techniques. Hamming codes are employed to detect and correct errors, ensuring data integrity and correcting misdirection attacks. Discrepancies between codewords and data words …

On Studying Transformer Networks For Volume-To-Surface Registration Of Inhomogeneous Soft Bodies For Liver Laparoscopy, Michael Adam Young

Theses

An important practical consideration in laparoscopic liver surgery is the limited visual information relative to open surgery. In laparoscopic interventions, the surgeon’s view of the liver surface is generally limited to the scene provided by a single scope with a narrow field-of-view. This limits the ability to navigate towards internal lesions previously identified through pre-procedural imaging. Surgical navigation during laparoscopy could be enhanced by registration of full preoperative liver models derived from pre-procedural imaging scans onto the partial laparoscopic view of the liver. This entails both a rigid registration to match the liver surface view to the preoperative volume, as …

Insights Into Covid-19 Dynamics Via The Integration Of Subpopulations, Vaccinations, And Bayesian Calibration In A Semi-Enclosed Community Model Framework, Meghan Rowan Childs

Theses

From the beginning of the COVID-19 pandemic, universities have experienced unique challenges due to their multifaceted nature as a place of education, residence, and employment. Previous work has used mathematical models to explore varying approaches to combating COVID-19 and tailored these models to local contexts, such as hospitals. However, previous work has not broadly incorporated pharmaceutical mitigation strategies into university models. We address this gap in research by integrating subpopulations, vaccines, and COVID-19 variants into a semi-enclosed population model framework. We further improve the quantification of uncertainty in the model parameters by implementing a formal Bayesian model calibration, fusing real-world …

An Ultra-Low-Power, Area-Efficient All-Analog Trojan With Temporal Power Supply Triggering, Roberto Ramos-Brito

Theses

With the rapid expansion of the Internet of Things and rising concerns over intellectual property protection, hardware security has become increasingly vital—particularly in defending against hardware trojans. While much of the existing research targets digital trojans, analog hardware trojans remain largely unexplored, presenting unique opportunities for developing innovative attack vectors and mitigation strategies. This thesis introduces a novel all-analog temporal trojan that functions entirely within the analog domain. The design incorporates power supply noise detection and charge-accumulator techniques to enable precise control over attack execution and payload delivery. It also leverages novel circuit techniques to reduce power and area overhead, …

Improving Reciprocating Compressor Valve Condition Monitoring Via An Accelerated Lifetime Tester And Deep Learning, Jacob Chesnes

Theses

The goal of this research is to improve the current condition monitoring method for diagnosing reciprocating compressor valves. Leakage, seat wear, and spring degradation are three common valve faults that are seeded into all the valves of a Dresser-Rand ESH-1 dual-acting reciprocating compressor. The method converts raw vibration data into the time-frequency domain and determines if the Continuous Wavelet Transformation or the Smoothed Pseudo Wigner-Ville Spectrum can achieve a higher classification accuracy. The Continuous Wavelet Transformation can vary its resolution through two parameters while the Smoothed Pseudo Wigner-Ville Spectrum can vary its resolution through smoothing window sizes. Both time-frequency transforms …

Machine Learning For Complex Networks: Generalization, Fairness, And Model Selection In Graph-Based Systems Across Social And Biological Domains, Angelina Brilliantova

Theses

Networks serve as a fundamental framework for modeling complex systems across diverse domains, from biological interactions and social structures to economic markets and information networks. Machine learning has significantly enhanced the ability to extract meaningful patterns from graph-based systems, improving predictive accuracy in applications such as market design, recommendation systems, and biomedical discovery. Despite the successes of machine learning in analyzing complex networks, several fundamental challenges remain. A major issue is the reliance on simplistic models for theoretical analysis, simulation, and benchmarking, which often fail to capture the complexities of real-world networked systems. Many theoretical studies focus on oversimplified generative …

Investigation Of Innovative Heat Pipes / Fins System For Diesel Engine Cooling, Mohammad Shafiq Anees

Theses

Engine cooling is considered one of the most important factors that affect their efficiency and therefore their performance, so a new cooling method is proposed using both heat pipes and fins to improve the cooling efficiency of internal combustion engines and shall be examined theoretically and experimentally. A test rig has been designed and built using a specimen material with similar properties to the real engine materials with similar dimensions as the engine cylinder head / liner thickness. The experiment employed a specially designed testing apparatus that included a gas burner for consistent heating, a modifiable support structure, and an …

Machinery Imbalance Failure Prediction Based On Pump – Motor Vibration Readings, Abdulrahman Albuflaseh

Theses

Predictive maintenance has emerged as a transformative solution in industrial operations, reducing unexpected machine failures and minimizing downtime. This study explores the application of machine learning in vibration analysis to detect imbalances in pump-motor units, a critical issue that accelerates equipment wear and increases maintenance costs. Traditional maintenance methods, such as scheduled inspections, often fail to identify faults early, leading to significant operational disruptions, particularly in utility, oil and gas sectors. By leveraging real-time vibration data and advanced machine learning techniques, this research aims to develop an efficient imbalance detection model that enhances maintenance efficiency and reliability. The study employs …

Analytical Method For Solving Systems Of Fractional Initial Value Problems Using The Modified Atangana-Baleanu Derivative, Lana Mohammad Aref

Theses

This thesis presents an analytical method to solve systems of fractional initial value problems using the Modified Atangana-Baleanu Fractional Derivative. The proposed approach leverages an improved operational matrix method, incorporating an iterative, direct computation technique to simplify the solution process and reduce computational costs. Foundational concepts in fractional calculus and block pulse functions are introduced, followed by the derivation of operational matrices tailored for fractional systems. Theoretical analysis establishes the existence and uniqueness of solutions, along with uniform convergence and error estimates. Practical examples and applications are presented that demonstrate the efficiency, accuracy, and versatility of the method in solving …

Towards Characterizing And Quantifying Interpretability Of Knowledge Graph Embedding Models, Narayanan Asuri Krishnan

Theses

Knowledge graphs are structured representations of real-world information, where entities are connected by edges with labels known as predicates. These graphs contain logical patterns, known as inference patterns, such as symmetry, transitivity and composition, to name a few. To leverage the information contained within a knowledge graph, embedding methods have been developed to encode entities and predicates as numerical vectors in a multi-dimensional space. These embeddings aim to capture the semantic and structural information of the graph, including the inference patterns. However, the sub-symbolic nature of these embeddings present significant challenges in terms of interpretability. Additionally, each embedding model utilizes …

Climate Influence On Both Spatial And Temporal Patterns Of Rocky Intertidal Community Structure In San Diego, California, Emily Jenkins

Theses

The rocky intertidal ecosystem, the coastal area between high and low tides, serves as a critical buffer between land and sea. This ecosystem supports a diverse array of species and provides rich opportunities for exploration and education. With the influence of climate change, this ecosystem faces significant shifts in species distribution, resilience and recovery from environmental disturbances, as well as habitat loss (Jackson & McIlvenny 2011; Jueterbock et al. 2013; Menge & Gravem 2022; Rilov et al. 2021; Sagarin & Barry 1999). Between 2014-2016, climate anomalies El Niño (ENSO) and the Pacific Decadal Oscillation (PDO) combined to create a period …

Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei

Theses

The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the …

Prediction Of Vessel Arrival Time To Optimize Berth Allocation In Ports Using Machine Learning Methods, Anfal Mansour Albloushi

Theses

80% of the world trade is carried out through the sea, which shows the importance of maintaining transportation efficiency in the maritime industry. The vessel arrival time and berth allocation pose a significant challenge in the marine field's day-to-day operation, especially when a high number of vessels are waiting at the anchorage to deliver the goods on time. This increases the pressure on the responsible stakeholders and good owners as some cargo must be delivered urgently. Artificial intelligence and machine learning play a vital role in improving the operation of different fields; utilizing such technologies in the maritime industry will …

A Comparative Analysis Of Machine Learning Models For Predictive Process Monitoring Using Objectcentric Event Logs, Buthaina Hasan Aidaros Alhebshi

Theses

The increasing complexity of business processes drives the need for advanced predictive monitoring solutions capable of handling multi-entity interactions. Traditional case-centric event logs fall short of capturing these complexities, leading to the emergence of Object-Centric Event Logs (OCEL). Despite the growing interest in OCELs, their structural complexity and impact on predictive performance remain unexplored. This thesis investigates how process complexity influences the performance of machine learning models in predictive process monitoring using OCEL. A framework combining Graph Neural Networks and graph embedding-based models is applied across three predictive tasks: next activity, next timestamp, and remaining time prediction. The evaluation covers …

Aviation Weather Forecasting Utilizing An Artificial Neural Network, Joshua Mote

Theses

Weather forecasting is critical to minimize risk and maximize efficiency for flying operations and is challenging due to the uncertainty involved in atmospheric changes. Operational area weather is becoming more critical with the growing reliance on air domain for transportation. This study investigates whether the forecasting accuracy using a Hybrid Long-Short Term Memory (LSTM) Artificial Neural Network (ANN) outperforms the San Antonio International Airport (KSAT) Terminal Aerodrome Forecast (TAF) by integrating diverse data sources – regional Meteorological Aerodrome Report (METAR), TAF, Avian Advisory System (AHAS) Data and geomagnetic activity K Index Data. When tested on one year of historical data, …

Sliding Mode Control Design Using Generalized Relative Degree Approach : Aerospace Application, Robert J. Jesionowski

Theses

The relative degree (RD) approach is a powerful tool, for obtaining a system’s input-output dynamics of an output tracking controller design with minimum phase dynamics. Designs using RD alone can fail due to insufficient control authority in minimum phase systems, and instability of internal/zero dynamics attributed to non-minimum phase systems. Generalized RD (GRD) in minimum phase systems can identify parasitic control terms (PCTs). An Alternate GRD (AGRD) is investigated, to determine the impact of discarding these PCTs. A novel definition for Practical GRD (PGRD) is proposed and used in concert with Sliding Mode Control (SMC) compensating system perturbations in minimum …

Experimental Evaluation Of Recent Theoretical Real-Time Signal Higher-Order Sliding Mode Differentiators On Noisy Sensors, Jessica Hamer

Theses

This thesis explores the performance of higher-order sliding mode differentiators applied to sensor signals under varying sampling rates and switching functions. Two differentiator designs are evaluated: a hybrid algorithm that combines a super-twisting algorithm with a linear observer, and a fixed-time higher-order sliding mode differentiator. Each was tested on a physical sensor testbed using position data from a servomotor and electronic compass. The differentiators were implemented in a ROS2 environment, and their estimates were evaluated by obtaining the minimum and maximum position and velocity values, as well as analyzing graphs to capture the nature of the algorithms' noise reduction. The …

Particle Image Velocimetry (Piv) Measurements Of Flow Characteristics Of An Unsteady Oblique Shock Wave Train, Andrew Girgis

Theses

Presented is the development of a Particle Image Velocimetry (PIV) apparatus to quantify the velocity field produced by an oblique shock wave and its reflections in a supersonic/transonic wind tunnel. A flow seeding system is developed using mesquite wood smoke generated in a metal vessel heated with a Vevor TDGC-2KVA variable autotransformer. A pressurized K-bottle drives the smoke through a cooled copper coil and moisture trap to remove contaminants before injection. A dual-pulsed class IV Nd:YAG Big Sky laser with Thorlabs optomechanical components, including two cylindrical lenses, is used to create a pulsed light sheet to illuminate tracer particles. Image …

Safe Reinforcement Learning For Trajectory Tracking Of Mobile Robots With Minimal Intermittent Observations, Mahtab Noor Shaan

Theses

Autonomous wheeled mobile robots (WMRs) are widely used for safe operation in safety-critical systems, such as robotic visual inspection of confined spaces in energy infrastructure, warehouse automation, delivery robots, and autonomous vehicles. The operating environments for these safety-critical systems are often uncertain. Therefore, in such environments, it is essential for WMRs to reliably follow predetermined paths while effectively maintaining lane position and avoiding collisions. However, frequent observations required for control execution to account for environmental uncertainty result in increased sensing, computation, and energy costs. This necessity drives the research for safe and resource-aware trajectory-tracking control methods. Although several state-of-the-art trajectory …