Digital Commons Network^™

A High-Efficiency Ldo Regulator With Adaptive Psrr And Transient Enhancements, Daniel Zeznick

Theses

Reliable power delivery is essential for all computing systems, particularly those operating with limited or constrained energy sources, such as batteries. Power management circuits must provide energy efficiency, stability, and resilience to disturbances to support accurate and consistent system performance. This thesis presents a high-efficiency low-dropout (LDO) regulator featuring adaptive power supply rejection ratio (PSRR) and transient response enhancement techniques. By leveraging adaptive analog design strategies, the proposed regulator dynamically boosts performance at high load currents, maintaining optimal efficiency across the full load range while circumventing key trade-offs inherent to conventional LDO architectures. The design incorporates several novel circuit techniques …

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, …

Human-Aware Reinforcement Learning For Adaptive Human-Robot Teaming, Saurav Singh

Theses

The rise of Industry 5.0 has shifted robotic systems toward human-centric design, emphasizing collaboration between humans and machines. While collaborative robots (cobots) are equipped with sensors for safety, they typically overlook human internal states such as fatigue, stress, and workload—factors that significantly impact performance. This lack of awareness limits their ability to adapt and collaborate effectively, particularly in dynamic, real-time environments where human conditions fluctuate. This thesis introduces a novel framework that integrates human internal states into robot decision-making to enhance human-robot collaboration. This research leverages reinforcement learning (RL) to develop methods that enable robots to adapt their behavior based …

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 …

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 …

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 …

A Bayesian Belief Networks Approach To Prioritize Road Safety Mitigation Measures For Eco-Mobility Modes, Fatmah Mohamed Alyammahi

Theses

This study presents an innovative decision-making approach that enables the prioritization of road safety measures for eco-mobility users—pedestrians, cyclists and light-electric micromobility riders—in Abu Dhabi. The analysis uses 2032 eco-mobility crash records (1016 pedestrian, 567 bicycle, 449 e-scooter) from 2020-2023 supplied by Abu Dhabi Police and the Department of Municipalities and Transport. A supervised Bayesian Belief Network (BBN) was constructed around six empirically estimated variables: visibility, predictability, road curvature, obstructions, lighting and speed limit. Maximum-likelihood conditional-probability tables were derived from the cleaned dataset; Beta priors were applied only where cell counts were sparse. Ten conventional machine-learning models—including Random Forest, Gradient …

Assessing Readiness For Transformation From Rulebased To Ai-Based Chatbot In Uae Healthcare: A Case Study Of A Rehabilitation Hospital In Abu Dhabi, Mubarak Alketbi

Theses

This study investigates the readiness for transforming rule-based chatbots to AI-based chatbots in UAE healthcare, examining a rehabilitation hospital in Abu Dhabi through quantitative research involving healthcare professionals (N=96) and technical analysis. Findings revealed positive perceptions of the current system alongside enhancement opportunities through AI capabilities, with perceived usefulness strongly correlating with behavioural intention, high service quality ratings for empathy and responsiveness, midcareer professionals demonstrating the highest AI acceptance levels, and system integration identified as the highest priority implementation area.

The research contributes to healthcare technology transformation knowledge in the UAE by providing a structured implementation framework addressing technical requirements, …

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 …

Performance Comparison Of Iot-Powered Indoor Hydroponic Systems And Outdoor Traditional Environment, Saleha Alharthi

Theses

Traditional agriculture faces challenges, including high water consumption, greenhouse gas emissions, and fluctuations in environmental conditions. The research aims to develop alternative sustainable solutions to address issues related to traditional farming. Hydroponics provides opportunities to grow different types of vegetables indoors, where conventional agriculture is challenging. This thesis presents a comparative study of soil-based and hydroponic arugula cultivation using the nutrient film technique (NFT). The proposed methodology focuses on maintaining high similarity in the implementation of components across systems to ensure fair comparison. This thesis utilized distinct approaches. The first approach compared an outdoor soil-based system with an indoor hydroponic …

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 …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Theses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …

Hybrid Treatment Method Of Biofiltration Preceded With Advanced Oxidation Process For The Removal Of Emerging Contaminants From The Wastewater Treatment Plant Effluent, Anjali Mohan Payathuparambil

Theses

The global demand for potable quality water has been on a continuous rise. Consequently, water reuse is now considered as an alternative to significantly expand supplies of freshwater in communities facing water shortages. Most of the conventional wastewater treatment plants (WWTP) are not efficient in the removal of emerging contaminants from the water, which will cause serious issues to the environment, human and animal life. This research investigated the possibilities of treating wastewater for reuse. First, the feasibility of utilizing a biofiltration system without pre-treatment for removing emerging contaminants from the WWTP effluent was investigated. Two types of biofilter media- …

Design And Evaluation Of Geopolymer Composite As A Potential Sorbent Of Heavy Metals From Water, Ebtesam Mohammed Alghafri

Theses

Reducing the levels of heavy metals in water is crucial, given their severe environmental and health impacts. Various methods exist for heavy metals removal. Yet, they mostly come with multiple drawbacks, such as costly treatments and limited efficiency. Previous studies have found geopolymer to be a promising sorbent because it is synthesized using by-product materials, making it an eco-friendly and economically sustainable alternative. Limited studies explored the sorption potential of fly ash-slag blended geopolymers; none examined the effect of mix design factors synergically on sorption, mechanical properties, and durability, and few considered solution characteristics and operating conditions simultaneously. Therefore, this …

Monitoring Sand Migration In Al Ain City Utilizing Remote Sensing Techniques, Sanad Salem Mohammad Fareaa

Theses

Sand migration significantly impacts urban development, infrastructure, and ecosystems in arid regions such as Al Ain city, United Arab Emirates (UAE). This study employs advanced remote sensing techniques to monitor and predict sand dune migration across the Sieh Al Hama dune field, a critical area west of Al Ain. The research objectives include quantifying dune migration rates over three years (2018–2020) using monthly Sentinel-2 satellite imagery, identifying distinct dune fields, and analyzing the textural and mineralogical properties of dune sediments to infer their provenance. Field sampling from four dunes (Large Sabra Dune, Dune 1, Dune 2, and Dune 3) was …

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.

From Skepticism To Loyalty: A Whitepaper For Companies On Building Consumer Trust In Digital Marketing, Grace Darcy Pora

Theses

Trust is the foundation of business and works as a dynamic relationship between customers and businesses. As social media continues to evolve as the main way that companies promote and sell their products, trust must align with these marketing efforts. Through the analysis of past research, case studies, and current surveys, data, and trends, the relationship between consumer trust and online advertisements can be examined. Consumer trust online is experiencing a consistent downward trend, where consumers are losing trust in the companies and products they purchase from online. There is a heavy amount of skepticism online with the content that …

The Use Of Robotic Surveillance In The Uae In 2035: Balancing, Innovation, Regulation, And Privacy, Oasha Yousuf Almohier

Theses

This research explores the future of robotic surveillance in the United Arab Emirates (UAE) by 2035, aiming to balance innovation, regulatory oversight, and privacy protection. It investigates the intersection of national security objectives, ethical AI governance, and public trust within the UAE’s rapidly advancing smart city and law enforcement environments. The focus areas include regulatory gaps in current AI laws, ethical concerns such as data privacy and algorithmic bias, and the societal perception of AI-powered surveillance systems. The background of this study stems from the UAE’s ambition to lead in AI deployment, especially within public security initiatives such as autonomous …

Detecting Compromised Hardware Integrity With Machine Learning In Multicore Processors, Alexander Beekman

Theses

Due to the globalization of the Integrated Circuits (IC) manufacturing process, the trust surrounding hardware reliability has become compromised through methods such as injection of malicious hardware (including hardware trojans), hardware backdoors, IP theft, and counterfeit hardware. This thesis will focus on the issue of counterfeit hardware. As the complexity of modern day hardware has increased, detecting counterfeits has become increasingly difficult. Current methods of physical and electrical inspection are limited in both depth and size limitations. Physical inspections are only useful for identifying counterfeits that manifest physically, while electrical testing requires expensive and extensive setups. Computational evaluation of all …

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.

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 …

Enhancing The Immunogenicity Of Extracellular Vesicle-Based Vaccines Against Pathogenic Nthi Bacteria, Christina Ciko

Theses

Acute otitis media (AOM), or ear infections, are highly prevalent in children, with nearly 80% experiencing at least one AOM by the age of three. As such, there is an urgent need for a vaccine against Nontypeable Haemophilus influenzae (NTHi), a main cause of AOM in the US. Most vaccines contain multiple antigens from the pathogen, inducing an immune response and antibodies to those antigens. Here, we describe a novel approach to creating a vaccine against NTHi, incorporating a unique recombinant trivalent protein (Tri) featuring three NTHi antigens (OMP26, P6, and protein F) in a single peptide chain with an …

Enhanced 3d Sub-Canopy Mapping Via Airborne Full-Waveform Lidar, Robert J. I. Wible

Theses

Airborne light detection and ranging (LiDAR) systems have been used to gather information about forests, their canopies and what lies beneath them for many decades. Recent advances in LiDAR sensor technology have enabled higher sampling rates, leading to increased point densities for discrete point clouds. However, vast portions of the forest sub-canopy still remain either unsampled or occluded. We contend, that waveform LiDAR, as a specific type of structural modality that digitizes the intensity of the laser backscatter as a function of time (range), contains additional information that can be extracted using modern artificial intelligence (AI) and machine learning (ML) …

Deepfake Image Detection Using Explainable Ai And Deep Learning, Abdallah Abdulfattah Mohammed Abdulrahman

Theses

Deepfake technology has grown exponentially, and it has changed our perspective of managing digital content. The technology has many applications, but its ease of access has brought many risks. These risks include identity theft, misinformation spread, development of non-consensual content, and a decrease in trust in media. To address these challenges, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deepfake image detection. We developed a CNN model and four hybrid CNN-LSTM models in our study. Our approach integrates the strengths of CNNs for spatial feature extraction and LSTMs for …

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 …

عقد المقاولة من الباطن, سالم صالح العامري

Theses

Subcontract

This research addresses the subject of subcontracting agreements, a significant aspect in construction and engineering fields due to the increasing complexity of engineering projects and the need to distribute work among specialized contractors. The study explores the challenges associated with subcontracting agreements in terms of legal obligations, disputes, and mechanisms for resolving them. It also focuses on analyzing the relationship between the main contractor and the subcontractor, as well as the legal frameworks governing this relationship to ensure a balance between the rights of the different parties.

The research employs a descriptive and analytical approach, utilizing data from legal …

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 …

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 …

Femtosecond Laser Figuring And Polishing For Freeform Optics Fabrication, Gong Chen

Theses

Freeform optics revolutionizes imaging systems by enabling compact designs and high-performance capabilities, which are crucial for applications such as augmented and virtual reality, medical diagnostics, biosensing, space exploration, remote sensing, and EUV lithography. However, fabricating freeform optical components presents significant challenges: conventional chemical and mechanical methods generate chemical waste and introduce mid-spatial-frequency errors that degrade imaging contrast. This research seeks to overcome these limitations by establishing ultrafast-laser-based figuring and finishing methods. These methods offer a fast, non-contact, flexible, and precise green solution, paving the way for sustainable, high-quality freeform optics fabrication. We expanded a laser pulse propagation model to simulate …