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Articles 571 - 600 of 10019
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Optimization Methods For Parameter Estimation In A Scalar Partial Differential Equation: Stochastic Variants, Yidan Yang
Optimization Methods For Parameter Estimation In A Scalar Partial Differential Equation: Stochastic Variants, Yidan Yang
Theses
In this work, we present stochastic variants of an optimization method for the problem of recovering a spatially varying parameter in a scalar partial differential equation (PDE) from measurement(s) of the state variable. In many inverse problems associated with PDE models, the goal is to utilize observational measurements of the state variable in the estimation of the model parameter(s). In the problem being studied, the hydraulic conductivity of an aquifer (a function of location) is the parameter/coefficient to be recovered and the hydraulic (or piezometric) head is the quantity observed/measured. We discuss the choices of objective functional (such as output …
Multimodal Data Fusion And Model Compression Methods For Computer Vision, Manish Sharma
Multimodal Data Fusion And Model Compression Methods For Computer Vision, Manish Sharma
Theses
This dissertation explores the advanced integration of deep learning (DL) techniques in remote sensing (RS) and computer vision (CV), with a focus on optimizing convolutional neural networks (CNNs) for enhanced detection performance and efficient computational deployment. The first part of the research addresses the underperformance of conventional object detection methods when applied to RS data. Traditional techniques often struggle due to the small size of targets, limited training data, and diverse modalities involved. To overcome these challenges, we introduce YOLOrs, a novel CNN designed specifically for real-time object detection within multimodal RS imagery. YOLOrs is adept at detecting objects across …
Sustainable Energy Access And Irrigation Planning In Sub Saharan Africa, Fhazhil Wamalwa
Sustainable Energy Access And Irrigation Planning In Sub Saharan Africa, Fhazhil Wamalwa
Theses
Sub-Saharan Africa (SSA) is the least developed and most vulnerable region to climate change globally, with electricity access and irrigation rates at 45% and 5%, respectively. The region's high reliance on rain-fed farming results in significant yield gaps, increasing dependence on food imports and aid. Projected declines in rainfed agricultural productivity due to climate-induced rainfall variability, coupled with a rapid population growth rate of 2.8% per year, will likely exacerbate food insufficiency. SSA is largely rural, with an urbanization rate of 42%. Much of its population lives in sparsely populated settlements characterized by low electricity demand, which poses a significant …
Modeling The Reverberation Response Of The Broad Line Region In Active Galactic Nuclei, Sara A. Rosborough
Modeling The Reverberation Response Of The Broad Line Region In Active Galactic Nuclei, Sara A. Rosborough
Theses
The supermassive black holes (SMBHs) that reside at the centers of massive galaxies are observed as active galactic nuclei (AGN) when they are rapidly consuming matter from a surrounding accretion disk, which releases large amounts of electromagnetic radiation. Closely surrounding this engine are the dense gas clouds of the broad line region (BLR), which are photoionized by the continuum radiation emitted by the accretion disk. As these clouds are deep within the potential well of the SMBH, the gas emission lines (the broad emission lines; BELs) are Doppler-broadened by several 1,000 km/s. Measuring the size and velocity dispersion of the …
Tribological Performance Of Ceramic On Textured Polyethylene Contact For Artificial Hip Implants, Sandhya Vaidyanathan
Tribological Performance Of Ceramic On Textured Polyethylene Contact For Artificial Hip Implants, Sandhya Vaidyanathan
Theses
Revision total hip arthroplasty (rTHA) is an expensive and surgically risky procedure that is necessary in the case of a failed primary total hip arthroplasty (pTHA). The cause of failure in artificial hip joints is often rooted in the tribological performance of the bearing surfaces such as increased wear and reduced lubrication. Surface texturing is a surface modification technique which has been studied for aerospace and automotive applications and has shown promise in the biomedical field of artificial human joints in the last two decades. Several studies have been done to assess the tribological performance of introducing surface micro-textures as …
Complementing Vehicle Trajectories Using Two Camera Viewpoints, Katsuaki Nakano
Complementing Vehicle Trajectories Using Two Camera Viewpoints, Katsuaki Nakano
Theses
Traffic volume surveying is a crucial activity to get traffic statistics for road management and traffic congestion control. In recent years, the target environment of traffic volume surveying has become more complex, such as the fully automated surveillance of many-way intersections. Further compounding this complexity, some local governments may not be able to install a camera at a high enough elevation to capture the entire intersection due to environmental, legal, safety, or cost restrictions. Therefore, bigger objects such as buses and trucks often occlude other vehicles in the captured image. This occlusion degrades the accuracy of counting and is one …
A Programmable Look-Up Table-Based Processing In Memory Architecture For Massively Parallel, Energy-Efficient Processing Of Data-Intensive Applications Within Dram, Purab Ranjan Sutradhar
A Programmable Look-Up Table-Based Processing In Memory Architecture For Massively Parallel, Energy-Efficient Processing Of Data-Intensive Applications Within Dram, Purab Ranjan Sutradhar
Theses
As modern computing workloads become increasingly data-intensive, the limitations of traditional digital computing hardware, especially in terms of communication bandwidth between memory and processors, are becoming more apparent. Surprisingly, the performance and energy efficiency of the state-of-the-art are not primarily constrained by the computing power of advanced processing architectures but rather by the low communication bandwidth between the memory and the processor. This challenge necessitates a clever re-architecting of digital computing hardware to make the communication between the processor and the memory more energy-efficient, faster, and capable of supporting significantly higher bandwidth. Memory-centric computing emerges as a novel, alternate computing …
Human-Machine Interactions For Behavioral Health Care, Zhiwei Yu
Human-Machine Interactions For Behavioral Health Care, Zhiwei Yu
Theses
About one in four American adults (26%) and 20.3% of children aged 8 to 16 years reported behavioral health concerns in 2023. However, a substantial gap remains between demand for behavioral healthcare services and access to such services. In response, research efforts have been made to advance effective and accessible approaches to provide assessments and treatments for populations with behavioral health needs. Yet, two primary challenges exist in current practice and research: 1) self-reports and manual observation-based assessments are subjective and labor-intensive, as evaluations are limited to the availability of clinical professionals; 2) they only target the general experiences but …
Spatiotemporal Alignment Of Event Stream With Images And Active Illumination, Abhijan Wasti
Spatiotemporal Alignment Of Event Stream With Images And Active Illumination, Abhijan Wasti
Theses
Unlike conventional frame-based cameras that form images by sampling all pixels within the duration of the global/rolling shutter, a pixel in an event camera can be triggered independently when the log intensity change in scene luminance at the pixel exceeds a threshold. This unique feature provides several advantages over conventional sensors, including high dynamic range (HDR) (≈120dB), high temporal rate (≈10,000Hz), low latency (< 1ms), and low power requirements (≈10mW). These properties make them excellent candidates for applications such as high-speed photography, HDR image reconstruction, object tracking, depth estimation, simultaneous localization and mapping, and surveillance and monitoring. Despite their potential, the asynchronous and spatially sparse nature of events poses challenges to event processing and interpretation. This is because most advanced image processing and computer vision algorithms are designed to work with conventional image formats, and not with temporally dense streams of asynchronous pixel events (i.e., the event stream). Although emerging techniques in supervised machine learning demonstrate promise, continued and rapid progress relies on the availability of labeled event datasets, which are scarce, and difficult to produce. Moreover, generating reliable events for training models is challenging due to the scene-dependent nature of event generation, which is further complicated by varying illumination and relative motion. In this thesis, we attempt to address these limitations with a novel imaging paradigm involving the capture of frames from a conventional frame-based camera that has been spa tially aligned and temporally synchronized with an event sensor. Our active illumination source allows us to generate events more consistently even under challenging illumination and motion in the scene. We demonstrate the feasibility of such a setup for a mobile eye tracking system and acquire subpixel and microsecond accurate spatiotemporal alignment. Our method facilitates the use of pre-trained models that operate on conventional images to detect features that can then be used to train models that can be applied directly to the event stream. This serves as the next step in event-based imaging and unlocks new possibilities in computer vision and imaging technology.
Unravelling Micro-Mysteries: Separating Microscopic Entities Using Electrokinetic Microsystems, Nuzhet Nihaar Nasir Ahamed
Unravelling Micro-Mysteries: Separating Microscopic Entities Using Electrokinetic Microsystems, Nuzhet Nihaar Nasir Ahamed
Theses
There is an increasing demand for the development of rapid and reliable methods for microparticle and cell separations in applications that range from clinical analysis to food and environmental safety assessments. Microfluidic systems, being portable and offering a response within minutes, are particularly attractive. Electrokinetic (EK) methods are a growing area in microfluidics, due to their robustness, rapid response time, low cost, and label-free analysis of micron-sized particles, including intact microorganisms. EK methods require the application of an electric stimulation, such as direct-current (DC) or alternating-current (AC) potentials, to induce different EK phenomena, such as electrophoresis, electroosmotic flow and/or dielectrophoresis, …
Machine Learning Models For Enhanced Stock Trading Strategies, Alyaa Al Ali
Machine Learning Models For Enhanced Stock Trading Strategies, Alyaa Al Ali
Theses
The purpose of this research is to develop sophisticated machine-learning models that can predict stock price movements, taking into account the multifaceted influences that create volatility in financial markets. To navigate the complexities of market dynamics effectively, investors, financial institutions, and academia rely on precision stock price predictions. This research generates actionable buy and sell signals by using historical stock data, technical market indicators, Linear Support Vector Machines (LSVM), Neural Networks, and Logistic Regression. A distinctive feature of this model is it uses market indicators such as PPO, MACD, RSI Signal, Bollinger Bands, ROC Signal, and DX Signal instead of …
Deep Learning Approach For Detecting And Classifying Chaotic Driving Activity Using Video Surveillance, Fatma Abdulla Desmal Alhaddad
Deep Learning Approach For Detecting And Classifying Chaotic Driving Activity Using Video Surveillance, Fatma Abdulla Desmal Alhaddad
Theses
The growing rate of urbanization in the city of Dubai has contributed to the increase in vehicular activity and the prevalence of risky driving behavior on the road. This project proposes a deep-learning-based system, VGG-16, for the identification and classification of different patterns of driving behavior. The research methodology used for the study relies on the compilation of datasets from various resources, including ImageNet, Kaggle, UAH-DriverSet, D²-City Dataset, and confidential videos obtained from the Dubai Police command center. The findings of the study will be categorized into two parts. The first part will involve the compilation of the features based …
Using Prediction Ml Algorithm For Predicting Early Student Attrition In Higher Education, Noora Ali Mohsen Alattar Alhashmi
Using Prediction Ml Algorithm For Predicting Early Student Attrition In Higher Education, Noora Ali Mohsen Alattar Alhashmi
Theses
Abstract This study aims to use predictive models in order to figure out students that are in likelihood of attrition as well as determine the factors that may contribute to this attrition. Student attrition is the occurrence in which students leave the institution without finishing their studies or taking the degree. It includes students either willingly or involuntarily terminating their education and failing to graduate. The findings will enable relevant parties to establish successful strategies or approaches and activities to assist in reducing the number of students who leave. With the testing and analysis approaches, it was recognized that SVM …
Nano Biosensing Platform For Infectious Disease Diagnostics, Li Liu
Nano Biosensing Platform For Infectious Disease Diagnostics, Li Liu
Theses
CRISPR-Cas enabled biosensors offer great potential to be at the forefront of diagnostic medicine. This thesis focuses on the principles of CRISPR-Cas technology and its application to biosensors and virus diagnostics. It also provides a detailed discussion of the different detection methods available at this stage. The breakthrough achievements of CRISPR biosensors in nucleic acid detection are summarized. The CRISPR-Cas nano biosensors exhibit high accuracy, sensitivity, selectivity, and versatility, offering great potential for next-generation diagnostic and point-of-care devices. The prospects and future trends of CRISPR biosensors are also described. From an engineering perspective, this thesis develops and applies several methods …
Using Machine Learning Algorithm For Detecting Distributed Denial Of Service Attack, Zainab Alblooshi
Using Machine Learning Algorithm For Detecting Distributed Denial Of Service Attack, Zainab Alblooshi
Theses
DDoS attacks, which stand for Distributed Denial of Service attacks, play a significant role in impacting the reliability and availability of online services and networks. Since no system is completely immune to cybersecurity threats, which evolve daily with new techniques, studying this topic is crucial for exploring machine learning methods that can effectively detect DDoS attacks. An approach has been utilized to conduct Exploratory Data Analysis (EDA) to identify patterns suggesting the presence of DDoS attacks. Multiple machine learning models have been employed, such as Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Logistic Regression. These models have undergone training and …
The Future Of Smart Transport Technologies: Examining Dubai's Technological Readiness Levels (Trls) To Adopt New Smart Transport Technologies, Hamad Omran Alshizawi
The Future Of Smart Transport Technologies: Examining Dubai's Technological Readiness Levels (Trls) To Adopt New Smart Transport Technologies, Hamad Omran Alshizawi
Theses
This research aims to understand what Dubai is willing to do and to provide in terms of smIntelligent Transportation Systems with emphasis on capacity optimization of traffic and understanding TRLs and absorption capacity for AI in Intelligent Transportation Systems Traffic Prediction and Control Systems. The research is important in the context of the concept of a smart city for Dubai and the argument that perfect for the object of examination – transport – can and should integrate new technologies easily into the existing context. Drawing from a population of smart city literature development, traffic and Smart technology, the present study …
High Heat Flux Dissipation Techniques Using Enhancement Structures And Subcooled Boiling Chamber For High Performance Computing Applications, Maharshi Y. Shukla
High Heat Flux Dissipation Techniques Using Enhancement Structures And Subcooled Boiling Chamber For High Performance Computing Applications, Maharshi Y. Shukla
Theses
With a rising need for high-performance computing in data centers, enhanced heat dissipation from microprocessors becomes critical for maintaining their surface temperature in a compact setting. While air cooling has been sufficient until now, liquid cooling proves to be superior in its heat dissipation capabilities. The present work investigates enhancing liquid cooling with boiling and enhanced boiling structures in a novel subcooled boiling chamber. The single-phase forced convection heat transfer is first studied by enhancing the heat transfer performance (CHF and HTC) by more than 50 % with the injection of air bubbles at the heater surface. A self-sustaining method …
Adaptive Parameter Tuning For Robust Event-Based Slam In Diverse Scenarios, Saber Muawiyah Sakhrieh
Adaptive Parameter Tuning For Robust Event-Based Slam In Diverse Scenarios, Saber Muawiyah Sakhrieh
Theses
In the ever-changing field of contemporary robotics, Visual Simultaneous Localization and Mapping (V-SLAM) stands as a crucial technology, facilitating navigation and mapping in various environments. While traditional V-SLAM approaches excel in static environments, the need for their adaptation to handle dynamic and realistic scenes is becoming increasingly apparent. This thesis delves into the advancements within the V-SLAM field, with a focus on enhancing its adaptability and performance in dynamic and challenging conditions. Initially, a comprehensive review of V-SLAM methods in the literature is presented in this thesis, offering an in-depth examination of current cutting-edge techniques, their strengths, weaknesses, and prospects …
A Study Of Gate Valve Supply Chain And Life Cycle Impacts, Saud Bindarwish
A Study Of Gate Valve Supply Chain And Life Cycle Impacts, Saud Bindarwish
Theses
and sustainable water supplies depend on water infrastructure. However, old infrastructure, especially valves that leak and break, can cause significant water loss, economic strain, and environmental damage. This study explores best choice of gate valves to use while replacing defected existing gate valve in the networks by examining gate valve supply chains and their life cycle consequences. Traditional gate valve supply chains are lengthy and complicated. Extracting iron ore and copper destroys habitats and pollutes air and water. Factory processing and production of these materials increases energy use and air/water pollution. Valve shipping, usually overseas, emits lots of greenhouse gases. …
Smart Technology For Enhancing Indoor Air Quality, Abdulaziz Mohammed
Smart Technology For Enhancing Indoor Air Quality, Abdulaziz Mohammed
Theses
The COP28 emission reduction plan underscores the imperative of transitioning away from fossil fuels to combat climate change. Concurrently, the rapid adoption of Internet of Things technology has emerged as a transformative force in urban governance, offering cities enhanced resilience and operational frameworks through data analytics and real-time monitoring. In response to these trends, the present research focused on leveraging IoT technology to monitor indoor air quality within a “restaurant A” in an urban area in Dubai. Employing advanced data analytics and smart sensor technologies, particularly LoRa, the project aimed to analyze and monitor air quality parameters. Disposing the sensor …
Investigating Key Challenges In Managing Inventories Of Spares In The Ev Sector, Keasav Ram R
Investigating Key Challenges In Managing Inventories Of Spares In The Ev Sector, Keasav Ram R
Theses
Exploring the major issues in spare inventory management in the EV industry is the main purpose of the study. The current practices and challenges associated with inventory management of spares in the US EV sector have been investigated in this study. A "secondary qualitative data collection" method along with an exploratory approach has been utilised in this study to drive the study in a proper way to achieve a successful outcome. The collected data has been analysed in terms of using a thematic analysis. Objective and research questions specific themes have been generated to analyse the collected secondary data. It …
Decentralisation Of Drone Authentication Within Iod Networks., Jarryd Tayler Brits
Decentralisation Of Drone Authentication Within Iod Networks., Jarryd Tayler Brits
Theses
The applications of drones have become increasingly more versatile, with their popularity also increasing rapidly in the last decade. These applications span from entertainment within civilian markets to more military-based applications, including private security firms. However, with this rise in use, the security risks involved have also increased drastically, predominantly in a drone’s lack of ability to authenticate surrounding drones efficiently and securely without the aid of a third party. Although there have been copious amounts of research to support drone authentication, they predominantly rely on a single source of truth, such as a ground station server (GCS), which presents …
A Framework For Filtering Irrelevant Notams, Roudha Abdulrahman
A Framework For Filtering Irrelevant Notams, Roudha Abdulrahman
Theses
The Notice to Airmen (NOTAM) system is essential for aviation safety, giving critical information on risks and operating limitations. However, the volume and complexity of NOTAM data complicate interpretation, which could compromise safety and efficiency. This study tries to address these issues by creating a predictive algorithm for analyzing NOTAM data and predicting whether to keep or remove them. The methodology combines effective machine learning algorithms to reveal insights that improve safety and operational efficiency. Using a dataset of NOTAM entries, multiple prediction algorithms are tested to forecast possible problems and enable proactive risk management. The study's findings help to …
Improving Automatic Refactoring Candidate Identification, Ryan Devoe
Improving Automatic Refactoring Candidate Identification, Ryan Devoe
Theses
Extract method refactoring is pivotal for enhancing code readability, maintainability, and modularity by segmenting complex code into clearer, isolated methods. Identifying opportunities for such refactorings necessitates a deep understanding of the codebase’s evolution and its intricate relationships. Current methodologies utilize developer commit messages, advanced graph analysis, and diverse machine learning approaches to automate this identification process. This research delves into the application of deep learning-based Large Language Models (LLMs) to tackle the complexities inherent in extract method refactoring. We introduce innovative approaches, including the use of LLMs to cluster code blocks based on complex patterns and dependencies, and the analysis …
Unraveling The Role Of Asymmetry In Structural Porous Media On The Growth Of Fluid Fingers, Amir Ibrahim
Unraveling The Role Of Asymmetry In Structural Porous Media On The Growth Of Fluid Fingers, Amir Ibrahim
Theses
Instabilities at fluid-fluid interfaces are ubiquitous in porous media, even at small flow rates. The growth of fluid fingers during fluid-fluid immiscible displacement in a porous medium results in partial saturation of the medium, and inefficiency in displacement has a tremendous economic impact on oil recovery, chemical reactors, and water filtration industries. In this study, we demonstrate that an inherent asymmetry in the structure of a porous medium controls the development of fluid fingers due to instabilities at fluid interfaces. We construct a 2D porous medium consisting of a matrix of triangles using soft lithography to investigate air displacement by …
Utilization And Justification Of Generative Ai Technologies For Schematic Architectural Design, Aaric Celeste
Utilization And Justification Of Generative Ai Technologies For Schematic Architectural Design, Aaric Celeste
Theses
Today, architecture is heavily assisted by computer programs. As technology advances, these programs will be more impactful in the design space and will become more inseparable as a result. It is nearly certain that architectural design will no longer be as time-consuming and reiterative as it is today. Computer software and AI-assisted tools will be able to interpret information and produce practical designs that architects otherwise couldn’t. It may seem futuristic and a not-in-our-lifetime phenomenon, but it in fact already exists today. Algorithms are already developed to assist and in some cases take over design work. This thesis discovered existing …
Autowaze: Automated, Real-Time, And Accurate Traffic Analytics, Vijay Bajracharya
Autowaze: Automated, Real-Time, And Accurate Traffic Analytics, Vijay Bajracharya
Theses
A key factor leading to the inefficiency of transportation systems is the lack of real-time, fine-grained traffic analytics. An average motorist in the United States spends a significant amount of time looking for parking spots during their daily commute. Existing systems for traffic analytics are either not scal- able to large metropolitan areas or require a human-in-the-loop. This project focuses on automating the detection of fine-grained traffic analytics. Lever- aging on-board stereo cameras, Autowaze utilizes a crowd-sourced strategy to take time-windowed snapshots of the road containing 3D map points of the environment. These snapshots are used to extract changes in …
On The Naming Of Identifiers: A Survey Of Undergraduate Students, Stacy Skalicky
On The Naming Of Identifiers: A Survey Of Undergraduate Students, Stacy Skalicky
Theses
Alsuhaibani et al. state that 60% of the participants do not have strict or any naming standards in the workplace and developers surveyed desired clear method naming standards to promote code comprehension [2]. Martin indi-cated identifier names are responsible for 90% of what makes software readable and understandable [25] . Borstler et al. indicated readability was important to developers at every level of experience but few report their academic influences being effectively leveraged to make quality identifiers [5]. Keuning et al. surveyed teachers in programming-related fields and found that code quality although important, was not a big part of their …
Improving Design And Physical Boundary In Temporary Post-Disaster Relief Shelters: Analysis Of Existing Literature, Min-Hye Jang
Improving Design And Physical Boundary In Temporary Post-Disaster Relief Shelters: Analysis Of Existing Literature, Min-Hye Jang
Theses
Natural disasters such as floods, hurricanes, cyclones, storms, earthquakes, wildfires, and landslides displace people. As responses at the community resilience level, the temporary post-relief shelter is critical for the community's rapid recovery. Converting existing public facilities is the fastest way to respond to a crisis. However, the Hurricane Katrina Superdome Shelter alerted society to the prevalence of sexual assault in transitory catastrophe contexts. Not only has sexual abuse occurred, but prior shelters did not equip every user with physical barriers to suit their needs. This study examined academic journals, articles, and books to identify issues with natural disaster shelters created …
Electron Counting, Dopant Implementation, & Electron Phonon Interaction Mesh Sensitivity Testing For Nanoscale Energy Transport Model, Dominick Velez
Electron Counting, Dopant Implementation, & Electron Phonon Interaction Mesh Sensitivity Testing For Nanoscale Energy Transport Model, Dominick Velez
Theses
Managing thermal energy generation and heat transfer within nanoscale devices (transistors) of modern-day electronics is important as it limits speed, carrier mobility, and affects device reliability. In the nanoscale, heat conduction occurs primarily via phonon transport and heat generation is a result of electron-phonon interactions in these devices. Traditional methods of predicting physical behavior have proven to lack either physical accuracy, computational efficiency, or flexibility. The Nanoscale Energy Transport Model (NETM) is an engineering design tool introduced to calculate non-equilibrium transport of energy carriers in nanoscale devices and overcome the deficiencies of traditional models of energy-carrier transport The NETM previously …