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Title: DEVELOPING AMINE-MODIFIED SILICA MATERIALS FOR CARBON DIOXIDE CAPTURE FROM DIFFERENT GAS STREAMS.
Creator: Ahmadian, Amirjavad Hosseini, Lashaki, Masoud Jahandar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: The atmospheric concentration of CO2 increased from 320 to 425 parts per million by volume (ppmv; 0.0425 vol.%) between 1960 and 2024. Sample CO2 reduction strategies include shifting to renewable energy sources and employing CO2 capture. CO2 capture from the air (also known as direct air capture; DAC) has recently received increased attention. CO2 has the potential to act as an asphyxiant at high concentrations, particularly in enclosed environments (e.g., spacecraft, submarines), requiring...
Show moreThe atmospheric concentration of CO2 increased from 320 to 425 parts per million by volume (ppmv; 0.0425 vol.%) between 1960 and 2024. Sample CO2 reduction strategies include shifting to renewable energy sources and employing CO2 capture. CO2 capture from the air (also known as direct air capture; DAC) has recently received increased attention. CO2 has the potential to act as an asphyxiant at high concentrations, particularly in enclosed environments (e.g., spacecraft, submarines), requiring air revitalization to remove CO2. Hence, the U.S. Occupational Safety and Health Administration determined a permissible exposure limit of 5,000 ppmv CO2 (0.5 vol.%) throughout an 8-hour work shift. Considering the trace levels of CO2 and the presence of humidity in DAC and air revitalization applications, similar materials can be developed for implementation in both cases. CO2 capture involving amine-functionalized silica materials (“aminosilicas”) can achieve high CO2 uptakes at low concentrations due to high selectivity. Additionally, moisture in CO2-laden gases enhances the CO2 uptake and stability of aminosilicas. Therefore, this research investigated the potential of aminosilicas for removing CO2 from dilute streams, including DAC and air revitalization applications. Aminosilicas were produced using mesoporous silica supports with different particle sizes that were modified with tetraethylenepentamine (TEPA) or branched polyethylenimine (PEI) with different molecular weights (600, 1200, and 1800), or grafted with 3-aminopropyltrimethoxysilane (APTMS). The performance of aminosilicas was assessed to determine equilibrium CO2 adsorption capacity, adsorption kinetics, and cyclic stability.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014479
Subject Headings: Carbon dioxide mitigation, Adsorption
Format: Document (PDF)

Title: INTEGRATING GEOSPATIAL ANALYSIS AND TRAFFIC SIMULATION TO MODEL FLOOD IMPACTS IN RURAL AREAS.
Creator: Reginato, Attilio Junior, Kaisar, Evangelos I., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: This study aims to address the unique challenges of transportation in rural and disconnected communities through innovative data-driven methodologies. The primary methods employed in this research involve Geographic Information Systems (GIS) tools and simulation techniques to model and assess the impact of flood zones on rural traffic dynamics. The study recognizes the distinct mobility patterns and limited infrastructure prevalent in rural areas, emphasizing the need for tailored solutions...
Show moreThis study aims to address the unique challenges of transportation in rural and disconnected communities through innovative data-driven methodologies. The primary methods employed in this research involve Geographic Information Systems (GIS) tools and simulation techniques to model and assess the impact of flood zones on rural traffic dynamics. The study recognizes the distinct mobility patterns and limited infrastructure prevalent in rural areas, emphasizing the need for tailored solutions to manage flood-induced disruptions. By leveraging GIS tools, the study intends to spatially analyze existing transportation networks, population distribution, flood-prone areas, and key points of interest to formulate a comprehensive understanding of the local context. Simulation-based approaches using the PTV VISSIM platform will be employed to model and assess various flood scenarios and their effects on traffic flow and accessibility. This study’s outcomes aim to contribute valuable insights into improving accessibility, efficiency, and safety in transportation for these underserved areas during flood events. By combining GIS tools and simulation techniques, this research seeks to provide a robust framework for data-driven decision-making and policy formulation in the realm of rural and disconnected community mobility, particularly in the context of flood risks.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014472
Subject Headings: Rural transportation, Geospatial data, Floods, Urban planning
Format: Document (PDF)

Title: NANOPARTICLE-INDUCED CATALYTIC CARBON CAPTURE: A MICROFLUIDICS APPROACH.
Creator: Donjuan, Joshua, Kim, Myeongsub, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Due to technological advancement, energy consumption and demand have been increasing significantly, primarily satisfied by fossil fuel consumption. This reliance on fossil fuels results in substantial greenhouse gas emissions, with CO₂ being the most prominent contributor to global warming. To mitigate this issue and prevent CO₂ emissions, Carbon Capture, Utilization, and Storage (CCUS) technologies are employed. Among these, the amine scrubbing method is widely used due to its high CO2...
Show moreDue to technological advancement, energy consumption and demand have been increasing significantly, primarily satisfied by fossil fuel consumption. This reliance on fossil fuels results in substantial greenhouse gas emissions, with CO₂ being the most prominent contributor to global warming. To mitigate this issue and prevent CO₂ emissions, Carbon Capture, Utilization, and Storage (CCUS) technologies are employed. Among these, the amine scrubbing method is widely used due to its high CO2 capture efficiency and regenerative ability. However, this method has drawbacks, including high toxicity, corrosion, and substantial freshwater consumption. To develop an environmentally sustainable carbon capture solution, researchers are exploring alternatives such as the use of seawater and enhanced CO2 capture with catalysts. In this study, we analyze the catalytic performance of nickel nanoparticles (NiNPs) in seawater with carboxymethyl cellulose (CMC) polymers. Using flow-focusing geometry-based microfluidic channels, we investigated CO₂ dissolution at various concentrations of nanoparticles and CMC polymers. The objective is to optimize the concentration of nanoparticles and CMC polymers for effective CO₂ dissolution. We utilized NiNPs with diameters of 100 nm and 300 nm in CMC concentrations of 100 ml/L, 200 ml/L, and 300 ml/L. Additionally, NiNP concentrations ranging from 6 mg/L to 150 mg/L were tested for CO₂ dissolution in seawater. The results indicated that a concentration of 10 mg/L NiNPs in 100 mg/L CMC provided a CO₂ dissolution of 57%, the highest for this specific CMC concentration. At CMC concentrations of 200 ml/L and 300 ml/L, NiNP concentrations of 70 mg/L and 90 mg/L achieved CO₂ dissolution rates of 58.8% and 67.2%, respectively.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014483
Subject Headings: Carbon sequestration, Global warming, Polymer chemistry, Nanoparticles
Format: Document (PDF)

Title: AN ASSESSMENT OF WATER-RELATED VULNERABILITY FOR DEVELOPED PROPERTIES IN COASTAL FORT LAUDERDALE.
Creator: Salazar, Stephanya Lotero, Bloetscher, Frederick, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Flooding disasters pose a significant threat worldwide, with 2022 seeing them as the most common type of disaster. In the U.S. alone, four flooding disasters in 2023 cost more than $9.2 billion. Coastal urban areas face increasing threats from flooding disasters due to rising sea levels, changing precipitation patterns, and intensifying extreme weather events. This study focuses on Central Beach, Fort Lauderdale; the area's unique geographical, environmental, historical, and socio-economic...
Show moreFlooding disasters pose a significant threat worldwide, with 2022 seeing them as the most common type of disaster. In the U.S. alone, four flooding disasters in 2023 cost more than $9.2 billion. Coastal urban areas face increasing threats from flooding disasters due to rising sea levels, changing precipitation patterns, and intensifying extreme weather events. This study focuses on Central Beach, Fort Lauderdale; the area's unique geographical, environmental, historical, and socio-economic characteristics make it a prime candidate for this analysis. The research objective is to comprehensively examine the factors contributing to water-related vulnerabilities of developed properties in Central Beach and assess localized impacts using regional models. The methodology involves developing probabilistic flood maps using GIS tools and the Cascade 2001 routing model. The flood scenarios consider groundwater table rise, extreme rainfall, high tides, storm surge, and sea level rise. Results indicate significant inundation risks, particularly for commercial and mobility infrastructure, under storm surge and sea level rise scenarios. The analysis highlights the importance of targeted mitigation efforts to protect these areas and reinforce resilience against future flooding events. The findings contribute valuable insights for policymakers, urban planners, and stakeholders, emphasizing the need for comprehensive strategies to mitigate flood risks in coastal urban areas.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014484
Subject Headings: Floods, Fort Lauderdale (Fla.), Urban planning
Format: Document (PDF)

Title: AI COMPUTATION OF L1-NORM-ERROR PRINCIPAL COMPONENTS WITH APPLICATIONS TO TRAINING DATASET CURATION AND DETECTION OF CHANGE.
Creator: Varma, Kavita, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent...
Show moreThe aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent potentially catastrophic system failures. Our research in data analysis focuses on creating new mathematical theories and algorithms for outlier-resistant matrix decomposition using L1-norm principal component analysis (PCA). L1-norm PCA has demonstrated robustness against irregular data points and will be pivotal for future AI learning and autonomous system operations. This dissertation presents a comprehensive exploration of L1-norm techniques and their diverse applications. A summary of our contributions in this manuscript follows: Chapter 1 establishes the foundational mathematical notation and linear algebra concepts critical for the subsequent discussions, along with a review of the complexities of the current state-of-the-art in L1-norm matrix decomposition algorithms. In Chapter 2, we address the L1-norm error decomposition problem by introducing a novel method called ”Individual L1-norm-error Principal Component Computation by 3-layer Perceptron” (Perceptron L1 error). Extensive studies demonstrate the efficiency of this greedy L1-norm PC calculator.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014460
Subject Headings: Artificial intelligence, Machine learning, Neural networks (Computer science), Data Analysis
Format: Document (PDF)

Title: SPONTANEOUS HYDROGEN GENERATION WITH SILICON NANOPARTICLES AND WATER.
Creator: Axelrod, Kevin Eric, Kim, Myeongsub, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Over the past decade, hydrogen gas generation has been a critical component toward clean energy due to its high specific energy content. Generating hydrogen gas from water is crucial for future applications, including space transportation. Recent studies show promising results using silicon nanoparticles (SiNPs) for spontaneous hydrogen generation, but most methods require external energy like high temperature or pressure. In this work, we investigated hydrogen production from SiNPs without...
Show moreOver the past decade, hydrogen gas generation has been a critical component toward clean energy due to its high specific energy content. Generating hydrogen gas from water is crucial for future applications, including space transportation. Recent studies show promising results using silicon nanoparticles (SiNPs) for spontaneous hydrogen generation, but most methods require external energy like high temperature or pressure. In this work, we investigated hydrogen production from SiNPs without external energy by leveraging high pH water using sodium hydroxide and optimizing the process with a microfluidic approach. When comparing the physical dispersion methods using the 0.1 mg/mL case, ultrasonic bath produced more hydrogen than magnetic stirrer. In this thesis, 0.01% dextran with pure SiNPs at concentrations of 0.1 mg/mL, 0.2 mg/mL, and 0.3 mg/mL was analyzed. The highest concentration with dextran generated at least 40% less hydrogen than silicon alone, thus dextran did not increase hydrogen gas.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014480
Subject Headings: Hydrogen, Hydrogen as fuel, Silicon, Nanoparticles
Format: Document (PDF)

Title: EVALUATION OF INFLUENCES OF THE EL NIÑO-SOUTHERN OSCILLATION (ENSO) EVENTS ON CHANGES IN TEMPERATURE EXTREMES AND RESIDENTIAL ENERGY CONSUMPTION IN SOUTH FLORIDA.
Creator: Thakker, Kuntal S., Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: El Niño Southern Oscillation (ENSO) occurrences have a well-established impact on regional hydroclimatic variability and alterations in crucial climatic factors such as temperature and precipitation. The impact of ENSO on temperature extremes can cause fluctuations in energy consumption, leading to the need for energy utilities to implement more effective management measures. This study aims to evaluate the potential impacts of El Niño Southern Oscillation (ENSO) events on local temperature...
Show moreEl Niño Southern Oscillation (ENSO) occurrences have a well-established impact on regional hydroclimatic variability and alterations in crucial climatic factors such as temperature and precipitation. The impact of ENSO on temperature extremes can cause fluctuations in energy consumption, leading to the need for energy utilities to implement more effective management measures. This study aims to evaluate the potential impacts of El Niño Southern Oscillation (ENSO) events on local temperature patterns & extremes and residential energy usage in South Florida. The region of focus consists of three Counties: Miami-Dade, Broward, and Palm Beach. The impact of ENSO occurrences on temperature is assessed by analyzing long-term monthly average, minimum, and maximum temperature data from numerous weather stations in these counties, spanning from 1961 to 2018. The study analyzes variations of monthly electricity usage data acquired from a local power utility company (e.g., Florida Power & Light) and temperature data from 2001 to 2018. Temporal frames that align with the three phases of ENSO (namely warm, cool, and neutral) are employed to assess variations in temperature and energy consumption. Nonparametric hypothesis tests are employed to validate statistically significant variations in temperature and residential energy consumption across the stages of ENSO. This study aims to analyze the potential regional and temporal impacts of ENSO episodes on temperature and residential energy consumption in South Florida. Initial findings indicate that the non-uniform distribution of temperature, affected by El Niño and La Niña occurrences, impacts the amount of energy consumed by households in South Florida.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014493
Subject Headings: Energy consumption, Florida, South, Climate change, El Niño Current, La Niña Current
Format: Document (PDF)

Title: MACHINE LEARNING TO PREDICT BUSINESS SUCCESS: THEORIES, FEATURES, AND MODELS.
Creator: Gangwani, Divya, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Businesses are the driving force behind economic systems and are the lifeline of the community as they help in the prosperity and growth of the nation. Hence it is important for the business to succeed in the market. The business’s success provides economic stability and sustainability that helps preserve resources for future generations. The success of a business is not only important to the owners but is also critical to the regional/domestic economic system, or even the global economy....
Show moreBusinesses are the driving force behind economic systems and are the lifeline of the community as they help in the prosperity and growth of the nation. Hence it is important for the business to succeed in the market. The business’s success provides economic stability and sustainability that helps preserve resources for future generations. The success of a business is not only important to the owners but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc.. Yet, millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long-lasting question that puzzles many economists, entrepreneurs, and government officials. Recent advancements in artificial intelligence, especially machine learning, has lent researchers the powers to use data to model and predict business success. However, due to the data-driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations, particularly in the field of business prediction. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on three main categories Investment, Business, and Market, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014506
Subject Headings: Machine learning, Business enterprises, Business education
Format: Document (PDF)

Title: EXPLAINABLE GRAPH LEARNING FOR POWER GRID FAULT DETECTION.
Creator: Bosso, Richard George, Tang, Yufei, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Short-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning...
Show moreShort-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning methods. However, despite their high performance, GNN models are often viewed as ”black-box” systems, making their decision logic difficult to interpret. This thesis demonstrates that explanation methods can be applied to GNN models to enhance their transparency by clarifying the reasoning behind fault location predictions. By systematically benchmarking several explanation techniques for a GNN model trained for fault location detection, we assess and recommend the most effective methods for elucidating fault detection predictions in power grid systems.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014528
Subject Headings: Fault location (Engineering), Neural networks (Computer science)
Format: Document (PDF)

Title: DEEP LEARNING-ASSISTED EPILEPSY DETECTION AND PREDICTION.
Creator: Saem, Raghdah Aldahr, Ilyas, Mohammad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Epilepsy is a multifaceted neurological disorder characterized by superfluous and recurrent seizure activity. Electroencephalogram (EEG) signals are indispensable tools for epilepsy diagnosis that reflect real-time insights of brain activity. Recently, epilepsy researchers have increasingly utilized Deep Learning (DL) architectures for early and timely diagnosis. This research focuses on resolving the challenges, such as data diversity, scarcity, limited labels, and privacy, by proposing...
Show moreEpilepsy is a multifaceted neurological disorder characterized by superfluous and recurrent seizure activity. Electroencephalogram (EEG) signals are indispensable tools for epilepsy diagnosis that reflect real-time insights of brain activity. Recently, epilepsy researchers have increasingly utilized Deep Learning (DL) architectures for early and timely diagnosis. This research focuses on resolving the challenges, such as data diversity, scarcity, limited labels, and privacy, by proposing potential contributions for epilepsy detection, prediction, and forecasting tasks without impacting the accuracy of the outcome. The proposed design of diversity-enhanced data augmentation initially averts data scarcity and inter-patient variability constraints for multiclass epilepsy detection. The potential features are extracted using a graph theory-based approach by analyzing the inherently dynamic characteristics of augmented EEG data. It utilizes a novel temporal weight fluctuation method to recognize the drastic temporal fluctuations and data patterns realized in EEG signals. Designing the Siamese neural network-based few-shot learning strategy offers a robust framework for multiclass epilepsy detection.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014523
Subject Headings: Deep learning (Machine learning), Epilepsy, Electroencephalography
Format: Document (PDF)

Title: DEVELOPMENT AND METHODOLOGY FOR AUV-BASED GEOMAGNETIC SURVEYS IN SUPPORT OF GEOPHYSICAL NAVIGATION.
Creator: Jepsen, Joshua, Dhanak, Manhar, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: This thesis investigates geomagnetic survey methodology in support of the development of a geophysical navigation system for an Autonomous Underwater Vehicle (AUV). Traditional AUV navigation methods are susceptible to cumulative errors and often rely on external infrastructure, limiting their effectiveness in complex underwater environments. This research leverages geomagnetic field anomalies as an additional navigational reference to these traditional systems, particularly in the absence of...
Show moreThis thesis investigates geomagnetic survey methodology in support of the development of a geophysical navigation system for an Autonomous Underwater Vehicle (AUV). Traditional AUV navigation methods are susceptible to cumulative errors and often rely on external infrastructure, limiting their effectiveness in complex underwater environments. This research leverages geomagnetic field anomalies as an additional navigational reference to these traditional systems, particularly in the absence of Global Positioning System (GPS) and acoustics navigation systems. Geomagnetic surveys were conducted over known shipwreck sites off the coast of Fort Lauderdale, Florida, to validate the system's ability to detect and map magnetic anomalies. Data from these surveys were processed to develop high-resolution geomagnetic contour maps, which were then analyzed for accuracy, reliability, and modeling in identifying geomagnetic features.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014527
Subject Headings: Geomagnetism, Geophysical surveys, Autonomous underwater vehicles
Format: Document (PDF)

Title: OPTIMIZATION OF BATTERY OPERATION USING ARTIFICIAL INTELLIGENCE TO MINIMIZE THE ELECTRICITY COST IN A MICROGRID WITH RENEWABLE ENERGY SOURCES AND ELECTRIC VEHICLES.
Creator: Colucci, Raymond A., Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids presents both opportunities and challenges in terms of optimizing energy use and minimizing electricity costs. This dissertation explores the development of an advanced optimization framework using artificial intelligence (AI) to enhance battery operation in microgrids. The proposed solution leverages AI techniques to dynamically manage the charging and discharging of batteries,...
Show moreThe increasing integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids presents both opportunities and challenges in terms of optimizing energy use and minimizing electricity costs. This dissertation explores the development of an advanced optimization framework using artificial intelligence (AI) to enhance battery operation in microgrids. The proposed solution leverages AI techniques to dynamically manage the charging and discharging of batteries, considering fluctuating energy demands, variable electricity pricing, and intermittent RES generation. By employing a fuzzy logic-based control algorithm, the system intelligently allocates energy from solar power, grid electricity, and battery storage, while coordinating EV charging schedules to reduce peak demand charges. The optimization framework integrates predictive modeling for energy consumption and generation, alongside real-time data from weather forecasts and electricity markets, to make informed decisions. Additionally, the approach considers the trade-off between maximizing renewable energy usage and minimizing reliance on costly grid power during peak hours.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014502
Subject Headings: Electric vehicles, Electric vehicles--Batteries, Renewable energy, Artificial intelligence
Format: Document (PDF)

Title: SPACE-TIME GRAPH-BASED VEHICULAR TRAJECTORY PLANNER: AN AUTONOMOUS INTERSECTION MANAGEMENT SYSTEM.
Creator: Mutlu, Caner, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Every passenger vehicle must rely on a safe and optimal trajectory to eliminate traffic incidents and congestion as well as to reduce environmental impact, and travel time. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicular trajectories with connected and autonomous vehicles (CAVs). The first contribution of this dissertation is the fastest trajectory planner (FTP) method which is geared for computing the fastest waypoint trajectories via performing...
Show moreEvery passenger vehicle must rely on a safe and optimal trajectory to eliminate traffic incidents and congestion as well as to reduce environmental impact, and travel time. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicular trajectories with connected and autonomous vehicles (CAVs). The first contribution of this dissertation is the fastest trajectory planner (FTP) method which is geared for computing the fastest waypoint trajectories via performing graph search over a discretized space-time (ST) graph (Gt), thereby constructing collision-free space-time trajectories with variable vehicular speeds adhering to traffic rules and dynamical constraints of vehicles. The benefits of navigating a connected and autonomous vehicle (CAV) truly capture effective collaboration between every CAV during the trajectory planning step. This requires addressing trajectory planning activity along with vehicular networking in the design phase. For complementing the proposed FTP method in decentralized scenarios, the second contribution of this dissertation is an application layer V2V solution using a coordinator-based distributed trajectory planning method which elects a single leader CAV among all the collaborating CAVs without requiring a centralized infrastructure. The leader vehicular agent calculates and assigns a trajectory for each node CAV over the vehicular network for the collision-free management of an unsignalized road intersection. The proposed FTP method is tested in a simulated road intersection scenario for carrying out trials on scheduling efficiency and algorithm runtime. The resulting trajectories allow high levels of intersection sharing, high evacuation rate, with a low algorithm single-threaded runtime figures even with large scenarios of up to 1200 vehicles, surpassing comparable systems.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014539
Subject Headings: Autonomous vehicles, Computer engineering, Transportation
Format: Document (PDF)

Title: SYNERGETIC COMBINATION OF SEAWATER AND POLYMER-COATED NICKEL NANOPARTICLES FOR CO2 CAPTURE.
Creator: Abhishek, Kim, Myeongsub, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Due to technological advancement, energy consumption and demand have been increasing significantly, primarily satisfied by fossil fuel utilization. The dependence on fossil fuels results in substantial greenhouse gas emissions, with CO₂ being the principal factor in global warming. Carbon capture technologies are employed to mitigate the escalated CO₂ emissions into the atmosphere. Among various carbon capture methods, amine scrubbing is widely utilized because of its high CO2 capture...
Show moreDue to technological advancement, energy consumption and demand have been increasing significantly, primarily satisfied by fossil fuel utilization. The dependence on fossil fuels results in substantial greenhouse gas emissions, with CO₂ being the principal factor in global warming. Carbon capture technologies are employed to mitigate the escalated CO₂ emissions into the atmosphere. Among various carbon capture methods, amine scrubbing is widely utilized because of its high CO2 capture efficiency and ease of adaptability to the existing power plants. This method, however, presents drawbacks, including increased toxicity, corrosiveness, and substantial freshwater use. To overcome these shortcomings and simultaneously develop an environmentally sustainable carbon capture solution, this study aims to evaluate the CO2 capture performance of seawater associated with polyvinylpyrrolidone (PVP) polymer-coated nickel nanoparticles (NiNPs) catalysts. Using high-speed bubble-based microfluidics, we investigated time-dependent size variations of CO2 bubbles in a flow-focusing microchannel, which is directly related to transient CO₂ dissolution into the surrounding solution. We hypothesize that the higher surface-to-volume ratio of polymer-coated NiNPs could provide a higher CO2 capture rate and solubility under the same environmental conditions. To test this hypothesis and to find the maximum performance of carbon capture, we synthesized polymer-coated NiNPs with different sizes of 5 nm, 10 nm, and 20 nm. The results showed that 5 nm polymer-coated NiNPs attained a CO₂ dissolution rate of 77% while it is 71% and 43% at 10 nm and 20 nm NPs, respectively. This indicates that our hypothesis is proven to be valid, suggesting that the smaller NPs catalyze CO2 capture effectively with using the same amount of material, which could be a game changer for future CO2 reduction technologies. This unique strategy promotes the future improvement of NiNPs as catalysts for CO2 capture from saltwater.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014557
Subject Headings: Nickel nanoparticles, Polyvinylpyrrolidone, Seawater, Carbon dioxide mitigation
Format: Document (PDF)

Title: HUMAN ACTIVITY RECOGNITION: INTEGRATING SENSOR FUSION AND ARTIFICIAL INTELLIGENCE.
Creator: Alanazi, Munid, Ilyas, Mohammad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Human Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes....
Show moreHuman Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes. Recent advancements in computational technology and sensor availability have driven significant progress in this field, enabling the integration of these sensors into smartphones and other devices. The first study outlines the foundational aspects of HAR and reviews existing literature, highlighting the importance of machine learning applications in healthcare, athletics, and personal use. In the second study, the focus shifts to addressing challenges in handling large-scale, variable, and noisy sensor data for HAR systems. The research applies machine learning algorithms to the KU-HAR dataset, revealing that the LightGBM classifier outperforms others in key performance metrics such as accuracy, precision, recall, and F1 score. This study underscores the continued relevance of optimizing machine learning techniques for improved HAR systems. The study highlights the potential for future research to explore more advanced fusion techniques to fully leverage different data modalities for HAR. The third study focuses on overcoming common challenges in HAR research, such as varying smartphone models and sensor configurations, by employing data fusion techniques.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014496
Subject Headings: Artificial intelligence, Human activity recognition, Detectors
Format: Document (PDF)

Title: AN ADAPTIVE DEEP LEARNING FRAMEWORK TO ENHANCE THE PERFORMANCE OF MONITORING SYSTEMS FOR BIOMEDICAL APPLICATIONS.
Creator: Shuqair, Mustafa, Ghoraani, Behnaz, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Deep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep...
Show moreDeep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep learning-based systems depend highly. Additionally, obtaining high-quality labeled data to train deep learning models is often expensive, requiring significant time and resources. Another significant challenge is ensuring the practicality, accessibility, and convenience of the monitoring systems. This dissertation proposes an innovative deep learning framework to overcome these challenges and improve system generalization performance in classification and regression tasks, specifically monitoring patients with neurological disorders like Parkinson’s.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014542
Subject Headings: Deep learning (Machine learning), Monitoring systems, Biomedical engineering
Format: Document (PDF)

Title: ENHANCING LOCATION INFORMATION PRIVACY AND SECURITY IN IOBT USING DECEPTION-BASED TECHNIQUES.
Creator: Alkanjr, Basmh Ibrahim, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: IoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may...
Show moreIoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may compromise operational confidentiality and impede the element of surprise in military operations. Therefore, ensuring the security of location data is crucial for the success and efficiency of military operations. We propose two systems to address this issue. First, we propose a novel deception-based scheme to enhance the location-information security of IoBT nodes. The proposed scheme uses a novel encryption method, dummy IDs, and dummy packets technology. We develop a mathematical model to evaluate our scheme in terms of safety time (ST), probability of failure (PF), and the probability of identifying the real packet in each location information update (PIRP). Then, we develop NetLogo simulations to validate the mathematical model. The proposed scheme increases ST, reduces PF and PIRP.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014497
Subject Headings: Internet of things, Artificial intelligence, Machine learning, Deception
Format: Document (PDF)

Title: BINARY AND MULTI-CLASS INTRUSION DETECTION IN IOT USING STANDALONE AND HYBRID MACHINE AND DEEP LEARNING MODELS.
Creator: Akif, MD Ahnaf, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support...
Show moreMaintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were used. Furthermore, two hybrid models were created by combining machine learning techniques: RF, XGBoost, AdaBoost, KNN, and SVM and these hybrid models were voting based hybrid classifier. Where one is for binary, and the other one is for multi-class classification. These models were tested using precision, recall, accuracy, and F1-score criteria and compared the performance of each model. This work thoroughly explains how hybrid, standalone ML and DL techniques could improve IDS (Intrusion Detection System) in terms of accuracy and scalability in IoT (Internet of Things).
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014514
Subject Headings: Internet of things, Machine learning, Deep learning (Machine learning), Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: ARIAL PHOTOGRAMMETRY AND LIDAR POINT CLOUD REGISTRATION USING DEEP LEARNING.
Creator: Mandal, Anil Kumar, Yong, Yan, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the...
Show moreThis research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014538
Subject Headings: Deep learning (Machine learning), Photogrammetry, Three-dimensional modeling
Format: Document (PDF)

Title: ANALYSIS OF THE RELATIONSHIP BETWEEN STUDENTS' SOCIOECONOMIC STATUS AND THEIR ACADEMIC PERFORMANCE.
Creator: Dulcio, Gamalie Haldas, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Promoting diversity in STEM fields is essential to fostering innovation and addressing global challenges. Despite extensive efforts, the representation of minority groups, including women, in undergraduate computer science and engineering programs remains low, posing significant barriers to equity and inclusivity in STEM education (Nicole & DeBoer, 2020). This systematic review explores the socio-economic and cultural challenges discouraging minority students from pursuing degrees,...
Show morePromoting diversity in STEM fields is essential to fostering innovation and addressing global challenges. Despite extensive efforts, the representation of minority groups, including women, in undergraduate computer science and engineering programs remains low, posing significant barriers to equity and inclusivity in STEM education (Nicole & DeBoer, 2020). This systematic review explores the socio-economic and cultural challenges discouraging minority students from pursuing degrees, specifically computer science and engineering disciplines. A comprehensive literature search was conducted across databases such as IEEE Xplore, Google Scholar, and Scopus using specific search terms. Studies were chosen based on clear inclusion and exclusion criteria. Data was carefully extracted and analyzed, focusing on primary obstacles such as the scarcity of role models, biases, and educational barriers. To evaluate the quality of the studies included in the review, Covidence’s quality assessment tools were used, ensuring methodological rigor and consistency across the studies.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014554
Subject Headings: Socioeconomic status, Students--Economic conditions, Students--Social conditions, Academic achievement, Educational sociology
Format: Document (PDF)

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