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Title: SORPTIVITY, RESISTIVITY AND POROSITY OF CONCRETE CONTAINING SUPPLEMENTARY CEMENTITIOUS MATERIALS.
Creator: Barman, Sanjoy, Presuel-Moreno, Francisco, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Supplementary cementitious materials (SCMs), are beneficial when used as partial replacement of cement in concrete mixtures for coastal concrete structures, blended with Portland cement (binary or ternary mixes), i.e., high-performance concrete provides improved properties when exposed to marine harsh environment. In order to characterize selected durability properties of different concrete mixtures, a testing program was established. The intent of this study consists of testing 10cm diameter...
Show moreSupplementary cementitious materials (SCMs), are beneficial when used as partial replacement of cement in concrete mixtures for coastal concrete structures, blended with Portland cement (binary or ternary mixes), i.e., high-performance concrete provides improved properties when exposed to marine harsh environment. In order to characterize selected durability properties of different concrete mixtures, a testing program was established. The intent of this study consists of testing 10cm diameter x 20cm long concrete specimens prepared with a range of different mix designs. 1) to evaluate the rate of water absorption due to capillary suction, referred to as sorptivity, 2) to evaluate the concrete surface resistivity, 3) to evaluate and compare the total porosity of specimens with different mixes, and 4) to obtain correlations between resistivity and sorptivity. All of these experimental tests were carried out according to ASTM International Standards (Sorptivity, Porosity) and Florida Method of Test (Resistivity). The tests were performed on concrete samples at various ages. Moreover, The results provided a fast and reasonable approximation of the concrete durability over time. Ordinary portland cement was partially replaced with supplementary cementitious materials including: fly ash (20%), silica fume (8%) and blast furnace slag (50%). These SCMs are highly effective in creating more durable concrete design mixtures. The water-to-cementitious (w/cm) ratios of 0.41 and 0.35 were investigated. The concrete that contains pozzolanic materials has demonstrated progress in extending the time for initiation of corrosion. The test results obtained indicate that the concurrent inclusion of fly ash and silica fume greatly reduced water penetration. The mixes containing slag also showed lower porosity and water absorption result, when compared to specimens containing fly ash only. Ternary concrete mixtures specimens showed much higher surface resistivity values than binary mixture specimens. These results suggest that reducing w/cm ratio, adding SCMs to concrete mixtures improved the concrete durability. The possibilities for the risks of corrosion initiation would be minimized (delayed) by prescriptive and then performance-based concrete blends with SCM materials optimized for service exposure in aggressive environments.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013633
Subject Headings: Fly ash, High performance concrete, Porosity, Silica fume, Slag
Format: Document (PDF)

Title: Hydrodynamic Interactions of Pitching Hydrofoils in Close Formation.
Creator: Boltri, Michael A., Curet, Oscar M., Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Hydrodynamics interaction is a factor in the performance of fish schooling or underwater vessels in close formation. In this work, we visualized the wake structure of pitching hydrofoils using an inclined soap film. We considered one-, two-, three- and nine-foil configurations with different spacing and actuation parameters: amplitude (A), frequency (f), phase difference (), and flow speed (U). The wake structures were recorded with a high-speed camera and analyzed to measure the vortex...
Show moreHydrodynamics interaction is a factor in the performance of fish schooling or underwater vessels in close formation. In this work, we visualized the wake structure of pitching hydrofoils using an inclined soap film. We considered one-, two-, three- and nine-foil configurations with different spacing and actuation parameters: amplitude (A), frequency (f), phase difference (), and flow speed (U). The wake structures were recorded with a high-speed camera and analyzed to measure the vortex angle created. The wake structure of two- and three-foil configurations were compared with the Strouhal number, St = fA/U, of a single foil. For the nine-foil configuration, the wake velocity and the standard deviation of the velocity were used to interpret the hydrodynamic interaction. It was found that both spacing and phase difference between foils are relevant in the hydrodynamic interaction. Qualitative observations are also made, and vortex street behavior characteristics are identified.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013627
Subject Headings: Hydrodynamics, Hydrofoils
Format: Document (PDF)

Title: ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE.
Creator: Garbin, Christian, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The...
Show moreArtificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013580
Subject Headings: Machine learning, Artificial intelligence, Healthcare
Format: Document (PDF)

Title: SPATIAL NETWORK BIG DATA APPROACHES TO EMERGENCY MANAGEMENT INFORMATION SYSTEMS.
Creator: Herschelman, Roxana M., Yang, KwangSoo, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Emergency Management Information Systems (EMIS) are defined as a set of tools that aid decision-makers in risk assessment and response for significant multi-hazard threats and disasters. Over the past three decades, EMIS have grown in importance as a major component for understanding, managing, and governing transportation-related systems. To increase resilience against potential threats, the main goal of EMIS is to timely utilize spatial and network datasets about (1) locations of hazard...
Show moreEmergency Management Information Systems (EMIS) are defined as a set of tools that aid decision-makers in risk assessment and response for significant multi-hazard threats and disasters. Over the past three decades, EMIS have grown in importance as a major component for understanding, managing, and governing transportation-related systems. To increase resilience against potential threats, the main goal of EMIS is to timely utilize spatial and network datasets about (1) locations of hazard areas (2) shelters and resources, (3) and how to respond to emergencies. The main concern about these datasets has always been the very large size, variety, and update rate required to ensure the timely delivery of useful emergency information and response for disastrous events. Another key issue is that the information should be concise and easy to understand, but at the same time very descriptive and useful in the case of emergency or disaster. Advancement in EMIS is urgently needed to develop fundamental data processing components for advanced spatial network queries that clearly and succinctly deliver critical information in emergencies. To address these challenges, we investigate Spatial Network Database Systems and study three challenging Transportation Resilience problems: producing large scale evacuation plans, identifying major traffic patterns during emergency evacuations, and identifying the highest areas in need of resources.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013576
Subject Headings: Emergency management, Big data, Emergency management--Information technology
Format: Document (PDF)

Title: LOCALIZED FLOW MODIFICATION TO INCREASE POWER CAPTURE OF A SMALL-SCALE FLOATING UNDERSHOT WATERWHEEL.
Creator: Hess, Sullivan, Dhanak, Manhar, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: The goal of the work described in this thesis is to design a flow augmentation device to increase the power capture and efficiency of a small-scale floating Under-Shot Water Wheel (USWW) currently being developed by Florida Atlantic University research funded by the U.S Department of Energy. The flow concentrator subsystem is intended to maximize the kinetic energy extracted by the marine hydrokinetic (MHK) energy collection device through modification of the local flow field across the...
Show moreThe goal of the work described in this thesis is to design a flow augmentation device to increase the power capture and efficiency of a small-scale floating Under-Shot Water Wheel (USWW) currently being developed by Florida Atlantic University research funded by the U.S Department of Energy. The flow concentrator subsystem is intended to maximize the kinetic energy extracted by the marine hydrokinetic (MHK) energy collection device through modification of the local flow field across the capture plane. The primary objective is to increase the velocity and/or rate of mass inflow through the turbine through inserting a streamlined body in the region of interest. By utilizing the resulting flow field to increase hydraulic forcing on the waterwheel blades, the torque and/or RPM of the USWW can be increased. Based on experimental testing in the FAU wave tank at 1:5 prototype scale (280 mm wheel diameter) the flow concentrator was shown to produce an increase in device power coefficient of 17-55% measured over a velocity range of 0.16-0.45 m/s.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013613
Subject Headings: Water-wheels, Renewable energy
Format: Document (PDF)

Title: Corrosion Propagation of Reinforcing Steel Embedded in Binary and Ternary Concrete.
Creator: Hoque, Kazi Naimul, Presuel-Moreno, Francisco, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: The Florida Department of Transportation (FDOT) has been using supplementary cementitious materials while constructing steel reinforced concrete marine bridge structures for over three decades. It has been found from previous studies that such additions in concrete mix makes the concrete more durable. This research was conducted to better understand the corrosion propagation stage of steel rebar embedded in high performance concrete exposed to high humidity environment. Reinforced concrete...
Show moreThe Florida Department of Transportation (FDOT) has been using supplementary cementitious materials while constructing steel reinforced concrete marine bridge structures for over three decades. It has been found from previous studies that such additions in concrete mix makes the concrete more durable. This research was conducted to better understand the corrosion propagation stage of steel rebar embedded in high performance concrete exposed to high humidity environment. Reinforced concrete samples that were made with binary mixes, and ternary mixes were considered. None of these concretes had any admixed chloride to start with. An accelerated chloride transport method was used to drive chloride ions into the concrete so that chlorides reached and exceed the chloride threshold at the rebar surface and hence the corrosion process initiated after a short period of time (within few days to few months). Once corrosion has initiated the corrosion propagation can be studied. Electrochemical measurements such as rebar potential measurements, Linear Polarization Resistance (LPR), Electrochemical Impedance Spectroscopy (EIS), and Galvanostatic Pulse (GP) measurements were taken at regular intervals (during and after the electro-migration process) to observe the corrosion propagation in each sample. During the propagation stage, reinforcement eventually reached negative potentials values (i.e., Ecorr≤ –0.200 Vsce) for all the samples. The corrected polarization resistance (Rc) was calculated by subtracting the concrete solution resistance from the apparent polarization resistance measured. The Rc values obtained from LPR and GP measurements were converted to corrosion current (as the corroding area is unknown), and these corrosion current values measured over time were used to obtain the calculated mass loss (using Faraday’s Law). A comparison was made of the calculated corrosion current obtained using the LPR and GP tests. A comparison of mass loss was also obtained from the values measured from LPR and GP tests. From the experimental results, it was observed that the corrosion current values were largely dependent on the length of solution reservoirs. For specimens cast with single rebar as well as three rebars, the most recent corrosion current values (measurements taken between July 2018 to October 2020) in general were larger for the rebars that are embedded in specimens prepared with SL mix, followed by specimens prepared with FA, T1, and T2 mixes respectively. The range of corrosion current values (most recent) were 0.8-33.8 μA for SL samples, 0.5-22.5 μA for FA samples, 0.8-14.8 μA for T1 samples, and 0.7-10.4 μA for T2 samples respectively. It was also found that the calculated mass loss values were larger for rebars that are embedded in specimens (single rebar and three rebars) prepared with SL mix, followed by specimens prepared with FA, T1, and T2 mixes respectively. The range of calculated mass loss values were 0.07-1.13 grams for SL samples, 0.06-0.62 grams for FA samples, 0.12-0.54 grams for T1 samples, and 0.06-0.40 grams for T2 samples respectively. A variety of corrosion related parameters (Ecorr, Rs, Rc, and Icorr) and calculated theoretical mass loss values observed, were due to the changing parameters such as concrete compositions, concrete cover thickness, rebar diameter, total ampere-hour applied, and reservoir size. The specimens showed no visual signs of corrosion such as cracks or corrosion products that reached the concrete surface. The actual size of the corroding sites was unknown as the specimens were not terminated for forensic analysis. The size of the corroding sites could affect how much corrosion products are required to crack the concrete. It is speculated that the corrosion products in liquid form penetrated the pore structure but did not build up enough to cause cracks. No cracks or corrosion bleed outs were observed within the monitored propagation period of approximately 1600 days.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013634
Subject Headings: Concrete, Concrete bridges--Corrosion, Carbon steel
Format: Document (PDF)

Title: STATISTICAL MODELING OF SHIP AIRWAKES INCLUDING THE FEASIBILITY OF APPLYING MACHINE LEARNING.
Creator: Krishnan, Vaishakh, Gaonkar, Gopal, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Airwakes are shed behind the ship’s superstructure and represent a highly turbulent and rapidly distorting flow field. This flow field severely affects pilot’s workload and such helicopter shipboard operations. It requires both the one-point statistics of autospectrum and the two-point statistics of coherence (normalized cross-spectrum) for a relatively complete description. Recent advances primarily refer to generating databases of flow velocity points through experimental and computational...
Show moreAirwakes are shed behind the ship’s superstructure and represent a highly turbulent and rapidly distorting flow field. This flow field severely affects pilot’s workload and such helicopter shipboard operations. It requires both the one-point statistics of autospectrum and the two-point statistics of coherence (normalized cross-spectrum) for a relatively complete description. Recent advances primarily refer to generating databases of flow velocity points through experimental and computational fluid dynamics (CFD) investigations, numerically computing autospectra along with a few cases of cross-spectra and coherences, and developing a framework for extracting interpretive models of autospectra in closed form from a database along with an application of this framework to study the downwash effects. By comparison, relatively little is known about coherences. In fact, even the basic expressions of cross-spectra and coherences for three components of homogeneous isotropic turbulence (HIT) vary from one study to the other, and the related literature is scattered and piecemeal. Accordingly, this dissertation begins with a unified account of all the cross-spectra and coherences of HIT from first principles. Then, it presents a framework for constructing interpretive coherence models of airwake from a database on the basis of perturbation theory. For each velocity component, the coherence is represented by a separate perturbation series in which the basis function or the first term on the right-hand side of the series is represented by the corresponding coherence for HIT. The perturbation series coefficients are evaluated by satisfying the theoretical constraints and fitting a curve in a least squares sense on a set of numerically generated coherence points from a database. Although not tested against a specific database, the framework has a mathematical basis. Moreover, for assumed values of perturbation series constants, coherence results are presented to demonstrate how coherences of airwakes and such flow fields compare to those of HIT.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013629
Subject Headings: Ships--Aerodynamics, Turbulence--Statistical methods, Machine learning
Format: Document (PDF)

Title: APPLYING BLIND SOURCE SEPARATION TO MAGNETIC ANOMALY DETECTION.
Creator: Nieves, Eric, Beaujean, Pierre-Philippe, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: The research shows a novel approach for the Magnetic Anomaly Differentiation and Localization Algorithm, which simultaneously localizes multiple magnetic anomalies with weak total field signatures (tens of nT). In particular, it focuses on the case where there are two homogeneous targets with known magnetic moments. This was done by analyzing the magnetic signals and adapting Independent Component Analysis (ICA) and Simulated Annealing (SA) to solve the problem statement. The results show the...
Show moreThe research shows a novel approach for the Magnetic Anomaly Differentiation and Localization Algorithm, which simultaneously localizes multiple magnetic anomalies with weak total field signatures (tens of nT). In particular, it focuses on the case where there are two homogeneous targets with known magnetic moments. This was done by analyzing the magnetic signals and adapting Independent Component Analysis (ICA) and Simulated Annealing (SA) to solve the problem statement. The results show the groundwork for using a combination of fastICA and SA to give localization errors of 3 meters or less per target in simulation and achieved a 58% success rate. Experimental results experienced additional errors due to the effects of magnetic background, unknown magnetic moments, and navigation error. While one target was localized within 3 meters, only the latest experimental run showed the second target approaching the localization specification. This highlighted the need for higher signal-to-noise ratio and equipment with better navigational accuracy. The data analysis was used to provide recommendations on the needed equipment to minimize observed errors and improve algorithm success.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013610
Subject Headings: Magnetic anomalies, Simulated annealing (Mathematics), Independent component analysis, Unmanned vehicles
Format: Document (PDF)

Title: Characterization and Modeling of Profiling Oceanographic Lidar for Remotely Sampling Ocean Optical Properties.
Creator: Strait, Christopher, Nayak, Aditya, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Lidar has the ability to supplant or compliment many current measurement technologies in ocean optics. Lidar measures Inherent Optical Properties over long distances without impacting the orientation and assemblages of particles it measures, unlike many systems today which require pumps and flow cells. As an active sensing technology, it has the benefit of being independent of time of day and weather. Techniques to interpret oceanographic lidar lags behind atmospheric lidar inversion...
Show moreLidar has the ability to supplant or compliment many current measurement technologies in ocean optics. Lidar measures Inherent Optical Properties over long distances without impacting the orientation and assemblages of particles it measures, unlike many systems today which require pumps and flow cells. As an active sensing technology, it has the benefit of being independent of time of day and weather. Techniques to interpret oceanographic lidar lags behind atmospheric lidar inversion techniques to measure optical properties due to the complexity and variability of the ocean. Unlike in the atmosphere, two unknowns in the lidar equation backscattering at 180o (𝛽𝜋) and attenuation (c) do not necessarily covary. A lidar system developed at the Harbor Branch Oceanographic Institute is used as a test bed to validate a Monte-Carlo model to investigate the inversion of optical properties from lidar signals. Controlled tank experiments and field measurements are used to generate lidar waveforms and provide optical situations to model. The Metron EODES backscatter model is used to model waveforms. A chlorophyll based forward optical model provides a set of 1500 unique optical situations which are modeled to test inversion techniques and lidar geometries. Due to issues with the lidar system and model the goal of validating the model as well as a more mature inversion experiment were not completed. However, the results are valuable to show the complexity and promise of lidar systems.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013631
Subject Headings: Lidar, Remote sensing, Seawater--Optical properties
Format: Document (PDF)

Title: CONNECTED MULTI-DOMAIN AUTONOMY AND ARTIFICIAL INTELLIGENCE: AUTONOMOUS LOCALIZATION, NETWORKING, AND DATA CONFORMITY EVALUATION.
Creator: Tountas, Konstantinos, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to...
Show moreThe objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to protect from -potentially catastrophic- system failures. With our research in data quality assurance, we develop new mathematical theory and algorithms for outlier-resistant decomposition of high-dimensional matrices (tensors) based on L1-norm principal-component analysis (PCA). L1-norm PCA has been proven to be resistant to irregular data-points and will drive critical real-world AI learning and autonomous systems operations in the future. At the same time, one of the most important tasks of autonomous systems is self-localization. In GPS-deprived environments, localization becomes a fundamental technical problem. State-of-the-art solutions frequently utilize power-hungry or expensive architectures, making them difficult to deploy. In this dissertation work, we develop and implement a robust, variable-precision localization technique for autonomous systems based on the direction-of-arrival (DoA) estimation theory, which is cost and power-efficient. Finally, communication between autonomous systems is paramount for mission success in many applications. In the era of 5G and beyond, smart spectrum utilization is key.. In this work, we develop physical (PHY) and medium-access-control (MAC) layer techniques that autonomously optimize spectrum usage and minimizes intra and internetwork interference.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013617
Subject Headings: Artificial intelligence, Machine learning, Tensor algebra
Format: Document (PDF)

Title: NETWORK FEATURE ENGINEERING AND DATA SCIENCE ANALYTICS FOR CYBER THREAT INTELLIGENCE.
Creator: Wheelus, Charles, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: While it is evident that network services continue to play an ever-increasing role in our daily lives, it is less evident that our information infrastructure requires a concerted, well-conceived, and fastidiously executed strategy to remain viable. Government agencies, Non-Governmental Organizations (\NGOs"), and private organizations are all targets for malicious online activity. Security has deservedly become a serious focus for organizations that seek to assume a more proactive posture; in...
Show moreWhile it is evident that network services continue to play an ever-increasing role in our daily lives, it is less evident that our information infrastructure requires a concerted, well-conceived, and fastidiously executed strategy to remain viable. Government agencies, Non-Governmental Organizations (\NGOs"), and private organizations are all targets for malicious online activity. Security has deservedly become a serious focus for organizations that seek to assume a more proactive posture; in order to deal with the many facets of securing their infrastructure. At the same time, the discipline of data science has rapidly grown into a prominent role, as once purely theoretical machine learning algorithms have become practical for implementation. This is especially noteworthy, as principles that now fall neatly into the field of data science has been contemplated for quite some time, and as much as over two hundred years ago. Visionaries like Thomas Bayes [18], Andrey Andreyevich Markov [65], Frank Rosenblatt [88], and so many others made incredible contributions to the field long before the impact of Moore's law [92] would make such theoretical work commonplace for practical use; giving rise to what has come to be known as "Data Science".
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013620
Subject Headings: Cyber security, Computer security, Information infrastructure, Predictive analytics
Format: Document (PDF)

Title: Online Parameter Learning for Structural Condition Monitoring System.
Creator: Alqazzaz, Jaffar, Jang, Jinwoo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In...
Show moreThe purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013540
Subject Headings: Kalman filtering, Kalman filtering--Data processing, Simulations, Parameter estimation
Format: Document (PDF)

Title: MACHINE LEARNING DEMODULATOR ARCHITECTURES FOR POWER-LIMITED COMMUNICATIONS.
Creator: Gorday, Paul E., Nurgun, Erdol, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited...
Show moreThe success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited communication systems, such as those found in wireless sensor networks. Such networks will provide greater connection to the world around us and are expected to contain orders of magnitude more devices than cellular networks. A number of standard and proprietary protocols span this space, with modulations such as frequency-shift-keying (FSK), Gaussian FSK (GFSK), minimum shift keying (MSK), on-off-keying (OOK), and M-ary orthogonal modulation (M-orth). These modulations enable low-cost radio hardware with efficient nonlinear amplification in the transmitter and noncoherent demodulation in the receiver.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013511
Subject Headings: Deep learning, Machine learning--Technique, Demodulators, Wireless sensor networks, Computer network architectures
Format: Document (PDF)

Title: Development of MnO2 Hollow Nanoparticles for Drug Delivery.
Creator: Greene, Allison, Kang, Yunqing, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: This thesis reports the development of a novel drug delivery system consisting of hollow nanoparticles, formed from manganese dioxide (δ-MnO2) sheets, that are coated with polydopamine and folic acid to selectively target cancer cells. The biodegradability and colloidal stability of the uncoated hollow nanoparticles were investigated in comparison to solid MnO2 nanoparticles and graphene oxide sheets. The MnO2 hollow nanoparticles degraded at a faster rate and seem to have a higher surface...
Show moreThis thesis reports the development of a novel drug delivery system consisting of hollow nanoparticles, formed from manganese dioxide (δ-MnO2) sheets, that are coated with polydopamine and folic acid to selectively target cancer cells. The biodegradability and colloidal stability of the uncoated hollow nanoparticles were investigated in comparison to solid MnO2 nanoparticles and graphene oxide sheets. The MnO2 hollow nanoparticles degraded at a faster rate and seem to have a higher surface area and better colloidal dispersion than solid MnO2 nanoparticles. Xanthan gum was proven to improve colloidal dispersion of these hollow nanoparticles and were used for further cell studies. In this study, cancer and healthy cells were treated with coated hollow nanoparticles, and results indicate that this novel hollow nanoparticle may preferentially target and kill cancer cells. Particle aggregation has shown to be toxic to cells. Further studies with this novel drug delivery system may lead to a groundbreaking solution to targeted cancer therapy.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013513
Subject Headings: Drug Delivery Systems, Nanoparticles, Manganese dioxide, Xanthan gum, Cancer cells
Format: Document (PDF)

Title: HPCC based Platform for COPD Readmission Risk Analysis with implementation of Dimensionality reduction and balancing techniques.
Creator: Jain, Piyush, Agarwal, Ankur, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts....
Show moreHospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in real world and a Machine learning based solution which uses patients discharge summaries as a dataset to train and test the machine learning model created. Current systems does not take into consideration one of the very important aspect of solving readmission problem by taking Big data into consideration. This study also takes into consideration Big data aspect of solutions which can be deployed in the field for real world use. We have used HPCC compute platform which provides distributed parallel programming platform to create, run and manage applications which involves large amount of data. We have also proposed some feature engineering and data balancing techniques which have shown to greatly enhance the machine learning model performance. This was achieved by reducing the dimensionality in the data and fixing the imbalance in the dataset. The system presented in this study provides a real world machine learning based predictive modeling for reducing readmissions which could be templatized for other diseases.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013560
Subject Headings: Machine learning, Big data, Patient Readmission, Hospitals--Admission and discharge--Data processing, High performance computing
Format: Document (PDF)

Title: The Effect of Shear Sheltering on Trailing Edge Noise: A Theoretical Study.
Creator: Jimenez, Ignacio, Glegg, Stewart, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Shear sheltering is defined as the effect of the mean flow velocity profile in a boundary layer on the turbulence caused by an imposed gust. In aeroacoustic applications turbulent boundary layers interacting with blade trailing edges or roughness elements are an important source of sound, and the effect of shear sheltering on these noise sources has not been studied in detail. Since the surface pressure spectrum below the boundary layer is the primary driver of trailing edge and roughness...
Show moreShear sheltering is defined as the effect of the mean flow velocity profile in a boundary layer on the turbulence caused by an imposed gust. In aeroacoustic applications turbulent boundary layers interacting with blade trailing edges or roughness elements are an important source of sound, and the effect of shear sheltering on these noise sources has not been studied in detail. Since the surface pressure spectrum below the boundary layer is the primary driver of trailing edge and roughness noise, this thesis considers the effect that shear sheltering has on the surface pressure spectrum below a boundary layer. This study presents a model of the incoming turbulence as a vortex sheet at a specified height above the surface and shows, using canonical boundary layers and approximations to numerical results, how the mean flow velocity profile can be manipulated to alter the surface pressure spectrum and hence the associated trailing edge noise. The results from this model demonstrate that different mean velocity profiles drive significant changes in the unsteady characteristics of the flow. The surface pressure fluctuations results also suggest that boundary layers where the shear in the mean velocity profile is significant can be beneficial for the reduction of trailing edge noise at particular frequencies.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013535
Subject Headings: Turbulent boundary layer, Trailing edges (Aerodynamics), Aeroacoustics, Boundary layer noise, Shear sheltering
Format: Document (PDF)

Title: NEURALSYNTH - A NEURAL NETWORK TO FPGA COMPILATION FRAMEWORK FOR RUNTIME EVALUATION.
Creator: Lanham, Grant Jr, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Artificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists....
Show moreArtificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists. There are tools to program FPGAs from a high level description of a network, but there is no unified interface for programmers across these tools. In this thesis, I present the design and implementation of NeuralSynth, a prototype Python framework which aims to bridge the gap between data scientists and FPGA programming for neural networks. My method relies on creating an extensible Python framework that is used to automate programming and interaction with an FPGA. The implementation includes a digital design for the FPGA that is completed by a Python framework. Programming and interacting with the FPGA does not require leaving the Python environment. The extensible approach allows multiple implementations, resulting in a similar workflow for each implementation. For evaluation, I compare the results of my implementation with a known neural network framework.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013533
Subject Headings: Artificial neural networks, Neural networks (Computer science)--Design, Field programmable gate arrays, Python (Computer program language)
Format: Document (PDF)

Title: ESTABLISHING A SCREENING TOOL TO SUPPORT DEVELOPMENT AND PRIORITIZATION OF WATERSHED BASED FLOOD PROTECTION PLANS.
Creator: Rojas, Gerardo, Bloetscher, Frederick, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Flood risk analysis is the instrument for utility managers to create a sound strategy and adaptation plans into their communities. Local municipalities are being continuously challenged every year by the impacts of climate change. The need to develop a screening tool to analyze watersheds and find risk areas is the goal of this research. Open source high-quality data is allowing climate scientists to create innovative ways to study watersheds when performing spatial analysis for inundation...
Show moreFlood risk analysis is the instrument for utility managers to create a sound strategy and adaptation plans into their communities. Local municipalities are being continuously challenged every year by the impacts of climate change. The need to develop a screening tool to analyze watersheds and find risk areas is the goal of this research. Open source high-quality data is allowing climate scientists to create innovative ways to study watersheds when performing spatial analysis for inundation areas. The development procedures for a screening tool involved combining readily available data on topography, groundwater, surface water, tidal information for coastal communities, soils, open space, and rainfall data. All efforts to help develop a planning level framework that allows investigators to target the optimal set of outcomes for a given community. This framework appears to be viable across cities that may be inundated with water due to sea-level rise, rainfall, runoff upstream, and other natural events.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013538
Subject Headings: Watersheds, Floods--Risk assessment, Watersheds--Analysis, Flood protection
Format: Document (PDF)

Title: MULTIFACETED EMBEDDING LEARNING FOR NETWORKED DATA AND SYSTEMS.
Creator: Shi, Min, Tang, Yufei, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Network embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization....
Show moreNetwork embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary facets. This thesis focuses on multifaceted network embedding learning, which aims to efficiently incorporate distinct aspects of information such as node labels and node contents for cooperative low-dimensional representation learning together with node topology.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013516
Subject Headings: Embedded computer systems, Neural networks (Computer science), Network embedding, Machine learning
Format: Document (PDF)

Title: On the Drainage Vortices of Liquid in a Container with Two Outlets.
Creator: Stankovic, Radivoje, Su, Tsung-Chow, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: When a liquid drains through a hole in a container, a vortex may form between the surface and the drainage hole. An interesting phenomenon occurs in the presence of two drainage holes. Only one vortex forms, while the other hole will mostly drain as sink flow. In addition, the vortex can switch between one hole and the other with regular periodicity. The primary goal of this study is to measure this periodicity under varying conditions (height of water in the container, diameter of the...
Show moreWhen a liquid drains through a hole in a container, a vortex may form between the surface and the drainage hole. An interesting phenomenon occurs in the presence of two drainage holes. Only one vortex forms, while the other hole will mostly drain as sink flow. In addition, the vortex can switch between one hole and the other with regular periodicity. The primary goal of this study is to measure this periodicity under varying conditions (height of water in the container, diameter of the drainage holes, and distance between drainage holes). Additionally, a study concerning the volume flow rates of vortical vs. sink flow out of the drainage holes was conducted. In the case of two drainage holes, when the height of the water was decreased in the container, the diameter of drainage holes decreased, or the distance between drainage holes was increased, the switching period was shown to decrease.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013572
Subject Headings: Drainage, Vortex-motion
Format: Document (PDF)

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