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Title: A REFERENCE ARCHITECTURE FOR NETWORK FUNCTION VIRTUALIZATION.
Creator: Alwakeel, Ahmed M., Fernandez, Eduardo B., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including...
Show moreCloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including faster module development for network functions, more rapid deployment, enhancement of the network on cloud infrastructures, and lowering the overall cost of having a network system. All these benefits can be achieved in NFV by turning physical network functions into Virtual Network Functions (VNFs). However, since this technology is still a new network paradigm, integrating this virtual environment into a legacy environment or even moving all together into NFV reflects on the complexity of adopting the NFV system. Also, a network service could be composed of several components that are provided by different service providers; this also increases the complexity and heterogeneity of the system. We apply abstract architectural modeling to describe and analyze the NFV architecture. We use architectural patterns to build a flexible NFV architecture to build a Reference Architecture (RA) for NFV that describe the system and how it works. RAs are proven to be a powerful solution to abstract complex systems that lacks semantics. Having an RA for NFV helps us understand the system and how it functions. It also helps us to expose the possible vulnerabilities that may lead to threats toward the system. In the future, this RA could be enhanced into SRA by adding misuse and security patterns for it to cover potential threats and vulnerabilities in the system. Our audiences are system designers, system architects, and security professionals who are interested in building a secure NFV system.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013434
Subject Headings: Virtual computer systems, Cloud computing, Computer network architectures, Computer networks
Format: Document (PDF)

Title: A Mathematical Modeling Approach Using Time Constraints: The Case of Economies of Scale and Sustainability in Intermodal Facilities.
Creator: Goulianou, Panagiota, Kaisar, Evangelos I., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Over the last thirty years, intermodal freight transportation has been a constantly expanding sector. The vast increase of freight volumes contributes to the increase of various issues in the freight corridors as well as the urban environment. The deterioration of congestion in the urban environment and the increase on freight movements on the highways have resulted in the increase of emissions. For this reason, new policies and regulations are put forth to address the environmental effects...
Show moreOver the last thirty years, intermodal freight transportation has been a constantly expanding sector. The vast increase of freight volumes contributes to the increase of various issues in the freight corridors as well as the urban environment. The deterioration of congestion in the urban environment and the increase on freight movements on the highways have resulted in the increase of emissions. For this reason, new policies and regulations are put forth to address the environmental effects of freight transportation. This study deals with the intermodal freight network design problem from the shipping company's perspective, aiming to simultaneously minimize emission levels and cost of freight transportation. We propose a mathematical model for optimizing the design of an intermodal freight network and the location of intermodal hubs between the origins and the destinations, under delivery time constraints. The goal is to identify the mode choice patterns considering transport cost and emissions, and the effects of new emission regulations on network costs. We consider a network with marine terminals as the origins, inland intermodal terminals as the hubs, and fulfillment centers as the destinations. Numerical experiments highlight that the proposed model can provide useful insights to the shipper.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013439
Subject Headings: Intermodal transportation, Containerization, Containerization--Mathematical models, Container ships--Environmental aspects, Economies of scale
Format: Document (PDF)

Title: META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS.
Creator: Liu, Feng, Dingding, Wang, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Deep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep...
Show moreDeep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep learning equipped with meta learning and ensemble learning to study specific problems. We first propose a new deep learning based method for suggestion mining. The major challenges of suggestion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multi-model Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013481
Subject Headings: Neural networks (Computer science), Deep learning, Neural Networks in Applications, Machine learning--Technique
Format: Document (PDF)

Title: Empirical Analysis of the Dissipated Acoustic Energy in Wave Breaking.
Creator: Francke, Kristina, Dhanak, Manhar, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: In this research an attempt is made at explaining the physical processes behind energy dissipation during wave breaking, through spectral analysis of the resulting sound. The size of an air bubble can be directly linked to the frequency of the sound that is heard using the simple harmonic solution to the Rayleigh–Plesset equation. It indicates the inverse relationship between frequency and bubble size. And this relationship has been used to identify wave breaking in general [MANASSEH 2006]....
Show moreIn this research an attempt is made at explaining the physical processes behind energy dissipation during wave breaking, through spectral analysis of the resulting sound. The size of an air bubble can be directly linked to the frequency of the sound that is heard using the simple harmonic solution to the Rayleigh–Plesset equation. It indicates the inverse relationship between frequency and bubble size. And this relationship has been used to identify wave breaking in general [MANASSEH 2006]. Now this research goes a step farther and looks at how the frequency spectrum of the sound changes with time, in an effort to understand the general pattern and from that to deduce an empirical equation that describes the breaking down of turbulence during a wave breaking event. Two main processes have been identified, with the second process having three main indicators that are necessary to evidence wave breaking. The first process is a near instantaneous shattering of the initial air bubble into much smaller metastable bubbles of a size that appears to be common for all waves independent of wave height. Then in the second process, the bubbles continue to break down following a recognisable pattern.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013504
Subject Headings: Waves, Energy dissipation, Spectral analysis, Fluid dynamics, Acoustic energy
Format: Document (PDF)

Title: SYNTHETIC FIBER REINFORCED CONCRETE PERFORMANCE AFTER PROLONGED ENVIRONMENTAL EXPOSURE UTILIZING THE MODIFIED INDIRECT TENSILE TEST.
Creator: Ellis, Spencer G., Presuel-Moreno, Francisco, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: In order to study the mechanical performance of dry-cast synthetic fiber reinforced concrete (SynFRC), samples of varying geometry, fiber content, and environmental exposure were developed and tested using the modified indirect tensile test. The samples created consisted of three different thicknesses (with two different geometries), and six different fiber contents that differed in either type, or quantity, of fibers. Throughout the duration of this research, procedures for inflicting...
Show moreIn order to study the mechanical performance of dry-cast synthetic fiber reinforced concrete (SynFRC), samples of varying geometry, fiber content, and environmental exposure were developed and tested using the modified indirect tensile test. The samples created consisted of three different thicknesses (with two different geometries), and six different fiber contents that differed in either type, or quantity, of fibers. Throughout the duration of this research, procedures for inflicting detrimental materials into the concrete samples were employed at a number of different environments by implementing accelerated rates of deterioration using geometric adjustments, increased temperature exposure, wetting/drying cycles, and preparation techniques. The SynFRC samples studied were immersed in a wide range of environments including: the exposure of samples to high humidity and calcium hydroxide environments, which served at the control group, while the sea water, low pH, and barge conditioning environments were used to depict the real world environments similar to what would be experienced in the Florida ecosystem. As a result of this conditioning regime, the concrete was able to imitate the real-world effects that the environments would have inflicted if exposed for long durations after an exposure period of only 20-24 months. Having adequately conditioned the samples in their respective environments, they were then tested (and forensically investigated) using the modified indirect tensile testing method to gather data regarding each sample’s toughness and load handling capability. By analyzing the results from each sample, the toughness was calculated by taking the area under the force displacement curve. From these toughness readings it was found that possible degradation occurred between the fiber-matrix interface of some of the concrete samples conditioned in the Barge environment. From these specimens that were immersed in the barge environment, a handful of them exhibited multiple episodes of strain softening characteristics within their force displacement curves. In regard to the fibers used within the samples, the PVA fibers tended to pull off more while the Tuff Strand SF fibers had the highest tendency to break (despite some of the fibers showing similar pull off and breaking failure characteristics). When it comes to the overall thickness of the sample, there was clear correlation between the increase in size and the increase in sample toughness, however the degree to which it correlates varies from sample to sample.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013466
Subject Headings: Reinforced concrete, Fiber-reinforced concrete--Testing, Reinforced concrete--Mechanical properties, Tensile Strength, Concrete—Environmental testing
Format: Document (PDF)

Title: SUSTAINING CHAOS USING DEEP REINFORCEMENT LEARNING.
Creator: Vashishtha, Sumit, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Numerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable...
Show moreNumerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable chaos, without requiring any a-priori knowledge about the underlying dynamics. The autonomous decisions taken by the RL algorithm are analyzed to understand how the system’s dynamics are impacted. Learning from this analysis, a simple control-law capable of restoring chaotic behavior is formulated. The reverse-engineering approach adopted in this work underlines the immense potential of the techniques used here to discover effective control strategies in complex dynamical systems. The autonomous nature of the learning algorithm makes it applicable to a diverse variety of non-linear systems, and highlights the potential of RLenabled control for regulating other transient-chaos like catastrophic events.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013498
Subject Headings: Machine learning--Technique, Reinforcement learning, Algorithms, Chaotic behavior in systems, Nonlinear systems
Format: Document (PDF)

Title: CRITICAL EVALUATION OF LEACHATE CLOGGING POTENTIAL IN GRAVITY COLLECTION SYSTEMS AND MANAGEMENT SOLUTIONS.
Creator: Shaha, Bishow Nath, Meeroff, Daniel E., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Leachate clogging in the Leachate Collection System (LCS) due to chemical precipitations and biofilms produced by microbial activities is a common phenomenon in any Municipal Solid Waste (MSW) landfill. This study focuses on quantifying the factors that impact the micro-environment of leachate; and microbial activities that help the precipitates to form and attach to the LCS. It also evaluates the performance of operational changes that have been implemented or the potential alternatives and...
Show moreLeachate clogging in the Leachate Collection System (LCS) due to chemical precipitations and biofilms produced by microbial activities is a common phenomenon in any Municipal Solid Waste (MSW) landfill. This study focuses on quantifying the factors that impact the micro-environment of leachate; and microbial activities that help the precipitates to form and attach to the LCS. It also evaluates the performance of operational changes that have been implemented or the potential alternatives and recommends the possible measures to reduce the severity of clogging. A field scale side-by-side pipe network, and several laboratory setups were used in this study. Calcite is identified to be the predominant phase present in the precipitates using XRD/XRF analysis which, concur with the previous studies. Microbial growth and activities enhance the precipitation of CaCO3 in LCS. Clogging in LCS pipes can be controlled if not eliminated by continuous monitoring along with frequent cleaning with physiochemical processes.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013493
Subject Headings: Leachate, Solid waste management, Sanitary landfills, Calcite, Leachate--Purification
Format: Document (PDF)

Title: DEVELOPMENT OF A BIOSENSOR FOR OBJECTIVELY QUANTIFYING ODORANTS.
Creator: Rahman, Sharmily, Meeroff, Daniel E., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Nuisance odor levels produced by solid waste management operations are subject to regulatory standards due to their impacts on the quality of life of the residents living nearby the facility. Failure to meet regulatory standards may result in fines, litigation, inability to acquire permits, mitigation, and re-siting operations. Since measurement of environmental nuisance odors is currently limited to subjective techniques, monitoring odor levels to meet such standards is often problematic....
Show moreNuisance odor levels produced by solid waste management operations are subject to regulatory standards due to their impacts on the quality of life of the residents living nearby the facility. Failure to meet regulatory standards may result in fines, litigation, inability to acquire permits, mitigation, and re-siting operations. Since measurement of environmental nuisance odors is currently limited to subjective techniques, monitoring odor levels to meet such standards is often problematic. This is becoming more acute as increasing residential populations begin to encroach on properties adjacent to landfills. In order to ensure that nuisance odor issues are minimized, it is necessary to provide an objective measurement. The objective of the current research is to develop a biosensor for providing an objective, standard measurement of odors. The approach is to modify the human odorant binding protein (hOBPIIa), isolated using published biomolecular techniques, by fluorescently tagging it with a chromophore functional group. When this protein is tagged with a fluorophore marker and excited in a spectrofluorometer, it emits light of a certain wavelength that can be detected and quantified. Once odorant molecules are exposed to this complex, they start replacing the fluorophore, and as a result, the emitted light intensity decreases in proportion to the number of odorant molecules. Since the protein response depends on odorant concentration, following an inverse Beer’s Law relationship, the odorants can be quantified accurately and rapidly using fluorometric measurements. The results establish quantitation ranges for different pure and mixture of odorant gases as well as the amount of gas that can be quantified across various flow rates.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013491
Subject Headings: Biosensors, Odors--Measurement, Landfills, Odorant-binding protein, Fluorescence--Measurement
Format: Document (PDF)

Title: DEVELOPMENT OF POINT-OF-CARE ASSAYS FOR DISEASE DIAGNOSTIC AND TREATMENT MONITORING FOR RESOURCE CONSTRAINED SETTINGS.
Creator: Sher, Mazhar, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This thesis aims to address the challenges of the development of cost-effective and rapid assays for the accurate counting of CD4+ T cells and quantification of HIV-1 viral load for resource-constrained settings. The lack of such assays has severely affected people living in disease prevalent areas. CD4+ T cells count information plays a vital role in the effective management of HIV-1 disease. Here, we present a flow-free magnetic actuation platform that uses antibody-coated magnetic beads to...
Show moreThis thesis aims to address the challenges of the development of cost-effective and rapid assays for the accurate counting of CD4+ T cells and quantification of HIV-1 viral load for resource-constrained settings. The lack of such assays has severely affected people living in disease prevalent areas. CD4+ T cells count information plays a vital role in the effective management of HIV-1 disease. Here, we present a flow-free magnetic actuation platform that uses antibody-coated magnetic beads to efficiently capture CD4+ T cells from a 30 μL drop of whole blood. On-chip cell lysate electrical impedance spectroscopy has been utilized to quantify the isolated CD4 cells. The developed assay has a limit of detection of 25 cells per μL and provides accurate CD4 counts in the range of 25–800 cells per μL. The whole immunoassay along with the enumeration process is very rapid and provides CD4 quantification results within 5 min time frame. The assay does not require off-chip sample preparation steps and minimizes human involvement to a greater extent. The developed impedance-based immunoassay has the potential to significantly improve the CD4 enumeration process especially for POC settings.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013495
Subject Headings: Point-of-care testing, Diagnostic tests, Immunoassay, HIV-1, Microfluidic devices
Format: Document (PDF)

Title: DEVELOPMENT OF GUIDELINES FOR IMPLEMENTATION OF FREIGHT AND TRANSIT SIGNAL PRIORITIES TO ENHANCE ROAD TRAFFIC SUSTAINABILITY.
Creator: Ardalan, Taraneh, Kaisar, Evangelos I., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Lately, the attractiveness of cities has contributed to a rise in vehicle movements to and from cities. The growth of freight movements in cities predictably will be one of the critical issues of the near future. Congestion caused by the increased movements of freight impacts the flow of private and transit vehicles. Thus, it is crucial to reduce the congestion on multimodal corridors. Components of the Intelligent Transportation System (ITS) such as Freight Signal Priority (FSP) and Transit...
Show moreLately, the attractiveness of cities has contributed to a rise in vehicle movements to and from cities. The growth of freight movements in cities predictably will be one of the critical issues of the near future. Congestion caused by the increased movements of freight impacts the flow of private and transit vehicles. Thus, it is crucial to reduce the congestion on multimodal corridors. Components of the Intelligent Transportation System (ITS) such as Freight Signal Priority (FSP) and Transit Signal Priority (TSP) that promote the freight and transit vehicles may not only help solve these conditions but may assist with the sustainability of the system. The primary objective of this research is to develop guidelines for traffic agencies to implement signal priorities based on identified decision factors on certain corridors. Besides, this study evaluates the efficiency of FSP and TSP in improving the performance of freight and transit systems. Finally, inclusive guidelines are drawn up based on the literature and the conducted simulation. The developed guidelines apply to corridors where freight delay plays a vital role in the assessment of corridor benefits.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013484
Subject Headings: Freight and freightage, Traffic signs and signals—Control systems, Traffic congestion, Freight transportation
Format: Document (PDF)

Title: CEREBROSPINAL FLUID SHUNT SYSTEM WITH AUTO-FLOW REGULATION.
Creator: Mutlu, Caner, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: A cerebrospinal fluid (CSF) shunt system is used for treatment of hydrocephalus and abnormal intracranial pressure (ICP) conditions. Mostly a shunt system is placed under skin for creating a low resistance pathway between intracranial space and appropriate discharge sites within body by doing so excess CSF volume can exit the intracranial space. Displaced intracranial CSF volume normally results in lowered ICP. Thereby, a CSF shunt can manage ICP. In a healthy person, normal ICP is primarily...
Show moreA cerebrospinal fluid (CSF) shunt system is used for treatment of hydrocephalus and abnormal intracranial pressure (ICP) conditions. Mostly a shunt system is placed under skin for creating a low resistance pathway between intracranial space and appropriate discharge sites within body by doing so excess CSF volume can exit the intracranial space. Displaced intracranial CSF volume normally results in lowered ICP. Thereby, a CSF shunt can manage ICP. In a healthy person, normal ICP is primarily maintained by CSF production and reabsorption rate as a natural tendency of body. If intracranial CSF volume starts increasing due to under reabsorption, this mostly results in raised ICP. Abnormal ICP can be treated by discharging excess CSF volume via use of a shunt system. Once a shunt system is placed subcutaneously, a patient is expected to live a normal life. However, shunt failure as well as flow regulatory problems are major issues with current passive shunt systems which leaves patients with serious consequences of under-/over CSF drainage condition. In this research, a shunt system is developed which is resistant to most shunt-related causes of under-/over CSF drainage. This has been made possible via use of an on-board medical monitoring (diagnostic) and active flow control mechanism. The developed shunt system, in this research, has full external ventricular drainage (EVD) capability. Further miniaturization will make it possible for an implantable shunt.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013489
Subject Headings: Cerebrospinal Fluid Shunts
Format: Document (PDF)

Title: COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.
Creator: Andrews, Whitney Angelica Johanna, Furht, Borko, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre...
Show moreGliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre-trained networks to reduce computational costs. However, with various MRI modalities, MRI machines, and poor image scan quality cause different network structures to have different performance metrics. Each pre-trained network is designed with a different structure that allows robust results given specific problem conditions. This thesis aims to cover the gap in the literature to compare the performance of popular pre-trained networks on a controlled dataset that is different than the network trained domain.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013450
Subject Headings: Gliomas, Neural networks (Computer science), Deep Learning, Convolutional neural networks
Format: Document (PDF)

Title: NOISE PREDICTION METHODS.
Creator: Perry, Nicole Kent, Glegg, Stewart, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Noise prediction methods are necessary in aspects of aerodynamic and hydrodynamic engineering. Predictive models of noise from rotating machinery ingesting turbulence is of much interest and relatively recently studied. This thesis presents a numerical method processed in a series of three codes that was written and edited to receive input for geometrical features of rotating machinery, as well as, adjustments to turbulent operating conditions. One objective of this thesis was to create a...
Show moreNoise prediction methods are necessary in aspects of aerodynamic and hydrodynamic engineering. Predictive models of noise from rotating machinery ingesting turbulence is of much interest and relatively recently studied. This thesis presents a numerical method processed in a series of three codes that was written and edited to receive input for geometrical features of rotating machinery, as well as, adjustments to turbulent operating conditions. One objective of this thesis was to create a platform of analysis for any rotor design to obtain five parameters necessary for noise prediction; 1) the hydrodynamic inflow angle to each blade section, 2) chord length as a function of radius, 3) the cylindrical radius of each blade section, 4) & 5) the leading edge as a function of span in both the rotor-plane and as a function of axial distance downstream. Another objective of this thesis was to use computational fluid dynamics (CFD), specifically by using a Reynold’s-Averaged Navier-Stokes (RANS) Shear Stress Transport (SST) 𝑘 − 𝜔 model simulation in ANSYS Fluent, to obtain the turbulent kinetic energy distribution, also necessary in the noise prediction method presented. The purpose of collecting the rotor geometry data and turbulent kinetic energy data was to input the values into the first of the series of codes and run the calculation so that the output spectra could be compared to experimental noise measurements conducted at the Stability Wind Tunnel at Virginia Tech. The comparison shows that the prediction method results in data that can be reliable if careful attention is payed to the input parameters and the length scale used for analysis. The significance of this research is the noise prediction method presented and used simplifies the model of turbulence by using a correlation function that can be determined by a one-dimensional function while also simplifying the iterations completed on rotor blade to calculate the unsteady forces.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013487
Subject Headings: Noise, Aerodynamic noise, Hydrodynamics, Noise control--Mathematical models
Format: Document (PDF)

Title: SMARTPHONE BASED SICKLE CELL DISEASE DETECTION AND ITS TREATMENT MONITORING FOR POINT-OF-CARE SETTINGS.
Creator: Ilyas, Shazia, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The majority of Sickle Cell Disease (SCD) prevalence is found in Sub-Saharan Africa, where 80% of the world’s population who suffer from this disease are born. Due to a lack of diagnosis and early treatments, 50-90% of these children will die before they reach the age of five. Current methods used for diagnosing SCD are based on hemoglobin analysis such as capillary electrophoresis, ion-exchange high-performance liquid chromatography, and isoelectric focusing. They require expensive...
Show moreThe majority of Sickle Cell Disease (SCD) prevalence is found in Sub-Saharan Africa, where 80% of the world’s population who suffer from this disease are born. Due to a lack of diagnosis and early treatments, 50-90% of these children will die before they reach the age of five. Current methods used for diagnosing SCD are based on hemoglobin analysis such as capillary electrophoresis, ion-exchange high-performance liquid chromatography, and isoelectric focusing. They require expensive laboratory equipment and are not feasible in these low-resource countries. It is, therefore, imperative to develop an alternative and cost-effective method for diagnosing and monitoring of SCD. This thesis aims to address the development and evaluation of a smartphone-based optical setup for the detection of SCD. This innovative technique can potentially be applied for low cost and accurate diagnosis of SCD and improve disease management in resource-limited settings where the disease exhibits a high prevalence. This Point-of-Care (POC) based device offers the potential to improve SCD diagnosis and patient care by providing a portable and cost effective device that requires minimal training to operate and analyze.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013475
Subject Headings: Anemia, Sickle Cell, Point-of-Care Systems, Sickle cell anemia--Treatment, Sickle cell anemia--Diagnosis, Smartphones
Format: Document (PDF)

Title: OBJECT-BASED LAND COVER CLASSIFICATION OF UAV TRUE COLOR IMAGERY.
Creator: Castillo, Stephen M., Nagarajan, Sudhagar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Land cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover...
Show moreLand cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover classification. In addition to the objective of land cover classification, comparison of varying spatial resolutions of the imagery will be analyzed in the accuracy assessment of the output thematic maps. These resolutions will also be compared at varying training sample sizes to see which configuration performed best.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013454
Subject Headings: Land cover, Unmanned aerial vehicles, Drone aircraft in remote sensing, Images, Classification
Format: Document (PDF)

Title: Non Destructive Testing for the Influence of Infill Pattern Geometry on Mechanical Stiffness of 3D Printing Materials.
Creator: Hlinka, Michael, Jang, Jinwoo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: This experiment investigated the effect of infill pattern shape on structural stiffness for 3D printed components made out of carbon fiber reinforced nylon. In order to determine the natural frequency of each specimen, nondestructive vibrational testing was conducted and processed using data acquisition software. After obtaining the acceleration information of each component, in response to ambient vibrational conditions and excitation, frequency response functions were generated. These...
Show moreThis experiment investigated the effect of infill pattern shape on structural stiffness for 3D printed components made out of carbon fiber reinforced nylon. In order to determine the natural frequency of each specimen, nondestructive vibrational testing was conducted and processed using data acquisition software. After obtaining the acceleration information of each component, in response to ambient vibrational conditions and excitation, frequency response functions were generated. These functions provided the natural frequency of each component, making it possible to calculate their respective stiffness values. The four infill patterns investigated in this experiment were: Zig Zag, Tri-Hex, Triangle, and Concentric. Results of the experiment showed that changing the infill pattern of a 3D printed component, while maintaining a constant geometry and density, could increase mechanical stiffness properties by a factor of two. Comprehensively, the experiment showed that infill pattern geometry directly attributes to the mechanical stiffness of 3D printed components.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013470
Subject Headings: 3D printing, Three-dimensional printing--Materials, Materials--Mechanical properties
Format: Document (PDF)

Title: TOWARDS A SECURITY REFERENCE ARCHITECTURE FOR NETWORK FUNCTION VIRTUALIZATION.
Creator: Alnaim, Abdulrahman K., Fernandez, Eduardo B., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Network Function Virtualization (NFV) is an emerging technology that transforms legacy hardware-based network infrastructure into software-based virtualized networks. Instead of using dedicated hardware and network equipment, NFV relies on cloud and virtualization technologies to deliver network services to its users. These virtualized network services are considered better solutions than hardware-based network functions because their resources can be dynamically increased upon the consumer’s...
Show moreNetwork Function Virtualization (NFV) is an emerging technology that transforms legacy hardware-based network infrastructure into software-based virtualized networks. Instead of using dedicated hardware and network equipment, NFV relies on cloud and virtualization technologies to deliver network services to its users. These virtualized network services are considered better solutions than hardware-based network functions because their resources can be dynamically increased upon the consumer’s request. While their usefulness can’t be denied, they also have some security implications. In complex systems like NFV, the threats can come from a variety of domains due to it containing both the hardware and the virtualize entities in its infrastructure. Also, since it relies on software, the network service in NFV can be manipulated by external entities like third-party providers or consumers. This leads the NFV to have a larger attack surface than the traditional network infrastructure. In addition to its own threats, NFV also inherits security threats from its underlying cloud infrastructure. Therefore, to design a secure NFV system and utilize its full potential, we must have a good understanding of its underlying architecture and its possible security threats. Up until now, only imprecise models of this architecture existed. We try to improve this situation by using architectural modeling to describe and analyze the threats to NFV. Architectural modeling using Patterns and Reference Architectures (RAs) applies abstraction, which helps to reduce the complexity of NFV systems by defining their components at their highest level. The literature lacks attempts to implement this approach to analyze NFV threats. We started by enumerating the possible threats that may jeopardize the NFV system. Then, we performed an analysis of the threats to identify the possible misuses that could be performed from them. These threats are realized in the form of misuse patterns that show how an attack is performed from the point of view of attackers. Some of the most important threats are privilege escalation, virtual machine escape, and distributed denial-of-service. We used a reference architecture of NFV to determine where to add security mechanisms in order to mitigate the identified threats. This produces our ultimate goal, which is building a security reference architecture for NFV.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013435
Subject Headings: Computer network architectures--Safety measures, Virtual computer systems, Computer networks, Modeling, Computer
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: 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: 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)

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