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In this dissertation, a novel approach to modeling the scattered field of a periodic corrugated cylinder, from an oblique incident planewave, is presented. The approach utilizes radial waveguide approximations for fields within the corrugations, which are point matched to approximated scattered fields outside of the corrugation to solve for the expansion coefficients. The point matching is done with TMz and TEz modes simultaneously, allowing for hybrid modes to exist. The derivation of the fields and boundary conditions used are discussed in detail. Axial and radial propagating modes for the scattered fields are derived and discussed. Close treatment is given to field equations summation truncation and conversion to matrix form, for numerical computing. A detailed account of the modeling approach using Mathematica® and NCAlgebra for the noncommutative algebra, involved in solving for the expansion coefficients, are also given. The modeling techniques offered provide a full description and prediction of the scattered field of a periodic corrugated cylinder. The model is configured to approximate a smooth cylinder, which is then compared against that of a textbook standard smooth cylinder. The methodology and analysis applied in this research provide a solution for computational electromagnetics, RF communications, Radar systems and the like, for the design, development, and analysis of such systems. Through the rapid modeling techniques developed in this research, early knowledge discovery can be made allowing for better more effective decision making to be made early in the design and investigation process of an RF project.

2017

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Bagby, Jonathan Dr. (Thesis advisor), College of Engineering and Computer Science (Degree grantor).

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Summary: Identifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the disease propagations and to identify possibly infected individuals which will help perform trace – back analysis to locate the possible source of infection for a social group. This model will visualize and identify the families and tightly connected social groups who have had contact with an Ebola patient and is a proactive approach to reduce the risk of exposure of Ebola spread within a community or geographic location.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Agarwal, Ankur (Thesis advisor), Furht, Borko (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Scour is the process of sediment erosion around bridge piers and abutments due to natural and man-made hydraulic activities. Excessive scour is a critical problem that is typically handled by enforcing design requirements that make the submerged structures more resilient. The purpose of this research is to demonstrate the feasibilities of the Optical- Based Green Laser Scanner and HydroLite Sonar in a laboratory setting to capture the 3D profile of simulated local scour holes. The Green Laser had successfully reconstructed a 3D point-cloud imaging of scour profiles under both dry and clear water conditions. The derived scour topography after applying water refraction correction was compared with the simulated scour hole, and was within 1% of the design dimensions. The elevations at the top and bottom surfaces of the 6.5-inch scour hole were -46.6 and -53.11 inches from the reference line at the origin (0,0,0) of the laser scanner. The HydroLite Sonar recorded hydrographical survey points of the scour’s interior surface. The survey points were then processed using MATLAB to obtain a 3D mesh triangulation.

2018

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2018.

Collection: FAU

Advisors/Committee Members: Arockiasamy, Madasamy (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Summary: SRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and analyzed both the short term (12 hours) and long term (48 hours) effects of SRSF1 expression in a human cell line. Furthermore, we analyzed and compared the effect of the expression of a naturally occurring deletion mutant of SRSF1 (RRM12) to the full-¬‐length protein. Our analysis reveals that shortly after SRSF1 is over-¬‐expressed the transcription of several histone coding genes is down-¬‐regulated, allowing for a more relaxed chromatin state and efficient transcription by RNA Polymerase II. This effect is reversed at 48 hours. At the same time key genes for the immune pathways are activated, more notably Tumor Necrosis Factor-¬‐Alpha (TNF-¬‐α), suggesting a role for SRSF1 in T cell functions.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Asghar, Waseem (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity.

2019

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2019.

Collection: FAU

Advisors/Committee Members: Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: In this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the context of Blockchain and mining, we illustrate that by using the same socio-rational model, miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We therefore show that honest mining strategies become Nash Equilibrium in our setting. This thesis is laid out in the following manner. In chapter 2 an introduction to game theory is provided followed by a survey of previous works in game theoretic network security, in chapter 3 a new reputation-based model is introduced to be used within the context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution concept is introduced to force cooperation by each mining entity in Blockchain, and finally, in chapter 5, the concluding remarks and future works are presented.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Nojoumian, Mehrdad (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: Food availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such networks can be used to drive ambient displays designed to educate a target audience, and ultimately reduce the amount of waste generated. We present WASTE REDUCE, a system for automating the measurement of food waste and affecting behavioral change. The challenges and results of deploying such a system are presented. To assess the bene ts of using WASTE REDUCE, two case studies are conducted. The rst study evaluates three different displays, and the second reevaluates one of these displays in a separate location. These studies con rm that the combination of automated monitoring and ambient feedback can reduce food waste for targeted groups.

2017

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Hallstrom, Jason O. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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The amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable knowledge, such as chat, comments, and discussion boards. People usually do not care about spelling and accurate grammatical construction of a sentence in everyday life conversations. Therefore, extracting information from such datasets are more complicated. Text mining can be a solution to this problem. Text mining is a knowledge discovery process used to extract patterns from natural language. Application of text mining techniques on social networking websites can reveal a significant amount of information. Text mining in conjunction with social networks can be used for finding a general opinion about any special subject, human thinking patterns, and group identification. In this study, we investigate machine learning methods in textual data in six chapters.

2018

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2018.

Collection: FAU

Advisors/Committee Members: Wang, Dingding (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Healthcare organizations, realizing the potential of the Internet of Things (IoT) technology, are rapidly adopting the technology to bring signi cant improvements in the quality and e ectiveness of the service. However, these smart and interconnected devices can act as a potential \back door" into a hospital's IT network, giving attack- ers access to sensitive information. As a result, cyber-attacks on medical IoT devices have been increasing since the last few years. It is a growing concern for all the stakeholders involved, as the impact of such attacks is not just monetary or privacy loss, but the lives of many patients are also at risk. Considering the various kinds of IoT devices one may nd connected to a hospital's network, traditional host-centric security solutions (e.g. antivirus, software patches) are at odds with realistic IoT infrastructure (e.g. constrained hardware, lack of proper built-in security measures). There is a need for security solutions which consider the challenges of IoT devices like heterogeneity of technology and protocols used, limited resources in terms of battery and computation power, etc. Accordingly, the goals of this thesis have been: (1) to provide an in-depth understanding of vulnerabilities of medical IoT devices; (2) to in- troduce a novel approach which uses a microservices-based framework as an adaptive and agile security solution to address the issue. The thesis focuses on OS Fingerprint- ing attacks because of its signi cance for attackers to understand a target's network. In this thesis, we developed three microservices, each one designed to serve a speci c functionality. Each of these microservices has a small footprint with RAM usage of approximately 50 MB. We also suggest how microservices can be used in a real-life scenario as a software-based security solution to secure a hospital's network consisting of di erent IoT devices.

2018

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2018.

Collection: FAU

Advisors/Committee Members: Shankar, Ravi (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: Automatic target recognition capabilities in autonomous underwater vehicles has been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack of publicly available sonar data. Machine learning techniques have made great strides in tackling this feat, although not much research has been done regarding deep learning techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object detection method is adapted for side-scan sonar imagery, with results supporting a simple yet robust method to detect objects/anomalies along the seabed. A systematic procedure was employed in transfer learning a pre-trained convolutional neural network in order to learn the pixel-intensity based features of seafloor anomalies in sonar images. Using this process, newly trained convolutional neural network models were produced using relatively small training datasets and tested to show reasonably accurate anomaly detection and classification with little to no false alarms.

2018

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2018.

Collection: FAU

Advisors/Committee Members: Dhanak, Manhar R. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: A three-dimensional parabolic equation (PE) and perturbation approach is used to invert for the depth- and range-dependent geoacoustic characteristics of the seabed. The model assumes that the sound speed profile is the superposition of a known range-independent profile and an unknown depth- and range-dependent perturbation. Using a Green’s function approach, the total measured pressure field in the water column is decomposed into a background field, which is due to the range-independent profile, and a scattered field, which is due to the range-dependent perturbation. When the Born approximation is applied to the resulting integral equation, it can be solved for the range-dependent profile using linear inverse theory. Although the method is focused on inverting for the sound speed profile in the bottom, it can also invert for the sound speed profile in the water column. For simplicity, the sound speed profile in the water column was assumed to be known with a margin of error of ± 5 m/s. The range-dependent perturbation is added to the index of refraction squared n2(r), rather than the sound speed profile c(ro). The method is implemented in both Cartesian (x,y,z) and cylindrical (r,q,z) coordinates with the forward propagation of the field in x and r, respectively. Synthetic data are used to demonstrate the validity of the method [1]. Two inversion methods were combined, a Monte Carlo like algorithm, responsible for a starting approximation of the sound speed profile, and a steepest descent method, that fine-tuned the results. In simulations, the inversion algorithm is capable of inverting for the sound speed profile of a flat bottom. It was tested, for three different frequencies (50 Hz, 75 Hz, and 100 Hz), in a Pekeris waveguide, a range-independent layered medium, and a range-dependent medium, with errors in the inverted sound speed profile of less than 3%. Keywords: Three-dimensional parabolic equation method, geoacoustic inversion, range-dependent sound speed profile, linear inversion, Born approximation, Green’s functions.

2017

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Frisk, George V. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: The monitoring of traffic signal systems can be of great importance for identifying problems, self-assessment, budgeting, creating the strategy for future steps, etc. Monitoring procedure was developed through a set of dashboards with relevant signal performance and reliability measures. The dashboards were created to reflect performance and reliability of a specific signal system on a weekly or monthly level. The author used data from ATMS.now signal system central software to illustrate how similar dashboards could be developed from any central software to enable operators to promptly and efficiently monitor various parameters of traffic signals. The main outcome of the study is a pair of Excel dashboards accompanied with appropriate user manual. The dashboards represent the tool for monitoring which can be helpful in the process of evaluation for traffic signal systems.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Stevanovic, Aleksandar (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Summary: Mangrove trees play a prominent role in coastal tropic and subtropical regions, providing habitat for many organisms and protecting shorelines against storm surges, high winds, erosion, and tsunamis. The motivation of this proposal is to understand the complex interaction of mangrove roots during tidal flow conditions using simplified physical models. In this dissertation, the mangrove roots were modeled with a circular array of cylinders with different porosities and spacing ratios. In addition, we modeled the flexibility of the roots by attaching rigid cylinders to hinge connectors. The models were tested in a water tunnel for a range of Reynolds number from 2200 to 11000. Additionally, we performed 2D flow visualization for different root models in a flowing soap film setup. We measured drag force and the instantanous streamwise velocity downstream of the models. Furthermore, we investigated the fluid dynamics downstream of the models using a 2-D time-resolved particle image velocimetry (PIV), and flow visualization. The result was analyzed to present time-averaged and time-resolved flow parameters including the velocity distribution, vorticity, streamline, Reynolds shear stress and turbulent kinetic energy. We found that the frequency of the vortex shedding increases as the diameter of the small cylinders decreases while the patch diameter is constant, therefore increasing the Strouhal number, St=fD/U By comparing the change of Strouhal numbers with a single solid cylinder, we introduced a new length scale, the “effective diameter”. In addition, the effective diameter of the patch decreases as the porosity increases. In addition, patch drag decreases linearly as the spacing ratio increases. For flexible cylinders, we found that a decrease in stiffness increases both patch drag and the wake deficit behind the patch in a similar fashion as increasing the blockage of the patch. The average drag coefficient decreased with increasing Reynolds number and with increasing porosity. We found that the Reynolds stress (−u′v′) peak is not only shifted in the vortex structure because of shear layer interference, but also the intensity was weakened by increasing the porosity, which causes a weakening of the buckling of vorticity layers leading to a decline in vortex strength as well as increase in wake elongation.

2017

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Curet, Oscar M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: This thesis presents two-dimensional hydrodynamic analysis of flapping foils for the propulsion of underwater vehicles using a source-vortex panel. Using a simulation program developed in MatLab, the hydrodynamic forces (such as the lift and the drag) as well as the propulsion thrust and efficiency are computed with this method. The assumptions made in the analysis are that the flow around a hydrofoil is two-dimensional, incompressible and inviscid. The analysis is first considered for the case of a deeply submerged hydrofoil followed by the case where it is located in shallow water depth or near the free surface. In the second case, the presence of the free surface and wave effects are taken into account, specifically at high and low frequencies and small and large amplitudes of flapping. The objective is to determine the thrust and efficiency of the flapping –foils under the influence of added effects of the free surface. Results show that the free-surface can significantly affect the foil performance by increasing the efficiency particularly at high Frequencies.

2015

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2015.

Collection: FAU

Advisors/Committee Members: Ananthakrishnan, Palaniswamy (Thesis advisor), Dhanak, Manhar R. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: Numerical simulations of waterjet inlets have been conducted in order to understand inlet performance during ship turning maneuvers. During turning maneuvers waterjet systems may experience low efficiency, cavitation, vibration, and noise. This study found that during turns less energy arrived at the waterjet pump relative to operating straight ahead, and that the flow field at the entrance of the waterjet pump exhibited a region of both low pressure and low axial velocity. The primary reason for the change in pump inflow uniformity is due to a streamwise vortex. In oblique inflow the hull boundary layer separates when entering the inlet and wraps up forming the streamwise vortex. These changes in pump inflow during turning maneuvers will result in increased unsteady loading of the pump rotor and early onset of pump rotor cavitation.

2015

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2015.

Collection: FAU

Advisors/Committee Members: von Ellenrieder, Karl (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: Trends in streamflow extremes at a regional scale linked to the possible influences of four major oceanic-atmospheric oscillations are analyzed in this study. Oscillations considered include: El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The main emphasis is low flows in the South-Atlantic Gulf region of the United States. Several standard drought indices of low flow extremes during two different phases (warm/positive and cool/negative) of these oscillations are evaluated. Long-term streamflow data at 43 USGS sites in the region from the Hydro-Climatic Data Network that are least affected by anthropogenic influences are used for analysis. Results show that for ENSO, low flow indices were more likely to occur during La Niña phase; however, longer deficits were more likely during El Niño phase. Results also show that for PDO (AMO), all (most) low flow indices occur during the cool (warm) phase.

2015

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2015.

Collection: FAU

Advisors/Committee Members: Teegavarapu, Ramesh (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Summary: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.

2015

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2015.

Collection: FAU

Advisors/Committee Members: Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: A confounding factor for sea level rise (SLR) is that it has a slow, steady creep, which provides a false sense for coastal communities. Stresses caused by SLR at today’s rate are more pronounced in southeastern Florida and as the rate of SLR accelerates, the exposure areas will increase to a point where nearly all the state’s coastal infrastructure will be challenged. The research was conducted to develop a method for measuring the impact of SLR on the City of West Palm Beach (City), assess its impact on the stormwater system, identify vulnerable areas in the City, provide an estimate of long-term costs of improvements, and provide a toolbox or strategies to employ at the appropriate time. The assessment was conducted by importing tidal, groundwater, topographic LiDAR and infrastructure improvements into geographic modeling software and performing analysis based on current data. The data revealed that over $400 million in current dollars might be needed to address stormwater issues arising from SLR before 2100.

2016

Degree granted:

Collection: FAU

Advisors/Committee Members: Bloetscher, Frederick (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

▼ Summary: This research is the array processing help wireless communication techniques to increase the signal accuracy. This technique has an important part of prevalent applications. The wireless communication system, radar, and sonar. Beamforming is one of methods in array processing that filters signals based on their capture time at each element in an array of antennas spatially. Numerous studies in adaptive array processing have been proposed in the last several decades, which are divided in two parts. The first one related to non-adaptive beamforming techniques and the next one related to digitally adaptive Beamforming methods. The trade-off between computational complexity and performance make them different. In this thesis, we concentrate on the expansion of array processing algorithms in both non-adaptive and adaptive ones with application of beamforming in 4G mobile antenna and radar systems. The conventional and generalized side-lobe canceller (GSC) structures beamforming algorithms were employed with a phase array antenna that changed the phase of arrivals in array antenna with common phased array structure antennas. An eight-element uniform linear array (ULA), consisting of di-pole antennas, represented as the antenna array. An anechoic chamber measures the operation of beamforming algorithms performance. An extended modified Kaiser weighting function is proposed to make a semi-adaptive structure in phased array beamforming. This technique is extended to low complexity functions like hyperbolic cosine and exponential functions. Furthermore, these algorithms are used in GSC beamforming. The side-lobe levels were so lower than other algorithms in conventional beamforming around -10 dB. On the other hand, a uniform linear arrays for smart antenna purposes designed to utilize in implementing and testing the proposed algorithms. In this thesis, performance of smart antenna with rectangular aperture coupled microstrip linear array which experimental investigations carried out for obtaining X-band operation of rectangular microstrip antenna by using aperture coupled feeding technique. Frequency range set at approximately 8.6 to 10.9 GHz, by incorporating frequency range of the antenna resonates for single wideband with an impedance bandwidth of 23%. The enhancement of impedance bandwidth and gain does not affect the nature of broadside radiation characteristics. This thesis describes the design, operation, and realization of the beamforming such as Sidelobe level (SLL) control and null forming array antenna are examined with the prototype. An antenna radiation pattern beam maximum can be simultaneously placed towards the intended user or Signal of interest (SOl), and, ideally nulls can be positioned towards directions of interfering signals or signals not of interest (SNOIs). Finally, we focused on the adaptive digitally algorithms in compact antenna that faces with mutual coupling. The variable step-size normalized lease mean square (VS-NLMS) algorithm is implemented in beamforming. This algorithm utilizes… Advisors/Committee Members: Bagby, Jonathan S. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: Research on collaboration among unmanned platforms is essential to improve the applications for autonomous missions, by expanding the working environment of the robotic systems, and reducing the risks and the costs associated with conducting manned operations. This research is devoted to enable the collaboration between an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV), by allowing the first one to launch and recover the second one. The objective of this dissertation is to identify possible methods to launch and recover a REMUS 100 AUV from a WAM-V 16 USV, thus developing this capability by designing and implementing a launch and recovery system (LARS). To meet this objective, a series of preliminary experiments was first performed to identify two distinct methods to launch and recover the AUV: mobile and semi-stationary. Both methods have been simulated using the Orcaflex software. Subsequently, the necessary control systems to create the mandatory USV autonomy for the purpose of launch and recovery were developed. Specifically, a series of low-level controllers were designed and implemented to enable two autonomous maneuvers on the USV: station-keeping and speed & heading control. In addition, a level of intelligence to autonomously identify the optimal operating conditions within the vehicles' working environment, was derived and integrated on the USV. Lastly, a LARS was designed and implemented on the vehicles to perform the operation following the proposed methodology. The LARS and all subsystems developed for this research were extensively tested through sea-trials. The methodology for launch and recovery, the design of the LARS and the experimental findings are reported in this document.

2016

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2016.

Collection: FAU

Advisors/Committee Members: Dhanak, Manhar R. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: Scene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel intensities, texture, color gradient) of sub-regions of an image to identify objects. The initially assigned objects names must be further examined to ensure they are compatible with each other and the scene. By enforcing inter-object dependencies (e.g., co-occurrence, spatial and semantical priors) and object to scene constraints as background information, a concept classifier predicts the most semantically consistent set of names for discovered objects. The additional background information that describes concepts is called context. In this dissertation, a framework for building context-based concept detection is presented that uses a combination of multiple contextual relationships to refine the result of underlying feature-based object detectors to produce most semantically compatible concepts. In addition to the lack of ability to capture semantical dependencies, object detectors suffer from high dimensionality of feature space that impairs them. Variances in the image (i.e., quality, pose, articulation, illumination, and occlusion) can also result in low-quality visual features that impact the accuracy of detected concepts. The object detectors used to build context-based framework experiments in this study are based on the state-of-the-art generative and discriminative graphical models. The relationships between model variables can be easily described using graphical models and the dependencies and precisely characterized using these representations. The generative context-based implementations are extensions of Latent Dirichlet Allocation, a leading topic modeling approach that is very effective in reduction of the dimensionality of the data. The discriminative contextbased approach extends Conditional Random Fields which allows efficient and precise construction of model by specifying and including only cases that are related and influence it. The dataset used for training and evaluation is MIT SUN397. The result of the experiments shows overall 15% increase in accuracy in annotation and 31% improvement in semantical saliency of the annotated concepts.

2016

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2016.

Collection: FAU

Advisors/Committee Members: Furht, Borko (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: In fabrication of nanoparticle-reinforced polymers, two critical factors need to be taken into account to control properties of the final product; nanoparticle dispersion/distribution in the matrix; and interfacial interactions between nanoparticles and their surrounding matrix. The focus of this thesis was to examine the role of these two factors through experimental methodologies and molecular-level simulations. Carbon nanotubes (CNTs) and vinyl ester (VE) resin were used as nanoparticles and matrix, respectively. In a parametric study, a series of CNT/VE nanocomposites with different CNT dispersion conditions were fabricated using the ultrasonication mixing method. Thermomechanical properties of nanocomposites and quality of CNT dispersion were evaluated. By correlation between nanocomposite behavior and CNT dispersion, a thermomechanical model was suggested; at a certain threshold level of sonication energy, CNT dispersion would be optimal and result in maximum enhancement in properties. This threshold energy level is also related to particle concentration. Sonication above this threshold level, leads to destruction of nanotubes and renders a negative effect on the properties of nanocomposites. In an attempt to examine the interface condition, a novel process was developed to modify CNT surface with polyhedral oligomeric silsesquioxane (POSS). In this process, a chemical reaction was allowed to occur between CNTs and POSS in the presence of an effective catalyst. The functionalized CNTs were characterized using TEM, SEM-EDS, AFM, TGA, FTIR and Raman spectroscopy techniques. Formation of amide bonds between POSS and nanotubes was established and verified. Surface modification of CNTs with POSS resulted in significant improvement in nanotube dispersion. In-depth SEM analysis revealed formation of a 3D network of well-dispersed CNTs with POSS connections to the polymer. In parallel, molecular dynamics simulation of CNT-POSS/VE system showed an effective load transfer from polymer chains to the CNT due to POSS linkages at the interface. The rigid and flexible network of CNTs is found to be responsible for enhancement in elastic modulus, strength, fracture toughness and glass transition temperature (Tg) of the final nanocomposites.

2016

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2016.

Collection: FAU

Advisors/Committee Members: Mahfuz, Hassan (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively.

2016

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2016.

Collection: FAU

Advisors/Committee Members: von Ellenrieder, Karl (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: Substantial savings in operations can be achieved by reducing the amount of wastewater that must be pumped and treated. Utilities have long dealt with the infiltration and inflow (I and I) issues in their system by televising their pipes and identifying leak points, but this primarily addresses only the infiltration part of “I and I.” Inflow, which creates hydraulic issues during rain events, leads to sanitary sewer overflows and can subject the utility to fines from regulatory agencies. As a result, dealing with the inflow portion of I and I is needed. The goal of this thesis is to differentiate inflow and infiltration from baseflow and to determine the effectiveness of different methods used to reduce inflow and infiltration in sanitary sewer lines. An analysis was conducted on the benefits and cost effectiveness of different inflow/infiltration approaches (slip-lining sewer lines, stormwater manhole inserts, replacing sewer lines, smoke testing, etc.) and cost savings municipalities can expect to receive from each.

2015

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2015.

Collection: FAU

Advisors/Committee Members: Bloetscher, Frederick (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Summary: This document reports on a hands-on project aimed at learning and experiencing the concept of system-of-systems. The motivation behind this project is to study and implement the concept of System of Systems in the generation of a RF-based communication and control complex system. The goal of this project is to develop a multi-level integrated and complete system in which the vehicles that belong to a same network can become aware of their location, communicate with nearby vehicles (sometimes with no visible line of sight), be notified of the presence of different objects located in their immediate vicinity (obstacles, such as abundant vehicles), and generate a two dimensional representation of the vehicles’ location for a remote user. In addition, this system will be able to transmit back messages from the remote user to a specific or to all local vehicles. The end result is a demonstration of a complex, functional, and robust system built and tested for other projects to use and learn from.

2015

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2015

Collection: FAU

Advisors/Committee Members: Raviv, Daniel (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: A traditional machine learning environment is characterized by the training and testing data being drawn from the same domain, therefore, having similar distribution characteristics. In contrast, a transfer learning environment is characterized by the training data having di erent distribution characteristics from the testing data. Previous research on transfer learning has focused on the development and evaluation of transfer learning algorithms using real-world datasets. Testing with real-world datasets exposes an algorithm to a limited number of data distribution di erences and does not exercise an algorithm's full capability and boundary limitations. In this research, we de ne, implement, and deploy a transfer learning test framework to test machine learning algorithms. The transfer learning test framework is designed to create a wide-range of distribution di erences that are typically encountered in a transfer learning environment. By testing with many di erent distribution di erences, an algorithm's strong and weak points can be discovered and evaluated against other algorithms. This research additionally performs case studies that use the transfer learning test framework. The rst case study focuses on measuring the impact of exposing algorithms to the Domain Class Imbalance distortion pro le. The next case study uses the entire transfer learning test framework to evaluate both transfer learning and traditional machine learning algorithms. The nal case study uses the transfer learning test framework in conjunction with real-world datasets to measure the impact of the base traditional learner on the performance of transfer learning algorithms. Two additional experiments are performed that are focused on using unique realworld datasets. The rst experiment uses transfer learning techniques to predict fraudulent Medicare claims. The second experiment uses a heterogeneous transfer learning method to predict phishing webgages. These case studies will be of interest to researchers who develop and improve transfer learning algorithms. This research will also be of bene t to machine learning practitioners in the selection of high-performing transfer learning algorithms.

2017

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science.

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Summary: The Gulf Stream current in the Straits is typically dominated by a strong northerly current, associated shear, and eddies. The water column also includes a prominent thermocline and periodically features internal waves centered on the upper or lower edges of the thermocline. Despite numerous previous related studies, there is limited available field data on internal waves in the Straits of Florida. Here, study and analysis of velocity, temperature and conductivity data acquired in the Straits over a period of time are described, in support of identifying presence of internal waves in the flow. A systematic procedure is employed in modifying the universal Garrett- Munk spectrum for internal waves in the open ocean for application to flow in the Straits of Florida. Using this process, identified internal waves are characterized and related velocity fluctuations in the time series are isolated to facilitate consideration of their correlations with simultaneously observed magnetic fields.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Dhanak, Manhar R. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.

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Summary: A comprehensive study is conducted to evaluate global sea levels for trends and variations due to climate change and variability by using non-parametric methods. Individual and coupled effects of inter-annual ENSO, decadal PDO, multi-decadal AMO, and quasi-decadal NAO on sea levels are evaluated. Combined influences of different phases (cool or warm) of PDO, AMO, and NAO influences and ENSO are also evaluated. The results from this study showed that sea level at 60% of the sites is increasing with time with all four oscillations impacting global sea levels. AMO warm phase individually and PDO warm combined with La-Niña phase contribute to higher sea levels throughout the world. Trends and variations in sea levels are noted to be spatially non-uniform. Understanding and quantifying climate variability influenced variations in sea levels and assessment of long-term trends enables protection of coastal regions of the world from sea level rise.

2017

Degree granted: Thesis (M.S.) – Florida Atlantic University, 2017.

Collection: FAU

Advisors/Committee Members: Teegavarapu, Ramesh (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Advanced electrochemical oxidation processes have emerged as a promising method for the destruction of persistent organic material in variable waste streams. Although the process has been successfully employed for wastewater treatment applications, high energy requirements, and the risk of formation of undesirable by-products may limit its application in the field of leachate treatment. This study focuses on the investigation of the feasibility of removing organics and ammonia by electrochemical oxidation coupled with ozone, Fenton or lime. Landfill leachate was treated by two different bench scale electrochemical oxidation reactors coupled with ozone oxidation, Fenton coagulation or lime precipitation. The electrochemical oxidation was conducted using a titanium anode coated with multi-metal oxides (MMO) at three-different current densities for different durations. Treatment performance was determined based on the removal of COD, ammonium-N, and turbidity. A three-level factorial design was established, and response surface methodology (RSM) was introduced to determine the optimum process parameters. The results suggest that the process can remove appreciable amounts of ammonium-N and COD in a very short time, demonstrating that the process is effective in rapidly degrading recalcitrant organics in leachate.

2019

Degree granted:

Collection: FAU

Advisors/Committee Members: Meeroff, Daniel (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering.

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Summary: As humans explore greater depths of Earth’s oceans, there is a growing need for the installation of subsea structures. 71% of the earth’s surface is ocean but there are limitations inherent in current detection instruments for marine applications leading to the need for the development of underwater platforms that allow research of deeper subsea areas. Several underwater platforms including Autonomous Underwater Vehicles (AUVs), Remote Operated Vehicles (ROVs), and wave gliders enable more efficient deployment of marine structures. Deployable structures are able to be compacted and transported via AUV to their destination then morph into their final form upon arrival. They are a lightweight, compact solution. The wrapped package includes the deployable structure, underwater pump, and other necessary instruments, and the entire package is able to meet the payload capability requirements. Upon inflation, these structures can morph into final shapes that are a hundred times larger than their original volume, which extends the detection range and also provides long-term observation capabilities. This dissertation reviews underwater platforms, underwater acoustics, imaging sensors, and inflatable structure applications then proposes potential applications for the inflatable structures. Based on the proposed applications, a conceptual design of an underwater tubular structure is developed and initial prototypes are built for the study of the mechanics of inflatable tubes. Numerical approaches for the inflation process and bending loading are developed to predict the inflatable tubular behavior during the structure’s morphing process and under different loading conditions. The material properties are defined based on tensile tests. The numerical results are compared with and verified by experimental data. The methods used in this research provide a solution for underwater inflatable structure design and analysis. Several ocean morphing structures are proposed based on the inflatable tube analysis.

2016

Degree granted: Dissertation (Ph.D.) – Florida Atlantic University, 2016.

Collection: FAU

Advisors/Committee Members: Su, Tsung-Chow (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering.