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Title: Electric Power Distribution Systems: Optimal Forecasting of Supply-Demand Performance and Assessment of Technoeconomic Tariff Profile.
Creator: Melendez, Roxana, De Groff, Dolores, Neelakanta, Perambur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence,...
Show moreThis study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013242
Subject Headings: Electric power distribution, Supply and demand--Forecasting, Artificial neural networks, Tariff
Format: Document (PDF)

Title: Enhancement of Deep Neural Networks and Their Application to Text Mining.
Creator: Prusa, Joseph Daniel, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Many current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level...
Show moreMany current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level concepts from text, such as those found in many applications of text classi- cation, is a challenging task due to the many challenges associated with text mining and classi cation. Recently, classi ers trained using arti cial neural networks have been shown to be e ective for a variety of text mining tasks. Convolutional neural networks have been trained to classify text from character-level input, automatically learn high-level abstract representations and avoiding the need for human engineered features. This dissertation proposes two new techniques for character-level learning, log(m) character embedding and convolutional window classi cation. Log(m) embedding is a new character-vector representation for text data that is more compact and memory e cient than previous embedding vectors. Convolutional window classi cation is a technique for classifying long documents, i.e. documents with lengths exceeding the input dimension of the neural network. Additionally, we investigate the performance of convolutional neural networks combined with long short-term memory networks, explore how document length impacts classi cation performance and compare performance of neural networks against non-neural network-based learners in text classi cation tasks.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00005959
Subject Headings: Text Mining, Neural networks (Computer science), Machine learning
Format: Document (PDF)

Title: Fattitude The Movie: Theory and Praxis of Creating a Documentary that Examines Fat Representation and Fat Social Justice.
Creator: Averill, Lindsey, Caputi, Jane, Hagood, Taylor, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Center for Women, Gender and Sexuality Studies
Abstract/Description: This dissertation explores the making of and research for the film, Fattitude, a social justice based documentary that looks to awaken viewers to the reality of weight bias in media representation. This dissertation reviews the filmmaking process and then engages with the nature of stereotypes about fat bodies. Deeply tied to feminist and fat studies theory, the work here seeks to categorize and shape the understanding of weight bias in the media by linking fat tropes to clearly understood...
Show moreThis dissertation explores the making of and research for the film, Fattitude, a social justice based documentary that looks to awaken viewers to the reality of weight bias in media representation. This dissertation reviews the filmmaking process and then engages with the nature of stereotypes about fat bodies. Deeply tied to feminist and fat studies theory, the work here seeks to categorize and shape the understanding of weight bias in the media by linking fat tropes to clearly understood images of oppression, for example the monstrous, the fool, they hypersexual and the asexual. The work also seeks to present theory on the nature of creating media representations of fatness that are not oppressive – making note of current media created by grassroots movements for body acceptance and fat positivity.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004900, http://purl.flvc.org/fau/fd/FA00004900
Subject Headings: Fattitude., Body image--Social aspects., Discrimination against overweight persons., Feminine beauty (Aesthetics), Obesity., Body image in women., Self-esteem in women., Physical-appearance-based bias.
Format: Document (PDF)

Title: Feminist Phenomenologies of Illness.
Creator: O’Connell, Emily, Morse, Nicole, Florida Atlantic University, Center for Women, Gender and Sexuality Studies, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: The experiences of those with difficult to diagnose conditions, chronic illnesses, and disability lack intelligibility in an able-bodied world. Much of this originates in the disjuncture between first- and third- person experience as accounted for between patients and their doctors, caregivers, and the greater public. Utilizing the insights of feminist philosophy and disability studies, I will explore how these marginalized identities face consequences in the real world for their embodiment....
Show moreThe experiences of those with difficult to diagnose conditions, chronic illnesses, and disability lack intelligibility in an able-bodied world. Much of this originates in the disjuncture between first- and third- person experience as accounted for between patients and their doctors, caregivers, and the greater public. Utilizing the insights of feminist philosophy and disability studies, I will explore how these marginalized identities face consequences in the real world for their embodiment. I propose that the best methodology to examine the experiences of chronically ill, hard to diagnose, and disabled individuals’ experiences is through the phenomenological perspective. Through utilizing case studies, I will demonstrate the importance of first- to third- person encounters in medicine and receiving adequate treatment. By examining such experiences, as well as my own, through such a perspective, I argue we can work towards creating a more equitable world for the chronically ill, hard to diagnose, and disabled.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013614
Subject Headings: Feminist philosophy, Feminist theory, Disability studies
Format: Document (PDF)

Title: Distributed Algorithms for Energy-Efficient Data Gathering and Barrier Coverage in Wireless Sensor Networks.
Creator: Aranzazu-Suescun, Catalina, Cardei, Mihaela, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Wireless sensor networks (WSNs) provide rapid, untethered access to information, eliminating the barriers of distance, time, and location for many applications in national security, civilian search and rescue operations, surveillance, border monitoring, and many more. Sensor nodes are resource constraint in terms of power, bandwidth, memory, and computing capabilities. Sensor nodes are typically battery powered and depending on the application, it may be impractical or even impossible to...
Show moreWireless sensor networks (WSNs) provide rapid, untethered access to information, eliminating the barriers of distance, time, and location for many applications in national security, civilian search and rescue operations, surveillance, border monitoring, and many more. Sensor nodes are resource constraint in terms of power, bandwidth, memory, and computing capabilities. Sensor nodes are typically battery powered and depending on the application, it may be impractical or even impossible to recharge them. Thus, it is important to develop mechanisms for WSN which are energy efficient, in order to reduce the energy consumption in the network. Energy efficient algorithms result in an increased network lifetime. Data gathering is an important operation in WSNs, dealing with collecting sensed data or event reporting in a timely and efficient way. There are various scenarios that have to be carefully addressed. In this dissertation we propose energy efficient algorithms for data gathering. We propose a novel event-based clustering mechanism, and propose several efficient data gathering algorithms for mobile sink WSNs and for spatio-temporal events. Border surveillance is an important application of WSNs. Typical border surveillance applications aim to detect intruders attempting to enter or exit the border of a certain region. Deploying a set of sensor nodes on a region of interest where sensors form barriers for intruders is often referred to as the barrier coverage problem. In this dissertation we propose some novel mechanisms for increasing the percentage of events detected successfully. More specifically, we propose an adaptive sensor rotation mechanism, which allow sensors to decide their orientation angle adaptively, based on the location of the incoming events. In addition, we propose an Unmanned Aerial Vehicle UAV aided mechanism, where an UAV is used to cover gaps dynamically, resulting in an increased quality of the surveillance.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013180
Subject Headings: Wireless sensor networks, Distributed algorithms, Wireless sensor nodes
Format: Document (PDF)

Title: Effects of Adaptive Antenna Array Beamforming and Power Management with Antenna Element Selection.
Creator: Abazari Aghdam, Sajjad, Bagby, Jonathan S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: 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...
Show moreThis 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 continuous adaptation. The weights are attuned that the final weight vector to the most satisfied result. The gradient vector can be achieved by iterative beamforming algorithm from the available data. This algorithm is compared with LMS, NLMS, VSS-NLMS algorithms, it is determined that the VSS-NLMS algorithm is better performance to other algorithms. Finally, we introduced novel adaptive IP-NNLMS beamformer. This beamformer reaches to faster convergence and lower error floor than the previous adaptive beamformers even at low SNRs in presence of mutual coupling. The experimental results verified the simulation results that the proposed technique has better performance than other algorithms in various situations.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004789, http://purl.flvc.org/fau/fd/FA00004789
Subject Headings: Global system for mobile communications., Long-Term Evolution (Telecommunications), Wireless communication systems., Antennas (Electronics), Antenna arrays., Array processors., Time-domain analysis.
Format: Document (PDF)

Title: A Collaborative Approach for Real-Time Measurements of Human Trust, Satisfaction and Frustration in Human-Robot Teaming.
Creator: Gonzalez Moya, Iker Javier, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis aims at real-time measurements of human trust, satisfaction, and frustration in human-robot teaming. Recent studies suggest that humans are inclined to have a negative attitude towards using autonomous systems. These ndings elevate the necessity of conducting research to better understand the key factors that a ect the levels of trust, satisfaction and frustration in Human-Robot Interaction (HRI). We utilized a new sequential and collaborative approach for HRI data collection that...
Show moreThis thesis aims at real-time measurements of human trust, satisfaction, and frustration in human-robot teaming. Recent studies suggest that humans are inclined to have a negative attitude towards using autonomous systems. These ndings elevate the necessity of conducting research to better understand the key factors that a ect the levels of trust, satisfaction and frustration in Human-Robot Interaction (HRI). We utilized a new sequential and collaborative approach for HRI data collection that employed trust, satisfaction and frustration as primarily evaluative metrics. We also used haptic feedback through a soft actuator armband to help our human subjects control a robotic hand for grabbing or not grabbing an object during our interaction scenarios. Three experimental studies were conducted during our research of which the rst was related to the evaluation of aforementioned metrics through a collabora- tive approach between the Baxter robot and human subjects. The second experiment embodied the evaluation of a newly fabricated 3D- nger for the I-Limb robotic hand through a nuclear-waste glove. The third experiment was based on the two previous studies that focused on real-time measurements of trust, satisfaction and frustration in human-robot teaming with the addition of pressure feedback to the system through soft actuators. In the last case, human subjects had more controls over our robotic systems compared to earlier experiments leading to a more collaborative interaction and teaming. The results of these experiments illustrated that human subjects can rebuild their trust and also increase their satisfaction levels while lowering their frus- tration levels after failures or any faulty behavior. Furthermore, our analyses showed that our methods are highly e ective for collecting honest and genuine data from hu- man subjects and lays the foundation for more-involved future research in the domain of human-robot teaming.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013064
Subject Headings: Human-robot interaction., Haptic devices.
Format: Document (PDF)

Title: ACCURATE DETECTION OF SELECTIVE SWEEPS WITH TRANSFER LEARNING.
Creator: Sigler, Priya, DeGiorgio, Michael, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Positive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw...
Show morePositive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw population genomic data, convolutional neural networks have most recently been employed as they can handle large input arrays and maintain correlations among elements. Yet, such models often require massive amounts of training data and can be computationally expensive to train for a given problem. Instead, transfer learning has recently been used in the image analysis literature to improve machine learning models by learning the important features of images from large unrelated datasets beforehand, and then refining these models through subsequent application on smaller and more relevant datasets. We combine transfer learning with convolutional neural networks to improve classification of selective sweeps from raw population genomic data. We show that the combination of transfer learning with convolutional neural networks allows for accurate classification of selective sweeps.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013785
Subject Headings: Transfer learning (Machine learning), Neural networks (Computer science), Natural selection, Genomes
Format: Document (PDF)

Title: Absence.
Creator: Johnson, Sarah, McConnell, Brian E., Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Visual Arts and Art History
Abstract/Description: Absence is the thesis exhibition resulting from the culmination of my three years in the graduate program. In this documentation, I outline historical and contextual influences that shape my ceramic practice. I specify that my work is centered on my life and describe how my work has evolved since I first started in the program. In the process I have gathered source material and have explored key points in the theory and history of Minimalism. As I developed the work, I was able to discuss how...
Show moreAbsence is the thesis exhibition resulting from the culmination of my three years in the graduate program. In this documentation, I outline historical and contextual influences that shape my ceramic practice. I specify that my work is centered on my life and describe how my work has evolved since I first started in the program. In the process I have gathered source material and have explored key points in the theory and history of Minimalism. As I developed the work, I was able to discuss how my work applied to these sources, and where it differed. I ended with introspective installation work that paralleled the themes I was working with at the start of my graduate experience that shows my evolution from being a child continually bounced around from one family member to another, to being the confidant of my mother and holding her darkest secrets, to feeling alone and unloved, to finally beginning to heal and accept who I am.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004383, http://purl.flvc.org/fau/fd/FA00004383
Subject Headings: Artists -- Conduct of life, Bee culture, Ceramics -- Exhibitions, Emotional intelligence, Minimalism (Literature), Self actualization (Psychology), Stress (Psychology)
Format: Document (PDF)

Title: Adaptive hierarchical weighted fair queuing scheduling in WiMAX networks.
Creator: AL-Ghanem, Waleed Khalid, Ilyas, Mohammad, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The growing demand for faster connection to the Internet service and wireless multimedia applications has motivated the development of broadband wireless access technologies in recent years. WiMAX has enabled convergence of mobile and fixed broadband networks through a common wide-area radio-access technology and flexible network architecture. Scheduling is a fundamental component in resource management in WiMAX networks and plays the main role in meeting QoS requirements such as delay,...
Show moreThe growing demand for faster connection to the Internet service and wireless multimedia applications has motivated the development of broadband wireless access technologies in recent years. WiMAX has enabled convergence of mobile and fixed broadband networks through a common wide-area radio-access technology and flexible network architecture. Scheduling is a fundamental component in resource management in WiMAX networks and plays the main role in meeting QoS requirements such as delay, throughput and packet loss for different classes of service. In this dissertation work, the performance of uplink schedulers at the fixed WiMAX MAC layer has been considered, we proposed an Adaptive Hierarchical Weighted Fair Queuing Scheduling algorithm, the new scheduling algorithm adapts to changes in traffic, at the same time; it is able to heuristically enhance the performance of WiMAX network under most circumstances. The heuristic nature of this scheduling algorithm enables the MAC layer to meet the QoS requirements of the users. The performance of this adaptive WiMAX Uplink algorithm has been evaluated by simulation using MATLAB. Results indicate that the algorithm is efficient in scheduling the Base Stations’ traffic loads, and improves QoS. The utilization of relay stations is studied and simulation results are compared with the case without using relay stations. The results show that the proposed scheduling algorithm improves Quality of Service of WiMAX system.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004178, http://purl.flvc.org/fau/fd/FA00004178
Subject Headings: Computer networks -- Technological innovations, Optical fiber communication, Wireless communication systems -- Technological innovations, Wireless metropolitan area networks -- Security measures
Format: Document (PDF)

Title: APPLICATION OF BLOCKCHAIN NETWORK FOR THE USE OF INFORMATION SHARING.
Creator: Zamir, Linir, Liu, Feng-Hao, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The Blockchain concept was originally developed to provide security in the Bitcoin cryptocurrency network, where trust is achieved through the provision of an agreed-upon and immutable record of transactions between parties. The use of a Blockchain as a secure, publicly distributed ledger is applicable to fields beyond finance, and is an emerging area of research across many other fields in the industry. This thesis considers the feasibility of using a Blockchain to facilitate secured...
Show moreThe Blockchain concept was originally developed to provide security in the Bitcoin cryptocurrency network, where trust is achieved through the provision of an agreed-upon and immutable record of transactions between parties. The use of a Blockchain as a secure, publicly distributed ledger is applicable to fields beyond finance, and is an emerging area of research across many other fields in the industry. This thesis considers the feasibility of using a Blockchain to facilitate secured information sharing between parties, where a lack of trust and absence of central control are common characteristics. Implementation of a Blockchain Information Sharing system will be designed on an existing Blockchain network with as a communicative party members sharing secured information. The benefits and risks associated with using a public Blockchain for information sharing will also be discussed.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013351
Subject Headings: Blockchains (Databases), Blockchains (Databases)--Industrial applications, Data encryption (Computer science), Personal data protection, Bitcoin
Format: Document (PDF)

Title: Imaging through ground-level turbulence by fourier telescopy: simulations and preliminary experiments.
Creator: Randunu-Pathirannehelage, Nishantha, Rhodes, William T., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Fourier telescopy imaging is a recently-developed imaging method that relies on active structured-light illumination of the object. Reflected/scattered light is measured by a large “light bucket” detector; processing of the detected signal yields the magnitude and phase of spatial frequency components of the object reflectance or transmittance function. An inverse Fourier transform results in the image. In 2012 a novel method, known as time-average Fourier telescopy (TAFT), was introduced by...
Show moreFourier telescopy imaging is a recently-developed imaging method that relies on active structured-light illumination of the object. Reflected/scattered light is measured by a large “light bucket” detector; processing of the detected signal yields the magnitude and phase of spatial frequency components of the object reflectance or transmittance function. An inverse Fourier transform results in the image. In 2012 a novel method, known as time-average Fourier telescopy (TAFT), was introduced by William T. Rhodes as a means for diffraction-limited imaging through ground-level atmospheric turbulence. This method, which can be applied to long horizontal-path terrestrial imaging, addresses a need that is not solved by the adaptive optics methods being used in astronomical imaging. Field-experiment verification of the TAFT concept requires instrumentation that is not available at Florida Atlantic University. The objective of this doctoral research program is thus to demonstrate, in the absence of full-scale experimentation, the feasibility of time-average Fourier telescopy through (a) the design, construction, and testing of smallscale laboratory instrumentation capable of exploring basic Fourier telescopy datagathering operations, and (b) the development of MATLAB-based software capable of demonstrating the effect of kilometer-scale passage of laser beams through ground-level turbulence in a numerical simulation of TAFT.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004401, http://purl.flvc.org/fau/fd/FA00004401
Subject Headings: Fourier analysis, Fourier integral operators, Interconnects (Integrated circuit technology), Remote sensing, Spread spectrum communications, Wireless sensor networks
Format: Document (PDF)

Title: How interpersonal coordination changes the self: Theory, experiment, and adaptive HKB model of social memory.
Creator: Nordham, Craig A, Kelso, J. A. Scott, Tognoli, Emmanuelle, Florida Atlantic University, Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description: How one behaves after interacting with a friend may not be the same as before the interaction began What factors a ect the formation of social interactions between people and, once formed, how do social interactions leave lasting changes on individual behavior? In this dissertation, a thorough review and conceptual synthesis is provided Major features of coordination dynamics are demonstrated with examples from both the intrapersonal and interpersonal coordination literature that are...
Show moreHow one behaves after interacting with a friend may not be the same as before the interaction began What factors a ect the formation of social interactions between people and, once formed, how do social interactions leave lasting changes on individual behavior? In this dissertation, a thorough review and conceptual synthesis is provided Major features of coordination dynamics are demonstrated with examples from both the intrapersonal and interpersonal coordination literature that are interpreted via a conceptual scheme, the causal loops of coordination dynamics An empirical, behavioral study of interpersonal coordination was conducted to determine which spontaneous patterns of coordination formed and whether a remnant of the interaction ensued ("social memory") To assess social memory in dyads, the behavior preceding and following episodes of interaction was compared In the experiment, pairs of people sat facing one another and made continuous flexion-extension finger movements while a window acted as a shutter to control whether partners saw each other's movements Thus, vision ("social contact") allowed spontaneous information exchange between partners through observation Each trial consisted of three successive intervals lasting twenty seconds: without social contact ("me and you"), with social contact ("us"), and again without ("me and you") During social contact, a variety of patterns was observed ranging from phase coupling to transient or absent collective behavior Individuals also entered and exited social coordination differently In support of social memory, compared to before social contact, after contact ended participants tended to remain near each other's movement frequency Furthermore, the greater the stability of coupling, the more similar the partners' post-interactional frequencies were Proposing that the persistence of behavior in the absence of information exchange was the result of prior frequency adaptation, a mathematical model of human movement was implemented with Haken-Kelso-Bunz oscillators that reproduced the experimental findings, even individual dyadic patterns Parametric manipulations revealed multiple routes to persistence of behavior via the interplay of adaptation and other HKB model parameters The experimental results, the model, and their interpretation form the basis of a proposal for future research and possible therapeutic applications
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004793
Subject Headings: Ergodic theory, Dynamics, Conversation analysis--Social aspects, Social interaction, Social acceptance, Identity (Philosophical concept), Motivation (Psychology), Applied mathematics, Statistical physics
Format: Document (PDF)

Title: Implementation of the IEEE 1609.2 WAVE Security Services Standard.
Creator: Mandy, Chad Christopher Jr., Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This work presents the implementation of the the IEEE 1609.2 WAVE Security Services Standard. This implementation provides the ability to generate a message signature, along with the capability to verify that signature for wave short messages transmitted over an unsecured medium. Only the original sender of the message can sign it, allowing for the authentication of a message to be checked. As hashing is used during the generation and verification of signatures, message integrity can be...
Show moreThis work presents the implementation of the the IEEE 1609.2 WAVE Security Services Standard. This implementation provides the ability to generate a message signature, along with the capability to verify that signature for wave short messages transmitted over an unsecured medium. Only the original sender of the message can sign it, allowing for the authentication of a message to be checked. As hashing is used during the generation and verification of signatures, message integrity can be verified because a failed signature verification is a result of a compromised message. Also provided is the ability to encrypt and decrypt messages using AES-CCM to ensure that sensitive information remains safe and secure from unwanted recipients. Additionally this implementation provides a way for the 1609.2 specific data types to be encoded and decoded for ease of message transmittance. This implementation was built to support the Smart Drive initiative’s VANET testbed, supported by the National Science Foundation and is intended to run on the Vehicular Multi-technology Communication Device (VMCD) that is being developed. The VMCD runs on the embedded Linux operating system and this implementation will reside inside of the Linux kernel.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004693, http://purl.flvc.org/fau/fd/FA00004693
Subject Headings: Application software -- Security measures, Expert systems (Computer science), Vehicular ad hoc networks (Computer networks), Wireless LANs, Wireless communication systems -- Security measures, Wireless sensor networks
Format: Document (PDF)

Title: Numerical Simulation of an Ocean Current Turbine Operating in a Wake Field.
Creator: Pyakurel, Parakram, VanZwieten, James H., Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: An Ocean Current Turbine (OCT) numerical simulation for creating, testing and tuning flight and power takeoff controllers, as well as for farm layout optimization is presented. This simulation utilizes a novel approach for analytically describing oceanic turbulence. This approach has been integrated into a previously developed turbine simulation that uses unsteady Blade Element Momentum theory. Using this, the dynamical response and power production of a single OCT operating in ambient...
Show moreAn Ocean Current Turbine (OCT) numerical simulation for creating, testing and tuning flight and power takeoff controllers, as well as for farm layout optimization is presented. This simulation utilizes a novel approach for analytically describing oceanic turbulence. This approach has been integrated into a previously developed turbine simulation that uses unsteady Blade Element Momentum theory. Using this, the dynamical response and power production of a single OCT operating in ambient turbulence is quantified. An approach for integrating wake effects into this single device numerical simulation is presented for predicting OCT performance within a farm. To accomplish this, far wake characteristics behind a turbine are numerically described using analytic expressions derived from wind turbine wake models. These expressions are tuned to match OCT wake characteristics calculated from CFD analyses and experimental data. Turbine wake is characterized in terms of increased turbulence intensities and decreased mean wake velocities. These parameters are calculated based on the performance of the upstream OCT and integrated into the environmental models used by downstream OCT. Simulation results are presented that quantify the effects of wakes on downstream turbine performance over a wide range of relative downstream and cross stream locations for both moored and bottom mounted turbine systems. This is done to enable the development and testing of flight and power takeoff controllers designed for maximizing energy production and reduce turbine loadings.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004737, http://purl.flvc.org/fau/fd/FA00004737
Subject Headings: Turbulence--Mathematical models., Marine turbines--Mathematical models., Wind turbines--Aerodynamics--Mathematical models., Structural dynamics., Computational fluid dynamics., Fluid dynamic measurements., Atmospheric circulation.
Format: Document (PDF)

Title: Models and Implementations of Online Laboratories; A Definition of a Standard Architecture to Integrate Distributed Remote Experiments.
Creator: Zapata Rivera, Luis Felipe, Larrondo Petrie, Maria M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Hands-on laboratory experiences are a key part of all engineering programs. Currently there is high demand for online engineering courses, but offering lab experiences online still remain a great challenge. Remote laboratories have been under development for more than 20 years and are part of a bigger category, called online laboratories, which includes also virtual laboratories. Development of remote laboratories in academic settings has been held back because of the lack of standardization...
Show moreHands-on laboratory experiences are a key part of all engineering programs. Currently there is high demand for online engineering courses, but offering lab experiences online still remain a great challenge. Remote laboratories have been under development for more than 20 years and are part of a bigger category, called online laboratories, which includes also virtual laboratories. Development of remote laboratories in academic settings has been held back because of the lack of standardization of technology, processes, operation and their integration with formal educational environments. Remote laboratories can be used in educational settings for a variety of reasons, for instance, when the equipment is not available in the physical laboratory; when the physical laboratory space available is not sufficient to either set up the experiments or permit access to all on-site students in the course; or when the teacher needs to provide online laboratory experiences to students taking courses via distance education. This dissertation proposes a new approach for the development and deployment of online laboratories over online platforms. The research activities performed include: The design and implementation of an architecture of a system for Smart Adaptive Remote Laboratories (SARL) integrated to educational environments to improve the remote laboratory users experience through the implementation of a modular architecture and the use of context information about the users and laboratory activities; the design pattern and implementation for the Remote Laboratory Management System (RLMS); the definition and implementation of an xAPI-based activity tracking system for online laboratories with support for both centralized and distributed architectures of Learning Record Stores (LRS); the definition of Smart Laboratory Learning Object (SLLO) capable of being integrated in different educational environments, including the implementation of a Lab Authoring module; and finally, the definition of a reliability model to detect and report failures and possible causes and countermeasures applying ruled based systems. The architecture proposed complies with the just approved IEEE 1876 Standard for Networked Smart Learning for Online Laboratories and supports virtual, remote, hybrid and mobile laboratories. A full set of low-cost online laboratory experiment stations were designed and implemented to support the Introduction to Logic Design course, providing true hands-on lab experience to students through the a low-cost, student-built mobile laboratory platform connected via USB to the SARL System. The SARL prototype have been successfully integrated to a Virtual Learning Environment (VLE) and a variety of configurations tested that can support privacy and security requirements of different stakeholders. The prototype online laboratory experiments developed have contributed and been featured in IEEE 1876 standard, as well as been integrated into an Industry Connections Actionable Data Book (ADB) that was featured in the Frankfurt Book Fair in 2017. SARL is being developed as the infrastructure to support a Latin American and Caribbean network of online laboratories.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013282
Subject Headings: Remote laboratories, Online laboratories, Engineering Education, Software architecture
Format: Document (PDF)

Title: Performance Analysis of Spectrum Sensing Schemes Based on Fractional Lower Order Moments for Cognitive Radios in Alpha- Stable Noise Environments.
Creator: Ackie, A-Bon Elfick, Aalo, Valentine A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Natural and manmade noise signals tend to exhibit impulsive behaviors. Therefore modeling those signals as α-stable processes is better suited towards the development of a practical spectrum sensing scheme. However, the performances of detectors operating in an α-stable noise environment are difficult to evaluate. This is because an α-stable random variable can usually only be modeled by the characteristic function since closed-form expressions are usually not available except for the special...
Show moreNatural and manmade noise signals tend to exhibit impulsive behaviors. Therefore modeling those signals as α-stable processes is better suited towards the development of a practical spectrum sensing scheme. However, the performances of detectors operating in an α-stable noise environment are difficult to evaluate. This is because an α-stable random variable can usually only be modeled by the characteristic function since closed-form expressions are usually not available except for the special values of the characteristic exponent that correspond to the Cauchy and Gaussian noise distributions. In this thesis, we derive a general closed-form expression for the probability density function (PDF) of symmetric alpha stable processes having rational characteristic exponent (0<α≤2). Consequently, we obtain analytical expressions for the PDF and corresponding complementary cumulative distribution function (CCDF) of the proposed fractional lower order moment (FLOM) detector. Utilizing false alarm and detection probabilities, the performance analysis of the proposed spectrum sensing scheme is conducted with the assumption that the cognitive radio (CR) users are operating in non-fading channels. We validate the analytical results with Monte Carlo simulations. The effect of the distribution parameters on the receiver operating characteristic (ROC) curves is verified.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004561, http://purl.flvc.org/fau/fd/FA00004561
Subject Headings: Cognitive radio networks., Radio frequency allocation., Radio resource management (Wireless communications), Wireless communication systems.
Format: Document (PDF)

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

Title: My representations: entropic manipulations.
Creator: Cardoso, De Gouveia Isabel, Prusa, Carol, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Visual Arts and Art History
Abstract/Description: My thesis body of work developed from a desire to examine my nonrepresentational artwork in relation to the concept of entropy - the law of thermodynamics that measures the gradual, steady disintegration in a system such as our world. Experimenting with a range of approaches and mediums, I resolved to radically manipulate the inkjet printing of my digital photography files to introduce chance and provoke decay. The resulting prints operate as an orchestrated chaos alluding to environmental...
Show moreMy thesis body of work developed from a desire to examine my nonrepresentational artwork in relation to the concept of entropy - the law of thermodynamics that measures the gradual, steady disintegration in a system such as our world. Experimenting with a range of approaches and mediums, I resolved to radically manipulate the inkjet printing of my digital photography files to introduce chance and provoke decay. The resulting prints operate as an orchestrated chaos alluding to environmental decline and collapse, and by extension, potential social degeneration. My art reflects my perceptions of our times as well as adds to the problem. I continue to produce waste through the consumption of materials. I contaminate through inks. I add to landfills with failures. My artwork points out the inevitable end.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004088
Subject Headings: Aesthetics -- Psychological aspects, New media art, Photography, Artistic, Postmodernism, Technology and the arts
Format: Document (PDF)

Title: Predictive Models for Ebola using Machine Learning Algorithms.
Creator: Jain, Abhishek, Agarwal, Ankur, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: 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...
Show moreIdentifying 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.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004919, http://purl.flvc.org/fau/fd/FA00004919
Subject Headings: Communicable diseases--Epidemiology., Public health surveillance., Ebola virus disease--Transmission., Machine learning., Computer algorithms., Virtual reality., Interactive multimedia., Computer graphics., History--Graphic methods., Historiography--Technological innovations.
Format: Document (PDF)

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