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Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Prognosis and health monitoring communications quality of service.
Creator: Tavtilov, Timur, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis research was funded by the Southeast National Marine Renewable Energy Center (SNMREC) at Florida Atlantic University. Its objective is the development of Quality of Service (QoS) mechanisms for the wireless communications architecture used by the Prognosis and Health Monitoring (PHM) subsystem. There are numerous technical challenges that the PHM Communications Subsystem tries to solve. Due to ocean platform mobility from waves, currents, and other environmental factors, signal...
Show moreThis thesis research was funded by the Southeast National Marine Renewable Energy Center (SNMREC) at Florida Atlantic University. Its objective is the development of Quality of Service (QoS) mechanisms for the wireless communications architecture used by the Prognosis and Health Monitoring (PHM) subsystem. There are numerous technical challenges that the PHM Communications Subsystem tries to solve. Due to ocean platform mobility from waves, currents, and other environmental factors, signal quality can vary significantly. As a result, the wireless link between the electric generator platform and shore systems will have variable quality in terms of data rate, delay, and availability. In addition, the data traffic that flows from generator sensors and PHM applications to the shore systems consists of numerous types of messages that have different QoS demands (e.g. delay) and priority that depends on the message type, user ID, sensor location, and application-dependent parameters. The PHM Communications subsystem must handle effectively high priority messages, such as alarms, alerts, and remote control commands from shore systems. It also performs QoS in the application layer, so it can read the contents of every message to prioritize them. In order to perform QoS in the application layer the PHM subsystem relies on Java Servlet multithreaded technology and different queuing techniques to control message transmission order. Furthermore, it compresses all traffic that comes from the ocean-based electric generator/turbine platform to reduce the load on the wireless link. The PHM Communications subsystem consists of three components: the wireless link, the Link Manager, and the Web Services Network Proxy. We present experimental results for the Web Services Network Proxy and demonstrate the effectiveness of XML data compression and semantic-based message scheduling over a link with variable capacity.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3334098
Subject Headings: Computer network architecture, Service-oriented architecture (Computer science), Wireless communication services, Technological innovations, Wireless communication services, Quality control
Format: Document (PDF)

Title: Firewall formulation driven by risk analysis.
Creator: Srinivasan, Sriram, Jr., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: At the turn of the new millennium, the focus of Information Technology Management turned to Information and Systems Security, as opposed to competitive advantage investment. In catering to the security needs of various firms and institutions, it is seen that different entities require varying Information Security configurations. This thesis attempts to utilize Risk Analysis, a commonly used procedure in business realms, to formulate customized Firewalls based on the specific needs of a...
Show moreAt the turn of the new millennium, the focus of Information Technology Management turned to Information and Systems Security, as opposed to competitive advantage investment. In catering to the security needs of various firms and institutions, it is seen that different entities require varying Information Security configurations. This thesis attempts to utilize Risk Analysis, a commonly used procedure in business realms, to formulate customized Firewalls based on the specific needs of a network, subsequently building an effective system following the "Defense in Depth" strategy. This is done by first choosing an efficient Risk Analysis model which suits the process of creating Firewall policies, and then applying it to a particular case study. A network within Florida Atlantic University is used as an experimental test case, and by analyzing the traffic to which it is subject while behind a single Firewall layer, a specific Security Policy is arrived at and implemented.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13348
Subject Headings: Computer networks--Security measures, Electronic data processing departments--Security measures, Firewalls (Computer security), Risk assessment
Format: Document (PDF)

Title: Parallel architectures and algorithms for digital filter VLSI implementation.
Creator: Desai, Pratik Vishnubhai., Florida Atlantic University, Sudhakar, Raghavan
Abstract/Description: In many scientific and signal processing applications, there are increasing demands for large volume and high speed computations, which call for not only high-speed low power computing hardware, but also for novel approaches in developing new algorithms and architectures. This thesis is concerned with the development of such architectures and algorithms suitable for the VLSI implementation of recursive and nonrecursive 1-dimension digital filters using multiple slower processing elements. As...
Show moreIn many scientific and signal processing applications, there are increasing demands for large volume and high speed computations, which call for not only high-speed low power computing hardware, but also for novel approaches in developing new algorithms and architectures. This thesis is concerned with the development of such architectures and algorithms suitable for the VLSI implementation of recursive and nonrecursive 1-dimension digital filters using multiple slower processing elements. As the background for the development, vectorization techniques such as state-space modeling, block processing, and look ahead computation are introduced. Concurrent architectures such as systolic arrays, wavefront arrays and appropriate parallel filter realizations such as lattice, all-pass, and wave filters are reviewed. A fully hardware efficient systolic array architecture termed as Multiplexed Block-State Filter is proposed for the high speed implementation of lattice and direct realizations of digital filters. The thesis also proposes a new simplified algorithm, Alternate Pole Pairing Algorithm, for realizing an odd order recursive filter as the sum of two all-pass filters. Performance of the proposed schemes are verified through numerical examples and simulation results.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15155
Subject Headings: Integrated circuits--Very large scale integration, Parallel processing (Electronic computers), Computer network architectures, Algorithms (Data processing), Digital integrated circuits
Format: Document (PDF)

Title: QoS Driven Communication Backbone for NOC Based Embedded Systems.
Creator: Agarwal, Ankur, Shankar, Ravi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: With the increasing complexity of the system design, it has become very critical to enhance system design productivity to meet with the time-to-market demands. Real Time embedded system designers are facing extreme challenges in underlying architectural design selection. It involves the selection of a programmable, concurrent, heterogeneous multiprocessor architecture platform. Such a multiprocessor system on chip (MPSoC) platform has set new innovative trends for the real-time systems and...
Show moreWith the increasing complexity of the system design, it has become very critical to enhance system design productivity to meet with the time-to-market demands. Real Time embedded system designers are facing extreme challenges in underlying architectural design selection. It involves the selection of a programmable, concurrent, heterogeneous multiprocessor architecture platform. Such a multiprocessor system on chip (MPSoC) platform has set new innovative trends for the real-time systems and system on Chip (SoC) designers. The consequences of this trend imply the shift in concern from computation and sequential algorithms to modeling concurrency, synchronization and communication in every aspect of hardware and software co-design and development. Some of the main problems in the current deep sub-micron technologies characterized by gate lengths in the range of 60-90 nm arise from non scalable wire delays, errors in signal integrity and un-synchronized communication. These problems have been addressed by the use of packet switched Network on Chip (NOC) architecture for future SoCs and thus, real-time systems. Such a NOC based system should be able to support different levels of quality of service (QoS) to meet the real time systems requirements. It will further help in enhancing the system productivity by providing a reusable communication backbone. Thus, it becomes extremely critical to properly design a communication backbone (CommB) for NOC. Along with offering different levels of QoS, CommB is responsible directing the flow of data from one node to another node through routers, allocators, switches, queues and links. In this dissertation I present a reusable component based, design of CommB, suitable for embedded applications, which supports three types of QoS (real-time, multi-media and control applications).
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012566
Subject Headings: Computer networks--Quality control, Data transmission systems, Embedded computer systems--Quality control, Interconnects (Integrated circuit technology)
Format: Document (PDF)

Title: An analysis of professional development in technology for elementary school teachers.
Creator: Meltzer, Sarah T., Florida Atlantic University, Bryan, Valerie
Abstract/Description: The identification of effective practices is of significant interest to school administrators, faculty, and staff planning and implementing professional development initiatives in technology. This study identified recommended practices for professional development in technology in elementary schools and determined if current practices reflected those recommendations. Studies by Wenglinsky (1998) for the Educational Testing Service reported the effective use of technology by classroom teachers...
Show moreThe identification of effective practices is of significant interest to school administrators, faculty, and staff planning and implementing professional development initiatives in technology. This study identified recommended practices for professional development in technology in elementary schools and determined if current practices reflected those recommendations. Studies by Wenglinsky (1998) for the Educational Testing Service reported the effective use of technology by classroom teachers has a positive impact on student performance. More recent studies frequently indicate teachers are not being trained to use technology effectively; and consequently, limited integration of technology in the classroom is taking place (Catchings, 2000; Howery, 2001; Johnson, 2002). Ham's assertion in 1999 that very few studies make the process of professional development the object of research remains true today. A literature review of current research revealed commonly recommended professional and governmental guidelines, standards, and principles. Published recommended practices of professional development in technology indicated similar practices in the areas of planning, implementation, and follow up/support. The Staff Development in Technology Survey was sent via the Internet to 200 participants including 56 providers of professional development and 144 receivers. Actual practices as described by providers and receivers were compared with recommended practices from the literature review. An analysis of variances (ANOVA) indicated a significant difference between the responses of the providers and receivers in the areas of planning (p < .02), implementation (p < .01), and follow up/support (p < .01). The providers' mean ratings of perception of the effectiveness of planning (p < .01) and effectiveness of follow up/support (p < .05), was significantly different from the receivers' perceptions. There was no significance between responses regarding the effectiveness of implementation. A Model of Effective Professional Development in Technology, developed from the analysis of the literature reviewed and responses from providers and receivers, provides a foundation for school administrators, faculty, and staff in planning, implementing, and providing follow up/support for professional development in technology. Professional development should take place in a collaborative environment with extensive support and resources available. Administrators, faculty, and staff working together using the model ought to be able to implement effective professional development in technology.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12217
Subject Headings: Elementary school teachers--Training of, Education, Elementary--Computer network resources, Educational technology, Computer-assisted instruction
Format: Document (PDF)

Title: An intelligent approach to system identification.
Creator: Saravanan, Natarajan, Florida Atlantic University, Duyar, Ahmet, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: System identification methods are frequently used to obtain appropriate models for the purpose of control, fault detection, pattern recognition, prediction, adaptive filtering and other purposes. A number of techniques exist for the identification of linear systems. However, real-world and complex systems are often nonlinear and there exists no generic methodology for the identification of nonlinear systems with unknown structure. A recent approach makes use of highly interconnected networks...
Show moreSystem identification methods are frequently used to obtain appropriate models for the purpose of control, fault detection, pattern recognition, prediction, adaptive filtering and other purposes. A number of techniques exist for the identification of linear systems. However, real-world and complex systems are often nonlinear and there exists no generic methodology for the identification of nonlinear systems with unknown structure. A recent approach makes use of highly interconnected networks of simple processing elements, which can be programmed to approximate nonlinear functions to identify nonlinear dynamic systems. This thesis takes a detailed look at identification of nonlinear systems with neural networks. Important questions in the application of neural networks for nonlinear systems are identified; concerning the excitation properties of input signals, selection of an appropriate neural network structure, estimation of the neural network weights, and the validation of the identified model. These questions are subsequently answered. This investigation leads to a systematic procedure for identification using neural networks and this procedure is clearly illustrated by modeling a complex nonlinear system; the components of the space shuttle main engine. Additionally, the neural network weights are determined by using a general purpose optimization technique known as evolutionary programming which is based on the concept of simulated evolution. The evolutionary programming algorithm is modified to include self-adapting step sizes. The effectiveness of the evolutionary programming algorithm as a general purpose optimization algorithm is illustrated on a test suite of problems including function optimization, neural network weight optimization, optimal control system synthesis and reinforcement learning control.
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Date Issued: 1994
PURL: http://purl.flvc.org/fcla/dt/12371
Subject Headings: Neural networks (Computer science), System identification, Nonlinear theories, System analysis, Space shuttles--Electronic equipment, Algorithms--Computer programs
Format: Document (PDF)

Title: A method for adding multimedia knowledge for improving intrusion detection systems.
Creator: Baillargeon, Pierre Elliott., Florida Atlantic University, Marques, Oge
Abstract/Description: Intrusion Detection Systems (IDS) are security tools which monitor systems and networks for malicious activity. In saturated network links the amount of data present for analysis can overwhelm them, resulting in potentially undetected attacks. Many of these network links contain significant amounts of multimedia traffic which may seem to contribute to the problem, however our work suggests otherwise. This thesis proposes a novel method to classify and analyze multimedia traffic in an effort...
Show moreIntrusion Detection Systems (IDS) are security tools which monitor systems and networks for malicious activity. In saturated network links the amount of data present for analysis can overwhelm them, resulting in potentially undetected attacks. Many of these network links contain significant amounts of multimedia traffic which may seem to contribute to the problem, however our work suggests otherwise. This thesis proposes a novel method to classify and analyze multimedia traffic in an effort to maximize the efficiency of IDS. By embedding multimedia-specific knowledge into IDS, trusted multimedia contents can be identified and allowed to bypass the detection engine, thereby allowing IDS to focus its limited resources on other traffic. The proposed framework also enables IDS to detect multimedia-specific exploits which would otherwise pass under the radar. Results of our experiments confirm our claims and show substantial CPU savings in both streaming and non-streaming scenarios.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/13242
Subject Headings: Computer networks--Security measures, Computers--Access control, Electronic countermeasures, Digital watermarking, Multimedia systems--Security measures
Format: Document (PDF)

Title: A class-based search system in unstructured peer-to-peer networks.
Creator: Huang, Juncheng., Florida Atlantic University, Wu, Jie
Abstract/Description: Efficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) [29] is such a system. GES derives node vector , a semantic summary of all documents on a node based on vector space model (VSM). The node-based topology adaptation algorithm and search protocol are then discussed. However, when there are many categories of...
Show moreEfficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) [29] is such a system. GES derives node vector , a semantic summary of all documents on a node based on vector space model (VSM). The node-based topology adaptation algorithm and search protocol are then discussed. However, when there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a class-based search system (CSS). It makes use of a document clustering algorithm: OSKM [27] to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. As a result, class vector replaces node vector and plays an important role in class-based topology adaptation and search process, which makes CSS very efficient. Our simulation demonstrates that CSS outperforms GES.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13367
Subject Headings: Peer-to-peer architecture (Computer networks), Management information systems, Computer security, Cascading style sheets, Web sites--Design
Format: Document (PDF)

Title: Intrusion detection in wireless networks: A data mining approach.
Creator: Nath, Shyam Varan., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The security of wireless networks has gained considerable importance due to the rapid proliferation of wireless communications. While computer network heuristics and rules are being used to control and monitor the security of Wireless Local Area Networks (WLANs), mining and learning behaviors of network users can provide a deeper level of security analysis. The objective and contribution of this thesis is three fold: exploring the security vulnerabilities of the IEEE 802.11 standard for...
Show moreThe security of wireless networks has gained considerable importance due to the rapid proliferation of wireless communications. While computer network heuristics and rules are being used to control and monitor the security of Wireless Local Area Networks (WLANs), mining and learning behaviors of network users can provide a deeper level of security analysis. The objective and contribution of this thesis is three fold: exploring the security vulnerabilities of the IEEE 802.11 standard for wireless networks; extracting features or metrics, from a security point of view, for modeling network traffic in a WLAN; and proposing a data mining-based approach to intrusion detection in WLANs. A clustering- and expert-based approach to intrusion detection in a wireless network is presented in this thesis. The case study data is obtained from a real-word WLAN and contains over one million records. Given the clusters of network traffic records, a distance-based heuristic measure is proposed for labeling clusters as either normal or intrusive. The empirical results demonstrate the promise of the proposed approach, laying the groundwork for a clustering-based framework for intrusion detection in computer networks.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/13246
Subject Headings: Wireless communication systems, Data warehousing, Data mining, Telecommunication--Security measures, Computer networks--Security measures, Computer security
Format: Document (PDF)

Title: Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
Creator: Dabbas, Mohammad A., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
Subject Headings: Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
Format: Document (PDF)

Title: Analysis of quality of service (QoS) in WiMAX networks.
Creator: Talwalkar, Rohit., College of Engineering and Computer Science, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This thesis focuses on analysis of quality of service as implemented by...
Show moreIn last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This thesis focuses on analysis of quality of service as implemented by the WiMAX networks. First, it presents the details of the quality of service architecture in WiMAX network. In the analysis, a WiMAX module developed based on popular network simulator ns-2, is used. Various real life scenarios like voice call, video streaming are setup in the simulation environment. Parameters that indicate quality of service, such as, throughput, packet loss, average jitter and average delay, are analyzed for different types of service flows as defined in WiMAX. Results indicate that better quality of service is achieved by using service flows designed for specific applications.
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Date Issued: 2008
PURL: http://purl.flvc.org/fcla/flaent/EN00154040/68_2/98p0143h.pdf, http://purl.flvc.org/FAU/58012
Subject Headings: Wireless communication systems, Broadband communication systems, Wireless LANs, Design and construction, Computer networks, Management, Quality control
Format: Document (PDF)

Title: Cloud-based Skin Lesion Diagnosis System using Convolutional Neural Networks.
Creator: Akar, Esad, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Skin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural...
Show moreSkin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural networks (CNNs) with near dermatologist level accuracy has been designed and implemented in part to increase early detection of skin cancer. A large range of client devices can connect to the system to upload digital lesion images and request diagnosis results from the diagnosis pipeline. The diagnosis is handled by a two-stage CNN pipeline hosted on a server where a preliminary CNN performs quality check on user requests, and a diagnosis CNN that outputs lesion predictions.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013150
Subject Headings: Skin Diseases--diagnosis, Skin--Cancer--Diagnosis, Diagnosis--Methodology, Neural networks, Cloud computing
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: Activity analysis and detection of falling and repetitive motion.
Creator: Carryl, Clyde, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more...
Show moreThis thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
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Date Issued: 2013
PURL: http://purl.flvc.org/FAU/3360774
Subject Headings: Perpetual-motion processes, Human locomotion, Neural networks (Computer science), Artificial intelligence
Format: Document (PDF)

Title: The triangle of reflections.
Creator: Torres, Jesus, Yiu, Paul Y., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: This thesis presents some results in triangle geometry discovered using dynamic software, namely, Geometer’s Sketchpad, and confirmed with computations using Mathematica 9.0. Using barycentric coordinates, we study geometric problems associated with the triangle of reflections T of a given triangle T, yielding interesting triangle centers and simple loci such as circles and conics. These lead to some new triangle centers with reasonably simple coordinates, and also new properties of some...
Show moreThis thesis presents some results in triangle geometry discovered using dynamic software, namely, Geometer’s Sketchpad, and confirmed with computations using Mathematica 9.0. Using barycentric coordinates, we study geometric problems associated with the triangle of reflections T of a given triangle T, yielding interesting triangle centers and simple loci such as circles and conics. These lead to some new triangle centers with reasonably simple coordinates, and also new properties of some known, classical centers. Particularly, we show that the Parry reflection point is the common point of two triads of circles, one associated with the tangential triangle, and another with the excentral triangle. More interestingly, we show that a certain rectangular hyperbola through the vertices of T appears as the locus of the perspector of a family of triangles perspective with T, and in a different context as the locus of the orthology center of T with another family of triangles.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004167, http://purl.flvc.org/fau/fd/FA00004167
Subject Headings: Geometer's Sketchpad, Geometry -- Study and teaching, Geometry, Hyperbolic, Mathematics -- Computer network resources, Problem solving
Format: Document (PDF)

Title: The role of advertising and information asymmetry on firm performance.
Creator: Fine, Monica B., College of Business, Department of Marketing
Abstract/Description: Research linking marketing to financial outputs has been gaining significance in the marketing discipline. The pertinent questions are, therefore: how can marketing improve measures of firm performance and draw potential investors to the company, and where is the quantitative proof to back up these assertions? This research investigates the role of marketing expenditures in the context of initial public offerings (IPOs). The proposed theoretical framework comes from marketing and finance...
Show moreResearch linking marketing to financial outputs has been gaining significance in the marketing discipline. The pertinent questions are, therefore: how can marketing improve measures of firm performance and draw potential investors to the company, and where is the quantitative proof to back up these assertions? This research investigates the role of marketing expenditures in the context of initial public offerings (IPOs). The proposed theoretical framework comes from marketing and finance literature, and uses econometric models to test the hypotheses. First, we replicate the results of a previous study by Luo (2008) showing a relationship between the firm's pre-IPO marketing spending and IPO underpricing. Next, we extend the previous study by looking at the IPO's long-run returns, types of risk, analyst coverage, and market/industry characteristics. The results of this study, based on a sample of 2,103 IPOs from 1996 to 2008, suggest that increased marketing spending positively impac ts firm performance. We examine different measures of firm performance, such as risk and long-run performance, whose results are important to the firm, its shareholders, and potential investors. This study analyzes the impact marketing spending has on IPO characteristics (IPO underpricing in the short-run and cumulative abnormal returns in the long run); risk characteristics (systematic, unsystematic, bankruptcy risk, and total risk); analyst coverage characteristics (the number of analysts, optimistic coverage, and forecast error) and market characteristics (market volatility and industry type). We control for variables such as firm size, profitability, and IPO characteristics. In this paper, the results show that increased marketing spending lowers underpricing, lowers bankruptcy risk, lowers total risk, leads to greater analyst coverage, leads to more favorable analyst coverage, and lowers analyst forecast error. For theory, this paper advances the literature on the, marketing-financ e interface by extending the market-based assets and signaling theories. For practice, the results indicate that spending more money on marketing before the IPO and disclosing this information produces positive bottom-line results for the firm. KEYWORDS: Marketing-Finance, Risk, Financial Analysts, Marketing Spending, Firm Performance, Marketing Strategy Meets Wall Street, Long-Run Firm Performance, Underpricing, Stock Recommendations, Initial Public Offering, Marketing Strategy, Econometric Model.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342050
Subject Headings: Investment analysis, Organizational effectiveness, Measurement, Advertising, Financial services industry, Marketing, Financial services industry, Computer network resources
Format: Document (PDF)

Title: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
Creator: Thakkar, Jay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data...
Show moreDuring the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174078
Subject Headings: Neural networks (Computer science), Scientific applications, GMDH algorithms, Pattern recognition systems, Genetic algorithms, Fuzzy logic
Format: Document (PDF)

Title: A planar cable-driven robotic device for physical therapy assistance.
Creator: Morris, Melissa M., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact...
Show moreThe design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact with the cables as well as a "break-away" joystick that the user utilizes to perform the rehabilitation tasks. In addition, this device is portable, aesthetically acceptable and easy to operate. Other uses of this system include sports therapy, virtual reality and teleoperation of remote devices.
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Date Issued: 2007
PURL: http://purl.flvc.org/FAU/FADTsup3358410p
Subject Headings: Medical physics, Robotics, Biomechanics, Physical therapy, Technological innovations, Neural networks (Computer science)
Format: Set of related objects

Title: Real Time Traffic Monitoring System from a UAV Platform.
Creator: Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013188
Subject Headings: Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
Format: Document (PDF)

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