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Title: Performance evaluation of blind equalization techniques in the digital cellular environment.
Creator: Boccuzzi, Joseph., Florida Atlantic University, Sudhakar, Raghavan
Abstract/Description: This thesis presents simulation results evaluating the performance of blind equalization techniques in the Digital Cellular environment. A new method of a simple zero memory non-linear detector for complex signals is presented for various forms of Fractionally Spaced Equalizers (FSE). Initial simulations are conducted with Binary Phase Shift Keying (BPSK) to study the characteristics of FSEs. The simulations are then extended to complex case via $\pi/$4-Differential Quaterny Phase Shift...
Show moreThis thesis presents simulation results evaluating the performance of blind equalization techniques in the Digital Cellular environment. A new method of a simple zero memory non-linear detector for complex signals is presented for various forms of Fractionally Spaced Equalizers (FSE). Initial simulations are conducted with Binary Phase Shift Keying (BPSK) to study the characteristics of FSEs. The simulations are then extended to complex case via $\pi/$4-Differential Quaterny Phase Shift Keying ($\pi/$4-DQPSK) modulation. The primary focus in this thesis is the performance of this complex case when operating in Additive White Gaussian Noise (AWGN) and Rayleigh Multipath Fading channels.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/14859
Subject Headings: Equalizers (Electronics), Computer algorithms, Data transmission systems, Programming electronic computers
Format: Document (PDF)

Title: Novel Techniques in Genetic Programming.
Creator: Fernandez, Thomas, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Three major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a...
Show moreThree major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a paper[36] for the Federal Reserve Bank, authors Neely and Weller state “a perennial problem with using flexible, powerful search procedures like Genetic Programming is overfitting, the finding of spurious patterns in the data. Given the well-documented tendency for the genetic program to overfit the data it is necessary to design procedures to mitigate this.” The third is the difficulty of determining optimal control parameters for the Genetic Programming process. Control parameters control the evolutionary process. They include settings such as, the size of the population and the number of generations to be run. In his book[45], Banzhaf describes this problem, “The bad news is that Genetic Programming is a young field and the effect of using various combinations of parameters is just beginning to be explored.” We address these problems by implementing and testing a number of novel techniques and improvements to the Genetic Programming process. We conduct experiments using data sets of various degrees of difficulty to demonstrate success with a high degree of statistical confidence.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012570
Subject Headings: Evolutionary programming (Computer science), Genetic algorithms, Genetic programming (Computer science)
Format: Document (PDF)

Title: Probabilistic predictor-based routing in disruption-tolerant networks.
Creator: Yuan, Quan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Disruption-Tolerant Networks (DTNs) are the networks comprised of a set of wireless nodes, and they experience unstable connectivity and frequent connection disruption because of the limitations of radio range, power, network density, device failure, and noise. DTNs are characterized by their lack of infrastructure, device limitation, and intermittent connectivity. Such characteristics make conventional wireless network routing protocols fail, as they are designed with the assumption the...
Show moreDisruption-Tolerant Networks (DTNs) are the networks comprised of a set of wireless nodes, and they experience unstable connectivity and frequent connection disruption because of the limitations of radio range, power, network density, device failure, and noise. DTNs are characterized by their lack of infrastructure, device limitation, and intermittent connectivity. Such characteristics make conventional wireless network routing protocols fail, as they are designed with the assumption the network stays connected. Thus, routing in DTNs becomes a challenging problem, due to the temporal scheduling element in a dynamic topology. One of the solutions is prediction-based, where nodes mobility is estimated with a history of observations. Then, the decision of forwarding messages during data delivery can be made with that predicted information. Current prediction-based routing protocols can be divided into two sub-categories in terms of that whether they are probability related: probabilistic and non-probabilistic. This dissertation focuses on the probabilistic prediction-based (PPB) routing schemes in DTNs. We find that most of these protocols are designed for a specified topology or scenario. So almost every protocol has some drawbacks when applied to a different scenario. Because every scenario has its own particular features, there could hardly exist a universal protocol which can suit all of the DTN scenarios. Based on the above motivation, we investigate and divide the current DTNs scenarios into three categories: Voronoi-based, landmark-based, and random moving DTNs. For each category, we design and implement a corresponding PPB routing protocol for either basic routing or a specified application with considering its unique features., Specifically, we introduce a Predict and Relay routing protocol for Voronoi-based DTNs, present a single-copy and a multi-copy PPB routing protocol for landmark-based DTNs, and propose DRIP, a dynamic Voronoi region-based publish/subscribe protocol, to adapt publish/subscribe systems to random moving DTNs. New concepts, approaches, and algorithms are introduced during our work.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/359928
Subject Headings: Routers (Computer networks), Computer network protocols, Computer networks, Reliability, Computer algorithms, Wireless communication systems, Technological innovations
Format: Document (PDF)

Title: Label routing protocol: A new cross-layer protocol for multi-hop ad hoc wireless network.
Creator: Wang, Yu., Florida Atlantic University, Wu, Jie, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Compared to the traditional wireless network, the multi-hop ad hoc wireless network (simply called ad hoc networks) is self-configurable, dynamic, and distributed. During the past few years, many routing protocols have been proposed for this particular network environment. While in wired and optical networks, multi-protocol label switching (MPLS) has clearly shown its advantages in routing and switching such as flexibility, high efficiency, scalability, and low cost, however MPLS is complex...
Show moreCompared to the traditional wireless network, the multi-hop ad hoc wireless network (simply called ad hoc networks) is self-configurable, dynamic, and distributed. During the past few years, many routing protocols have been proposed for this particular network environment. While in wired and optical networks, multi-protocol label switching (MPLS) has clearly shown its advantages in routing and switching such as flexibility, high efficiency, scalability, and low cost, however MPLS is complex and does not consider the mobility issue for wireless networks, especially for ad hoc networks. This thesis migrates the label concept into the ad hoc network and provides a framework for the efficient Label Routing Protocol (LRP) in such a network. The MAC layer is also optimized with LRP for shorter delay, power saving, and higher efficiency. The simulation results show that the delay is improved significantly with this cross-layer routing protocol.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13321
Subject Headings: Computer network protocols, Wireless communication systems, Mobile computing, Computer algorithms, MPLS standard, Operating systems (Computers)
Format: Document (PDF)

Title: Design and implementation of efficient routing protocols in delay tolerant networks.
Creator: Liu, Cong., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Delay tolerant networks (DTNs) are occasionally-connected networks that may suffer from frequent partitions. DTNs provide service despite long end to end delays or infrequent connectivity. One fundamental problem in DTNs is routing messages from their source to their destination. DTNs differ from the Internet in that disconnections are the norm instead of the exception. Representative DTNs include sensor-based networks using scheduled intermittent connectivity, terrestrial wireless networks...
Show moreDelay tolerant networks (DTNs) are occasionally-connected networks that may suffer from frequent partitions. DTNs provide service despite long end to end delays or infrequent connectivity. One fundamental problem in DTNs is routing messages from their source to their destination. DTNs differ from the Internet in that disconnections are the norm instead of the exception. Representative DTNs include sensor-based networks using scheduled intermittent connectivity, terrestrial wireless networks that cannot ordinarily maintain end-to-end connectivity, satellite networks with moderate delays and periodic connectivity, underwater acoustic networks with moderate delays and frequent interruptions due to environmental factors, and vehicular networks with cyclic but nondeterministic connectivity. The focus of this dissertation is on routing protocols that send messages in DTNs. When no connected path exists between the source and the destination of the message, other nodes may relay the message to the destination. This dissertation covers routing protocols in DTNs with both deterministic and non-deterministic mobility respectively. In DTNs with deterministic and cyclic mobility, we proposed the first routing protocol that is both scalable and delivery guaranteed. In DTNs with non-deterministic mobility, numerous heuristic protocols are proposed to improve the routing performance. However, none of those can provide a theoretical optimization on a particular performance measurement. In this dissertation, two routing protocols for non-deterministic DTNs are proposed, which minimizes delay and maximizes delivery rate on different scenarios respectively. First, in DTNs with non-deterministic and cyclic mobility, an optimal single-copy forwarding protocol which minimizes delay is proposed., In DTNs with non-deterministic mobility, an optimal multi-copy forwarding protocol is proposed. which maximizes delivery rate under the constraint that the number of copies per message is fixed . Simulation evaluations using both real and synthetic trace are conducted to compare the proposed protocols with the existing ones.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/210522
Subject Headings: Computer network protocols, Computer networks, Reliability, Computer algorithms, Wireless communication systems, Technological innovations
Format: Document (PDF)

Title: A utility-based routing scheme in multi-hop wireless networks.
Creator: Lu, Mingming., College of Engineering and Computer Science, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Multi-hop wireless networks are infrastructure-less networks consisting of mobile or stationary wireless devices, which include multi-hop wireless mesh networks and multi-hop wireless sensor networks. These networks are characterized by limited bandwidth and energy resources, unreliable communication, and a lack of central control. These characteristics lead to the research challenges of multi-hop wireless networks. Building up routing schemes with good balance among the routing QoS (such as...
Show moreMulti-hop wireless networks are infrastructure-less networks consisting of mobile or stationary wireless devices, which include multi-hop wireless mesh networks and multi-hop wireless sensor networks. These networks are characterized by limited bandwidth and energy resources, unreliable communication, and a lack of central control. These characteristics lead to the research challenges of multi-hop wireless networks. Building up routing schemes with good balance among the routing QoS (such as reliability, cost, and delay) is a paramount concern to achieve high performance wireless networks. These QoS metrics are internally correlated. Most existing works did not fully utilize this correlation. We design a metric to balance the trade-off between reliability and cost, and build up a framework of utility-based routing model in multi-hop wireless networks. This dissertation focuses on the variations with applications of utility-based routing models, designing new concepts, and developing new algorithms for them. A review of existing routing algorithms and the basic utility-based routing model for multi-hop wireless networks has been provided at the beginning. An efficient algorithm, called MaxUtility, has been proposed for the basic utility-based routing model. MaxUtility is an optimal algorithm that can find the best routing path with the maximum expected utility., Various utility-based routing models are extended to further enhance the routing reliability while reducing the routing overhead. Besides computing the optimal path for a given benefit value and a given source-destination pair, the utility-based routing can be further extended to compute all optimal paths for all possible benefit values and/or all source-destination pairs. Our utility-based routing can also adapt to different applications and various environments. In the self-organized environment, where network users are selfish, we design a truthful routing, where selfish users have to tell the truth in order to maximize their utilities. We apply our utility-based routing scheme to the data-gathering wireless sensor networks, where a routing scheme is required to transmit data sensed by multiple sensor nodes to a common sink node.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/77647
Subject Headings: Wireless communication systems, Security measures, Computer network protocols, Computer algorithms, Computer networks, Security measures
Format: Document (PDF)

Title: Application level intrusion detection using a sequence learning algorithm.
Creator: Dong, Yuhong., Florida Atlantic University, Hsu, Sam, Rajput, Saeed, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: An un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed...
Show moreAn un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed approach, a hash table is used to store a sparse data matrix in triple data format that is collected from a web transition log instead of an n-by-n dimension matrix. Furthermore, in GSLA, the sequence learning matrix can be dynamically changed according to a different volume of data sets. Therefore, this approach is more efficient, easy to manipulate, and saves memory space. To validate the effectiveness of the algorithm, extensive simulations have been conducted by applying the GSLA algorithm to the homework submission system at our computer science and engineering department. The performance of GSLA is evaluated and compared with traditional Markov Model (MM) and K-means algorithms. Specifically, three major experiments have been done: (1) A small data set is collected as a sample data, and is applied to GSLA, MM, and K-means algorithms to illustrate the operation of the proposed algorithm and demonstrate the detection of abnormal behaviors. (2) The Random Walk-Through sampling method is used to generate a larger sample data set, and the resultant anomaly score is classified into several clusters in order to visualize and demonstrate the normal and abnormal behaviors with K-means and GSLA algorithms. (3) Multiple professors' data sets are collected and used to build the normal profiles, and the ANOVA method is used to test the significant difference among professors' normal profiles. The GSLA algorithm can be made as a module and plugged into the IDS as an anomaly detection system.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12220
Subject Headings: Data mining, Parallel processing (Electronic computers), Computer algorithms, Computer security, Pattern recognition systems
Format: Document (PDF)

Title: Connected Dominating Set in Wireless Ad Hoc Networks: Variations with Applications.
Creator: Yang, Shuhui, Wu, Jie, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Wireless ad hoc networks (or simply ad hoc networks) are infrastructureless multihop networks consisting of mobile or stationary wireless devices, which include mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs). These networks are characterized by limited bandwidth and energy resources, frequent topology changes, and a lack of central control. These characteristics lead to the research challenges of ad hoc networks. The algorithms designed for ad hoc networks should be...
Show moreWireless ad hoc networks (or simply ad hoc networks) are infrastructureless multihop networks consisting of mobile or stationary wireless devices, which include mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs). These networks are characterized by limited bandwidth and energy resources, frequent topology changes, and a lack of central control. These characteristics lead to the research challenges of ad hoc networks. The algorithms designed for ad hoc networks should be localized, selforganizing, and energy efficient. A connected dominating set (CDS) is frequently used in ad hoc networks as a virtual backbone to support efficient routing, service discovery, and area monitoring. In addition, efficient broadcasting (i.e., finding a small set of forward nodes to ensure full delivery) can be viewed as forming a CDS on-the-fly. The periodically maintained virtual backbone is called a static CDS, and the temporarily formed forward node set is called a dynamk CDS. For efficiency and robustness, the ideal CDS construction algorithm is lightweight, has fast convergence, and minimizes the CDS size. Recently, due to some specific applications and new techniques, the concept of a connected dominating set can be modified or further extended for more efficient usage. This dissertation focuses on the variations with applications of the connected dominating set, designing new concepts, and developing new algorithms for them. A review of CDS construction algorithms for ad hoc networks has been provided at the beginning. An efficient scheme, called Rule K, has been proposed for static CDS construction. Rule K achieves a probabilistic constant upper bound on the expected CDS size, which is currently the best known performance guarantee for localized CDS algorithms. Several CDS algorithms are extended to generate the extended CDS, which exploits the cooperative communication technique to further reduce the size of CDS. A k-coverage set is developed for higher robustness. With the equipment of directional antennas , the transmission can be restricted to some certain directions to reduce interference and energy consumption. The corresponding directional CDS is discussed. Finally, a wireless sensor and actor network (WSAN) is introduced and localized algorithms are designed for it.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012580
Subject Headings: Computer network protocols, Wireless communication systems--Design and construction, Mobile computing, Computer algorithms
Format: Document (PDF)

Title: Multi-Path Intelligent Virtual Mobile Nodes for Ad Hoc Networks.
Creator: Qian, Binbin, Wu, Jie, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In mobile ad hoc networks, it is challenging to solve the standard problems encountered in fixed network because of the unpredictable motion of mobile nodes. Due to the lack of a fixed infrastructure to serve as the backbone of the network, it is difficult to manage nodes' locations and ensure the stable node performance. The virtual mobile node (VMN) abstraction that has been applied implements an virtual mobile node that consists of a set of real nodes traveling on one predetermined virtual...
Show moreIn mobile ad hoc networks, it is challenging to solve the standard problems encountered in fixed network because of the unpredictable motion of mobile nodes. Due to the lack of a fixed infrastructure to serve as the backbone of the network, it is difficult to manage nodes' locations and ensure the stable node performance. The virtual mobile node (VMN) abstraction that has been applied implements an virtual mobile node that consists of a set of real nodes traveling on one predetermined virtual path to collect messages and deliver them to the destinations when they meet. It conquers the unpredictable motion with virtual nodes' predictable motion. But it encounters unavoidable failure when all the nodes leave the VMN region and stop emulating the VMN. We extend the idea of the VMN abstraction to the Multi-path Intelligent Virtual Mobile Node (MIVMN) abstraction, which allows the messages to switch between multiple Hamiltonian paths to increase the message delivery ratio and decrease the failure rate of the virtual nodes. Through simulation results we show that the MIVMN abstraction successfully meets our goals.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012542
Subject Headings: Routers (Computer networks), Computer network architectures, Wireless communication systems, Computer algorithms
Format: Document (PDF)

Title: Performance analysis of K-means algorithm and Kohonen networks.
Creator: Syed, Afzal A., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: K-means algorithm and Kohonen network possess self-organizing characteristics and are widely used in different fields currently. The factors that influence the behavior of K-means are the choice of initial cluster centers, number of cluster centers and the geometric properties of the input data. Kohonen networks have the ability of self-organization without any prior input about the number of clusters to be formed. This thesis looks into the performances of these algorithms and provides a...
Show moreK-means algorithm and Kohonen network possess self-organizing characteristics and are widely used in different fields currently. The factors that influence the behavior of K-means are the choice of initial cluster centers, number of cluster centers and the geometric properties of the input data. Kohonen networks have the ability of self-organization without any prior input about the number of clusters to be formed. This thesis looks into the performances of these algorithms and provides a unique way of combining them for better clustering. A series of benchmark problem sets are developed and run to obtain the performance analysis of the K-means algorithm and Kohonen networks. We have attempted to obtain the better of these two self-organizing algorithms by providing the same problem sets and extract the best results based on the users needs. A toolbox, which is user-friendly and written in C++ and VC++ is developed for applications on both images and feature data sets. The tool contains K-means algorithm and Kohonen networks code for clustering and pattern classification.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13112
Subject Headings: Self-organizing maps, Neural networks (Computer science), Cluster analysis--Computer programs, Computer algorithms
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: Applications of evolutionary algorithms in mechanical engineering.
Creator: Nelson, Kevin M., Florida Atlantic University, Huang, Ming Z., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Many complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve...
Show moreMany complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve complex engineering problems by optimizing the entire system, rather than sub-components of the system. Three standard forms of evolutionary algorithms have been developed: evolutionary programming, genetic algorithms and evolution strategies. Mathematical and algorithmic details are described for each of these methods. In this dissertation, four engineering problems are explored using evolutionary programming and genetic algorithms. Exploiting the inherently parallel nature of evolution, a parallel version of evolutionary programming is developed and implemented on the MasPar MP-1. This parallel version is compared to a serial version of the same algorithm in the solution of a trial set of unimodal and multi-modal functions. The parallel version had significantly improved performance over the serial version of evolutionary programming. An evolutionary programming algorithm is developed for the solution of electronic part placement problems with different assembly devices. The results are compared with previously published results for genetic algorithms and show that evolutionary programming can successfully solve this class of problem using fewer genetic operators. The finite element problem is cast into an optimization problem and an evolutionary programming algorithm is developed to solve 2-D truss problems. A comparison to LU-decomposition showed that evolutionary programming can solve these problems and that it has the capability to solve the more complex nonlinear problems. Finally, ordinary differential equations are discretized using finite difference representation and an objective function is formulated for the application of evolutionary programming and genetic algorithms. Evolutionary programming and genetic algorithms have the benefit of permitting over-constraining a problem to obtain a successful solution. In all of these engineering problems, evolutionary algorithms have been shown to offer a new solution method.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/12514
Subject Headings: Mechanical engineering, Genetic algorithms, Evolutionary programming (Computer science)
Format: Document (PDF)

Title: Asynchronous distributed algorithms for multi-agent supporting systems.
Creator: Jin, Kai., Florida Atlantic University, Larrondo-Petrie, Maria M.
Abstract/Description: Based on multi-agent supporting system (MASS) structures used to investigate the synchronous algorithms in my previous work, the partially and totally asynchronous distributed algorithms are proposed in this thesis. The stability of discrete MASS with asynchronous distributed algorithms is analyzed. The partially asynchronous algorithms proposed for both 1- and 2-dimensional MASS are proven to be convergent, if the vertical disturbances vary sufficiently slower than the convergent time of the...
Show moreBased on multi-agent supporting system (MASS) structures used to investigate the synchronous algorithms in my previous work, the partially and totally asynchronous distributed algorithms are proposed in this thesis. The stability of discrete MASS with asynchronous distributed algorithms is analyzed. The partially asynchronous algorithms proposed for both 1- and 2-dimensional MASS are proven to be convergent, if the vertical disturbances vary sufficiently slower than the convergent time of the system. The adjacent error becomes zero when the system converges. It is also proven that in 1-dimensional MASS using the proposed totally asynchronous algorithm, the maximum of the absolute value of the adjacent error is non-increasing over time. Finally, the simulation results for all the above cases are presented to demonstrate the theoretical findings.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15277
Subject Headings: Electronic data processing--Distributed processing, Computer algorithms
Format: Document (PDF)

Title: Binary representation of DNA sequences towards developing useful algorithms in bioinformatic data-mining.
Creator: Pandya, Shivani., Florida Atlantic University, Neelakanta, Perambur S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis refers to a research addressing the use of binary representation of the DNA for the purpose of developing useful algorithms for Bioinformatics. Pertinent studies address the use of a binary form of the DNA base chemicals in information-theoretic base so as to identify symmetry between DNA and complementary DNA. This study also refers to "fuzzy" (codon-noncodon) considerations in delinating codon and noncodon regimes in a DNA sequences. The research envisaged further includes a...
Show moreThis thesis refers to a research addressing the use of binary representation of the DNA for the purpose of developing useful algorithms for Bioinformatics. Pertinent studies address the use of a binary form of the DNA base chemicals in information-theoretic base so as to identify symmetry between DNA and complementary DNA. This study also refers to "fuzzy" (codon-noncodon) considerations in delinating codon and noncodon regimes in a DNA sequences. The research envisaged further includes a comparative analysis of the test results on the aforesaid efforts using different statistical metrics such as Hamming distance Kullback-Leibler measure etc. the observed details supports the symmetry aspect between DNA and CDNA strands. It also demonstrates capability of identifying non-codon regions in DNA even under diffused (overlapped) fuzzy states.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13089
Subject Headings: Bioinformatics, Data mining, Nucleotide sequence--Databases, Computer algorithms
Format: Document (PDF)

Title: PRGMDH algorithm for neural network development and its applications.
Creator: Tangadpelli, Chetan., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm...
Show moreThe existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm provides visualization of the network displaying all the neurons in the network. The algorithm is general enough that it will accept any number of inputs and any sized training set. To show the flexibility of the Pruning based Regenerated Network, this algorithm is used to analyze different combinations of drugs and determine which pathways in these networks interact and determine the combination of drugs that take advantage of these interactions to maximize a desired effect on genes.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13397
Subject Headings: Neural networks (Computer science), GMDH algorithms, Pattern recognition systems
Format: Document (PDF)

Title: A new GMDH type algorithm for the development of neural networks for pattern recognition.
Creator: Gilbar, Thomas C., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Researchers from a wide range of fields have discovered the benefits of applying neural networks to pattern recognition problems. Although applications for neural networks have increased, development of tools to design these networks has been slower. There are few comprehensive network development methods. Those that do exist are slow, inefficient, and application specific, require predetermination of the final network structure, and/or result in large, complicated networks. Finding optimal...
Show moreResearchers from a wide range of fields have discovered the benefits of applying neural networks to pattern recognition problems. Although applications for neural networks have increased, development of tools to design these networks has been slower. There are few comprehensive network development methods. Those that do exist are slow, inefficient, and application specific, require predetermination of the final network structure, and/or result in large, complicated networks. Finding optimal neural networks that balance low network complexity with accuracy is a complicated process that traditional network development procedures are incapable of achieving. Although not originally designed for neural networks, the Group Method of Data Handling (GMDH) has characteristics that are ideal for neural network design. GMDH minimizes the number of required neurons by choosing and keeping only the best neurons and filtering out unneeded inputs. In addition, GMDH develops the neurons and organizes the network simultaneously, saving time and processing power. However, some of the qualities of the network must still be predetermined. This dissertation introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design. The new algorithm is faster, more flexible, and more accurate than traditional network development methods. It is also more dynamic than current GMDH based methods, capable of creating a network that is optimal for an application and training data. Additionally, the new algorithm virtually guarantees that the number of neurons progressively decreases in each succeeding layer. To show its flexibility, speed, and ability to design optimal networks, the algorithm was used to successfully design networks for a wide variety of real applications. The networks developed using the new algorithm were compared to other development methods and network architectures. The new algorithm's networks were more accurate and yet less complicated than the other networks. Additionally, the algorithm designs neurons that are flexible enough to meet the needs of the specific applications, yet similar enough to be implemented using a standardized hardware cell. When combined with the simplified network layout that naturally occurs with the algorithm, this results in networks that can be implemented using Field Programmable Gate Array (FPGA) type devices.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/11994
Subject Headings: GMDH algorithms, Neural networks (Computer science), Pattern recognition systems
Format: Document (PDF)

Title: Indexed resource auction multiple access (I-RAMA): A new medium access scheme for third generation wireless networks.
Creator: Barrantes-Sliesarieva, Elena Gabriela., Florida Atlantic University, Ulyas, Mohammad
Abstract/Description: Indexed Resource Auction Multiple Access (I-RAMA), a new medium access protocol for wireless cellular networks based on Resource Auction Multiple Access (RAMA) is presented. I-RAMA relies in variable length resource auctions, whose length depends on the time it takes the Base Station to uniquely identify the Mobile Station. This identification is done by using dynamic Base Station information about the users present in the cell at any moment. I-RAMA effectively reduces the amount of time...
Show moreIndexed Resource Auction Multiple Access (I-RAMA), a new medium access protocol for wireless cellular networks based on Resource Auction Multiple Access (RAMA) is presented. I-RAMA relies in variable length resource auctions, whose length depends on the time it takes the Base Station to uniquely identify the Mobile Station. This identification is done by using dynamic Base Station information about the users present in the cell at any moment. I-RAMA effectively reduces the amount of time spent in the resource auctions without introducing contention or excessive complexity at the Base Station. The effects of introducing data users in the system are investigated using a simulation, and it is shown that I-RAMA guarantees Quality of Service for isochronous users while maintaining a bounded delay for data users at much higher loads than RAMA.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15204
Subject Headings: Wireless communication systems, Cellular radio, Mobile communication systems, Computer algorithms
Format: Document (PDF)

Title: Software reliability engineering: An evolutionary neural network approach.
Creator: Hochman, Robert., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: This thesis presents the results of an empirical investigation of the applicability of genetic algorithms to a real-world problem in software reliability--the fault-prone module identification problem. The solution developed is an effective hybrid of genetic algorithms and neural networks. This approach (ENNs) was found to be superior, in terms of time, effort, and confidence in the optimality of results, to the common practice of searching manually for the best-performing net. Comparisons...
Show moreThis thesis presents the results of an empirical investigation of the applicability of genetic algorithms to a real-world problem in software reliability--the fault-prone module identification problem. The solution developed is an effective hybrid of genetic algorithms and neural networks. This approach (ENNs) was found to be superior, in terms of time, effort, and confidence in the optimality of results, to the common practice of searching manually for the best-performing net. Comparisons were made to discriminant analysis. On fault-prone, not-fault-prone, and overall classification, the lower error proportions for ENNs were found to be statistically significant. The robustness of ENNs follows from their superior performance over many data configurations. Given these encouraging results, it is suggested that ENNs have potential value in other software reliability problem domains, where genetic algorithms have been largely ignored. For future research, several plans are outlined for enhancing ENNs with respect to accuracy and applicability.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15474
Subject Headings: Neural networks (Computer science), Software engineering, Genetic algorithms
Format: Document (PDF)

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: DATA COLLECTION FRAMEWORK AND MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF CYBER SECURITY ATTACKS.
Creator: Calvert, Chad, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors...
Show moreThe integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013289
Subject Headings: Machine learning, Algorithms, Anomaly detection (Computer security), Intrusion detection systems (Computer security), Big data
Format: Document (PDF)

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