Vehicular Ad hoc Networks (VANET) is a wireless ad-hoc network that includes two types of communications, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). In VANET there are two types of messages. The first type is the event-driven messages that are only triggered in case of emergency. The second type is the periodical messages named beacons that are exchanged frequently between vehicles. A beacon message contains basic information about the sending vehicle such as id, location and velocity. Beacons are frequently exchanged to increase the cooperative awareness between vehicles. Increasing beacon frequency helps increasing neighborhood awareness and improving information accuracy. However, this causes more congestion in the network, specially when the number of vehicles increases. On the other hand, reducing beacon frequency alleviates network congestion, but results in out-dated information. In this dissertation, we address the aforementioned challenges and propose a number of smart beaconing protocols and evaluate their performance in di↵erent environments and network densities. The four adaptive beaconing protocols are designed to increase the cooperative awareness and information freshness, while alleviating the network congestion. All the proposed protocols take into account the most important aspects, which are critical to beaconing rate adaptation. These aspects include channel status, traffic conditions and link quality. The proposed protocols employ fuzzy logic-based techniques to determine the congestion rank, which is used to adjust beacon frequency. The first protocol considers signal to interference-noise ratio (SINR), number of neighboring nodes and mobility to determine the congestion rank and adjust the beacon rate accordingly. This protocol works well in sparse conditions and highway environments. The second protocol works well in sparse conditions and urban environments. It uses channel busy time (CBT), mobility and packet delivery ratio (PDR) to determine the congestion rank and adjust the beacon rate. The third protocol utilizes CBT, SINR, PDR, number of neighbors and mobility as inputs for the fuzzy logic system to determine the congestion rank and adjust the beacon rate. This protocol works well in dense conditions in both highway and urban environments. Through extensive simulation experiments, we established that certain input parameters are more e↵ective in beacon rate adaptation for certain environments and conditions. Based on this, we propose a high awareness and channel efficient scheme that adapts to di↵erent environments and conditions. First, the protocol estimates the network density using adaptive threshold function. Then, it looks at the spatial distribution of nodes using the quadrat method to determine whether the environment is highway or urban. Based on the density conditions and nodes distribution, the protocol utilizes the appropriate fuzzy input parameters to adapt the beaconing rate. In addition, the protocol optimizes the performance by adapting the transmission power based on network density and nodes distribution. Finally, an investigation of the impact of adaptive beaconing on broadcasting is conducted. The simulation results confirm that our adaptive beaconing scheme can improve performance of the broadcast protocols in terms of reachability and bandwidth consumption when compared to a fixed rate scheme. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Vehicular Ad hoc Networks (VANETs) have the potential to enable various kinds of applications aiming at improving road safety and transportation efficiency. These applications require uni-cast routing, which remains a significant challenge due to VANETs characteristics. Given VANET dynamic topology, geographic routing protocols are considered the most suitable for such network due to their scalability and low overhead. However, the optimal selection of next-hop nodes in geographic routing is a challenging problem where the routing performance is highly affected by the variable link quality and bandwidth availability. In this dissertation, a number of enhancements to improve geographic routing reliability in VANETs are proposed. To minimize packet losses, the direction and link quality of next-hop nodes using the Expected Transmission Count (ETX) are considered to select links with low loss ratios. To consider the available bandwidth, a cross-layer enchantment of geographic routing, which can select more reliable links and quickly react to varying nodes load and channel conditions, is proposed. We present a novel model of the dynamic behavior of a wireless link. It considers the loss ratio on a link, in addition to transmission and queuing delays, and it takes into account the physical interference e ect on the link. Then, a novel geographic routing protocol based on fuzzy logic systems, which help in coordinating di erent contradicting metrics, is proposed. Multiple metrics related to vehicles' position, direction, link quality and achievable throughput are combined using fuzzy rules in order to select the more reliable next-hop nodes for packet forwarding. Finally, we propose a novel link utility aware geographic routing protocol, which extends the local view of the network topology using two-hop neighbor information. We present our model of link utility, which measures the usefulness of a two-hop neighbor link by considering its minimum residual bandwidth and packet loss rate. The proposed protocol can react appropriately to increased network tra c and to frequent topology dis-connectivity in VANETs. To evaluate the performance of the proposed protocols, extensive simulation experiments are performed using network and urban mobility simulation tools. Results confirm the advantages of the proposed schemes in increased traffic loads and network density. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

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 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. / by Jay Thakkar. / Pagination error. "References" should be leaves 63-67, and pagination end with leaf 67. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Esta Tese estuda a implementação de um novo mecanismo de análise e atuação em Redes de Sensores Sem Fio (RSSF) com múltiplos saltos baseado em características de cognição aplicadas aos nós que compõem a rede. Para tanto, é proposto um algoritmo de detecção de variabilidade dos nós sensores, envolvendo movimentação do nó, alcance do sinal da antena do sensor, quantidade de nós que fazem parte da rede e o número de conexões possíveis com nós vizinhos. Além do algoritmo de detecção de variabilidade, propõe-se um sistema multilayer denominado Adaptive Cognitive System (ACS) com base na arquitetura de Cognitive Networks (CN), que abrange: coleta, tratamento e tomada de decisão. O tratamento se refere à parte cognitiva do sistema, contemplando a criação do Cognitive Processor Module (CPMod), que por sua vez, abrange a semântica da rede, aplicação de Lógica Fuzzy e interação com um simulador de Wireless Sensor Networks (WSN) e a tomada de decisão é realizada pelo CPMod com base no resultado de análises executadas em rounds e histórico da rede com o uso de funções de pertinência de fuzzificação e defuzzificação, regras Fuzzy e inferência sobre informações coletadas da rede. Observou-se com os testes realizados na rede, utilizando-se o algoritmo de detecção, que a variabilidade dos nós sensores afeta diretamente o desempenho da rede, devido à necessidade de reestabelecimento de links e rotas entre os nós. Através de testes realizados via software na WSN, identificou-se que com o uso do ACS houve melhora significativa no desempenho em relação ao atraso fim-a-fim, latência, quantidade de pacotes descartados e de energia consumida pelos nós na rede. O ACS demonstrou potencial para a solução de problemas relacionados com as métricas destacadas, realizando ajustes em múltiplas camadas de rede do padrão IEEE 802.15.4 para até 200 nós na rede. / This Dissertation examines the implementation of a mechanism to analyze and act on multi-hop Wireless Sensor Networks (WSN) with the use of cognitive features applied to the network nodes. For this purpose, a variation detection algorithm was proposed for monitoring sensor nodes, involving the node\'s mobility features, signal range of the sensor antenna, the number of nodes in the network and the number of possible connections to neighboring nodes. In addition to the detection algorithm, a multi-layer system is proposed, named Adaptive Cognitive System (ACS). It is based on Cognitive Networks (CN) architecture, including data gathering, information treatment and decision making. The main part of the system is the Cognitive Processor Module (CPMod), which extracts the information about the WSN. In turn the Fuzzy Logic block works in tandem with the semantic engine to feed the codes to CPMod in the decision making process. The codes are the result of analysis performed on rounds using fuzzification and defuzzification membership functions, fuzzy rules and inference over information collected from the network. It was observed in tests performed in the WSN, using the detection algorithm, that the variability in sensor nodes directly affects the network performance due to the effort spent in rerounting links and paths. Through WSN testing performed via software, it was found that using the ACS implies in significant improvement in performance over the end-to-end delay, network latency, dropped packets and amount of energy consumed by nodes on the network. The ACS potential is proven for solving problems related to the previously mentioned metrics, performing adjustments on multiple network layers standardized by IEEE 802.15.4 up to 200 nodes in the network.

Este trabalho propõe uma alternativa para o problema de classificação de espécies de abelhas a partir da implementação de um algoritmo com base na Morfométria Geométrica e estudo das Formas dos marcos anatômicos das imagens obtidas pelas asas das abelhas. O algoritmo implementado para este propósito se baseia no algoritmo dos k-Vizinho mais Próximos (do inglês, kNN) e na Lógica Fuzzy kNN (Fuzzy k-Nearest Neighbor) aplicados a dados analisados e selecionados de pontos bidimensionais referentes as características geradas por marcos anatômicos. O estudo apresentado envolve métodos de seleção e ordenação de marcos anatômicos para a utilização no algoritmo por meio da implementação de um método matemático que utiliza o calculo dos marcos anatômicos mais significativos (que são representados por marcos matemáticos) e a formulação da Ordem de Significância onde cada elemento representa variáveis de entrada para a Fuzzy kNN. O conhecimento envolvido neste trabalho inclui uma perspectiva sobre a seleção de características não supervisionada como agrupamentos e mineração de dados, analise de pré-processamento dos dados, abordagens estatísticas para estimação e predição, estudo da Forma, Analise de Procrustes e Morfométria Geométrica sobre os dados e o tópico principal que envolve uma modificação do algoritmo dos k- Vizinhos mais Próximos e a aplicação da Fuzzy kNN para o problema. Os resultados mostram que a classificação entre amostras de abelhas no seu próprio grupo apresentam acuracia de 90%, dependendo da espécie. As classificações realizadas entre as espécies de abelhas alcançaram acuracia de 97%. / This work presents a proposal to solve the bees classification problem by implementing an algorithm based on Geometrics Morphometrics and the Shape analysis of landmarks generated from bees wings images. The algorithm is based on the K-Nearest Neighbor (K-Nearest Neighbor) algorithm and Fuzzy Logic KNN applied to the analysis and selection of two-dimensional data points relating to landmarks. This work is part of the Architecture Reference Model for Automatic identification and Taxonomic Classification System of Stingless Bee using the Wing Morphometry. The study includes selection and ordering methods for landmarks used in the algorithm by developing a mathematical model to represent the significance order, generating the most significant mathematical landmarks as input variables for Fuzzy Logic kNN. The main objective of this work is to develop a classification system for bee species. The knowledge involved in the development of this work include an overview of feature selection, unsupervised clustering and data mining, analysis of data pre-processing, statistical approaches for estimation and prediction, study of Shape, Procrustes Analysis on data that comes from Geometric Morphometry and the modification of the k-Nearest Neighbors algorithm and the Fuzzy Logic kNN. The results show that the classification in bee samples of the same species presents a accuracy above 90%, depending on the specie in analysis. The classification done between the bees species reach accuracies of 97%.

Considerando a segurança e extensão da vida de uma planta industrial, especificamente das válvulas de controle de processo, o estudo de confiabilidade de componentes é um ponto importante a ser investigado em usinas nucleares e em outras áreas, tais como refinaria ou plataforma de petróleo offshore. O desenvolvimento de monitorização não intrusiva e método de diagnóstico possibilita a identificação de defeitos em componentes da planta durante sua operação normal. O objetivo deste trabalho é apresentar uma análise e diagnóstico de válvulas de controle de uma planta de vapor que simula parte do circuito secundário de um reator de água pressurizada. Esta instalação faz parte do laboratório de testes de equipamentos de propulsão da Marinha do Brasil, em Iperó-SP. A metodologia utilizada no projeto é baseada na análise gráfica de dois parâmetros: a pressão de ar do atuador da válvula e o deslocamento de seu obturador. Estes dados são extraídos por um posicionador inteligente do Sistema de Automação Delta VTM. É implementada uma análise para detecção de anomalias por meio de uma abordagem que utiliza Sistemas Especialistas baseados na Lógica Fuzzy, considerando regras e conhecimento de inteligência artificial (IA). Uma vez que as medidas de base de válvulas de controle são tomadas, é possível detectar sintomas de falha, vazamento, atrito, fricção, danos, etc. O monitoramento e o sistema de diagnóstico foram projetados utilizando o programa MATLAB® versão 2009a com o FUZZY LOGIC TOOLBOX, que é um pacote integrante de subrotinas dedicado à lógica nebulosa. A monitoração e o diagnóstico das válvulas de controle são realizados por meio de uma técnica não-invasiva. Desta maneira, é possível conhecer o real status da válvula. O software ValveLink® (desenvolvido pela empresa EMERSON) recebe sinais do componente de hardware, posicionador inteligente, o qual é instalado ao lado da válvula de controle de processos. Estes sinais (corrente eléctrica) transformados em informação são utilizados como parâmetros de entrada: Pressão de ar do atuador e deslocamento do obturador da válvula. Com o uso da lógica fuzzy, esses parâmetros são interpretados. Eles sofrem inferências por regras escritas por especialistas em válvulas. Após essas inferências, as informações são tratadas e enviadas como sinais de saída. Esses sinais contém a informação de diagnóstico do estado da válvula. / The question of components reliability, specifically process control valves, has become an important issue to be investigated in nuclear power plants and other areas such as refinery or offshore oil rig, considering the safety and life extension of the plant. The development of non-intrusive monitoring and diagnostic method allows the identification of defects in components of the plant during normal operation. The objective of this dissertation is to present an analysis and diagnosis of control valves of a steam plant part that simulates the secondary circuit of a pressurized water reactor. This installation is part of propulsion equipment testing laboratory of the Brazilian Navy, at Iperó-SP. The methodology for design is based on graphical analysis of two parameters, the valve air pressure actuator and the displacement of the valve plug. These data are extracted by a smart positioner, part of Delta VTM Automation System. An analysis is implemented in detecting anomalies by an approach using Expert Systems by the technique of fuzzy logic. Once the basic measures of control valves are taken, it is possible to detect symptoms of failure, leakage, friction, damage, etc. The monitoring and diagnostic system has been designed in MATLAB® version 2009th by the complement \"FUZZY LOGIC TOOLBOX \". It is a noninvasive technique. Thus, it is possible to know what is happening with the chosen components, just analyzing the parameters of the valve. The software called ValveLink® (developed by Emerson) receives signals from hardware component (intelligent positioner) installed next to the control valve. These signals (electrical current) are transformed into information which are used input parameters: air pressure valve actuator and valve plug displacement. With the use of fuzzy logic, these parameters are interpreted. They suffer inferences by rules written by experts in valves. After these inferences, the information is processed and sent as output signals. These signals lead to diagnostic information of the state of the valve.

O Sistema Proteu Fuzzy é um sistema de tomada de decisão para compra e venda de ativos financeiros que visa auxiliar a figura do analista técnico (de modo imparcial e racional), informando quando existe uma boa oportunidade para se comprar ou vender um determinado ativo (e.g. ações). Utilizaram-se, como base para as suas decisões, técnicas de inteligência artificial (Lógica Fuzzy) e indicadores técnicos (Médias Móveis, MACD e RSI). As simulações mostram que o sistema conseguiu gerar resultados de forma consistente e com menor volatilidade que o mercado para alguns ativos. / The Proteu Fuzzy System is a decision-making system with the purpose of supporting a technical analyst issuing (impartial and rational) buy and sell signals for a financial asset. The system use, for the decision-making process, an inference engine based on Fuzzy Logic and technical indicators (e.g. Moving Averages, MACD and RSI). The simulation shows that the system is able to generate profits in a consistent manner and with a lower volatility then the market for some assets.

A presente tese de doutorado discorre sobre o método da espectroscopia na região do Infravermelho Próximo, usando Transformada de Fourier (IVP-TF), aplicada à identificação de plantas medicinais, método de análise química qualitativa, que se propõe para o controle de qualidade de fitoterápicos. É apresentado, também, um método computacional, implementado para realizar esta identificação, utilizando modelo e lógica nebulosos (fuzzy). Remete-se a discussão da espectroscopia IVP-TF como um método rápido e de baixo custo, para taxonomia de plantas medicinais brasileiras, bem como promove-se a comparação dos resultados obtidos nos diferentes métodos utilizados. / This doctoral thesis is about the method of Near Infrared spectroscopy using Fourier transform (FT-NIR), applied to identification of medicinal plants, method of the chemical analysis qualitative, which is proposed to the quality control of herbal medicines. It is presented, also, a computational method, implemented to make this identification using fuzzy logic model. Refers to discussion of FT-NIR spectroscopy as a rapid method with low cost for taxonomy of Brazilian medicinal plants, and makes comparison of results obtained in the different methods used.

With the increased deterioration of infrastructure in this country, it has become important to find ways to maintain the strength and integrity of a structure over its design life. Being able to control the amount a structure displaces or vibrates during a seismic event, as well as being able to model this nonlinear behavior, provides a new challenge for structural engineers. This research proposes a wavelet-based adaptive neuro- fuzzy inference system for use in system identification and structural control of civil engineering structures. This algorithm combines aspects of fuzzy logic theory, neural networks, and wavelet transforms to create a new system that effectively reduces the number of sensors needed in a structure to capture its seismic response and the amount of computation time needed to model its nonlinear behavior. The algorithm has been tested for structural control using a three-story building equipped with a magnetorheological damper for system identification, an eight-story building, and a benchmark highway bridge. Each of these examples has been tested using a variety of earthquakes, including the El-Centro, Kobe, Hachinohe, Northridge, and other seismic events.

Made available in DSpace on 2017-07-21T18:15:33Z (GMT). No. of bitstreams: 1 Alex Caetano da Silva.pdf: 6965557 bytes, checksum: 462615e031e7afd8c789b6ee564942a9 (MD5) Previous issue date: 2013-06-19 / This research identifies the different environmental vulnerability indices from fuzzy logic in urban area of Ponta Grossa, Paraná. Noteworthy is the use of geotechnology, and representation in the making cartograms and maps as well as in intersection and thematic analysis. The research raises the environmental profile of urban space of the city, through the data collected by IBGE Census 2010 and the steepness of the municipality. Employs the methodology for creating fuzzy indices, characterizing the data in (5) analysis groups, housing conditions, conditions as to access to education and economic conditions, subnormal agglomerates and relief conditions with rates ranging from 1 better and 0 the worst conditions of the studied phenomenon. By Fuzzy operators AND, OR, PRODUCT, SUM and GAMMA, intersect the vulnerability groups representing different levels of vulnerability in different fuzzy operators, representing the socioeconomic vulnerability. The environmental vulnerability index is the end result, results from overlapping fuzzy with all the groups that make up the index of socioeconomic vulnerability added to the group index slope by AND and GAMMA. The operators performed well, identifying the different levels of vulnerability and the least vulnerable sectors are present in the district center and in its vicinity, in more distant neighborhoods mostly vulnerabilities are higher. / A presente pesquisa identifica os diferentes índices de vulnerabilidade socioambiental a partir da lógica Fuzzy na área urbana de Ponta Grossa, Paraná. Destaca-se o emprego das geotecnologias, na confecção e representação dos cartogramas e mapas bem como no cruzamento e análise da temática. A pesquisa levanta o perfil socioambiental do espaço urbano do município, por meio dos dados coletados pelo CENSO IBGE 2010 e da declividade do município. Emprega-se a metodologia Fuzzy para criação de índices, caracterizando os dados em (5) grupos de análise, condições de moradia, condições quanto ao acesso a educação e das condições econômicas, aglomerados subnormais e condições do relevo com índices variando entre 1 melhores condições e 0 as piores condições do fenômeno estudado. Mediante os operadores Fuzzy AND, OR, PRODUCT, SUM e GAMMA, cruzam-se os grupos de vulnerabilidade representando os diferentes níveis de vulnerabilidade nos diferentes operadores Fuzzy, representando a vulnerabilidade socioeconômica. O índice de vulnerabilidade socioambiental é o resultado final, resulta da sobreposição difusa com todos os grupos que compõem o índice de vulnerabilidade socioeconômica agregado ao grupo índice de declividade mediante os operadores AND e GAMMA. Os operadores apresentaram bons resultados, identificando os diferentes níveis de vulnerabilidade e os setores menos vulneráveis estão presentes no Bairro Centro e nas suas proximidades, nos bairros mais distantes em sua maioria encontram-se as vulnerabilidades mais altas.