L'augmentation du parallélisme, sur des puces dont la densité d'intégration est en constante croissance, soulève un certain nombre de défis tels que le routage de l'information qui se confronte au problème de "goulot d'étranglement de données", ou la simple difficulté à exploiter un parallélisme massif et grandissant avec les paradigmes de calcul modernes issus pour la plupart, d'un historique séquentiel.Nous nous inscrivons dans une démarche bio-inspirée pour définir un nouveau type d'architecture, basée sur le concept d'auto-adaptation, afin de décharger le concepteur au maximum de cette complexité. Mimant la plasticité cérébrale, cette architecture devient capable de s'adapter sur son environnement interne et externe de manière homéostatique. Il s'inscrit dans la famille du calcul incorporé ("embodied computing") car le substrat de calcul n'est plus pensé comme une boite noire, programmée pour une tâche donnée, mais est façonné par son environnement ainsi que par les applications qu'il supporte.Dans nos travaux, nous proposons un modèle de carte neuronale auto-organisatrice, le DMADSOM (pour Distributed Multiplicative Activity Dependent SOM), basé sur le principe des champs de neurones dynamiques (DNF pour "Dynamic Neural Fields"), pour apporter le concept de plasticité à l'architecture. Ce modèle a pour originalité de s'adapter sur les données de chaque stimulus sans besoin d'un continuum sur les stimuli consécutifs. Ce comportement généralise les cas applicatifs de ce type de réseau car l'activité est toujours calculée selon la théorie des champs neuronaux dynamique. Les réseaux DNFs ne sont pas directement portables sur les technologies matérielles d'aujourd'hui de part leurs forte connectivité. Nous proposons plusieurs solutions à ce problème. La première consiste à minimiser la connectivité et d'obtenir une approximation du comportement du réseau par apprentissage sur les connexions latérales restantes. Cela montre un bon comportement dans certain cas applicatifs. Afin de s'abstraire de ces limitations, partant du constat que lorsqu'un signal se propage de proche en proche sur une topologie en grille, le temps de propagation représente la distance parcourue, nous proposons aussi deux méthodes qui permettent d'émuler, cette fois, l'ensemble de la large connectivité des Neural Fields de manière efficace et proche des technologies matérielles. Le premier substrat calcule les potentiels transmis sur le réseau par itérations successives en laissant les données se propager dans toutes les directions. Il est capable, en un minimum d'itérations, de calculer l'ensemble des potentiels latéraux de la carte grâce à une pondération particulière de l'ensemble des itérations.Le second passe par une représentation à spikes des potentiels qui transitent sur la grille sans cycles et reconstitue l'ensemble des potentiels latéraux au fil des itérations de propagation.Le réseau supporté par ces substrats est capable de caractériser les densités statistiques des données à traiter par l'architecture et de contrôler, de manière distribuée, l'allocation des cellules de calcul. / The increasing degree of parallelism on chip which comes from the always increasing integration density, raises a number of challenges such as routing information that confronts the "bottleneck problem" or the simple difficulty to exploit massive parallelism thanks to modern computing paradigms which derived mostly from a sequential history.In order to discharge the designer of this complexity, we design a new type of bio-inspired self-adaptive architecture. Mimicking brain plasticity, this architecture is able to adapt to its internal and external environment and becomes homeostatic. Belonging to the embodied computing theory, the computing substrate is no longer thought of as a black box, programmed for a given task, but is shaped by its environment and by applications that it supports.In our work, we propose a model of self-organizing neural map, DMADSOM (for Distributed Multiplicative Activity Dependent SOM), based on the principle of dynamic neural fields (DNF for "Dynamic Neural Fields"), to bring the concept of hardware plasticity. This model is able to adapt the data of each stimulus without need of a continuum on consecutive stimuli. This behavior generalizes the case of applications of such networks. The activity remains calculated using the dynamic neural field theory. The DNFs networks are not directly portable onto hardware technology today because of their large connectivity. We propose models that bring solutions to this problem. The first is to minimize connectivity and to approximate the global behavior thanks to a learning rule on the remaining lateral connections. This shows good behavior in some application cases. In order to reach the general case, based on the observation that when a signal travels from place to place on a grid topology, the delay represents the distance, we also propose two methods to emulate the whole wide connectivity of the Neural Field with respect to hardware technology constraints. The first substrate calculates the transmitted potential over the network by iteratively allowing the data to propagate in all directions. It is capable, in a minimum of iterations, to compute the lateral potentials of the map with a particular weighting of all iterations.The second involves a spike representation of the synaptic potential and transmits them on the grid without cycles. This one is hightly customisable and allows a very low complexity while still being capable to compute the lateral potentials.The network supported, by these substrates, is capable of characterizing the statistics densities of the data to be processed by the architecture, and to control in a distributed manner the allocation of computation cells.

L’Europe a subi la crise la plus sévère de sa récente histoire à la suite de la crise financière globale de 2008. C’est pourquoi cette thèse a l’objectif d’identifier de façon empirique les déterminants de cette crise dans le cadre de 15 principaux membres de l’UE. Dans ce sens, nous développons d’abord un index de pression fiscale continu, contrairement aux travaux empiriques précédents, afin d’identifier des périodes de crise dans les pays UE-15 de 2003 à 2015. Ensuite, nous utilisons trois différentes techniques d’estimation, à savoir Cartes auto-organisatrices, Logit et Markov. Nos résultats d’estimation démontrent que notre indicateur de crise identifie le timing et la durée de la crise de dette dans chacun des pays de UE-15. Résultats empiriques indiquent également que l’occurrence de la crise de dette dans l’UE-15 est la conséquence de la détérioration de balances macroéconomiques et financières sachant que les variables comme le ratio des prêts non-performants sur les crédits totaux du secteur bancaire, la croissance du PIB, chômage, balance primaire / PIB, le solde ajusté du cycle PIB. De plus, variables démontrant la qualité de gouvernance tel que participation et responsabilisation, qualité de la réglementation, et de l'efficacité gouvernementale, jouent également un rôle important dans l’occurrence et sur la durée de la crise de dette dans le cadre de l’UE-15. Étant donne que les résultats économétriques indiquent l’importance de la détérioration fiscale dans l’occurrence de la crise de dette européenne, nous testons la convergence fiscale des pays membre de l’UE. Les résultats montrent que Portugal, Irlande, Italie, Grèce et Espagne diverge des autres pays de l’UE-15 en termes de dette publique / PIB alors qu’ils convergent, à part la Grèce, avec les autres pays membres de l’UE-15 en termes de déficit budgétaires / PIB. / Europe went through the most severe economic crisis of its recent history following the global financial crisis of 2008. Hence, this thesis aims to empirically identify the determinants of this crisis within the framework of 15 core EU member countries (EU-15). To do so, the study develops a continuous fiscal stress index, contrary to previous empirical studies that tend to use event-based crisis indicators, which identifies the debt crises in the EU-15 and the study employs three different estimation techniques, namely Self-Organizing Map, Multivariate Logit and Panel Markov Regime Switching models. Our estimation results show first that the study identifies correctly the time and the length of the debt crisis in each EU-15-member country by developing a fiscal stress index. Empirical results also indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Besides, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15. As the econometric results clearly indicate the importance of fiscal deterioration on the occurrence of the European debt crisis, this study also aims to test the fiscal convergence among the EU member countries. The results indicate that Portugal, Ireland, Italy, Greece, and Spain diverge from other EU-15 countries in terms of public debt-to-GDP ratio. In addition, results also show that all PIIGS countries except for Greece converge to EU-10 in terms of budget deficit-to-GDP ratio.

The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and timedemanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Atributos visuais de textura e forma foram investigados para a recuperação baseada em conteúdo de imagens mamográficas (CBIR). Para a similaridade de imagens, foi considerada a estrutura de densidade mamária, representada principalmente pelos tecidos fibro-glandulares. A pesquisa consistiu de três etapas: (1) Preparação e processamento das imagens; (2) Extração e seleção de atributos visuais de textura e forma; (3) Implementação de um sistema de recuperação de imagem. A primeira etapa consistiu dos processos de retirada de ruído do fundo da imagem, segmentação da região da mama, detecção da região de músculo peitoral, localização do mamilo e da segmentação da região de tecidos fibro-glandulares. Utilizou-se a equação de Difusão Anisotrópica com filtro de Wiener para retirada e suavização de ruídos encontrados na imagem e preservação da borda da mama. Para a segmentação da região da mama, foram utilizadas as técnicas de limiarização de Princípio de Máxima Entropia, Método de Preservação de Momento, Método de Otsu, Método interativo de Ridler & Carvard, Método de Reddi e Método da Matriz de Co-ocorrência. A melhor imagem foi escolhida numa tarefa supervisionada. A detecção automática da região do músculo peitoral foi feita com a combinação do operador de Canny e a transformada de Radon como detector de linha. A posição do mamilo foi detectada com a transformada de Radon como detector de direção de densidade. A segmentação da região de tecidos fibro-glandulares foi feita também com as técnicas de limiarização do Princípio de Máxima Entropia, Método de Preservação de Momento, e Método de Otsu. Momentos Estatísticos extraídos do Histograma, Medida de Granulometria, Momentos Estatísticos extraídos do Domínio de Radon, Momento de Hu, e Textura de Haralick foram investigados como atributos de textura. Medida de Área, Circularidade e Razão de Diâmetro foram investigados como atributos de forma. A rede de Mapas Auto-Organizáveis de Kohonen foi utilizada como sistema de recuperação de imagem. Foram utilizadas, neste trabalho, 1080 imagens do projeto de Banco de Imagens do HCFMRP-USP, módulo Mamografia. O treinamento e teste foram feitos com a técnica de \"leaving-one-out\" e os melhores resultados obtidos foram: Taxa de precisão de 91,07% para a combinação dos cinco grupos de atributos de Forma, Estatísticos Extraídos do Histograma, Momento de Hu, Espectral no Domínio de Radon e de Medida de Granulometria; taxa de precisão e revocação do coeficiente de correlação médio representadas pela área sob a curva com valor de 0,02351 dos grupos de atributos de forma, de Textura de Haralick e Momento de Hu. Os resultados obtidos indicaram a relevância de nosso trabalho e seu potencial de utilização para a recuperação baseada em conteúdo de imagens mamográficas. / Visual texture based on texture and shape features were investigated for content-based mammographic images retrieval (CBIR). For similarity of images, the mammary density structures were considered, mainly represented by fibro-glandular tissues. This research consisted of three stages: (1) Images preparation and processing; (2) Extraction and selection of the visual features; (3) Implementation of a retrieval system. The first stage consisted of noisy removing from the image background, breast region segmentation, pectoral muscle region detection, nipple localization and the fibro-glandular tissues region segmentation. The equation of Anisotropic Diffusion was used with Wiener filter for noisy removing with the breast region edge preservation. For the breast region segmentation, the Thresholding techniques were used of Maximum Entropy Principle, Moment Preserving Method, Otsu Method, Ridler & Carvard Method, Reddi Method and Co-occurrence Matrix Method. The better image was chosen in a supervised task. The automatic pectoral muscle region detection was made with the Canny operator and Radon Transform combination as straight line detector. The nipple position was detected with the Radon Transform as density direction detector. The fibro-glandular tissues region was also defined with the thresholding techniques of the Maximum Entropy Principle, Moment Preserving Method, and Otsu Method. The Statistical Moments extracted from the Histogram, Measured of Granulometry, Statistical Moments extracted in Radon Domain, Moment of Hu, and Haralick Textures were investigated as texture features. Area, Circularity and Diameter Ratio were investigated as shape features. The Self-Organizing Maps of Kohonen was used as image retrieval system. One thousand and eighty images of the HCFMRP-USP Database Project, Mammography Module, were used in this work. The training and test processes were realized with the \"leaving-one-out\" technique and the best results obtained were: The precision rate of 91,07% for the combination of the five following features group: Shape, Statistical Moments extracted of the Histogram, Moment of Hu, Statistical Moments extracted in Radon Domain and Measure of Granulometry; precision and revocation rates of the average coefficient of correlation represented by the area under the curve with value of 0,02351 for the three following features group: Shape, Haralick Textures and Moment de Hu. The results obtained indicated the relevance of our work for the content-based mammographic images retrieval.

Depuis son introduction en 1982, la carte auto-organisatrice de Kohonen (Self-Organizing Map : SOM) a prouvé ses capacités de classification et visualisation des données multidimensionnelles dans différents domaines d’application. Les implémentations matérielles de la carte SOM, en exploitant le taux de parallélisme élevé de l’algorithme de Kohonen, permettent d’augmenter les performances de ce modèle neuronal souvent au détriment de la flexibilité. D’autre part, la flexibilité est offerte par les implémentations logicielles qui quant à elles ne sont pas adaptées pour les applications temps réel à cause de leurs performances temporelles limitées. Dans cette thèse nous avons proposé une architecture matérielle distribuée, adaptable, flexible et extensible de la carte SOM à base de NoC dédiée pour une implantation matérielle sur FPGA. A base de cette approche, nous avons également proposé une architecture matérielle innovante d’une carte SOM à structure croissante au cours de la phase d’apprentissage / Since its introduction in 1982, Kohonen’s Self-Organizing Map (SOM) showed its ability to classify and visualize multidimensional data in various application fields. Hardware implementations of SOM, by exploiting the inherent parallelism of the Kohonen algorithm, allow to increase the overall performances of this neuronal network, often at the expense of the flexibility. On the other hand, the flexibility is offered by software implementations which on their side are not suited for real-time applications due to the limited time performances. In this thesis we proposed a distributed, adaptable, flexible and scalable hardware architecture of SOM based on Network-on-Chip (NoC) designed for FPGA implementation. Moreover, based on this approach we also proposed a novel hardware architecture of a growing SOM able to evolve its own structure during the learning phase

As redes ópticas passivas (PON), em virtude da oferta de maior largura de banda a custos relativamente baixos, vêm se destacando como possível candidata para suprir a demanda dos novos serviços como, tráfego de voz, vídeo, dados e de serviços móveis, exigidos pelos usuários finais. Uma importante candidata, para realizar o controle de acesso nas PONs, é a técnica de acesso múltiplo por divisão de código óptico (OCDMA), por apresentar características relevantes, como maior segurança e capacidade flexível sob demanda. No entanto, agentes físicos externos, como as variações de temperatura ambiental no enlace, exercem uma influência considerável sobre as condições de operação das redes ópticas. Especificamente, nas OCDMA-PONs, os efeitos da variação de temperatura ambiental no enlace de transmissão, afetam o valor do pico do autocorrelação do código OCDMA a ser detectado, degradando a qualidade de serviço (QoS), além do aumento da taxa de erro de bit (BER) do sistema. O presente trabalho apresenta duas novas propostas de técnicas, utilizando sistemas inteligentes, mais precisamente, controladores lógicos fuzzy (FLC) aplicados nos transmissores e nos receptores das OCDMA-PONs, com o objetivo de mitigar os efeitos de variação de temperatura. Os resultados das simulações mostram que o desempenho da rede é melhorado quando as abordagens propostas são empregadas. Por exemplo, para a distância de propagação de 10 km e variações de temperatura de 20°C, o sistema com FLC, suporta 40 usuários simultâneos com a BER = 10-9, enquanto que, sem FLC, acomoda apenas 10. Ainda neste trabalho, é proposta uma nova técnica de classificação de códigos OCDMA, com o uso de redes neurais artificiais, mais precisamente, mapas auto-organizáveis de Kohonen (SOM), importante para que o sistema de gerenciamento da rede possa oferecer uma maior segurança para os usuários. Por fim, sem o uso de técnica inteligente, é apresentada, uma nova proposta de código OCDMA, cujo formalismo desenvolvido, permite generalizar a obtenção de código com propriedades distintas, como diversas ponderações e comprimentos de códigos. / Passive optical networks (PON), due to the provision of higher bandwidth at relatively low cost, have been excelling as a possible candidate to meet the demand of new services, such as voice traffic, video, data and mobile services, as required by end users. An important candidate to perform access control in PONs, is the Optical Code-Division Multiple-Access (OCDMA) technique, due to relevant characteristics, such as improved security and flexible capacity on demand. However, external physical agents, such as variations in environmental temperature on the Fiber Optic Link, have considerable influence on the operating conditions of optical networks. Specifically, in OCDMA-PONs, the effects of environmental temperature variation in the transmission link affect the peak value on the autocorrelation of the OCDMA code to be detected, degrading the quality of service (QoS), in addition to increasing the Bit Error Rate (BER) of the system. This thesis presents two new proposals of techniques using intelligent systems, more precisely, Fuzzy Logic Controllers (FLC) applied on the transmitters and receivers of OCDMA-PONs, in order to mitigate the effects of temperature variation. The simulation results show that the network performance is improved when the proposed approaches are employed. For example, for the propagation distance of 10 kilometers and temperature variations of 20°C, the FLC system supports 40 simultaneous users at BER = 10-9, whereas without the FLC, the system can accommodate only 10. Furthermore, in this work is proposed a new technique of OCDMA codes classification, using Artificial Neural Networks (ANN), more precisely, the Self-Organizing Maps (SOM) of Kohonen, important for the network management system to provide increased security for users. Finally, without the use of intelligent technique, it is presented a new proposal of OCDMA code, whose formalism developed, allows to generalize the code acquisition with distinct properties, such as different weights and length codes.

Atributos visuais de textura e forma foram investigados para a recuperação baseada em conteúdo de imagens mamográficas (CBIR). Para a similaridade de imagens, foi considerada a estrutura de densidade mamária, representada principalmente pelos tecidos fibro-glandulares. A pesquisa consistiu de três etapas: (1) Preparação e processamento das imagens; (2) Extração e seleção de atributos visuais de textura e forma; (3) Implementação de um sistema de recuperação de imagem. A primeira etapa consistiu dos processos de retirada de ruído do fundo da imagem, segmentação da região da mama, detecção da região de músculo peitoral, localização do mamilo e da segmentação da região de tecidos fibro-glandulares. Utilizou-se a equação de Difusão Anisotrópica com filtro de Wiener para retirada e suavização de ruídos encontrados na imagem e preservação da borda da mama. Para a segmentação da região da mama, foram utilizadas as técnicas de limiarização de Princípio de Máxima Entropia, Método de Preservação de Momento, Método de Otsu, Método interativo de Ridler & Carvard, Método de Reddi e Método da Matriz de Co-ocorrência. A melhor imagem foi escolhida numa tarefa supervisionada. A detecção automática da região do músculo peitoral foi feita com a combinação do operador de Canny e a transformada de Radon como detector de linha. A posição do mamilo foi detectada com a transformada de Radon como detector de direção de densidade. A segmentação da região de tecidos fibro-glandulares foi feita também com as técnicas de limiarização do Princípio de Máxima Entropia, Método de Preservação de Momento, e Método de Otsu. Momentos Estatísticos extraídos do Histograma, Medida de Granulometria, Momentos Estatísticos extraídos do Domínio de Radon, Momento de Hu, e Textura de Haralick foram investigados como atributos de textura. Medida de Área, Circularidade e Razão de Diâmetro foram investigados como atributos de forma. A rede de Mapas Auto-Organizáveis de Kohonen foi utilizada como sistema de recuperação de imagem. Foram utilizadas, neste trabalho, 1080 imagens do projeto de Banco de Imagens do HCFMRP-USP, módulo Mamografia. O treinamento e teste foram feitos com a técnica de \"leaving-one-out\" e os melhores resultados obtidos foram: Taxa de precisão de 91,07% para a combinação dos cinco grupos de atributos de Forma, Estatísticos Extraídos do Histograma, Momento de Hu, Espectral no Domínio de Radon e de Medida de Granulometria; taxa de precisão e revocação do coeficiente de correlação médio representadas pela área sob a curva com valor de 0,02351 dos grupos de atributos de forma, de Textura de Haralick e Momento de Hu. Os resultados obtidos indicaram a relevância de nosso trabalho e seu potencial de utilização para a recuperação baseada em conteúdo de imagens mamográficas. / Visual texture based on texture and shape features were investigated for content-based mammographic images retrieval (CBIR). For similarity of images, the mammary density structures were considered, mainly represented by fibro-glandular tissues. This research consisted of three stages: (1) Images preparation and processing; (2) Extraction and selection of the visual features; (3) Implementation of a retrieval system. The first stage consisted of noisy removing from the image background, breast region segmentation, pectoral muscle region detection, nipple localization and the fibro-glandular tissues region segmentation. The equation of Anisotropic Diffusion was used with Wiener filter for noisy removing with the breast region edge preservation. For the breast region segmentation, the Thresholding techniques were used of Maximum Entropy Principle, Moment Preserving Method, Otsu Method, Ridler & Carvard Method, Reddi Method and Co-occurrence Matrix Method. The better image was chosen in a supervised task. The automatic pectoral muscle region detection was made with the Canny operator and Radon Transform combination as straight line detector. The nipple position was detected with the Radon Transform as density direction detector. The fibro-glandular tissues region was also defined with the thresholding techniques of the Maximum Entropy Principle, Moment Preserving Method, and Otsu Method. The Statistical Moments extracted from the Histogram, Measured of Granulometry, Statistical Moments extracted in Radon Domain, Moment of Hu, and Haralick Textures were investigated as texture features. Area, Circularity and Diameter Ratio were investigated as shape features. The Self-Organizing Maps of Kohonen was used as image retrieval system. One thousand and eighty images of the HCFMRP-USP Database Project, Mammography Module, were used in this work. The training and test processes were realized with the \"leaving-one-out\" technique and the best results obtained were: The precision rate of 91,07% for the combination of the five following features group: Shape, Statistical Moments extracted of the Histogram, Moment of Hu, Statistical Moments extracted in Radon Domain and Measure of Granulometry; precision and revocation rates of the average coefficient of correlation represented by the area under the curve with value of 0,02351 for the three following features group: Shape, Haralick Textures and Moment de Hu. The results obtained indicated the relevance of our work for the content-based mammographic images retrieval.

This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theoretical aspects of the semantic path merger packet classification algorithm are investigated, and the algorithm's hardware-based implementation is evaluated along with comparative analysis versus content addressable memory. Experimental results show that the hardware implementation of the semantic path merger algorithm significantly outperforms content addressable memory in terms of energy consumption and operational timing.

Made available in DSpace on 2014-12-17T14:08:50Z (GMT). No. of bitstreams: 1 PauloRMP_capa_ate_pag32.pdf: 5057325 bytes, checksum: bf8da0b02ad06ee116c93344fb67e976 (MD5) Previous issue date: 2012-02-06 / Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization / Originalmente voltadas ao monitoramento da opera??o, as medi??es cont?nuas de press?o e temperatura no fundo de po?o, realizadas atrav?s de PDGs (do ingl?s, Permanent Downhole Gauges), encontram vasta aplicabilidade no gerenciamento de reservat?rios. Para tanto, permitem o monitoramento do desempenho de po?os e a estimativa de par?metros de reservat?rios no longo prazo. Contudo, a despeito de sua inquestion?vel utilidade, os dados adquiridos de PDG apresentam grande conte?do de ru?do. Outro aspecto igualmente desfavor?vel reside na ocorr?ncia de valores esp?rios (outliers) imersos entre as medidas registradas pelo PDG. O presente trabalho aborda o tratamento inicial de sinais de press?o e temperatura, mediante t?cnicas de suaviza??o, mapas auto-organiz?veis e transformada wavelet discreta. Ademais, prop?e-se um sistema de detec??o de transientes relevantes para an?lise no longo hist?rico de registros, baseado no acoplamento entre clusteriza??o fuzzy e redes neurais feed-forward. Os resultados alcan?ados mostraram-se de todo satisfat?rios para po?os marinhos, atendendo a requisitos reais de utiliza??o dos sinais registrados por PDGs

As redes ópticas passivas (PON), em virtude da oferta de maior largura de banda a custos relativamente baixos, vêm se destacando como possível candidata para suprir a demanda dos novos serviços como, tráfego de voz, vídeo, dados e de serviços móveis, exigidos pelos usuários finais. Uma importante candidata, para realizar o controle de acesso nas PONs, é a técnica de acesso múltiplo por divisão de código óptico (OCDMA), por apresentar características relevantes, como maior segurança e capacidade flexível sob demanda. No entanto, agentes físicos externos, como as variações de temperatura ambiental no enlace, exercem uma influência considerável sobre as condições de operação das redes ópticas. Especificamente, nas OCDMA-PONs, os efeitos da variação de temperatura ambiental no enlace de transmissão, afetam o valor do pico do autocorrelação do código OCDMA a ser detectado, degradando a qualidade de serviço (QoS), além do aumento da taxa de erro de bit (BER) do sistema. O presente trabalho apresenta duas novas propostas de técnicas, utilizando sistemas inteligentes, mais precisamente, controladores lógicos fuzzy (FLC) aplicados nos transmissores e nos receptores das OCDMA-PONs, com o objetivo de mitigar os efeitos de variação de temperatura. Os resultados das simulações mostram que o desempenho da rede é melhorado quando as abordagens propostas são empregadas. Por exemplo, para a distância de propagação de 10 km e variações de temperatura de 20°C, o sistema com FLC, suporta 40 usuários simultâneos com a BER = 10-9, enquanto que, sem FLC, acomoda apenas 10. Ainda neste trabalho, é proposta uma nova técnica de classificação de códigos OCDMA, com o uso de redes neurais artificiais, mais precisamente, mapas auto-organizáveis de Kohonen (SOM), importante para que o sistema de gerenciamento da rede possa oferecer uma maior segurança para os usuários. Por fim, sem o uso de técnica inteligente, é apresentada, uma nova proposta de código OCDMA, cujo formalismo desenvolvido, permite generalizar a obtenção de código com propriedades distintas, como diversas ponderações e comprimentos de códigos. / Passive optical networks (PON), due to the provision of higher bandwidth at relatively low cost, have been excelling as a possible candidate to meet the demand of new services, such as voice traffic, video, data and mobile services, as required by end users. An important candidate to perform access control in PONs, is the Optical Code-Division Multiple-Access (OCDMA) technique, due to relevant characteristics, such as improved security and flexible capacity on demand. However, external physical agents, such as variations in environmental temperature on the Fiber Optic Link, have considerable influence on the operating conditions of optical networks. Specifically, in OCDMA-PONs, the effects of environmental temperature variation in the transmission link affect the peak value on the autocorrelation of the OCDMA code to be detected, degrading the quality of service (QoS), in addition to increasing the Bit Error Rate (BER) of the system. This thesis presents two new proposals of techniques using intelligent systems, more precisely, Fuzzy Logic Controllers (FLC) applied on the transmitters and receivers of OCDMA-PONs, in order to mitigate the effects of temperature variation. The simulation results show that the network performance is improved when the proposed approaches are employed. For example, for the propagation distance of 10 kilometers and temperature variations of 20°C, the FLC system supports 40 simultaneous users at BER = 10-9, whereas without the FLC, the system can accommodate only 10. Furthermore, in this work is proposed a new technique of OCDMA codes classification, using Artificial Neural Networks (ANN), more precisely, the Self-Organizing Maps (SOM) of Kohonen, important for the network management system to provide increased security for users. Finally, without the use of intelligent technique, it is presented a new proposal of OCDMA code, whose formalism developed, allows to generalize the code acquisition with distinct properties, such as different weights and length codes.