Impacto da Estratégia Saúde da Família com equipe de saúde bucal sobre a utilização de serviços odontológicos

PEREIRA, C. R. S. et al. Impacto da estratégia saúde da família com equipe de saúde bucal sobre a utilização de serviços odontológicos. Cad. Saúde Pública, v. 25, n. 5, p.985-996. Maio, 2009. ISSN 0102-311X.

Impacto da Estratégia Saúde da Família com equipe de saúde bucal sobre a utilização de serviços odontológicos

PEREIRA, Carmen Regina dos Santos et al. Impacto da Estratégia Saúde da Família com equipe de saúde bucal sobre a utilização de serviços odontológicos. Cadernos de Saúde Pública, Rio de Janeiro, v. 25, n. 5, p. 985-996, maio 2009.

Realidade aumentada aplicada em bibliotecas: multiplicidade no acesso a informação

Conceitua Realidade Aumentada do ponto de vista histórico de vários autores e como surgiu esse segmento através da evolução tecnológica até os dias atuais. Aborda seu funcionamento, como também, seus sistemas e aplicações em diversos campos de pesquisas e estudos científicos. Diferencia Realidade Aumentada de Realidade Virtual, visando melhor esclarecimento entre ambas na intenção de descaracterizá-las com uma única “realidade”. Apresenta a Realidade Aumentada e sua aplicação dentro de um contexto de uma unidade de informação, promovendo uma melhor interação com os usuários e as adaptações pelas quais as bibliotecas terão que passar futuramente para se adequarem a “explosão” tecnológica. Descreve o funcionamento da biblioteca ARToolKIT, baseada em RA e suas principais etapas de funcionamento para visualização de objetos virtuais em 3D. Exemplifica os benefícios que uma unidade de informação, que utiliza Realidade Aumentada, promove aos usuários portadores de deficiência, além de sua inclusão no meio digital e sua inserção no mercado de trabalho

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

Caracterização do nicho alimentar do polvo octopus insularis (cephalopoda: octopodidae): da população ao indivíduo

Octopus insularis, target species in this study, is the dominant benthic octopus of the North and Northeast Brazil. Studies on behavior and ecology of the species have been conducted primarily on oceanic islands, with little information on the continental populations. In this study, two regions of the coast of RN, Rio do Fogo and Pirangi, were chosen for the characterization of the niche by O. insularis populations. The dietary niche, habitat and distribution of O. insularis of oceanic islands and the mainland, were compared. In addition, individual characteristics of feeding behavior in a population at Atol das Rocas was studied, taking into account the size of individuals, the proximity of the dens and characteristics of their "personality". The diet of the Rio do Fogo population was composed mainly of bivalve molluscs (82%), unlike Pirangi population that has a diet consisting mainly of crustaceans Decapoda (68%), similar to that described for the populations of the islands. Consequently, the feeding niches of the island populations were more similar, with greater overlap, but the niche breadth of the continent was larger. The habitats of occurrence on the coast includes reefs, rocks, gravel and an environment called Restinga, a plateau composed of biogenic gravel, sand, sponges and algae, showed a high density of animals. Similarly to that found in the islands, O. insularis in the continent, had a clumped distribution, and a bathymetric segregation between small and large individuals. The differences in diet composition among populations were explained by differences in habitat and coverage of the substrate, which may be directly influencing the diversity and prey availability in these environments. The individual analyzes of the population at Atol das Rocas, showed no relationship between the degree of individual specialization and the different personalities, or the distance between dens. The results suggest that the foraging strategy with greater availability of prey in the environment has an influence on diet octopuses over preferences or personalities

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Controle adaptativo robusto para um modelo desacoplado de um robô móvel

This thesis presents a new structure of robust adaptive controller applied to mobile robots (surface mobile robot) with nonholonomic constraints. It acts in the dynamics and kinematics of the robot, and it is split in two distinct parts. The first part controls the robot dynamics, using variable structure model reference adaptive controllers. The second part controls the robot kinematics, using a position controller, whose objective is to make the robot to reach any point in the cartesian plan. The kinematic controller is based only on information about the robot configuration. A decoupling method is adopted to transform the linear model of the mobile robot, a multiple-input multiple-output system, into two decoupled single-input single-output systems, thus reducing the complexity of designing the controller for the mobile robot. After that, a variable structure model reference adaptive controller is applied to each one of the resulting systems. One of such controllers will be responsible for the robot position and the other for the leading angle, using reference signals generated by the position controller. To validate the proposed structure, some simulated and experimental results using differential drive mobile robots of a robot soccer kit are presented. The simulator uses the main characteristics of real physical system as noise and non-linearities such as deadzone and saturation. The experimental results were obtained through an C++ program applied to the robot soccer kit of Microrobot team at the LACI/UFRN. The simulated and experimental results are presented and discussed at the end of the text

Uma contribuição ao estudo das categorias internas e de sua proliferação em redes ARTMAP

ART networks present some advantages: online learning; convergence in a few epochs of training; incremental learning, etc. Even though, some problems exist, such as: categories proliferation, sensitivity to the presentation order of training patterns, the choice of a good vigilance parameter, etc. Among the problems, the most important is the category proliferation that is probably the most critical. This problem makes the network create too many categories, consuming resources to store unnecessarily a large number of categories, impacting negatively or even making the processing time unfeasible, without contributing to the quality of the representation problem, i. e., in many cases, the excessive amount of categories generated by ART networks makes the quality of generation inferior to the one it could reach. Another factor that leads to the category proliferation of ART networks is the difficulty of approximating regions that have non-rectangular geometry, causing a generalization inferior to the one obtained by other methods of classification. From the observation of these problems, three methodologies were proposed, being two of them focused on using a most flexible geometry than the one used by traditional ART networks, which minimize the problem of categories proliferation. The third methodology minimizes the problem of the presentation order of training patterns. To validate these new approaches, many tests were performed, where these results demonstrate that these new methodologies can improve the quality of generalization for ART networks

Análise de Agrupamentos Com Base na Teoria da Informação: Uma Abordagem Representativa

Currently, one of the biggest challenges for the field of data mining is to perform cluster analysis on complex data. Several techniques have been proposed but, in general, they can only achieve good results within specific areas providing no consensus of what would be the best way to group this kind of data. In general, these techniques fail due to non-realistic assumptions about the true probability distribution of the data. Based on this, this thesis proposes a new measure based on Cross Information Potential that uses representative points of the dataset and statistics extracted directly from data to measure the interaction between groups. The proposed approach allows us to use all advantages of this information-theoretic descriptor and solves the limitations imposed on it by its own nature. From this, two cost functions and three algorithms have been proposed to perform cluster analysis. As the use of Information Theory captures the relationship between different patterns, regardless of assumptions about the nature of this relationship, the proposed approach was able to achieve a better performance than the main algorithms in literature. These results apply to the context of synthetic data designed to test the algorithms in specific situations and to real data extracted from problems of different fields

Calibração cega de receptores cinco-portas baseada em separação cega de fontes

The exponential growth in the applications of radio frequency (RF) is accompanied by great challenges as more efficient use of spectrum as in the design of new architectures for multi-standard receivers or software defined radio (SDR) . The key challenge in designing architecture of the software defined radio is the implementation of a wide-band receiver, reconfigurable, low cost, low power consumption, higher level of integration and flexibility. As a new solution of SDR design, a direct demodulator architecture, based on fiveport technology, or multi-port demodulator, has been proposed. However, the use of the five-port as a direct-conversion receiver requires an I/Q calibration (or regeneration) procedure in order to generate the in-phase (I) and quadrature (Q) components of the transmitted baseband signal. In this work, we propose to evaluate the performance of a blind calibration technique without additional knowledge about training or pilot sequences of the transmitted signal based on independent component analysis for the regeneration of I/Q five-port downconversion, by exploiting the information on the statistical properties of the three output signals

A Influência da observabilidade e da visualização radial no projeto de sistemas de monitoramento de redes de computadores

Este trabalho apresenta um levantamento dos problemas associados à influência da observabilidade e da visualização radial no projeto de sistemas de monitoramento para redes de grande magnitude e complexidade. Além disso, se propõe a apresentar soluções para parte desses problemas. Através da utilização da Teoria de Redes Complexas, são abordadas duas questões: (i) a localização e a quantidade de nós necessários para garantir uma aquisição de dados capaz de representar o estado da rede de forma efetiva e (ii) a elaboração de um modelo de visualização das informações da rede capaz de ampliar a capacidade de inferência e de entendimento de suas propriedades. A tese estabelece limites teóricos a estas questões e apresenta um estudo sobre a complexidade do monitoramento eficaz, eficiente e escalável de redes

Sistema híbrido para detecção e diagnóstico de falhas em sistemas dinâmicos

The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

Técnica de controle adaptativo robusto aplicada a filtros ativos de potência e paralelo

The Methods for compensation of harmonic currents and voltages have been widely used since these methods allow to reduce to acceptable levels the harmonic distortion in the voltages or currents in a power system, and also compensate reactive. The reduction of harmonics and reactive contributes to the reduction of losses in transmission lines and electrical machinery, increasing the power factor, reduce the occurrence of overvoltage and overcurrent. The active power filter is the most efficient method for compensation of harmonic currents and voltages. The active power filter is necessary to use current and voltage controllers loop. Conventionally, the current and voltage control loop of active filter has been done by proportional controllers integrative. This work, investigated the use of a robust adaptive control technique on the shunt active power filter current and voltage control loop to increase robustness and improve the performance of active filter to compensate for harmonics. The proposed control scheme is based on a combination of techniques for adaptive control pole placement and variable structure. The advantages of the proposed method over conventional ones are: lower total harmonic distortion, more flexibility, adaptability and robustness to the system. Moreover, the proposed control scheme improves the performance and improves the transient of active filter. The validation of the proposed technique was verified initially by a simulation program implemented in C++ language and then experimental results were obtained using a prototype three-phase active filter of 1 kVA