Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Supervisório Inteligente de Processos na Idústria do Petróleo e Gás :Aplicação em uma Coluna de Destilação Simulada Integrada a Instrumentação Real

The need to implement a software architecture that promotes the development of a SCADA supervisory system for monitoring industrial processes simulated with the flexibility of adding intelligent modules and devices such as CLP, according to the specifications of the problem, it was the motivation for this work. In the present study, we developed an intelligent supervisory system on a simulation of a distillation column modeled with Unisim. Furthermore, OLE Automation was used as communication between the supervisory and simulation software, which, with the use of the database, promoted an architecture both scalable and easy to maintain. Moreover, intelligent modules have been developed for preprocessing, data characteristics extraction, and variables inference. These modules were fundamentally based on the Encog software

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

Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional

This thesis describes design methodologies for frequency selective surfaces (FSSs) composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4, RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to the simple design of efficient band-stop spatial filters with applications in the range of microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or T fractal). The transmission properties of these proposed periodic arrays are investigated through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial softwares that run full-wave methods. To validate the employed methodology, FSS prototypes are selected for fabrication and measurement. The obtained results point to interesting features for FSS spatial filters: compactness, with high values of frequency compression factor; as well as stable frequency responses at oblique incidence of plane waves. This thesis also approaches, as it main focus, the application of an alternative electromagnetic (EM) optimization technique for analysis and synthesis of FSSs with fractal motifs. In application examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal design of FSS structures. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated to the full-wave parametric analyzes. To this end, fast and accurate multilayer perceptron (MLP) neural network models are developed using different parameters as design input variables. These neural network models aim to calculate the cost function in the iterations of population-based search algorithms. Continuous genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are used for FSSs optimization with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of computational cost and numerical convergence. Consistent results can be verified by the excellent agreement obtained between simulations and measurements related to FSS prototypes built with a given fractal iteration

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Especificação de um bloco funcional implementando rede neural para redes industriais foundations fieldbus

This work proposes the specification of a new function block according to Foundation Fieldbus standards. The new block implements an artificial neural network, which may be useful in process control applications. The specification includes the definition of a main algorithm, that implements a neural network, as well as the description of some accessory functions, which provide safety characteristics to the block operation. Besides, it also describes the block attributes emphasizing its parameters, which constitute the block interfaces. Some experimental results, obtained from an artificial neural network implementation using actual standard functional blocks on a laboratorial FF network, are also shown, in order to demonstrate the possibility and also the convenience of integrating a neural network to Fieldbus devices

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Análise de um controlador baseado no Jacobiano estimado da planta através de uma rede neural

This work presents an analysis of the control law based on an indirect hybrid scheme using neural network, initially proposed for O. Adetona, S. Sathanathan and L. H. Keel. Implementations of this control law, for a level plant of second order, was resulted an oscillatory behavior, even if the neural identifier has converged. Such results had motivated the investigation of the applicability of that law. Starting from that, had been made stability mathematical analysis and several implementations, with simulated plants and with real plants, for analyze the problem. The analysis has been showed the law was designed being despised some components of dynamic of the plant to be controlled. Thus, for plants that these components have a significant influence in its dynamic, the law tends to fail

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Controle Robusto Aplicado a Conversor Buck-Boost em Sistemas Fotovoltaicos

The need to implement a software architecture that promotes the development of a SCADA supervisory system for monitoring industrial processes simulated with the flexibility of adding intelligent modules and devices such as CLP, according to the specifications of the problem, it was the motivation for this work. In the present study, we developed an intelligent supervisory system on a simulation of a distillation column modeled with Unisim. Furthermore, OLE Automation was used as communication between the supervisory and simulation software, which, with the use of the database, promoted an architecture both scalable and easy to maintain. Moreover, intelligent modules have been developed for preprocessing, data characteristics extraction, and variables inference. These modules were fundamentally based on the Encog software

Memórias da prisão: literatura e liberdade m estudo sobre Graciliano Ramos e Antonio Gramsci

La tese avvicina le Memorie dal carcere, di Graciliano Ramos e i Quaderni dal carcere di Antonio Gramsci, in una prospettiva in cui essi erano uniti per lo stesso ideale politico della prima metá del XX secolo e rappresentavano la resistenza intellettuale di fronte alla repressione. Entrambi furono vittime dell autoritarismo dei poteri fascisti e registrarono il periodo di prigione in differenti forme memorialistiche. Questi scrittori sono uniti anche per il contesto nazionale molto simile per la differenza economica tra le regioni. Condividono, inoltre, anche il concetto di arte e la certezza che l alienzazione dell intelligenza può essere superata solamente attraverso la ricostruzione delle basi nazionali per mezzo della conoscenza, dell educazione e della cultura. Si vuole mostrare come i concetti di Gramsci sono presenti non solo nella convinzione politica di Graciliano, ma in tutto il suo stile letterario memorialístico. Il lavoro, ancora, paragona le Memorie del carcere con due testi memorialistici di prigione in Italia, che sono Le mie prigioni, di Silvio Pellico, e Se questo è un uomo, di Primo Levi, per dimostrare che la proposta della letteratura gramsciana è molto più vicina allo scrittore brasiliano che ai due suoi conazionali.

A memória de curto prazo e a síndrome de down: a relação entre contextos de desenvolvimento

Down syndrome (DS) is one of the most frequent causes of intellectual disability, affecting one in every 600 to 1000 live births. Studies have demonstrated that people with DS have a lower capacity for short-term memory (STM) and working memory (WM), which affects their capability to learn new words and to follow spoken instructions, specially when they involve multiple information or consecutive orders/orientations. It seems that the basis of the learning process, as it happens with language and mathematics comprehension and reasoning, relies in the STM and WM systems. Individuals with DS are increasingly included in mainstream education, and yet, very few researches have been conducted to investigate the influence of memory development and the type of enrollment (regular school and special school). This study investigated the relationship between the type of school enrollment with the performance on STM tests and also, the relationship of this performance with early stimulation (ES). The tests used in the first research were the digit span, free recall, word recognition and subtests of the Wechsler Intelligence Scale for Children Third Edition (WISC-III). Individuals enrolled in the regular schools group had higher scores on the digit span test and the subtests of the WISC-III. In the free recall and recognition tests, no differences were found. This study indicates that the type of enrollment might influence the memory development of individuals with DS and clearly points the need for future investigations. In the second research, the tests used were the digit span, free word recall and subtests of the WISC-III. The test results showed better performance by adults that received ES before six months of age. The studies showed improvement in STM both in people who attended or were attending regular school, as well as those who benefited from ES before six months of age. However, some issues still need to be better understood. What is the relation between this stimulation with the individual s education? Since ES may reflect a greater family involvement with the individual, what is the role of emotional components derived from this involvement in the cognitive improvement? These and other questions are part of the continuity of this study

A relação de amizade nas redes sociais de estudantes universitários de Natal/RN

The basis of sociability in humans is based on cooperation. The relationship of friendship is vital to the social, emotional and cognitive development of an individual and can be understood as a consequence of selection for reciprocal altruism in humans. The period of adulthood is considered very suitable and appropriate for the investigation of the relations of friendship, but the Brazilian literature on friendship in adults is still nascent. Therefore, the objective was to characterize the relationship of friendship among college students. The study gathered 500 students from higher education institutions in the city of Natal-RN, Brazil, and 250 women (average age 24.1 ± 7.66 years) and 250 men (mean age 26.77 ± 9.64 years). Two questionnaires anonymously and individual were applied: a sociodemographic questionnaire and the other with the desired characteristics in idealized friends. Study 1 assessed the degree of importance of characteristics in the process of choosing a friend of the same sex and opposite sex of the participant. Study 2 investigated the relationship between patterns of idealization of friends and self-assessment of participants. Overall, were the preferred characteristics "Companionship" and "Sincerity" to idealized friends. We also found the influence of sex on the characteristics attributed to an female ideal friend, with emphasis on men for "Beauty/Good looks" and "Intelligence" and women to "Companionship" and "Sincerity". Finally, we observed a positive correlation between participants' self-assessment and preferences for the characteristics of the friends devised. This study revealed important elements for understanding the relationship of friendship, specifically the process of choosing friends. The results reinforce the importance of studying the relationship of friendship to a better understanding of human social behavior.

Validade e confiabilidade da versão informatizada do inventário Million de estilos de personalidade

Currently, several psychological and non-psychological tests can be found in publishes without standardization on procedures set in different psychological areas, like intelligence, emotional states, attitudes, social skills, vocation, preferences and others. The computerized psychological testing is a extension of traditional testing psychological practices. However, it has own psychometrics qualities, either by its matching in a computerized environment or by the extension that can be developed in it. The current research, developed from a necessity to study process of validity and reliability on a computerized test, drew a methodological structure to provide parallel applications in numerous kinds of operational groups, evaluating the influences of the time and approach in the computerization process. This validity refers to normative values groups, reproducibility in computerized applications process and data processing. Not every psychological test can be computerized. Therefore, our need to find a good test, with quality and plausible properties to transform in computerized application, leaded us to use The Millon Personality Inventory, created by Theodore Millon. This Inventory assesses personality according to 12 bipolarities distributed in 24 factors, distributed in categories motivational styles, cognitive targets and interpersonal relations. This instrument doesn t diagnose pathological features, but test normal and non adaptive aspects in human personality, comparing with Theodore Millon theory of personality. In oder to support this research in a Brazilian context in psychological testing, we discuss the theme, evaluating the advantages and disadvantages of such practices. Also we discuss the current forms in computerization of psychological testing and the main specific criteria in this psychometric specialized area of knowledge. The test was on-line, hosted in the site http://www.planetapsi.com, during the years of 2007 and 2008, which was available a questionnaire to describe social characteristics before test. A report was generated from the data entry of each user. An application of this test was conducted in a linear way through a national coverage in all Brazil regions, getting 1508 applications. Were organized nine groups, reaching 180 applications in test and retest subject, where three periods of time and three forms of retests for studies of on-line tests were separated. Parallel to this, we organized multi-application session offline group, 20 subjects who received tests by email. The subjects of this study were generally distributed by the five Brazilian regions, and were noticed about the test via the Internet. The performance application in traditional and on-line tested groups subsidies us to conclude that on-line application provides significantly consistency in all criteria for validity studied and justifies its use. The on-line test results were related not only among themselves but were similar to those data of tests done on pencil and paper (0,82). The retests results demonstrated correlation, between 0,92 and, 1 while multisessions had a good correlation in these comparisons. Moreover, were assessed the adequacy of operational criteria used, such as security, the performance of users, the environmental characteristics, the organization of the database, operational costs and limitations in this on-line inventory. In all these five items, there were excellent performances, concluding, also, that it s possible a self-applied psychometric test. The results of this work are a guide to question and establish of methodologies studies for computerization psychological testing software in the country