920 resultados para Partial data fusion
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O artigo apresenta dados parciais de uma pesquisa sobre a construção do conhecimento social a partir da perspectiva piagetiana, mais especificamente as ideias das crianças a respeito da escola e do professor. Os participantes do estudo foram 52 crianças entre 7 e 8 anos inseridas em ambientes educacionais diferenciados: um considerado como ambiente tradicional de ensino e o outro considerado como ambiente sociomoral construtivista. O instrumento apresentado aqui, utilizado para coleta de dados, é uma história envolvendo uma situação de não aprendizagem. Os participantes eram convidados a pensar sobre as questões inerentes à história, bem como o papel da escola e do professor na situação proposta. Os dados indicaram não haver diferença entre os dois ambientes no que se refere à construção desse conhecimento social. No entanto, houve diferença muito significativa na maneira utilizada pelos alunos para resolverem os problemas da história: no ambiente tradicional a coerção e a expiação foram mais mencionadas e no ambiente sociomoral construtivista, o diálogo e a cooperação. Os dados apontam ainda para a necessidade do trabalho com esse tipo de conhecimento em sala de aula, visto que as respostas dos sujeitos caracterizaram-se por uma compreensão parcial da realidade, centrada em aspectos mais visíveis e aparentes dos fatos e na não consideração de processos ocultos.
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Oral administration with solid dosage forms is a common route in the drug therapy widely used. The drug release by the disintegration process occurs in several gastrointestinal tract (GIT) regions. AC Biosusceptometry (ACB) was originally proposal to characterize the disintegration process of tablets in vitro and in the human stomach, through changes in magnetic signals. The aim of this work was to employ a multisensor ACB system to monitoring magnetic tablets and capsules in the human GIT and to obtain the magnetic images of the disintegration process. The ACB showed accuracy to quantify the gastric residence time, the intestinal transit time and the magnetic images allowed to visualize the disintegration of magnetic formulations in the GIT. The ACB is a non-invasive, radiation free technique, completely safe and harmless to the volunteers and had demonstrated potential to evaluate pharmaceutical dosage forms in the human gastrointestinal tract. © 2005 IEEE.
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This paper proposes a rank aggregation framework for video multimodal geocoding. Textual and visual descriptions associated with videos are used to define ranked lists. These ranked lists are later combined, and the resulting ranked list is used to define appropriate locations for videos. An architecture that implements the proposed framework is designed. In this architecture, there are specific modules for each modality (e.g, textual and visual) that can be developed and evolved independently. Another component is a data fusion module responsible for combining seamlessly the ranked lists defined for each modality. We have validated the proposed framework in the context of the MediaEval 2012 Placing Task, whose objective is to automatically assign geographical coordinates to videos. Obtained results show how our multimodal approach improves the geocoding results when compared to methods that rely on a single modality (either textual or visual descriptors). We also show that the proposed multimodal approach yields comparable results to the best submissions to the Placing Task in 2012 using no extra information besides the available development/training data. Another contribution of this work is related to the proposal of a new effectiveness evaluation measure. The proposed measure is based on distance scores that summarize how effective a designed/tested approach is, considering its overall result for a test dataset. © 2013 Springer Science+Business Media New York.
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A fusão de imagens multisensor tem sido um procedimento amplamente utilizado em função da natureza complementar dos vários conjuntos de dados. Este artigo compara o desempenho de quatro métodos diferentes para fusão de imagens Landsat-7 ETM+ e RADARSAT-1 W1. A comparação foi baseada nas características espectrais das imagens, utilizando-se de análise estatística e visual dos produtos gerados. Quatro métodos foram usados para a fusão das imagens Landsat-7 ETM+ e RADARSAT-1 W1: i) fusão do SAR (radar de abertura sintética) com a tríade selecionada pelo OIF (Optimum Index Factor); ii) realce por decorrelação da tríade selecionada pelo OIF, seguida da fusão com SAR; iii) ACP (Análise por Componentes Principais) para as seis bandas ETM+ do espectro refletido (1, 2, 3, 4, 5 e 7) e posterior fusão das três primeiras componentes principais (1CP; 2CP; 3CP) com o SAR; iv) SPC-SAR (Principal Componente Seletivo - SAR). O produto SPC-SAR mostrou melhor desempenho na identificação das feições costeiras e permitiu o realce mais efetivo dos diferentes ambientes.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Este trabalho procura examinar o que o leitor brasileiro contemporâneo lê, com o propósito de explicar as razões que levam esse leitor a realizar suas escolhas. Nesse sentido, portanto, o objetivo central do trabalho será examinar o perfil desse leitor brasileiro. O levantamento dos dados para estabelecer o corpus da pesquisa foi realizado por meio do registro das listas de livros mais vendidos, publicadas em dois jornais brasileiros. O primeiro jornal fonte da pesquisa foi o Leia, periódico mensal que circulou no território nacional durante o período de abril de 1978 a setembro de 1991. O segundo, foi o Jornal do Brasil, diário carioca que publicou listas dos livros mais vendidos no Brasil a partir de 1966 até o mês de dezembro de 2004, data de encerramento da pesquisa, em caderno destinado à leitura. Como o segundo jornal interrompeu a publicação das listas dos mais vendidos durante o período de fevereiro de 1976 a abril de 1984, propusemos uma fusão dos dados dos dois jornais de forma a cobrir um período que compreende os anos de 1966 até 2004. A base teórica a partir da qual se estabeleceu o exame do perfil do leitor brasileiro foi a semiótica da escola de Paris. Para o tratamento da questão da leitura elegeu-se o exame das manifestações da enunciação no discurso, as projeções do enunciador e do enunciatário e o tratamento das paixões. Foram observados em cada um dos textos do corpus como essas categorias enunciativas projetam-se em cada um dos textos mais lidos pelos leitores brasileiros e, posteriormente, como, nas listas dos livros mais vendidos, esse leitor manifesta-se como enunciador. Para tanto propôs-se a contraposição entre o ethos do enunciador-leitor das listas e o pathos do enunciatário dos discursos de leitura. Uma vez que o corpus da pesquisa revelou um crescimento na opção pelos textos de auto-ajuda, foi examinada a questão específica... (Resumo completo, clicar acesso eletrônico abaixo)
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This papaer's main objective is to discuss partial data collected for an ongoing reserch wich aims at investigating the pedagogical pratice (re) construction process in Teaching Pratice as part of a pre-service teacher education course.
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Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.
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In this work we study localized electric potentials that have an arbitrarily high energy on some given subset of a domain and low energy on another. We show that such potentials exist for general L-infinity-conductivities (with positive infima) in almost arbitrarily shaped subregions of a domain, as long as these regions are connected to the boundary and a unique continuation principle is satisfied. From this we deduce a simple, but new, theoretical identifiability result for the famous Calderon problem with partial data. We also show how to construct such potentials numerically and use a connection with the factorization method to derive a new non-iterative algorithm for the detection of inclusions in electrical impedance tomography.
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We present a user supported tracking framework that combines automatic tracking with extended user input to create error free tracking results that are suitable for interactive video production. The goal of our approach is to keep the necessary user input as small as possible. In our framework, the user can select between different tracking algorithms - existing ones and new ones that are described in this paper. Furthermore, the user can automatically fuse the results of different tracking algorithms with our robust fusion approach. The tracked object can be marked in more than one frame, which can significantly improve the tracking result. After tracking, the user can validate the results in an easy way, thanks to the support of a powerful interpolation technique. The tracking results are iteratively improved until the complete track has been found. After the iterative editing process the tracking result of each object is stored in an interactive video file that can be loaded by our player for interactive videos.
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Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Growth codes show improved intermediate performance which is particularly useful in applications where partial data presents some utility. In this paper, we investigate the asymptotic performance of Growth codes using the Wormald method, which was proposed for studying the Peeling Decoder of LDPC and LDGM codes. Compared to previous works, the Wormald differential equations are set on nodes' perspective which enables a numerical solution to the computation of the expected asymptotic decoding performance of Growth codes. Our framework is appropriate for any class of Rateless codes that does not include a precoding step. We further study the performance of Growth codes with moderate and large size codeblocks through simulations and we use the generalized logistic function to model the decoding probability. We then exploit the decoding probability model in an illustrative application of Growth codes to error resilient video transmission. The video transmission problem is cast as a joint source and channel rate allocation problem that is shown to be convex with respect to the channel rate. This illustrative application permits to highlight the main advantage of Growth codes, namely improved performance in the intermediate loss region.
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In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.
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O uso de veículos aéreos não tripulados (VANTs) tem se tornado cada vez mais comum, principalmente em aplicações de uso civil. No cenário militar, o uso de VANTs tem focado o cumprimento de missões específicas que podem ser divididas em duas grandes categorias: sensoriamento remoto e transporte de material de emprego militar. Este trabalho se concentra na categoria do sensoriamento remoto. O trabalho foca a definição de um modelo e uma arquitetura de referência para o desenvolvimento de sensores inteligentes orientados a missões específicas. O principal objetivo destas missões é a geração de mapas temáticos. Neste trabalho são investigados processos e mecanismos que possibilitem a geração desta categoria de mapas. Neste sentido, o conceito de MOSA (Mission Oriented Sensor Array) é proposto e modelado. Como estudos de caso dos conceitos apresentados são propostos dois sistemas de mapeamento automático de fontes sonoras, um para o caso civil e outro para o caso militar. Essas fontes podem ter origem no ruído gerado por grandes animais (inclusive humanos), por motores de combustão interna de veículos ou por atividade de artilharia (incluindo caçadores). Os MOSAs modelados para esta aplicação são baseados na integração de dados provenientes de um sensor de imageamento termal e uma rede de sensores acústicos em solo. A integração das informações de posicionamento providas pelos sensores utilizados, em uma base cartográfica única, é um dos aspectos importantes tratados neste trabalho. As principais contribuições do trabalho são a proposta de sistemas MOSA, incluindo conceitos, modelos, arquitetura e a implementação de referência representada pelo sistema de mapeamento automático de fontes sonoras.
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Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.
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1973-74 not published.