Nuvens

Ilustração componente do jogo “Escola Mágica (http://www.loa.sead.ufscar.br/escola-magica.html)” desenvolvido pela equipe do Laboratório de Objetos de Aprendizagem da Universidade Federal de São Carlos (LOA/UFSCar).

Aplicação do método de análise hierárquica na tomada de decisão para adoção de computação em nuvem: um estudo de caso na federação das indústrias do RN

The progresses of the Internet and telecommunications have been changing the concepts of Information Technology IT, especially with regard to outsourcing services, where organizations seek cost-cutting and a better focus on the business. Along with the development of that outsourcing, a new model named Cloud Computing (CC) evolved. It proposes to migrate to the Internet both data processing and information storing. Among the key points of Cloud Computing are included cost-cutting, benefits, risks and the IT paradigms changes. Nonetheless, the adoption of that model brings forth some difficulties to decision-making, by IT managers, mainly with regard to which solutions may go to the cloud, and which service providers are more appropriate to the Organization s reality. The research has as its overall aim to apply the AHP Method (Analytic Hierarchic Process) to decision-making in Cloud Computing. There to, the utilized methodology was the exploratory kind and a study of case applied to a nationwide organization (Federation of Industries of RN). The data collection was performed through two structured questionnaires answered electronically by IT technicians, and the company s Board of Directors. The analysis of the data was carried out in a qualitative and comparative way, and we utilized the software to AHP method called Web-Hipre. The results we obtained found the importance of applying the AHP method in decision-making towards the adoption of Cloud Computing, mainly because on the occasion the research was carried out the studied company already showed interest and necessity in adopting CC, considering the internal problems with infrastructure and availability of information that the company faces nowadays. The organization sought to adopt CC, however, it had doubt regarding the cloud model and which service provider would better meet their real necessities. The application of the AHP, then, worked as a guiding tool to the choice of the best alternative, which points out the Hybrid Cloud as the ideal choice to start off in Cloud Computing. Considering the following aspects: the layer of Infrastructure as a Service IaaS (Processing and Storage) must stay partly on the Public Cloud and partly in the Private Cloud; the layer of Platform as a Service PaaS (Software Developing and Testing) had preference for the Private Cloud, and the layer of Software as a Service - SaaS (Emails/Applications) divided into emails to the Public Cloud and applications to the Private Cloud. The research also identified the important factors to hiring a Cloud Computing provider

Uma Aplicação de Redes Neurais Auto-Organizaveis à Reconstrução Tridimensional de Superfícies

We propose a multi-resolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen s self-organizing map. Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multi-resolution, iterative scheme. Reconstruction was experimented with several point sets, induding different shapes and sizes. Results show generated meshes very dose to object final shapes. We include measures of performance and discuss robustness.

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Síntese de superfícies seletivas de frequência atráves de técnicas de computação natural

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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

Modelo arquitetural de comunicação para monitoramento de pacientes baseado em middleware, computação móvel e ubíqua

Hospital Automation is an area that is constantly growing. The emergency of new technologies and hardware is transforming the processes more efficient. Nevertheless, some of the hospital processes are still being performed manually, such as monitoring of patients that is considered critical because it involves human lives. One of the factors that should be taken into account during a monitoring is the agility to detect any abnormality in vital signs of patients, as well as warning of this anomaly to the medical team involved. So, this master's thesis aims to develop an architecture to automate this process of monitoring and reporting of possible alert to a professional, so that emergency care can be done effectively. The computing mobile was used to improve the communication by distributing messages between a central located into the hospital and the mobile carried by the duty

Morfologia matemática aplicada à detecção de sombras e nuvens em imagens de alta resolução

The spatial resolution improvement of orbital sensors has broadened considerably the applicability of their images in solving urban areas problems. But as the spatial resolution improves, the shadows become even a more serious problem especially when detailed information (under the shadows) is required. Besides those shadows caused by buildings and houses, clouds projected shadows are likely to occur. In this case there is information occlusion by the cloud in association with low illumination and contrast areas caused by the cloud shadow on the ground. Thus, it's important to use efficient methods to detect shadows and clouds areas in digital images taking in count that these areas care for especial processing. This paper proposes the application of Mathematical Morphology (MM) in shadow and clouds detection. Two parts of a panchromatic QuickBird image of Cuiab-MT urban area were used. The proposed method takes advantage of the fact that shadows (low intensity - dark areas) and clouds (high intensity - bright areas) represent the bottom and top, respectively, of the image as it is thought to be a topographic surface. This characteristic allowed MM area opening and closing operations to be applied to reduce or eliminate the bottom and top of the topographic surface.