969 resultados para Compressão de dados (Telecomunicações)


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In recent decades, changes have been occurring in the telecommunications industry, allied to competition driven by the policies of privatization and concessions, have fomented the world market irrefutably causing the emergence of a new reality. The reflections in Brazil have become evident due to the appearance of significant growth rates, getting in 2012 to provide a net operating income of 128 billion dollars, placing the country among the five major powers in the world in mobile communications. In this context, an issue of increasing importance to the financial health of companies is their ability to retain their customers, as well as turn them into loyal customers. The appearance of infidelity from customer operators has been generating monthly rates shutdowns about two to four percent per month accounting for business management one of its biggest challenges, since capturing a new customer has meant an expenditure greater than five times to retention. For this purpose, models have been developed by means of structural equation modeling to identify the relationships between the various determinants of customer loyalty in the context of services. The original contribution of this thesis is to develop a model for loyalty from the identification of relationships between determinants of satisfaction (latent variables) and the inclusion of attributes that determine the perceptions of service quality for the mobile communications industry, such as quality, satisfaction, value, trust, expectation and loyalty. It is a qualitative research which will be conducted with customers of operators through simple random sampling technique, using structured questionnaires. As a result, the proposed model and statistical evaluations should enable operators to conclude that customer loyalty is directly influenced by technical and operational quality of the services offered, as well as provide a satisfaction index for the mobile communication segment

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This work presents simulation results of an identification platform compatible with the INPE Brazilian Data Collection System, modeled with SystemC-AMS. SystemC-AMS that is a library of C++ classes dedicated to the simulation of heterogeneous systems, offering a powerful resource to describe models in digital, analog and RF domains, as well as mechanical and optic. The designed model was divided in four parts. The first block takes into account the satellite s orbit, necessary to correctly model the propagation channel, including Doppler effect, attenuation and thermal noise. The identification block detects the satellite presence. It is composed by low noise amplifier, band pass filter, power detector and logic comparator. The controller block is responsible for enabling the RF transmitter when the presence of the satellite is detected. The controller was modeled as a Petri net, due to the asynchronous nature of the system. The fourth block is the RF transmitter unit, which performs the modulation of the information in BPSK ±60o. This block is composed by oscillator, mixer, adder and amplifier. The whole system was simulated simultaneously. The results are being used to specify system components and to elaborate testbenchs for design verification

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We propose a new approach to reduction and abstraction of visual information for robotics vision applications. Basically, we propose to use a multi-resolution representation in combination with a moving fovea for reducing the amount of information from an image. We introduce the mathematical formalization of the moving fovea approach and mapping functions that help to use this model. Two indexes (resolution and cost) are proposed that can be useful to choose the proposed model variables. With this new theoretical approach, it is possible to apply several filters, to calculate disparity and to obtain motion analysis in real time (less than 33ms to process an image pair at a notebook AMD Turion Dual Core 2GHz). As the main result, most of time, the moving fovea allows the robot not to perform physical motion of its robotics devices to keep a possible region of interest visible in both images. We validate the proposed model with experimental results

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Digital signal processing (DSP) aims to extract specific information from digital signals. Digital signals are, by definition, physical quantities represented by a sequence of discrete values and from these sequences it is possible to extract and analyze the desired information. The unevenly sampled data can not be properly analyzed using standard techniques of digital signal processing. This work aimed to adapt a technique of DSP, the multiresolution analysis, to analyze unevenly smapled data, to aid the studies in the CoRoT laboratory at UFRN. The process is based on re-indexing the wavelet transform to handle unevenly sampled data properly. The was efective presenting satisfactory results

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The last years have presented an increase in the acceptance and adoption of the parallel processing, as much for scientific computation of high performance as for applications of general intention. This acceptance has been favored mainly for the development of environments with massive parallel processing (MPP - Massively Parallel Processing) and of the distributed computation. A common point between distributed systems and MPPs architectures is the notion of message exchange, that allows the communication between processes. An environment of message exchange consists basically of a communication library that, acting as an extension of the programming languages that allow to the elaboration of applications parallel, such as C, C++ and Fortran. In the development of applications parallel, a basic aspect is on to the analysis of performance of the same ones. Several can be the metric ones used in this analysis: time of execution, efficiency in the use of the processing elements, scalability of the application with respect to the increase in the number of processors or to the increase of the instance of the treat problem. The establishment of models or mechanisms that allow this analysis can be a task sufficiently complicated considering parameters and involved degrees of freedom in the implementation of the parallel application. An joined alternative has been the use of collection tools and visualization of performance data, that allow the user to identify to points of strangulation and sources of inefficiency in an application. For an efficient visualization one becomes necessary to identify and to collect given relative to the execution of the application, stage this called instrumentation. In this work it is presented, initially, a study of the main techniques used in the collection of the performance data, and after that a detailed analysis of the main available tools is made that can be used in architectures parallel of the type to cluster Beowulf with Linux on X86 platform being used libraries of communication based in applications MPI - Message Passing Interface, such as LAM and MPICH. This analysis is validated on applications parallel bars that deal with the problems of the training of neural nets of the type perceptrons using retro-propagation. The gotten conclusions show to the potentiality and easinesses of the analyzed tools.

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Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature

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The control of industrial processes has become increasingly complex due to variety of factory devices, quality requirement and market competition. Such complexity requires a large amount of data to be treated by the three levels of process control: field devices, control systems and management softwares. To use data effectively in each one of these levels is extremely important to industry. Many of today s industrial computer systems consist of distributed software systems written in a wide variety of programming languages and developed for specific platforms, so, even more companies apply a significant investment to maintain or even re-write their systems for different platforms. Furthermore, it is rare that a software system works in complete isolation. In industrial automation is common that, software had to interact with other systems on different machines and even written in different languages. Thus, interoperability is not just a long-term challenge, but also a current context requirement of industrial software production. This work aims to propose a middleware solution for communication over web service and presents an user case applying the solution developed to an integrated system for industrial data capture , allowing such data to be available simplified and platformindependent across the network

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There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

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The use of waste heat of energy conversion equipment to produce a cooling effect, consists currently in a very interesting way of efficiency improvement of energy systems. The present research has as intention the theoretical and experimental study of a new intermittent refrigeration system ejector cycle characteristics, with use of waste heat. Initially, was doing a bibliographical survey about the vapor ejector refrigeration system technology. In the following stage was doing a simulation of the corresponding thermodynamic cycle, with preliminarily intention to evaluate the performance of the system for different refrigerants fluids. On the basis of the results of the simulation were selected the refrigerant fluid and developed an experimental group of benches of the refrigeration system considered, where pressure and temperature sensory had been inserted in strategical points of the refrigeration archetype and connected to a computerized data acquisition system for measure the refrigerant fluid properties in the thermodynamic cycle. The test results obtained show good agreement with the literature

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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OBJETIVO: Avaliar o efeito da corrente catódica de alta voltagem sobre a dor em um modelo experimental de ciatalgia. MÉTODOS: Foram utilizados 16 ratos Wistar, machos, submetidos a um modelo de ciatalgia experimental no membro pélvico direito. Os sujeitos foram divididos em grupo simulacro (GS) e grupo tratado com corrente catódica (GP-) por 20 min diários durante 10 dias. O modelo de compressão foi realizado com amarria por fio catgut 4.0 cromado, em quatro pontos ao longo do nervo isquiático. A avaliação da nocicepção foi realizada, de forma funcional, com o tempo de elevação da pata (TEP), e à pressão, pelo limiar de retirada, via analgesímetro eletrônico. Os dados foram coletados antes do modelo de ciatalgia (AV1), três dias depois da compressão (antes, AV2, e após o tratamento, AV3), após o quinto dia de tratamento (AV4) e em seguida ao décimo dia de tratamento (AV5). RESULTADOS: Pela avaliação funcional, em ambos os grupos houve aumento da nocicepção, sem redução da mesma em qualquer momento da avaliação. À pressão, no entanto, o GS mostrou redução do limiar de retirada em todos os momentos, enquanto o GP- apresentou redução do limiar apenas inicialmente - em AV5 o limiar foi restaurado. CONCLUSÃO: Não houve alteração na nocicepção pela avaliação funcional; porém, à pressão, o tratamento com corrente catódica mostrou efeito com a somatória de terapias.

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Introdução: Trombose Venosa Profunda (TVP) é o resultado de inúmeros fatores, incluindo estase venosa, injuria endotelial e hipercoagulação do sangue. O embolismo pulmonar é a maior complicação da TVP e ocorre quando um trombo ou um coágulo sangüíneo desloca-se em direção aos pulmões. O tromboembolismo é um sério problema de saúde e é responsável por um considerável número de mortes súbitas e por significantes custos de hospitalizações em todo o mundo. Entre os métodos profiláticos usados na prevenção da TVP, há os classificados como mecânicos e os farmacológicos. Ambos são efetivos e podem ser usados sempre que necessário, tendo sido recomendados por consenso internacional. Objetivos: Avaliar a incidência da TVP em pacientes cirúrgicos através da Compressão pneumática Intermitente CPI (coxa/perna ou perna/pé) contra nenhuma forma de profilaxia ou contra fármacos. Métodos: Revisão sistemática de ensaios clínicos randomizados, utilizando a metodologia Cochrane, através de buscas eletrônica e manual. Foram incluídos pacientes cirúrgicos, de ambos os sexos, cuja intervenção foi o uso de aparelhos de compressão pneumática intermitente. Resultados: 4269 pacientes cirúrgicos investigados por 31 ensaios submetidos a um número de intervenções cirúrgicas, incluindo cirurgia ortopédica (joelho e quadris), geral, ginecológica, urológica e neurológica. Dados combinados demonstraram efeitos de tratamento virtualmente iguais entre CPI e abordagens farmacológicas [0.95 (95%CI 0.82, 1.10)], em relação à incidência de TVP. Entretanto, o risco de hemorragia foi maior e estatisticamente significante em pacientes tratados com intervenções farmacológicas [0.37(95%CI 0.20, 0.69)]. Conclusão: Comparações entre as estratégias farmacológicas e mecânicas indicam que ambas são similares na prevenção de trombose venosa profunda. Entretantio, a evidência atual suportaria a escolha das abordagens mecâncias, uma vez que o risco de eventos hemorrágicos favorece os métodos mecânicos.