941 resultados para On-line data


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Este guião de apoio à formação tem como objectivo apoiar docentes na (1) configuração e utilização da actividade teste do moodle e (2) criação de bancos de perguntas.

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To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

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Dynamically reconfigurable systems have benefited from a new class of FPGAs recently introduced into the market, which allow partial and dynamic reconfiguration at run-time, enabling multiple independent functions from different applications to share the same device, swapping resources as needed. When the sequence of tasks to be performed is not predictable, resource allocation decisions have to be made on-line, fragmenting the FPGA logic space. A rearrangement may be necessary to get enough contiguous space to efficiently implement incoming functions, to avoid spreading their components and, as a result, degrading their performance. This paper presents a novel active replication mechanism for configurable logic blocks (CLBs), able to implement on-line rearrangements, defragmenting the available FPGA resources without disturbing those functions that are currently running.

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Relatório de estágio apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Jornalismo.

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In this paper we exploit the nonlinear property of the SiC multilayer devices to design an optical processor for error detection that enables reliable delivery of spectral data of four-wave mixing over unreliable communication channels. The SiC optical processor is realized by using double pin/pin a-SiC:H photodetector with front and back biased optical gating elements. Visible pulsed signals are transmitted together at different bit sequences. The combined optical signal is analyzed. Data show that the background acts as selector that picks one or more states by splitting portions of the input multi optical signals across the front and back photodiodes. Boolean operations such as EXOR and three bit addition are demonstrated optically, showing that when one or all of the inputs are present, the system will behave as an XOR gate representing the SUM. When two or three inputs are on, the system acts as AND gate indicating the present of the CARRY bit. Additional parity logic operations are performed using four incoming pulsed communication channels that are transmitted and checked for errors together. As a simple example of this approach, we describe an all-optical processor for error detection and then provide an experimental demonstration of this idea. (C) 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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The main goal of the present work is the use of mineralogical data corresponding to sediment fine fractions (silt and clay) of Quaternary littoral deposits for the definition of a more detailed vertical zonography and to discriminate the most significant morphoclimatic changes concerned with sediment source areas and sediment deposition areas. The analysis of the available mineralogical data reveals a vertical evolution of the mineral composition. The following aspects deserve particular reference: 1) fine fractions (<38 nm) are composed of quartz and phyllosilicates associated to feldspars, prevailing over other minerals; however in certain sections iron hydroxides and evaporitic minerals occur in significant amounts; 2) clay fractions (<2 nm) show a general prevalence of illite associated with kaolinite and oscillations, in relative terms, of kaolinite and illite contents. Qualitative and quantitative lateral and vertical variations of clay and non clay minerals allow the discrimination of sedimentary sequences and the establishment of the ritmicity and periodicity of the morphoclimatic Quaternary episodes that occurred in the Cortegaça and Maceda beaches. To each one of the sedimentary sequences corresponds, in a first stage, a littoral environment that increasingly became more continental. Climate would be mild to cold, sometimes with humidity - aridity oscillations. Warmer and moister episodes alternated with cooler and dryer ones.

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Monitoring systems have traditionally been developed with rigid objectives and functionalities, and tied to specific languages, libraries and run-time environments. There is a need for more flexible monitoring systems which can be easily adapted to distinct requirements. On-line monitoring has been considered as increasingly important for observation and control of a distributed application. In this paper we discuss monitoring interfaces and architectures which support more extensible monitoring and control services. We describe our work on the development of a distributed monitoring infrastructure, and illustrate how it eases the implementation of a complex distributed debugging architecture. We also discuss several issues concerning support for tool interoperability and illustrate how the cooperation among multiple concurrent tools can ease the task of distributed debugging.

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In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

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Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

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Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação da Mestre Inês Veiga Pereira “Esta versão contém as críticas e sugestões dos elementos do júri”

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This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

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Trabalho de Projecto apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Gestão de Sistemas de e-Learning

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Dissertation to obtain the degree of Master in Electrical and Computer Engineering

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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Gestão de Informação