A importância da internacionalização: candidaturas à União Europeia: trabalho de projecto

Trabalho de Projecto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro, especialização em Produção.

Como polir uma montanha: trabalho de projeto

Esta reflexão visa contextualizar o processo criativo da componente prática do Trabalho de Projecto – Objecto conferente do grau de Mestre em Teatro, especialização em Artes Performativas – Interpretação, e que consiste no solo Como polir uma montanha que teve lugar no Lavadouro Público de Carnide com Acolhimento do Teatro do Silêncio em Maio de 2014. O objectivo foi criar um espectáculo com base em acções de limpeza, utilizando os gestos quotidianos como elemento catalisador do processo criativo. Procurei, desta forma, espelhar algumas rotinas do dia-a-dia no campo abstracto das artes performativas, desconstruindo o espaço privado e servindo-me do corpo como ferramenta primordial na comunicação destas acções através do movimento. A premissa para este solo partiu da limpeza enquanto acção passível de gerar transformação no espaço e no tempo, e também no corpo e na mente. Inicialmente pensei que a limpeza era fundamental para a organização destes elementos, contudo o resultado levou-me a compreender que limpeza e organização podem ter pressupostos muito distintos. Fundamentei a minha pesquisa observando acções quotidianas de limpeza em contextos diversificados como a vivência rural, a vivência urbana e ao nível da memória – a minha e a de outros indivíduos – de como processamos determinados comportamentos observados desde a infância. Durante o processo de criação de Como polir uma montanha, surgiram diversas inquietações que me levaram a reflectir também sobre a problemática de ser criadora e intérprete a solo, e de como esta questão é transversal à noção de si mesmo perante o outro. Esta noção leva-me a questionar o limiar que separa, mas também une, o palco e a plateia. Por isso integrei o público na acção cénica, através de elementos que propunham uma observação participativa do espectáculo.

Reversible current power supply for fast-field cycling nuclear magnetic resonance equipment

The Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) is a technique used to study the molecular dynamics of different types of materials. The main elements of this equipment are a magnet and its power supply. The magnet used as reference in this work is basically a ferromagnetic core with two sets of coils and an air-gap where the materials' sample is placed. The power supply should supply the magnet being the magnet current controlled in order to perform cycles. One of the technical issues of this type of solution is the compensation of the non-linearities associated to the magnetic characteristic of the magnet and to parasitic magnetic fields. To overcome this problem, this paper describes and discusses a solution for the FFC-NMR power supply based on a four quadrant DC/DC converter.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Sparse matrix multiplication on a reconfigurable many-core architecture

Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.