Study in real conditions and in laboratory of the application of expanded agglomerated cork as exterior wall covering

The interest of the study on the implementation of expanded agglomerated cork as exterior wall covering derives from two critical factors in a perspective of sustainable development: the use of a product consisting of a renewable natural material-cork-and the concern to contribute to greater sustainability in construction. The study aims to assess the feasibility of its use by analyzing the corresponding behaviour under different conditions. Since this application is relatively recent, only about ten years old, there is still much to learn about the reliability of its long-term properties. In this context, this study aims to deepen and approach aspects, some of them poorly studied and even unknown, that deal with characteristics that will make the agglomerate a good choice for exterior wall covering. The analysis of these and other characteristics is being performed by testing both under actual exposure conditions, on an experimental cell at LNEC, and on laboratory. In this paper the main laboratory tests are presented and the obtained results are compared with the outcome of the field study. © (2015) Trans Tech Publications, Switzerland.

Matemática II : apoio às aulas de funções reais de mais de uma variável real

Este texto apresenta os conceitos fundamentais das funções reais de mais de duas variáveis. Estes slides são um complemento às aulas de Matemática II para o tema em estudo para alunos da licenciatura em gestão.

System-on-chip field-programmable gate array design for onboard real-time hyperspectral unmixing

Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the last usually done on a ground station, onboard systems have emerged to process data, reducing the volume of information to transfer from the satellite to the ground station. For this purpose, compact reconfigurable hardware modules, such as field-programmable gate arrays (FPGAs), are widely used. This paper proposes an FPGA-based architecture for hyperspectral unmixing. This method based on the vertex component analysis (VCA) and it works without a dimensionality reduction preprocessing step. The architecture has been designed for a low-cost Xilinx Zynq board with a Zynq-7020 system-on-chip FPGA-based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low-cost embedded systems, opening perspectives for onboard hyperspectral image processing.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. 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 proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.