Estratégias e internacionalização na manutenção e produção

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Stock Options: impacte nas empresas cotadas em Portugal

Mestrado em Contabilidade

Hyperspectral subspace identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Signal Subspace Identification in Hyperspectral Linear Mixtures

Chpater in Book Proceedings with Peer Review Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II

Especulação em mercado bolsista : análise técnica : o caso Nasdaq-100 perante a crise subprime

Mestrado em Controlo de Gestão e dos Negócios

Viability of the use of thin-film A-SiC:H photodiodes for protein identification

In this paper, we present a multilayer device based on a-Si:H/a-SiC:H that operates as photodetector and optical filter. The use of such device in protein detection applications is relevant in Fluorescence Resonance Energy Transfer (FRET) measurements. This method demands the detection of fluorescent signals located at specific wavelengths bands in the visible part of the electromagnetic spectrum. The device operates in the visible range with a selective sensitivity dependent on electrical and optical bias. Several nanosensors were tested with a commercial spectrophotometer to assess the performance of FRET signals using glucose solutions of different concentrations. The proposed device was used to demonstrate the possibility of FRET signals detection, using visible signals of similar wavelength and intensity. The device sensitivity was tuned to enhance the wavelength band of interest using steady state optical bias at 400 nm. Results show the ability of the device to detect signals in this range. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The impact of the 2008 and 2010 financial crises on international stock markets : contagion and long memory

Nesta tese estudamos os efeitos de contágio financeiro e de memória longa causados pelas crises financeiras de 2008 e 2010 em alguns mercados acionistas internacionais. A tese é composta por três ensaios interligados. No Ensaio 1, recorremos à teoria das cópulas para testar a existência de contágio e revelar os canais “investor induced” de transmissão da crise de 2008 aos mercados da Bélgica, França, Holanda e Portugal (grupo NYSE Euronext). Concluímos que existe contágio nestes mercados, que o canal “portfolio rebalancing” é o mecanismo mais importante de transmissão da crise, e que o fenómeno “flight to quality” está presente nos mercados. No Ensaio 2, usando novamente modelos de cópulas, avaliamos os efeitos de contágio provocados pelo mercado acionista grego nos mercados do grupo NYSE Euronext, no contexto da crise de 2010. Os resultados obtidos sugerem que durante a crise de 2010 apenas o mercado português foi objeto de contágio; além disso, conclui-se que os efeitos de contágio provocados pela crise de 2008 são claramente superiores aos efeitos provocados pela crise de 2010. No Ensaio 3, abordamos o tema da memória longa através do estudo do expoente de Hurst dos mercados acionistas da Bélgica, E.U.A., França, Grécia, Holanda, Japão, Reino Unido e Portugal. Verificamos que as propriedades de memória longa dos mercados foram afetadas pelas crises, especialmente a de 2008 – que aumentou a memória longa dos mercados e tornou-os mais persistentes. Finalmente, usando cópulas mais uma vez, verificamos que as crises provocaram, em geral, um aumento na correlação entre os expoentes de Hurst locais dos mercados foco das crises (E.U.A. e Grécia) e os expoentes de Hurst locais dos outros mercados da amostra, sugerindo que o expoente de Hurst pode ser utilizado para detetar efeitos de contágio financeiro. Em síntese, os resultados desta tese sugerem que comparativamente com períodos de acalmia, os períodos de crises financeiras tendem a provocar ineficiência nos mercados acionistas e a conduzi-los na direção da persistência e do contágio financeiro.

PV systems linked to the grid: parameter identification with a heuristic procedure

This paper focuses on a PV system linked to the electric grid by power electronic converters, identification of the five parameters modeling for photovoltaic systems and the assessment of the shading effect. Normally, the technical information for photovoltaic panels is too restricted to identify the five parameters. An undemanding heuristic method is used to find the five parameters for photovoltaic systems, requiring only the open circuit, maximum power, and short circuit data. The I- V and the P- V curves for a monocrystalline, polycrystalline and amorphous photovoltaic systems are computed from the parameters identification and validated by comparison with experimental ones. Also, the I- V and the P- V curves under the effect of partial shading are obtained from those parameters. The modeling for the converters emulates the association of a DC-DC boost with a two-level power inverter in order to follow the performance of a testing commercial inverter employed on an experimental system. © 2015 Elsevier Ltd.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Unsupervised hyperspectral signal subspace identification

Hyperspectral imaging sensors provide image data containing both spectral and spatial information from the Earth surface. The huge data volumes produced by these sensors put stringent requirements on communications, storage, and processing. This paper presents a method, termed hyperspectral signal subspace identification by minimum error (HySime), that infer the signal subspace and determines its dimensionality without any prior knowledge. The identification of this subspace enables a correct dimensionality reduction yielding gains in algorithm performance and complexity and in data storage. HySime method is unsupervised and fully-automatic, i.e., it does not depend on any tuning parameters. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.