Distributed controllers modeling through Petri nets with multi-asynchronous-channels

This paper proposes the concept of multi-asynchronous-channel for Petri nets. Petri nets extended with multi-asynchronous-channels and time-domains support the specification of distributed controllers, where each controller has a synchronous execution but the global system is asynchronous (globally-asynchronous locally-synchronous systems). Each multi-asynchronous-channel specify the interaction between two or more distributed controllers. These channels, together with the time-domain concept, ensure the creation of network-independent models to support implementations using heterogeneous communication networks. The created models support not only the systems documentation but also their validation and implementation through simulation tools, verification tools, and automatic code generators. An application example illustrates the use of a Petri net class extended with the proposed channels. © 2015 IEEE.

INA verde : diagnóstico dos processos de separação e recolha selectiva de resíduos consumíveis no INA, I.P.

Esta apresentação decorre do projecto realizado pela equipa: Helena Rato, César Madureira, David Ferraz e Margarida Quintela Martins

O Instituto Nacional de Administração, I.P. e a política de valorização do capital humano da Administração Pública Portuguesa

Comunicação apresentada no curso avançado em gestão da formação, 2010.

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.

O contributo da avaliação de desempenho para a profissionalização da A.P.

Apresentação realizada no 7º Congresso Nacional da Administração Pública: Estado e Administração na Resposta à Crise, em Lisboa, de 10 a 11 de Novembro.

Instituto Nacional de Administração I.P.

Apresentação realizada no âmbito do projecto Leonardo da Vinci no Institute for Public Administration em Praga a 5 de Dezembro de 2011

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Recompondo a cultura da matemática escolar: a intervenção da folha informativa dos professores do 1º Grupo (E.T.P.)

XX Seminário de Investigação em Educação Matemática (pp. 228-238). Viana do Castelo

Comportamento da tibiotársica na sequência de movimento de sentar para de pé em crianças prematuras

Introdução: A prematuridade constitui um fator de risco para a ocorrência de lesões ao nível do sistema nervoso central, sendo que uma idade gestacional inferior a 36 semanas potencia esse mesmo risco, nomeadamente para a paralisia cerebral (PC) do tipo diplegia espástica. A sequência de movimento de sentado para de pé (SPP), sendo uma das aprendizagens motoras que exige um controlo postural (CP) ao nível da tibiotársica, parece ser uma tarefa funcional frequentemente comprometida em crianças prematuras com e sem PC. Objetivo(s): Descrever o comportamento dos músculos da tibiotársica, tibial anterior (TA) e solear (SOL), no que diz respeito ao timing de ativação, magnitude e co-ativação muscular durante a fase I e início da fase II na sequência de movimento de SPP realizada por cinco crianças prematuras com PC do tipo diplegia espástica e cinco crianças prematuras sem diagnóstico de alteração neuromotoras, sendo as primeiras sujeitas a um programa de intervenção baseado nos princípios do conceito de Bobath – Tratamento do Neurodesenvolvimento (TND). Métodos: Foram avaliadas 10 crianças prematuras, cinco com PC e cinco sem diagnóstico de alterações neuromotoras, tendo-se recorrido à eletromiografia de superfície para registar parâmetros musculares, nomeadamente timings, magnitudes e valores de co-ativação dos músculos TA e SOL, associados à fase I e inico da fase II da sequência de movimento de SPP. Procedeu-se ao registo de imagem de modo a facilitar a avaliação dos componentes de movimento associados a esta tarefa. Estes procedimentos foram realizados num único momento, no caso das crianças sem diagnóstico de alterações neuromotoras e em dois momentos, antes e após a aplicação de um programa de intervenção segundo o Conceito de Bobath – TND no caso das crianças com PC. A estas foi ainda aplicado o Teste da Medida das Funções Motoras (TMFM–88) e a Classificação Internacional da Funcionalidade Incapacidade e Saúde – crianças e jovens (CIF-CJ). Resultados: Através da eletromiografia constatou-se que ambos os grupos apresentaram timings de ativação afastados da janela temporal considerada como ajustes posturais antecipatórios (APAs), níveis elevados de co-ativação, em alguns casos com inversão na ordem de recrutamento muscular o que foi possível modificar nas crianças com PC após o período de intervenção. Nestas, verificou-se ainda que, a sequência de movimento de SPP foi realizada com menor número de compensações e com melhor relação entre estruturas proximais e distais compatível com o aumento do score final do TMFM-88 e modificação positiva nos itens de atividade e participação da CIF-CJ. Conclusão: As crianças prematuras com e sem PC apresentaram alterações no CP da tibiotársica e níveis elevados de co-ativação muscular. Após o período de intervenção as crianças com PC apresentaram modificações positivas no timing e co-ativação muscular, com impacto funcional evidenciado no aumento do score final da TMFM-88 e modificações positivas na CIF-CJ.

Influência do alinhamento do pé nos Ajustes Posturais Antecipatórios em crianças com Paralisia Cerebral

Introdução: A dificuldade na organização dos ajustes posturais antecipatórios (APAs) é frequentemente associada ao défice de controlo postural (CP) em crianças/jovens com um quadro motor de hemiplegia espástica, resultante de paralisia cerebral. As alterações biomecânicas da tibiotársica e do pé são características comummente observadas nestas crianças/jovens e influenciam o CP na sua globalidade. Objectivo(s): descrever o comportamento dos APAs associados ao início da marcha, face à modificação do alinhamento do pé em crianças/jovens com hemiplegia espástica, após 12 semanas de intervenção, segundo o Conceito Bobath-TND e aplicação de uma Ligadura Funcional (LF). Métodos: Foram avaliadas quatro crianças/jovens num momento inicial (M0) e após 12 semanas de intervenção e de aplicação de uma LF (M1). Recorrendo à eletromiografia de superfície, registaram-se os timings de activação dos músculos tibial anterior, solear, recto abdominal e erector da espinha (bilateralmente). O início do movimento foi calculado a partir da alteração do sinal obtido através da plataforma de pressões. Recorreu-se à aplicação da TMFM-88 para avaliar a função motora global e à aplicação da CIF-CJ para classificar a funcionalidade mediante as actividades e a participação. Procedeu-se ao registo de imagem para facilitar a observação/avaliação das componentes de movimento das crianças/jovens em estudo. Resultados: Após o período de intervenção, verificou-se uma modificação nos valores dos timings de ativação dos músculos em análise, que se aproximaram da janela temporal definida como APAs, bem como na distribuição de carga na base de suporte, nos scores da TMFM-88 e nos qualificadores das “Actividades e Participação”, sugestivos de uma melhor organização dos mecanismos de controlo postural. Conclusão: As crianças/jovens em estudo evidenciaram, após uma intervenção de fisioterapia baseada no Conceito Bobath- TND e aplicação de uma LF, uma evolução favorável tanto ao nível do CP da tibiotársica e do pé, apresentando timings de ativação muscular temporalmente mais ajustados à tarefa, com repercussões positivas nas actividades e participação.