ESEC - Comunicações em conferências e congressos
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- Aprender sexualidade desde cedo : menino ou menina - eis a questãoPublication . Veiga, Maria Luísa Ferreira Cabral dos Santos; Teixeira, Filomena; Couceiro, Fernanda; Martins, Isabel
- Extension to CORBA event service for a conference control systemPublication . Orvalho, Joao; Andrade, Tiago; Boavida, Fernando
- Kernel-PCA denoising of artifact-free protein NMR spectraPublication . Stadlthanner, K.; Lang, E.W.; Gruber, P.; Theis, E J.; Tomé, A.M.; Teixeira, Ana; Puntonet, C. G.Multidimensional 'H NMR spectra of hiomolecules dissolved in light water are contaminated by an intense water artifact. Generalized eigenvalue decomposition methods using congruent matrix pencils are used to separate the water artefact from the protein spectra. Due to the statistical separation process, however, noise is introduced into the reconstructed spectra. Hence Kernel - based denoising techniques are discussed lo obtain noise- and artifact - free 2D NOESY NMR spectra of proteins.
- Blind source separation using time-delayed signalsPublication . Tomé, A.M.; Teixeira, Ana; Lang, E.W.; Stadlthanner, K.; Rocha, A.P.; Almeida, R.In this work a modified version of AMUSE, called MMUSE, is proposed. The main modification consists in increasing the dimension of the data vectors by joining delayed versions of the observed mixed signals. With the new data a matrix pencil is computed and its generalized eigendecomposition is performed as in AMUSE. We will show that in this case the output (or independent) signals are filtered versions of the source signals. Some numerical simulations using artificially mixed signals as well as biological data (RR and QT intervals of Electrocardiogram) are presented.
- Identidade e políticas de reconhecimento social na sociedade de redePublication . Baptista Ferreira, GilEste artigo considera a importância dos novos media no processo de formação das identidades nas sociedades modernas, ao constituíremse em espaço privilegiado e instrumento poderoso em relação aos processos simbólicos de reconhecimento intersubjectivo. O meu objectivo é demonstrar como a categoria do reconhecimento social, associada às dinâmicas que os novos media propiciam, pode surgir não apenas como categoria fundamental para uma reflexão acerca das novas questões do mundo em que vivemos, mas ainda como opção válida para a compreensão de aspectos decisivos dos actuais processos de constituição das identidades.
- Estratégias de educação para a saúde no jardim de infância e no 1º ciclo do ensino básicoPublication . Veiga, Maria Luísa Ferreira Cabral dos Santos; Teixeira, Filomena
- Middleware for embedded sensors and actuators in mobile pervasive augmented realityPublication . Ferreira, Pedro Miguel da Fonseca Marques; Orvalho, Joao; Boavida, Fernando
- Nonlinear projective techniques to extract artifacts in biomedical signalsPublication . Teixeira, Ana; Tomé, A. M.; Stadlthanner, K.; Lang, E. W.Biomedical signals are generally contaminated with artifacts and noise. In case the artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multidimensional signal vectors for the latter techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. Using this embedding we propose to cluster the resulting feature vectors and apply a singular spectrum analysis (SSA) locally in each cluster to recover the undistorted signals. We also compare the reconstructed signals to results obtained with kernel-PCA. Both nonlinear subspace projection techniques are applied to artificial data to demonstrate the suppression of random noise signals as well as to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference.
- On the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical SignalsPublication . Teixeira, Ana; Tome, A.; Lang, E.; Schachtner, R.; Stadlthanner, K.Kernel principal component analysis(KPCA) is a nonlinear projective technique that can be applied to decompose multi-dimensional signals and extract informative features as well as reduce any noise contributions. In this work we extend KPCA to extract and remove artifact-related contributions as well as noise from one-dimensional signal recordings. We introduce an embedding step which transforms the one-dimensional signal into a multi-dimensional vector. The latter is decomposed in feature space to extract artifact related contaminations. We further address the preimage problem and propose an initialization procedure to the fixed-point algorithm which renders it more efficient. Finally we apply KPCA to extract dominant Electrooculogram (EOG) artifacts contaminating Electroencephalogram (EEG) recordings in a frontal channel.
- Exploiting low-rank approximations of kernel matrics in denoising applicationSPublication . Teixeira, Ana; Tomé, A. M.; Lang, E.W.The eigendecomposition of a kernel matrix can present a computational burden in many kernel methods. Nevertheless only the largest eigenvalues and corresponding eigenvectors need to be computed. In this work we discuss the Nystrom low-rank approximations of the kernel matrix and its applications in KPCA denoising tasks. Furthermore, the low-rank approximations have the advantage of being related with a smaller subset of the training data which constitute then a basis of a subspace. In a common algebraic framework we discuss the different approaches to compute the basis. Numerical simulations concerning the denoising are presented to compare the discussed approaches.