Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

Caracterização mecânica de misturas betuminosas a aplicar em infra-estruturas de transportes

A maioria das infra-estruturas de transportes, nomeadamente os pavimentos rodoviários e aeroportuários, são constituídas por misturas betuminosas, o que permite um bom desempenho e uma adequada durabilidade, nas condições usuais de serviço. As misturas betuminosas são ainda amplamente utilizadas na construção de zonas de estacionamento de veículos, tendo-se verificado recentemente a sua aplicação também em infra-estruturas ferroviárias. Face à necessidade de melhorar o desempenho das vias-férreas, permitindo uma concepção mais durável de linhas de alta velocidade e uma redução dos custos da sua manutenção, tem-se vindo a desenvolver diversos estudos para promover a utilização de novos materiais, principalmente através da incorporação de misturas betuminosas. O presente trabalho tem como objectivo a caracterização do comportamento mecânico de misturas betuminosas a aplicar em infra-estruturas de transportes. Como metodologia para o estudo do comportamento mecânico das misturas betuminosas foram realizados em laboratório ensaios de cargas repetidas, nomeadamente, ensaios de flexão em quatro pontos para determinação da rigidez e da resistência à fadiga e ensaios de compressão triaxiais cíclicos para avaliação do comportamento à deformação permanente. A resistência à fadiga das misturas betuminosas em estudo foi avaliada através do ensaio de flexão em quatro pontos, com extensão controlada, e aplicação de um carregamento sinusoidal com diferentes frequências, de acordo com o procedimento de ensaio da norma europeia EN 12697-24 (2004 + A1: 2007). A resistência à deformação permanente das misturas betuminosas foi analisada através de ensaios de compressão triaxiais cíclicos, submetendo-as a uma tensão de confinamento estática pela aplicação parcial de vácuo e a uma pressão axial cíclica sob a forma rectangular, de acordo com a norma europeia EN 12697-25 (2004). O conhecimento destas propriedades mecânicas assume particular importância ao nível da formulação das misturas betuminosas, do dimensionamento de uma estrutura ou do estabelecimento de uma adequada solução para uma obra de reabilitação duma infra-estrutura de transportes. Para este estudo foi utilizado um modelo físico construído numa fossa no LNEC, com o propósito de serem testadas três substruturas ferroviárias não convencionais, utilizando sub-balastro betuminoso. A selecção das substruturas foi efectuada após uma análise de várias secções de estruturas já testadas e aplicadas noutros países, de forma a proporcionar comparações fiáveis entre elas. Os resultados obtidos mostraram que a mistura betuminosa AC20 base 50/70 (MB) aplicada na camada de sub-balastro é adequada para ser aplicada nas infra-estruturas de transportes pois apresenta um bom desempenho à fadiga e à deformação permanente. Através dos ensaios efectuados foi ainda possível entender a importante influência das características volumétricas, principalmente da porosidade para o bom comportamento da mistura betuminosa.

Application of GIS-based multi-criteria analysis for site selection of aquifer recharge with reclaimed water

Reclaimed water from small wastewater treatment facilities in the rural areas of the Beira Interior region (Portugal) may constitute an alternative water source for aquifer recharge. A 21-month monitoring period in a constructed wetland treatment system has shown that 21,500 m(3) year(-1) of treated wastewater (reclaimed water) could be used for aquifer recharge. A GIS-based multi-criteria analysis was performed, combining ten thematic maps and economic, environmental and technical criteria, in order to produce a suitability map for the location of sites for reclaimed water infiltration. The areas chosen for aquifer recharge with infiltration basins are mainly composed of anthrosol with more than 1 m deep and fine sand texture, which allows an average infiltration velocity of up to 1 m d(-1). These characteristics will provide a final polishing treatment of the reclaimed water after infiltration (soil aquifer treatment (SAT)), suitable for the removal of the residual load (trace organics, nutrients, heavy metals and pathogens). The risk of groundwater contamination is low since the water table in the anthrosol areas ranges from 10 m to 50 m. Oil the other hand, these depths allow a guaranteed unsaturated area suitable for SAT. An area of 13,944 ha was selected for study, but only 1607 ha are suitable for reclaimed water infiltration. Approximately 1280 m(2) were considered enough to set up 4 infiltration basins to work in flooding and drying cycles.

Solubility of Ethene in Water and in a Medium for the Cultivation of a Bacterial Strain

The solubility of ethene in water and in the fermentation medium of Xanthobacter Py(2) was determined with a Ben-Naim-Baer type apparatus. The solubility measurements were carried out in the temperature range of (293.15 to 323.15) K and at atmospheric pressure with a precision of about +/- 0.3 %. The Ostwald coefficients, the mole fractions of the dissolved ethene, at the gas partial pressure of 101.325 kPa, and the Henry coefficients, at the water vapor pressure, were calculated using accurate thermodynamic relations. A comparison between the solubility of ethene in water and in the cultivation medium has shown that this gas is about 2.4 % more soluble in pure water. On the other hand, from the solubility temperature dependence, the Gibbs energy, enthalpy, and entropy changes for the process of transferring the solute from the gaseous phase to the liquid solutions were also determined. Moreover, the perturbed-chain statistical associating fluid theory equation of state (PC-SAFT EOS) model was used for the prediction of the solubility of ethene in water. New parameters, k(ij), are proposed for this system, and it was found that using a ky temperature-dependent PC-SAFT EOS describes more accurately the behavior solubilities of ethene in water at 101.325 kPa, improving the deviations to 1 %.

Deformação miocárdica analisada por 2D strain em indivíduos com lúpus eritematoso sistémico

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular. Área de especialização: Ultrassonografia Cardiovascular.

Avaliação ecocardiográfica da função sistólica ventricular direita em doentes com disfunção ventricular esquerda: parâmetros convencionais versus strain bidimensional

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular.

Strain global longitudinal avaliado por VVI-Speckle Tracking numa população de indivíduos normais

O VVI-Speckle Tracking é um método visual e quantitativo de avaliação dos parâmetros de deformação miocárdica utilizando a imagem bidimensional. Tem com principal vantagem em relação a outras metodologias a sua facilidade de execução. O SGL apresenta-se como o parâmetro de deformação miocárdica que mais precocemente deteta a alteração da função contrátil. Objectivo do estudo: determinar os valores do Strain Global Longitudinal (SGL), numa população de indivíduos normais utilizando a mais recente versão desta tecnologia e compará-los com outos valores já publicados, assim como a sua eventual variação com alguns parâmetros demográficos, como seja o género, idade e IMC.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

In-plane displacement and strain image analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements. © 2015Computer-Aided Civil and Infrastructure Engineering.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.