Species richness and beta-diversity of aquatic macrophytes assemblages in three floodplain tropical lagoons: evaluating the effects of sampling size and depth gradients

Using aquatic macrophyte data gathered in three lagoons of the Paraná River floodplain we showed a strong effect of sample size on species richness. Incidence-based species richness estimators (Chao 2, jackknife 1, jackknife 2, incidence-based coverage estimator and bootstrap) were compared to evaluate their performance in estimating the species richness throughout transect sampling rnethod. Our results suggest that the best estimate of the species richness was gave by jackknife 2 estimator. Nevertheless, the transect sampling design was considered inappropriate to estimate aquatic macrophytes species richness. Depth gradient was not a good predictor of the species richness and species turnover (beta diversity). The dynamics of these environments, subject to high water-level fluctuation prevents the formation and permanence of a clear floristic depth-related gradient

“Small Sample Size”: a methodological problem in bayes plug-in classifier for image recognition

New technologies in the form of improved instrumentation have made it possible to take detailed measurements over recognition patterns. This increase in the number of features or parameters for each pattern of interest not necessarily generates better classification performance. In fact, in problems where the number of training samples is less than the number of parameters, i.e. “small sample size” problems, not all parameters can be estimated and traditional classifiers often used to analyse lower dimensional data deteriorate. The Bayes plug-in classifier has been successfully applied to discriminate high dimensional data. This classifier is based on similarity measures that involve the inverse of the sample group covariance matrices. However, these matrices are singular in “small sample size” problems. Thus, several other methods of covariance estimation have been proposed where the sample group covariance estimate is replaced by covariance matrices of various forms. In this report, some of these approaches are reviewed and a new covariance estimator is proposed. The new estimator does not require an optimisation procedure, but an eigenvectoreigenvalue ordering process to select information from the projected sample group covariance matrices whenever possible and the pooled covariance otherwise. The effectiveness of the method is shown by some experimental results

A maximum uncertainty LDA-based approach for limited sample size problems – with application to Face Recognition

A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or pre-processed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this paper, a new LDA-based method is proposed. It is based on a straighforward stabilisation approach for the within-class scatter matrix. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other LDA-based methods. The results indicate that our method improves the LDA classification performance when the within-class scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features

Far-Field Species Distribution Measurements on the BHT-600 Hall Thruster Cluster

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76148/1/AIAA-2007-5304-543.pd

Análise de logs do sistema Agritempo por meio do log do PHPNuke e WebAlizer.

O foco deste trabalho é a análise dos logs do sistema Agritempo, um sistema de monitoramento agrometeorológico que disponibiliza informações meteorológicas e agrometeorológicas de diferentes regiões brasileiras gratuitamente na internet3. O Agritempo possui um amplo público-alvo: produtores, extensionistas, consultores, agentes do governo, estudantes e professores universitários, além da iniciativa privada

Using an holistic method based on prior information to represent global and local variations on face images

Faces are familiar objects that can be easily perceived and recognized by ourselves. However, the computational modeling of such apparently natural human ability remains challenging. Recent studies in the literature have suggested that face processing is a cognition task composed of configural (or global) and featural (or local) sources of information, but with controversial arguments about the combination of these two types of information. In this work, we describe an holistic method that combines variance used in Principal Component Analysis (PCA) with some prior knowledge about the underlying visual perception task, including systematically the global and local information in the common multivariate representation of face images. We have showed that, with prior information, important local variations represented by principal components with small eigenvalues may not be discarded augmenting the classification accuracy of the first orthogonal basis vectors. Most interestingly, PCA with prior knowledge provides a specialized feature selection procedure, where the mapping of high-dimensional data into a lower-dimensional space has been able to handle local variations and capture not only the entire facial appearance but also some sample group facial features

Efeito da aplicação de silício na resistência às condições ambientais em Eucalyptus grandis.

EVINCI. Resumo 029

Poder calorífico da madeira e do carvão vegetal: lignina e carbono fixo como determinantes.

Resumo