Deterministic and Reliability-based Optimization of a Belt Conveyor Bridge

Civil Comp Press, Stirlingshire, U.K

Factorial validity and group invariance of the Portuguese short version of the Social Physique Anxiety Scale in adolescents

Social physique anxiety (SPA) has been associated with a range of psychosocial and health-related variables, thus it can be considered is an indicator of social-psychological adjustment. The purpose of this paper is to determine factorial validity and group invariance of Motl and Conroy (2000) translated 7-item SPA among Portuguese adolescents. A nationally representative sample of 3330 8th and 10th grade students (15.07 1.34 years old; 47.5% males and 52.5% females) answered a survey as a part of a larger collaborative cross-national survey, the Health Behaviour in School-Aged Children (HBSC) 2006 study. Exploratory and confirmatory factor analyses resulted on a uni-dimensional factor structure of 6 items [Satorra-Bentler c2 = 30.85, df = 8, p<.01; CFI = .996; NNFI = .992; RMSEA = .038 (90% C.I.: .024 - .052); SRMR = .010]. Further analises, confirmed configurational (all CFI and RMSEA > .99) and metric invariances (CFI difference between restricted and unrestricted models were lower than .01 for all groups) across gender, grade level, diet beliefs, physical activity, perception of body, and BMI. The present version can be used confidently by researchers in analysing and interpreting scores of SPA across a variety of samples in Portuguese adolescents, and that this instrument can be used in cross-cultural research

A Framework for Fast Image Deconvolution with Incomplete Observations

In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods, or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast. We iteratively alternate the estimation of the unknown pixels and of the deconvolved image, using, e.g., an FFT-based deconvolution method. This framework is an efficient, high-quality alternative to existing methods of dealing with the image boundaries, such as edge tapering. It can be used with any fast deconvolution method. We give an example in which a state-of-the-art method that assumes periodic boundary conditions is extended, through the use of this framework, to unknown boundary conditions. Furthermore, we propose a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM). We provide a proof of convergence for the resulting algorithm, which can be seen as a "partial" ADMM, in which not all variables are dualized. We report experimental comparisons with other primal-dual methods, where the proposed one performed at the level of the state of the art. Four different kinds of applications were tested in the experiments: deconvolution, deconvolution with inpainting, superresolution, and demosaicing, all with unknown boundaries.Comment: IEEE Trans. Image Process., to be published. 15 pages, 11 figures. MATLAB code available at https://github.com/alfaiate/DeconvolutionIncompleteOb

A convex formulation for hyperspectral image superresolution via subspace-based regularization

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images which combine the high spectral and high spatial resolutions of HSIs and MSIs, respectively, is a data fusion problem that has been the focus of recent active research due to the increasing availability of HSIs and MSIs retrieved from the same geographical area. We formulate this problem as the minimization of a convex objective function containing two quadratic data-fitting terms and an edge-preserving regularizer. The data-fitting terms account for blur, different resolutions, and additive noise. The regularizer, a form of vector Total Variation, promotes piecewise-smooth solutions with discontinuities aligned across the hyperspectral bands. The downsampling operator accounting for the different spatial resolutions, the non-quadratic and non-smooth nature of the regularizer, and the very large size of the HSI to be estimated lead to a hard optimization problem. We deal with these difficulties by exploiting the fact that HSIs generally "live" in a low-dimensional subspace and by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction Method of Multipliers (ADMM), to this optimization problem, by means of a convenient variable splitting. The spatial blur and the spectral linear operators linked, respectively, with the HSI and MSI acquisition processes are also estimated, and we obtain an effective algorithm that outperforms the state-of-the-art, as illustrated in a series of experiments with simulated and real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe