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Out of sample fusion is a computational method where real and independent computer generated data are fused. The method hinges on a density ratio model whereby the distributions of the real and the generated data are related. The method is applied in the interval estimation of very small binomial proportions from moderately large samples.
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
The research presented in this paper aims at developing and validating a predictive tool of individual exposure to solar Ultra-Violet (UV). UV exposure depends on ambient irradiation level and individual factors related to activity (position to the sun, clothing, duration of exposure, and other forms of sun protection). We predict exposure levels of body parts on basis of ambient irradiation levels...
This paper introduces volume springs that provide the volume effect to a surface model when it is deformed. The estimation of the properties of the model takes the real material properties into consideration, where each spring stiffness is derived based on the elasticity, rigidity and compressibility modulus. The proposed model can be adopted to simulate soft objects such as a deformable human breast,...
For several decades, the output from semiconductor manufacturers has been high volume products with process optimisation being continued throughout the lifetime of the product to ensure a satisfactory yield. However, product lifetimes are continually shrinking to keep pace with market demands. Furthermore there is an increase in dasiafoundrypsila business where product volumes are low; consequently...
The size of unified modeling language (UML) models used in practice is very large and ranges up to hundreds and thousands of classes. Querying of these models is used to support their quality assessment by information filtering and aggregating. For both, human cognition and automated analysis, there is a need for fast querying. In this context performance of model queries becomes an important issue...
In this paper, we present a simple method to find networks of time-correlated brain sources, using a singular value decomposition (SVD) analysis of the source matrix estimated after any linear distributed inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Despite the high dimension of the source space, our method allows for the rapid computation of the source matrix...
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