Biomechanics of a branch-stem junction in softwood

Direct measurement of strain field in a mechanically loaded Norway spruce branch-stem junction was performed by means of electronic speckle pattern analysis. Results were compared with strain distribution in a polyester cast of identical shape as the branch-stem junction, and a simplified polyester model consisting of two half-cylinders. Compared to polyester models, the branch-stem junction was characterised by a very homogeneous distribution of strain, which can be interpreted as a homogeneous distribution of stress in terms of fraction of material strength. This optimised transfer of mechanical load from the branch to the stem is achieved by a combination of naturally optimised shape with, additionally, optimised mechanical wood properties in the junction area.

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.