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Let M be a finite set of random uniformly distributed points lying in a unit cube. Every four points from M make a tetrahedron and the tetrahedron can either contain some of the other points from M, or it can be empty. This diploma thesis brings an upper bound of the expected value of the number of empty tetrahedra with respect to size of M. We also show how precise is the upper bound in comparison to an approximation computed by a straightforward algorithm. In the last section we move from the three- dimensional case to a general dimension d. In the general d-dimensional case we have empty d-simplices in a d-hypercube instead of empty tetrahedra in a cube. Then we compare the upper bound for d-dimensional case to the results from another paper on this topic. 1

We derive useful general concentration inequalities for functions of Gibbs fields in the uniqueness regime. We also consider expectations of random Gibbs measures that depend on an additional disorder field, and prove concentration w.r.t the disorder field. Both fields are assumed to be in the uniqueness regime, allowing in particular for non-independent disorder field. The modification of the bounds compared to the case of an independent field can be expressed in terms of constants that resemble the Dobrushin contraction coefficient, and are explicitly computable. On the basis of these inequalities, we obtain bounds on the deviation of a diffraction pattern created by random scatterers located on a general discrete point set in the Euclidean space, restricted to a finite volume. Here we also allow for thermal dislocations of the scatterers around their equilibrium positions. Extending recent results for independent scatterers, we give a universal upper bound on the probability of a deviation of the random scattering measures applied to an observable from its mean. The bound is exponential in the number of scatterers with an upper bound rate that involves only the minimal distance between points in the point set.

In a recent work, Blanc, Le Bris, and Lions defined a notion of increment-stationarity for random point sets, which allowed them to prove the existence of a thermodynamic limit for two-body potential energies on such point sets (under the additional assumption of ergodicity), and to introduce a variant of stochastic homogenization for increment-stationary coefficients. Whereas stationary random point sets are increment-stationary, it is not clear a priori under which conditions incrementstationary random point sets are stationary. In the present contribution, we give a characterization of the equivalence of both notions of stationarity based on elementary PDE theory in the probability space. This allows us to give conditions on the decay of a covariance function associated with the random point set, which ensure that increment-stationary random point sets are stationary random point sets up to a random translation with bounded second moment in dimensions d > 2. In dimensions d = 1 and d = 2, we show that such sufficient conditions cannot exist., SCOPUS: ar.j, info:eu-repo/semantics/published

We consider diffraction at random point scatterers on general discrete point sets in ℝν, restricted to a finite volume. We allow for random amplitudes and random dislocations of the scatterers. We investigate the speed of convergence of the random scattering measures applied to an observable towards its mean, when the finite volume tends to infinity. We give an explicit universal large deviation upper bound that is exponential in the number of scatterers. The rate is given in terms of a universal function that depends on the point set only through the minimal distance between points, and on the observable only through a suitable Sobolev-norm. Our proof uses a cluster expansion and also provides a central limit theorem.

We describe a morphological image analysis method to characterize images in terms of geometry and topology. We present a method to compute the morphological properties of the objects building up the image and apply the method to triply periodic minimal surfaces and to images taken from polymer chemistry.