The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from global EM scattering measurements, at various incidences and wave frequencies. This large scale ill-posed inverse problem is explored by an intensive exploitation of an efficient 2D Maxwell solver, distributed on high performance compu...

Given two shapes in relative motion, an important class of inverse configuration problems are solved by determining relative configurations that maintain set-inclusion relationships (non-interference, containment, or contact) between the shapes. This class of inverse problems includes the well-known problem of constructing a configuration space obstacle, as well as many other problems in comput...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

In this talk, we will be mainly concerned with a novel multilevel linear sampling method (MLSM) for reconstructing the obstacle from its corresponding scattering amplitude. The new method is shown to possess asymptotically optimal computational complexity. For an n×n sampling mesh in R2 or an n×n×n sampling mesh in R3, the proposed algorithm requires to solve only O(nN−1) far-field equations fo...

SUMMARY This paper discusses the log-linear model for multi-way contingency table , where the cell values represent the frequency counts that follow an extended negative multinomial distribution. This is an extension of negative multinomial log-linear model described by Evans (1989). The parameters of the new model are estimated by maximum likelihood method. The likelihood ratio test for the ge...

A closed-form expression and an upper bound are obtained for the mean absolute error of the unbiased estimator of a probability in inverse binomial sampling. The results given permit the estimation of an arbitrary probability with a prescribed level of the normalized mean absolute error.

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

In this paper we present a novel method to calculate the gamut boundary of a color printing device using a sparse number of measured values. This method belongs to the SMGBD (Segment Maxima Gamut Boundary Descriptor ) family. At first, we construct an inverse printer model based on a set of printed color patches. This model allows us to find the colorant values that have to be sent to the print...

It is a ubiquitous problem to estimate the means of bounded random variables. Specially, the problem of estimating the probability of an event can be formulated as the estimation of the mean of a Bernoulli variable. Moreover, in many applications, one needs to estimate a quantity μ which can be bounded in [0, 1] after proper operations of scaling and translation. A typical approach is to design...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...