In this paper, a parametric level set method for reconstruction of obstacles in general inverse problems is considered. General evolution equations for the reconstruction of unknown obstacles are derived in terms of the underlying level set parameters. We show that using the appropriate form of parameterizing the level set function results a significantly lower dimensional problem, which bypass...

[1] Monte Carlo inversion techniques were first used by Earth scientists more than 30 years ago. Since that time they have been applied to a wide range of problems, from the inversion of free oscillation data for whole Earth seismic structure to studies at the meter-scale lengths encountered in exploration seismology. This paper traces the development and application of Monte Carlo methods for ...

Beams, made of brittle materials like concrete or cement, show increasing crack development during their service life due to mechanical and environmental loadings. This local damage can be translated into a reduction of the local bending stiffness. Stiffness modifications, while assuming constant mass distribution, can be observed by monitoring the vibrational behavior of the beam. In this pape...

Two numerical methods one continuous and the other discrete are proposed for solving inverse singular value problems The rst method consists of solving an ordinary di erential equation obtained from an explicit calculation of the projected gradient of a certain objective function The second method generalizes an iterative process proposed originally by Friedland et al for solving inverse eigenv...

In this paper we discuss general regularization estimators. This class includes Tikhonov type and spectral cut-off estimators as well as iterative methods, such as ν-methods and the Landweber iteration. The latter estimators achieve the same (optimal) convergence rates as spectral cut-off, but do not require explicit spectral information on the operator and are often much faster to compute than...

A new era in cancer radiation therapy has been gradually emerging during the last decade with the rapid development of many new and powerful methods for plan­ ning treatment and for optimizing the delivery of radiation doses. This process parallels in many respects the rapid devel­ opment in medical imaging technology that took place during the last 20 years, where computed tomography (CT), mag...

In the uncertainty treatment framework considered in this paper, the intrinsic variability of the inputs of a physical simulation model is modelled by a multivariate probability distribution. The objective is to identify this probability distribution the dispersion of which is independent of the sample size since intrinsic variability is at stake based on observation of some model outputs. More...

Procedural modeling is an elegant and fast way to generate huge complex and realistically looking urban sites. Due to its generative nature it can also be referred to as forward-procedural modeling. Its major drawback is the usually quite complicated way of control. To overcome this difficulty a novel modeling paradigm has been introduced: it is commonly referred to as inverse procedural modeli...

Recovering electrical activity of the brain from MEG/EEG measurements is known as the MEEG inverse problem. It is an ill-posed problem in several senses. One is that there is further less data observed than data to recover. One way to address this issue is to search for regular solutions. We present here a framework for applying image processing filtering techniques to the MEEG inverse problem....