Climatic changes in Eurasia and Africa at the last glacial maximum and mid-Holocene: reconstruction from pollen data using inverse vegetation modelling

IEECAS SKLLQG

A new method of measuring alcohol clusters in polyimide membrane: combination of inverse gas chromatography with equilibrium swelling

A new method of measuring the mean size of solvent clusters in swollen polymer membrane is presented in this paper. This method is based on a combination of inverse gas chromatography (IGC) and equilibrium swelling. The mechanism is that weight fraction activity coefficient of solvent in swollen polymer is influenced by its clusters size. The mean clusters size of solvent in swollen polymer can be calculated as the quotient of the weight fraction activity coefficient of clustering system dividing the weigh fraction activity coefficient of non-clustering system. In this experiment, the weigh fraction activity coefficient of non-clustering system was measured with IGC. Methanol, ethanol and polyimide systems were tested with the new method at three temperatures, 20, 40, and 60degreesC. The mean clusters size of methanol in polyimide was five, four, and three at each temperature condition, respectively. Ethanol did not form clusters (the mean clusters size was one). In contrast to the inherent narrow temperature range in DSC, XRD, and FTIR methods, the temperature range in IGC and equilibrium swelling is broad. Compared with DSC. XRD. and FTIR, this new method can detect the clusters of solvent-polymer system at higher temperature.

Probabilistic Solution of Inverse Problems

In this thesis we study the general problem of reconstructing a function, defined on a finite lattice from a set of incomplete, noisy and/or ambiguous observations. The goal of this work is to demonstrate the generality and practical value of a probabilistic (in particular, Bayesian) approach to this problem, particularly in the context of Computer Vision. In this approach, the prior knowledge about the solution is expressed in the form of a Gibbsian probability distribution on the space of all possible functions, so that the reconstruction task is formulated as an estimation problem. Our main contributions are the following: (1) We introduce the use of specific error criteria for the design of the optimal Bayesian estimators for several classes of problems, and propose a general (Monte Carlo) procedure for approximating them. This new approach leads to a substantial improvement over the existing schemes, both regarding the quality of the results (particularly for low signal to noise ratios) and the computational efficiency. (2) We apply the Bayesian appraoch to the solution of several problems, some of which are formulated and solved in these terms for the first time. Specifically, these applications are: teh reconstruction of piecewise constant surfaces from sparse and noisy observationsl; the reconstruction of depth from stereoscopic pairs of images and the formation of perceptual clusters. (3) For each one of these applications, we develop fast, deterministic algorithms that approximate the optimal estimators, and illustrate their performance on both synthetic and real data. (4) We propose a new method, based on the analysis of the residual process, for estimating the parameters of the probabilistic models directly from the noisy observations. This scheme leads to an algorithm, which has no free parameters, for the restoration of piecewise uniform images. (5) We analyze the implementation of the algorithms that we develop in non-conventional hardware, such as massively parallel digital machines, and analog and hybrid networks.

Birkhoff normal forms in semi-classical inverse problems

Iantchenko, A.; Sj?strand, J.; Zworski, M., (2002) 'Birkhoff normal forms in semi-classical inverse problems', Mathematical Research Letters 9(3) pp.337-362 RAE2008

The inverse limit of GIT quotients of Grassmannians by the maximal torus

There are finitely many GIT quotients of

A regularized iterative algorithm for limited-angle inverse Radon transform

The tomography problem is investigated when the available projections are restricted to a limited angular domain. It is shown that a previous algorithm proposed for extrapolating the data to the missing cone in Fourier space is unstable in the presence of noise because of the ill-posedness of the problem. A regularized algorithm is proposed, which converges to stable solutions. The efficiency of both algorithms is tested by means of numerical simulations. © 1983 Taylor and Francis Group, LLC.

Stability and resolution in electromagnetic inverse problems

info:eu-repo/semantics/published

On the regularization of linear inverse problems in Fourier optics

info:eu-repo/semantics/published

Ill-posed Inverse Problems and Logarithmic Continuity in Electromagnetics

info:eu-repo/semantics/published

The Stability of Inverse Problems

info:eu-repo/semantics/published