Inverse Problem

Electrical capacitance tomography (ECT) is considered as a promising process tomography (PT) technology. Image reconstruction algorithms play an important role in the successful applications of ECT. In this paper, a generalized objective functional, which has been developed using the combinational minimax estimation and a generalized stabilizing functional, is proposed. The Newton algorithm is employed to solve the proposed objective functional.

We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio.

The possibility of developing a photochemical setup on the basis of an optically pumped ammonia laser with an intracavity photoreactor is proved. The obtained values of the cavity 'implication' factor γ are comparable with those of intracavity systems based on a CO2 laser. The conditions for achieving the maximum energy in the focusing cavity are determined and the ways to control the shape of its caustic are indicated.

... a function that is assumed to have two continuous derivatives, adding the side condition guarantees positivity and produces, uniquely, the Epanechnikov kernel. ... It will be clear from the previous paragraphs that, in the problem of noise-free density estimation, the side constraints ...

A Petschek-type model of magnetic reconnection is used to describe the behaviour of nightside flux transfer events (NFTEs). Based on the Cagniard-deHoop method we calculate the magnetic field and plasma flow time series observed by a satellite. The reconstruction of the reconnection electric field is an ill-posed inverse problem, which we treat with the method of regularisation, since the solution of the Cagniard-deHoop method is given in the form of a convolution integral, which is a well- known problem in the theory of inverse problems. This method is applied to Cluster measurements from September 8 th , 2002, and from August, 13 th , 2002, where a series of Earth-ward propagating 1-minute scale magnetic field and plasma flow variations are observed which are consistent with the theoretical picture of NFTEs. Methods to estimate the satellite position with respect to the reconnection site as well as the Alfven velocity are presented because they are necessary parameters used in the...

Robot arms are used in modern work cells and flexible manufacturing systems (FMS) in handling work pieces and loading/unloading processes. The robot arm links may interfere with the bodies of the system components, which are considered as obstacles in the robot workspace. In order that the robot works in safe conditions, study of robot workspace in a free space and

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne

Many works related learning from examples to regularization techniques for inverse problems, emphasizing the strong algorithmic and conceptual analogy of certain learning algorithms with regularization algorithms. In particular it is well known that regularization schemes such as Tikhonov regularization can be effectively used in the context of learning and are closely related to algorithms such as support vector machines. Nevertheless the connection with inverse problem was considered only for the discrete (finite sample) problem and the probabilistic aspects of learning from examples were not taken into account. In this paper we provide a natural extension of such analysis to the continuous (population) case and study the interplay between the discrete and continuous problems. From a theoretical point of view, this allows to draw a clear connection between the consistency approach in learning theory and the stability convergence property in ill-posed inverse problems. The main mat...

This article describes the first attempt to conduct electrical resistivity tomography (ERT) in solid permafrost-affected rock faces. Electrode design, instrument settings, and processing routines capable of measuring under relevant conditions were developed. Four transects, with ...

We formulate the inverse problem of solving Fredholm integral equa- tions of the first kind as a nonparametric Bayesian inference problem, using Levy random fields (and their mixtures) as prior distributions. Posterior distributions for all featues of interest are computed using novel Markov chain Monte Carlo numerical methods in infinite-dimensional spaces, based on generalizations and extensions of the authors' Inverse

In an effort to understand the present-day thermal structure of the Jeanne d'Arc Basin, we derive matrix thermal conductivity values, using a geometric mean model, from thermal conductivity values of thirteen lithologic end-members (of which seven are measurements on materials from the Jeanne d'Arc Basin) and lithological data. Temperature corrections to thermal conductivity are made for both matrix and pore water. In-situ thermal conductivities were obtained after correcting for the effect of porosity, which we estimated from both lithological information and digital well logs. Calculated thermal conductivities of the formations range from 1.46 to 2.65 W m -1K -1. The heterogeneity of formation thermal conductivity is caused by at least two factors. The first is lithological variation due to facies change. The second is due to different degrees of compaction. Under-compacted intervals (overpressure zones) are characteristically of lower thermal conductivity. The surface heat flow density calculated with an inverse method in seventeen wells varies from 37.8 to 60.7 mW/m 2. This study indicates that surface heat flow density values estimated without porosity corrections may be 15% higher than those with porosity corrections. We also demonstrate the sensitivity of modelled timing of maturation to thermal conductivity estimations.