2010
DOI: 10.2528/pier09111201
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Three-Dimensional Nonlinear Inversion of Electrical Capacitance Tomography Data Using a Complete Sensor Model

Abstract: Abstract-Electrical Capacitance Tomography (ECT) is a noninvasive and non-destructive imaging technique that uses electrical capacitance measurements at the periphery of an object to generate map of dielectric permittivity of the object. This visualization method is a relatively mature industrial process tomography technique, especially in 2D imaging mode. Volumetric ECT is a new method that poses major computational challenges in image reconstruction and new challenges in sensor design. This paper shows a non… Show more

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Cited by 74 publications
(73 citation statements)
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“…In all these cases the amount of memory requirement for inversion calculation will increase significantly, therefore iterative methods become more suitable techniques for large scale computation. The same problem may occur in a number of other inverse problems [9][10][11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…In all these cases the amount of memory requirement for inversion calculation will increase significantly, therefore iterative methods become more suitable techniques for large scale computation. The same problem may occur in a number of other inverse problems [9][10][11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…This linear approximation comes with a price of system accuracy, as shown in figure 10 (pictures from [67]). In order to solve a nonlinear inverse problem, the forward problem needs to be updated in every iteration cycle.…”
Section: Reconstruction Algorithmsmentioning
confidence: 99%
“…Most of the mentioned disadvantages would be eliminated if it was possible to develop a new method of technical condition assessment that would gather a current stream of data concerning differences in the internal structure of the flood defence. One of the potential methods to achieve this goal is electrical tomography [2,12]. The tomography generates the image of the tested object interior.…”
Section: Introductionmentioning
confidence: 99%