Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors

Abstract

A software tool that facilitates the development of image reconstruction algorithms, and the design of optimal capacitance sensors for a capacitance-based 12-electrode tomographic flow imaging system are described. The core of this software tool is the finite element (FE) model of the sensor, which is implemented in OCCAM-2 language and run on the Inmos T800 transputers. Using the system model, the in-depth study of the capacitance sensing fields and the generation of flow model data are made possible, which assists, in a systematic approach, the design of an improved image-reconstruction algorithm. This algorithm is implemented on a network of transputers to achieve a real-time performance. It is found that the selection of the geometric parameters of a 12-electrode sensor has significant effects on the sensitivity distributions of the capacitance fields and on the linearity of the capacitance data. As a consequence, the fidelity of the reconstructed images are affected. Optimal sensor designs can, therefore, be provided, by accommodating these effects.

The influence of quantization process on the performance of global entropic thresholding algorithms using electrical capacitance tomography data

“…The implemented LBP reconstruction algorithm (Xie et al, 1992) is described by the following equation.…”

“…The technique tends to bias toward splitting the grey level histograms into equal halves (Kapur, 1989). However, improvement in higher gas concentration ranges can be done by incorporating the truncation mechanism of Xie et al (1992) as already demonstrated elsewhere (Mwambela and Johansen, 2001). It is difficult to minimize distortions in lower gas concentration ranges because of the following reason.…”

Direct imaging of two-phase flows by electrical impedance measurements

“…Problems issued from diffractive or scattering systems can be usually solved by means of algorithms based on moment and pseudo inversion methods (Caorsi et al [5]; Chiu et al [6]). Problems issued from electric sensing systems can be frequently treated with several back-projection methods (Huang et al [7]; Xie et al [8]) and by means of iteration algorithms based on optimization techniques (Isaksen & Nordtvedt [9]), perturbation methods (Kim & Woo [10]; Yorkey et al [11]) or the NewtonRaphson method (Yorkey et al [12]; Abdullah et al [13]). …”

“…In addition, the algorithms available for image reconstruction assume in general the electrical field to be static and two-dimensional. Calculation results may consequently exhibit some over and undershooting as for instance void fractions above 100% and under 0% (Xie et al [8]). In the same way, 3D effects lead to the presence of "phantom objects" in the reconstructed images, when the flow configuration is not bidimensional (Peytraud [16]).…”

Comparative Performance Evaluation of Entropic Thresholding Algorithms Based on Shannon, Renyi and Tsallis Entropy Definitions for Electrical Capacitance Tomography Measurement Systems

“…Some reconstructions algorithms have been proposed to address the problem [16][17][18][19][20][21][22][23][24]. Others have improved images from LBP by further preprocessing the images using different approaches such as filtering [25], segmentation [26] and curve fitting [27].…”