A Finite Volume Method using a Quadtree Non-Uniform Structured Mesh for Modeling in Electrical Capacitance Tomography

An electric field solver based on a finite volume method using refined structural mesh is proposed to implement a quadtree structure and estimate the electric flux in the mesh cell. Numerical experiments were carried out using uniform and non-uniform meshes to assess quality of numerical modeling. The proposed method of verification of the quality of numerical calculations based on circular symmetry of the electrical capacitance tomography (ECT) probe allows to assess the effectiveness of mesh refinement and to reduce the number of mesh elements. Experiments showed that even a moderate level of mesh refinement is sufficient to significantly reduce the simulation error that occurs in modeling of cylindrical probes. The reduced number of mesh elements and applied implementation of the quadtree ensures high speed of forward problem calculations.

Electrical Capacitance Tomography (ECT) Electrode Size Simulation Study for Cultured Cell

Cell sensing and monitoring using capacitive sensors are widely used in cell monitoring because of the flexible and uncomplicated design and fabrication. Previous work from many different fields of applications has integrated capacitive sensing technique with tomography to produce cross-sectional images of the internal dielectric distribution. This paper carried an investigation on the capabilities of four 16-channel sensor electrodes with different electrode sizes to detect the change in the dielectric distribution of the cultured cells. All three 16-channel sensor electrodes are designed and simulate on COMSOL 6.3a Multiphysics. The pre-processing results obtained from three finite element models (FEM) of ECT sensor configurations in detecting the cell phantom shows that bigger electrodes size are more sensitive to permittivity distribution.

Analysis and Performance Evaluation of Entropic Thresholding Image Processing Techniques for Electrical Capacitance Tomography Measurement System

To improve image quality generated from the electrical capacitance tomography measurement system, the use of entropic thresholding techniques is investigated in this article. Based on the analysis of the principle of Electrical Capacitance Tomography (ECT) image reconstruction and entropic thresholding, various algorithms have been proposed for easy extraction of quantitative information from tomograms generated from the ECT system. Experiments indicate that proposed algorithms can provide high-quality images at no or minimum computational cost. It is easier to implement and integrate with classical algorithms such as Linear Back Projection, Singular value decomposition, Tikhonov regularization, and Landweber. Entropic thresholding techniques present a feasible and effective way toward the industrial utilization of ECT measurement systems. Keywords: Electrical Capacitance Tomography; Inverse Problem; Image Reconstruction; Entropic Thresholding

Iterative Algorithms for Deblurring of Images in Case of Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) has been used to measure flow by applying gas-solid flow in coal gasification, pharmaceutical, and other industries. ECT is also used for creating images of physically confined objects. The data collected by the acquisition system to produce images undergo blurring because of ambient conditions and the electronic circuitry used. This research includes the principle of ECT techniques for deblurring images that were created during measurement. The data recorded by the said acquisition system ascends a large number of linear equations. This system of equations is sparse and ill-conditioned and hence is ill-posed in nature. A variety of reconstruction algorithms with many pros and cons are available to deal with ill-posed problems. Large-scale systems of linear equations resulting during image deblurring problems are solved using iterative regularization algorithms. The conjugate gradient algorithm for least-squares problems (CGLS), least-squares QR factorization (LSQR), and the modified residual norm steepest descent (MRNSD) algorithm are the famous variations of iterative algorithms. These algorithms exhibit a semiconvergence behavior; that is, the computed solution quality first improves and then reduces as the error norm decreases and later increases with each iteration. In this work, soft thresholding has been used for image deblurring problems to tackle the semiconvergence issues. Numerical test problems were executed to indicate the efficacy of the suggested algorithms with criteria for optimal stopping iterations. Results show marginal improvement compared to the traditional iterative algorithms (CGLS, LSQR, and MRNSD) for resolving semiconvergence behavior and image restoration.