Research Article Open Access

Image Reconstruction Algorithm for Electrical Charge Tomography System

M. F. Rahmat1, M. D. Isa1, K. Jusoff1, T. A. Hussin1 and S. Md. Rozali2
  • 1 ,
  • 2 , Afganistan
American Journal of Applied Sciences
Volume 7 No. 9, 2010, 1254-1263

DOI: https://doi.org/10.3844/ajassp.2010.1254.1263

Submitted On: 22 September 2010 Published On: 30 September 2010

How to Cite: Rahmat, M. F., Isa, M. D., Jusoff, K., Hussin, T. A. & Rozali, S. M. (2010). Image Reconstruction Algorithm for Electrical Charge Tomography System. American Journal of Applied Sciences, 7(9), 1254-1263. https://doi.org/10.3844/ajassp.2010.1254.1263

Abstract

Problem statement: Many problems in scientific computing can be formulated as inverse problem. A vast majority of these problems are ill-posed problems. In Electrical Charge Tomography (EChT), normally the sensitivity matrix generated from forward modeling is very ill-condition. This condition posts difficulties to the inverse problem solution especially in the accuracy and stability of the image being reconstructed. The objective of this study is to reconstruct the image cross-section of the material in pipeline gravity dropped mode conveyor as well to solve the ill-condition of matrix sensitivity. Approach: Least Square with Regularization (LSR) method had been introduced to reconstruct the image and the electrodynamics sensor was used to capture the data that installed around the pipe. Results: The images were validated using digital imaging technique and Singular Value Decomposition (SVD) method. The results showed that image reconstructed by this method produces a good promise in terms of accuracy and stability. Conclusion: This implied that LSR method provides good and promising result in terms of accuracy and stability of the image being reconstructed. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.

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Keywords

  • Tomography system
  • inverse problem
  • image reconstruction
  • least square with regularization