A BB-PSO Image Reconstruction Algorithm for Electrical Capacitance Tomography System

  • Xingwu Sun
  • Yu Chen
  • Deyun Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 139)


The abstract To solve the electrical capacitance tomography(ECT) technology "soft field" effect and pathological problem, a new BB-PSO algorithm for electrical capacitance tomography is presented. On the basis of analyzing ECT system measurement principle, constructing corrector formula of secant approximation algorithm in second-order information items of objective functions. The feasibility of using this algorithm for ECT problems is also discussed.It shows that it is easy to meet the convergence condition and error of image reconstruction is small. Experimental results and simulation data indicate that the algorithm can provide high quality images and high speed compared with LBP , Steepest Descent algorithm and Tikhonov algorithm and this new algorithm presents presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.


electrical capacitance tomography image reconstruction PSO Barzilai-Borwein 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, S., Yang, W.Q., Wang, H., Jiang, F., Su, Y.: Investigation of square fluidized beds using capacitance tomography: preliminary results. Measurement Science and Technology 12(8), 1120–1125 (2002)CrossRefGoogle Scholar
  2. 2.
    Xi, S., Zhao, F.: Computation Methods for Optimization, vol. 7, pp. 113–117. Shanghai Scientifie & Technical Publishers, Shanghai (1983)Google Scholar
  3. 3.
    Yang, W.Q., Peng, L.H.: Image reconstruction algorithms for electrical capacitance tomography. Meas. Sci. Technol. 14, R1–R3 (2003)CrossRefGoogle Scholar
  4. 4.
    Wang, H., Zhu, X., Zhang, L.: Conjugate gradient algorithm for electrical capacitance tomography. Journal of Tianjin University 38(1), 1–4 (2005)Google Scholar
  5. 5.
    Liu, S., Fu, L., Yang, W.Q., et al.: Prior-online iteration for image reconstruction with electrical capacitance tomography. IEE Proc. Sci. Meas. Technol. 151(3), 195–200 (2004)CrossRefGoogle Scholar
  6. 6.
    Zhao, J., Fu, W., Li, T.: Image reconstruction new algorithm for electrical capacitance tomography. Computer Engineering 30(8), 54–57 (2004)Google Scholar
  7. 7.
    Nashed, M.Z., Walter, G.G.: General sampling theorems for functions in reproducing kernel Hilbert spaces. Math. Control Signals Systems (4), 363–390 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Barzilai, J., Borwein, J.: Two-point step size gradient methods. IMA Journal of Numerical Analysis (8), 141–148 (1988)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xingwu Sun
    • 1
  • Yu Chen
    • 1
  • Deyun Chen
    • 2
  1. 1.Northeast Forestry UniversityHarbinChina
  2. 2.Computer InstituteHarbin University of Science and TechnologyHarbinChina

Personalised recommendations