Electrical Capacitance Tomography: Current sensors/algorithms and future advances

Electrical Capacitance Tomography (ECT) forms an inverse problem aiming on the determination of material specifics x in the domain ΩROI from capacitance measurements d̃ taken at the boundary ∂ΩROI of the region of interest (ROI). In this paper we give an overview about the current state of the art of ECT for process tomography. This includes aspects about instrumentation and reconstruction algorithms. Further we will give an outlook about current developments, future advances and applications for ECT techniques.

[1]  A. Nachman,et al.  Global uniqueness for a two-dimensional inverse boundary value problem , 1996 .

[2]  Lijun Xu,et al.  Electrical capacitance tomography with a non-circular sensor using the dbar method , 2009 .

[3]  Hannes Wegleiter,et al.  Formulation of cost functionals for different measurement principles in nonlinear capacitance tomography , 2006 .

[4]  Wuqiang Yang,et al.  Dynamic imaging in electrical capacitance tomography and electromagnetic induction tomography using a Kalman filter , 2007 .

[5]  Wuqiang Yang,et al.  Comparison of three electrical capacitance tomography systems , 2010, 2010 IEEE International Conference on Imaging Systems and Techniques.

[6]  Christian F. Wallinger,et al.  Adaptive monotonicity method for permittivity imaging , 2009 .

[7]  P. Hansen.  Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .

[8]  Wuqiang Yang,et al.  New AC-based capacitance tomography system , 1999 .

[9]  E. Somersalo,et al.  Statistical and computational inverse problems , 2004 .

[10]  Hugh McCann,et al.  Process Imaging For Automatic Control (Electrical and Computer Enginee) , 2005 .

[11]  Wuqiang Yang,et al.  Hardware design of electrical capacitance tomography systems , 1996 .

[12]  Matti Lassas,et al.  Reconstructions of piecewise constant conductivities by the D-bar method for electrical impedance tomography , 2008 .

[13]  Subhas Chandra Mukhopadhyay,et al.  New Developments and Applications in Sensing Technology , 2011 .

[14]  Robert Banasiak,et al.  Spatial imaging with 3D capacitance measurements , 2007 .

[15]  Wuqiang Yang,et al.  Prior-online iteration for image reconstruction with electrical capacitance tomography , 2004 .

[16]  Daniel Watzenig,et al.  A particle filter approach for tomographic imaging based on different state-space representations , 2006 .

[17]  G. Holler,et al.  Adaptive regularization parameter adjustment for reconstruction problems , 2004, IEEE Transactions on Magnetics.

[18]  Lihui Peng,et al.  Image reconstruction algorithms for electrical capacitance tomography , 2003 .

[19]  Antonello Tamburrino,et al.  A new non-iterative inversion method for electrical resistance tomography , 2002 .

[20]  Sheng Liu,et al.  Preliminary study on ECT imaging of flames in porous media , 2008 .

[21]  Manuchehr Soleimani,et al.  Nonlinear image reconstruction for electrical capacitance tomography using experimental data , 2005 .

[22]  H. Zangl,et al.  A novel sensor fusion concept for distance measurement in automotive applications , 2010, 2010 IEEE Sensors.

[23]  Richard A Williams,et al.  Process tomography : principles, techniques and applications , 1995 .

[24]  E. Somersalo,et al.  Statistical inverse problems: discretization, model reduction and inverse crimes , 2007 .

[25]  Hannes Wegleiter,et al.  Development of a displacement current-based sensor for electrical capacitance tomography applications , 2008 .

[26]  Daniel Watzenig,et al.  A review of statistical modelling and inference for electrical capacitance tomography , 2009 .

[27]  W. Ahmed,et al.  Capacitance Sensors for Void-Fraction Measurements and Flow-Pattern Identification in Air–Oil Two-Phase Flow , 2006, IEEE Sensors Journal.

[28]  Daniel Watzenig,et al.  An H∞ approach for robust estimation of material parameters in electrical capacitance tomography , 2011 .

[29]  C. Fox,et al.  Accelerated Markov chain Monte Carlo sampling in electrical capacitance tomography , 2011 .

[30]  Hugh McCann,et al.  Process Imaging For Automatic Control , 2005 .