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Advances in electrical capacitance tomography

Marashdeh, Qussai Mohammad
http://rave.ohiolink.edu/etdc/view?acc_num=osu1148591259

Abstract Details

2006, Doctor of Philosophy, Ohio State University, Electrical Engineering.
Electrical tomography techniques for process imaging are very prominent for industrial applications due to their low cost, safety, high capture speed, and suitability for different vessel sizes. Among electrical tomography techniques, electrical capacitance tomography has been the subject of extensive recent research due to its noninvasive nature and capability of differentiating between different phases based on permittivity distribution. Research in electrical capacitance tomography is inherently interdisciplinary, and areas of research in it can be categorized as: (1) sensor design, (2) hardware electronics, (3) and image reconstruction. Work presented in this dissertation includes developments in image reconstruction and sensor design. Work on image reconstruction presented in this dissertation include developments of both forward and inverse solutions. A feed forward neural network based forward solver has been developed for fast and relatively accurate forward solutions. The forward solver has been integrated into a Hopfield optimization reconstruction technique to provide a fully non-linear image reconstruction process. In addition, a 3D volume image reconstruction has been developed by extending the 2D it neural network multi objective image reconstruction technique (NN-MOIRT) to 3D applications, and inclusion of new objective functions tailored for 3D imaging. Developments on sensor related topics provided in this dissertation are 3D capacitance sensor designs for 3D imaging and non-invasive capacitance sensors for simultaneous permittivity/conductivity imaging. In the former case, a 3D sensor with axial variation in field distribution has been used for volume imaging based on the developed Hopfield 3D optimization image reconstruction. In the latter case, an extension of the conventional capacitance sensor based on capacitance and power measurements has been provided for simultaneous imaging of permittivity and conductivity distributions.
Fernando Teixeira (Advisor)
169 p.
Electrical Capacitacne Tomography; ECT; Multi-modal; Feed Forward Neural Networks; Hopfield Networks; Forward Problem; Image Reconstruction; Inverse Problem; Optimization; EIT; Sensor; Soft Field Tomography; Multi-Phase Imaging; Volume Tomography

Recommended Citations

Citations

  • Marashdeh, Q. M. (2006). Advances in electrical capacitance tomography [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1148591259

    APA Style (7th edition)

  • Marashdeh, Qussai. Advances in electrical capacitance tomography. 2006. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1148591259.

    MLA Style (8th edition)

  • Marashdeh, Qussai. "Advances in electrical capacitance tomography." Doctoral dissertation, Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1148591259

    Chicago Manual of Style (17th edition)

osu1148591259
4,369
© 2006, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.