Detail Cantuman

Electrical capacitance volume tomography is a volumetric tomography
technique that utilizes capacitance and fringing to capture behavior or
perturbation in the sensing domain. One of the crucial issues in developing
ECVT technology is the reconstruction algorithm. In practice, ILBP is most used
due to its simplicity. However, it still presents elongation errors for certain
dielectric contrasts. The high undersampling measurement of the ECVT imaging
system, which is mathematically defined as an undetermined linear system, is
one of the most challenging issues. Compressive sensing (CS) is a framework
that enables the recovery of a sparse signal or a signal that can be represented as
sparse in a certain domain, by having a lower dimension of measurement data
compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework
is promising for solving an undetermined linear system such as the ECVT
imaging system. This paper discusses the possibility of developing an ECVT
imaging technique for static objects based on a CS framework. Based on the
simulation results, Non-optimized CS does not completely succeed in providing
better ECVT imaging quality. However, it does provide more localized imaging
compared to ILBP. In addition, by having fewer requirements for the
measurement data dimension, the CS framework is promising for reducing the
number of required electrodes.