Title :
Electrical impedance imaging of binary mixtures with boundary estimation approach based on multilayer neural network
Author :
Jeon, Hae Jin ; Kim, Jae Hyoung ; Choi, Bong Yeol ; Kim, Kyung Youn ; Kim, Min Chan ; Kim, Sin
Author_Institution :
Dept. of Electron. Eng., Kyungpook Nat. Univ., Daegu, South Korea
fDate :
4/1/2005 12:00:00 AM
Abstract :
This paper presents a boundary estimation approach in electrical impedance imaging for binary-mixture fields based on multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the multilayer neural network. Results from numerical experiments show that the proposed approach is insensitive to the measurement noise and has a good possibility in the visualization of binary mixtures for a real time monitoring.
Keywords :
Fourier series; electric impedance imaging; flow visualisation; image processing; mixtures; neural nets; real-time systems; Fourier coefficients; Fourier series; binary mixtures visualization; boundary estimation; electrical impedance imaging; interfacial boundaries; measurement noise; multilayer neural network; real time monitoring; Conductivity; Electrodes; Image reconstruction; Impedance; Multi-layer neural network; Neural networks; Power engineering and energy; Tomography; Visualization; Voltage; Binary mixtures; boundary estimation; electrical impedance tomography; multilayer neural network;
Journal_Title :
Sensors Journal, IEEE
DOI :
10.1109/JSEN.2004.841868
