Processing of Tomographic Data Using Weightless

Neural Networks

Dr. T.A. York and Mr. P.M. Duggan

The potential of simple weightless neural networks for fast inexpensive, processing of tomographic data is described. Image reconstruction and parameter estimation for two-component distributions in an electrical capacitance tomography system have been considered. Results concentrate on simulated data from finite element analysis but importantly, these have been verified using direct measurements on a tomographic rig. Typically, for image reconstruction, 95% of the binary pixels can be classified correctly for previously unseen measurements. It can be expected that 97% of the estimates of component ratio are within 10% of the actual value. Similarly flow regimes are correctly classified for 92% of patterns. This is the first report to describe direct estimates of these parameters without recourse to the time consuming process of image reconstruction. A particularly interesting result is that the WNN approach demands only modest accuracy from the measurements and this has significant and beneficial implications for noise immunity. Proposed hardware implementation requires less than 4 Mbits of RAM in all cases. This will enable 1000 images per second to be reconstructed and process parameters to be estimated for 10,000 frames per second.

Keywords: Weightless Neural Networks, Electrical Impedance Tomography, Parameter Estimation.