IEEE TIE features FSE research in tomography and deep learning

The top Journal IEEE TIE features Faculty of Science and Engineering research regarding tomography and deep learning

Omar Costilla Reyes, Patricia Scully and Krikor Ozanyan (EEE and CEAS) have been featured in the IEEE TIE Journal regarding their innovative work on deep learning for industrial tomography. Industrial process tomography is a well-established area that uses methods to create an image of a cross section of a pipe for flow monitoring. The author's work addresses the problem by removing the image reconstruction stage and thus the need for image interpretation. Instead, the information contents of the spatio-temporal raw data acquired from tomography are used directly in deep learning models to enable classifications of the process conditions for process control. If you would like to find further information you can read the journal on the IEE Explore website; the journal manuscript was published as open access with funding from EPSRC.

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