ENG  Vol.12 No.10 , October 2020
Application of FPGA in Process Tomography Systems
Abstract: This paper will provide some insights on the application of Field Programmable Gate Array (FPGA) in process tomography. The focus of this paper will be to investigate the performance of the technology with respect to various tomography systems and comparison to other similar technologies including the Application Specific Integrated Circuit (ASIC), Graphics Processing Unit (GPU) and the microcontroller. Fundamentally, the FPGA is primarily used in the Data Acquisition System (DAQ) due to its better performance and better trade-off as compared to competitor technologies. However, the drawback of using FPGA is that it is relatively more expensive.
Cite this paper: Hong, L. and Yunos, Y. (2020) Application of FPGA in Process Tomography Systems. Engineering, 12, 790-809. doi: 10.4236/eng.2020.1210056.

[1]   International Atomic Energy Agency (2008) Industrial Process Gamma Tomography: Final Report of a Coordinated Research Project 2003-2007. Vienna.

[2]   Reinecke, N., et al. (1998) Tomographic Measurement Techniques: Visualization of Multiphase Flows. Chemical Engineering Technology, 21, 7-18.<7::AID-CEAT7>3.0.CO;2-K

[3]   Ibrahim, S. (2000) Modelling to Optimize the Design of Optical Tomography Systems for Process Measurement. Paper Presented at WARSAW 2000, T-14.

[4]   Ibrahim, S., et al. (2000) Optical Tomography for Process Measurement and Control. UKACC Conference, Cambridge, 4-7 September 2000, 188-190.

[5]   Deng, X. and Yang, W.Q. (2012) Fusion Research of Electrical Tomography with Other Sensors for Two-Phase Flow Measurement. Measurement Science Review, 12, 62-67.

[6]   Industrial Tomography Systems 2010. An Oil Company Used Dual-Modality ECT and ERT to Study the Flow of Multiphase Oil-Water-Gas Systems Reducing Energy Costs and Improving Plant Yields [Annual Buyers’ Guide 2010]. Reader Reply Card No 168.

[7]   King, M., et al. (2015) Software-Driven Hardware Development. FPGA’15, Monterey, 22-24 February 2015, 13-22.

[8]   Qasim, S.M., et al. (2009) Hardware Realization of Matrix Multiplication Using Field Programmable Gate Array. MASAUM Journal of Computing, 1, 21-25.

[9]   Moore, A. (2014) FPGAs for Dummies. Altera Special Edition, John Wiley & Sons, Inc., Hoboken.

[10]   Tan, C., et al. (2013) Gas-Water Two-Phase Pattern Characterization with Multivariate Multiscale Entropy. IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, 22-23 October 2013, 40-44.

[11]   Zhang, L. (2014) Digital Electrical Resistance Tomography System and Its Experimental Research. Journal of Chemical and Pharmaceutical Research, 6, 520-526.

[12]   Zhang, Z., et al. (2011) Data Acquisition System Based on Compact PCI Bus and FPGA for Electrical Resistance Tomography. Control and Decision Conference, Mianyang, 23-25 May 2011, 3538-3543.

[13]   Yang, W.Q., et al. (2003) Analysis of the Effect of Stray Capacitance on an AC-Based Capacitance Tomography Transducer. IEEE Transactions on Instrumentation and Measurement, 52, 1674-1681.

[14]   Warsito, W., et al. (2007) Electrical Capacitance Volume Tomography. IEEE Sensors Journal, 7, 525-535.

[15]   Muttakin, I., et al. (2015) Design and Simulation of Quadrature Phase Detection in Electrical Capacitance Volume Tomography. Telkomnika, 13, 55-64.

[16]   Zhang, X., et al. (2007) A Novel ECT System Based on FPGA and DSP. Second International Conference on Innovative Computing, Information and Control, Kumamoto, 5-7 September 2007, 510-513.

[17]   Wang, P., et al. (2015) An Image Reconstruction Algorithm For Electrical Capacitance Tomography Based on Simulated Annealing Particle Swarm Optimization. Journal of Applied Research and Technology, 13, 197-204.

[18]   Firadus, A. and Meribout, M. (2013) A New Energy Aware Embedded Architecture for Real-Time Electrical Capacitance Tomography. 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC), Istanbul, 16-18 December 2013, 93-96.

[19]   Henderson, R.P. and Webster, J.G. (1978) An Impedance Camera for Spatially Specific Measurements of the Thorax. IEEE Transactions on Biomedical Engineering, 25, 250-254.

[20]   Sohal, H., et al. (2014) Electrical Impedance Imaging System Using FPGAs for Flexibility and Interoperability. BioMedical Engineering OnLine, 13, 126.

[21]   Hong, H., et al. (2009) Comparison of a New Integrated Current Source with the Modified Howland Circuit for EIT Applications. Physiological Measurement, 30, 999-1007.

[22]   Wi, H., et al. (2014) Multi-Frequency Electrical Impedance Tomography System with Automatic Self-Calibration for Long-Term Monitoring. IEEE Transactions on Biomedical Circuits and Systems, 8, 119-128.

[23]   Khan, S., et al. (2013) FPGA Based High Speed Data Acquisition System for Electrical Impedance Tomography. IOP Publishing Journal of Physics: Conference Series, 434, Article ID: 012081.

[24]   Wu, J., et al. (2013) Digital Biomedical Electrical Impedance Tomography Based on FPGA. Journal of Biosciences and Medicines, 1, 14-18.

[25]   Zhang, X.-H. and Wang, H.-X. (2008) Phase Sensitive De-Modulation in ECT System. Microcomputer Information, 24, 300-302.

[26]   Chen, X., et al. (2014) A PXI-Based Biomedical Electrical Impedance Tomography System. Applied Mechanics and Materials, 670-671, 1205-1209.

[27]   Sagar, S.P. (2008) Modern Ultrasonic Techniques for Defect Detection in Cast Materials. Special Metal Casting and Forming Processes (CAFP 2008), Jamshedpur, 25-26 February 2008, 88-94.

[28]   Bharath, R., et al. (2014) FPGA Based Implementation of Low Complex Adaptive Speckle Suppression Filter for B-Mode Medical Ultrasound Images. IEEE Conference on Biomedical Engineering and Sciences, Miri, 8-10 December 2014, 545-550.

[29]   Krishna, K.D., et al. (2014) FPGA Based Preliminary CAD for Kidney on IoT Enabled Portable Ultrasound Imaging System. IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), Natal, 15-18 October 2014, 198-202.

[30]   Wei, D. and Rajashekar, U. (2005) Handbook of Image and Video Processing, 157-165.

[31]   Choi, Y., et al. (2014) FPGA Implementation of EM Algorithm for 3D CT Reconstruction. 22nd Annual International Symposium on Field-Programmable Custom Computing Machines, IEEE, Boston, 11-13 May 2014, 157-160.

[32]   Chen, J., et al. (2012) A Hybrid Architecture for Compressive Sensing 3-D CT Reconstruction. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2, 616-625.

[33]   Chidlow, K. and Möller, T. (2003) Rapid Emission Tomography Reconstruction. Proc. 2003 Eurographics/IEEE TVCG Workshop on Volume Graphics, July 2003, 15-26.

[34]   Yan, G., et al. (2008) Fast Cone-Beam CT Image Reconstruction Using GPU Hardware. Journal of X-Ray Science and Technology, 16, 225-234.

[35]   Shinde, A. and Pathrikar, A.K. (2014) VLSI Implementation Using DWT for Image Compression and Image Fusion for Medical Applications. International Journal of Advanced Research in Computer and Communication Engineering, 3, 8300-8302.

[36]   Kumar, A.R. and Suhasini, E.R. (2014) NOC Based FPGA Acceleration for Monte Carlo Simulations with Applications to SPECT Imaging. International Journal of Ethics in Engineering & Management Education, 1, 76-80.

[37]   Chen, J., et al. (2012) FPGA-Accelerated 3D Reconstruction Using Compressive Sensing. FPGA’12, Monterey, 22-24 February 2012, 163-166.

[38]   Yang, T., et al. (2014) A High Frequency Digital Induction System for Conductive Flow Level. Measurements, Flow, Measurement and Instrumentation, 37, 83-91.

[39]   Griffiths, H. (2001) Magnetic Induction Tomography. Measurement Science and Technology, 12, 1126-1131.

[40]   Peyton, A., et al. (2003) Addressing the Difficulties in Using Inductive Methods to Evaluating Human Body Composition. Biom Hum Anthropol, 21, 69-77.

[41]   Korzhenevsky, A. and Sapetsky, S. (2001) Visualisation of the Internal Structure of Extended Conducting Objects by Magnetoinduction Tomography. Bulletin of the Russian Academy of Sciences: Physics, 65, 1945-1949.

[42]   Merwa, R., et al. (2004) Detection of Brain Oedema Using MIT: A Feasibility Study of the Likely Sensitivity and Detectability. Physiological Measurement, 25, 347-354.

[43]   Zolgharni, M., Ledger, P.D., Armitage, D.W., Griffiths, H. and Holder, D.S. (2008) Detection of Haemorrhagic Cerebral Stroke by Magnetic Induction Tomography: FE and TLM Numerical Modeling. Proceedings of the Electrical Impedance Tomography Conference, Dartmouth College, Hanover NH, USA, 16-18 June 2008, p. 54.

[44]   Yin, W., et al. (2008) Simultaneous Noncontact Measurement of Water-Level and Conductivity. IEEE Transactions on Instrumentation and Measurement, 57, 2665-2669.

[45]   Todman, T.J., et al. (2005) Reconfigurable Computing: Architectures and Design Methods. IEE Proceedings—Computers and Digital Techniques, 152, 193-207.

[46]   Singhal, L. and Bozorgzadeh, E. (2007) Multi-Layer Floorplanning for Reconfigurable Designs. IET: Computers and Digital Techniques, 1, 276-294.

[47]   Almashary, B., et al. (2005) Realization of Linear Back-Projection Algorithm for Capacitance Tomography Using FPGA. 4th World Congress on Industrial Process Tomography, Aizu, 87-93.

[48]   Qasim, S.M., et al. (2008) FPGA Based Parallel Architecture for the Computation of Third-Order Cross Moments. International Journal of Computer Systems Science & Engineering, 2, 216-220.

[49]   Birk, M., et al. (2014) A Comprehensive Comparison of GPU- and FPGA-Based Acceleration of Reflection Image Reconstruction for 3D Ultrasound Computer Tomography. Journal of Real-Time Image Processing, 9, 159-170.

[50]   Doctor, S., et al. (1986) SAFT the Evolution of a Signal Processing Technology for Ultrasonic Testing. NDT International, 19, 163-167.

[51]   Asano, S., et al. (2009) Performance Comparison of FPGA, GPU and CPU in Image Processing. International Conference on Field Programmable Logic and Applications, Prague, 31 August-2 September 2009, 126-131.

[52]   Chase, J., et al. (2008) Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study. 16th International Symposium on Field Programmable Custom Computing Machines, FCCM’08, Palo Alto, 14-15 April 2008, 173-182.

[53]   Kalarot, R. and Morris, J. (2010) Comparison of FPGA and GPU Implementations of Real-Time Stereo Vision. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, 13-18 June 2010, 9-15.

[54]   Che, S., et al. (2008) Accelerating Compute-Intensive Applications with GPUs and FPGAs. Symposium on Application Specific Processors, SASP, Anaheim, 8-9 June 2008, 101-107.

[55]   Nicolas, G.A.C., et al. (2008) High Speed 3D Tomography on CPU, GPU, and FPGA. Journal on Embedded Systems, 2008, Article ID: 930250.

[56]   Li, J., et al. (2011) Scalable, High Performance Fourier Domain Optical Coherence Tomography: Why FPGAs and Not GPGPUs. IEEE International Symposium on Field-Programmable Custom Computing Machines, Salt Lake City, 1-3 May 2011, 49-56.

[57]   Xu, J., et al. (2009) In Vivo Imaging of the Mouse Model of X-Linked Juvenile Retinoschisis with Fourier Domain Optical Coherence Tomography. Investigative Ophthalmology & Visual Science, 50, 2989.

[58]   Li, J., et al. (2011) Performance and Scalability of Fourier Domain Optical Coherence Tomography Acceleration Using Graphics Processing Units. Applied Optics, 50, 1832.

[59]   Aguilar, A., et al. (2014) Time of Flight Measurements Based on FPGA and SiPMs for PET-MR. Nuclear Instruments and Methods in Physics Research A, 734, 127-131.