Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System
Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.
Keywords:Electrical impedance tomography (EIT), Adaptive Noise cancellation, FPGA-based EIT system, Time-varying noise model, Adaptive filter
 X. Liu, J. Yao, T. Zhao, H. Obara, Y. Cui, and M. Takei. Image Reconstruction Under Contact mpedance Effect in Micro Electrical Impedance Tomography Sensors. IEEE Trans. Biomed. Circuits Syst., 2018,12(3): 623-631 DOI: https://doi.org/10.1109/TBCAS.2018.2816946
 A. Sapkota, T. Fuse, M. Seki, O. Maruyama, M. Sugawara, and M. Takei. Application of electrical resistance tomography for thrombus visualization in blood. Flow Meas Instrum., 2015, 46: 334–340. DOI: https://doi.org/10.1016/j.flowmeasinst
 B. S. Kim, A. K. Khambampati, Y. J. Hong, S. Kim, and K. Y. Kim. Multiphase flow imaging using an adaptive multi-threshold technique in electrical resistance tomography. Flow Meas. Instrum., 2013, 31: 25–34. DOI: https://doi.org/10.1016/j.flowmeasinst.2012.11.003
 Z. Wang, T. Zhao, and M. Takei. Morphological Structure Characterizations in Lithium-Ion Battery (LIB) Slurry under Shear Rotational Conditions by On-Line Dynamic Electrochemical Impedance Spectroscopy (EIS) Method. J. Electrochem. Soc., 2017, 164(9): A2268–A2276. DOI: https://doi.org/10.1149/2.0391712jes
 A. McEwan, G. Cusick, and D. S. Holder. A review of errors in multi-frequency EIT instrumentation. Physiol. Meas., 2007, 8(7): S197-215. DOI: https://doi.org/10.1088/0967-3334/28/7/S15
 J. Rosell, D. Murphy, R. Pallas, and P. Rolfe. Analysis and assessment of errors in a parallel data acquisition system for electrical impedance tomography. Clin. Phys. Physiol. Meas., 1988, 9(4A): 93–99. DOI: https://doi.org/10.1088/0143-0815/9/4A/016
 M. Rafiei-Naeini and H. McCann. Low-noise current excitation sub-system for medical EIT. Physiol. Meas., 2008, 29(6): S173–S184. DOI: https://doi.org/10.1088/0967-3334/29/6/S15
 M. Yasin, S. Böhm, P. O. Gaggero, and A. Adler. Evaluation of EIT system performance. Physiol. Meas., 2011, 32(7): 851.
 M. R. Baidillah, A.-A. S. Iman, Y. Sun, and M. Takei. Electrical Impedance Spectro-Tomography based on Dielectric Relaxation Model. IEEE Sens. J., 2017, 17(24): 8251–8262. DOI: https://doi.org/10.1109/JSEN.2017.2710146
 R. W. M. Smith, I. L. Freeston, B. H. Brown, and A. M. Sinton, “Design of a phase-sensitive detector to maximize signal-to-noise ratio in the presence of Gaussian wideband noise,” Meas. Sci. Technol. 1992, 3(11): 1054–1062. DOI: https://doi.org/10.1088/0957-0233/3/11/006
 N. Liu, G. J. Saulnier, and J. C. Newell. A multi channel synthesizer and voltmeter for electrical impedance tomography. in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003: 3110–3113.
 DOI: https://doi.org/10.1109/IEMBS.2003.1280800 E. K. Murphy, M. Takhti, J. Skinner, R. J. Halter, and K. Odame. Signal-to-Noise Ratio Analysis of a Phase-Sensitive Voltmeter for Electrical Impedance Tomography. IEEE Trans. Biomed. Circuits Syst., 2017, 11(2): 360–369. DOI: https://doi.org/10.1109/TBCAS.2016.2601692
 J. Yao and M. Takei. Application of Process Tomography to Multiphase Flow Measurement in Industrial and Biomedical Fields: A Review. IEEE Sens. J., 2017, 17(24): 8196–8205 DOI: https://doi.org/10.1109/JSEN.2017.2682929
 R. J. Halter, A. Hartov, and K. D. Paulsen. A Broadband High-Frequency Electrical Impedance Tomography System for Breast Imaging. IEEE Trans. Biomed. Eng., 2008, 55(2): 650–659. DOI: https://doi.org/10.1109/TBME.2007.903516
 C. Brady, J. Arbona, I. S. Ahn, and Y. Lu. FPGA-based adaptive noise cancellation for ultrasonic NDE application. in 2012 IEEE International Conference on Electro/Information Technology, 2012: 1–5. DOI: https://doi.org/10.1109/EIT.2012.6220729
 A. Rosado-Munoz, M. Bataller-Mompean, E. Soria-Olivas, C. Scarante, and J. F. uerrero-Martinez. FPGA Implementation of an Adaptive Filter Robust to Impulsive Noise: Two Approaches. IEEE Trans. Ind. Electron., 2011, 58(3) 860–870. DOI: https://doi.org/10.1109/TIE.2009.2023641
 R. Kusche, A. Malhotra, M. Ryschka, G. Ardelt, P. Klimach, and S. Kaufmann. A FPGA-Based Broadband EIT System for Complex Bioimpedance Measurements—Design and Performance Estimation. Electronics, , 2015, 4(3): 507–525. DOI: https://doi.org/10.3390/electronics4030507
 Z. Xu et al.. Development of a Portable Electrical Impedance Tomography System for Biomedical Applications. IEEE Sens. J., 2018, 18(19): 8117–8124. DOI: https://doi.org/10.1109/JSEN.2018.2864539
 X. Yang, Y. Xu, and F. Dong. A FPGA-based multi-frequency current source for biological EIT system. Conf. Rec. - IEEE Instrum. Meas. Technol. Conf., 2016, 2016(61302122): 1–6. DOI: https://doi.org/10.1109/I2MTC.2016.7520538
 G. Kou and L. Rong. FPGA-based digital phase-sensitive demodulator for EIT system. 2007 8th Int. Conf. Electron. Meas. Instruments, ICEMI, 2007: 4845–4848. DOI: https://doi.org/10.1109/ICEMI.2007.4351274
 S. A. Santos, A. Robens, A. Boehm, S. Leonhardt, and D. Teichmann. System description and first application of an FPGA-based simultaneous multi-frequency electrical impedance tomography.Sensors (Switzerland), 2016, 16(8). DOI: https://doi.org/10.3390/s16081158
 X. Yue and C. McLeod. FPGA design and implementation for EIT data acquisition. Physiol. Meas., 2008. DOI: https://doi.org/10.1088/0967-3334/29/10/007
 M. R. Naeini and H. McCann. A High Performance, Space Effective FPGA-Based Signal Generation and Measurement System for Medical EIT. in World Congress on Medical Physics and Biomedical Engineering 2006. IFMBE Proceedings, vol. 14, R. Magjarevic and J. H. Nagel, Eds. Springer, Berlin, Heidelberg, 2006: 3878–3881. DOI: https://doi.org/10.1007/978-3-540-36841-0_981
 Chang-Min Kim, Hyung-Min Park, Taesu Kim, Yoon-Kyung Choi, and Soo-Young Lee, “FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling,” IEEE Trans. Neural Networks, 2003, 14(5): 1038–1046. DOI:https://doi.org/10.1109/TNN.2003.818381
 A. Di Stefano, A. Scaglione, and C. Giaconia. Efficient FPGA Implementation of an Adaptive Noise Canceller. in Seventh International Workshop on Computer Architecture for Machine Perception (CAMP’05), 2005: 87–89. DOI: https://doi.org/10.1109/CAMP.2005.22
 T. Lan and J. Zhang. FPGA Implementation of an Adaptive Noise Canceller. in 2008 International Symposiums on Information Processing, 2008: 553–558. DOI: https://doi.org/10.1109/ISIP.2008.107
 B. Widrow et al.. Adaptive noise cancelling: Principles and applications. Proc. IEEE, 1975, 63(12): 1692–1716. DOI: https://doi.org/10.1109/PROC.1975.10036
 J. Nagumo and A. Noda. A learning method for system identification. IEEE Trans. Automat. Contr., 1967, 12(3): 282–287. DOI:https://doi.org/10.1109/TAC.1967.1098599
 B. Widrow and M. Hoff. Adaptive switching circuits. IRE WESCON Conv. Rec., 1960, 4: 96–104. DOI: https://doi.org/10.1088/0264-9381/23/9/024
 K. Mayyas. Performance analysis of the deficient length LMS adaptive algorithm. IEEE Trans. Signal Process., 2005, 53(8): 2727–2734. DOI: https://doi.org/10.1109/TSP.2005.850347
 H. C. So. Modified LMS algorithm for unbiased impulse response estimation in nonstationary noise. Electron. Lett., 1999, 35(10): 791. DOI: https://doi.org/10.1049/el:19990523
 S. Gazor. Prediction in LMS-type adaptive algorithms for smoothly time varying environments. IEEE Trans. Signal Process., 1999, 47(6): 1735–1739. DOI: https://doi.org/10.1109/78.765152
 A. Devices Inc, LTC2145-14 Datasheet, 2018. [Online]. Available: http://www.analog.com/en/products/analog-to-digital-converters/standard-adc/high-speed-ad-10msps/ltc2145-14.html#product-overview [Accessed: 25-Jun-2018].
 A. Adler et al. GREIT: a unified approach to 2D linear EIT reconstruction of lung images. Physiol. Meas. Physiol. Meas, 2009, 30(30): 35–55. DOI: https://doi.org/10.1088/0967-3334/30/6/S03
 L. Fabrizi, A. McEwan, E. Woo, and D. S. Holder. Analysis of resting noise characteristics of three EIT systems in order to compare suitability for time difference imaging with scalp electrodes during epileptic seizures. Physiol. Meas., 2007, 28(7): S217–S236. DOI: https://doi.org/10.1088/0967-3334/28/7/S16
 A. R. Frangi, P. J. Riu, J. Rosell, and M. A. Viergever. Propagation of measurement noise through backprojection reconstruction in electrical impedance tomography. IEEE Trans. Med. Imaging, 2002, 21(6): 566–578. DOI: https://doi.org/10.1109/TMI.2002.800612
 M. Oljaca and B. Baker. How the voltage reference affects ADC performance, Part 2. Analog Appl. J., 2009, 3Q: 13–16.
 M. Figureueiredo, J. Goes, and G. Evans, Reference-Free CMOS Pipeline Analog-to-Digital Converters. New York, USA: Springer-Verlag, 2013. DOI: https://doi.org/10.1007/978-1-4614-3467-2
 J. J. Blair and T. E. Linnenbrink. Corrected rms error and effective number of bits for sine wave ADC tests. Comput. Stand. Interfaces, 2004, 26(1): 43–49. DOI: https://doi.org/10.1016/S0920-5489(03)00061-8
 D. Belega, D. Dallet, and D. Petri. A High-Performance Procedure for Effective Number of Bits Estimation in Analog-to-Digital Converters. IEEE Trans. Instrum. Meas., 2011, 60(5): 1522–1532. DOI: https://doi.org/10.1109/TIM.2010.2089151
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Copyright © 2019 Marlin Ramadhan Baidillah, Zengfeng Gao, Al-Amin S Iman, Masahiro Takei
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