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Hardware Implementation of a High-Speed Adaptive Filter Using a Combination of Systolic and Convex Architectures

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Abstract

In this paper, an improved high-speed adaptive filter is proposed and implemented using a field-programmable gate array platform. Specifically, a new filter structure combining systolic and convex architectures has been analyzed and compared with conventional filter architectures. The new filter structure efficiently removes power line interference noise from electrocardiogram signals at high convergence speeds. The systolic architecture is used to improve the convergence speed of the filter, and the convex architecture is used in combination to improve the signal-to-noise ratio of the filter. One fast filter was designed using the retimed delay recursive least square algorithm (recursive least square filter design with systolic architecture), and it was combined with one slow filter designed using the least mean square algorithm based on convex combination architecture. The proposed filter architectures are assessed for electrocardiogram noise cancellation, obtained from the MIT-BIH database, and the performance is compared with various filter structures in terms of the signal-to-noise ratio, convergence speed, learning behaviors, and complexity. The results show an improvement in signal-to-noise ratio of 24.2% and an increase in convergence speed of 50% when compared with conventional filter structures.

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Data Availability

Data are available in a public (MIT-BIH arrhythmia database).

References

  1. M. Bahoura, H. Ezzaidi, FPGA-implementation of parallel and sequential architectures for adaptive noise cancelation. Circuits Syst Signal Process 30, 1521–1548 (2011). https://doi.org/10.1007/s00034-011-9310-0

    Article  Google Scholar 

  2. M. Chandra, P. Goel, A. Anand, A. Kar, Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising. Biomed. Signal Process. Control (2021). https://doi.org/10.1016/j.bspc.2020.102221

    Article  Google Scholar 

  3. F. Ding, Y. Wang, J. Ding, Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model. Digit. Signal Process. 37, 100–108 (2015). https://doi.org/10.1016/j.dsp.2014.10.005

    Article  Google Scholar 

  4. V. Filipovic, N. Nedic, V. Stojanovic, Robust identification of pneumatic servo actuators in the real situations. Forsch. Ing. 75, 183–196 (2011). https://doi.org/10.1007/s10010-011-0144-5

    Article  Google Scholar 

  5. P. Goel, M. Chandra, FPGA implementation of adaptive filtering algorithms for noise cancellation—a technical survey. in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, ed by V. Nath, J. Mandal. vol 556. (Springer, Singapore, 2019) doi:https://doi.org/10.1007/978-981-13-7091-5_42

  6. S. Gao, H. Yoshida, K. Seto, S. Komatsu, M. Fujita, Interconnect-aware pipeline synthesis for array based architectures. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E92–A(6), 1464–1475 (2009). https://doi.org/10.1587/transfun.E92.A.1464

    Article  ADS  Google Scholar 

  7. Y. Hu, Iterative and recursive least squares estimation algorithms for moving average systems. Simul. Model. Pract. Theory 34, 12–19 (2013). https://doi.org/10.1016/j.simpat.2012.12.009

    Article  Google Scholar 

  8. T. Jamel, Absolute mean error based adaptive step size affine projection algorithm for adaptive filtering systems. Int. J. Inf. Electron. Eng. (2010). https://doi.org/10.7763/IJIEE.2013.V3.278

    Article  Google Scholar 

  9. T.M. Jamel, K.K. Al-Magazachi. Simple variable step size LMS algorithm for adaptive identification of IIR filtering system, in The 5th International Conference on Communications, Computers and Applications (MIC-CCA2012), pp. 23–28 (2012)

  10. A.Z. Khan, I. Shafi, Removing artifacts from raw electrocardiogram signals using adaptive filter in state space. Circuits Syst. Signal Process. 39, 1713 (2020). https://doi.org/10.1007/s00034-019-01149-3

    Article  Google Scholar 

  11. D. Kalaiyarasi, T. Kalpalatha Reddy, Design and implementation of Least Mean Square adaptive FIR filter using offset binary coding based Distributed Arithmetic, Microprocessors and Microsystems, Volume 71 (2019). doi:https://doi.org/10.1016/j.micpro.2019.102884.

  12. M. Kazemi, M.M. Arefi, A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems. ISA Trans. 67, 382–388 (2017). https://doi.org/10.1016/j.isatra.2016.12.002

    Article  PubMed  Google Scholar 

  13. V. Kavitha, P. Kaviya Priya, T. Sugapriyaa. Efficient implementation of adaptive filter architecture using gate level modification for ECG denoising, in Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering, pp. 171–177, Bangkok, (2018). doi:https://doi.org/10.18178/wcse.2018.06.031

  14. D.C. Le, J. Zhang, Y. Pang, A novel pipelined neural FIR architecture for nonlinear adaptive filter. Neurocomputing 440, 220–229 (2021). https://doi.org/10.1016/j.neucom.2020.11.036

    Article  Google Scholar 

  15. L. Lu, H. Zhao, Z. He, B. Chen, A novel sign adaptation scheme for convex combination of two adaptive filters. AEU-Int. J. Electron. C. 69(11), 1590–1598 (2015). https://doi.org/10.1016/j.aeue.2015.07.009

    Article  Google Scholar 

  16. J. Raghuwanshi, A. Mishra, N. Singh, The wavelet transform-domain adaptive filter for nonlinear acoustic echo cancellation. Multimed. Tools Appl. 79, 25853–25871 (2020). https://doi.org/10.1007/s11042-020-09218-5

    Article  Google Scholar 

  17. M. Sumalatha, P.V. Naganjaneyulu, K. S. Prasad. Low power and low area VLSI implementation of vedic design FIR filter for ECG signal de-noising, Microprocessors and Microsystems, Volume 71, (2019). doi:https://doi.org/10.1016/j.micpro.2019.102883.

  18. V. Stojanovic, V. Filipovic, Adaptive input design for identification of output error model with constrained output. Circuits Syst. Signal Process. 33, 97–113 (2014). https://doi.org/10.1007/s00034-013-9633-0

    Article  MathSciNet  Google Scholar 

  19. G. Swaminathan, G. Murugesan, S. Sasikala, L. Murali, A novel implementation of combined systolic and folded architectures for adaptive filters in FPGA. Microprocess. Microsyst. (2020). https://doi.org/10.1016/j.micpro.2020.103018

    Article  Google Scholar 

  20. P. Song, H. Zhao, Filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control. Mech. Syst. Signal Process. 120(2019), 69–82 (2019). https://doi.org/10.1016/j.ymssp.2018.10.009

    Article  ADS  Google Scholar 

  21. M. Salah, M. Dessouky, B. Abdelhamid, Design and implementation of an improved variable step-size NLMS-based algorithm for acoustic noise cancellation. Circuits Syst. Signal Process. 41, 551–578 (2022). https://doi.org/10.1007/s00034-021-01796-5

    Article  Google Scholar 

  22. V. Stojanovic, N. Nedic, D. Prsic, L. Dubonjic, Optimal experiment design for identification of ARX models with constrained output in non-Gaussian noise. Appl. Math. Model. 40(13–14), 6676–6689 (2016). https://doi.org/10.1016/j.apm.2016.02.014

    Article  MathSciNet  Google Scholar 

  23. S. Sasikala, G. Murugesan. Efficient digit serial architecture for sign least mean square adaptive filter for denoising of artifacts in ECG signals, Int. J. Biomed. Eng. Technol. 335–344 (2017). doi:https://doi.org/10.1504/IJBET.2017.082672.

  24. M.V. Sudhakar, M.P. Charan, G.N. Pranai, L. Harika, P. Yamini, Audio signal noise cancellation with adaptive filter techniques. Mater. Today: Proc. (2021). https://doi.org/10.1016/j.matpr.2021.07.080

    Article  Google Scholar 

  25. C. Sathya, S. Sasikala, G. Murugesan. Denoising ECG signal using combination of ENSLMS and ZA-LMS algorithms, in 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, India, pp. 1–6 (2015). doi:https://doi.org/10.1109/ICSCN.2015.7219911

  26. Á.A. Vázquez, J.G. Avalos, G. Sánchez, J.C. Sánchez, H. Pérez, A comparative survey of convex combination of adaptive filters. IETE J. Res. (2020). https://doi.org/10.1080/03772063.2020.1844075

    Article  Google Scholar 

  27. Z. Zhu, X. Gao, L. Cao, D. Pan, Y. Cai, Y. Zhu, Analysis on the adaptive filter based on LMS algorithm. Optik 127(11), 4698–4704 (2016). https://doi.org/10.1016/j.ijleo.2016.02.005

    Article  ADS  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Technology, Baghdad, Iraq

    Harith H. Thannoon & Ivan A. Hashim

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  1. Harith H. Thannoon
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Thannoon, H.H., Hashim, I.A. Hardware Implementation of a High-Speed Adaptive Filter Using a Combination of Systolic and Convex Architectures. Circuits Syst Signal Process 43, 1773–1791 (2024). https://doi.org/10.1007/s00034-023-02539-4

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