Advertisement

A Brief Survey on Hardware Realization of Two-Dimensional Adaptive Filters

  • Prabhat Chandra ShrivastavaEmail author
  • Prashant Kumar
  • Manish Tiwari
  • Amit Dhawan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 587)

Abstract

The efficient recognition of hardware of two-dimensional (2-D) adaptive filters is an immense problem of present state of art. The concept of the adaptive filter is given by Widrow in the decade of sixty and the mathematical expression of 2-D adaptive filters is introduced by Hadhoud in the decade of ninety. Further, several researchers give the different type of adaptive algorithms for the hardware realization of 2-D adaptive filters. The least mean square (LMS) algorithms are too renowned due to its accomplished convergence properties and simplicity to implement in hardware. In this paper, we present a concise compendium of the efficient hardware structure of 2-D adaptive filters.

Keywords

1-D and 2-D adaptive filter LMS algorithms Mean square error Normalized LMS 2-D LMS algorithm 

References

  1. 1.
    Haykin, S.: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ (1986)zbMATHGoogle Scholar
  2. 2.
    Sid-Ahmed, M.A.: Image Processing: Theory, Algorithms and Architectures. McGraw-Hill, New York (1995)Google Scholar
  3. 3.
    Lu, W.S., Antoniou, A.: Two-Dimensional Digital Filters. Marcel Dekker, New York (1992)zbMATHGoogle Scholar
  4. 4.
    Madisetti, V.K., Williams, D.B.: The Digital Signal Processing Handbook. IEEE Press, New York (1998)Google Scholar
  5. 5.
    Long, G., Ling, F., Proakis, J.G.: The LMS algorithm with delayed coefficient adaptation. IEEE Trans. Acoust., Speech, Signal Process. ASSP 37(9), 1397–1405 (1989)CrossRefGoogle Scholar
  6. 6.
    Herzberg, H., Haimi-Cohen, R., Be’ery, Y.: A systolic array realization of an LMS adaptive filter and the effects of delayed adaptation. IEEE Trans. Signal Process. 40(11), 2799–2803 (1992)CrossRefGoogle Scholar
  7. 7.
    Meyer, M.D., Agrawal, D.P.: A high sampling rate delayed LMS filter architecture. IEEE Trans. Circuits Syst.-II CAS 40(11), 727–729 (1993)CrossRefGoogle Scholar
  8. 8.
    Douglas, S.C., Zhu, Q., Smith, K.F.: A pipelined LMS adaptive FIR filter architecture without adaptation delay. IEEE Trans., Signal Process. 46(3), 775–779 (1998)CrossRefGoogle Scholar
  9. 9.
    Matsubara, K., Nishikawa, K., Kiya, H.: Pipelined LMS adaptive filter using a new look-ahead transformation. IEEE Trans. Circuits Syst., 11, CAS 46(1), 51–55 (1999)CrossRefGoogle Scholar
  10. 10.
    Hadhoud, M.M., Thomas, D.W.: The two-dimensional adaptive LMS (TDLMS) algorithm. IEEE Trans. Circuits Syst. 35, 485–494 (1988)CrossRefGoogle Scholar
  11. 11.
    Widrow, B., Glover, J.R., McCool, J.M., Kaunitz, J., Williams, C.S., Doodlin, R.C., Zeidler, J.R., Hearn, R.H., Dong, E.: Adaptive noise cancelling: principles and applications. Proc. IEEE 63, 1692–1716 (1975)CrossRefGoogle Scholar
  12. 12.
    Tan, A.C., Chen, S.T.: Two-dimensional adaptive LMS IIR algorithm. In: 1993 IEEE International Symposium on Circuits and Systems, vol. 1, pp. 299–302, May (1993)Google Scholar
  13. 13.
    Wu, F.H.: The optimum adaptive algorithms and applications for noise cancelation. M.S. thesis, West Virginia University, Morgantown, WV, May (1984)Google Scholar
  14. 14.
    Mikhael, W.B., Wu, F.H., Kazovsky, L.G., Kang, G.S., Fransen, L.L.: Adaptive filters with individual adaption of parameters. IEEE Trans. Circuits Syst., CAS 33, 677–686 (1986)CrossRefGoogle Scholar
  15. 15.
    Wiener, N.: Extrapolation, Interpolation and Smoothing of Stationary Time Series, with Engineering Applications. Wiley, New York (1949)zbMATHGoogle Scholar
  16. 16.
    Widrow, B., Hoff, M.E.: Adaptive switching circuits. In: Proceedings of WESCON Convention Record, part 4, pp. 96–140 (1960)Google Scholar
  17. 17.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall (1985)Google Scholar
  18. 18.
    Sayed, A.H.: Fundamentals of Adaptive Filtering. Wiley, Hoboken, NJ (2003)Google Scholar
  19. 19.
    Treichler, J.R., Johnson Jr., C.R., Larimore, M.G.: Theory and Design of Adaptive Filters. Wiley, NewYork, NY (1987)zbMATHGoogle Scholar
  20. 20.
    Macchi, O.: Adaptive Processing: The Least Mean Squares Approach with Applications in Transmission. Wiley (1995)Google Scholar
  21. 21.
    Soraghan, J.J., Stewart, R.W., Durrani, T.S.: Comparative analysis between non-canonical LMS and LMS adaptive filtering. Electron. Lett. 27(11), 947–950 (1991) (Department of Electrical & Electronic Engineering, Strathclyde University, Glasgow, UK)Google Scholar
  22. 22.
    Harris, R.W., Chabries, D.M., Bishop, F.A.: A variable step (VS) adaptive filter algorithm. IEEE Trans. Acoust., Speech, Signal Process., ASSP 34, 309–316 (1986)CrossRefGoogle Scholar
  23. 23.
    Nagal1, R., Kumar, P., Bansal, P.: A survey with emphasis on adaptive filter, structure, LMS and NLMS adaptive algorithm for adaptive noise cancellation system. J. Eng. Sci. Technol. Rev. 10(2), 150–160 (2017)CrossRefGoogle Scholar
  24. 24.
    Diniz, P.S.R.: Adaptive Filtering: Algorithms and Practical Implementation, 3rd edn. Springer, New York, NY, USA (2008)CrossRefGoogle Scholar
  25. 25.
    Soni, T., Zeidler, J.R., Ku, W.H.: Performance Evaluation of 2-D adaptive prediction filters for detection of small objects in image data. IEEE Trans. Image Process. 2(3), 327–340 (1993)CrossRefGoogle Scholar
  26. 26.
    Youlal, H., Janati, M., Najim, M.: Two-dimensional joint process lattice for adaptive restoration of images. IEEE Trans. Image Process. 1, 366–378 (1992), ISSN 1057-7149CrossRefGoogle Scholar
  27. 27.
    Cho, H., Priemer, R.: Automatic step size adjustment of the two-dimensional LMS algorithm. In: Proceedings of the 37th Midwest Symposium on Circuits and Systems 1994, vol. 2, pp. 864–867 (1994)Google Scholar
  28. 28.
    Mikhael, W.B., Ghosh, S.M.: Two dimensional block adaptive filtering algorithms. In: Proceedings of ISCAS’gB, pp. 1219–1222, May 1992Google Scholar
  29. 29.
    Mikhael, W.B., Ghosh, S.M.: Two-dimensional block adaptive filtering algorithms with optimum convergence factors. IEEE Trans. Circuit Syst.-11: Analog. Digit. Signal Process. 42(8), 505–515 (1995)CrossRefGoogle Scholar
  30. 30.
    Wang, T., Wang, C.-L.: A new two-dimensional block adaptive FIR filtering algorithm and its application to image restoration. IEEE Trans. Image Process. 7, 238–246 (1998). ISSN 1057-7149CrossRefGoogle Scholar
  31. 31.
    Tan, A., Chen, S.T.: Image enhancement with 2-D adaptive LMS-based recursive filters. In: Proceedings of International Conference on Signal Processing, Applications and Technology, Boston, MA, vol. 2, pp 1068–1072, Oct. 24–26 1995Google Scholar
  32. 32.
    Cho, H., Priemer, R.: Automatic step size adjustment of the two-dimensional LMS algorithm. IEEE conference, 0-7803-2428-5/95, pp. 864–867Google Scholar
  33. 33.
    Matsubara, K., Nishikawa, K., Kiya, H.: 2-D pipelined adaptive filters based on 2-D delayed LMS algorithm. IEICE Trans. E80 A(6), 1009–1014 (1997)Google Scholar
  34. 34.
    Kimijima, T., Nishikawa, K., Kiya, H.: Pipelining of 2-dimensional adaptive filters based on the LDLMS algorithm. In: Proceedings of the 1998 IEEE International Symposium on Circuits and Systems 1998. ISCAS ’98, vol. 5, pp. 190–193 (1998)Google Scholar
  35. 35.
    Harada, A., Nishikawa, K., Kiya, H.: A pipelined architecture for the normalized LMS adaptive digital filters. In: IEEE Asia-Pacific Conference on Circuits and Systems, Chiangmai, Thailand, 24–27 Nov. 1998Google Scholar
  36. 36.
    Van, L.D., Tenqchen, S., Chang, C.H., Feng, W.S.: A tree-systolic array of DLMS adaptive filter. IEEE J. Accoustics, Speech Signal Process. 3, 1253–1256 (1999)Google Scholar
  37. 37.
    Van, L.D., Feng, W.S.: Efficient systolic architectures for 1-D and 2-D DLMS adaptive digital filters. In: Proceedings of IEEE Asia Pacific Conference on Circuits Systems, Tianjin, China, Dec. 2000, pp. 399–402Google Scholar
  38. 38.
    Van, L.D., Feng, W.S.: An efficient systolic architecture for the DLMS adaptive filter and its applications. IEEE Trans. Circuits Systems—II: Analog. Digit. Signal Process. 48(4) (2001)Google Scholar
  39. 39.
    Santhaa, K.R., Vaidehi, V.: Design of efficient architectures for 1-D and 2-D DLMS adaptive filters. Sciencedirect VLSI J. 40, 209–225 (2007)CrossRefGoogle Scholar
  40. 40.
    Dutta, H., Hannig, F., Teich, J., Heigl, B., Hornegger, H.: A design methodology for hardware acceleration of adaptive filter algorithms in image processing. In: The IEEE Computer Society, Application-specific Systems, Architectures and Processors (ASAP ’06), 0-7695-2682-9/06Google Scholar
  41. 41.
    Meher, P., Park, S.: High-throughput pipelined realization of adaptive FIR filter based on distributed arithmetic. In: VLSI Symposium on Technology Digital, pp. 428–433 (2011)Google Scholar
  42. 42.
    Meher, P., Park, S.: Critical-path analysis and low-complexity implementation of the LMS adaptive algorithm. IEEE Trans. Circuit Syst.-I, Regul. Pap. 61(3), 778–788 (2014)CrossRefGoogle Scholar
  43. 43.
    Mohanty, B., Meher, P., Singhal, S., Swamy, M.: A high performance VLSI architecture for reconfigurable FIR using distributive arithmetic. J. Integr., VLSI J. 54, 37–46 (2016)CrossRefGoogle Scholar
  44. 44.
    Park, S., Meher, P.: Efficient FPGA and ASIC realization of a DA-based reconfigurable FIR digital filter. IEEE Trans. Circuits Syst. II. Express Briefs 61(7), 511–515 (2014)CrossRefGoogle Scholar
  45. 45.
    Tiwari, A., Kumar, P., Tiwari, M.: High throughput adaptive block FIR filter using distributed arithmetic. In: 2016 1st India International Conference on Information Processing (IICIP), Delhi, 2016, pp. 1–6.  https://doi.org/10.1109/iicip.2016.7975385
  46. 46.
    Kumar, P., Shrivastava, P.C., Tiwari, M., Dhawan, A.: ASIC Implementation of area-efficient, high-throughput 2-D IIR filter using distributed arithmetic. Circuits Syst. Signal Process. 37, 2934 (2018).  https://doi.org/10.1007/s00034-017-0698-zMathSciNetCrossRefGoogle Scholar
  47. 47.
    Kumar, P., Shrivastava, P.C., Tiwari, M., Mishra, G.R.: High-throughput, area-efficient architecture of 2-D block FIR filter using distributed arithmetic algorithm. Circuits Syst. Signal Process., 1–15 (2018).  https://doi.org/10.1007/s00034-018-0897-2CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Prabhat Chandra Shrivastava
    • 1
    Email author
  • Prashant Kumar
    • 1
  • Manish Tiwari
    • 1
  • Amit Dhawan
    • 1
  1. 1.Department of Electronics & Communication EngineeringMNNITAllahabadIndia

Personalised recommendations