ETRI Journal

  • 제46권4호
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  • Pages.619-632
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  • 2024
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Finite impulse response design based on two-level transpose Vedic multiplier for medical image noise reduction

  • Joghee Prasad (Department of ECE, KPR Institute of Engineering and Technology) ;
  • Arun Sekar Rajasekaran (Department of ECE, SR University) ;
  • J. Ajayan (Department of ECE, SR University) ;
  • Kambatty Bojan Gurumoorthy (Department of ECE, KPR Institute of Engineering and Technology)
  • 투고 : 2023.08.16
  • 심사 : 2023.11.22
  • 발행 : 2024.08.20
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초록

Medical signal processing requires noise and interference-free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two-level transpose Vedic multiplier. The proposed architecture identifies the zero-noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero-noise segregation. This is performed independently for the zero- and nonzero-noise intervals. Finally, the finite impulse response is estimated as the sum of zero- and nonzero-noise inputs at any finite classification.

키워드

과제정보

We acknowledge the receipt of this ETRI Journal submission. Thank you for your consideration of our manuscript for publication.

참고문헌

  1. W. Qin, M. T. Gamba, E. Falletti, and F. Dovis, An assessment of impact of adaptive notch filters for interference removal on the signal processing stages of a GNSS receiver, IEEE Trans. Aerosp. Electron. Syst. 56 (2020), no. 5, 4067-4082, DOI 10.1109/taes.2020.2990148.
  2. J. Wouters, P. Patrinos, F. Kloosterman, and A. Bertrand, Multi-pattern recognition through maximization of signal-to-peak-interference ratio with application to neural spike sorting, IEEE Trans. Signal Process. 68 (2020), no. 2020, 6240-6254, DOI 10.1109/tsp.2020.3033973.
  3. Z. Xiang, G. Ou, and A. Rashidi, Robust cascaded frequency filters to recognize rebar in GPR data with complex signal interference, Autom. Constr. 124 (2021), 103593, DOI 10.1016/j.autcon.2021.103593.
  4. C. Hellings, F. Askerbeyli, and W. Utschick, Two-user SIMO interference channel with treating interference as noise: improper signaling versus time-sharing, IEEE Trans. Signal Process. 68 (2020), 6467-6480, DOI 10.1109/tsp.2020.3027903.
  5. J. Kim, Signal processing and noise analysis on realistic radiation detector model, Nucl. Inst. Methods Phys. Res. A 1038 (2022), 166931, DOI 10.1016/j.nima.2022.166931.
  6. A. V. Lvov, V. A. Karaseva, O. A. Potapov, and A. M. Sokov, Adaptive active wideband acoustic noise cancellation system with dynamic calibration, Acoust Phys. 69 (2023), no. 3, 372-380, DOI 10.1134/s1063771022700026.
  7. V. D. Christilda and A. Milton, Speed, power and area efficient 2D fir digital filter using Vedic multiplier with predictor and reusable logic, Analog Integr. Circuits Signal Process. 108 (2021), no. 2, 323-333, DOI 10.1007/s10470-021-01853-8.
  8. P. Paliwal, J. B. Sharma, and V. Nath, Comparative study of FFA architectures using different multiplier and adder topologies, Microsyst. Technol. 26 (2019), no. 5, 1455-1462, DOI 10.1007/s00542-019-04678-8.
  9. M. M. da Rosa, P. U. da Costa, E. A. C. da Costa, S. J. M. Almeida, G. Paim, and S. Bampi, Design of a low power and robust VLSI power line interference canceler with optimized arithmetic operators, Analog Integr. Circuits Signal Process. 112 (2022), no. 2, 247-261, DOI 10.1007/s10470-022-02050-x.
  10. P. Chowdari and J. B. Seventline, VLSI implementation of distributed arithmetic based block adaptive finite impulse response filter, Mater. Today: Proc. 33 (2020), 3757-3762, DOI 10.1016/j.matpr.2020.06.206.
  11. A. Carini, R. Forti, and S. Orcioni, A polynomial multiple variance method for impulse response measurement, SSRN Electron. J. 207 (2022), DOI 10.2139/ssrn.4258609.
  12. J. Liu and J. Li, Analytical performance of rank-one signal detection in subspace interference plus Gaussian noise, IEEE Trans Aerosp. Electron. Syst. 56 (2020), no. 2, 1595-1601, DOI 10.1109/taes.2019.2950075.
  13. P. A. Lopes, Low-delay and low-cost sigma-delta adaptive controller for active noise control, Circuits Syst. Signal Process. 41 (2022), no. 5, 2988-2999, DOI 10.1007/s00034-021-01913-4.
  14. R. Walia and S. Ghosh, Design of active noise control system using hybrid functional link artificial neural network and finite impulse response filters, Neural Comput. Applic. 32 (2018), no. 7, 2257-2266, DOI 10.1007/s00521-018-3697-5.
  15. J. Brunnstrom, T. Van Waterschoot, and M. Moonen, Signal-to-interference-plus-noise ratio based optimization for sound zone control, IEEE Open J. Signal Process. 4 (2023), 257-266, DOI 10.1109/ojsp.2023.3246398.
  16. M. Qiu, Y.-C. Huang, and J. Yuan, Discrete signaling and treating interference as noise for the Gaussian interference channel, IEEE Trans. Inf. Theory 67 (2021), no. 11, 7253-7284, DOI 10.1109/tit.2021.3111551.
  17. Y. Guo, X. Li, and Q. Meng, An outlier robust finite impulse response filter with maximum correntropy, IEEE Access. 9 (2021), 17030-17040, DOI 10.1109/access.2021.3053212.
  18. R. Qiu, P. Qu, X. Zheng, and G. Tang, Design of a finite impulse response filter for rapid single-flux-quantum signal processors based on stochastic computing, Superconductivity 6 (2023), 100045, DOI 10.1016/j.supcon.2023.100045.
  19. R. G. Ramirez-Chavarria and M. Schoukens, Nonlinear finite impulse response estimation using regularized neural networks, IFAC-PapersOnLine 54 (2021), no. 7, 174-179, DOI 10.1016/j.ifacol.2021.08.354.
  20. E. M. Carlini, F. del Pizzo, G. M. Giannuzzi, D. Lauria, F. Mottola, and C. Pisani, Online analysis and prediction of the inertia in power systems with renewable power generation based on a minimum variance harmonic finite impulse response filter, Int. J. Electr. Power & Energy Syst. 131 (2021), 107042, DOI 10.1016/j.ijepes.2021.107042.
  21. S. Li, M. Zhang, Y. Ji, Z. Zhang, R. Cao, B. Chen, H. Li, and Y. Yin, Agricultural machinery GNSS/IMU-integrated navigation based on fuzzy adaptive finite impulse response Kalman filtering algorithm, Comput. Electron. Agric. 191 (2021), 106524, DOI 10.1016/j.compag.2021.106524.
  22. Y. Wu, Z. Mao, Y. Li, and S. Liu, Finite impulse response filter based fault estimation with computational efficiency for linear discrete time-varying systems subject to multiplicative noise, J. Frankl Instit. 359 (2022), no. 6, 2737-2754, DOI 10.1016/j.jfranklin.2022.01.044.
  23. Y. Pu, Y. Yang, Y. Rong, and J. Chen, Expectation maximization algorithm for time-delay output-error models based on finite impulse response method, Int. J. Contr. Autom. Syst. 19 (2021), no. 12, 3914-3923, DOI 10.1007/s12555-021-0241-7.
  24. X. Wang, Y. Rong, C. Wang, F. Ding, and T. Hayat, Gradientbased iterative parameter estimation for a finite impulse response system with saturation nonlinearity, Int. J. Contr. Autom. Syst. 20 (2022), no. 1, 73-83, DOI 10.1007/s12555-020-0872-0.
  25. V. Jain, P. Chakrabarti, M. Mitolo, Z. Leonowicz, M. Jasinski, A. Vinogradov, and V. Bolshev, A power-efficient multichannel low-pass filter based on the cascaded multiple accumulate finite impulse response (CMFIR) structure for digital image processing, Circuits Syst. Signal Process. 41 (2022), no. 7, 3864-3881, DOI 10.1007/s00034-022-01960-5.
  26. R. Karthick, A. Senthilselvi, P. Meenalochini, and S. Senthil Pandi, Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA, Circuits Syst. Signal Process. 41 (2022), no. 9, 5254- 5282, DOI 10.1007/s00034-022-02034-2.
  27. M. M. Ganatra and C. H. Vithalani, FPGA design of a variable step-size variable tap length denlms filter with hybrid systolicfolding structure and compressor-based booth multiplier for noise reduction in ECG signal, Circuits Syst. Signal Process. 41 (2022), no. 6, 3592-3622, DOI 10.1007/s00034-021-01933-0.
  28. Y. Zhang, X. Zhu, A. Liu, and S. Yi, A Lorentzian-IP norm regularization based algorithm for recovering sparse signals in two types of impulsive noise, J. Comput. Appl. Math 430 (2023), 115251, DOI 10.1016/j.cam.2023.115251.
  29. Y. MejiaCruz and B. T. Davis, Event reconstructing adaptive spectral evaluation (ERASE) approach to removing noise in structural acceleration signals, Exp. Tech. 47 (2022), no. 4, 827-837, DOI 10.1007/s40799-022-00598-x.
  30. J. Prasad, D. Mohana Geetha, and K. Srinivasan, Experimental setup of stretchable arid dry pad sensors for the signal acquisition FIR filter design using Vedic approach, Measurement 141 (2019), 209-216.
  31. Y. Yamanashi, T. Kainuma, N. Yoshikawa, I. Kataeva, H. Akaike, A. Fujimaki, and M. Hidaka, 100 GHz demonstrations based on the single-flux-quantum cell library for the 10 kA/cm2 Nb multi-layer process, IEICE Trans. Electron. 93 (2010), no. 4, 440444.