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Noise Cancellation Using Adaptive Filters in FPGA

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Author Affiliations

  • 1Embedded Systems, Karunya University, Coimbatore, INDIA
  • 2Dept of EIE, Karunya University, Coimbatore, INDIA

Res. J. Recent Sci., Volume 3, Issue (ISC-2013), Pages 4-8, (2014)

Abstract

Adaptive filters have gained popularity over the years due to their ability to adapt themselves to different environmental situations without substantial intervention by the user. The implementation of an adaptive noise cancellation filter process is done here. The filter is designed using the Recursive least square (RLS) algorithm due to its computational simplicity, robust behavior when implemented in finite-precision hardware and well understood convergence behavior. The correctness and response of the adaptive noise cancellation filter can be checked by the RLS algorithm using the Matlab/ Simulink tool. To implement this algorithm the Simulink model is used as a reference using the Xilinx Tool Box. To implement the adaptive filter on Xilinx, the System Generator (“SysGen”) tool in the Xilinx block set is used to generate the bit file which can be downloaded onto the FPGA through hardware co-simulation. This project presents the adaptive noise cancellation filter using RLS algorithm suitable for noise cancellation and the results are verified by plotting the output using MATLAB.

References

  1. M.A. Gandhi, C. Ledoux and L. Mili, Robust estimation method for impulsive noise suppression in speech, IEEE Int. Symp. SignalProcess. Inf. Technol,3(7), 755–760 (2004)
  2. J. Armstrong, H. A. Suraweera, C. Chai and M. Feramez, Impulse noise mitigation techniques for OFDM receivers and their application in digital video broadcasting, Mediterr, J. Electron. Commun, 1(1), 1–10 (2005)
  3. E.S. Nejevenko and A.A. Sotnikov, Adaptive modeling for hydroacoustic signal processing, Pattern Recognit, Image Analysis,16(1), 5–8 (2006)
  4. Tuning Fuzzy Control Rules via Genetic Algorithms: An Experimental Evaluation Pitalúa Díaz N., Lagunas Jiménez R. and González Angelesa, Res. J. Recent Sci., 2(10), 81-87 (2013)
  5. J. Benesty, T. Gänsler, D.R. Morgan, M.M. Sondhi and S.L. Gay, Advances in Network and Acoustic Echo Cancellation, Berlin, Germany: Springer-Verlag, (2001)
  6. Comparative Survey on Time Interleaved Analog to Digital Converter Mismatches Compensation Techniques Hafiz Allah Nawaz, Abida Sharif and Muhammad Sharif, Research Journal of Recent Sciences,2(9), 95-100 (2013)
  7. I. Aizenberg, T. Bregin and D. Paliy, Method for the impulsive noise detection and its application for the improvement of the impulsive noise filtering algorithms, in Image Process. Conf., San Jose, CA, 4667, 204–214 (2002)
  8. U.M. Baese, Digital Signal Processing With Field Programmable Gate Arrays, 3rd ed. Berlin, Germany:Springer-Verlag, (2007)
  9. A.Ordaz-Moreno, R. de Jesus Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, and A. Garcia-Perez, Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation, IEEE Trans. Ind. Electron., 55(5), 2193–2202 (2008)
  10. Enhanced SLAM for a Mobile Robot using Unscented Kalman Filter and Radial Basis Function Neural Network Panah Amir, Res. J. Recent Sci.,2(2), 69-75 (2013)
  11. D. Zhang and H. Li, A stochastic-based FPGA controller for an induction motor drive with integrated neural network algorithms, IEEE Trans. Ind.Electron., 55(2),551–561 (2008) 4-8 (2014)
  12. Z. Shu, Y. Guo and J. Lian, Steady-state and dynamic study of active power filter with efficient FPGA-based control algorithm, IEEE Trans.Ind. Electron.,55(4), 1527–1536 (2008)
  13. T.P. Pander, A suppression of an impulsive noise in ECG signal processing, in Proc. Conf. IEEE Eng. Med. Biol. Soc.,1(3), 596–599 (2005)
  14. E. Soria, J.D. Martín, G. Camps, A.J. Serrano, J. Calpe and L. Gómez, A low-complexity fuzzy activation function for artificial neural networks, IEEE Trans. Neural Netw.,14(6), 1576– 1579 (2003)