Skip to main content
Log in

CORDIC-Based VLSI Architecture for Implementing Kaiser-Bessel Window in Real Time Spectral Analysis

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Windowing techniques have been widely used for preprocessing of samples before fast Fourier transform (FFT) in real time spectral analysis to minimize spectral leakage and picket fence effect. Among all popular window functions, Kaiser-Bessel window is an obvious choice for its better spectral characteristics. In this paper, CORDIC (CO-ordinate Rotation DIgital Computer) based VLSI architecture for implementing Kaiser-Bessel window has been proposed for real time applications. The parallel-pipelined technique has been adopted for the present design to ensure high throughput. Various architectural design and implementation issues have been discussed. The physical synthesis for ASIC implementation of proposed architecture using Synopsys design compiler(Design Vision) and commercially available 0. 18 μm CMOS yields the core area of 52 mm 2and worst case dynamic power of 890 mW at an operating frequency and voltage of 400 MHz and 1.8 V respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  1. Zivanovic, M., & Carlosena, A. (2006). On asymmetric analysis windows for detection of closely spaced signal components. Mechanical Systems and Signal Processing, 20(3), 702–717.

    Article  Google Scholar 

  2. Muthuswamy, J., & Thakor, N.V. (1998). Spectral analysis methods for neurological signals. Journal of Neuroscience Methods, 83(1), 1–14.

    Article  Google Scholar 

  3. Banerjee, A., Dhar, A.S., Banerjee, S. (2001). FPGA realization of a CORDIC based FFT processor for biomedical signal processing. Microprocessors and Microsystems, 25(3), 131–142.

    Article  Google Scholar 

  4. Sansaloni, T., Perez-Pascual, A., Torres, V., Almenar, V., Toldo, J.F., Valls, J. (2008). FFT spectrum analyzer project for teching digital signal processing with FPGA devices. IEEE Transactions on Education, 50(3), 229–235.

    Article  Google Scholar 

  5. Barros, J., & Diego, R.I. (2006). Effects of windowing on the measurement of harmonics and interharmonics in the IEC standard framework. In Instrumentation and measurement technology conference (IMTC2006) (pp. 2294–2299).

  6. Xu, F. (2006). Algorithm to remove spectral leakage, close-in noise, and its application to converter test. In Instrumentation and measurement technology conference (IMTC2006) (pp. 1038–1042).

  7. Prabhu, K.M.M., & Bhoopathy Bagan, B.K. (1989). Variable parameter window families for digital spectral analysis. IEEE Transactions on Acoustics, Speech and Signal Processing, 37(6), 946–949.

    Article  Google Scholar 

  8. Higgins, R.J. (1990). Digital signal processing in VLSI. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  9. Prabhu, K.M.M., & Agrawal J.P. (1978). Selection of data windows for digital signal processing. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (IEEE-ICASSP-1978) (pp. 79–82).

  10. Yuegang, W., Shao, J., Hongtao, X. (2007). Non-stationary signals processing based on STFT. In The eighth international conference on electronic measurement & instruments (pp. 301–304).

  11. Zhang, S., Yu, D., Sheng, S. (2006). A discrete STFT processor for real-time spectrum analysis. In Proceedings on circuits and systems, IEEE Asia-Pacific conference, (IEEE-APCCAS6) (pp. 1943–1946).

  12. Cheng-Ying, Y., Sau-Gee, C., Jen-Chuan, C. (2006). Efficient CORDIC design for multimode OFDM FFT. In Proceedings on acoustics, speech and signal processing, (ICASSP06) (pp. 1036–1039).

  13. Shuenn-shyang, W., & Chien-Sung, L. (2008). An area-efficient design of variable length fast Fourier transform processor. Journal of Signal Processing Systems, 51, 245–256.

    Article  Google Scholar 

  14. Zhong, K., Zhu, G., He, H. (2003). A single-chip, ultra high-speed FFT architecture. In Proceedings on ASIC 5th international conference.

  15. Samad, A., Ragoub, A., Othman, M., Shariff, Z.A.M. (1998). Implementation of a high speed fast Fourier transform VLSI chip. Microelectronics Journal, 29(11), 881–887.

    Article  Google Scholar 

  16. Ray, K.C., & Dhar, A.S. (2006). CORDIC based unified VLSI architecture for implementing window functions for real time spectral analysis. IEE Proceedings - Circuits Devices and Systems, 153(6), 539–544.

    Article  Google Scholar 

  17. Haviland, G.L., & Tuszynsky, A.A. (1980). A CORDIC arithmetic processor chip. IEEE Transactions on Computers, 29(2), 68–79.

    Article  Google Scholar 

  18. Arulalan, M.R., Jamadagni, H.S., Rao, A. (2008). Novel Window functions for digital filters. In Fifth international conference on information technology: new generations (pp. 1184–1185).

  19. Hou, W., & Kwon, H.M. (1999). Complementary filter design for testing of IS-95 Code Division Multiple Access wireless communication systems. IEEE Transactions on Instrumentation and Measurement, 48(1), 34–38.

    Article  Google Scholar 

  20. Gnecchi, J.A.G., Garcia, J.C.H., Alvarado, J.D. (2007). Auxiliary neurofeedback system for diagnostic of attention deficit hyperactivity disorder. In Electronics, robotics and automotive mechanics conference (pp. 135–138).

  21. Bergen, S.W.A., & Antoniou, A. (2005). Application of Parametric window functions to the STDFT Method for Gene Prediction. In Proceedings on communication, computers and signal processing, (IEEE-PACRIM05) (pp. 324–327).

  22. Rade, L., & Westergren, B.B. (1992). Beta mathematics hand books (3rd Ed.). Cleveland: CRC Press.

    Google Scholar 

  23. Hu, X., Harber, R.G., Bass, S.C. (1991). Expanding the range of convergence of the CORDIC algorithm. IEEE Transactions on Computers, 40(1), 13–21.

    Article  Google Scholar 

  24. Padala, S.K., & Prabhu, K.M.M. (1999). Pipelined CORDIC processors for generating Gaussian random numbers. Signal Processing, 72(3), 177–181.

    Article  MATH  Google Scholar 

  25. Hu, Y.H. (1992). CORDIC-based VLSI architectures for digital signal processing. IEEE Signal Processing Magazine, 9(3), 16–35.

    Article  Google Scholar 

  26. Boudabous, A., Fahmi Ghozzi, M., Kharrat, W., Masmoudi, N. (2004). Implementation of hyperbolic functions using CORDIC algorithm. In Proceedings on international conference on microelectronics, (ICM04) (pp. 738–741).

  27. Harris, D. (2003). A taxonomy of parallel prefix networks. In IEEE conference on signals, systems, and computers (pp. 2213–2217).

  28. Hu, Y.H. (1992). The quantization effects of the CORDIC algorithm. IEEE Transactions on Signal Processing, 40(4), 834–844.

    Article  MATH  Google Scholar 

  29. Sung, T.U., & Hsin, H.C. (2007). Fixed point error analysis of CORDIC arithmetic for special-purpose signal processing. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, A(9), 2006–2013.

    Article  Google Scholar 

  30. Park, S.Y., & Cho, N.I. (2004). Fixed-point error analysis of CORDIC processor based on the variance propagation formula. IEEE Transactions on Circuits and Systems-1: Regular Papers, 51(3), 573–584.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kailash Chandra Ray.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ray, K.C., Dhar, A.S. CORDIC-Based VLSI Architecture for Implementing Kaiser-Bessel Window in Real Time Spectral Analysis. J Sign Process Syst 74, 235–244 (2014). https://doi.org/10.1007/s11265-013-0781-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-013-0781-z

Keywords

Navigation