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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 476))

Abstract

The present chapter discusses Ramanujan Sums and its various signal processing applications. VLSI architectures to calculate Ramanujan Sums and DFT using it are also presented here. This chapter clearly articulates the usage and importance of Ramanujan Sums in a number of signal processing aspects. Ramanujan Sums can be calculated in an exact quantization error-free manner. As it works on integers, it is very suitable to realize hardware which consumes less machine cycles and also requires less hardware resources. All these properties may put Ramanujan Sums as a necessary technique for VLSI signal processing in near future.

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References

  1. Hardy G. H., Seshu Iyer P. V., and Wilson B. M., Collected papers of Srinivasa Ramanujan, Cambridge University Press, London, 1927.

    Google Scholar 

  2. Berndt B. C., Ramanujan’s notebooks, Springer-Verlag, Inc., N. Y., 1991.

    Google Scholar 

  3. Andrews G. E., Berndt B. C., Ramanujan’s lost notebook, Springer, N. Y., 2005.

    Google Scholar 

  4. Cohen E., “A class of arithmetical functions,” in Proc. Nat. Acad. Sci. U.S.A., vol. 41, 1955, pp. 939–944.

    Google Scholar 

  5. Cohen E., “Representations of even functions (mod r). I. Arithmetical identities,” Duke Math. J., vol. 25, pp. 401–421, 1958.

    Google Scholar 

  6. Samadi S., Ahmad M. O., Swamy M., Ramanujan sums and discrete fourier transforms, IEEE Signal Processing Letters 12 (4) (2005) pp. 293–296.

    Google Scholar 

  7. Planat M., Ramanujan sums for signal processing of low frequency noise, in: Frequency Control Symposium and PDA Exhibition, 2002. IEEE International, IEEE, 2002, pp. 715–720.

    Google Scholar 

  8. Mainardi L., Pattini L., Cerutti S., 2007. Application of the Ramanujan Fourier transform for the analysis of secondary structure content in amino acid sequences. Methods Inf Med 46, pp. 126–129.

    Google Scholar 

  9. Lagha M., Bensebti M., 2009. Doppler spectrum estimation by Ramanujan Fourier transform (RFT). Digital Signal Processing 19, pp. 843–851.

    Google Scholar 

  10. Mainardi L., Bertinelli M., Sassi R., 2008. Analysis of t-wave alternans using the Ramanujan transform, in: Computers in Cardiology, 2008, IEEE. pp. 605–608.

    Google Scholar 

  11. Sugavaneswaran L., Xie S., Umapathy K., and Krishnan S., “Time frequency analysis via Ramanujan sums,” IEEE Signal Processing Letters, vol. 19, pp. 352–355, June 2012.

    Google Scholar 

  12. Chen G., Krishnan S., Bui T. D., “Matrix Based Ramanujan Sums Transforms”, IEEE Signal Processing Letters, vol. 20, No. 10, pp. 941–944, October, 2013.

    Google Scholar 

  13. Yin C., Yin X.E., Wang J., “ A Novel Method for comparative analysis of DNA sequences by Ramanujan Fourier transform”, https://doi.org/10.1089/cmb.2014.0120.

  14. Ramanujan S., “On certain trigonometrical sums and their applications in the theory of numbers,” Trans. Cambridge Philosoph. Soc., vol. XXII, no. 13, pp. 259–276, 1918.

    Google Scholar 

  15. Hardy G. H. and Wright E. M., An Introduction to the Theory of Numbers. New York, NY, USA: Oxford Univ. Press, 2008.

    Google Scholar 

  16. Hardy G. H., “Note on Ramanujan’s trigonometrical function, and certain series of arithmetical functions,” in Proc. Cambridge Philosoph. Soc., 1921, vol. 20, pp. 263–271.

    Google Scholar 

  17. Vaidyanathan P. P., “Ramanujan sums in the context of signal processing Part I: Fundamentals,” IEEE Trans. Signal Process., vol. 62, no. 16, pp. 4145–4157, 2014.

    Google Scholar 

  18. Maddox J., “Möbius and problems of inversion,” Nature, vol. 344, no. 29, p. 377, Mar. 1990.

    Google Scholar 

  19. Carmichael R. D., “Expansions of arithmetical functions in infinite series,” in Proc. London Math. Soc., 1932, pp. 1–26.

    Google Scholar 

  20. Vaidyanathan P. P., “Ramanujan sums in the context of signal processing: Part II: FIR representations and applications,” IEEE Trans. on Signal Proc., vol. 62, no. 16, pp. 4158–4172, Aug., 2014.

    Google Scholar 

  21. Haukkanen P., “Discrete Ramanujan Fourier transform of even functions (mod r)”, Indian J. Math. Math. Sci., vol. 3, no. 1, pp. 75–80, 2007.

    Google Scholar 

  22. Toth L. and Haukkanen P.,”The Discrete Fourier transform of r-even functions”, Acta Univ. Sapientiae, Mathematica, vol. 3, no. 1, pp. 5–25, 2011.

    Google Scholar 

  23. Laohakosol V., Ruengsinsub P. and Pabhapote N.,” Ramanujan Sums for signal processing of low frequency noise”, Phys. Rev. E., 2006.

    Google Scholar 

  24. Anderson D.R., Apostol T.M., “The Evolution of Ramanujan Sums and Generalizations”, Duke Mathematical Journal, vol. 20, no. 2, pp. 211–216.

    Google Scholar 

  25. Apostol T.M., “Arithmetical properties of generalized Ramanujan Sums”, Pacific J. of Mathematics, vol 41, No. 2, 1972.

    Google Scholar 

  26. McCarthy P.J.,” A generalization of Smith’s determinant”, Canad. Math. Bull,, vol. 29, no. 1, pp. 109–113, 1986.

    Google Scholar 

  27. Pei S.-C. and Chang K.-W., “Odd Ramanujan sums of complex roots of unity,” IEEE Signal Process. Letters, vol. 14, pp. 20–23, Jan. 2007.

    Google Scholar 

  28. Debaprasad De, Archisman Ghosh, K Gaurav Kumar, Mrinal Kanti Naskar, “An efficient FPGA Implementation of Discrete Fourier and Inverse Discrete Fourier Transforms using Ramanujan Sums”, Circuits, Systems, and Signal Processing, Springer (submitted).

    Google Scholar 

  29. H. G. Gadiyar and R. Padma, “Ramanujan-Fourier series, the Wiener-Khintchine formula, and the distribution of prime numbers,” Physica A, vol. 269, pp. 503–510, 1999.

    Google Scholar 

  30. Cohen L., “Time-frequency distributions—A review,” Proc. IEEE, vol. 77, no. 7, pp. 941–981, Jul. 1989.

    Google Scholar 

  31. Planat M., Minarovjech M.and Saniga M., “Ramanujan sums analysis of long-period sequences and 1/f noise”, EPL J.; https://doi.org/10.1209/0295-5075/85/400052008.

  32. Cohen E., “An extension of Ramanujan’s sum. III. Connections with totient functions,” Duke Math. J. 23, 1956, pp: 623–630

    Google Scholar 

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Appendix

Appendix

  1. 1.

    σ(n) {Sum of divisors}

    The function σ(n) is the sum of positive divisors of n, i.e., \(\sigma \left( n \right) = \sum d \;{\text{if}}\; d|n\).

  2. 2.

    (n) {Euler’s totient function}

    The function (n) is defined as the number of positive integers which are less than and coprime with n. For example, (6) = 2 since {1, 5} are the only two positive integers which are less than and coprime with 6.

    $$\begin{aligned} & \psi \left( n \right) = n\mathop \prod \limits_{i} \left( {1 - \frac{1}{{n_{i} }}} \right)\;{\text{Such that}}\;n = \mathop \prod \limits_{i} n_{i}^{{\alpha_{i} }} \;{\text{where }}(\alpha_{i} )\;{\text{is prime}}. \\ & \psi_{2} \left( n \right) = n^{2} \mathop \prod \limits_{i} \left( {1 - \frac{1}{{n_{i}^{2} }}} \right) \\ \end{aligned}$$
  3. 3.

    µ(n) {Mobius function}

    Mobius function µ(n) is a number-theoretic function and is defined as

    $$\begin{array}{*{20}l} {\mu \left( n \right)} \hfill & { = 1} \hfill & {i{\text{f}}\;n = 1} \hfill \\ {} \hfill & { = \left( { - 1} \right)^{k} } \hfill & {{\text{if}}\;n = p_{1} p_{2} \ldots .p_{k} } \hfill \\ {} \hfill & { = 0} \hfill & {\text{otherwise}} \hfill \\ \end{array}$$

    Here, pi are distinct prime numbers.

  4. 4.

    ˄(n) {von Mangoldt function}

    $$\begin{array}{*{20}c} { \wedge \left( n \right)} & = & {\{ \ln p\quad {\text{if}}\;n = p^{\beta } ,\;p\;\;{\text{is prime}}} \\ {} & = & {\text{Otherwise}} \\ \end{array}$$
  5. 5.

    C(n)

    The function C(n) is defined as

    $$\begin{array}{*{20}l} {C\left( n \right)} \hfill & { = 2C_{2} \mathop \prod \limits_{p|n} \frac{p - 1}{p - 2},} \hfill & {{\text{if}}\;n\,{\text{is odd}}} \hfill \\ {} \hfill & { = 0,} \hfill & {{\text{if}}\;n\,{\text{is even}}} \hfill \\ \end{array}$$

    Here, p > 2 is a prime and \(p|n\) implies p divides n. The value of twin prime constant, \(C_{2}\) is 0.660

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De, D., Gaurav Kumar, K., Ghosh, A., Naskar, M.K. (2019). Ramanujan Sums and Signal Processing: An Overview. In: Nath, V., Mandal, J. (eds) Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017). Lecture Notes in Electrical Engineering, vol 476. Springer, Singapore. https://doi.org/10.1007/978-981-10-8234-4_34

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  • DOI: https://doi.org/10.1007/978-981-10-8234-4_34

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