Skip to main content

Transformations of Functions and Signals

  • Chapter
  • First Online:
Computational Methods in Physics

Part of the book series: Graduate Texts in Physics ((GTP))

Abstract

The Fourier transformation \(\mathcal{F}\) of the function f on the real axis is defined as \(F(\omega ) = \mathcal{F}[f](\omega ) = \int _{-\infty }^\infty f(x)\, \mathrm {e}^{-\mathrm {i}\omega x } \, \mathrm {d}x \>. \) The sufficient conditions for the existence of F are that f is absolutely integrable, i.e. \(\int _{-\infty }^\infty |f(x)| \, \mathrm {d}x < \infty \), and that f is piecewise continuous or has a finite number of discontinuities.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. B.D. MacCluer, Elementary Functional Analysis (Springer Science+Business Media, New York, 2010)

    MATH  Google Scholar 

  2. C. Shannon, Communication in the presence of noise. Proc. IEEE 86, 447 (1998) (reprint); See also M. Unser, Sampling-50 years after Shannon, Proc. IEEE 88, 569 (2000) and H. Nyquist, Certain topics in telegraph transmission theory, Proc. IEEE 90, 280 (2002) (reprint)

    Google Scholar 

  3. C. Canuto, M.Y. Hussaini, A. Quarteroni, T.A. Zang, Spectral Methods. Fundamentals In Single Domains (Springer, Berlin, 2006)

    MATH  Google Scholar 

  4. D. Donnelly, B. Rust, The fast fourier transform for experimentalists, Part I: concepts. Comput. Sci. Eng. p. 80 (Mar/Apr, 2005)

    Article  Google Scholar 

  5. F.J. Harris, On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. IEEE 66, 51 (1978)

    Article  ADS  Google Scholar 

  6. M. Frigo, S.G. Johnson, The design and implementation of FFTW3. Proc. IEEE 93, 216 (2005). documentation can be found at http://www.fftw.org

    Article  Google Scholar 

  7. W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling, Numerical Recipes: The Art of Scientific Computing, 3rd edn. (Cambridge University Press, Cambridge, 2007); See also the equivalent handbooks in Fortran, Pascal and C, as well as http://www.nr.com

  8. P. Duhamel, M. Vetterli, Fast Fourier transforms: a tutorial review and a state of the art. Signal Process. 19, 259 (1990)

    Article  MathSciNet  Google Scholar 

  9. J.C. Schatzman, Accuracy of the discrete Fourier transform and the fast Fourier transform. SIAM J. Sci. Comput. 17, 1150 (1996)

    Article  MathSciNet  Google Scholar 

  10. H. Hassanieh, P. Indyk, D. Katabi, E. Price, Simple and practical algorithm for sparse Fourier transform, in Proceedings of the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms, ed. by Y. Rabani (SIAM, Philadelphia, 2012), p. 1183

    Chapter  Google Scholar 

  11. Available at http://groups.csail.mit.edu/netmit/sFFT/

  12. H. Hassanieh, P. Indyk, D. Katabi, E. Price, Nearly optimal sparse Fourier transform, in Proceedings of the 44th Annual ACM Symposium on Theory of Computing (ACM, New York, 2012), p. 563

    Google Scholar 

  13. J. Schumacher, M. Püschel, High-performance sparse fast Fourier transforms, in Proceedings of the IEEE Workshop on Signal Processing Systems (IEEE, 2014), p. 1

    Google Scholar 

  14. Available at http://spiral.net/software/sfft.html

  15. P. Degroote et al., Evidence for nonlinear resonant mode coupling in the \(\beta \) Cephei star HD 180642 (V1449 Aquilae) from CoRoT photometry. Astron. Astrophys. 506, 111 (2009)

    Google Scholar 

  16. J.T. VanderPlas, Understanding the Lomb-Scargle Periodogram, arXiv:1703.0982 [astro-ph.IM]

  17. A. Dutt, V. Rokhlin, Fast Fourier transforms for nonequispaced data. SIAM J. Sci. Comput. 14, 1368 (1993)

    Article  MathSciNet  Google Scholar 

  18. A. Dutt, V. Rokhlin, Fast Fourier transforms for nonequispaced data, II. Appl. Comput. Harmon. Anal. 2, 85 (1995)

    Article  MathSciNet  Google Scholar 

  19. L. Greengard, J.-Y. Lee, Accelerating the nonuniform fast Fourier transform. SIAM Rev. 46, 443 (2004)

    Article  ADS  MathSciNet  Google Scholar 

  20. M. Lyon, J. Picard, The Fourier approximation of smooth but non-periodic functions from unevenly spaced data. Adv. Comput. Math. 40, 1073 (2014)

    Article  MathSciNet  Google Scholar 

  21. N. Seghouani, High-resolution spectral analysis of unevenly spaced data using a regularization approach. Mon. Not. R. Astron. Soc. 468, 3312 (2017)

    Article  ADS  Google Scholar 

  22. W.H. Press, G.B. Rybicki, Fast algorithm for spectral analysis of unevenly sampled data. Astrophys. J. 338, 277 (1989)

    Article  ADS  Google Scholar 

  23. J. Keiner, S. Kunis, D. Potts, Using NFFT3 – a software library for various nonequispaced Fourier transforms. ACM Trans. Math. Softw. 36, 1 (2009)

    Article  Google Scholar 

  24. D. Potts, G. Steidl, Fast summation at nonequispaced knots by NFFTS. SIAM J. Sci. Comput. 24, 2013 (2003)

    Article  MathSciNet  Google Scholar 

  25. D. Potts, G. Steidl, M. Tasche, Fast Fourier transforms for nonequispaced data: a tutorial, in Modern Sampling Theory: Mathematics and Applications, ed. by J.J. Benedetto, P.J.S.G. Ferreira (Birkhäuser, Boston, 2001), p. 247

    Chapter  Google Scholar 

  26. G. Steidl, A note on the fast Fourier transforms for nonequispaced grids. Adv. Comput. Math. 9, 337 (1998)

    Article  MathSciNet  Google Scholar 

  27. B. Leroy, Fast calculation of the Lomb-Scargle periodogram using nonequispaced fast Fourier transforms. Astron. Astrophys. 545, A50 (2012)

    Article  ADS  Google Scholar 

  28. G. Szegö, Orthogonal Polynomials (AMS, Providence, 1939)

    Book  Google Scholar 

  29. T.J. Rivlin, The Chebyshev Polynomials (Wiley, New York, 1974)

    MATH  Google Scholar 

  30. V. Rokhlin, A fast algorithm for discrete Laplace transformation. J. Complex. 4, 12 (1988) and J. Strain, A fast Laplace transform based on Laguerre functions. Math. Comput. 58, 275 (1992)

    Google Scholar 

  31. M. Abramowitz, I.A. Stegun, Handbook of Mathematical Functions, 10th edn. (Dover Publications, Mineola, 1972)

    Google Scholar 

  32. S. Zhang, J. Jin, Computation of Special Functions (Wiley-Interscience, New York, 1996)

    Google Scholar 

  33. W.E. Boyce, R.C. DiPrima, Elementary Differential Equations and Boundary Value Problems, 5th edn. (Wiley, New York, 1992)

    Google Scholar 

  34. J. Abate, P.P. Valkó, Multi-precision Laplace transform inversion. Int. J. Numer. Methods Eng. 60, 979 (2004)

    Article  Google Scholar 

  35. A.M. Cohen, Numerical Methods For Laplace Transform Inversion (Springer Science+Business Media, New York, 2007)

    MATH  Google Scholar 

  36. B. Davies, B. Martin, Numerical inversion of the Laplace transform: a survey and comparison of methods. J. Comput. Phys. 33, 1 (1979); See also D.G. Duffy, On the numerical inversion of Laplace transforms: comparison of three new methods on characteristic problems from applications. ACM Trans. Math. Softw. 19, 333 (1993)

    Google Scholar 

  37. C.L. Epstein, J. Schotland, The bad truth about Laplace’s transform. SIAM Rev. 50, 504 (2008)

    Article  MathSciNet  Google Scholar 

  38. R. Piessens, Gaussian quadrature formulas for the numerical integration of Bromwich’s integral and the inversion of the Laplace transform. J. Eng. Math. 5, 1 (1971); See also L.N. Trefethen, J.A.C. Weideman, T. Schmelzer, Talbot quadratures and rational approximations. BIT Numer. Math. 46, 653 (2006)

    Google Scholar 

  39. J. Abate, W. Whitt, Queueing Syst. 10, 5 (1992); See also P. den Iseger, Prob. Eng. Inf. Sci. 20, 1 (2006) and A. Yonemoto, T. Hisikado, K. Okumura, IEEE Proc.-Circuits Devices Syst. 150, 399 (2003). Among the most efficient are also the algorithms based on the expansion of the function \(f\) in terms of Laguerre polynomials and the corresponding computation of \({\cal{L}}[f]\) and \({\cal{L}}^{-1}[F]\); See J. Abate, G. L. Choudhury, Inf. J Comput. 8, 413 (1996)

    Google Scholar 

  40. E. Titchmarsh, Introduction to the Theory of Fourier Integrals, 2nd edn. (Clarendon press, Oxford, 1948)

    Google Scholar 

  41. L. Grafakos, Classical And Modern Fourier Analysis (Prentice Hall, New Jersey, 2003)

    MATH  Google Scholar 

  42. M.L. Glasser, Some useful properties of the Hilbert transform. SIAM J. Math. Anal. 15, 1228 (1984)

    Article  MathSciNet  Google Scholar 

  43. R. Wong, Asymptotic expansion of the Hilbert transform. SIAM J. Math. Anal. 11, 92 (1980)

    Article  MathSciNet  Google Scholar 

  44. S.L. Hahn, Hilbert Transform in Signal Processing (Artech House, Boston, 1996)

    Google Scholar 

  45. W.J. Freeman, Origin, structure, and role of background EEG activity, Part 1. Analytic amplitude, Clin. Neurophys. 115, 2077 (2004); Part 2. Analytic phase, Clin. Neurophys. 115, 2089 (2004); Part 3. Neural frame classification, Clin. Neurophys. 116, 1118 (2005); Part 4. Neural frame simulation, Clin. Neurophys. 117, 572 (2006)

    Article  Google Scholar 

  46. M. West, A. Krystal, EEG Recorded (Duke University)

    Google Scholar 

  47. C. Kittel, Introduction to Solid State Physics, 8th edn. (Wiley, New York, 2005)

    Google Scholar 

  48. M. Nandagopal, N. Arunajadai, On finite evaluation of finite Hilbert transform. Comput. Sci. Eng. 9, 90 (2007)

    Article  Google Scholar 

  49. T. Hasegawa, T. Torii, Hilbert and Hadamard transforms by generalized Chebyshev expansion. J. Comput. Appl. Math. 51, 71 (1994)

    Article  MathSciNet  Google Scholar 

  50. W. Gautschi, J. Waldvogel, Computing the Hilbert transform of the generalized Laguerre and Hermite weight functions. BIT Numer. Math. 41, 490 (2001)

    Article  MathSciNet  Google Scholar 

  51. V.R. Kress, E. Martensen, Anwendung der Rechteckregel auf die reelle Hilberttransformation mit unendlichem Intervall. Z. Angew. Math. Mech. 50, 61 (1970)

    Article  MathSciNet  Google Scholar 

  52. F.W. King, G.J. Smethells, G.T. Helleloid, P.J. Pelzl, Numerical evaluation of Hilbert transforms for oscillatory functions: a convergence accelerator approach. Comput. Phys. Commun. 145, 256 (2002)

    Article  ADS  MathSciNet  Google Scholar 

  53. J.A.C. Weideman, Computing the Hilbert transform on the real line. Math. Comput. 64, 745 (1995). Attention: the authors use a non-standard definition \({{\rm sign}}(0)=1\); in Eq. (22) correct \(1/N\rightarrow 1/(2N)\)

    Article  ADS  MathSciNet  Google Scholar 

  54. X. Wang, Numerical implementation of the Hilbert transform, Ph.D. thesis, University of Saskatchewan, Saskatoon (2006)

    Google Scholar 

  55. H. Boche, M. Protzmann, A new algorithm for the reconstruction of the band-limited functions and their Hilbert transform. IEEE Trans. Instrum. Meas. 46, 442 (1997)

    Article  Google Scholar 

  56. A.V. Oppenheim, R.W. Schafer, Discrete-Time Signal Processing, 2nd edn. (Prentice Hall, New Jersey, 1989)

    Google Scholar 

  57. R. Bracewell, The Fourier Transform and its Applications, 2nd edn. (McGraw-Hill Reading, New York, 1986)

    Google Scholar 

  58. F.W. King, Hilbert Transforms, Vol. 1 & 2 (Encyclopedia of Mathematics and its Applications, Vol. 124 & 125) (Cambridge University Press, Cambridge, 2009)

    Google Scholar 

  59. H.-G. Stark, Wavelets and Signal Processing. An Application-Based Introduction (Springer, Berlin, 2005)

    Google Scholar 

  60. P.S. Addison, The Illustrated Wavelet Transform Handbook, (Institute of Physics, Bristol, 2002)

    Google Scholar 

  61. G. Kaiser, A Friendly Guide to Wavelets (Birkhäuser, Boston, 1994). A physicist might find particular pleasure in physical (acoustic and electro-magnetic) wavelets: See G. Kaiser. J. Phys. A: Math. Gen. 36, R291 (2003)

    Google Scholar 

  62. J.F. Kirby, Which wavelet best reproduces the Fourier power spectrum? Comput. Geosci. 31, 846 (2005)

    Article  ADS  Google Scholar 

  63. C. Torrence, G.P. Compo, A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61 (1998)

    Article  ADS  Google Scholar 

  64. D. Jordan, R.W. Miksad, E.J. Powers, Implementation of the continuous wavelet transform for digital time series analysis. Rev. Sci. Instrum. 68, 1484 (1997)

    Article  ADS  Google Scholar 

  65. G. Strang, T. Nguyen, Wavelets and Filter Banks, 2nd edn. (Wellesley-Cambridge Press, Cambridge, 1996)

    Google Scholar 

  66. D.B. Percival, A.T. Walden, Wavelet Methods For Time Series Analysis (Cambridge University Press, Cambridge, 2000)

    Book  Google Scholar 

  67. J.S. Walker, A Primer on Wavelets and their Scientific Applications, 2nd edn. (Chapman & Hall/CRC, Boca Raton, 2008)

    Google Scholar 

  68. I. Daubechies, The wavelet transform time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36, 961 (1990)

    Article  ADS  MathSciNet  Google Scholar 

  69. I. Daubechies, Ten Lectures On Wavelets (SIAM, Philadelphia, 1992)

    Book  Google Scholar 

  70. D.S. Taubman, M.W. Marcellin, Jpeg2000: Image Compression Fundamentals, Standards And Practice (Kluwer, Boston, 2002)

    Book  Google Scholar 

  71. A. Cohen, I. Daubechies, J.-C. Feauveau, Biorthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 45, 485 (1992)

    Article  MathSciNet  Google Scholar 

  72. A.R. Calderbank et al., Wavelet transforms that map integers to integers. Appl. Comput. Harmon. Anal. 5, 332 (1998)

    Article  MathSciNet  Google Scholar 

  73. See http://sourceforge.net/projects/libdwt/

  74. See http://pywavelets.readthedocs.io

  75. See http://ltfat.sourceforge.net

  76. See http://www-stat.stanford.edu/~wavelab

  77. See http://github.com/ricedsp/rwt

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Simon Širca .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Širca, S., Horvat, M. (2018). Transformations of Functions and Signals. In: Computational Methods in Physics. Graduate Texts in Physics. Springer, Cham. https://doi.org/10.1007/978-3-319-78619-3_4

Download citation

Publish with us

Policies and ethics