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Computación y Sistemas

versão On-line ISSN 2007-9737versão impressa ISSN 1405-5546

Comp. y Sist. vol.9 no.3 Ciudad de México Jan./Mar. 2006

 

Artículos

 

Sampling – Reconstruction Procedure of Gaussian Fields

 

Procedimiento para el Muestreo y Reconstrucción de Campos Gausianos

 

Vladimir Kazakov1 and Sviatoslav Afrikanov2

 

1 Dept. of Telecommunications, the Superior School of Mechanical and Electrical Engineering of the National
Polytechnical Institute of Mexico. Unidad Zacatenco,
C. P. 07738, D.F.; Mexico. Tel: (5255) 5729–60–00, ext. 54757.

vkazakov41@hotmail.com

 

2 The Corporation "Fazotron–NIR", Moscow, Russia.
africanov@mail.ru

 

Article received on June 04, 2004; accepted on August 11, 2005

 

Abstract

The description of the optimal Sampling – Reconstruction Procedure (SRP) of Gaussian fields is given on the basis of the conditional mean rule when the quantity of samples is limited. The Gaussian fields are described by two types of space covariance function: exponential and Gaussian. A lot of both reconstruction and reconstruction error surfaces are obtained by numerical calculation. We changed the type of the covariance functions; the type of sampling (uniform: triangular, square, etc. and non – uniform: polar, spiral, and arbitrary); the quantity of the samples; the distances between the samples; and radii of the covariance functions of both axes. We demonstrate how all above mentioned factors influence on principal optimal SRP characteristics. The results of the calculations have clear interpretations.

Keywords: Gaussian Fields, Uniform and Non – Uniform Sampling, Reconstruction Functions, Reconstruction Error Functions.

 

Resumen

La descripción del Procedimiento óptimo de Muestreo – Reconstrucción de los procesos Gaussianos esta dada en base a la regla de la media condicional cuando la cantidad de las muestras es limitada. Los Campos Gaussianos están descritos por dos diferentes funciones espaciales de covarianza: exponencial y Gaussiana. Varias superficies de reconstrucción y de error de reconstrucción son obtenidas a partir de los cálculos numéricos. Cambiamos el tipo de las funciones de covarianza; el modo de muestreo (uniforme: triangular, cuadrada, etc. y no uniforme: polar, espiral y arbitraria); la cantidad de muestras; la distancia entre las muestras; el radio de las funciones de covarianza en ambos ejes. Demostramos como estos factores influyen en las principales características del Procedimiento óptimo de Muestreo – Reconstrucción.

Palabras claves: Campos Gaussianos, Muestreo Uniforme y no Uniforme, Funciones de Reconstrucción, Funciones de Error de Reconstrucción.
2000 Mathematics subjects classification –60Hxx, 94A20

 

DESCARGA ARTICULO EN FORMATO PDF

 

Acknowledgement

This work has been partially supported by Consejo Nacional de Ciencia y Tecnologia (CONACYT) of Mexico under Project No 31472 and by National Polytecnical Institute of Mexico (IPN) under Project No 990350.

 

References

1. Bourgeois M., Wajer F.T.A.W, Van Ormondt D., Graveron–Demilly D., Reconstruction of MRI Images from Non–Uniform Sampling and Its Application to Intrascan Motion Correction in Functional MRI.– Chapter 16 in the book: J.J.Benedetto, P.J.S.G.Ferrejra (Editors) Modern Sampling Theory. Birkhauser, Boston, 2001, pp. 343–363.        [ Links ]

2. Clark J. J., Palmer M. R., Lawrence P. D., A Transformation Method for the Reconstruction of Functions from Non–uniformly Spaced Samples. IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. ASSP–33, No 4, October 1985, pp. 1151–1165.        [ Links ]

3. Cramer H., Mathematical Methods of Statistics, Princeton, N.J.: Princeton University Press, 1946.        [ Links ]

4. Kazakov V. A., Sampling–Reconstruction Procedure of Gaussian Fields. Abstracts of the International Conference "Sampling Theory and Applications" (SAMPTA–2003), Strobl, Salzburg, Austria, May, 2003, p. 49.        [ Links ]

5. Kazakov V. A., Regeneration of samples of random processes following nonlinear inertialess conversions. Telecommunication and Radioengineering, vol. 43, No 10, 1988, pp. 94–96.        [ Links ]

6. Kazakov V. A., Afrikanov S. A., Beliaev M. A., Comparison of two algorithms of the realization restorations using random numbers of counts. Radioelectronics and Communication Systems, vol. 37, No 4, 1994, pp. 43–45.        [ Links ]

7. Kazakov V. A., Beliaev M. A., Reconstruction of realizations of Gaussian processes from readings of the process and linear transformation of it. Telecommunication and Radioengineering, vol. 49, No 9, 1995, pp. 97–102.        [ Links ]

8. Kazakov V. A., Beliaev M. A., Sample–reconstruction procedure of some non–stationary processes. Radioelectronics and Communication Systems, vol. 40, No 9, 1997, pp. 43–49.        [ Links ]

9. Kazakov V. A., Sampling and reconstruction procedure of stochastic processes at the output of nonlinear non–memory converters. Proceedings of the 2001 International Conference on Sampling Theory and Applications (SAMPTA–2001), May 13–17, 2001, Orlando, USA, pp. 103–106.        [ Links ]

10. Kazakov V. A., Beliaev M. A., Sampling–Reconstruction Procedure for non–Stationary Gaussian processes Based on Conditional Expectation Rule. Sampling Theory in Signal and Image Processing, vol. 1, No 2, May 2002, pp. 135–153.        [ Links ]

11. Kazakov V. A., Sanchez S., Sampling–Reconstruction Procedure of Random Processes at the Output of Exponential Non – Linear Converters. Electromagnetic Waves and Electronic Systems, vol. 8, No 7 – 8, 2003, pp. 77 – 80.        [ Links ]

12. Klesov O. I., The restoration of a Gaussian random field with finite spectrum by readings on a lattice. Kibernetika, 4, pp. 41–46, 1985. (In Russian.)        [ Links ]

13. Klesov O. I., On the almost sure convergence of the multiple Kotel'nikov Shannon series. Problemy Peredachi Informatsii, XX, No 3, pp. 79–93, 1984. (In Russian.)        [ Links ]

14. Petersen D. P., Middleton D., Linear Interpolation, Extrapolation, and Prediction of Random Space–time Fields with a Limited Domain of Measurement. IEEE Trans. on Information Theory, vol. IT–11, No 1, 1965, pp. 18–30.        [ Links ]

15. Petersen D. P., Middleton D., Sampling and Reconstruction of wave–number–limited function in n–dimensional Euclidean spaces. Inform. Control, vol. 5, 1962, pp. 279–323.        [ Links ]

16. Pogany T., Almost sure sampling restoration of band–limited stochastic signals. Chapter 9 in the book: Sampling Theory in Fourier and Signal Analysis. Edited by J. R. Higgins and R. L. Stens. Oxford University Press, 1999, pp.209 – 232.        [ Links ]

17. Pogany T., Perunicic. On the sampling theorem for homogeneous random fields. Theory of Probability. and Mathemathical Statistics, N 53, 1995, (USA), pp. 153 – 159.        [ Links ]

18. Stark H., Polar, Spiral, and Generalized Sampling and Interpolation. In the book: R.J.Marx II (Editor), Advanced Topics in Shannon Sampling and Interpolation Theory. Springer Verlag, 1993, pp. 185–207.        [ Links ]

19. Stratonovich R. L., Topics in the Theory of Random Noise, New York: Gordon and Breach, 1963.        [ Links ]

20. Zeevi Y. Y., Shlomot E., Non–uniform Sampling and Anti–aliasing in Image Representation. IEEE Trans. on Signal Processing, vol. SP–41(3), March 1993, pp. 1223–1236.        [ Links ]

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