Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
It is shown that a number of equivalent choices for the calculation of the spectrum of a sampled signal are possible. Two such choices are presented in this paper. It is illustrated that the proposed calculations are more physically relevant than the definition currently in use.
Rocznik
Tom
Strony
395--401
Opis fizyczny
Bibliogr. 55 poz., wykr.
Twórcy
autor
- Department of Marine Telecommunications, Faculty of Electrical Engineering, Gdynia Maritime University, Gdynia, Poland
Bibliografia
- [1] A. Borys, “Spectrum aliasing does not occur in case of ideal signal sampling,” Intl Journal of Electronics and Telecommunications, vol. 67, no. 1, pp. 71-77, 2021.
- [2] J. H. McClellan, R. Schafer, M. Yoder, DSP First. London, England: Pearson, 2015.
- [3] M. Vetterli, J. Kovacevic, V. K. Goyal, Foundations of Signal Processing. Cambridge, England: Cambridge University Press, 2014.
- [4] A. V. Oppenheim, R. W. Schafer, J. R. Buck, Discrete-Time Signal Processing. New Jersey, USA: Prentice Hall, 1998.
- [5] R. J. Marks, Introduction to Shannon Sampling and Interpolation Theory. New York, USA: Springer-Verlag, 1991.
- [6] R. N. Bracewell, The Fourier Transform and Its Applications. New York, USA: McGraw-Hill , 2000.
- [7] V. K. Ingle, J. G. Proakis, Digital Signal Processing Using Matlab. Stamford, CT, USA: Cengage Learning, 2012.
- [8] P. Prandoni, M. Vetterli, Signal Processing for Communications. Lausanne, Switzerland: EPFL Press, 2008.
- [9] N.T. Thao, M. Vetterli, “Deterministic analysis of oversampled A/D conversion and decoding improvement based on consistent estimates”, IEEE Transactions on Signal Processing, vol. 42, no. 3, pp. 519-531, 1994.
- [10] K. Adam, A. Scholefield, M. Vetterli, “Encoding and Decoding Mixed Bandlimited Signals Using Spiking Integrate-and-Fire Neurons”, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9264-9268, May 2020.
- [11] R. Alexandru, P. L. Dragotti, “Reconstructing classes of non-bandlimited signals from time encoded information”, IEEE Transactions on Signal Processing, vol. 68, pp. 747-763, 2020.
- [12] A. Lazar, L. T. Toth, “Perfect recovery and sensitivity analysis of time encoded bandlimited signals,” IEEE Transactions on Circuits and Systems – I: Regular Papers, vol. 51, no. 10, pp. 2060-2073, 2004.
- [13] J. A. Urigueen, T. Blu, P. L. Dragotti, “FRI Sampling with arbitrary kernels”, IEEE Transactions on Signal Processing, vol. 61, pp. 5310-5323, 2013.
- [14] M. Vetterli, P. Marziliano, T. Blu, “Sampling signals with finite rate of innovation”, IEEE Transactions on Signal Processing, vol. 50, no. 6, pp. 1417-1428, 2002.
- [15] P. L. Dragotti, M. Vetterli, and T. Blu, “Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets strang-fix,” IEEE Transactions on Signal Processing, vol. 55, no. 5, pp. 1741-1757, 2007.
- [16] R. Tur, Y. C. Eldar, Z. Friedman, “Innovation rate sampling of pulse streams with application to ultrasound imaging,” IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1827-1842, 2011.
- [17] M. Unser, “Sampling – 50 years after Shannon,” Proceedings of the IEEE, vol. 88, no. 4, pp. 569-587, 2000.
- [18] G. Ortiz-Jimenez, M. Coutino, S. P. Chepuri, G. Leus, “Sparse sampling for inverse problems with tensors”, IEEE Transactions on Signal Processing, vol. 67, no. 12, pp. 3272-3286, 2019.
- [19] S. P. Chepuri, G. Leus, “Graph sampling for covariance estimation”, IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 3, pp. 451-466, 2017.
- [20] M. R. D. Rodrigues, Y. C., Eldar, Information-Theoretic Methods in Data Science. Cambridge, England: Cambridge University Press, 2021.
- [21] M. R. D. Rodrigues, H. Bölcskei, S. Draper, Y. Eldar, V. Tan, “Introduction to the issue on information-theoretic methods in data acquisition, analysis, and processing”, IEEE Journal on Selected Topics in Signal Processing, vol. 66, no. 9, pp. 2314-2329, 2018.
- [22] G. Matz, H. Bölcskei, and F. Hlawatsch, “Time-frequency foundations of communications”, IEEE Signal Processing Magazine, vol. 30, no. 6, pp. 87-96, 2013.
- [23] Y. Eldar, H. Bölcskei, “Geometrically uniform frames”, IEEE Transactions on Information Theory, vol. 49, no. 4, pp. 993-1006, 2003.
- [24] Y. Kopsinis, K. Slavakis, S. Theodoridis “On line sparse system identification and signal reconstruction using projections onto weighted 11 balls”, IEEE Transactions on Signal Processing, vol. 59, no. 3, pp. 936-952, 2011.
- [25] A. Morgado, R. del Río, J.M. de la Rosa, “High-efficiency cascade sigma-delta modulators for the next generation software-defined-radio mobile systems,” IEEE Trans. on Instrumentation and Measurement, vol. 61, pp. 2860-2869, 2012.
- [26] L. Zhao, Z. Chen, Y. Yang, L. Zou, Z. J. Wang, “ICFS clustering with multiple representatives for large data”, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 728-738, 2019.
- [27] V. Poor, An Introduction to Signal Detection and Estimation. Berlin, Germany: Springer-Verlag, 1994.
- [28] T. Kailath, V. Poor, “Detection of stochastic processes”, IEEE Transactions on Information Theory, vol. 44, no. 6, pp. 2230-2231, 1998.
- [29] A. Yeredor, “Blind channel estimation using first and second derivatives of the characteristic function”, IEEE Signal Processing Letters, vol. 9, no. 3 pp. 100-103, 2002.
- [30] J.J. Clark, M.R. Palmer, P.D. Lawrence, “A transformation method for the reconstruction of functions from non-uniformly spaced samples,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 33, pp. 1151-1165, 1985.
- [31] L. Heyoung, Z.Z. Bien, “A variable bandwidth filter for estimation of instantaneous frequency and reconstruction of signals with time-varying spectral content,” IEEE Transactions on Signal Processing, vol. 59, pp. 2052-2071, 2011.
- [32] E. Lee, D. Messerschmitt, Digital Communication. Boston, USA: Kluwer, 1994.
- [33] S. Mallat, A Wavelet Tour of Signal Processing. San Diego, USA: Academic, 1998.
- [34] P. Stoica, R. Moses, Introduction to Spectral Analysis. Englewood Cliffs, USA: Prentice-Hall, 2000.
- [35] H. P. E. Stern, S.A. Mahmoud, Communication Systems: Analysis and Design. Upper Saddle River, USA: Prentice-Hall, 2004.
- [36] M. Vetterli, J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, USA: Prentice-Hall, 1995.
- [37] E. J. Candè, M. B. Wakin, “An introduction to compressive sampling”, IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, 2008.
- [38] A. Zayed, Advances in Shannon’s Sampling Theory. Boca Raton, USA: CRC Press, 1993.
- [39] R. H. Walden, “Analog-to-digital converter survey and analysis,” IEEE Journal on Selected Areas in Communications, vol. 17, no. 4, pp. 539–550, 1999.
- [40] P. P. Vaidyanathan, “Generalizations of the sampling theorem: seven decades after Nyquist,” IEEE Trans. Circuits Systems I: Fundamental Theory and Applications, vol. 48, no. 9, pp. 1094–1109, 2001.
- [41] H. J. Landau, “Sampling, data transmission, and the Nyquist rate”, Proceedings of the IEEE, vol. 55, no. 10, pp. 1701-1706, 1967.
- [42] R.G. Lyons, Understanding Digital Signal Processing. Reading, USA: Addison-Wesley, 1997.
- [43] A. J. Jerri, “The Shannon sampling theorem - its various extensions and applications: a tutorial review,” Proceedings of the IEEE, vol. 65, no. 11, pp. 1565–1596, 1977.
- [44] Y. C. Eldar, T. Michaeli, “Beyond bandlimited sampling,” IEEE Signal Processing Magazine, vol. 26, no. 3, pp. 48–68, 2009.
- [45] A. Papoulis, “Error analysis in sampling theory,” Proceedings of the IEEE, vol. 54, no. 7, pp. 947–955, 1966.
- [46] A. Papoulis, “Generalized sampling expansion,” IEEE Transactions on Circuits and Systems, vol. 24, no. 11, pp. 652–654, 1977.
- [47] R. G. Vaughan, N. L. Scott, D. R. White, “The theory of bandpass sampling,” IEEE Transactions on Signal Processing, vol. 39, no. 9, pp. 1973–1984, 1991.
- [48] Y. M. Lu, M. N. Do, “A theory for sampling signals from a union of subspaces,” IEEE Transactions on Signal Processing, vol. 56, no. 6, pp. 2334–2345, 2008.
- [49] C. Herley, P. W. Wong, “Minimum rate sampling and reconstruction of signals with arbitrary frequency support,” IEEE Transactions on Information Theory, vol. 45, no. 5, pp. 1555–1564, 1999.
- [50] L. Schwartz, Théorie des Distributions. Paris, France: Hermann, 1950-1951.
- [51] A. Borys, “Spectrum aliasing does occur only in case of non-ideal signal sampling”, Intl Journal of Electronics and Telecommunications, vol. 67, no. 1, pp. 79-85, 2021.
- [52] S. Boyd, L. Chua, “Fading memory and the problem of approximating nonlinear operators with Volterra series,” IEEE Transactions on Circuits and Systems, vol. 32, no. 11, pp. 1150-1161, 1985.
- [53] L. V. Kantorovich, G. P. Akilov, Functional Analysis. Oxford, England: Pergamon Press, 1982.
- [54] I. W. Sandberg, “Linear maps and impulse responses,” IEEE Transactions on Circuits and Systems, vol. 35, no. 2, pp. 201-206, 1988.
- [55] I. W. Sandberg, “Causality and the impulse response scandal,” IEEE Transactions on Circuits and Systems–I: Fundamental Theory and Applications, vol. 50, no. 6, pp. 810-813, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f656bce3-55f2-4300-990f-7d08285fa5bf