Adaptive Anytime Data Transmission of Non-Stationary Signals

Annamária R. Várkonyi-Kóczy

doi:10.20965/jaciii.2010.p0240

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JACIII Vol.14 No.3 pp. 240-246

doi: 10.20965/jaciii.2010.p0240

(2010)

Paper:

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Adaptive Anytime Data Transmission of Non-Stationary Signals

Annamária R. Várkonyi-Kóczy^*,**

^*Institute of Mechatronics and Vehicle Engineering, Óbuda University, Népszínház u. 8., H-1081 Budapest, Hungary

^**Integrated Intelligent Systems Japanese-Hungarian Laboratory

Received:

December 25, 2009

Accepted:

March 1, 2010

Published:

April 20, 2010

Keywords:

transformed domain signal processing, nonstationary signals, overcomplete signal representation, anytime systems

Abstract

The never unseen information explosion in data transmission and communication called for new methods in signal coding and reconstruction. To minimize the channel capacity needed for the transmission urged researchers to find techniques which are flexible and can adapt to the available space and time. Anytime techniques are good candidates for such purposes. If the signal/data to be transmitted can be characterized as sequence of stationary intervals overcomplete signal representations can be applied. These techniques can be operated in an anytime manner as well, i.e., are excellent tools for handling the capacity problems.
This paper introduces the concept of anytime recursive overcomplete signal representations using different recursive signal processing algorithms. The novelty of the approach is that an on-going set of signal transformations together with appropriate (e.g., L₁ norm) minimization procedures can provide optimal and flexible anytime on-going representations, on-going signal segmentations into stationary intervals, and on-going feature extractions for immediate utilization in data transmission, communication, diagnostics, or other applications. The proposed technique may be advantageous if the transmission channel is overloaded and in case of processing non-stationary signals when complete signal representations can be used only with serious limitations because of their relative weakness in adaptive matching of signal structures.

Cite this article as:

A. Várkonyi-Kóczy, “Adaptive Anytime Data Transmission of Non-Stationary Signals,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.3, pp. 240-246, 2010.

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References

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[4] A. R. Várkonyi-Kóczy, “Efficient Polyphase DFT Filter Banks with Fading Memory,” IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, Vol.44, No.8, pp. 670-673, Aug. 1997.
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[9] A. R. Várkonyi-Kóczy and M. Fék, “Recursive Overcomplete Signal Representations,” IEEE Trans. on Instrumentation and Measurement, Vol.50, No.6, pp. 1698-1703, Dec. 2001.
[10] S. Zilberstein, “Using Anytime Algorithms in Intelligent Systems,” AI Magazine, Vol.17, No.3, pp. 73-83, 1996.
[11] A. R. Várkonyi-Kóczy, T. Kovácsházy, O. Takács, and Cs. Benedecsik, “Anytime Algorithms in Intelligent Measurement and Control,” In CD-ROM Proc. of the 2000 World Automation Congress, WAC’2000, Maui, USA, pp. ISIAC-156.1-6, June 11-16, 2000.
[12] A. R. Várkonyi-Kóczy, A. Ruano, P. Baranyi, and O. Takács, “Anytime Information Processing Based on Fuzzy and Neural Network Models,” In Proc. of the 2001 IEEE Instrumentation and Measurement Technology Conf., IMTC/2001, Budapest, Hungary, May 21-23, 2001, pp. 1247-1252.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. A. Ruzinsky and E. T. Olsen, “L₁ and L_∞ minimization Via a Variant of Karmarkar’s algorithm,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.37, pp. 245-253, 1989.

[2] [2] S. Mallat and Z. Zhang, “Matching Pursuit in a Time-Frequency Dictionary,” IEEE Trans. on Signal Processing, Vol.41, pp. 3397-3415, 1993.

[3] [3] N. Ahmed and K. R. Rao, “Orthogonal Transforms for Digital Signal Processing,” Springer-Verlag, New York, 1975.

[4] [4] A. R. Várkonyi-Kóczy, “Efficient Polyphase DFT Filter Banks with Fading Memory,” IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, Vol.44, No.8, pp. 670-673, Aug. 1997.

[5] [5] G. Péceli, “A Common Structure for Recursive Discrete Transforms,” IEEE Trans. on Circuits and Systems, Vol.33, pp. 1035-1036, Oct. 1986.

[6] [6] M. M. Goodwin, “Adaptive Signal Models: Theory, Algorithms, and Audio Applications,” Ph. D. Thesis, University of California, Berkeley, USA, 1997.

[7] [7] S. B. Chen and L. D. Donoho, “Examples of Basis Pursuit,” Wavelet Applications in Signal Processing III, Proc. of the SPIE, pt.2, pp. 564-574, 1995.

[8] [8] N. N. Abdelmarek, “Solution of Minimum Time Problem and Minimum Fuel Problem for Discrete Linear Admissible Control Systems,” Int. J. Syst. Sci., Vol.8, pp. 849-859, 1978.

[9] [9] A. R. Várkonyi-Kóczy and M. Fék, “Recursive Overcomplete Signal Representations,” IEEE Trans. on Instrumentation and Measurement, Vol.50, No.6, pp. 1698-1703, Dec. 2001.

[10] [10] S. Zilberstein, “Using Anytime Algorithms in Intelligent Systems,” AI Magazine, Vol.17, No.3, pp. 73-83, 1996.

[11] [11] A. R. Várkonyi-Kóczy, T. Kovácsházy, O. Takács, and Cs. Benedecsik, “Anytime Algorithms in Intelligent Measurement and Control,” In CD-ROM Proc. of the 2000 World Automation Congress, WAC’2000, Maui, USA, pp. ISIAC-156.1-6, June 11-16, 2000.

[12] [12] A. R. Várkonyi-Kóczy, A. Ruano, P. Baranyi, and O. Takács, “Anytime Information Processing Based on Fuzzy and Neural Network Models,” In Proc. of the 2001 IEEE Instrumentation and Measurement Technology Conf., IMTC/2001, Budapest, Hungary, May 21-23, 2001, pp. 1247-1252.

Adaptive Anytime Data Transmission of Non-Stationary Signals

Annamária R. Várkonyi-Kóczy*,**

Annamária R. Várkonyi-Kóczy^*,**