+7 (495) 957-77-43

Article-9_11-2018

Извините, этот техт доступен только в “Американский Английский”. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.

STUDY OF THE BASIC ALGORITHMS OF NON-UNIFORM SINUSOIDAL QUANTIFICATION

Ngoulou-A-Ndzeli, Universite Marien Ngouabi, Republic of Congo, becker20000@yahoo.fr

Abstract
This article is devoted to the quantitative estimation of a basic algorithmic analysis of non-uniform sinusoidal signals. Its aim is to successfully extract the maximum amount of useful information on a signal disturbed by the noise based on the resources of the algorithm and quantification. [Analytical derivations are based on Baranov’s method (define a differential and inverse function which purpose is to calculate the statistics of amplitude, standard deviation, entropy and correlation properties of the input signal) and Matlab (simulation software, modeling and marking curves)]. This article highlights, on the one hand, the improvement of sinusoidal signals and the transition process of a continuous set of signal values for a discrete set, which volume is equal to the number of quantization levels; on the other hand, the overall increase in the signal / noise reducing noise to dominate the weak signals by increasing the noise for strong rare signals. In addition, in this system, the quantization noise is the same for all signal amplitudes. Therefore, in this case, the quantization noise signal may be proportional.

Keywords: quantization, harmonic signal, uniform, non-uniform, minimizing the errors, algorithm,
sampling, dispersion, storage, memory.

References

  1. Baranov L.A. (1990). Quantization by level and temporal sampling in digital control systems. Moscow: Energoatomizdat. 304 p.
  2. Vlasenko A.V., Kljuchko V.I. (2003). The Theory of Information and Signals: A Textbook. Krasnodar: Publishing House of Kuban State Technical University. 97 p.
  3. Zyuko A.G., Klovskiy D.D. (1980). Theory of signal transmission: a textbook for high schools. Moscow: Svyaz’.
  4. Sergienko A.B. (2003). Digital signal processing. St. Petersburg: Peter. 604 p.
  5. Porshnev S.V., Kusaykin D.V. (2014). Methods for improving the accuracy of recovery unevenly sampled signals with unknown coordinate values nodes of the time grid. Bulletin of SibGUTI. No.1, pp. 24-34.
  6. Porshnev S.V., Kusaykin D.V. (2014). Recovery algorithms are uneven of sampled signals with unknown sample coordinates / Modern Information and Electronic Technologies: Works of the 15th International Scientific and Practical Conference conference. Ukraine, Odessa. Vol. 1, pp. 201-202.
  7. Kusaykin D.V., Porshnev S.V. (2014). On the possibility of increasing the accuracy recovery of a discrete signal specified on a non-uniform time grid with unknown values of the coordinates of its nodes // DSPA, pp. 216-220.
  8. Porshnev S.V., Kusaykin D.V. (2014). Accuracy algorithms recovery of discrete signals set on a non-uniform time grid with unknown values of the coordinates of nodes. News of Higher Educational Institutions Russia. Radioelectronics. No. 6, pp. 17-23.
  9. Hemming R.V. (1987). Digital filters. Moscow: Nedra. 221 p.
  10. Porshnev S.V., Kusaykin D.V. (2015). Investigation of recovery methods unevenly sampled signals with unknown node coordinates time grid. Telecommunications. No. 2, pp. 32-37.
  11. Fedorov V.V. (1978). Regression analysis in the presence of errors in determination of the predictor. Questions of cybernetics. Мoscow: Scientific advice on integrated the problem of «Cybernetics» of the Academy of Sciences of the USSR. Issue 47, pp. 69-75.
  12. Kotkinik V.Ya. (1985). Nonparametric identification and data smoothing: method of local approximation. Moscow: The main version of the physical and mathematical literature. 336 p.
  13. Zhilinskaya E.I., Tovmachenko N.L., Fedorov V.V. (1978). Regression methods Analysis in the presence of errors in predictor variables. Moscow: Publishing House of the USSR Academy of Sciences. 1978. P. 34.
  14. IEEE standard for terminology and test methods for analog-to-digital converters. IEEE Std 1241-2000. 2001. P. 92.
  15. Jenq Y.C., Li Q. (2002). Differential non-linearity, integral non-linearity, and signal to noise ratio of an analog to digital converter. IMEKO International Measurment Confederation. Chicago.
  16. Haideh Khorramabadi. (2010). Electrical Engineering 247, Lecture 12: Data ConvertersTesting. Lectures Notes. University of California, Berkeley.
  17. Li Q. (2015). INL, DNL and Performance of Analog to Digital Converter. Electrical and Computer Engineering. Portland State University. URL: http://web.cecs.pdx.edu/~edam/Reports/2002/Li.pdf (reference date: 01.02.2015).
  18. El-Chammas M., Murmann B. (2010). A 12-GS/s 81-mW 5-bit time-interleaved flash ADC with background timing skew calibration. Symposium on VLSI Circuits. 16-18 June 2010, pp. 157-158.
  19. Hajimiri A., Lee T.H. (1998). A general theory of phase noise in electrical oscillators. IEEE J. Solid-State Circuits. Dec. 1998. Vol. 28, pp. 1273-1282.
  20. 20. Kusaykin D.V., Porshnev S.V. Classification of uneven species Discretization. Theory, technique and economics of communication networks: Collection of scientific and technical and methodical works. Ekaterinburg: URTISI FGOBU VPO.

Information about author:
Ngoulou-A-Ndzeli, graduate student, teacher, Universite Marien Ngouabi, Brazzaville, Republic of Congo