INFORMATIONALITY OF NOISE-LIKE SIGNALS

1
Lviv Polytechnic National University
2
Lviv Polytechnic National University

With known methods of detecting and extracting a useful signal from a signal-noise mixture, another method of detecting an information signal is proposed - based on the analysis of the energy spectrum of such a mixture. Examples of informa- tional signals were noise-like signals generated by operators. The white noise signal was generated with a computer. The statistical parameters of the signals of the operators and the computer - average value, variance - were commensurate. The analysis of the operators' signals showed that with sufficient duration of these signals, their energy spectrum is similar to flicker noise. The energy spectra of most signals generated by natural dynamic systems are similar to the form of flicker noise. The informativeness of opera- tor signals, white noise, and additive signal-noise mixture was evaluated by the value of entropy, which was determined by the parameter τ of the approximating function of the energy spectrum. At the same time, the amount of information in white noise is zero, and the amount of information in noisy signals of operators is greater, the smaller the value of τ is.

  1. A. Khobotov, V. Kalinina, A. Khil’ko and A. Ma- lekhanov, “ Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound”, Remote Sensing, vol.14, p.4860, 2022. doi.org/10.3390/rs14194860.
  2. G. Ford, B. J. Foster, M. J. Liston, M. Kam ,“Unknown Signal Detection in Interference and Noise Using Hidden Markov Models”, IEEE Statistical Signal Processing Workshop (SSP), Rio de Janeiro, Brazil, 2021, pp. 406–410. doi.org/10.1109/SSP49050.2021.9513832.
  3. L. Wang, J. Zhang, X. Hua, M. Huang, “Weak Signal Detection Based on a Differential Dual-Coupling Method under Lévy Noise”, 15th International Conference on Electronic Measurement & Instruments (ICEMI), Nanjing,  China,     2021,       pp.           76-81. DOI: 10.1109/ICEMI52946.2021.9679557.
  4. G. Ierley, A. Kostinski, “Detection of unknown signals in arbitrary noise”, Physical review E 102, p. 032221, 2020. doi.org/10.1103/PhysRevE.102.032221.
  5. H. Orimoto, A. Ikuta, and K. Hasegawa, “ Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech”, Intelligent Information Management, vol.13, pp.199-213, 2021. doi: 10.4236/iim.2021.134011.
  6. Z. Li, A. V. Peterchev, J. C. Rothwell and S. M. Goetz, “Detection of motor-evoked potentials below the noise floor: rethinking the motor stimulation threshold”, Journal of Neural Engineering, vol. 19, № 5, p. 056040, 2022. DOI: 10.1088/1741-2552/ac7dfc.
  7. Z. Kolodiy and A. Kolodiy, “Fluctuations of flicker type in technical and natural systems,” 22nd International Conference on Noise and Fluctuations (ICNF), Corum de Montpellier, France, 2013, p.131. doi: 10.1109/ICNF.2013.6578927.
  8. A. Z .Kolodiy, Z. A. Kolodiy, “Quantitative assessment of noise signal information”, Aut. Control Comp. Sci., vol.48, pp.243–248, 2014. doi.org/10.3103/S014641161404004X.
  9. P. Szendro, G. Vincze , A. Szasz, “Bio-response to white noise excitation”, Journal Electro- and Magnetobiology, vol 20, no. 2, pp. 215-229, 2001. doi/abs/10.1081/JBC- 100104145?journalCode=iebm19.
  10. P. Allegrini et al., “ Spontaneous brain activity as a source of ideal 1 / f noise”, Physical review E, vol. 80, p. 061914, 2009. doi.org/10.1103/PhysRevE.80.061914.
  11. A. Diniz, M. Wijnants, K.Torre, J. Barreiros, “Contemporary theories of 1/f noise in motor control”, Human Movement Science, 30(5), pp. 889-905, 2010. doi:10.1016/j.humov.2010.07.006.
  12. A. Paris, G. Atia, A. Vosoughi , S. A. Berman, “Optimal causal filtering for 1 /fα-type noise in single-electrode EEG signals”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016,  pp. 997-1001. DOI: 10.1109/EMBC.2016.7590870.
  13. B. Voytek et al., “Age-Related Changes in 1/f Neural Electrophysiological Noise”, J. Neurosci., vol.35, no. 38, pp.13257-13265, 2015. doi.org/10.1523/JNEUROSCI.2332-14.2015.
  14. P. Bormann, E. Wielandt, “Seismic Signals and Noise. Chapter 4.”, New Manual of Seismological Observatory Practice 2 (NMSOP2), Potsdam: Deutsches GeoForschungsZentrum GFZ, 2013, pp. 1-62. doi.org/ 10.2312/GFZ.NMSOP-2_ch4.
  15. C. A. Varotsos, I. Melnikova, M. Efstathiou, C. Tzanis, “1/f noise in the UV solar spectral irradiance”, Theoretical & Applied Climatology, vol. 111, no. 3/4, pp.641-648, 2013. DOI: 10.1007/s00704-012-06997-8.
  16. S. F. Timashev, Yu. S. Polyakov, “Review of flicker - noise spectroscopy in electrochemistry”, arXiv:0812.0030 (physics), 2008. https://arxiv.org/abs/0812.0030
  17. H. Nyquist, “Thermal agitation of electric charge in con- ductors”, Phys. Rev., vol. 32, July. № 1. pp. 110-113, 1928. doi.org/10.1103/PhysRev.32.110.