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Polarisation-optical model of a controlled random number generator

https://doi.org/10.26425/2658-3445-2021-4-4-47-54

Abstract

The subject of the paper is an original model of a tunable optical random number generator. The purpose of the article is to analyse the possibilities of using the proposed model to ensure the protection of the control signals in the projected telecommunication management system of the robotized agro-industrial complex of the Republic of South Ossetia.
The research was carried out by methods of mathematical and information-logical modeling. The main results of the study are the information-logical model of the hardware implementation prototype, the descriptive mathematical model of its functioning and the obtained dependences of the quantitative characteristics of the generated random numbers statistical distributions on the main control parameter of the experimental setup.
These results can be used in the design and the operation of the remote production facilities monitoring and management telecommunication systems’ components. The possibilities of prototype creating and functioning were demonstrated by visualising the schematic chart of the experimental equipment and by the quantitative estimates of “one” and “zero” signals observation probabilities under the different polarisation rotator orientations relative to the plane of the optical signals detecting system.

About the Authors

A. V. Glazkov
State University of Management
Russian Federation

Aleksei V. Glazkov, Lecturer

Moscow 



I. K. Dzhioeva
South Ossetian State University
South Ossetia

Irina K. Dzhioeva, Cand. Sci. (Econ.), Assoc. Prof.

Tskhinval 



D. V. Pervukhin
State University of Management
Russian Federation

Dmitrii V. Pervukhin, Senior Lecturer

Moscow 



A. A. Pruchkina
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
Russian Federation

Anna A. Pruchkina, Cand. Sci. (Phys. and Math.), Senior Researcher

Moscow

ORCID: M-7550-2015 



G. O. Rytikov
LLC “Impact Electronics”
Russian Federation

Georgy O. Rytikov, CEO

Moscow 



References

1. Alcácer V. and Cruz-Machado V. (2019), “Scanning the industry 4.0: a literature review on technologies for manufacturing systems”, Engineering Science and Technology an International Journal, vol. 22, no. 3, pp. 899–919. https://doi.org/10.1016/j.jestch.2019.01.006

2. Baptista M.S. (1998), “Cryptography with chaos”, Physics Letters A, vol. 240, no. 1-2, pp. 50–54. https://dx.doi.org/10.1016/S0375-9601(98)00086-3

3. Barash L.Yu. and Shchur L.N. (2014), “PRAND: GPU accelerated parallel random number generation library: using most reliable algorithms and applying parallelism of modern GPUs and CPUs”, Computer Physics Communications, vol. 185, no. 4, pp. 1343–1353. https://doi.org/10.1016/j.cpc.2014.01.007

4. Barash L.Yu. and Shchur L.N. (2011), “RNGSSELIB: program library for random number generation, SSE2 realization”, Computer Physics Communications, vol. 182, no. 7, pp. 1518–1527. https://doi.org/10.1016/j.cpc.2011.03.022

5. Binder K. (1997), “Applications of Monte Carlo methods to statistical physics”, Reports on Progress in Physics, vol. 60, no. 5, pp. 487–559. https://doi.org/10.1088/0034-4885/60/5/001

6. Boneh D. and Lipton R.J. (1996), “Algorithms for black-box fields and their application to cryptography”, Lecture Notes in Computer Science, vol. 1109, pp. 283–297. https://doi.org/10.1007/3-540-68697-5_22

7. Breiman L. (2001), “Random forests”, Machine Learning, vol. 45, no. 1, pp. 5–32. https://doi.org/10.1023/A:1010933404324

8. Demchik V. (2011), “Pseudo-random number generators for Monte Carlo simulations on ATI graphics processing units”, Computer Physics Communications, vol. 182, no. 3, pp. 692–705. https://doi.org/10.1016/j.cpc.2010.12.008

9. Dzhioeva I.K. and Tehov A.V. (2017), “Initial terms of development of enterprise are in Republic of South Ossetia”, Journal of Economy and entrepreneurship, vol. 11, no. 9-2, pp. 434–438. (In Russian).

10. Djioeva I.K., Kochieva J.G., Techov A.V. and Dzhioeva A.K. (2018), “Strategy of restoration and increase of efficiency of the agro-food complex of the Republic of South Ossetia”, Journal of Economy and entrepreneurship, vol. 12, no. 2, pp. 335–342. (In Russian).

11. Djioeva I.K., Techov A.V. and Shelkunova T.G. (2019), “Key trends in the social economic transformation of modern society”, Journal of Economy and entrepreneurship, vol. 13, no. 8, pp. 293–295. (In Russian).

12. Fill J.A. (1998), “An interruptible algorithm for perfect sampling via Markov chains”, The Annals of Applied Probability, vol. 8, no. 1, pp. 131–162. https://doi.org/10.1145/258533.258664

13. Foulkes W.M.C., Mitas L., Needs R.J. and Rajagopal G. (2001), “Quantum Monte Carlo simulations of solids”, Reviews of Modern Physics, vol. 73, no. 1, pp. 33–83. https://doi.org/10.1103/REVMODPHYS.73.33

14. Frolova E.E., Polyakova T.A., Dudin M.N., Rusakova E.P. and Kucherenko P.A. (2018), “Information security of Russia in the digital economy: the economic and legal aspects”, Journal of Advanced Research in Law and Economics, vol. 9, no. 1, pp. 89–95. https://doi.org/10.14505/jarle.v9.1(31).12

15. Fürst M., Weier H., Nauerth S., Marangon D.G., Weinfurter H. and Kurtsiefer C. (2010), “High speed optical quantum random number generation”, Optics Express, vol. 18, no. 12, pp. 13029–13037. https://doi.org/10.1364/OE.18.013029

16. Gisin N., Ribordy G., Tittel W. and Zbinden H. (2002), “Quantum cryptography”, Reviews of Modern Physics, vol. 74, no. 1, pp. 145–195. https://doi.org/10.1103/REVMODPHYS.74.145

17. Hartley H.O. and Fitch E.R. (1951), “A chart for the incomplete beta-function and the cumulative binomial distribution”, Biometrika, vol. 38, no. 3-4, pp. 423–426. https://doi.org/10.2307/2332588

18. Hastings W.K. (1970), “Monte Сarlo sampling methods using markov chains and their applications”, Biometrika, vol. 57, no. 1, pp. 97–109. https://doi.org/10.1093/biomet/57.1.97

19. Iskhakov T.Sh., Lopaeva E.D., Penin A.N., Rytikov G.O. and Chekhova M.V. (2008), “Two methods for detecting nonclassical correlations in parametric scattering of light”, Journal of Experimental and Theoretical Physics Letters (JETP Letters), vol. 88, no. 10, pp. 660–664. https://doi.org/10.1134/S0021364008220050

20. Jennewein T., Achleitner U., Weihs G., Weinfurter H. and Zeilinger A. (1999), “A fast and compact quantum random number generator”, Review of Scientific Instruments, vol. 71, no. 4. pp. 1675–1680. https://doi.org/10.1063/1.1150518

21. Kamolov S.G. (2017), “Digital public governance: trends and risks”, Journal of Constitutional History, vol. 33, no. 1, pp.185–194.

22. Kim H.K. and Lebedev V. (2004), “On optimal superimposed codes”, Journal of Combinatorial Design, vol. 12, no. 2, pp. 79–91. https://doi.org/10.1002/jcd.10056

23. Liu J.-M., Chen H.-F. and Tang S. (2002), “Synchronized chaotic optical communications at high bit rates”, IEEE Journal of Quantum Electronics, vol. 38, no. 9, pp. 1184–1196. https://doi.org/10.1109/JQE.2002.802045

24. Maaranen H., Mäkelä M.M. and Miettinen K. (2004), “Quasi-random initial population for genetic algorithms”, Computers & Mathematics with Applications, vol. 47, no. 12, pp. 1885–1895. https://doi.org/10.1016/j.camwa.2003.07.011

25. Manssen M., Hartmann A.K. and Weigel M. (2012), “Random number generators for massively parallel simulations on GPU”, The European Physical Journal. Special Topics, vol. 210, no. 1, pp. 53–71. https://doi.org/10.1140/epjst/e2012-01637-8

26. Morozova A.N., Panov A.D., Pruchkina A.A., Rytikov G.O. and Tscherbina O.A. (2014), “Parametric down conversion frequency-angle spectrum modeling”, 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA 2014), Saint-Petersburg, June 30–July 4, ed. E.I. Veremey, IEEE Catalog number: CFP14BDA-POD, St.- Petersburg, Russia, pp. 122–123. https://doi.org/10.1109/ICCTPEA.2014.6893314

27. Myasnikov A. and Roman’Kov V. (2015), “A linear decomposition attack”, Groups, Complexity, Cryptology, vol. 7, no. 1, pp. 81–94. https://doi.org/10.1515/gcc-2015-0007

28. Noh J.D. and Rieger H. (2004), “Random walks on complex networks”, Physical Review Letters, vol. 92, no. 11, ant. 118701. https://doi.org/10.1103/PhysRevLett.92.118701

29. Nyberg K. (1994), “Differentially uniform mappings for cryptography”, Lecture Notes in Computer Science, vol. 765, pp. 55–64. https://doi.org/10.10007/3-540-48285-7_6

30. Phillips C.L., Glotzer S.C. and Anderson J.A. (2011), “Pseudo-random number generation for brownian dynamics and dissipative particle dynamics simulations on GPU devices”, Journal of Computational Physics, vol. 230, no. 19, pp.7191–7201. https://doi.org/10.1016/j.jcp.2011.05.021

31. Rytikov GO. (2007), “Technique of approximate solution of a class of quantum optics problems”, Vestnik MGUP imeni Ivana Fedorova, no. 3, pp. 74–82. (In Russian).

32. Sobol I.M. (1998), “On quasi-Monte Carlo integrations”, Mathematics and Computers in Simulation, vol. 47, no. 2, pp. 103–112. https://doi.org/10.1016/S0378-4754(98)00096-2

33. Sobol I.M. and Levitan Yu.L (1999), “A pseudo-random number generator for personal computers”, Computers & Mathematics with Applications, vol. 37, no. 4-5, pp. 33–40. https://doi.org/10.1016/S0898-1221(99)00057-7

34. Soshnikov A. (2000), “Determinantal random point fields”, Russian Mathematical Surveys, vol. 55, no. 5, pp. 923–975. https://doi.org/10.1070/RM2000v055n05ABEH000321

35. Srinivasan A., Mascagni M. and Ceperley D. (2003), “Testing parallel random number generators”, Parallel Computing, vol. 29, no. 1, pp. 69–94. https://doi.org/10.1016/S0167-8191(02)00163-1

36. Steane A.M. (1998), “Quantum computing”, Reports on Progress in Physics, vol. 61, no. 2, pp. 117–173. https://dx.doi.org/10.1088/0034-4885/61/2/002

37. Tarone R.E. (1979), “Testing the goodness of fit of the binomial distribution”, Biometrika, vol. 66, no. 3, pp. 585–590. https://doi.org/10.1093/BIOMET/66.3.585

38. Vadhan S.P. (2011), “Pseudorandomness”, Foundations and Trends in Theoretical Computer Science, vol. 7, no. 1-3, pp. 1–336. https://doi.org/10.1561/0400000010

39. Wood S.N., Pya N. and Säfken B. (2016), “Smoothing parameter and model selection for general smooth models”, Journal of the American Statistical Association, vol. 111, no. 516, pp. 1548–1563. https://doi.org/10.1080/01621459.2016.1180986

40. Yang T., Yang L.B. and Yang C.M. (1998), “Cryptanalyzing chaotic secure communications using return maps”, Physics Letters A, vol. 245, no. 6, pp. 495–510. https://doi.org/10.1016/S0375-9601(98)00425-3

41. Yu W., Liang F., He X., Hatcher W.G., Lu C., Lin J. and Yang X. (2017), “A survey on the edge computing for the internet of things”, IEEE Access, vol. 6, pp. 6900–6919. https://doi.org/10.1109/ACCESS.2017.2778504


Review

For citations:


Glazkov A.V., Dzhioeva I.K., Pervukhin D.V., Pruchkina A.A., Rytikov G.O. Polarisation-optical model of a controlled random number generator. E-Management. 2021;4(4):47-54. https://doi.org/10.26425/2658-3445-2021-4-4-47-54

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