Analyzing the Possibility of Increasing the Randomness of Noise Using a Continuous Wavelet Transform, Based on a Sequence of Numbers Generated by an Optical Random Noise Generator

The authors investigate the possibility of controlling the parameters of random number sequences using a continuous wavelet transform. It is shown that changing the energy of the scales of the continuous wavelet transform can increase the probability of passing the NIST LongestRun, FFT, and Runs tests. The possibility of increasing the probability of passing tests is demonstrated for different sizes of the studied experimental sequence of random numbers.

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Publication Details

Journal
Bulletin of the Russian Academy of Sciences Physics
Published
2024-12-01
DOI
https://doi.org/10.1134/s1062873824708547
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

Analyzing the Possibility of Increasing the Randomness of Noise Using a Continuous Wavelet Transform, Based on a Sequence of Numbers Generated by an Optical Random Noise Generator

L. R. Gilyazov, M. E. Sibgatullin, N. M. Arslanov, D. A. Mavkov
Bulletin of the Russian Academy of Sciences Physics
Neural Networks and Applications
article

Analyzing the Possibility of Increasing the Randomness of Noise Using a Continuous Wavelet Transform, Based on a Sequence of Numbers Generated by an Optical Random Noise Generator

L. R. Gilyazov, M. E. Sibgatullin, N. M. Arslanov, D. A. Mavkov
article en

Abstract

The authors investigate the possibility of controlling the parameters of random number sequences using a continuous wavelet transform. It is shown that changing the energy of the scales of the continuous wavelet transform can increase the probability of passing the NIST LongestRun, FFT, and Runs tests. The possibility of increasing the probability of passing tests is demonstrated for different sizes of the studied experimental sequence of random numbers.

Bulletin of the Russian Academy of Sciences PhysicsVol. 88(12)
Kazan State Technical University named after A. N. Tupolev (RU), Tatarstan Academy of Sciences (RU)
Openalex Percentile: Top 76%
Neural Networks and Applications
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