QUALITY EVALUATION OF "WHITE" NOISE:
IMPLEMENTATION OF THE "MONKEYS" TEST THROUGH A SET OF HAMMING CONVOLUTIONS CONSTRUCTED
FOR DIFFERENT NUMBER SYSTEMS 

Yunin Aleksey Petrovich, Specialist, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), E-mail: pniei@penza.ru
Ivanov Aleksandr Ivanovich, Doctor of engineering sciences, associate professor, head of the laboratory of biometric and neural network technologies, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), E-mail: ivan@pniei.penza.ru
Ratnikov Kirill Andreevich, Specialist, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), E-mail: pniei@penza.ru
Kol'chugina Elena Anatol'evna, Doctor of engineering sciences, professor, sub-department of computer software and application, Penza State University (40 Krasnayа street, Penza, Russia), E-mail: kea@pnzgu.ru 

519.2, 004.622, 004.056.531 

10.21685/2072-3059-2018-4-5 

Background. The aim of the work is to increase the correctness of calculating the entropy of long codes with weakly dependent bits generated by neural network biometrics-code converters or hashing of biometric data using lightweight cryptography.
Materials and methods. The classical Shannon procedures cannot be used for calculations, since they require the use of huge statistical material. To reduce the cost of computing resources, the display of ordinary codes to the Hamming convolution space is used.
Results. It is suggested to consider a portrait of white noise in the Hamming convolution system obtained in different namber systems. This is equivalent to the test of a set of monkeys, each of which simultaneously prints on a variety of printing machines of completely different designs, created for different languages and different writing systems. The more printing machines are used, the faster a lot of monkeys will jointly create an intelligent phrase in one of possible languages.
Conclusions. In the space of the Hamming convolution set, the white noise quality estimate becomes correct if we know in advance the distribution parameters of the controlled convolutions. At the same time, the reliability of the estimates is higher, the more the various Hamming convolutions are controlled and the higher the dimension of the calculated functionals. Since NIST USA recommends about 16 randomness tests, it is suggested to apply at least 16 types of Hamming convolutions constructed for various number systems. 

entropy of long codes with weakly dependent discharges, regularization of calculations, variety of Hamming convolutions, Hamming convolution with random positioning of digits 

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