The Perspective of Binary and Tertiary Artificial Neurons Mutual Application for White Noise Quality Analysis within Hamming Window Convolution Calculated by Different Modules

Abstract

The work is devoted to simplification of binary sequence quality testing, obtained either from physical noise generators, or from instable part of human biometric data. The problem originated from exponential computational complexity of a frequently used test “pressing keys by mistake”. As a result, such a test can be realized only on a powerful computer. The work shows that the simplest testing of white noise quality is performed by counting of units. It can be considered as a special case of Hamming window convolution, calculated by module two in comparison with unit code «111...11». A generalization of this test with its analogues using Hamming window convolutions calculated by modules 2 and 256 was presented. Programs based on MathCAD for performing numerical tests are given. Hamming spectral line probability amplitude distribution was presented. It was suggested to enhance verification of white noise with additional analysis using tertiary artificial neurons. The assessment of benefit from transition to mutual application of binary and tertiary artificial neurons when verifying the white noise hypothesis for one or another binary sequence was given. The calculational procedures considered in the article do not require significant computational resources. They are oriented on low power and consumption reliable microcontrollers of SIM-cards and SD-cards. This is stipulated by linear computational complexity of all tests built on Hamming window convolution irrespectively of module values, they are calculated by. The number of output quantizer levels of one or another artificial neurons has no effect on computational complexity.