Integrated circuit implementations of new models of neural networks with scale-invariant properties are presented. The specifics of such models are necessary in analysis of discrete mappings containing fractional power. We suggest an algorithm for increasing the power of a physical value by using a field-programmable gate array (FPGA).Comparisons between FPGA implementations and numerical results are demonstrated.

Prediction of parameters and classification of molecular outflows using convolutional neural networksMachine learning is gaining popularity in modern astrophysics for its incredibly powerful ability to make predictions or make assumptions over large amounts of data. We describe the application of machine learning to regression of molecular outflow parameters (mass, momentum, kinetic energy, and dynamic time) and classification of bipolar outflow using convolutional neural networks. The size of our training sample is ~ 125 sources of molecular outflow for classification, that is, 80% of the total amount of data, where 67 sources are bipolar outflow and ~ 75 sources of bipolar outflow for regression. The results show that the use of CNN improves the image classification accuracy up to 97%. The regression model predicts molecular outflow parameters with an average absolute percentage error of 37.7% for the training data and with an average absolute error of 88.0 (mass), 1237.7 (momentum), 193.3 (kinetic energy), and 3.0 (dynamic time) for test data. The machine learning algorithm reduces data processing time for predictions and classification, and this methodology has a broad prospect for future studies of astrophysics problems.

The classification of modulated signals under a low signal-to-noise ratio (SNR) environment has become a hot topic due to the complexity of the communication environment. Many relevant publications deal with signal recognition with stable SNR but are not applicable in time-varying SNR scenarios. To solve this problem, we propose a new method for determining the types of modulation based on entropy analysis. The proposed algorithm first extracts characteristics using different types of entropy and can separate the types of phase modulation (PSK): BPSK, QPSK, 8PSK, 16PSK, 32PSK, and 64PSK. In comparison with traditional feature extraction methods, the proposed algorithm increases the efficiency of signal classification. The results show that the algorithm can achieve better signal classification effects, even if SNR reaches -4 dB.

In this article, the efficient implementation multiplier of polynomials irreducible polynomials modulo for cryptographic encryption and decryption using FPGA is presented. For this, the Nexys 4 board based on the Artix-7 Field Programmable Gate Array (FPGA) from Xilinx was chosen. Verilog HDL is used to describe the circuit for reducing a number modulo. The results of a timing simulation of the device are presented in the form of time diagrams for a given 8-bit number, confirming the correct operation of the device. The developed encryption algorithm on the basis of non-positional polynomial notations is intended for software, hardware, and also software and hardware implementation. The main hardware-implemented device in non-positional algorithm of the cryptographic transformation is a device for the multiplication of polynomials irreducible polynomials modulo, which produces routine calculations on data encryption. These mathematical operations are computationally intensive and fundamental arithmetic operations, which are intensively used in many fields such as cryptography, number theory, and finite field arithmetic.

The work is devoted to study of the following problem: can we use any qualitative criteria for realization of such universal phenomenon as self-organization in open systems?We have defined values of information at fixed points of probability function of density of information and entropy . Physical meaning of these values as criteria of self-affinity and selfsimilarity in chaotic processes have been explained.We have shown that self-organization occurs if normalized information entropy S belongs to the range , where , . The validity of these findings is confirmed by calculation of value of S for hierarchical sets of well-known fractals.