An approach to the implementation of a neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable, especially for onboard communication systems in unmanned aerial vehicles (UAV), because of its suitability for hardware implementation. In this study, we evaluate the possibility of building such a system in hardware implementation at FPGA. Onboard implementation-oriented information technology of real-time neuro-like cryptographic data protection with symmetric keys (masking codes, neural network architecture, and matrix of weighting coefficients) has been developed. Due to the pre-calculation of matrices of weighting coefficients and tables of macro-partial products and the use of tabular-algorithmic implementation of neuro-like elements and dynamic change of keys, it provides increased cryptographic stability and hardware–software implementation on FPGA. The table-algorithmic method of calculating the scalar product has been improved. By bringing the weighting coefficients to the greatest common order, pre-computing the tables of macro-partial products, and using operations of memory read, fixed-point addition, and shift operations instead of floating-point multiplication and addition operations, it provides a reduction in hardware costs for its implementation and calculation time as well. Using a processor core supplemented with specialized hardware modules for calculating the scalar product, a system of neural network cryptographic data protection in real-time has been developed, which, due to the combination of universal and specialized approaches, software, and hardware, ensures the effective implementation of neuro-like algorithms for cryptographic encryption and decryption of data in real-time. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family, and it requires 3053 logic elements and 745 registers. The execution time of exclusively software realization of NN cryptographic data encryption procedure using a NanoPi Duo microcomputer based on the Allwinner Cortex-A7 H2+ SoC was about 20 ms. The hardware–software implementation of the encryption, taking into account the pre-calculations and settings, requires about 1 msec, including hardware encryption on the FPGA of four 2-bit inputs, which is performed in 160 nanoseconds.

This article explores the development of a video surveillance system that utilizes cuttingedge technology to analyze the video stream in real-time, identify motion, and recognize objects within the video stream. The functionality of this system enables it to provide a high level of accuracy in identifying objects, even in low-light conditions or with low-resolution cameras. The software system has been designed as a user-friendly desktop application with the latest technologies and features that will ensure its relevance and easy maintenance in the future. To ensure that the developed desktop application meets common optimization requirements, extensive testing has been conducted to evaluate its resource usage. The resulting system is an efficient and reliable tool for monitoring and detecting movement in various locations, providing enhanced security measures and public safety.

Rapid grows and expansion smart systems application, wide use data transmission networks require cryptographic protection of such data. Graph model of nonlinear neuro-like data encryption algorithm has been developed. This model provides a transition to the structure of hardware to process data of different intensity. The structure of the hardware for implementation basic operation of nonlinear neuro-like encryption is focused on FPGA. The method of implementing multi-input summation has been improved, which, due to the complex use of vertical and multi-operand approaches, ensured a time reduction of summation of a group of operands.

The requirements for a mobile robotic platform (MRP) with an intelligent traffic control system and data transmission protection are determined. Main requirements are the reduction of dimensions, energy consumption, and cost; remote and intelligent autonomous traffic control; real-time cryptographic data protection; preservation of working capacity in the conditions of action of external factors; adaptation to customer requirements; ability to perform tasks independently in conditions of uncertainty of the external environment. It is proposed to develop a mobile robotic platform based on an integrated approach including: navigation methods, methods of pre-processing and image recognition; modern methods and algorithms of intelligent control, artificial neural networks, and fuzzy logic; neuro-like methods of cryptographic data transmission protection; modern components and modern element base; methods of intellectual processing and evaluation of data from sensors in the conditions of interference and incomplete information; methods and means of automated design of MRP hardware and software. The following principles were chosen for the development of a mobile robotic platform with an intelligent control system and cryptographic protection of data transmission: hierarchical construction of an intelligent control system; systematicity; variable composition of equipment; modularity; software openness; compatibility; specialization and adaptation of hardware and software to the structure of algorithms for data processing and protection; use of a set of basic design solutions. The basic architecture of a mobile robotic platform with an intelligent traffic control system and data transmission protection has been developed, which is the basis for the construction of mobile robotic platforms with specified technical and operational parameters. To implement neuro-like tools, the method of tabular-algorithmic calculation of the scalar product was improved, which due to the simultaneous formation of k macroparticle products provides k times reduction of the time of the scalar product calculation. Keywords: mobile robotic platform; intelligent processing; architecture; neural network; autonomous control; sensors; data protection.

Досліджено та обґрунтовано вибір нейромережевих структур для оброблення статистичних даних з метою прогнозування та виявлення аномальних показників споживання енергоресурсів. Показано, що системам на основі нейронних мереж завжди протиставлялись експертні системи, які, на відміну від перших, очевидно програмувались. Середовище, в якому працює система, не завжди є статичним і потрібні методи опрацювання даних, які могли б адекватно реагувати на зміну середовища та вміти відповідно адаптувати отримувані результати. Нейронні мережі володіють такою особливістю, як вміння навчатись. Ця особливість і є основним аргументом для застосування таких структур у системах управління енергоефективністю. Розроблена архітектура мережі та застосований процес навчання дав змогу прогнозувати показники спожитої електроенергії з урахуванням багатьох параметрів. Особливістю розробленої архітектури є можливість здійснювати перенавчання у процесі функціонування, не перериваючи його. Використання адаптивного та безперервного навчання нейромережі дасть змогу виявляти аномальні показники даних. Точність такого виявлення було перевірено на реальній вибірці даних. Аналіз отриманих результатів показує, що використання нейронних мереж хоч і потребує швидкодії і часу на навчання, проте, під час класифікації вхідного вектора, швидкодія нейронної мережі перевищує будь-який алгоритм кластеризації.