System design of neurocomputers on microcontrollers in conditions of limited resources

The development of software tools for electronic equipment has led to the development and widespread use of neural network technology. They are used for processing and making decisions based on the received information, which is not discrete, but has a polymorphic essence. Processing entity data and computing decisions requires significant amounts of computing power and device operating memory. This problem does not allow the widespread use of neural network technologies in portable devices and devices based on microcontrollers. The aim of the article – adapt neural network technology for use on portable environments and microcontroller-based electronic devices. The chosen method of implementing a neural network based on the resource-saving Hamming algorithm, and the optimized program code in the C language made it possible to significantly reduce the requirements for the hardware of the device on which this technology can be implemented. The analysis of modern microcontrollers allowed us to choose and apply the optimal power-to-energysaving microcontroller-STM32, which allowed us to implement a simplified neural network on its basis. The developed algorithm was implemented on a debug board with an STM32 microcontroller in a device that allows you to recognize handwritten numbers entered from the touch screen. The created portable device for recognizing handwritten numbers is applicable as a module in other electronic equipment products. The prospects of using the latest variants of implementing neurocomputers on microcontrollers are shown.

Prospects for the implementation of neural network technologies in radar information processing systems

Science Intensive Technologies ◽

10.18127/j19998465-202102-07 ◽

2021 ◽

Author(s):

E.V. Egorova ◽

A.N. Rybakov ◽

M.H. Aksyaitov

Keyword(s):

Neural Network ◽

Information Processing ◽

Network Technology ◽

Network Methods ◽

External Conditions ◽

Network Technologies ◽

Radar Information ◽

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

Development of a Vision System for an Intelligent Robotic Hand Prosthesis Using Neural Network Technology

ITM Web of Conferences ◽

10.1051/itmconf/20203504006 ◽

2020 ◽

Vol 35 ◽

pp. 04006

Author(s):

Andrew A. Boshlyakov ◽

Alexander S. Ermakov

Keyword(s):

Neural Network ◽

Vision System ◽

Mathematical Education ◽

Robotic Hand ◽

Network Technology ◽

Technical Vision ◽

Recognition Of Objects ◽

Network Technologies ◽

A brief review of the existing auxiliary prosthetic control systems was carried out. The concept of an intelligent prosthesis is proposed, which will expand the possibilities of application and simplify the use of the prosthesis. The required actions of the vision system in automatic and manual capture modes are considered. The sequence of operation of the subsystems of the technical vision system is determined. The possibility of implementing a prosthesis vision system based on neural network technology is shown. The method of using a ready-made neural network for recognition of objects by a prosthesis is considered. The possibilities of using the considered neural network technologies in the mathematical education of engineers are presented. A version of the prosthesis design is proposed. The possibility of constructing the described prosthesis is shown.

Digital Watermarking Based on Neural Network Technology for Grayscale Images

Encyclopedia of Multimedia Technology and Networking ◽

10.4018/978-1-59140-561-0.ch029 ◽

2011 ◽

pp. 204-212

Author(s):

Jeanne Chen ◽

Tung-Shou Chen ◽

Keh-Jian Ma ◽

Pin-Hsin Wang

Keyword(s):

Neural Network ◽

Digital Watermarking ◽

The Internet ◽

Network Technology ◽

Traditional Methods ◽

Internet Environment ◽

Network Technologies ◽

Data Passing ◽

Neural Network Technology ◽

Grayscale Images

Great advancements made on information and network technologies have brought on much activity on the Internet. Traditional methods of trading and communication are so revolutionized that everything is quasi-online. Amidst the rush to be online emerge the urgent need to protect the massive volumes of data passing through the Internet daily. A highly dependable and secure Internet environment is therefore of utmost importance.

ON USING INDICATOR COUNTERS AND NEURAL NETWORK TECHNOLOGIES TO MONITOR THE TECHNICAL CONDITION OF ENERGY-SATURATED TRACTORS UNDER THE LEASE

AGRICULTURAL ENGINEERING ◽

10.26897/2687-1149-2021-6-4-10 ◽

2021 ◽

pp. 4-10

Author(s):

MIKHAIL N. KOSTOMAKHIN ◽

Keyword(s):

Neural Network ◽

Operational Reliability ◽

Technical Condition ◽

Automated System ◽

Diagnostic Tools ◽

Technical Diagnostics ◽

Warranty Period ◽

Network Technologies ◽

Reducing the risks and the equipment ownership costs for the lessor, especially in the post-warranty period, requires developing and implementing a new automated system for equipment maintenance. The article presents an analysis and off ers possibilities of using indicator counters and neural network technologies to monitor the technical condition of energy-rich tractors online. The authors give an example of using a neural network calculator to identify malfunctions in the transmission line and increase the controllability and objectivity of assessing the current technical condition of tractors under the lease. Counters-indicators are built-in express diagnostic tools. Their use helps minimize preparatory operations to determine the technical condition, visualize and analyze parameters for monitoring the technical condition of individual components and units, and increase the operational reliability of equipment under the lease. The use of neural network technology in the technical diagnostics of equipment will generalize the experience of diagnosticians and service experts for fault localization and enable specialists with little experience to assess the technical condition and determine the amount of work needed to eliminate malfunctions, thereby reducing the time and cost of repairs.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Nanoindustry Russia ◽

10.22184/1993-8578.2020.13.3s.585.588 ◽

2020 ◽

Vol 96 (3s) ◽

pp. 585-588

Author(s):

С.Е. Фролова ◽

Е.С. Янакова

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computer Vision ◽

High Performance ◽

Systems On Chip ◽

High Performance Systems ◽

On Chip ◽

Network Technologies ◽

Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.