The tasks performed by the intelligent components of mobile robotic systems (MRS) are analyzed and their features are determined. The operational basis for the implementation of hardware accelerators of artificial neural networks (ANN) is defined and divided into three groups of neurooperations: preprocessing, processing and calculation of transfer functions.
This paper presents the analysis and development of a smart noise information collection system utilizing the Spectrum Analyzer SVAN 958A to enhance real-time noise monitoring and data analysis. The study addresses the limitations of traditional noise measurement tools, which often lack real-time processing and comprehensive integration with modern data management platforms.
Проаналізовано особливості апаратної реалізації штучних нейронних мереж, вибрано принципи побудови, визначено шляхи підвищення ефективності використання обладнання, розроблено методи синтезу та базові структури нейрокомп’ютерних систем реального часу.
Features of hardware representation of artificial neural networks were analyzed, principles of construction were chosen, ways of efficiency increase of equipment use were determined, methods of synthesis and base structures of the neural computing, , real-time systems were developed.
As a result of the analytical review, it was established that the family of Yolo models is a promising area of search and recognition of objects. However, existing implementations do not support the ability to run the model on the iOS platform. To achieve these goals, a comprehensive scalable conversion system has been developed to improve the recognition accuracy of arbitrary models based on the Docker system. The method of improvement is to add a layer with the Mish activation function to the original model. The method of conversion is to quickly convert any Yolo model to CoreML format.
It is shown that for the processing of intensive data flows in industry (management of technological processes and complex objects), energy (optimization of load in power grids), military affairs (technical vision, mobile robot traffic control, cryptographic data protection), transport (traffic management and engine), medicine (disease diagnosis) and instrumentation (pattern recognition and control optimization) the real-time hardware neural networks with high efficiency of equipment use should be applied.
The features of the most common methods and systems for searching and recognizing objects in video are explored. The research shows the feasibility of building search and recognition tools for the iOS platform in real time. The method of functional adaptation of the algorithm of search and recognition of objects to features of video is offered, which consists in processing of video image by smoothing and minimization filters, which reduces the time of search and recognition of objects. The block diagram and algorithm of system functioning were designed.
It is determined that the main tasks for the monitoring of technological processes at an enterprise are the collection, storage, visualization, preliminary analytical and intellectual processing of technological data in real time. The requirements have been formed and the principles of developing a monitoring system for technological processes in the enterprise have been selected, the main ones are modularity, system integration, openness and compatibility.
Розглянуто алгоритм ефективного використання ресурсів мультитермінальної системи в режимі реального часу.
The requirements for the real-time hardware development have been formed. The principles of such development have been selected. Consistent flow graphs for sorting algorithms by merging data sets have been created. The hardware for sorting data with the high efficiency of equipment usage has been synthesized.