The article discusses modern approaches to the design and implementation of data processing architectures in intelligent transport systems (ITS) with a focus on ensuring technological sovereignty. Special attention is paid to the integration of machine learning practices to automate the full lifecycle of machine learning models: from data preparation and streaming to real-time monitoring and updating of models. Architectural solutions using distributed computing platforms such as Hadoop and Apache Spark, in-memory databases on Apache Ignite, as well as Kafka messaging brokers to ensure reliable transmission of events are analyzed. The importance of infrastructure flexibility and scalability, support for parallel operation of multiple models, and reliable access control, including security issues, and the use of transport layer security protocols, is emphasized. Recommendations are given on the organization of a logging and monitoring system for rapid response to changes and incidents. The presented solutions are focused on ensuring high fault tolerance, safety and compliance with the requirements of industrial operation, which allows for efficient processing of large volumes of transport data and adaptation of ITS systems to import-independent conditions.

Keywords: data processing, intelligent transport systems, distributed processing, scalability, fault tolerance

  The paper discusses the features of their own circuits and mutual compensation circuitry in a differential stage in CMOS transistors. It is shown that their composition can significantly increase the differential gain in symmetric cascades with a dynamic load, significantly reduce common mode gain and extend the range of operating frequencies. As an example, the evolution of the basic concept of a symmetric stage and the results of its simulation in Cadence Virtuoso.

Keywords: micro circuitry, structural synthesis, complex-function blocks, a differential stage, a CMOS transistor, system-on-chip

`

Keywords: