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Current trends in the design of electronic systems is the use of embedded systems based on systems on a chip (System-on-Chip (SoC)) or (VLSI SoC). The paper discusses the design features of electronic systems on a chip using the SystemC design and verification language. For the joint design and simulation of digital systems hardware and software, seven modeling levels are presented and discussed: executable specification, disabled functional model, temporary functional model, transaction-level model, behavioral hardware model, accurate hardware model, register transfer model. The SystemC design methodology with functional verification is presented, which reduces development time.The architecture of the SystemC language and its main components are shown. The expansion of SystemC–AMS for analog and mixed analog-digital signals and its use cases in the design of electronic systems are considered. Computing models are discussed: temporary data stream (TDF), linear signal stream (LSF) and electric linear networks (ELN). The architecture of the SystemC–AMS language standard is shown and examples of its application are given. It is shown that the design languages SystemC and SystemC–AMS are widely used by leading developers of computer-aided design systems for electronic devices.

рассматривается проблема классификации потоковых данных, поступающих из различных источников в случайные моменты времени. Это может быть поток данных, содержащих результаты измерения датчиков, расположенных в прибрежных зонах океанов, позволяющий оценивать параметры состояния экосистемы, и также поток текстов, получаемых, например, в письмах электронной почты и т. д. Интернет содержит большие объёмы неструктурированной информации, отсутствие организации которых делает работу с данными неудобной и ресурсоёмкой. Преодоление указанного недостатка является актуальной задачей. Классификация служит инструментом, позволяющим облегчить работу с неструктурированной информацией. Разработан алгоритм классификации потоковых данных на основе байесовского критерия. Построена математическая модель потоковых текстовых данных, позволяющая применять алгоритмы классификации текстов на естественном языке на потоковых данных. Предложена модификация наивного байесовского классификатора, использующая характеристику tf-idf как меру принадлежности терминов классам, позволяющая улучшить качество классификации. Классификатор был обучен с помощью машинного фонда русского языка. Разработано программное обеспечение, позволяющее извлекать потоковые текстовые данные из сети Интернет и производить классификацию разработанным алгоритмом в реальном времени, The paper describes the issue of stream data classification. Stream data is described as a set of objects arriving from different sources at random moments of time. It might be a stream of data containing ocean coastal area sensors measure information and describing the parameters of the ecosystem condition, as well, it might be a stream of texts acquired from incoming emails attachments, etc. The Internet contains vast volumes of unstructured information. The lack of organization makes data inconvenient and resource-intensive to work with. Addressing to such an issue considered to be a relevant problem. Classification provides an opportunity to make it easier to work with unstructured information. The paper describes the algorithm for stream data classification based on Bayesian criteria. Text stream data model is proposed. This model allows applying natural language text classification algorithms to stream data. Naive Bayes classifier modification using tf-idf measure for evaluating the proximity of a classified document to a particular class that allows improving the classification quality is proposed. The classifier has been trained using the machine Fund of the Russian language. Software allowing text data stream extraction from the Internet and its classification using the proposed algorithm in real-time scale is proposed, №1(28) (2020)

Объектом исследования является проблема классификации Ð¿Ð¾Ñ‚Ð¾ÐºÐ¾Ð²Ñ‹Ñ Ð´Ð°Ð½Ð½Ñ‹Ñ Ñ временной зависимостью. При исследовании проблемы были использованы различные программные инструменты, в частности система фреймворков Python Anaconda и MOA для анализа непосредственно потоков Ð´Ð°Ð½Ð½Ñ‹Ñ . Были рассмотрены различные классификаторы Ð¿Ð¾Ñ‚Ð¾ÐºÐ¾Ð²Ñ‹Ñ Ð´Ð°Ð½Ð½Ñ‹Ñ , метрики и способы адаптации ÑÑ‚Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸Ñ„Ð¸ÐºÐ°Ñ‚Ð¾Ñ€Ð¾Ð² к проблеме Ð¿Ð¾Ñ‚Ð¾ÐºÐ¾Ð²Ñ‹Ñ Ð´Ð°Ð½Ð½Ñ‹Ñ Ñ временной зависимостью. Результатом данной работы являются общие рекомендации при работе с потоками Ð´Ð°Ð½Ð½Ñ‹Ñ Ñ временной зависимостью и методика оценки Ð¸Ñ Ñ€Ð°Ð±Ð¾Ñ‚Ð¾ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑ‚Ð¸., The object of the research is the problem of streaming data classification with time dependence. Python Anaconda and MOA for analyzing open data streams. Various classifiers of streaming data, metrics and ways of adapting these classifiers to the problem of streaming data with time dependence were considered. The results of this work are general recommendations.

Modern big science projects are becoming highly data intensive to the point where offline processing of stored data is infeasible. High data throughput computing, or Data Stream Computing, for future projects is required to deal with terabytes of data per second which cannot be stored in long-term storage elements. Conventional data-centres based on typical server-grade hardware are expensive and are biased towards processing power. The overall I/O bandwidth can be increased with massive parallelism, usually at the expense of excessive processing power and high energy consumption. An ARM System on Chip (SoC) based processing unit may address the issue of system I/O and CPU balance, affordability and energy efficiency since ARM SoCs are mass produced and designed to be energy efficient for use in mobile devices. Such a processing unit is currently in development, with a design goal of 20 Gb/s I/O throughput and significant processing power. The I/O capabilities of consumer ARM System on Chips are discussed along with to-date performance and I/O throughput tests.

In this article we examine the possibility of hardware support for functions of mobile device management platform (MDM-platform) using a Multiple Instructions and Single Data stream computer system, developed within the framework of the project in Bauman Moscow State Technical University. At the universities the MDM-platform is used to provide various mobile services for the faculty, students and administration to facilitate the learning process: a mobile schedule, document sharing, text messages, and other interactive activities. Most of these services are provided by the extensive use of data stored in MDM-platform databases. When accessing the databases SQL- queries are commonly used. These queries comprise operators of SQL-language that are based on mathematical sets theory. Hardware support for operations on sets is implemented in Multiple Instructions and Single Data stream computer system (MISD System). This allows performance improvement of algorithms and operations on sets. Thus, the hardware support for the processing of SQL-queries in MISD system allows us to benefit from the implementation of SQL-queries in the MISD paradigm.The scientific novelty of the work lies in the fact that it is the first time a set of algorithms for basic SQL statements has been presented in a format supported by MISD system. In addition, for the first time operators INNER JOIN, LEFT JOIN and LEFT OUTER JOIN have been implemented for MISD system and tested for it (testing was done for FPGA Xilinx Virtex-II Pro XC2VP30 implementation of MISD system). The practical significance of the work lies in the fact that the results of the study will be used in the project "Development of the Russian analogue of the system software for centralized management of personal devices and platforms in enterprise networks" of the St. Petersburg Polytechnic University (with the financial support of the state represented by the Ministry of Education and Science of the Russian Federation).In this study, we have conducted the testing on the basis of data structures obtained during the approbation of MDM-platform in SPbPU. Test results have shown that on average MISD system provides a two-fold gain for SQL-operators such as INNER JOIN and LEFT JOIN. However, it was found that MISD system does not give gain in cycles for SQL-operator LEFT OUTER JOIN. A comparison was carried out with the processing of requests on Intel Core 2 Duo.The results of this work are areas of further development and improvement of MISD system, including the area of hardware support for new operations.

The paper considers the implementing Bellman-Ford and Lee algorithms to find the shortest graph path on a computer system with multiple instruction stream and single data stream (MISD). The MISD computer is a computer that executes commands of arithmetic-logical processing (on the CPU) and commands of structures processing (on the structures processor) in parallel on a single data stream. Transformation of sequential programs into the MISD programs is a labor intensity process because it requires a stream of the arithmetic-logical processing to be manually separated from that of the structures processing. Algorithms based on the processing of data structures (e.g., algorithms on graphs) show high performance on a MISD computer. Bellman-Ford and Lee algorithms for finding the shortest path on a graph are representatives of these algorithms. They are applied to robotics for automatic planning of the robot movement in-situ. Modification of Bellman-Ford and Lee algorithms for finding the shortest graph path in coprocessor MISD mode and the parallel MISD modification of these algorithms were first obtained in this article. Thus, this article continues a series of studies on the transformation of sequential algorithms into MISD ones (Dijkstra and Ford-Fulkerson 's algorithms) and has a pronouncedly applied nature. The article also presents the analysis results of Bellman-Ford and Lee algorithms in MISD mode. The paper formulates the basic trends of a technique for parallelization of algorithms into arithmetic-logical processing stream and structures processing stream. Among the key areas for future research, development of the mathematical approach to provide a subsequently formalized and automated process of parallelizing sequential algorithms between the CPU and structures processor is highlighted. Among the mathematical models that can be used in future studies there are graph models of algorithms (e.g., dependency graph of a program). Due to the high labor intensity of manual parallelization of sequential computer programs for MISD computer, automatic parallelization becomes a key issue for the development of a new MISD architecture. The paper lays down the foundation for the solution of this task.

В работе актуализируется проблема эффективности использования современных WEB-технологий при реализации WEB-приложений. Предлагаемый вариант решения позволяет успешно интегрировать стек современных WEB-технологий и применить его в разработке программного комплекса для бронирования и поиска свободного места на парковке. The thesis updates the problem of the effectiveness of using modern WEB-technologies in the implementation of the WEB user interface, as well as the problem of finding and reserving parking spots in large parking spaces. The offered solution allows to integrate the stack of modern WEB-technologies and apply it in the development of a software package for simple reservation and search of free parking spots.

Normal requirement for telemetry data compression algorithms is an ability to recover initial data “as is” without loss of information. This feature is very important in various telemetry processing applications. Precise recovery of the telemetry data as it is acquired from the original source of information is necessary for the analysis of any kind of abnormal events, recovery of bad sites within the telemetry data stream and for other types of post- or real-time data processing [1,2]. The effectiveness of methods of lossless compression is largely determined by the properties of the data under compression [3]. Compression algorithms show better compression ratios if they can adapt to the characteristics of the input data, which are in most cases rapidly change. In this paper we present the results of studies conducted to develop an efficient method of reversible telemetry data compression based on adaptive linear prediction of telemetry data packed according to IRIG-106 format. IRIG-106 is an open standard, developed specifically for aerospace industry, but now used in wide range of telemetry registration applications [4]. Data is packed to frames of fixed length and predefined internal structure. Frame can carry different sources of information: digitized samples of analog signals, as well as pure digital data. For each source a channel of the recording system is provided. The source sample in each channel is introduced by telemetry word. All words in the frame have the same bit width. Telemetry frame contains additional service information in purpose of detecting bit errors, frame synchronization, etc.Lossless data compression algorithm can be divided into two stages; the first stage - decorrelation stage, which exploits the redundancy between the neighboring samples in the data sequence, the second stage - entropy coding, which takes advantage from decreasing variance and lowering entropy of the data made on the first stage [5,6,7].