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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
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Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
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Обзор методов случайного множественного доступа к общему каналу для сценариев массовой межмашинной связи

Информационно-управляющие системы. 2019. № 6. С. 54–67.
Матвеев Н. В., Tyurlikov A., Н. Л., Турликов А., Матвээв Н.

Introduction: Intensive research is currently underway in the field of data transmission systems for the Internet of Things in relation to various scenarios of Massive Machine Type Communication. The presence of a large number of devices in such systems necessitates the use the methods of random multiple access to a common communication channel. It is proposed in some works to increase the channel utilization efficiency by the use of error correction coding methods for conflict resolution (Coded Random Access). The vast variety of options for using such communication systems has made it impossible to compare algorithms implementing this approach under the same conditions. This is a problem that restrains the development of both the theory and practice of using error correction code methods for conflict resolution. Purpose: Developing a unified approach to the description of random multiple access algorithms; performing, on the base on this approach, a review and comparative analysis of algorithms in which error correction code methods are used for conflict resolution. Results: A model of a random multiple access system is formulated in the form of a set of assumptions that reflect both the features of various scenarios of Massive Machine Type Communication and the main features of random multiple access algorithms, including Coded Random Access approaches. The system models are classified by the following features: 1) a finite or infinite number of subscribers; 2) stable, unstable or metastable systems; 3) systems with retransmissions or without them; 4) systems with losses or without them. For a lossy system, the main characteristics are Throughput (the proportion of successfully delivered messages) and Packet Loss Rate (probability of a message loss). For a lossless system, the basic characteristics are the algorithm speed and the average delay. A systematic review and comparative analysis of Coded Random Access algorithms have been carried out. The result of the comparative analysis is presented in a visual tabular form. Practical relevance: The proposed model of a random multiple access system can be used as a methodological basis for research and development of random multiple access algorithms for both existing and new scenarios of Massive Machine Type Communication. The systematic results of the review allow us to identify the promising areas of research in the field of data transmission systems for the Internet of Things.

Language: Russian
DOI
Keywords: medium access control5GSlotted ALOHAуровень доступа к средеслотовый ALOHAкрупномасштабные системы межмашинной связипоследовательное погашение интерференциислучайный кодовый доступалгоритм слотовый ALOHA с нерегулярным числом копийmassive machine type communicationsuccessive interference cancellationcoded random accessirregular repetition slotted ALOHA
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