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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.
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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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Packet Level Performance Assessment of mmWave Backhauling Technology for 3GPP NR Systems

IEEE Access. 2019. No. 7. P. 9860–9871.
Ometov A., Moltchanov D., Komarov M. M., Volvenko S., Koucheryavy E.

Recently standardized millimeter-wave (mmWave) band 3GPP New Radio systems are expected to bring extraordinary rates to the air interface efficiently providing commercial-grade enhanced mobile broadband services in hotspot areas. One of the challenges of such systems is efficient offloading of the data from access points (AP) to the network infrastructure. This task is of special importance for APs installed in remote areas with no transport network available. In this paper, we assess the packet level performance of mmWave technology for cost-efficient backhauling of remote 3GPP NR APs connectivity “islands”. Using a queuing system with arrival processes of the same priority competing for transmission resources, we assess the aggregated and per-AP packet loss probability as a function environmental conditions, mmWave system specifics, and generated traffic volume. We show that the autocorrelation in aggregated traffic provides a significant impact on service characteristics of mmWave backhaul and needs to be compensated by increasing either emitted power or the number of antenna array elements. The effect of autocorrelation in the per-AP traffic and background traffic from other APs also negatively affects the per-AP packet loss probability. However, the effect is of different magnitude and heavily depends on the load fraction of per-AP traffic in the aggregated traffic stream. The developed model can be used to parameterize mmWave backhaul links as a function of the propagation environment, system design, and traffic conditions.

Priority areas: IT and mathematics
Language: English
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Keywords: bandwidth mathematical modelwireless communications 5G mobile communicationmmWave
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