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October 19, 2026
'Cultural Studies Seeks to Understand What Lies Beneath the Surface of Everyday Things'
Maxim Zhiganov has been exploring different aspects of sound for many years—first in relation to his native Perm and then on a broader, global scale. In this interview for the HSE Young Scientists project, he talks about sound maps, the special issue of the journal Logos, and why it is important to make the familiar less clear and obvious.
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Li-WiGR: Легковесная система распознавания жестов на основе Wi-Fi с группированием поднесущих CSI

Информационные процессы. 2026. Т. 26. № 2. С. 429–445.
Dai T., Khorov E.

Modern Wi-Fi-based gesture recognition systems that use a single receiver require many computations. The paper proposes Li-WiGR, a  system that employs subcarrier grouping and a specialized preprocessing to reduce computational complexity. Experiments on the Widar3 dataset show that in the single-receiver scenario Li-WiGR achieves in-domain and cross-domain classification accuracy of 98.33% and 95.24%, respectively, with a computational complexity of 2.26 million multiply--accumulate operations, which is about 500 times lower than that of the WiZeCSi approach that is close in accuracy. These results demonstrate the potential of the proposed system for deployment on resource-constrained devices.

Language: Russian
Text on another site
Keywords: gesture recognitionLow complexityReal-time systemWi-FI sensingдетектирование с помощью сигналов Wi-Fiнизкая вычислительная сложностьраспознавание жестовсистема реального времени
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