DEVELOPMENT OF A PROTECTIVE COATING AGAINST CAVITATION AND ELECTRIC CORROSION EXPOSURE USING POWDER PAINT WITH ADDED CNT

Cavitation and electric corrosion exposure is poorly studied. Planed to study impact the cavitation and electric corrosion exposure on titanium alloy that coated powder paint with added carbon nanotubes

Improving Josephson junction reproducibility for superconducting quantum circuits: junction area fluctuation

Josephson superconducting qubits and parametric amplifiers are prominent examples of superconducting quantum circuits that have shown rapid progress in recent years. With the growing complexity of such devices, the requirements for reproducibility of their electrical properties across a chip have become stricter. Thus, the critical current $I_c$ variation of the Josephson junction, as the most important electrical parameter, needs to be minimized. Critical current, in turn, is related to normal-state resistance the Ambegaokar-Baratoff formula, which can be measured at room temperature. Here, we focus on the dominant source of Josephson junction critical current non-uniformity junction area variation. We optimized Josephson junctions fabrication process and demonstrate resistance variation of $9.8-4.4\%$ and $4.8-2.3\%$ across $22{\times}22$ $mm^2$ and $5{\times}10$ $mm^2$ chip areas, respectively. For a wide range of junction areas from $0.008$ ${\mu}m^2$ to $0.12$ ${\mu}m^2$ we ensure a small linewidth standard deviation of $4$ $nm$ measured over 4500 junctions with linear dimensions from $80$ to $680$ $nm$. The developed process was tested on superconducting highly coherent transmon qubits $(T_1 > 100\:{\mu}s)$ and a nonlinear asymmetric inductive element parametric amplifier

Bajesovskij podchod k realizacii interferjsa mozg-komp’juter, osnovannogo na predstavlenii dviženij

V stat'e rassmatrivaetsya ispol'zovanie baiesovskogo podkhoda k sozdaniyu klassifikatora dlya interfeisa mozg-komp'yuter, osnovannogo na raspoznavanii patternov EEG pri voobrazhenii fiksirovannogo nabora dvizhenii razlichnymi konechnostyami. Pokazano, chto prosteishii baiesovskii klassifikator, osnovannyi neposredstvenno na analize kovariatsionnykh matrits iskhodnogo signala mnogokanal'nykh zapisei EEG, ne ustupaet po effektivnosti klassifikatoru, osnovannomu na metode MCSP (Multiclass Common Spatial Patterns), obespechivayushchemu, po dannym literatury, nailuchshee raspoznavanie patternov EEG v interfeisakh mozg-komp'yuter. Issledovano vliyanie artefaktov, svyazannykh s dvizheniem glaz i morganiem, na kachestvo klassifikatsii patternov EEG i pokazano, chto ikh nalichie ne vliyaet na kachestvo raspoznavaniya.В статье рассматривается использование байесовского подхода к созданию классификатора для интерфейса мозг-компьютер, основанного на распознавании паттернов ЭЭГ при воображении фиксированного набора движений различными конечностями. Показано, что простейший байесовский классификатор, основанный непосредственно на анализе ковариационных матриц исходного сигнала многоканальных записей ЭЭГ, не уступает по эффективности классификатору, основанному на методе MCSP (Multiclass Common Spatial Patterns), обеспечивающему, по данным литературы, наилучшее распознавание паттернов ЭЭГ в интерфейсах мозг-компьютер. Исследовано влияние артефактов, связанных с движением глаз и морганием, на качество классификации паттернов ЭЭГ и показано, что их наличие не влияет на качество распознавания.Web of Science621998

Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

The paper examines sources of brain activity, contributing to EEG patterns which correspond to motor imagery during training to control brain-computer interface. To identify individual source contribution into electroencephalogram recorded during the training Independent Component Analysis was used. Then those independent components for which the BCI system classification accuracy was at maximum were treated as relevant to performing the motor imagery tasks, since they demonstrated well exposed event related de-synchronization and event related synchronization of the sensorimotor μ-rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these components we have solved the inverse EEG problem to locate the sources of brain activity causing these components to appear in EEG. The sources were located in hand representation areas of the primary sensorimotor cortex. Their positions practically coincide with the regions of brain activity during the motor imagination obtained in fMRI study. Individual geometry of brain and its covers provided by anatomical MR images was taken into account when localizing the sources.Web of Science221372