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Yangyu Liu

Publications and source records attributed to Yangyu Liu.

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Bidirectional Plasma Jets Driven by Magnetic Reconnection: Observations by GST and SDO

Using high-resolution photospheric and chromospheric observations taken by the Goode Solar Telescope (GST), we studied two groups of bidirectional plasma jets occurring in active region NOAA 13110. Supplementary observations are also provided by Solar Dynamics Observatory's (SDO) Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI). From the photospheric observations and magnetograms, the two successive bidirectional plasma jets were initially located in the vicinity of the polarity inversion lines and at the intersection of the umbra and penumbra of the sunspot, followed by magnetic flux emergence and cancellation. As the cool filamentary threads are continuously emerging from the lower chromosphere and interact with overlying horizontal magnetic loops, it leads to the bidirectional jets, erupting outflow plasmoids, and heating coronal magnetic loops. We find that the bidirectional jets extended from the central excitation location in opposite directions, at the speed of about dozens of km s$^{-1}$. For the first jet, the initial brightening first appears in 304 angstroms, about 30 s earlier than the H$\alpha$ observations, indicating that magnetic reconnection takes place in the transition region. While the initial reconnection for the second jet occurs simultaneously in H$\alpha$ and 304 angstroms, showing the recurrent eruptions. These observations confirm that the bidirectional plasma jets can be generated by magnetic reconnection between the rising filamentary threads or material and the overlying horizontal magnetic loops. Our results provide new insights into the generation of the bidirectional plasma jets and reconnection-based coronal heating.

astro-ph.SR

Channel Estimation for WiFi Prototype Systems with Super-Resolution Image Recovery

Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.

eess.SP