With the support of the Meridian Project II of China, Qinzhou mesosphere-stratosphere-troposphere (MST) radar, also named Chinese low-latitude atmosphere ionosphere radar (CLAIR), was built by Wuhan University in Qinzhou, Guangxi, China. The radar has been completed construction in March 2024. Two independent radar systems make up the CLAIR: one is a typical MST radar operated at 50-MHz frequency (CLAIR A) and the other is a dual-frequency radar working at 160- and 200-MHz frequencies (CLAIR B). CLAIR A has a very large quasi-circular antenna array of 155 m diameter composed of 1261 Yagi antennas with ~2-MW peak power. It is a full digital array radar and each antenna connects an independent signal channel. The hardware and software structure of CLAIR B is the same as that of CLAIR A, but the antenna array of CLAIR B is smaller with 32.7 m diameter and composed of 931 log-periodic antennas with ~0.58-MW peak power. This radar has two operating modes for atmosphere observation, including the ST mode for stratosphere and troposphere observation and the M mode for mesosphere observation. The observation results of the stratospheric and tropospheric wind field by the CLAIR with the three operating frequencies and the rawinsonde present good consistency, demonstrating the reliability of the CLAIR observations. The peak power of 2 MW enables CLAIR A to well observe wind field variations at an altitude of 60–110-km altitude. Moreover, the observation result of ionospheric field-aligned irregularities in the E-region is also displayed.

We propose a video feature representation learning frame-work called STAR-GNN, which applies a pluggable graph neural network component on a multi-scale lattice feature graph. The essence of STAR-GNN is to exploit both the temporal dynamics and spatial contents as well as vi-sual connections between regions at different scales in the frames. It models a video with a lattice feature graph in which the nodes represent regions of different granularity, with weighted edges that represent the spatial and temporal links. The contextual nodes are aggregated simultaneously by graph neural networks with parameters trained with re-trieval triplet loss. In the experiments, we show that STAR-GNN effectively implements a dynamic attention mechanism on video frame sequences, resulting in the emphasis for dy-namic and semantically rich content in the video, and is robust to noise and redundancies. Empirical results show that STAR-GNN achieves state-of-the-art performance for Content-Based Video Retrieval.

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

Both images and music can convey rich semantics and are widely used to induce specific emotions. Matching images and music with similar emotions might help to make emotion perceptions more vivid and stronger. Existing emotion-based image and music matching methods either employ limited categorical emotion states which cannot well reflect the complexity and subtlety of emotions, or train the matching model using an impractical multi-stage pipeline. In this paper, we study end-to-end matching between image and music based on emotions in the continuous valence-arousal (VA) space. First, we construct a large-scale dataset, termed Image-Music-Emotion-Matching-Net (IMEMNet), with over 140K image-music pairs. Second, we propose cross-modal deep continuous metric learning (CDCML) to learn a shared latent embedding space which preserves the cross-modal similarity relationship in the continuous matching space. Finally, we refine the embedding space by further preserving the single-modal emotion relationship in the VA spaces of both images and music. The metric learning in the embedding space and task regression in the label space are jointly optimized for both cross-modal matching and single-modal VA prediction. The extensive experiments conducted on IMEMNet demonstrate the superiority of CDCML for emotion-based image and music matching as compared to the state-of-the-art approaches.

Vector perturbation (VP) precoding which utilizes a scaled Gaussian vector to minimize the effective transmit power is proved can obtain better diversity compared with linear precoding techniques. In this paper, we apply VP precoding in wireless MIMO wiretap channels to obtain practical physical layer security which aims at maximizing eavesdropper's error probability. The proposed scheme can also avoid performance loss introduced by artificial noise (AN) based secure schemes. New limit of perturbation vector is developed to guarantee practical secrecy and a new sphere decoder is given to meet such limitation. Furthermore, a modified VP scheme is proposed to reduce the complexity introduced by sphere decoder. Simulation results show that the proposed scheme could also achieve practical secrecy as AN based schemes, and better performance is obtained at the intended user with proposed scheme at the cost of computation complexity.

We investigate the atmospheric and ionospheric response to the 2013 sudden stratospheric warming (SSW) by using multiple instruments located in Eastern Asia. Three meteor radars and five ionosondes are used to investigate the mesospheric zonal wind fields and ionospheric parameters of F-layer virtual height (h'F), F2-layer peak height (hmF2), and critical frequency (foF2) at midand low-latitudes (10.7°N to 40.3°N). The vertical total electron content (TEC) data derived from the groundbased global positioning system receiver network are analyzed to study the ionospheric perturbations in the equatorial ionization anomaly (EIA) region. The changes in equatorial electrojet (EEJ) are observed by using the magnetometer data from stations on and off the magnetic equator. The variations of the hmF2at Sanya and EEJ strength presented the semidiurnal pattern with increase/decrease and eastward/westward currents in the morning/afternoon hours. In addition, the EIA crest moved poleward/equatorward in the morning/afternoon. The foF2showed the most significant enhancements during daytime at Wuhan and Shaoyang but the foF2at Sanya and Chumphon reduced mildly. Most importantly, based on the time-period wavelet analysis, the diurnal tidal components in the foF2over Beijing, Wuhan, and Sanya seemed similar those in zonal winds and the semidiurnal tides in the low-latitude hmF2showed the similar temporal variations as those in EEJ strength during the later phase of SSW. Therefore, apart from the local tides propagating from lower atmosphere having influence on the mid- and low-latitude ionosphere directly during the early phase, the equatorial fountain effect modulated by the enhanced tides also disturbed the EIA region.

In the 2011 Tohoku tsunamigenic earthquake, ionospheric anomalies generated by tsunami-induced gravity waves were observed by many types of instruments. Three digisondes and one Doppler receiver located in East Asia were applied to investigate the far-field ionospheric response to the westward-propagating gravity waves generated by the earthquake. Based on time-period spectrum analysis, oscillations between 20 and 36 min on the $fo\text{F}2$ (critical frequency of F2 layer) curves and Doppler variations of each observation location were identified. The horizontal group speed, generation time, and source location estimated with the ray-tracing method all indicated that the periodic disturbances recorded by the four radio systems were gravity waves induced by the tsunami following the 2011 Tohoku earthquake. The plasma frequency variations at five fixed altitudes in the ionospheric F2 layer over I-Cheon were used to investigate the vertical propagation of the tsunami-associated gravity waves. The measured phase progression in the vertical direction was opposite that of the energy transport, which further confirmed that the recorded waves were atmospheric gravity waves. The use of multisite remote sensing for examining tsunami-induced waves in the ionosphere may open new perspectives in oceanic monitoring and future tsunami warning systems.