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This paper presents a novel wideband low-profile linear-to-circular polarizer based on high refractive-index metamaterials in the X band. The proposed design is a multilayer structure that is built from two kinds of metamaterial unit cells consisting of 4-H-shaped strips and 1-H-shaped strips. Combining the two unit cells realizes the flexible and independent control of the refractive index difference between two orthogonal E-field components and thus achieves a consistent 90-degree phase delay over a wide bandwidth. Moreover, the proposed unit cell shows a high refractive index, which contributes to lowering the height of the LP-CP polarizer. In order to improve the axial ratio (AR) bandwidth, two impedance-matching layers are introduced to combat the reflection loss on the metamaterial-air boundary. Nevertheless, the proposed design can provide a wide 3 dB AR bandwidth of 37.7% and a low insertion loss of 0.5 dB with a thin profile of $0.21~{\lambda }_{0}$ . The prototype was fabricated and measured, while the experimental results agree well with the simulated ones, thus validating the working mechanism and its practical feasibility.

In order to address the problem of incomplete data set of real seismic impact of cultural relics in collections, we propose a small sample set oriented center-of-mass positioning method to solve the center-of-mass attribute of cultural relics ontology. First, the method combines the cultural relics image data to construct a deep feature fusion cultural relics ontology recognition model, which extracts and learns the cultural relics image features through multi-layer feature extraction, global information perception and ontology fusion recognition, so as to realize the efficient and accurate recognition of the cultural relic’s ontology. Then, the parameter synchronous migration learning method is designed to train and initialize the model, and the data migration strategy of semi-supervised learning is used to retrain the model using the public dataset and the cultural relic image data, to achieve the fine-tuning of the model parameters to improve the accuracy of the cultural relic ontology recognition. Finally, the center-of-mass positioning method is designed to integrate the attention mechanism with the quality of relics and other ontological attributes, and the segmentation of the relic’s ontology region is achieved by rectangular stereo fitting and the calculation of the center-of-mass points. At the same time, the fusion self-attention mechanism is used to adjust the regional center-of-mass weights to achieve weighted positioning of the center-of-mass position of the cultural relics. The experimental results show that the present method achieves the best Dice, Acc and MIoU metrics in comparison with various classical models, with improvements of 8.3%, 5.1% and 3.4% respectively. The overall center-of-mass offset of cultural relics is less than 2%, which can achieve accurate identification and center-of-mass positioning of the cultural relics body, fully improve the seismic impact dataset of cultural relics, and enhance the ability of preventive protection of cultural relics against earthquakes.

This paper introduces a proof of concept design for the first phased array antenna design enabled by liquid metal (LM) technology. The proposed LM phased array antenna system is based on substrate integrated waveguides (SIW) to excite an $1\times 4$ printed dipole antenna array. To effectively control the phase distribution on SIW power dividers, the liquid metals performed as a shorting pin along the body of the SIW are implemented. The proposed LM integrated SIW structure enables a large-phase tuning ratio of 360°. This new LM based phase shifters perform stable, low loss and wideband performance for the phased antenna array. In addition, a reconfigurability of the phase controls on the proposed phased antenna array can be realized when the LMs are filled in and out on the designated vias on the SIWs. To validate the proposed antenna array, simulation and measurement are carried out in this work. A prototype of the proposed LM phased array antenna operates at 10 GHz is measured, which confirms the array can have a 12% impedance bandwidth and the maximum gain of 8.3dBi with the scanning range of ±38° along the end-fire direction. This proposed technology is reliable and cost-effective for wideband phased array applications

This work presents a design of a fan-beam metasurface with a low profile and a wide operating bandwidth. We introduce a new meta unit of four-loop structure which produces high refractive index over the wide operating bandwidth. The metasurface composes of impedance matching layers and core layers to contribute to the fan-beam radiation. A prototype working at X-band is designed, fabricated, and measured. The measured results agree well with simulated ones. The measured results exhibit a wide bandwidth of 51%. The measured fan-beam radiation performances show that the 3-dB beamwidths on E and H-plane are 17. and 45., respectively. The realized metasurface is with the height of about 0.24 $\lambda_{0}$ , where $\lambda_{0}$ is the wavelength at center frequency in free space. Measured realized gain is about 15.6 dBi at center frequency. In addition, the measured cross polarization discrimination is better than 25 dB across the entire bandwidth.

In this paper, an Archimedean spiral antenna has been developed to flexibly radiate orbital angular momentum (OAM) wave with tunable modes. The operation mechanism for generation of the OAM wave is elaborated in terms of phases of the currents flowing on the spiral arm of the Archimedean structure. Two pairs of feeding probes are introduced to excite the Archimedean spiral antenna. With the signals with different phase differences fed into the spiral antenna, the OAM waves with the modes of $l=\pm 1$ , $l=\pm 2$ , and their all mixed modes are radiated, respectively. Simulated and measured results are given to demonstrate that the proposed Archimedean spiral antenna radiates the tunable OAM waves from the sole mode to the mixed four modes in an overlapped band of 5~6 GHz.

Stable and reliable operation of high-speed railway requires continuous and reliable power supply of traction substation, and isolating switch is one of the most important electrical devices in high-speed railway traction substation. In this paper, to address the issue of isolating switch accurate localization and state recognition simultaneously, we present an automatic isolating switch segmentation and state recognition framework called ISSSR-Net using multitask learning that consists of two stages. First, an isolating switch segmentation network called ISS-Net is proposed for isolating switch pixel-level segmentation precisely, of which a new structure containing a strip pooling module, a channel attention and three pyramid pooling modules is designed to greatly improve the segmentation and recognition performance, even in complex conditions such as rain, snow and fog. Second, to improve state recognition accuracy, the segmentation map yielded from the ISS-Net and the feature map from the shared backbone are together fed into isolating switch recognition network called ISR-Net to recognize its three states. In addition, a global context block is integrated into ISR-Net to further improve state recognition accuracy. Extensive experimental results on a self-collected dataset from Heishan traction substation corroborate that this paper provides an effective and robust method to achieve isolating switch segmentation and state recognition simultaneously. The M IoU and ${F}~1$ -score of segmentation reach 0.93 and 0.94 respectively, which is better than U-Net and its variants, and other SOTA. The ${F}~1$ -score of recognition reach 1.00, which is higher than HOG+SVM and other deep learning methods that have been experimented. [ABSTRACT FROM AUTHOR]

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic and unpredictable link status. In this paper, we propose a cybertwin-driven learning-based transmission scheduling mechanism for software-defined vehicular networks, which can adaptively select/adjust transmission control methods, i.e., loss-based, delay-based and hybrid ones, to suit to the time-varying network environment. In particular, we first analyze the dynamic network characteristics of three realistic vehicular network scenarios in terms of network throughput, round-trip time (RTT) and RTT jitter. Furthermore, we propose a novel transmission scheduling model and formulate the SDVN transmission scheduling issue as a linear programming problem. To obtain the optimized scheduling policies and guarantee the effectiveness of transmission control, we further propose a Cybertwin-driven and Deep Reinforcement Learning based transmission control solution (TcpCDRL). Specifically, TcpCDRL is featured with: (i) using deep reinforcement learning (DRL) to adaptively adjust transmission control policy, (ii) using cybertwin-driven transmission controlling to improve the policy-making effectiveness and timeliness. Simulation results show that the proposed TcpCDRL approach outperforms the single well-known transmission control approach (i.e., TcpWestwood, TcpBic, TcpVeno and TcpVegas) in terms of network throughput and RTT. [ABSTRACT FROM AUTHOR]