China Electronics Technology Group Corporation

Thirty years ago, Coullet et al. proposed that a special optical field exists in laser cavities bearing some analogy with the superfluid vortex. Since then, optical vortices have been widely studied, inspired by the hydrodynamics sharing similar mathematics. Akin to a fluid vortex with a central flow singularity, an optical vortex beam has a phase singularity with a certain topological charge, giving rise to a hollow intensity distribution. Such a beam with helical phase fronts and orbital angular momentum reveals a subtle connection between macroscopic physical optics and microscopic quantum optics. These amazing properties provide a new understanding of a wide range of optical and physical phenomena, including twisting photons, spin-orbital interactions, Bose-Einstein condensates, etc., while the associated technologies for manipulating optical vortices have become increasingly tunable and flexible. Hitherto, owing to these salient properties and optical manipulation technologies, tunable vortex beams have engendered tremendous advanced applications such as optical tweezers, high-order quantum entanglement, and nonlinear optics. This article reviews the recent progress in tunable vortex technologies along with their advanced applications.

in area) with the requisite thickness for high-resolution X-ray imaging applications. We showcase prototype applications of these high-quality scintillating films as X-ray imaging screens for a cellphone panel and a standard central processing unit chip. Our radiography prototype combines large-area processability with high resolution and a strong penetration ability to sheath materials, such as resin and silicon. We reveal an energy transfer process inside those stacked nanosheet solids that is responsible for their superb scintillation performance. Our findings demonstrate a large-area solution-processed scintillator of stable and efficient RL as a promising approach for low-cost radiography and X-ray imaging applications.

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer.

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

The average valence, ValO, of the oxygen anions in the perovskite oxide BaTiO3, was found using O1s photoelectron spectra to be −1.55. This experimental result is close to the theoretical value for BaTiO3 (−1.63) calculated by Cohen [Nature 358, 136 (1992)] using density functional theory. Using the same approach, we obtained values of ValO for several monoxides, and investigated the dependence of ValO and the ionicity on the second ionization energy, V(M2+), of the metal cation. We found that the dependence of the ionicity on V(M2+) in this work is close to that reported by Phillips [Rev. Mod. Phys. 42, 317 (1970)]. We therefore suggest that O1s photoelectron spectrum measurements should be accepted as a general experimental method for estimating the ionicity and average valence of oxygen anions.

The development of information transmission technology towards high-frequency microwaves and highly integrated and multi-functional electronic devices has been the mainstream direction of the current communication technology. During signal transmission, resistance-capacitance time delay, crosstalk, energy consumption increase and impedance mismatch restrict the high density and miniaturization of Printed circuit board (PCB). In order to achieve high fidelity and low delay characteristics of high-frequency signal transmission, the development of interlayer dielectric materials with low dielectric constant (Dk) and low dielectric loss factor (Df) has become the focus of researchers. This review introduces the dielectric loss mechanism of polymer composites and the resin matrix commonly used in several high-frequency copper-clad laminates, and mainly describes how to reduce the dielectric constant and dielectric loss of materials from the level of molecular structure design, as well as the effect of fillers on the dielectric properties of polymer substrates. As a kind of potential functional fillers for dielectric polymeric composites, the carbon nanofillers are used to tailor the dielectric properties of their composites via different dimensions and loadings, as well as their proper preparation methods. This review finally summarizes the interface bonding failure mechanism and a feasible idea to optimize the dielectric properties of polymer matrix composites is also proposed.

This paper describes the scientific objectives and payloads of Tianwen-1, China’s first exploration mission to Mars. An orbiter, carrying a lander and a rover, lifted-off in July 2020 for a journey to Mars where it should arrive in February 2021. A suite of 13 scientific payloads, for in-situ and remote sensing, autonomously commanded by integrated payload controllers and mounted on the orbiter and the rover will study the magnetosphere and ionosphere of Mars and the relation with the solar wind, the atmosphere, surface and subsurface of the planet, looking at the topography, composition and structure and in particular for subsurface ice. The mission will also investigate Mars climate history. It is expected that Tianwen-1 will contribute significantly to advance our scientific knowledge of Mars.

Automatic modulation classification (AMC) is one of the essential technologies, and also a hard nut to crack in the field of cognitive radio (CR) and non-cooperative communication systems. In this work, we propose a novel AMC method based on the promising recurrent neural network (RNN), which is shown to have the capability to sufficiently exploit the temporal sequence characteristic of received communication signals. This method resorts to raw signals directly with limited data length, and avoids extracting signal features manually. The proposed method is compared with a convolutional neural network (CNN) based method and the result indicates the superiority of the proposed one, especially when signal-to-noise ratio (SNR) is above -4dB. Furthermore, a comparative study is presented to evaluate the availability of the other different RNN structures. And a more efficient structure is recommended based on two-layer gated recurrent unit (GRU) network. Additional numerical results demonstrate that the proposed structure achieves an improved performance from 80% to 91% in terms of classification accuracy.

The traditional perimeter‐based network protection model cannot adapt to the development of current technology. Zero trust is a new type of network security model, which is based on the concept of never trust and always verify. Whether the access subject is in the internal network or the external network, it needs to be authenticated to access resources. The zero trust model has received extensive attention in research and practice because it can meet the new network security requirements. However, the application of zero trust is still in its infancy, and enterprises, organizations, and individuals are not fully aware of the advantages and disadvantages of zero trust, which greatly hinders the application of zero trust. This paper introduces the existing zero trust architecture and analyzes the core technologies including identity authentication, access control, and trust assessment, which are mainly relied on in the zero trust architecture. The main solutions under each technology are compared and analyzed to summarize the advantages and disadvantages, as well as the current challenges and future research trends. Our goal is to provide support for the research and application of future zero trust architectures.

Permissionless blockchain, as a kind of distributed ledger, has gained considerable attention because of its openness, transparency, decentralization, and immutability. Currently, permissionless blockchain has shown a good application prospect in many fields, from the initial cryptocurrency to the Internet of Things (IoT) and Vehicular Ad-Hoc Networking (VANET), which is considered as the beginning of rewriting our digital infrastructure. However, blockchain confronts some privacy risks that hinder its practical applications. Though numerous surveys reviewed the privacy preservation in blockchain, they failed to reveal the latest advances, nor have they been able to conduct a unified standard comprehensive classification of the privacy protection of permissionless blockchain. Therefore, in this paper, we analyze the specific characteristics of permissionless blockchain, summarize the potential privacy threats, and investigate the unique privacy requirements of blockchain. Existing privacy preservation technologies are carefully surveyed and evaluated based on our proposed evaluation criteria. We finally figure out open research issues as well as future research directions from the perspective of privacy issues.

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

Human exploration of the Moon is associated with substantial risks to astronauts from space radiation. On the surface of the Moon, this consists of the chronic exposure to galactic cosmic rays and sporadic solar particle events. The interaction of this radiation field with the lunar soil leads to a third component that consists of neutral particles, i.e., neutrons and gamma radiation. The Lunar Lander Neutrons and Dosimetry experiment aboard China's Chang'E 4 lander has made the first ever measurements of the radiation exposure to both charged and neutral particles on the lunar surface. We measured an average total absorbed dose rate in silicon of 13.2 ± 1 μGy/hour and a neutral particle dose rate of 3.1 ± 0.5 μGy/hour.

Contact mechanics plays an important role in the design, performance analysis, simulation, and control of legged robots. The Hunt–Crossley model and the Coulomb friction model are often used as black-box models with limited consideration of the properties of the terrain and the feet. This paper analyzes the foot–terrain interaction based on the knowledge of terramechanics and reveals the relationship between the parameters of the conventional models and the terramechanics models. The proposed models are derived in three categories: deformable foot on hard terrain, hard foot on deformable terrain, and deformable foot on deformable terrain. A novel model of tangential forces as the function of displacement is proposed on the basis of an in-depth understanding of the terrain properties. Methods for identifying the model parameters are also developed. Extensive foot–soil interaction experiments have been carried out, and the experimental results validate the high fidelity of the derived models.

Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. This technology has been widely used in image segmentation, target detection, and other high-level image processing technologies. In recent years, considering the problems of thick image edge contour, inaccurate positioning, and poor detection accuracy, researchers have proposed a variety of edge detection algorithms based on deep learning, such as multi-scale feature fusion, codec, network reconstruction, and so on. This paper dedicates to making a comprehensive analysis and special research on the edge detection algorithms. Firstly, by classifying the multi-level structure of traditional edge detection algorithms, the theory and method of each algorithm are introduced. Secondly, through focusing on the edge detection algorithm based on deep learning, the technical difficulties, advantages of methods, and backbone network selection of each algorithm are analysed. Then, through the experiments on the BSDS500 and NYUD dataset, the performance of each algorithm is further evaluated. It can be seen that the performance of the current edge detection algorithms is close to or even beyond the human visual level. At present, there are a few comprehensive review articles on image edge detection. This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of edge detection and to make further improvements and innovations.

Purpose The purpose of this paper is to study tourists’ spatial and psychological involvement reflected through tourism destination image (TDI), TDI is divided into on-site and after-trip groups and the two groups are compared in the frame of three-dimensional continuums. Design/methodology/approach By conducting latent Dirichlet allocation (LDA) modeling to tourism user-generated content, structural topic models are established. The topics separated out from unstructured raw texts are structural themes and representations of TDI. Social network analysis (SNA) reveals the quantitative and structural differences of three-dimensional continuums of the two TDI groups. Findings The findings reveal that from the stage of on-site to after-trip, tourist perception of TDI shifts from psychologically to functionally-oriented, from common to unique, and from holistic to more attribute focused. Also, it is suggested that from a postmodernism perspective, TDI is never unique, fixed or universal, but has different image perceptions and feedbacks for different tourists. Research limitations/implications With the assistance of social sensing, a panoramic view of TDI could be established. Targeted and precision destination marketing and image promotion could be applied out to each individual tourist. Originality/value Combining with the perspectives of the tourist-destination space system and the tourism involvement theory, this research proposes a TDI transformation model and an explanation of the internal mechanism. The originality of research also lies in the methodological innovation of social sensing data and the LDA topic model.

This paper proposes a reconfigurable polarization converter based on a p-i-n diode controlled active metasurface (AMS). The AMS consists of a thin dielectric substrate and is tuned by two identical layers of elliptic split rings loaded with p-i-n diodes. The p-i-n diodes are positioned between the split gaps and biased through the interconnected elliptic split rings without extra bias network. The active design achieves conversion from linear to circular polarization when the p-i-n diodes are OFF, whereas no conversion takes place when the diodes are ON. A prototype of the proposed design is fabricated, and the operational characteristics are measured. Both the simulated and the experimental results verify the viability of the design. Subsequently, the converter is applied to a linearly polarized horn antenna as a superstrate, making the polarization of the antenna reconfigurable.

Target capturing is an essential and key mission for tethered space robot (TSR) in future on-orbit servicing, and it is quite meaningful to investigate the stabilization method for TSR during capture impact with target. In this paper, the stabilization control of TSR during target capturing is studied. The space tether is described by the lumped mass model, and the impact dynamic model for target capturing is derived using the Lagrange method with the consideration of space tether length, in/out-plane angles, and gripper attitude. Given the structure of the TSR's gripper, a position-based impedance control method is proposed for target capturing operation. The neural network is used to estimate and compensate the uncertainties in the dynamic model of TSR, and an adaptive robust controller is designed to overcome the influence of the space tether and track the desired position generated by impedance controller. Numerical simulations suggest that the proposed controller can realize the stabilization of TSR during target capturing; besides, the uncertainties of the TSR can effectively be compensated via adaptive law and the influence of the space tether can be suppressed via the robust control strategy, which lead to smaller overshoot, less convergence time, and higher control accuracy during capturing operation.

Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and other fields. However, existing detection methods have low accuracy due to the limitations presented by the strong scattering of SAR image targets, unclear edge contour information, multiple scales, strong sparseness, background interference, and other characteristics. In response, for SAR target detection tasks, this paper combines the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks (CNNs) to innovatively propose a visual transformer framework based on contextual joint-representation learning, referred to as CRTransSar. First, this paper introduces the latest Swin Transformer as the basic architecture. Next, it introduces the CNN’s local information capture and presents the design of a backbone, called CRbackbone, based on contextual joint representation learning, to extract richer contextual feature information while strengthening SAR target feature attributes. Furthermore, the design of a new cross-resolution attention-enhancement neck, called CAENeck, is presented to enhance the characterizability of multiscale SAR targets. The mAP of our method on the SSDD dataset attains 97.0% accuracy, reaching state-of-the-art levels. In addition, based on the HISEA-1 commercial SAR satellite, which has been launched into orbit and in whose development our research group participated, we released a larger-scale SAR multiclass target detection dataset, called SMCDD, which verifies the effectiveness of our method.

Abstract Within the framework of differential augmentation, this paper introduces the basic technical framework and performance of the BeiDou Global Navigation Satellite System (BDS-3) Satellite-Based Augmentation System (BDSBAS), including orbit products, satellite clock offset products, ionosphere and its integrity performance. The basic principle of BDS-3 Precise Point Positioning (PPP-B2b) is expounded, the similarities and differences between the PPP service provided by BDS-3 and International Global Navigation Satellite System (GNSS) Service (IGS) are discussed, and the limitations of PPP-B2b are analyzed. Since both the BDSBAS and PPP-B2b utilize a ground monitoring station network to determine the satellite orbits and clock offset corrections, and broadcast differential corrections through the three Geostationary Orbit (GEO) satellites of BDS-3, the feasibility of the co-construction of BDSBAS and PPP-B2b is analyzed, strategies for the infrastructure sharing and correction broadcasting are presented, and the influences of BDSBAS correction broadcasting strategy adjustment are evaluated. In addition, it assesses the possibility of broadcasting differential corrections through the Inclined Geosynchronous Orbit (IGSO) satellites of BDS-3, and the feasibility of augmenting satellite navigation with Low Earth Orbit (LEO) satellites.

Abstract Electromagnetic absorption (EMA) materials with light weight and harsh environmental robustness are highly desired and crucially important in the stealth of high-speed vehicles. However, meeting these two requirements is always a great challenge, which excluded the most attractive lightweight candidates, such as carbon-based materials. In this study, SiC nw -reinforced SiCNO (SiC nw /SiCNO) composite aerogels were fabricated through the in-situ growth of SiC nw in polymer-derived SiCNO ceramic aerogels by using catalyst-assisted microwave heating at ultra-low temperature and in short time. The phase composition, microstructure, and EMA property of the SiC nw /SiCNO composite aerogels were systematically investigated. The results indicated that the morphology and phase composition of SiC nw /SiCNO composite aerogels can be regulated easily by varying the microwave treatment temperature. The composite aerogels show excellent EMA property with minimum reflection loss of −23.9 dB@13.8 GHz, −26.5 dB@10.9 GHz, and −20.4 dB@14.5 GHz and the corresponding effective bandwidth of 5.2 GHz, 3.2 GHz, and 4.8 GHz at 2.0 mm thickness for microwave treatment at 600 °C, 800 °C, and 1000 °C, respectively, which is much better than that of SiCN ceramic aerogels. The superior EMA performance is mainly attributed to the improved impedance matching, multi-reflection, multi-interfacial polarization, and micro current caused by migration of hopping electrons.