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The development of augmented reality (AR) has brought innovation to the working mode in multiple fields to improve the efficiency of multi-party cooperation and the safety rate of operations. However, when users use AR technology, they will inevitably suffer from cybersickness, which may cause symptoms such as nausea, dizziness, eye fatigue, and headaches, severely reducing their experience. Still, there is limited research on AR cybersickness, compared to cybersickness in virtual reality (VR). The causes and symptoms of cybersickness in AR and VR are different, therefore some conclusions in VR may not be applicable. Also, the current research scenarios related to AR cybersickness lack diversity, which is not conducive to the study of the causes of cybersickness. The paper mainly studies the cybersickness in AR railway inspection scenarios, including the causes (latency and the number of interactions) and the symptoms. 54 volunteers were recruited for the experiment, and we defined three latency types and three interaction types based on related interaction criteria. Then we designed a 3 * 3 experiment with latency and interaction quantity as variables. We conducted statistical analysis on the ARSQ data and experimental data. The results show that the severity of cybersickness in AR railway inspections is significantly positively correlated with the number of interactions and latency. Oculomotor is also significantly correlated with the two variables, while the severity of disorientation is not significantly correlated with latency. Besides, it was also observed that in AR, the severity of oculomotor is much higher than disorientation.

Photonic neural networks are emerging as promising computing platforms for artificial intelligence (AI). Particularly, integrated photonic unitary neural networks (IPUNNs) are capable of mitigating gradient vanishing/explosion problems when deeper neural networks are constructed. Furthermore, their optical implementations are also much simpler compared to non-unitary counterparts. Meanwhile, real-valued datasets still dominate AI research and the encoding strategy is critical for IPUNNs' performances. However, there are few studies to compare different encoding strategies of IPUNNs to represent these real-valued datasets and their impacts on IPUNNs' performances. Here, in the scope of encoding strategies for real-valued features, we first compare different schemes, such as phase, amplitude and hybrid encoding using numerical simulations, with benchmarks of decision boundary and image recognition tasks. These encoding strategies of IPUNNs are also compared to non-unitary real-valued neural networks (RVNNs) with trainable biases for the same benchmarks. The results suggest that phase encoding outperforms amplitude and hybrid encoding, and exhibits comparable performances to non-unitary RVNNs. To verify the numerical results, a 10×10 IPUNN chip is designed and fabricated. The phase encoding is chosen to be implemented because of its superior performances in numerical studies. We reconfigure the IPUNN chip to perform decision boundary and image recognition tasks by on-chip in-situ training. The experimental results match the simulations well. Our work provides insights for implementing reconfigurable IPUNNs in AI computing.

The study of brightness temperature (TB) anomalies always postulates a new understanding of the subsurface deposits of the Moon. In this study, the TB performances of the lunar farside highlands were comprehensively studied using TB data derived from the Chang'e (CE)-2 microwave radiometer, accompanied by Kaguya multiband imager data and Lunar Reconnaissance Orbiter diviner thermal infrared (TIR) data. The main results are as follows: First, we identified two anomaly belts surrounding Giordano Bruno and King Craters, covering an area of about 244 806 km2. Second, through similarity analysis and theoretical simulation, the cause of the anomaly was ascribed to the existence of surface or subsurface rocks. Finally, combining the in situ observations from the Apollo, CE-3, and CE-4 missions and the sensitivity of the electromagnetic waves to the sizes of the rocks in the TIR and microwave domains, we conclude that the rocks should be in centimeter-meter scale and they are widely distributed in the abnormal belts. These findings provide a new understanding of the surface features in and surrounding the impact craters.

Marius Hills volcanic complex (MHVC) comprises one of the densest volcanic structures on the Moon and is of significant interest for comprehending lunar volcanism. In this article, brightness temperature (TB), normalized TB, and brightness temperature difference were first generated based on microwave radiometer (MRM) data from the ChangE-2 Lunar Orbiter and (FeO+TiO2) abundance was calculated from Clementine ultraviolet-visible data to investigate the microwave thermal emissions and evolutionary history of the MHVC. The results are as follows. First, surface composition derived from the optical data is strongly influenced by impact events, and MRM data can better reflect the thermophysical properties of surface deposits. Second, a notable concentration of high TB values at daytime suggests an enrichment of volcanic activities. Third, later volcanic activities are indicated by the TB performances at daytime and night in multiple regions. Finally, a hot anomaly was detected by MRM data, suggesting the higher substrate temperature and the existence of a potential hidden volcanic structure. The results suggest that MRM data provide new insights into the evolutionary history of the MHVC, as well as an important perspective for lunar thermal evolution.

Cryogenic data transfer has numerous applications at 4.2 K for extracting data from supercomputers and quantum computers to address interface bottlenecks. The external modulation of data transmission across temperature zones is achieved through the use of electro-optic modulators. The performance of a thin-film lithium niobate (TFLN) Mach-Zehnder modulator was examined at cryogenic temperatures. Low-frequency measurements indicate that the Vπ (voltage needed for π phase shift) of the modulator increases as the temperature decreases from 298 K to 4.2 K. A comparison was conducted between the readout performance of the electrical wire link and the optical fiber link, spanning from 4.2 K to 298 K. Additionally, high-speed data transmission was successfully demonstrated in a 4.2 K environment without the need for cryogenic electric amplifiers, achieving signals of up to 30 Gbps. The proposed solution holds potential benefits for both classical and quantum computers in alleviating interface bottlenecks.

Urban tree cover plays a pivotal role in bolstering the sustainability, livability, and resilience of cities. However, the changes in urban tree cover and exposure under the backdrop of urbanization in China remain unclear. This study investigates the spatial distribution and temporal trends of urban tree cover expansion and exposure change from 1985 to 2023. Our findings highlight significant progress in enhancing urban tree cover, with the average tree cover in Chinese urban areas reaching 11.9% in 2023, representing a remarkable 116% increase from 5.5% in 1985. However, this positive trend contrasts with persistent deficiencies in forest resources, as reflected by the fact that urban forest cover in Chinese cities was only 4.1% in 2023, compared with 0.1% in 1985. Furthermore, our study revealed substantial heterogeneity in the spatiotemporal variations of urban tree cover. Only five cities exhibited a decreasing trend in tree cover during the study period, with coastal cities demonstrating significantly higher tree cover compared with inland cities. Additionally, we observed that urban tree cover and expansion rates decreased significantly with increasing latitude. Moreover, nationwide, there has been a sustained and accelerated improvement in urban tree exposure levels, with average tree exposure increasing from 0.66% in 1985 to 7.45% in 2020. The 69% of cities experienced positive changes in tree exposure levels, while only 1% exhibited negative changes. These findings offer valuable insights for policymakers, planners, and stakeholders to foster greener, more resilient, and sustainable urban environments in China.

The area far from the Procellarum KREEP Terrane (PKT) is important for future landing exploration. Mare Crisium, located in the area, has been identified as an essential landing option for the Lunar Geophysical Network (LGN) mission. Utilizing high-resolution images, topographic data, surface compositions, and Chang'e-2 microwave radiometer (MRM) data, a systematic analysis and comparison have been done between Maria Crisium and Smythii in terms of geomorphological feature, regolith properties, lunar crust and mantle properties, and microwave thermal emission characteristics. The results indicate that, except for investigating central magnetic anomaly in situ, Mare Smythii can complete all the scientific goals in Mare Crisium proposed by predecessors. Moreover, Mare Smythii has more complex geomorphology; the regolith here has a high detection priority including the cryptomare, swirls, and the probable water ice and volatile; and there exists the MRM anomaly, probably representing a new material that has not been detected until now. Generally, Mare Smythii should have a high priority for future exploration of the lunar surface in the region far from the PKT.

Building height dataset is crucial in urban studies, holding significant importance in understanding the interactions of human activities and the built-up environment. However, high-resolution three-dimensional building datasets covering large areas are limited. A rapid and accurate method for revealing fine-scale urban morphology is required. In this study, we developed a method for estimating building heights at the building scale. First, we integrated multisource datasets (i.e., synthetic aperture radar, optical, terrain, social-economic, and vector-based datasets) and built the machine learning model for building height estimation. Second, we applied the model to 11 cities in the U.S. and assessed its performance. Our results were consistent with the reference data, indicating that the effectiveness of our method is applicable [i.e., the R2 was 0.82, and the root mean square error (RMSE) was 3.39 m]. The evaluated results in various cities, across different height intervals, and within distinct regions also show the good agreement with reference heights according to the correlation (R2: 0.51–0.86, RMSE: 2.57–5.97 m in cities) and similar height distribution. Moreover, our results also showed the superiority by comparing with other height datasets at different scales. Finally, we mapped the building-scale height to characterize the urban morphology. These results demonstrate our proposed method's usable accuracy and the vast application potential in estimating building heights. Our proposed method's refined building height information can significantly help socioeconomic and climatological urban studies.

Individual tree segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned aerial vehicles (UAVs) have become an essential data source for (ITS) due to their capability to capture ultra-fine details. However, current deep-learning-based ITS methods struggle to accurately handle densely overlapping fruit tree distributions with similar characteristics in UAVs images, primarily due to the intricate nature of spatial arrangements in such scenarios. In this article, we propose CEDAnet, a context enhancement, and density adjustment network, to address the challenge of dense fruit trees segmentation. Specifically, a transformer-based contextual aggregation module is designed to distinguish different instances and refine the boundary of the instances. We have proposed a density-guided nonmaximum suppression method to adaptively generate sufficient candidate bounding boxes, aiming to retain more potential instances in dense trees. To evaluate the effectiveness and robustness of our proposal, we curated two ITS datasets constructed with imagery captured by UAVs, namely instance segmentation in Conghua images dataset (iSCHID) and instance segmentation in Maoming images dataset (iSMMID) based on their respective spatial characteristics. Experimental results on both two datasets demonstrated that CEDAnet yields competitive results in ITS tasks, with the bounding box AP of 0.498, segmentation AP of 0.493 in iSCHID, and the bounding box AP of 0.706, segmentation AP of 0.703 in iSMMID.

An on-chip, high-extinction-ratio transverse electric (TE) pass polarizer utilizing a silicon oxynitride (SiON) slab has been proposed and experimentally verified. The power confinement ratio of the mode field is manipulated by using a SiON slab, where most of the power of the transverse magnetic (TM) mode is transferred to the upper SiON slab and then attenuated through radiation, while the TE mode passes through with relatively low propagation loss. Experimental results show that our proposed device can achieve an extinction ratio that varies from 20.5 to 32.7 dB in the wavelength range of 790 to 870 nm, with an insertion loss of 0.6 to 1.7 dB. Potentially, this design has lower material refractive index contrast, larger minimum etching size, smaller lengths, and less stray light crosstalk, which is beneficial for systems applications such as gyroscopes.

The welding stud is a widely used part in automobile manufacturing, and its welding quality plays a crucial role in component assembly efficiency and vehicle quality. In welded stud target inspection, the complex body environment and different lighting conditions will have a certain impact on the inspection accuracy, and most of the existing methods have limited efficiency. In this paper, in order to solve the problems of low accuracy and slow speed in the stud target inspection process, we propose an innovative welding stud target inspection method based on YOLOv7. First, the EfficientFormerV2 backbone network is adopted to utilize the new partial convolution, which can extract spatial features more efficiently, reduce redundant computation, and improve the detection speed. Secondly, the bounding box loss function is changed to NWD, which reduces the loss value, accelerates the convergence speed of the network model, and better improves the detection of studs. After the test, the improved YOLOv7 network model is better than the traditional network in both speed and accuracy of welded stud target detection. (1) The mAP0.5 increased from 94.6% to 95.2%, and the mAP0.5:0.95 increased from 63.7% to 65.4%. (2) The detection speed increased from 96.1 f/s to 147.1 f/s. The results of the study can provide technical support for the subsequent tasks of automatic detection and position estimation of body welding studs.

As the core component of photovoltaic grid-connected system, the performance improvement of inverter is an important means to improve the performance of photovoltaic grid-connected system. As the key components of the inverter system, capacitors and inductors play the role of energy storage and filtering in the system, and their electrical characteristics will directly affect the dynamic and static characteristics of the photovoltaic inverter system. The existing research showed that capacitors and inductors should be fractional in nature, and the modeling, analysis and control of inverter systems were carried out on the theoretical basis of integer calculus, which will inevitably produce some errors. Therefore, the fractional controller of three-phase photovoltaic inverter system based on the improved Oustaloup algorithm is introduced into the grid-connected photovoltaic inverter system in this paper for fractional modeling, analysis and control, which can provide a theoretical model for the simulation and analysis of fractional power electronic systems. The practical operation in Jining PV power generation base (installed capacity of 560MW) in Shandong Province, China, showed that the fractional-order PI $^{\mathrm {\lambda }}$ controller had a good control effect on the grid-connected PV system, and the power quality of grid-connected PV was also improved.

Accurate change detection using remote sensing data is crucial for understanding surface dynamics. Despite the impressive success of current convolutional neural network (CNN)-based techniques, their feature extraction and representation capabilities are limited, leading to pseudochanges and omissions. To address this issue, we propose a multiscale fusion CNN-transformer network (MSFCTNet), which incorporates the strengths of CNN and transformer to improve change detection capability. The network utilizes a Siamese CNN to extract features from the bi-temporal image pairs and then combines multiscale features using a CNN-transformer hybrid structure to extract global and local features. In the decode stage, a gated attention module is used to filter the extracted features layer by layer. Moreover, before outputting the prediction results, a feature refinement head is employed to further refine the features, suppress background noise, and improve detection capability. LEVIR-CD, SVCD, and SYSU-CD datasets are used to evaluate the proposed network. Experimental results indicate that MSFCTNet outperforms other state-of-the-art change detection methods, proving the potential of MSFCTNet for change detection tasks.

In the realm of legal artificial intelligence (AI), the spotlight has been cast on its remarkable precision and efficiency, especially in tasks such as similar case retrieval where the identification of pertinent cases in response to a given query is of paramount importance. This task, distinct from traditional text retrieval, presents a set of unique challenges that necessitate the availability of high-quality, annotated datasets to facilitate efficient model training. The intricacies of handling extended queries and candidate documents, coupled with the varied interpretations of similarity, further compound the complexity of this endeavor. This study introduces an innovative training approach, combining dense and sparse retrieval methods. Utilizing a sparse retrieval model, we extract unlabeled data from extensive legal cases. Subsequently, a dense retrieval model screens this data, merging it with labeled data to create pseudo-labeled data, iteratively training until convergence. The results demonstrate exceptional performance in the Chinese law retrieval task dataset, showcasing a notable 3.66% precision enhancement and a substantial 3.62% improvement in mean average precision (MAP). However, the dataset’s imbalance across different charges of cases poses a challenge, potentially affecting retrieval performance for long-tailed legal cases. Nonetheless, these outcomes signify accelerated and more efficient retrieval of similar cases for legal professionals. Additionally, they provide high-quality references for non-legal individuals lacking expertise in the field.

Distance sensors are important for mobile robots to perceive surrounding environment. Typical sensors like LiDARs and depth cameras have been widely used, yet each has its limitations, such as LiDARs’ relatively high cost, depth cameras’ limitation to indoor use, and their poor performance in detecting transparent objects directly. On the other hand, ultrasonic phased array that integrates multiple ultrasonic sensors not only enables 3D ranging and imaging, but also provides advantages of strong environmental adaptability, being cost-effective and being able to detect transparent objects. To explore the application of in-air ultrasonic phased arrays for mobile robots, we simulate a 40 kHz $5\times 5$ non-uniform sparse ultrasonic phased array. The simulator emulates the process of phased array transmission and reception, and utilizes algorithms such as beamforming and matched filtering to obtain depth information in three-dimensional space. Then, a multi-view indoor 3D reconstruction method fusing the ultrasonic phased array and a monocular camera is proposed, where two scanning strategies are developed to handle different scenarios. Finally, the method is validated in different Gazebo scenarios and compared with other baseline methods like LiDARs and depth cameras. The experimental results reveal the method’s strong performance in terms of accuracy, consistency and completeness.

Line commutated converter based high-voltage direct-current (LCC-HVDC) transmissions are prone to harmonic oscillation under weak grids. Impedance modeling is an effective method for assessing interaction stability. Firstly, this paper proposes an improved calculation method for the DC voltage and AC currents of commutation stations to address the complex linearization of the commutation process and constructs an overall harmonic state-space (HSS) model of an LCC-HVDC. Based on the HSS model, the closed-loop AC impedances on the LCC-HVDC sending and receiving ends are then derived and verified. The impedance characteristics of the LCC-HVDC are then analyzed to provide a physical explanation for the harmonic oscillation of the system. The effects of the grid strength and control parameters on system stability are also analyzed. To improve the impedance characteristics and operating stability of the LCC-HVDC system, a virtual impedance based stability enhancement control is proposed, and a parameter design method is considered to ensure satisfactory phase margins at both the sending and receiving ends. Finally, simulation results are presented to verify the validity of the impedance model and virtual impedance based stability enhancement control.

Phase sensitive amplification is indispensable in promoting applications such as all-optical regenerators, quantum communications, all-optical analog-to-digital conversion, and long-distance communications. In this article, we proposed a vector dual-pump nondegenerate phase-sensitive amplification scheme based on ultra-silicon-rich nitride (Si7N3) waveguide, and theoretically verified its capability for all-optical regeneration of phase-encoded polarization-division multiplexing (PDM) signal without the need for complex polarization diversity structures. We achieved a gain extinction ratio (GER) of ∼37.5 dB by using a 3-mm-long Si7N3 waveguide with a high nonlinear coefficient (∼279 /W/m). Signal quality before and after regeneration is characterized by constellation diagram and error vector magnitude (EVM). The results show that the EVM of the degraded PDM differential phase-shift keying (DPSK) signals with two polarization states of 54% and 53.8%, can be improved to 13.6% and 13.6%, respectively, after regeneration, directly illustrating the remarkable phase noise suppression effect. The applicability of the scheme in PDM quadrature phase shift keying (QPSK) signals was further investigated. Similarly, the EVMs of the two polarization states of the deteriorated QPSK signals are optimized from 28.9% and 29.3% to 13.7% and 13.9%, respectively. The proposed scheme has promising applications in integrated all-optical processing systems and long-distance transmission of optical communications.

Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder–decoder network was proposed. In this network, the backbone employing atrous convolution (AC) facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoder–decoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union and class weights, a joint loss function (JLF) was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.

Earlier studies have reported some calculation methods for commutation failure fault level (CFFL) in line-commutated-converter based high-voltage direct current (LCC-HVDC) system under single-line-to-ground (SLG) faults. The accuracy of earlier methods is limited because they only consider the commutating voltage drop and phase shift, while neglecting the DC current variation. Hence, this paper proposes a CFFL calculation method under SLG faults considering DC current variation, for better planning and designing of LCC-HVDC systems. First, the fault commutating voltage magnitude and phase shift are calculated. Then, the fault DC voltage during different commutation processes is deduced. Based on the commutating voltage magnitude and phase shift, and DC voltage during different commutation processes under SLG faults, the characteristics of CFFL with different fault time are demonstrated and analyzed. Next, the transient time-domain response of the DC current after the fault is obtained based on the DC transmission line model. Discrete commutation processes are constructed based on the commutation voltage-time area rule to solve the extinction angle under different fault levels and fault time. Finally, the CFFL is calculated considering the fault time, commutating voltage drop, phase shift, and DC current variation. The accuracy of the proposed method compared with the traditional method is validated based on the CIGRE benchmark model in PSCAD/EMTDC.

Deep learning techniques continue to be used in various applications in recent years. However, when it is difficult to obtain adequate training samples, the performance of the depth model will degrade. Although few-shot learning and data enhancement techniques can relieve this dilemma, the diversity of real data is too large to simulate. To tackle this challenge, we study a novel method, Data Augmentation Scheme For Few-Shot Object Detection (DA-FSOD), to improve the efficiency of model training on visual tasks. Specifically, to expand data augmentation space, we build a data augmentation operation pool (DAOP) based on several common-applied image process operations. Then we propose a novel data augmentation scheme, the series and parallel connection scheme, which superimposes the effects of different operations to generate diverse variants. To further explore and utilize the deep feature information, we leverage the semantic information of input image in model and propose imposed semantic data augmentation which augments training set semantically via deep features of augmented variants. The proposed method successfully enhanced the model performance. We validated our approach using extensive experiments on the domain of few-shot object detection. The results showed remarkable gains compared to state-of-the-art methods.

Transfer learning (TL) has been applied in seizure detection to deal with differences between different subjects or tasks. In this paper, we consider cross-subject seizure detection that does not rely on patient history records, that is, acquiring knowledge from other subjects through TL to improve seizure detection performance. We propose a novel domain adaptation method, named the Cluster Embedding Joint-Probability-Discrepancy Transfer (CEJT), for data distribution structure learning. Specifically, 1) The joint probability distribution discrepancy is minimized to reduce the distribution shift in the source and target domains, and strengthen the discriminative knowledge of classes. 2) A clustering is performed on the target domain, and the class centroids of sources is used as the clustering prototype of the target domain to enhance data structure. It is worth noting that the manifold regularization is used to improve the quality of clustering prototypes. In addition, a correlation-alignment-based source selection metric (SSC) is designed for most favorable subject selection, reducing the computational cost as well as avoiding some negative transfer. Experiments on 15 patients with focal epilepsy from the Children’s Hospital, Zhejiang University School of Medicine (CHZU) database shown that CEJT outperforms several state-of-the-art approaches, and can promote the application of seizure detection.

For the safe and fast recovery of line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems after faults, a DC current order optimization based strategy is proposed. Considering the constraint of electric and control quantities, the DC current order with the maximum active power transfer is calculated by Thevenin equivalent parameters (TEPs) and quasi-state equations of LCC-HVDC transmission systems. Meanwhile, to mitigate the subsequent commutation failures (SCFs) that may come with the fault recovery process, the maximum DC current order that avoids SCFs is calculated through imaginary commutation process. Finally, the minimum value of the two DC current orders is sent to the control system. Simulation results based on PSCAD/EMT-DC show that the proposed strategy mitigates SCFs effectively and exhibits good performance in recovery.

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

We present a quantum annealing-based solution method for topology optimization (TO). In particular, we consider TO in a more general setting, i.e., applied to structures of continuum domains where designs are represented as distributed functions, referred to as continuum TO problems. According to the problem's properties and structure, we formulate appropriate subproblems that can be solved on an annealing-based quantum computer. The methodology established can effectively tackle continuum TO problems formulated as mixed-integer nonlinear programs. To maintain the resulting subproblems small enough to be solved on quantum computers currently accessible with small numbers of qubits and limited connectivity, we further develop a splitting approach that splits the problem into two parts: the first part can be efficiently solved on classical computers, and the second part with a reduced number of variables is solved on a quantum computer. By such, a practical continuum TO problem of varying scales can be handled on the D-Wave quantum annealer. More specifically, we concern the minimum compliance, a canonical TO problem that seeks an optimal distribution of materials to minimize the compliance with desired material usage. The superior performance of the developed methodology is assessed and compared with the state-of-the-art heuristic classical methods, in terms of both solution quality and computational efficiency. The present work hence provides a promising new avenue of applying quantum computing to practical designs of topology for various applications.

Cross-eye jamming technology has been used in electronic warfare, which attempts to protect a military platform from monopulse tracking radars. The cross-eye jamming technology has the ability of inducing angular errors to the monopulse tracking radars by transmitting two jamming signals with equal amplitudes and opposite phases. At present, high operation frequency and broadband cross-eye jamming system has been rarely demonstrated. Therefore, the present cross-eye jamming systems are hard to jam frequency-agile radar or multi-band radar, whose carrier frequency covers a large spectral range. In this paper, a photonic-assisted broadband radio frequency (RF) cross-eye jamming system is proposed and experimentally demonstrated. To achieve effective jamming effect, the intercepted radar signal is modulated to optical carriers and the phase shift is realized by optical spectrum processing. The relationship between system parameter tolerances and jamming effects are also simulated. Furthermore, the RF transfer function of X-band, Ku-band and K-band has been obtained in the experiments. The experimental results show that the amplitude and phase mismatch are below 2.15 degrees and 0.4 dB, respectively. Calculated cross-eye gain is 39 dB.

Programmable optical unitary matrices (POUMs) in photonic integrated circuits (PICs) technology play a critical role in modern information technology. With the expansion of their scale and integration density, POUMs have their performance limited by fabrication errors and tuning phase errors of their building blocks, i.e., tunable 2 × 2 optical couplers. Here, we provide an in-depth theoretical analysis of a prior robust equivalent 2 × 2 optical coupler and demonstrate a general requirement of its passive beam splitters' split ratios, under which an arbitrary tunable transmission ratio can always be achieved. More importantly, we analyze the impact of the phase shifters' tuning phase errors which is always caused by the limited voltage/current resolution of the electronic driving interface or thermal fluctuation. By the analyzed result, we propose a novel working scheme for the first time by discovering an unconventional configuration with large beam split ratio errors, under which robust POUMs against fabrication and tuning phase errors can be achieved.

During the epidemic outbreak period, the number of infected people increased explosively. In order to effectively contain the further spread of the epidemic and treat infected people, it is necessary to make reasonable planning for the treatment of infected people and the distribution of materials. In this paper, a multi-objective mixed-integer programming model for the integrated design of emergency medical and material distribution networks is developed, in which the location of medical points, the allocation of infected people, and the distribution of medical materials are addressed in the multi-stage planning. Then, the multi-objective model is solved by the augmented $\varepsilon $ -constraint method. A realistic case study on Huangpu District of Shanghai in China is conducted to demonstrate the validity of the developed model. The results show that the integrated consideration of the emergency medical network and the material distribution network is more capable of reducing the total transportation distance and decreasing the total operating cost.

The eastern Pamir Plateau, with a mean altitude of 5000 m, forms a large-scale glacier region with a size of 2054 km2 that serves as an important environmental climate condition for central Asia. In this article, we estimated the spatio-temporal patterns of glacier activities in this region by using the time series sub-pixel offsets derived from 63 sentinel-2 optical images between December 31, 2020 and December 31, 2021. Our results indicated the mean glacier flow velocity in this region was 0.531 m/d ± 0.007 m/d in 2021, and the Kongur Tagh Glacier was much more active than the Kingata Glacier and Muztagh Ata Glacier. The time series observations revealed that the glacier motion involves a pre-melting period (January–April) with a velocity of 0.600 m/d ± 0.012 m/d, a melting period (May–August) with a velocity of 0.608 m/d ± 0.003 m/d, and a post-melting period (September–December) with a velocity of 0.659 m/d ± 0.006 m/d. In order to get insights into the characteristics of these glacier activities, we carried out a correlation analysis between the glacier flow velocity change and its potential caused reasons (i.e., topography, temperature, precipitation, glacier surges, debris-cover, and glacier thickness), and current results suggest that the glacier flow velocity is influenced by a combination of these factors.

The decode-and-forward (DF) based free-space optical-radio frequency (FSO-RF) hybrid system combines the advantages of both FSO links and RF links, which makes the system easy to be deployed and enables the extended transmission coverage. In the case of multiple users, the unreasonable dynamic antenna selection algorithm will lead to multi-user interference (MUI) and reduce the spectral efficiency of the system. Therefore, we propose hybrid precoding based on manifold learning with the antenna partitioning algorithm for dual-hop hybrid FSO-RF systems. In the hybrid precoding of dynamic subarray structure, the high-dimensional channels are embedded into the low-dimensional manifolds by the optimized multidimensional scaling (MDS). The potential spatial correlations of the high-dimensional channel are preserved by the scaling by majorizing a complicated function (SMACOF) algorithm. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. Meanwhile, an antenna subarray partitioning algorithm is proposed, so that each antenna unit can be assigned to an RF chain based on the increment of the user’s maximum signal to interference noise ratio (SINR). By calculating the simulated equivalent channel SINR for the selected users, the antenna division can greatly reduce the computational complexity and the size of the search space, and ensure fairness among users. Simulation results show that this method can obtain almost the best summation rate and higher spectral efficiency compared with the conventional method.

Stock market volatility research has long been the focus of industry and academia, and stock trend forecasting is challenging. Existing research focuses more on how to aggregate historical price features into graph networks, ignoring the effects of other information such as news and current events on forecasts. Most existing graph-based learning methods create stock graphs by manually constructing stock relationships, ignoring the complexity of stock relationships. Based on these, we propose a novel multilevel graph attention network (ML-GAT) for predicting the stock market trends. To be more specific, we initialize the node representations captured by each feature extraction module, then update the stock nodes in the isomorphic graph converted from Wikidata using ML-GAT, selectively aggregate the information of various relation types, aggregate the information to the node representation, and finally feed the result to a particular forecast layer for forecasting, completing the trend forecast of 423 stocks in the S&P 500 index and 286 stocks in the CSI 300 index. By comparing 5 popular approaches, the experimental research verifies ML-GAT’S state-of-the-art performance in prediction tasks. In comparison with the best performance benchmark model, the F1-score and accuracy increased by 11.82% and 12.6%, respectively, and the average daily return and Sharpe rate rose by 5.06 percent and 94.81 percent, respectively. More significantly, our model’s stock linkages are interpretable and consistent with real-world interactions.

In-band full-duplex (IBFD) technology has become important for future wireless communications, due to its ability of increasing the radio-frequency (RF) spectrum efficiency. However, the benefits can only be realized if the self-interference (SI) is sufficiently suppressed. RF cancellation is important for IBFD systems due to its ability of SI mitigation. But it becomes more difficult for RF cancellation while the frequency and bandwidth increase. In real scenarios, the response of the multi-path SI channel is not frequency-flat, which also limits the cancellation performance. To solve these problems, we present a photonic-enabled multi-tap RF canceller in this paper. The proposed RF canceller has the ability of cancelling multi-path SI. The tap coefficients are precisely adjusted by optical spectrum processing. A prototype system with 8 taps is demonstrated in this paper. The measured results show 25 dB average cancellation depth over 1 GHz cancellation bandwidth under over-the-air conditions. To verify the signal of interest (SOI) recovery capability of the novel RF canceller, an IBFD wireless communication experiment based on 16-quadrature amplitude modulation (16-QAM) was demonstrated, the SOI was successfully recovered.

Due to the high energy efficiency per bit and high sensitivity, Return-to-zero differential-phase-shift-keying (RZ-DPSK) is perfectly suitable for free-space laser communications. However, the conventional generation method of RZ-DPSK optical signal requires two modulators, which is costly, bulky, and heavy, significantly hindering the application of RZ-DPSK in size, weight, and power (SWaP)-constrained satellite platforms. In this paper, we propose and experimentally demonstrate a modulator-free variable multi-rate RZ-DPSK free-space optical (FSO) communication system based on chirp-managed laser (CML). Based on the proposed scheme, an FSO outfield experiment over 1 km has been successfully undertaken, achieving receiving sensitivities of −48.9 dBm and −45.6 dBm at 2.5 Gbps and 5 Gbps, with bit error rate (BER) of 1 × 10−3 without forward error correction (FEC), respectively. The performance of the proposed system is also investigated by studying the eye diagrams under two different test conditions of back-to-back transmission and 1-km free space transmission. In addition to the small size, lightweight and low cost, the proposed scheme shows great potential for a variety of FSO communication applications ranging from Cube-Star to larger satellite laser communication platforms.

The commutation failure (CF) mitigation effectiveness is normally restricted by the delay of extinction angle (EA) measurement or the errors of existing prediction methods for EA or firing angle (FA). For this purpose, this paper proposes a CF mitigation method based on the imaginary commutation process. For each sample point, an imaginary commutation process is constructed to simulate the actual commutation process. Then, the imaginary EA is calculated by comparing the imaginary supply voltage-time area and the imaginary demand voltage-time area, which can update the imaginary EA earlier than the measured EA. In addition, the proposed method considers the impacts of commutation voltage variation, DC current vari-ation, and phase angle shift of commutation voltage on the commutation process, which can ensure a more accurate EA calculation. Moreover, the DC current prediction is proposed to improve the CF mitigation performance under the single-phase AC faults. Finally, the simulation results based on CIGRE model prove that the proposed method has a good performance in CF mitigation.

Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.

A feature-tracking algorithm algorithm utilizing the proposed K-nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines with local neighborhood correspondences to optimize the elimination of outliers while retaining inliers as many as possible. The proposed KVFC was evaluated and compared with several algorithms on three standard datasets and Sentinel-1 image pairs in the Fram Strait and the Beaufort Sea. The KVFC obtained more sea-ice drift vectors than the nearest neighbor similarity ratio (NNSR) with a 0.7 threshold and generated dense distribution of sea-ice drift vectors combined with the HH and HV channels. Using buoy datasets to calculate the sea-ice drift speed and evaluate algorithm performance, the proposed approach yielded a lower mean error (KVFC: −0.150 cm/s, NNSR: 0.407 cm/s), lower root mean square error (KVFC: 0.476 cm/s, NNSR: 1.817 cm/s), and lower angle deviation (KVFC: 3.542°, NNSR: 10.318°) compared to the NNSR.

The mitigation of commutation failure (CF) depends on the accuracy of CF prediction. In terms of the large error of the existing extinction angle (EA) calculation during the fault transient period, a method for CF prediction and mitigation is proposed. Variations in both DC current and overlap angle (OA) are considered in the proposed method to predict the EA rapidly. In addition, variations in critical EA and the effect of firing angle (FA) on both DC current and OA are considered in the proposed method to obtain the accurate FA order for the control system. The proposed method can achieve good performance in terms of CF mitigation and reduce reactive consumption at the inverter side when a fault occurs. Simulation results based on the PSCAD/EMTDC show that the proposed method predicts CF rapidly and exhibits good performance in terms of CF mitigation.

High-altitude long-endurance (HALE) solar-powered unmanned aerial vehicles (UAVs) have outstanding advantages in military and civilian fields. In view of the energy management problem of HALE solar aircraft in a wind field, and considering the combined use of solar energy, gravitational potential energy and the wind gradient, this study uses a reinforcement learning (RL) method to train a neural network controller as an integrated aircraft navigation and guidance controller. It is used to optimize the flight trajectory and improve the energy management capability of HALE solar aircraft to enhance long-endurance performance. Simulation experiments of a hover mission show that after 24 hours of flight, the aircraft guided by the RL controller has 3.53% more final total energy, 8.84% more final remaining battery energy and 10.20 kJ more energy from the wind gradient than the L1 state machine. This indicates that the RL controller offers certain advantages in the energy management of HALE solar aircraft and wind gradient energy acquisition compared with the traditional L1 state machine, and this advantage is mainly reflected in the climb and descent stages. The RL controller performs better in acquiring energy from the wind gradient in the descending stage than in the climbing stage. In addition, the robustness is verified.

The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multistep-ahead forecasting for SSTA is greatly challenging since the nonlinearity and nonstationarity of SSTA and the lag problem of prediction. Therefore, in this article, a multistep-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions that are more stationary than the original data. Then, GRU is used for multistep forecasting, in which three multistep forecasting strategies (recursive, direct, and multioutput) are compared. The proposed hybrid model is validated by multistep forecasting for monthly average SSTA at the Niño3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multimonth trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Niño3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.

Delay invariant convolutional network codes (abbreviated DI-CNCs) can guarantee multicast communication at asymptotically optimal rates in networks with any delay profile. For a cyclic network, it has been shown that one can associate it with an acyclic network consisting of nodes in five layers, and the acyclic algorithm of $\mathbb {F}$ -linear multicast can be employed to construct a DI- $\mathbb {F}$ -CNC, as long as the field size is larger than the number of sinks in the cyclic network. In this paper, we present a directly feasible construction algorithm for a DI- $\mathbb {F}$ -CNC over a cyclic network. Complexity of code construction and theoretical guarantees of algorithm implementation are also investigated in detail. The advantage of the straight construction algorithm is that for an existing code, when some sink nodes and associated edges are added, our algorithm just modifies the new assigned coding coefficients in an efficient localized manner, without the necessity to construct again the code in its expanding network.

Maximizing the absorption and utilization of solar energy is a bottleneck issue and hot research concern for high-altitude long-endurance (HALE) solar-powered UAVs (SP-UAVs). This paper investigates the overall design and task optimization for a deformable/reconfigurable SP-UAV platform that utilizes its modular characteristics to optimize the overall solar absorption and structural weight while maximizing its mission utility. First, we establish the energy absorption/consumption model, the aerodynamic model, and the subsystem mass prediction model. Based on these models, the overall parameter design and optimization scheme of a modular SP-UAV is proposed by using heuristic intelligence algorithms. The results show that the loading ratio of the deformable modular SP-UAV increases from 8.2% to 9.8%. Then, the flight processes in both the deformed and the reconfigured cases are verified under task-oriented scenarios. Simulation results indicate that compared with comparable benchmarks with conventional layouts, deformable SP-UAVs are capable of increasing the feasible zone from 72 to 142 days. Additionally, the separation and recombination points are optimized and determined at specific latitudes and dates. A performance analysis shows that separation in the daytime can multiply, thereby increasing the flight range, although the feasible zone shrinks with increasing UAV unit number.

In this paper, we propose TSFE-Net, two stream feature extraction networks for active stereo matching. First, we perform extra local contrast normalization (LCN) for dataset due to dependency between speckle intensity and distance. Second, we construct two stream feature extraction layers which consist of convolutional layers and deconvolutional layers in different scales to simultaneously learn the features of the original images and LCN images and aggregate context information to form the left and right features. Third, we convert the obtained depth map into disparity map in virtue of camera parameters to construct a supervised learning model. The TSFE-Net not only solves illumination effects between speckle intensity and distance but also reserves details of the original image. Our dataset are captured by RealSense D435 camera. We research extensive quantitative and qualitative evaluations based on a series of scenes, and achieve the end point error (EPE) accuracy of 0.335 on the TITAN XP platform only for valid pixel. The assessment results show that our network has the ability of real-time deep reconstruction for active pattern.

Massive high-resolution spaceborne synthetic aperture radar (SAR) data are available nowadays, effecting rapid development in the urban remote sensing technology. In this study, we use a set of 27 scenes from TerraSAR-X to reconstruct the three-dimensional (3D) point clouds of a skyscraper-North Star Times Tower in Beijing, China. We study in detail the derivation process of a 3D model of tomographic SAR (TomoSAR) from the two-dimensional (2D) imaging mathematical model of SAR. We observe the presence of a spatial shift in the elevation direction of TomoSAR, which yields poor and even incorrect results when greedy algorithms such as orthogonal matching pursuit (OMP) or regularized orthogonal matching pursuit (ROMP) algorithms are applied. Then, we propose a phase compensation method to eliminate the spatial shift. Both OMP and ROMP algorithms are used for TomoSAR imaging on high-resolution TerraSAR-X data. The experimental results with North Star Times Tower demonstrate the accuracy of the theoretical analysis and the effectiveness of the algorithms. Finally, we compare the ROMP and OMP algorithms and conclude that both algorithms can yield similar reconstruction results in TomoSAR imaging. The ROMP algorithm does not depend on sparsity and can suppress noise through regularization selection at the cost of time. The OMP algorithm introduces noise when the sparsity is inaccurate.