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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.

The sensitivity and noise of an all-fiber optic current transformer (AFOCT) scheme named resonant AFOCT are analyzed, which senses the current information by measuring the frequency difference between clockwise and counterclockwise resonances inside a fiber ring resonator (FRR). Compared with polarization-type and interference-type AFOCT, the sensitivity of the resonant AFOCT can be improved by increasing the finesse of the FRR. The mathematical model of the resonant AFOCT is established, and the sensitivity expression of the resonant AFOCT limited by the sensitivity of the photodetector is derived. For the small current measurement of 1 mA level, the carrier suppression level of phase modulation is required to be better than ${{\rho }_{\text{ccw}}}{{\rho }_{\text{cw}}}{\rm{ = }} - \mathrm{110 \, dB}$, and the intensity of unwanted polarization eigenstate of the FRR should be suppressed to less than −68 dBm. The non-ideal characteristics of fiber quarter-waveplate and the residual linear birefringence of spun fiber mainly affect the scale factor of the resonant AFOCT. The resonant AFOCT will be an important way to achieve high-precision of the current transformer.

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.

With the surging demands for higher current at sub-1-V supply level in high-performance digital systems, high-efficiency and high-current-density power converters are essential for system integration. Higher voltage supply buses are emerging for high-current applications to reduce the IR losses on the power delivery networks. Thus, there is a wide voltage gap between the power bus and the digital supply rails at the point of load (PoL). Meanwhile, battery-powered portable or wearable devices favor extremely high-power-density solutions, calling for novel power conversion topologies, which have been the hottest topic in the power management IC area in the past decade. This article reviews the switched-capacitor-inductor (SCI) hybrid dc–dc buck converters from the topology “seeds” to their “leaves.” Here, we define six seeds, they are: 1) three-level buck; 2) double-step down buck; 3) inductor-first buck; 4) always-dual-path buck; 5) buck–buck; and 6) multiple-output hybrid buck. We try to analyze and summarize their pros and cons, and to derive the evolution of the hybrid dc–dc converters, with milestone examples. Then, we share our observations, design intuitions, and suggestions to help the researchers and engineers to pick up and design a new SCI hybrid dc–dc converter.

Evaluating the complex surface deposits of the Schiller–Schickard cryptomare region can enhance our understanding of basaltic volcanic activity and lunar surface evolution. This study utilizes brightness temperature (TB), normalized TB (nTB), and TB difference (dTB) maps generated from the Chang'E (CE)-2 microwave radiometer data. The S–S region's surface deposits were also evaluated by combining the datasets from the Lunar Prospector Thorium, Diviner, LOLA, Clementine UV-VIS, Bouguer Gravity, and Lunar Reconnaissance Orbiter Wide Angle Camera. The main research findings are as follows. First, based on the TB distribution characteristics of craters, four types of craters are identified in the S–S region. The microwave radiation TB of each type of crater is affected by the varying degrees of influencing factors. Second, according to the nTB and dTB performances, combined with the distribution of surface compositions and high-density materials, the extent of mare-like cryptomare deposits (MCD) is considered more reasonable. It is speculated that there are at least two different periods of buried mare deposit eruptions in Region MCD. Finally, within Region MCD, two patches with low TB anomaly are discovered. These discoveries advance our knowledge of the Moon's surface evolution.

This paper investigates a backscatter communication (BackCom) non-orthogonal multiple access (NOMA) system with numerous base stations (BSs) and backscatter devices (BDs). The objective is to maximize sum uplink rate by adjusting reflection coefficients of BDs and establishing association policies between BSs and BDs. To achieve this, matching theory is utilized to address the association problem, and a quadratic transform iterative algorithm is employed to calculate optimal reflection coefficients under a given association policy. Meanwhile, hybrid successive interference cancellation (SIC) is applied to reduce the outage probability of the proposed system while optimizing the uplink sum data rate, where a low-complexity solution is developed. Simulation results validate the superiority of the proposed optimization scheme in enhancing uplink sum data rate. The comparison between BackCom NOMA and BackCom orthogonal multiple access (OMA) reveals superior performance of the former, which becomes increasingly evident as the number of BSs and BDs increases. The proposed iterative algorithm significantly enhances the system’s sum uplink data rate. The usage of hybrid SIC also notably declines the outage probability of the system.

Traffic flow prediction is a crucial aspect of Intelligent Transport Systems, offering a scientific foundation for urban transport system management and planning. However, predicting traffic flow becomes challenging due to its susceptibility to diverse static and dynamic external factors, such as the presence of emergency vehicles that necessitate priority treatment in the road network. To tackle this issue, this paper introduces an Emergency Vehicle Priority Scheduling Model based on Heterogeneous Feature Fusion in Graph Convolutional Networks (EVHF-GCN). This model concurrently considers road network and emergency vehicle information, dynamically adjusting signal control strategies based on traffic flow prediction outcomes. This approach ensures the prioritized passage of emergency vehicles and mitigates traffic congestion. The model utilizes a heterogeneous feature fusion mechanism within a Graph Convolutional Network (GCN) to propagate features and aggregate information from intersection nodes. It also integrates a Gated Recurrent Unit (GRU) network to capture dynamic traffic flow features. Additionally, we propose a Dynamic Signal Control Strategy (DSCS) that determines intersection green light durations based on prediction results and selects different control strategies as per the situation. Experimental results demonstrate that the model enhances traffic flow prediction accuracy and improves traffic system efficiency and safety in scenarios with and without emergency vehicles.

With the continuous advancement of remote sensing technology, satellite remote sensing images have become one of the important means of obtaining information on the earth surface. But in the current research on aircraft target detection and classification in remote sensing images, the imbalanced data samples, large variations in target scales and backgrounds, and target occlusion have led to low average precision and slow detection speed in detection and classification tasks. Therefore, this paper proposes the YOLO-class model. Firstly, the YOLO-Extract model is transferred to optimize the detection of small targets, dense targets, and occluded targets. Secondly, Representative Batch Normalization and Mish activation function are used to optimize the Conv module, and VariFocal loss is used to optimize the classification loss function to improve the accuracy caused by imbalanced data samples. Finally, RepVGG modules are designed in the Backbone to further improve the detection accuracy of the model. Simulation results show that compared with the YOLO-Extract model, YOLO-class improves the detection accuracy from 0.608 to 0.704 and FPS from 36.16 to 39.598.

It is shown in a recently published work that the GNN (gradient-based neural network) model activated by the msbp (modified sign-bi-power) function exhibits superior fixed-time convergence for solving the Sylvester equation in the noise-free case. Encouraged by this point, in this paper, we study its robust performance when it is disturbed by three kinds of noises, i.e., dynamic bounded vanishing noise, dynamic bounded non-vanishing noise, and constant noise. Detailed mathematical analyses are conducted to show the robustness properties (e.g., convergence property, the upper bound of steady-state solution error) of the GNN model, which are further verified by two simulation examples. Ultimately, the GNN model is applied to the path tracking of a planar four-link redundant robot arm.

Since the position of the classroom surveillance camera is not fixed, the angle of the face captured through the surveillance video is also different. The deep learning-based face verification model has achieved good results in controlled environments, but there is still much room for improvement in the face verification ability in uncontrolled environments. The performance of the model depends not only on the results of the network but also on the quality and diversity of the dataset. The current Asian face dataset in an uncontrolled environment is insufficient; for this reason, this paper constructs a Chinese face dataset (UCEC-Face) in an uncontrolled classroom environment, which is collected by 35 real classroom surveillance videos. The UCEU dataset contains 7395 images of 130 subjects, including 44 males and 86 females. To verify that there is still room for improving the performance of existing face verification models for Asian face verification, we further utilize four models such as OpenFace and ArcFace for face verification, as well as the VGG-Face model for gender, expression, and age recognition on the UCEC-Face. The experimental results show that the UCEC-Face constructed in this paper is more challenging and difficult to verify in face verification tasks because it is closer to the real environment, and the best results obtained on the existing models only reach 69.7%, which is largely below the average accuracy of the identification results of other datasets.

Non-orthogonal multiple access (NOMA) continues to receive enormous attention as a potential technique for improving the spectral efficiency (SE) of wireless networks. Although for several years most research efforts on the performance of NOMA systems focused on the ergodic sum-rate and outage probability, recent works have shifted towards error rate analysis of various NOMA configurations and designs. While the influx of publications on this topic is rich in lessons and innovations, the sheer volume of it makes it easy to get caught up in the details, so much so that one often loses sight of the overall picture. This paper serves as a survey on NOMA error rate analysis, painted in the large with bold and immediately recognizable strokes of insights to facilitate for the reader to understand and follow the up-to-date progress in this area. In addition to summarizing the principles of NOMA error rate analysis, this work aims to minimize redundancy and overlaps, identify research gaps, and outline future research directions.

Weakly supervised object detection (WSOD) is an increasingly important task in remote sensing images. However, mainstream WSOD methods often rely on low-quality proposals due to the complex backgrounds of remote sensing images. Moreover, applying strong data augmentations directly in WSOD methods can introduce significant noise, which can hinder training procedures that rely only on image-level ground truth labels. To address these issues, we propose a dynamic informative proposal-based iterative WSOD training procedure. Specifically, we implement an informative proposal reconstruction (IPR) method to generate more informative proposals dynamically. We also use a proposal-based contrastive learning (PBCL) technique to steadily improve the quality of generated proposals. In addition, we employ a pseudolabel learning-based multistage (PLMS) training procedure to progressively improve the quality of new informative proposals while alleviating the noise catastrophe caused by strong data augmentations. Extensive experiments demonstrate the effectiveness of our proposed method in generating higher quality proposals and enhancing model generalization. Our method achieves state-of-the-art results in optical remote sensing images (DIOR), Northwestern Polytechnical University (NWPU) VHR-10.v2, and HRSC2016 datasets.

Intelligent reflecting surface (IRS) has been recently integrated with emerging communication technologies to meet the demanding requirements of communication systems. This paper investigates the deployment of multi-IRS units in a hybrid time-domain multiple access (TDMA) and non-orthogonal multiple access (NOMA) system, referred to as a multi-IRS-aided hybrid TDMA-NOMA system. In particular, a self-sustainable scenario is proposed, in which the IRS units can harvest energy from the radio frequency signal to feed them-self with the required energy. With this self-sustainable IRS-aided hybrid TDMA-NOMA system, the available time is fragmented into a set of time slots, in which an IRS unit is assigned to serve a cluster of users during each time slot. Meanwhile, the remaining unassigned (i.e., idle) IRS units harvest energy to feed themselves with the required energy, which addresses the energy limitation challenge related to conventional communication systems. Specifically, we propose an efficient algorithm to group the users in clusters and, thus, assign an appropriate IRS unit for each cluster. To examine the capabilities of the proposed self-sustainable multi-IRS-aided hybrid TDMA-NOMA system, a resource allocation framework is formulated aiming to minimize the transmit power at the base station under a set of quality-of-service (QoS) constraints. Such QoS constraints include the minimum required rate for each user and the minimum harvested energy at each IRS. However, since the considered optimization problem is not convex, and the coupled nature of the design parameters (i.e., the per-user power allocation and per-IRS reflection phase matrix), solving such a problem is challenging. Thus, we develop an efficient iterative algorithm, based on the sequential convex approximation, to solve the original optimization problem. Simulation results reveal that the proposed self-sustainable IRS-aided TDMA-NOMA system with the proposed clustering approach and IRS assignment consumes less power while achieving the sustainability of IRS units compared to benchmark approaches.

As is well known, the classification performance of large deep neural networks is closely related to the amount of annotated data. However, in practical applications, the quantity of annotated data is minimal for many computer vision tasks, which poses a considerable challenge for deep convolutional neural networks that aim to achieve ideal classification performance. This paper proposes a new, fully supervised low-sample image classification model to alleviate the problem of limited marked sample quantity in real life. Specifically, this paper presents a new sample intrinsic consistency loss, which can more effectively update model parameters from a “fundamental” perspective by exploring the difference between intrinsic sample features and semantic information contained in sample labels. Secondly, a new uncertainty weighting method is proposed to weigh the original supervised loss. It can more effectively learn sample features by weighting sample losses one by one based on their classification status and help the model autonomously understand the importance of different local information. Finally, a sample generation model generates some artificial samples to supplement the limited quantity of actual training samples. The model adjusts parameters through the combined effect of sample intrinsic consistency loss and weighted supervised loss. This paper uses $25 \%$ of the SVHN dataset and $30 \%$ of the CIFAR-10 dataset as training samples to simulate scenarios with limited sample quantities in real life, achieving accuracies of $94.59 \%$ and $91.27 \%$ respectively, demonstrating the effectiveness of our method on small real datasets.

A 32-channel hybrid (de)multiplexer on silicon is designed and experimentally demonstrated to enable polarization division multiplexing (PDM) and wavelength division multiplexing (WDM) for improving the capacity of optical communication links. The hybrid (de)multiplexer is realized by monolithically integrating a polarization rotator and splitter (PRS) and two 16-channel microring resonators (MRRs) arrays. The through and cross ports of the PRS are connected to the bus waveguide of the MRR arrays, respectively. In order to ensure a larger free spectrum range (FSR) to cover all wavelength channels, the radius of the microring is about 3 $\mu$m and the wavelength-channel spacing is 1.6 nm. For the hybrid (de)multiplexer, the crosstalk between the adjacent channels and the non-adjacent channels are −20 dB and −25 dB, respectively. When TE$_{0}$ mode input, the insertion loss is 1.5 dB–3.8 dB, the crosstalk between the through and cross port is less than −24 dB. When TM$_{0}$ mode inputs, the insertion loss is 1.9 dB–4.5 dB and the polarization crosstalk is less than −22 dB. In order to eliminate the influence of the fabrication errors and temperature on resonance wavelength, a TiN thermal tuning electrode is added to calibrate the deviation of the wavelength.

Compared with natural images, remote sensing targets have small and dense target shapes as well as complex target backgrounds. As a result, insufficient detection accuracy and target location cannot be accurately identified. So, this paper proposes the YOLO-extract algorithm based on the YOLOv5 algorithm. Firstly, The YOLO-extract algorithm optimized the model structure of the YOLOv5 algorithm. The YOLO-extract algorithm not only deleted the feature layer and prediction head with poor feature extraction ability but also a new feature extractor with stronger feature extraction ability was integrated into the network. At the same time, YOLO-extract borrowed the idea of residual network to integrate Coordinate Attention into the network. Secondly, the mixed dilated convolution was combined with the redesigned residual structure to enhance the feature and location information extraction ability of the shallow layer of the model and optimize the feature extraction ability of the model for different scale targets. Finally, drawing on the idea of $\alpha $ -IoU Loss, Focal- $\alpha $ EIoU Loss was designed to replace CIoU Loss, which makes the model bounding box regression faster and the loss lower. The experimental results on the test data set show that compared with the YOLOv5 algorithm, the YOLO-extract algorithm has a faster convergence speed, reduces the calculation amount by 45.3GFLOPs and the number of parameters by 10.526M, but increases the mAP by 8.1% and the detection speed by 3 times.

With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% ( ${p} < 0.01$ ). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.

Aiming at the problem that the number of users and available bandwidth of the current satellite service are increasing significantly, and the spectrum utilization rate of the existing spectrum allocation scheme is low, this paper first establishes a space-earth integrated spectrum resource sharing model, and analyzes the scenario of using the idle spectrum of the ground network to recognize the satellite downlink. Furthermore, a spectrum allocation algorithm based on stable matching is proposed for cognitive satellite network. According to the transmission characteristics of satellite communication, utility functions of cognitive satellite are given. Users can form stable matching between PUs and SUs through independent negotiation. The spectrum allocation algorithm proposed in this paper has low complexity and can adapt to dynamic networks. Experiments show that the proposed algorithm is superior to greedy matching and random matching in utility value, average sum rate, throughput and spectrum utilization ratio, so as to effectively allocate satellite spectrum.

Relay assisted transmission can compensate atmospheric turbulence, improve transmission distance, and realize communication between non-linear targets in free space optical communication (FSO) system. Space division multiplexing (SDM) greatly improves the channel capacity of FSO system. This paper focuses on the relay assisted transmission oriented to SDM wireless optical communication system. We develop a dual-hop SDM FSO communication model with two spatial mode channels. At the relay, the received optical signals are amplified by the few mode EDFA and forwarded to the destination. Simulation results show that the bit error rate (BER) of the dual-hop SDM system with a few mode EDFA based relay outperforms the single-hop SDM system. Under BER = $1 \times {10}^{ - 5}$, the power budget is increased by 0.53 dB, 1.2 dB, and 4.88 dB, when ${\boldsymbol{C}}_{\boldsymbol{n}}^2 = \ 4 \times {10}^{ - 15}$, $7 \times {10}^{ - 15}$ and $1 \times {10}^{ - 14}$, respectively. Moreover, the BER performance is the best when the relay is (originally in the middle of the link) slightly closer to the source.

In this paper, a symmetric training sequence-based (STSB) carrier frequency offset estimation (FOE) scheme for coherent free-space optical (CFSO) communication is proposed. In the proposed technique, we exploit the training-sequence-based (TSB) carrier FOE algorithm to the CFSO communication system in the quadrature phase-shift keying (QPSK) modulation format. A symmetric training sequence, tailored to the carrier FOE algorithm for atmospheric turbulence channel, is formulated to accurately identify the correct temporal position of a data frame from received signals. Unlike the 4th power algorithm, the proposed algorithm requires only the identified training sequence and the symmetric sequence of the original training sequence in the FOE stage, performing a single multiplication to accurately remove the signal modulation phase. In this way, the proposed technique significantly improves the estimation accuracy. Furthermore, by avoiding the time-consuming 4th power operation, the proposed method reduces the computational complexity while expanding the FOE range. Therefore, when compared with existing algorithms, our proposed technique can achieve more accurate timing, a wider estimation range, and lower complexity. A 10-Gbps QPSK CFSO communication transmission experiment with different turbulence channels was carried out to validate the proposed technique and the improvement in receiver sensitivity.

This paper is concerned with the finite time annular domain robust stability (FTADRS) analysis and controller design for T-S fuzzy positive systems with interval uncertainties. The concept of finite time annular domain stability is first introduced for positive systems. Based on this and using the copositive Lyapunov function approach, some sufficient conditions for FTADRS are derived. Subsequently, the finite time annular domain robust controller is designed via the linear programming technique. Finally, two numerical examples and an application example are employed to show the effectiveness of our results.

High-resolution remote sensing images have the advantage of timeliness, and they can display feature information in more detail. Deep learning embodies its unique characteristics in land cover classification, target recognition, and other fields, which can automatically learn the in-depth feature information of images and make accurate classification decisions. However, when deep learning models extract high-dimensional abstract feature information, they often ignore and lose part of the underlying features essential for classification accuracy. This article proposes a dual-channel fully convolutional network (D-FCN), whose two channels, respectively, take image data and low-level features such as color, texture, and shape as the different input data to combine the underlying features with high-dimensional abstract features. To reduce the complexity of the model, we add a large number of skip connections between the model and make full use of the advantages of weight sharing and local connections to connect spatial context information. We used multifeature information as the model input and compared and analyzed the impact of different features on the land cover classification accuracy, and finally obtained the most suitable combination of multifeature information. In addition, we provide a small-scale land cover classification dataset with labels to verify the applicability and transferability of the D-FCN, and use the optimal combination of multifeature information to conduct comparative experiments on the small-scale dataset. The experimental results show that D-FCN has outstanding applicability and transferability. Compared with other state-of-the-art models, D-FCN has a more challenging performance and greatly reduces model complexity.

Low-dose CT images contain severe mottle noise and streak artifacts, which seriously affect the physician’s diagnosis of the disease. Hence, in this paper, we propose a novel anisotropic fourth-order diffusion model for low-dose CT image processing. The proposed diffusion model uses both image gradient magnitude and weighted residual local energy to determine the diffusion coefficient. Gradient magnitude is used to detect the image edges, while the weighted residual local energy preserves textures and details in the image. In addition, the fidelity term is introduced into the diffusion model to avoid excessive smoothing and weaken the blocky effects. Experimental results show that when compared with the anisotropic fourth-order diffusion model, the proposed algorithm protects the texture details and suppresses the blocky effects. In comparison with other state-of-the-art algorithms, the proposed model effectively suppresses mottle noise and streak artifacts while simultaneously improving the low-dose CT image quality.

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking. It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability. To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed. Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain. Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm. On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability. Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.

Accurate identification of lane-changing intention can effectively assist intelligent driving vehicles in terms of decision-making and trajectory planning, which plays a significant role in enhancing driving safety by reducing traffic accidents caused by lane-changing. Based on the trajectory characteristics and vehicle interaction information, an attention-enhanced bidirectional multi-layer residual long-short term memory neural network (Attention Enhanced Residual-MBi-LSTM) model is proposed for lane change intention recognition in this paper. Firstly, an EWMA filter is employed to smooth the noisy data collected from the vehicle. Then a four-layer bidirectional residual LSTM memory (Residual-MBi-LSTM) structure is used to extract lane-changing features from the historical driving trajectories of ego-vehicle and vehicle interaction information. Besides, the attention mechanism is added to adjust the weight of data in different time frames. After that, the current lane-changing probability is calculated and output by the Softmax function. Finally, the vehicle lane-changing intention recognition model is firstly trained and then verified in the HighD dataset. According to the HIL experiment, the proposed model has the ability to identify driver intention on average of 2.07 seconds in advance.

The estimation of the degree of polarization of backscattered light and target light is the key to realize scattering removal in underwater polarimetric imaging. In this paper, a new polarization scattering removal method is proposed by realizing polarimetric calculation of the target light with each pixel of the scene automatically, which is conducive to the restoration of the details of underwater scene. The global model of pixel level is established by utilizing the gradient prior of the polarization information of the total radiance image. Different from the previous study that the degree of polarization of the target light is a constant, the global calculation method can solve the global variable image of the degree of polarization of the target light and it is used for reconstruction experiments of underwater real-world scene to verify the effectiveness of the algorithm. The experimental results demonstrate that the global pixel calculation method has obvious effect on image detail recovery of the underwater scene to realize the clear underwater vision. The influence of the backscattered light on underwater imaging is well suppressed and image contrast has been significantly improved, which provides a new method for enhancing performance of underwater polarimetric imaging with better vision.

Foot-mounted inertial navigation systems are suitable in GNSS denied environment for special tasks such as fire rescue, soldier, etc. These tasks are often accompanied by vigorous movements such as running and jumping, which is a challenge for the Zero Velocity Update (ZUPT) based inertial navigation system. As the speed increases, the inertial sensor drift increases, while the zero-velocity interval shortens, making the gait detection more difficult and resulting in a limited error correction. And as the motion intensifies, the stability of the system decreases. A multi-node inertial pedestrian navigation system based on the lower limb model constraint is proposed in this paper. Our first contribution in this paper is studying a simplified adaptive zero-bias estimation algorithm. The changes in velocity and angular velocity between two steps are used to estimate and compensate for the drift of accelerometers and gyroscopes. Our second contribution is proposing a novel gait segmentation method by lower limb motion statistics feature. A hybrid detection model of dual IMU is conducted to improve the accuracy of gait recognition. After that, the maximum distance inequality constraint is studied to correct the position error when the still phase is not available in vigorous movement. The final contribution is studying the chi-square fault detection method to improve the stability of KF. Multiple experiments prove that the proposed multi-node inertial pedestrian navigation system is effective in dealing with various challenging in walking, running, and hybrid motion pedestrian inertial navigation problems which could achieve less than 0.3% average distance error.

This article presents a new ultrawideband wide-angle scanning tightly coupled dipole array loaded with multilayer metallic strips. The loaded multilayer metallic strips, which is composed of several narrow strips with different lengths, are used to equivalent the metal patch which has a specific shape. It can help achieve smoother impedance transform from the dipole to the free space. An equivalent circuit model (ECM) of the unit cell is proposed to help analyze and design the tightly coupled dipole array. Thanks to the loaded multilayer metallic strips, the array can be fed by a simple feeding structure, avoiding a matching network located below the ground plane. Also, all components of the proposed array are printed on a single dielectric substrate, resulting in a low-cost and light-weight array. The proposed tightly coupled dipole array can achieve 6.2:1 bandwidth with voltage stand wave ratio (VSWR)

The sharply growing demand for increased transmission capacity and bandwidth in last meter and last mile access networks together with the commercialization of fifth generation (5G) wireless systems has been opening up new opportunities for non-radio frequency (RF)-based wireless technologies. Visible light communications (VLC) technology is a potential candidate for access networking in 5G, which offers a higher spectral efficiency than RF-based Femtocell networks by three orders of magnitude. This paper proposes an all wireless optical bidirectional VLC multiple access scheme for pure VLC network access points in terminals. Centralized coordination is adopted to reduce the system complexity. And reservation of resource is introduced to guarantee collision avoidance during data frame transmission. The proposed multiple access scheme introduces special system parameters to achieve the balance of system throughput and access latency. The feasibility of the proposed scheme is verified by both theoretical analysis and experimental investigation. We show that the proposed scheme is suitable for a bidirectional pure VLC access network and can be used as a supplement in the IEEE 802.11 bb for 5G+bidirectional VLC application scenarios.

This article investigates a novel cooperative control system based on the sliding mode variable structure control theory for multi-missile formation flight. It is desired in practice to form and maintain the formation under the premise of uncontrollable missile speed. First, under the inertial coordinate system, we obtain the model of the formation control problem using the relative position of the leader to the follower. Then, we perform acceleration conversion and combine it to this model, getting the form of the formation problem model in the ballistic coordinate system. The finished model is useful for researchers to design the formation controller on this basis. Besides, the sliding mode variable structure control theory is used to design the formation controller for the system not considering disturbances and considering disturbances. Then we use the Lyapunov stability theory to analyze the stability of the formation control system. Finally, we compare our method with another method which requires controllable speed. According to numerical simulations, the method proposed in this article can achieve similar relative position errors under the condition of uncontrollable speed. And the robustness, versatility and formation adaptability of the method we propose are confirmed by simulation results.

This paper is concerned with the finite-time annular domain bounded control of Itô-type stochastic systems with Wiener and Poisson random disturbance. First, utilizing different quadratic function methods, some sufficient conditions for finite-time annular domain bounded-ness (FTADB) of the system are achieved. Second, two finite-time annular domain bounded controllers are skillfully developed to ensure the FTADB of the closed-loop system, of which one is state feedback controller and the other is dynamic output feedback controller. Furthermore, an algorithm is provided to deal with the obtained matrix inequalities. Finally, two examples are used to demonstrate the effectiveness of the theorems in this paper.

In this article, input-to-state stability (ISS) and stabilization are examined for sampled-data systems under deterministic aperiodic sampling and random sampling, respectively. Using the direct design method, the sampled-data systems are transformed into switched systems with switched time-varying delays. First, the ISS definition and criterion appropriate for these systems are provided. Based on this, the ISS criterion on sampled-data systems under deterministic aperiodic sampling is given. Second, after the stochastic ISS (SISS) definition and criterion are provided for switched nonlinear systems with randomly switching delays, the SISS criterion for the sampled-data systems under random sampling is provided. All of the ISS and SISS definitions are given in the form of $\mathcal {KL}$ function that is quite elegant and easy to work with. Then, sufficient conditions for input-to-state stabilization are obtained for sampled-data linear systems under deterministic aperiodic sampling and random sampling, respectively, via the Lyapunov-Krasovskii method. Finally, based on the criteria, a piecewise controller is designed by the matrix inequality approach for a sampled-data linear time invariant system, and simulation results are provided to illustrate our design method. The main conclusion of this article is that sampling intervals will affect the controller design of the systems, and the ISS properties are maintained using a piecewise controller.

Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.

Convolutional neural networks (CNNs) have been successfully used in remote sensing scene classification and identification due to their ability to capture deep spatial feature representations. However, the performance of deep models inevitably encounters a bottleneck when multimodality-dominated scene classification rather than single-modality-dominated scene classification is performed, due to the high similarity among different categories. In this study, we propose a novel multi-granularity fusion convolutional neural network (MGFN) to automatically capture the latent ontological features of remote sensing images. We firstly design a multigranularity module that can progressively crop input images to learn multigrained features, which can describe images to different degrees. Based on a comparison of different granularities, we then design a maxout-based module to learn the corresponding Gaussian covariance matrices of different granularities, which can extract second-order features to express the latent ontological essence of inputs and select the most distinguished inputs. We thirdly provide an adaptive fusion module to fuse all features via normalization to combine features of different degrees using the adaptive fused module. Finally, an SVM classifier is used to classify the fused matrix of every input image. Extensive experimentation and evaluations, particularly for multimodality-dominated scenes, demonstrate that the proposed network can achieve promising results for public remote sensing datasets.

Multispectral remote sensing images are often degraded by clouds, resulting in the reduced efficiency and accuracy of image interpretation. Thin cloud removal is one of the most important and significant tasks for optical multispectral images. In this article, we propose a novel thin cloud removal method for multispectral images, which is a combination of traditional methods and deep learning methods. First, we adopt U-Net to estimate the reference thin cloud thickness map of the cloudy image. Then, a convolutional neural network named Slope-Net is designed to estimate the thickness coefficient of each band relative to the reference thin cloud thickness map to obtain the thin cloud thickness maps of different bands. Finally, the recovered clear image can be obtained by subtracting the thin cloud thickness maps from the cloudy image according to the traditional thin cloud imaging model. To train U-Net and Slope-Net, a wavelength-dependent thin cloud simulation method is presented to generate a labeled dataset composed of synthetic cloudy images, corresponding clear images, reference thin cloud thickness maps, and thickness coefficients. Qualitative and quantitative comparison experiments are conducted on both synthetic cloudy images and real cloudy images from the Landsat 8 Operational Land Imager. The results indicate that the proposed method can effectively remove thin clouds in multispectral images with various land cover types and maintain good color fidelity.

With the development of ship detection in optical remote sensing images, it is convenient to obtain accurate detection results and ship images. Owing to the superior performance of convolutional neural networks (CNNs), one way to acquire the category of ship is to train a classifier using numerous ship images. However, the classification performance of CNN may degrade in the case of a small number of training samples. To solve this problem, we propose a metric-based few-shot method to generate novel concept (class) representation using nearest neighbor prototype. Different from image-to-image measure in common few-shot methods, we use an image-to-feature measure. We map small number of samples to the feature space through CNN, and generate prototypes by computing nearest neighbor value on each dimension of the feature separately. Our method is validated on patch-level ship image dataset, a reproduced ship classification dataset based on HRSC2016. The experimental results demonstrate the accuracy and robustness of our method for ship classification with a small amount of labeled data.

In the resonator fiber optic gyroscope (RFOG), both the residual intensity modulation (RIM)-induced error and backscattering-induced noise need to be effectively suppressed. When the clockwise and counterclockwise resonance detection adopt the same phase modulation and demodulation scheme, namely reciprocal detection, the reciprocity of the RFOG is improved, and the error induced by the RIM of the phase modulator used for detection (PMD) can be effectively suppressed. However, it is inevitable to add a phase modulator used for frequency shift (PMFS) to suppress the backscattering-induced noise. In this paper, the influence of the RIM of the PMFS on the performance of the RFOG is analyzed, and the model of the RIM-induced error in the RFOG with reciprocal detection is established. The analysis shows that in order to reduce the bias to the level of the shot noise, it is necessary to suppress the error induced by the RIM of the PMFS, and reducing the modulation frequency of the PMFS can effectively suppress the RIM-induced error. When a phase modulator is added to the RFOG with reciprocal detection to further suppress the backscattering-induced noise, the RIM-induced error can still be effectively suppressed by reducing the frequency of the added phase modulation.

Visible light communication (VLC) is considered an important complementary technology for extremely high sixth-generation (6G) data transmission and has become part of a hybrid 6G indoor network architecture with an ultradense deployment of VLC access points (APs) that presents severe challenges to user mobility. An adaptive handover mechanism, which includes a seamless handover protocol and a selection algorithm optimized with a deep reinforcement learning (DRL) method, is proposed to overcome these challenges. Experimental simulation results reveal that the average downlink data rate with the proposed algorithm is up to 48% better than those with traditional RL algorithms and that this algorithm also outperforms the deep Q-network (DQN), Sarsa and Q-learning algorithms by 8%, 13% and 13%, respectively.

According to the transmission mode of polarized light in Mueller ellipsometry, a characterization method for the thickness and optical constants of isotropic nano films based on Self-Adaptive Differential Evolution algorithm (SADE) is proposed. By establishing the least square model of the output light intensity with respect to the Mueller matrix of the standard sample to be measured, the elements of the Mueller matrix are solved by using the Sade algorithm, and the Mueller spectral curve obtained by fitting is compared with that measured by DRC-MME, and the film thickness is calculated by using the transfer matrix. The SiO2 / Si standard samples with calibration values of 100.4 nm and 121.56 nm are simulated and calculated. The experiment shows that: when numbers of iterations accumulated to 65 and 80 respectively, the residual square sum of the light intensity of the objective function converges to the minimum values of 1.24 and 1.02. The calculated film thickness values are 101.25nm and 120.53nm respectively, and the relative errors are both less than 1%. It proves the characteristics of simple calculation, fast convergence and accurate global optimal solution.

For multimodal medical image fusion problems, most of the existing fusion approaches are based on pixel-level. However, the pixel-based fusion method tends to lose local and spatial information as the relationships between pixels are not considered appropriately, which has much influence on the quality of the fusion results. To address this issue, a region-based multimodal medical image fusion framework is proposed based on superpixel segmentation and a post-processing optimization method in this paper. In this framework, the average image of the source medical images is firstly obtained by a weighted averaging method. To effectively obtain homogeneous regions and preserve the complete information of image details, the fast linear spectral clustering(LSC) superpixel algorithm is carried out to segment the average image and get superpixel labels. For each region of the medical images, log-gabor filter(LGF) and sum modified laplacian(SML) are adopted to extract texture feature and contrast feature for the measurement of region importance. The most important regions are selected and the decision map is generated by comparison. Moreover, to get a more accurate decision map, a new post-processing optimized method based on genetic algorithm(GA) is given. A weighted strategy is applied to the extracted features and the weighting factor can be adaptively adjusted by GA. The effectiveness of the proposed fusion method is validated by conducting experiments on eight pairs of medical images from diverse modalities. In addition, seven other mainstream medical image fusion methods are adopted for comparing the performance of fusion. Experimental results in terms of qualitative and quantitative evaluation demonstrate that the proposed method can achieve state-of-the-art performance for multimodal medical image fusion problems.

Nighttime light image (NTL) is an effective data for urban built-up area. Due to the shortcomings of low resolution and the blooming effect of NTL, the extraction result of the original NTL image is not ideal. We combined point of interest (POI), road network, and land surface temperature (LST) data to propose the POI, road density, and LST-adjusted NTL Index (PRLANI) to correct the luminance value of the NTL image. The proposed index was verified by NTL images of different developed areas and different spatial resolutions. The threshold segmentation method was used to obtain the result of the built-up area extraction from the original NTL and the image modified by the PRLANI. Then, we generated the vector boundary from the built-up area. Taking Changchun urban area as an example, the kappa coefficient based on PRLANI-VIIRS is 0.802, which is 0.293 higher than of NTL-VIIRS. The kappa coefficient based on PRLANI-LJ is 0.838, which is 0.179 higher than that of NTL-LJ. The results show that: the PRLANI can effectively suppress the blooming effect and improve the overestimation of the built-up area caused by light dimming; the PRLANI can enhance the weak light signal in the original NTL image and compensate for the missing information of the built-up area; and the fusion of road network data improves the extraction accuracy and refines the internal structure of built-up area, especially the NPP/VIIRS. The vector boundary was consistent with the built-up area range of reference data, and the visualization effect was good.

Mare Smythii is a special basin, which is one of the oldest mare basins with the considerably young mare basalts and the high density of floor-fractured craters. In this article, the microwave sounder, named CELMS, onboard Chang'E-2 lunar orbiter was first invested to assess the thermophysical characteristics of the floor materials in mare Smythii. Based on the numerical simulation with the radiative transfer model and the previous geological results, several special findings are noted as follows. First, the substrate temperature in mare Smythii is likely fairly high, and a special temperature structure is provided. Second, there likely exists an unrevealed deposit in the upper layer of the regolith with strong thermal absorption ability. Third, the CELMS results show a strong correlation with the subsurface features obtained by lunar radar sounder data and the surface wrinkle ridges. These findings will be of essential importance to strengthen knowing the thermal and magmatic evolutions of the lunar maria.

Chang'e-5 (CE-5) mission was successful to return samples up to 1.8 kg from the northeastern part of the Rümker region, the Moon. These samples can provide new understandings for the basaltic volcanism of northern Oceanus Procellarum. Therefore, the terrain-related analysis of the Rümker region will become a hotspot in the recent and latter studies. As a typical surface analyzing method, multiscale surface roughness has been used in planetary studies for a long time. However, fewer studies of topographic roughness analysis are applied in the Rümker region. The visual interpretations directly from digital elevation models (DEMs) or images potentially cause controversial results. To provide a way for reunderstanding the geomorphological features in Rümker region, we investigate a new roughness analyzing method to extract the topographic signatures by using a wavelet-based V-system of degree 1. Based on these signatures, the hectometer- and kilometer-scale roughness are calculated and mapped. The multiscale roughness textural changes are applied to provide new understandings of the geomorphological features of Rümker region. Our results demonstrate that: 1) Most of younger mare units show lower roughness textures following with the scale increases. Especially at kilometer scales, a new understanding is given to units Im1 and Em1 based on the statistical variations of the geologic units; 2) compared with the results obtained from the optical data, roughness maps provide a new way to recognize the wrinkle ridges in the Rümker region. The wrinkle ridges are reevaluated based on the hectometer- and kilometer-scale roughness textural changes.

Voting is one of the most prominent components of democracy. The one-vote veto, which requires the voting result to be only “yes” or “no”, is a particularly noteworthy type of voting with widespread application. When obtaining a “no” result, no useful information about the number of “no” votes and who votes “no” is disclosed. In this paper, we introduce a protocol for anonymous one-vote veto utilizing qubits and local Pauli operations $\mathbb {Z}$ and $\mathbb {X}$ , building simple processes for easy implementation under the current technology. For better elaboration, we give two examples: all voters cast “yes” votes and only one voter casts a “no.” Then, the corresponding experiment tests are conducted on the simulated IBM quantum computer to verify their feasibility. We also show that the proposed protocol has the desirable properties of privacy, fairness, verifiability and robustness. Furthermore, we analyze the proposed protocol’s security against cheating from eavesdroppers, a semi-honest server and malicious voters. This work is the first attempt to illustrate how qubits can be useful for building a secure anonymous one-vote veto strategy.

In this work, we design, model, and characterize the Bragg grating modulator (BGM) on silicon-rich nitride and thin-film lithium niobate (SRN-TFLN) platform. In particular, the slow-light effect on the electro-optical modulation response is modeled and experimentally validated. The presented modulator shows a 3-dB electro-optical (E-O) bandwidth beyond 40 GHz, which agrees well with the simulation results. High-speed modulation up to 60 Gbps and 70 Gbps are experimentally obtained with extinction ratio (ER) of 4.7 dB and 2.7 dB, respectively. A measured modulation efficiency of 2.47 pm/V is also achieved. With optimized slow-light effect and proper design, the proposed BGM has great potential in high-speed data transmission.

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.

In this paper, we propose a joint beamforming and power-splitter optimization technique for simultaneous wireless power and information transfer in the downlink transmission of a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. Accordingly, each user employs a power splitter to decompose the received signal into two parts, namely, the information decoding and energy harvesting. The former part is used to decode the corresponding transmitted information, whereas the latter part is utilized for harvesting energy. For this system model, we solve an energy harvesting problem with a set of design constraints at the transmitter and the receiver ends. In particular, the beamforming vector and the power splitting ratio for each user are jointly designed such that the overall harvested power is maximized subject to minimum per-user rate requirements and the available power budget constraints at the base station. As the formulated problem turns out to be non-convex in terms of the design parameters, we propose a sequential convex approximation technique and demonstrate a superior performance compared to a baseline scheme.

In this paper, we present a novel transmission line architecture in thin-film lithium niobate (TFLN) platforms to improve the velocity match between the microwave and the optical wave. Compared to conventional coplanar waveguide (CPW), the microwave index ($n_m$) of the proposed slotted electrodes can be optimized from 2.1 to 3 while maintaining the high modulation efficiency and 50-$\Omega$ impedance match. Equivalent-circuit model analysis and finite-element simulation are performed. The simulated half-wave voltage (V$_\pi$) of 1.2 V and E-O modulation bandwidth greater than 80 GHz is obtained for a 2-cm-long modulator. By utilizing the slotted slow-wave electrode, TFLN Mach-Zehnder modulators with CMOS-compatible operating voltage and 3-dB modulation bandwidth greater than 100 GHz are potentialized.

This paper considers the design of beamforming for orthogonal time frequency space modulation assisted non-orthogonal multiple access (OTFS-NOMA) networks, in which a high-mobility user is sharing the spectrum with multiple low-mobility NOMA users. In particular, the beamforming design is formulated as an optimization problem whose objective is to maximize the low-mobility NOMA users' data rates while guaranteeing that the high-mobility user's targeted data rate can be met. Both the cases with and without channel state information errors are considered, where low-complexity solutions are developed by applying successive convex approximation and semidefinite relaxation. Simulation results are also provided to show that the use of the proposed beamforming schemes can yield a significant performance gain over random beamforming.

The Proliferation of connected and embedded devices has played a critical role in the development of next generation Internet of Things (IoT), and consequently, the diffusion of low power networks. The sustainability of such networks entails energy aware devices that are capable of self-sustainable operations by harvesting and recycling energy from various sources. Accordingly, integration of wireless-powered networks (WPN) with ambient backscatter communications (AmBC), in which a backscatter transmitter conveys data to a destination by backscattering ambient signals, has recently emerged as a promising key technology for low power wireless networks. However, these technologies are not optimized and/or designed for large-scale networks and a massive number of IoT devices.