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3-D building models are one of the most intuitive and widely used forms for understanding urban buildings. Generating 3-D building models based on a single off-nadir satellite image is an economical and rapid method, particularly valuable in large-scale 3-D reconstruction scenarios with limited time. In this article, we propose a novel pipeline for automatically reconstructing level of detail 1 (LoD1) 3-D building models based on a single off-nadir satellite remote sensing image. Our pipeline is built upon a multitask neural network called off-nadir building reconstruction network (ONBuildingNet), which extracts building roof polygons and offsets from the image. Using this information, the pipeline computes the building footprint polygons and heights, constructs LoD1 building models, and then extract textures from the off-nadir image. ONBuildingNet introduces our proposed cross-field auxiliary task and multiscale mask head to extract building roof polygons with accurate shapes. We have demonstrated through extensive experiments that our pipeline can automatically and rapidly construct LoD1 3-D urban building models. In addition, our proposed ONBuildingNet outperforms current state-of-the-art methods in extracting more shape accurate building roof polygons, thereby enhancing the accuracy of the final 3-D models produced by our pipeline. Experimental results demonstrate that our method for reconstructing 3-D models of urban building scenes has strong visualization effects, with an average height error of 3.3 m.

Formation-containment control in multi-agent systems faces a critical challenge: how to stabilize all formation leaders when the $\alpha -$ leader’s influence diminishes over topology distance, especially under input delays? This challenge is particularly acute in PDE-based models, where information decay is intrinsic, making it difficult for the $\alpha -$ leader—a leader among leaders—to affect far-away agents, especially under speed constraints.Our work tackles this challenge by proposing a three-layer hierarchical control framework based on Partial Differential Equations (PDEs). Our key innovation is an integral-type delayed compensation input specifically designed for the $\alpha -$ leader. This control law uses kernel functions derived from backstepping transformation and equivalence principles, tailored to compensate for input delays. By providing the $L^{2}$ space expansion of these kernel functions, we show that formation control error for finite agents is acceptable, even with approximations.We organize formation leaders into a one-dimensional chain-like topology, categorizing them as leaders, anchors, or $\alpha -$ leaders based on their roles. For other agents, we derive distributed control laws from the discrete form of PDEs governing formation deployment. Followers converge to the convex hull spanned by leaders, forming an internal two-dimensional surface. This PDE-based approach ensures invariance in translation, rotation, and expansion.Our work also contributes to the mathematical foundations of PDE-based multi-agent systems. We discuss the numerical solution of kernel functions, offer a distributional interpretation, and—critically—analyze the discretization error between PDE and ODE models. This analysis reveals the relationship between stability, time step, spatial step, and control laws, addressing an often-ignored issue in the field.Simulation examples validate our theoretical findings, showing effective formation-containment control under our framework.

The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue. In recent years, deep learning methods, particularly autoencoders (AEs), have been progressively employed in blind HU due to their compatibility with linear mixture models. However, most of the current advanced AE unmixing networks are based on a single-stage framework that conducts the unmixing task solely from a spectral perspective. This makes the rich spatial information ignored and makes it difficult for the network to obtain discriminative compression features while being susceptible to spectral variability and noise outliers. To address these issues, we propose a new deep shared fully connected autoencoder (DSFC-AE) unmixing network. The proposed DSFC-AE network comprises dual branches that utilize distinct data inputs for feature extraction: the original spectral data and coarse-scale spectral data obtained through superpixel segmentation. Furthermore, shared weight strategies are applied to the corresponding dimension reduction layers of the encoder, facilitating effective feature fusion. In addition, we integrate two constraint terms into the loss function, harnessing the sparsity of abundances and the geometric features of endmembers. We evaluate the DSFC-AE method against three traditional methods and four state-of-the-art deep learning algorithms using multiple real datasets. The results unequivocally demonstrate that the proposed network achieves significant improvements in both accuracy and stability.

Traditional three-dimensional (3-D) reconstruction methods for satellite stereo images (SSIs) are limited by observation angles and image resolution, resulting in poor reconstruction results and only a rough 3-D model of the extracted target. Meanwhile, deep-learning methods require a large number of training samples and restoring the complete 3-D structure of the target is challenging when it is quite different from the training sample. To address these problems, we propose a 3-D extraction method for SSIs based on self-similar convolution and a deformation inference network constrained by a rational function model (RFM). Inspired by the implicit relationship between 2-D image features and 3-D shapes, we construct a 2-D–3-D mapping relationship to mine the depth features of remote-sensing images by incorporating the RFM. The deformation result of each point in the point cloud is inferred by a graph convolution network to iteratively optimize the 3-D reconstruction effect of the visible surface. We construct the self-similar convolution module by utilizing the self-similarity characteristics existing in the target itself. The reconstruction results of the invisible surface are optimized while establishing the mesh vertex connection relationship. Experiments on multiple datasets show that the target reconstruction results of our method outperform those of other classical methods, and the relative accuracy of root-mean-square error for targets such as buildings, planes, and ships can reach up to 3 m or less. The accuracy of the earth mover's distance is better than 0.5.

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length ($ {\mathtt {ARL}}$) and upper bounds for average-detection-delay ($ {\mathtt {ADD}}$) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast $\phi$-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially $\alpha$/$\beta$-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.

Mismatch removal is a crucial step in multiview matching of lunar orbiter images. This process involves complex challenges like terrain-induced distortion and nonunique geometric structures due to repetitive textures. Traditional methods, whether global constraints or local constraints, fall short in adequately addressing these issues in orbiter imagery. Therefore, this article proposes an effective method for mismatch removal of orbiter images based on global constraint and local geometry preservation combined with the imaging model. In this method, a clean neighborhood of each matching point based on the characteristic of centralized distribution of the back-projection residuals globally is constructed. In the local region, we define a local minimum geometric polygon consisting of the center feature point and its three neighbors, and combine the similarity of the back-projection difference vectors with the affine invariance of the polygon to distinguish the correct matches and mismatches by measuring the degree of local geometry preservation. A series of experiments encompassing parameter sensitivity analysis, comparison studies and ablation experiments were conducted on the lunar reconnaissance orbiter image datasets to demonstrate the effectiveness and reliability of the proposed method. The results indicate that our method exhibits a notable insensitivity to parameter variations, and outperforms other advanced methods in both qualitative and quantitative evaluations. Moreover, a large-scale orbiter images multiview matching tie points extraction framework is extended based on the proposed mismatch removal method, which can achieve better results than commonly used photogrammetric software in terms of the number and accuracy of tie points.

The lightning discharges generated by lightning strikes can reach several hundred kiloamperes, causing significant electromagnetic, mechanical, and thermal effects. These effects induce extremely high overvoltages in power transmission systems, leading to the tripping of transmission lines. Therefore, the study of lightning protection and early warning methods is crucial for safeguarding transmission lines. However, previous research in the realm of active lightning protection has been limited, focusing either solely on assessing the risk of lightning-induced tripping on the power grid side or merely predicting the patterns of lightning activity on the disaster side. This paper proposes a novel method for lightning protection and early warning of power transmission lines based on the analysis of lightning data. It encompasses both the risk of lightning-induced tripping of transmission lines on the grid side and the hazards of real-time lightning activities on the disaster side. In the warning phase, the method extensively mines historical lightning location data and, based on predictions of thunderstorm cloud movement trajectories, determines if thunderstorm clouds are nearing tightly packed transmission corridors. It then assesses whether these clouds present a high-risk storm or are approaching transmission lines with low lightning protection capabilities, subsequently integrating these findings to issue a continuous lightning-induced trip warning. In a case study conducted in a certain part of northern China, the proposed continuous lightning strike trip warning method achieved an accuracy rate of 80% for transmission corridors of particular interest to power grid companies’ dispatch departments, enhancing the efficiency of warnings and reducing false alarms and missed reports.

This paper investigates the dynamic observer (DO) design problem for a class of disturbed Lipschitz nonlinear vehicle systems with uncertainties, where the uncertainties are assumed to be in both system matrix and input matrix. The proposed DO has a unified form and the popularly used proportional observer and proportional integral observer can be considered as the particular cases of the proposed DO. Based on the algebraic constraints, the observer design issue is transformed into the asymptotically stability analysis problem. The sufficient criterion of the DO design is derived in terms of linear matrix equality (LMI), by constructing a Lyapunov function to guarantee the H-infinity performance. A numerical example is used to illustrate the performance of the proposed DO.

Accurate indoor positioning is critical to a variety of use cases including tracking, proximity, mapping, and navigation. Existing positioning methods are either inaccurate (e.g. RSSI-based ranging), impractical (e.g. fingerprinting), or uncommon (e.g. Ultra-Wide Band/UWB). WiFi Round-Trip Time (RTT) marries the ubiquity of WiFi infrastructure with the accuracy of such ranging mechanisms as those used in UWB. We present WhereArtThou, a commercial-grade, plug-and-play indoor positioning solution (IPS) based on WiFi RTT. Our base algorithm uses an extended Kalman filter with a random walk motion model (EKF-RW) which relies solely on RTT distance measurements. We propose two EKF components to enhance positioning accuracy. First, as distance measurements can further be fused with inertial sensor readings, we propose a step-and-heading-based filter (EKF-SH) when such readings are available. We devise a method to fit the non-Gaussian step error with a Gaussian random variable to remain within the computationally-efficient Kalman filtering framework. Second, we define a distance-dependent measurement model to match the true statistics of the measurement noise and approach optimal position estimation. Moreover, we measure the gain from the proposed enhancements not only through the almost-exclusively-used error metric in the indoor positioning literature, the Euclidean distance and its variations, but also through metrics commonly used in satellite and maritime navigation, the cross-track and along-track errors, in addition to a set of metrics of our own definition. Finally, we show that RTT is highly susceptible to the human body holding the device measuring it, and we case-study the impact of human-body blockage on positioning and ranging errors. We test our algorithms on over 18 hours of walking data collected on different devices, in different locations, and with different users, and we observe that the EKF-RW and EKF-SH achieve 90th percentile distance errors of 1.65 m and 1.45 m, and 90th percentile cross-track errors of 0.85 m and 1.55 m, making our solution primed for commercial deployment.

A lithium niobate thin-film(LNTF) electro-optic modulator(EOM) with segmented electrode structure is investigated, and the influence of the modulator's structural configuration on its performance is achieved. In contrast to conventional couplers, the design incorporates a 2 × 2 S-shaped directional coupler. Our modulator achieves a bandwidth of nearly 110 GHz and a half-wave voltage of 1.45 V $\cdot$ cm. A comparative analysis between two configurations: one with SiO$_{2}$ cladding covering the entire surface and the other with coverage limited to the electrode gaps, which reveals the latter potential for enhancing device performance. The accuracy of the simulation results is substantiated through both software analysis and theoretical calculations. The simulation results provide valuable insights for the design of LNTF EOM, highlighting the efficacy of the proposed segmented electrode structure.

Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU methods are seldom studied, and the simple spatial-spectral extension leads to unpleasant spectral distortion. To tackle these issues, we first propose a content-dependent 3-D convolutional sparse coding (CD-CSC) to jointly represent spatial-spectral feature. Specifically, the 3-D filters used in CD-CSC for each HSI are unique, which are determined by linear combination of base 3-D filters. Then, we develop a novel CD-CSC-inspired DU network for HSI denoising, called CD-CSCNet. Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a separable content-dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. SCD-Conv not only reduces computational complexity, but also can be viewed as the convolutional sparse coding with spatial and spectral dictionaries. Extensive experimental results on the ICVL, Zhuhai-1 OHS-3C, and GaoFen-5 datasets demonstrate that CD-CSCNet outperforms several recent pure data-driven and DU-based networks quantitatively and visually.

Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately capturing small abandoned cropland, particularly in regions, such as southern China, with fragmentized farmland, poses a significant challenge using the traditional optical image-based mapping methods due to their coarse spatial resolution. In addition, irregular and chaotic textures of abandoned cropland further complicate the accurate prediction using very high resolution (VHR) data. In this article, we propose a novel deep learning network termed pixel attention contrastive network (PACnet) to map fine-grained abandoned cropland based on VHR data. Cross-image pixel contrast learning is introduced to discern distinctive features distinguishing abandoned cropland from other land types across various interimages. Moreover, a criss-cross attention module is embedded to enhance the contrasting characteristics within individual intraimages. Experimental outcomes validate the efficacy of PACnet, showcasing the highest accuracy (OA = 93.8% and mIOU = 71.7%) when compared with classical semantic segmentation networks. Our proposal not only underscores the potency of VHR remote sensing data in finely delineating abandoned cropland but also carries significant implications for cropland abandonment impact analysis and informed policy formulation.

3-D convolutional neural networks (CNNs) offer a great potential spatial–spectral representation for hyperspectral image (HSI), and has achieved promising HSI denoising performance. However, the current 3-D CNNs still suffer from limited nonuniform multicomponent spatial–spectral features encoding, and incur a high computation burden. To address these issues, we draw inspiration from the success of multibranch and separable convolution perspectives, and propose a plug-and-play multibranch separable 3-D convolution block (MS3CB) with different spatial–spectral receptive fields. Specifically, MS3CB comprises mixed regular and nonregular separable 3-D branches, in which the nonregular separable 3-D branch attempts to provide a meaningful nonuniform spatial–spectral features extractor. The separable 3-D convolution in MS3CB factorizes the standard 3-D convolution into a 2-D spatial convolution and a 1-D spectral convolution, which not only reduces model size but also decouples spatial and spectral features in HSI for more flexible spatial–spectral representation. Based on MS3CB, we develop a novel 3-D U-Net for HSI denoising, called HSDU-Net, which uses MS3CB as the basic building block instead of a standard 3-D convolution block. We empirically verify that our separable 3-D convolution block reduces about 64.6% parameters and achieves a certain performance gain over standard 3-D convolution. Extensive experiments further demonstrate that HSDU-Net surpasses several latest baselines on various synthetic and real noisy HSI datasets.

This article presents an elliptical circumnavigation control strategy for multiple quadrotors subject to uncertainties. At translational level, a novel horizontal elliptical guidance law capable of regulating the relative distance between different quadrotors and a vertical control law are synchronized to educe the translation controller. At rotational level, an USDE characterized by laconic constructure and simple parameter tuning, is introduced to estimate the unknown lumped uncertainties online. The primary advantage of this paper is that the elliptical circumnavigation with regulatable distance between circling quadrotors is achieved. Meanwhile, all the error signals in the closed-loop system are analyzed to be bounded. Finally, practical experiments verify the efficacy of suggested strategy.

Atmospheric dispersion is a wavelength-dependent effect that affects astronomical observations performed using ground-based telescopes. The effect of atmospheric dispersion for a 4-meter telescope is analyzed and an atmospheric dispersion corrector (ADC) system was designed for its adaptive optical system. With the help of adaptive optical system and a line fit method, the science images can be conveniently used to measure the atmospheric dispersion and to control ADC to correct the dispersion, and thus the full-width at half-maximum of the final images can approach the diffraction limit of the telescope.

The scintillator crystal is a crystalline material that can light under the influence of high-energy rays and be widely used for detecting high-energy particles. With the development of industrial CT and other fields, the demand for inorganic scintillator crystals with high melting points and high performance is increasing yearly, which results in the abandonment of the traditional laboratory mode of manufacturing inorganic scintillator crystals. For achieving the needs of high-standard inorganic scintillator crystals, the market attaches great importance to the intelligent manufacturing of scintillator crystals. One of the critical technical difficulties in intelligent crystal manufacturing is the numerical assessment of crystal quality. The traditional laboratory research and development mode relies on manually measuring crystal quality, which has many defects, such as the inability to be numerical, and the quality evaluation varies from person to person. Therefore, this paper proposes a methodology for assessing crystal quality based on the residual depth network. At the same time, in order to make crystal quality evaluation more refined, this paper uses a target detection method based on depth learning to determine the crystal quality assessment area. The target detection model in this article is developed based on the yolov5 deep learning framework, which is suitable for crystal target detection, and its detection accuracy is 98%. The residual depth network proposed in this paper is based on the network developed by ResNet-18, which is more suitable for crystal grading, and its accuracy of crystal quality evaluation reaches 84.8%. The quality evaluation of inorganic scintillation crystals is a solution to the problem of numerical representation of the quality of inorganic scintillation crystals, which opens up a technical channel for closed-loop control of intelligent crystal manufacturing processes.

Few-Shot Segmentation (FSS) is challenging for intra-class diversity and support sample scarcity. Many works focus on the class-wise or pixel-wise similarity between the support foreground and query sample while neglecting the support background, which is vital for FSS to suppress the related query background. In this paper, we propose a Multi-Similarity Enhancement Network (MSENet) to remedy this issue by extracting the pixel-wise support-query similarity of the foreground and background. To remedy the shift issue, caused by the huge difference between support and query target objects, this study extracts and fuses multiple support-query similarity, and keep enhancing them with convolutional operations. Experimental results reveal that our approach achieves a performance of 66.8% in PASCAL and 43.8% in COCO, surpassing the state-of-the-art (SOTA) and outperforming other leading competitors.

Combining with the dispersion characteristics of ultrasonic guided waves, full waveform inversion method shows great application potential in the quantificational detections of defects in plate- and pipe- like structures. Owing to the inversion efficiency problem, the forward modeling is generally based on the acoustic equation approximation, and a reference signal unaffected by the defects needs to be artificially selected to correct the approximation forward simulation results. This study presents an auto-calibration (without artificial selection of the reference signal) and high-precision imaging method based on the combination of the full waveform inversion and ray tomography algorithm. The ray tomography results are not only used to automatically select the reference signal based on an auto-calibration process, but also used as the macro initial model for the full waveform inversion method, which decreases the possibility of losing in local minimum values during the inversion process and enhances the robustness of the inversion method. Therefore, compared with the classical guided wave tomography method based on full waveform inversion, relatively high-frequency transducers can be used to acquire high-frequency signals, and thus, the imaging accuracy could be effectively improved. Simulation and experiment results have verified that the global relative error of the auto-calibration method is smaller than the classical method. The good imaging results of irregular complex defects confirmed the effectiveness and applicability of the new method.

Two microwave photonic signal processing structures, which are capable to realise microwave frequency division with a tunable integer or non-integer division ratio, are presented. They are based on applying an RF signal into a Mach Zehnder modulator, which is biased to generate a carrier-suppressed double sideband (DSB) or single sideband (SSB) optical signal. The carrier-suppressed DSB optical signal consists of the upper and lower $1^{\rm st}$ order sidebands with a separation of two times the input RF signal frequency. The carrier of the SSB optical signal is suppressed by a fibre Bragg grating (FBG) connected to the modulator output. Hence the FBG output consists of the upper $1^{\rm st}$ order and lower $2^{\rm nd}$ order sidebands with a separation of three times the input RF signal frequency. The carrier-suppressed DSB or SSB optical signal is injected into a semiconductor laser. The semiconductor laser is oscillated in the period-one state that generates a number of equally spaced frequency components, which are frequency locked by the injection light wave. Beating of these optical frequency components at the photodetector produces an RF signal with a frequency of 2/ $N$ or 3/ $N$ times the input RF signal frequency where $N$ can be between 3 and 6. Hence the 2/ $N$ frequency divider can realise 2/3, 1/2, 2/5 and 1/3 frequency division operation, and the 3/ $N$ frequency divider can realise 3/4, 3/5 and 1/2 frequency division operation. The proposed 2/ $N$ and 3/ $N$ frequency dividers have a very simple structure compared to the reported photonics-based microwave frequency divider that can realise both integer and non-integer frequency divisions. The frequency division ratio can be tuned by simply adjusting the forward bias current of the semiconductor laser subject to optical injection. Experimental results demonstrate the two proposed structures can realise microwave frequency division with a tunable integer and non-integer division ratio for different input RF signal frequencies of 10 to 18 GHz, and over 60 dB output signal-to-noise ratio performance. More than 27 dB suppression in the unwanted frequency components around the frequency divided signal is also demonstrated.

The conventional laser triangulation displacement sensor (LTDS) is difficult to achieve large dynamic range and high precision measurement simultaneously, since LTDS is affected by factors such as limited detector size, nonlinear input and output variables, laser power fluctuation, speckle noise, electronic noise, etc. Given the above background, we proposed a modified LTDS using a diffraction grating to improve the dynamic range and precision simultaneously. Different from the conventional LTDS, the modified LTDS generates more than one spots on the image sensor, thereby the displacements with different ranges can be obtained from the multiple order diffraction spots. Ultimately, the dynamic range and measurement accuracy will be improved simultaneously by integrating these displacements. In this paper, the principle of the modified LTDS for large dynamic range and high precision measurement is described in detail, and we verified the validity and effectiveness of this idea through the experiment. Compared with the traditional LTDS, the experimental results show that the linearity of the modified LTDS is improved by a factor of 1.5685 and the dynamic range is improved 1.2986 times as well as maintaining the same linearity.

This paper presents a detailed experimental investigation of the nonlinear dynamics of a dual-beam optically injected semiconductor laser. Unlike previous works, we focus on the situation where the power and the frequency of the injection light wave are fixed and the free running frequency of the laser subject to optical injection is in between the two injection beams that have a large frequency separation. The nonlinear dynamics of the slave laser (SL), i.e., the laser subject to optical injection, for different SL forward bias currents are examined. Results show, in addition to the regenerated injection light wave, a fundamental oscillation together with its harmonics are generated when the SL interacts with its nearby injection beam. The SL output frequency components are stabilised when one of the harmonics of the fundamental oscillation is located at the injection beam that is far away from the free running SL. More than six-fold improvement in the frequency stability of the resultant output microwave signal, compared to that generated by optical mixing, is obtained. Experimental results also show the SL nonlinear dynamics have different behaviours when the two injection beams have different frequency separations.

Most heterogeneous change detection methods based on transfer learning may not yield satisfactory results due to the lack of comprehensive utilization of the global and local characteristics of the image. In this article, we propose an unsupervised heterogeneous change detection method based on iterative joint global–local translation (IJGLT). The two heterogeneous images are first segmented into superpixels with the same boundary. Unlike the transfer learning methods for classification tasks that directly reduce the distance of the distribution of all data, we construct a translation function to decrease the difference between unchanged superpixels while increasing the difference of changed superpixels. During the translation process, the overall distribution is constrained using pseudo change labels, and the local manifold preserving is utilized to maintain the spatial structure and local adjacency relationship of superpixels. By comparing the translated superpixels of the heterogeneous images, the change map is obtained and then refined with pseudo change labels taking an iterative approach until convergence. Using the public and self-made optical-SAR heterogeneous datasets with different resolutions, the experimental results demonstrate that IJGLT is superior to several typical comparison methods with a maximum overall accuracy of 0.98 and a kappa coefficient of 0.9.

The production of high-resolution digital terrain models (DTMs) based on images is often hampered by the lack of appropriate stereo observations. Here, we propose a deep learning-based reconstruction of pixel-resolution DTMs from Lunar Reconnaissance Orbiter (LRO) single-view narrow angle camera (NAC) images, constrained by Selenological and Engineering Explorer and LRO LOLA Elevation Models (SLDEM). The procedure is carried out for a set of adjacent images, and the mosaicking of a contiguous large-area DTM is demonstrated. The approach is applied to the CE-3 and CE-4 landing sites, involving six multiple coverage and eight adjacent NAC L/R image pairs, respectively. For the DTM reconstruction, we use an improved convolutional neural network architecture with a weighted sum loss function involving three loss terms. We demonstrate that our method is robust and can deal with images acquired under varying illumination conditions. The DTM mosaic (1.5 m pixel size) covering the CE-4 landing area (72.8 × 30.3 km) is without apparent seams between the individual image boundaries and consistent with the SLDEM (60 m pixel size) in terms of overall elevation, trend, and scale, but is showing considerably more morphologic detail.

With the improvement of the quality of life, the demands on button mushroom (Agaricus Bisporus) increase significantly. But traditional farming methods mainly rely on costly human picking, which is currently facing the time-consuming and labor-intensive problem. As machine vision technology becomes more mature, harvesting robot with flexible picking end-effector is an alternative method to address this issue. Though the conventional harvesting robot is capable of mushrooms picking, but the damage rate is relatively high for the improper picking force and motion. Thus, a novel picking end-effector for button mushrooms is designed based on vacuum negative pressure picking in this paper. The harvesting robot with flexible end-effector is proposed to solve the problem: 1) to avoid the injury when the end-effector touch the mushroom; 2) to increase the picking efficiency without damage rate increase. The structure of robotic mushroom picking end-effector is described in detail, and then the kinematic modeling and picking force analysis of this robot are presented. Lastly, the numerical simulation via the MATLAB is carried out to study the influence of the robot parameters. The bruise tests indicated that the maximal allowable stress is 0.196 MPa and mean allowable pressure is 13.82 N. Preliminary results demonstrate that the robot achieved 88.2% success rate and 2.9% damage rate in the factory environment, and there is potential for the automatic mushroom harvesting with the proposed harvesting robot.

Big data-driven technologies, especially machine learning and deep learning technologies, have been extensively employed in mineral prospectivity prediction. Several approaches have been proposed to learn the deep characteristics of geoscience data, enhance the accuracy of prediction and reduce uncertainty. Nevertheless, the approaches always contain the following two limitations. Firstly, the formation of mineral resources often involves the coupling of multiple factors on a certain spatio-temporal scale, resulting in rare labelled deposits and insufficient number of training samples. Secondly, training Deep Neural Network (DNN) is very challenging. Many approaches are subject to weak interpretability and lack of organic combination with geoscience knowledge. To address these two problems, we propose Geo-Rnet and GCAE (Geological Convolutional Autoencoder). Geo-Rnet is a multi-class mineral prospectivity prediction approach based on improved DNN. GCAE is able to effectively augment multi-disciplinary geoscience data by constructing upon an optimized Convolutional Autoencoder. The experimental results show that most of prospective areas predicted by Geo-Rnet overlap with the labelled mineralization locations, with an average accuracy of 91.1%. In addition, 89.98% of the ore deposits are located in the predicted areas. The results indicate the effectiveness of Geo-Rnet and GCAE for multi-class prediction of mineral resources. Finally, we classify the target area into several mineral prospectiviy areas according to their different mineral types. The research provides an innovative approach for mineral prospectivity prediction in the target area.

We propose and experimentally demonstrate an RF signal multiplier based on optical injection locking in a semiconductor laser followed by an optoelectronic oscillator (OEO). In the proposed scheme, the modulated master laser is injected into a slave laser, a single mode Fabry-Pérot laser diode (SMFP-LD), in such a way that one of the harmonics of the modulated beam is locked to the corresponding mode of the SMFP-LD. By optical beating at the output of the SMFP-LD, stable multiple RF signals with ahigh signal-to-noise ratio (SNR) are generated due to SMFP-LD, which provides a sufficient gain to the RF signal. Further, we employ an OEO structure at the output of the SMFP-LD to optimize one of the generated multiple RF signals which are determined by the electrical bandpass filter (EBPF) and the low noise amplifier (LNF) in the OEO loop. As the gain of the OEO loop is higher than the loss, a high-purity oscillating signal, which is the selected multiple RF frequency, can be generated. In the experiment, a sextuple frequency (20 GHz) of the modulated signal is observed with an SNR and the phase noise of 54.44 dB and −102.44 dBc/Hz@10 kHz, respectively. Compared with other photonic schemes, the proposed method provides high SNR, higher multiplication factor, and low phase noise.

The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real-world scenarios. Because of the high complexity of the optimisation problem for modeling the traffic, experimental settings of existing works are often inconsistent. Moreover, it is not trivial to control multiple intersections properly in real complex traffic scenarios due to its vast state and action space. Failing to take intersection topology relations into account also results in inferior solutions. To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios. Additionally, we propose a novel baseline model with strong performance. It is based on deep reinforcement learning with an encoder-decoder structure: an edge-weighted graph convolutional encoder to excavate multi-intersection relations; and an unified structure decoder to jointly model multiple junctions in a comprehensive manner, which significantly reduces the number of the model parameters. By doing so, the proposed model is able to effectively deal with the multi-intersection traffic control optimisation problem. Models are trained/tested on both synthetic and real maps and traffic data with the Simulation of Urban Mobility (SUMO) simulator. Experimental results show that the proposed model surpasses multiple competitive methods. The traffic data and the code can be found at https://git.io/JPdU1.

Orthogonal Time Frequency Space (OTFS) as a two-dimensional modulation scheme designed in the delay-Doppler domain, is realized by inverse symplectic finite Fourier transform (ISFFT) and symplectic finite Fourier transform (SFFT), for combating the high Doppler channels toward future wireless communications. Meanwhile, generalized spatial modulation (GSM) offers an efficient implementation for multi-input multi-output (MIMO) systems, by activating only part of the transmit antennas to alleviate inter-channel interference (ICI). In this paper, the combination of the above two concepts is exploited, for bridging their unique advantages. On the other hand, an iterative detector based on generalized approximate message passing (GAMP) is also developed for GSM-OTFS. Simulation results demonstrate that the proposed GAMP detector can achieve better performance than the conventional minimum mean square error (MMSE) detector.

An all-optical photonic microwave frequency divider (PMFD) is presented. It is based on injecting an RF phase modulated optical signal into a semiconductor laser oscillating in the period-two state. New optical frequency components with frequency separation of half of the input RF signal frequency are generated at the laser output. A frequency divided signal can be obtained by the beating of these optical frequency components at a photodetector. Large harmonic suppression can be achieved by using an optical filter to select only two optical frequency components to be detected by the photodetector. The proposed PMFD is free of electrical components and does not suffer from the modulator bias drift problem. It has the potential to operate over a 100 GHz frequency range. System parameters required to realise divide-by-two frequency division operation for different input RF signal frequencies are investigated. Experimental results are presented for the novel PMFD, which demonstrate the generation of a 1/2 frequency component for different input RF signal frequencies by controlling the forward bias current of an off-the-shelf semiconductor laser, and which also show the important advantages of large harmonic suppression and high signal-to-noise ratio performance. Wide input RF signal power range and high output stability performance are also demonstrated experimentally.

The role of demand response resources is particularly important under the new-type power system with new energy as the main source. Demand response resources are important for generation adequacy and can provide electricity capacity value for power system. The United States and the UK are the only countries in the world that offer capacity services from demand response resources as generation resources. Their experiences and lessons learned are of great significance to other countries and regions. This paper first analyzes the significance of DR resource participating in the capacity market from two perspectives: the development of demand response resources and system generation capacity. Secondly, a detailed analysis of the mechanism design of demand response resources in the US capacity market is presented and the development status of demand response resources since 2007 is provided, so as to provide meaningful reference for other countries and regions. Thirdly, this study introduces the current situation and dilemma of demand response resources in the capacity market of the UK, and provides corresponding suggestions so that demand response resources in other countries and regions can avoid repeating the pace of the UK in the capacity market. Finally, suggestions for demand response resources to participate in the capacity market are given from four aspects.

By improving a previously introduced Lyapunov functional,this paper addressed the stability issue about neutral-type Cohen-Grossberg neural networks with multiple time-varying delays in the states of neurons and the time derivative of states of neurons, a sufficient condition for global asymptotic stability is obtained. Since the Cohen-Grossberg neural networks considered in this paper is more general than those in the former literature, the obtained result developed stability condition of Cohen-Grossberg neural networks. At the end of the paper, two examples are employed to demonstrate the validity and superiority of the conclusion.

High-power wireless power transfer (WPT) is an attractive option for fast charging of electric vehicles (EVs), but the power capability of the WPT system is limited by power devices and resonant elements. This paper presents a scalable modular four-channel WPT system for fast EV charging. Owing to the modular design, the four-channel system achieves 50 kW output power with low-power Silicon devices, and the reliability and robustness of the system are significantly improved. Through the coil structure and placement design, the four-channel magnetic coupler achieves inter-channel decoupling, eliminating interference between channels, simplifying control logic, and enabling scalable modular design. The four-channel magnetic coupler also has rotational symmetry, making the system have a balanced tolerance to misalignment in both lateral and vertical directions. The proposed magnetic coupler is verified by 3-D finite element method (FEM) simulation. A modular four-channel WPT prototype is built and tested. Experiments show that the prototype can deliver 50.8 kW to load with 97% dc-to-dc efficiency over a 200-mm airgap in aligned case, and has a balanced tolerance to misalignment in both the lateral and vertical directions.

In 5G New Radio (NR), low-density parity-check (LDPC) codes are included as the error correction codes (ECC) for the data channel. While LDPC codes enable a low, near Shannon capacity, bit error rate (BER), they also become a computational bottleneck in the physical layer processing. Moreover, 5G LDPC has new challenges not seen in previous LDPC implementations, such as Wi-Fi. The LDPC specification in 5G includes many reconfigurations to support a variety of rates, block sizes, and use cases. 5G also creates targets for supporting high-throughput and low-latency applications. For this new, flexible standard, traditional hardware-based solutions in FGPA and ASIC may struggle to support all cases and may be cost-prohibitive at scale. Software solutions can trivially support all possible reconfigurations but struggle with performance. This article demonstrates the high-throughput and low-latency capabilities of graphics processing units (GPUs) for LDPC decoding as an alternative to FPGA and ASIC decoders, effectively providing the high performance needed while maintaining the benefits of a software-based solution. In particular, we highlight how by varying the parallelization strategy for mapping GPU kernels to blocks, we can use the many GPU cores to compute one codeword quickly to target low-latency, or we can use the cores to work on many codewords simultaneously to target high throughput applications. This flexibility is particularly useful for virtualized radio access networks (vRAN), a next-generation technology that is expected to become more prominent in the coming years. In vRAN, the hardware computational resources will become decoupled from the specific computational functions in the RAN through virtualization, allowing for benefits such as load-balancing, improved scalability, and reduced costs. To highlight and investigate how the GPU can accelerate tasks such as LDPC decoding when containerizing vRAN functionality, we integrate our decoder into the Open Air Interface (OAI) NR software stack. With our GPU-based decoder, we measure a best case-latency of 87 μs and a best-case throughput of nearly 4 Gbps using the Titan RTX GPU.

Accurate prediction of the large-scale channel fading is fundamental to planning and optimization in 5G millimeter-wave cellular networks. The current prediction methods, which are either too computationally expensive or inaccurate, are unsuitable for city-scale cell planning and optimization. This paper presents FadeNet, a convolutional neural-network enabled alternative for predicting large-scale fading with high computation speed and accuracy. By using carefully designed input features and neural-network architecture to capture topographical information, FadeNet accurately predicts the large-scale fading from a base station to each location in its coverage area. Evaluations on realistic data, derived from millimeter-wave cells across multiple cities, suggest that FadeNet can achieve a prediction accuracy of 5.6 decibels in root mean square error. In addition, by leveraging the parallel processing capabilities of a graphics processing unit, FadeNet can reduce the prediction time by 40X - 1000X in comparison to industry prevalent methods like ray-tracing. Generalizations of FadeNet, that can handle variable topographies and base station heights, and its use for optimal cell site selection are also explored.

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts.

A new topology that generates a DC voltage whose value is depending on the phase difference of two input RF signals is presented. It is implemented by the mixer concept. With the use of microwave photonics, the mixer-based phase detector can be operated over a very wide frequency range. It also has the advantages of high phase detection resolution, small phase detection error, and small DC offset and phase offset. The phase detector can be constructed using off-the-shelf components including a single integrated optical modulator and a low-speed balanced detector. Measured results on the phase detector demonstrate a 0° to 180° RF signal phase difference detection range for 3.5 GHz to 18 GHz input RF signal frequencies, less than ±3° phase detection error and 2° phase detection resolution.

Power line carrier communication (PLC) is one of the important technologies to construct and realize ubiquitous power Internet of Things, while the degree of network matching severely affects the communication quality of the network. Combined with the actual topology of a medium voltage (MV) distribution network, the transmission line theory is used to derive the impedance matching relationship between the two slave nodes in the master-slave network mode. Based on this, a matching optimization method for the carrier communication of MV distribution Internet of Things based on network looseness is proposed. Firstly, a large complex distribution communication network is selected and divided into several small impedance-matching networks according to the network looseness on the trunk lines. Then, in each region, the particle swarm algorithm is applied for matching coordination to avoid power wasting and impedance mismatching among multiple communication nodes. The coordinated matching method has fast optimization speed and strong portability, and its feasibility and effectiveness are verified by theoretical derivation, simulation and laboratory test results, which provides an important theoretical basis for the planning, design and popularization of PLC technology in the ubiquitous power Internet of Things.

Map is an essential medium for people to understand our changing planet. Recently, research on generating and updating maps through remote sensing images has been an important and challenging task in geographic information. Traditional methods for map generation are time-consuming and labor-intensive. Besides, most supervised learning methods for map generation lack labeled training samples. It is challenging to generate maps quickly and efficiently for emergency rescue operations such as earthquakes, fire disasters, or tsunami. In this article, we propose an unsupervised domain mapping model based on adversarial learning called MapGen-GAN. MapGen-GAN is a generative adversarial network (GAN) that can do end-to-end translation from remote sensing images to general map quickly, and trained with no human annotation data. In order to improve the fidelity and the geometry precision of generated maps, we employ circularity-consistency and geometrical-consistency constraints as a part of the loss function of the proposed model. And then, an improved residual block Unet is designed and adopted as the generator of MapGen-GAN to capture the geographic structure information of buildings, roads, and topography outlines under different resolutions in the map generation. By applying the proposed model to two distinct datasets, experiments demonstrate that our model can generate maps efficiently and quickly and outperform the state-of-the-art approaches.

Existing deep learning-based remote sensing images semantic segmentation methods require large-scale labeled datasets. However, the annotation of segmentation datasets is often too time-consuming and expensive. To ease the burden of data annotation, self-supervised representation learning methods have emerged recently. However, the semantic segmentation methods need to learn both high-level and low-level features, but most of the existing self-supervised representation learning methods usually focus on one level, which affects the performance of semantic segmentation for remote sensing images. In order to solve this problem, we propose a self-supervised multitask representation learning method to capture effective visual representations of remote sensing images. We design three different pretext tasks and a triplet Siamese network to learn the high-level and low-level image features at the same time. The network can be trained without any labeled data, and the trained model can be fine-tuned with the annotated segmentation dataset. We conduct experiments on Potsdam, Vaihingen dataset, and cloud/snow detection dataset Levir_CS to verify the effectiveness of our methods. Experimental results show that our proposed method can effectively reduce the demand of labeled datasets and improve the performance of remote sensing semantic segmentation. Compared with the recent state-of-the-art self-supervised representation learning methods and the mostly used initialization methods (such as random initialization and ImageNet pretraining), our proposed method has achieved the best results in most experiments, especially in the case of few training data. With only 10% to 50% labeled data, our method can achieve the comparable performance compared with random initialization. Codes are available at https://github.com/flyakon/SSLRemoteSensing.

Automatic extraction of buildings from high-resolution remote sensing images becomes an important research. Since the convolutional neural network can perform pixel-level segmentation, this technology has been applied in this field. But the increase in resolution prone to blurry segmentation because the model needs more edge detail and multi-scale detail learning. To solve this problem, a method is proposed in this paper, which consists of three parts: (1) an improved model named Holistically-Nested Attention U-Net (HA U-Net) is designed, which integrates the attention mechanism and multi-scale nested modules to supervise prediction; (2) During model training, an improved weighted loss function is proposed to make the designed model more focused on learning boundary features; (3) watershed algorithm is exploited for image post-processing to optimize segmentation results. The designed HA U-Net performs well on WHU Building Dataset and Urban3d Challenge dataset, and achieves 9.31%, 2.17% better F1-score and 10.78%, 1.77% better IOU than the standard U-Net respectively. The experimental results indicate that the proposed method can well solve the building adhesion problem. The research can serve as updating geographic databases.

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods; then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

A compact system with multiple signal processing functions is of interest in many applications. The purpose of this paper is to present a microwave photonic structure that is capable of simultaneously realising in-phase/quadrature (I/Q) mixing and phase shifting operations. The multi-function signal processor has a simple structure and can be constructed using off-the-shelf components. It is designed to enable commercial modulator bias controllers to be incorporated into the system to provide accurate phase shift and to improve system stability. Non-ideal effects in the system that cause deviation in the two output IF signal phase difference from 90° can be mitigated by adjusting a modulator bias voltage. The multi-function signal processor is theoretically analysed and experimentally demonstrated. Its performance including two output IF signals with a quadrature phase difference, wideband operation, continuous 0° to 360° IF signal phase shift and long-term stable operation are verified experimentally.

According to a previously built situation-awareness (SA) model based on attention allocation, with the ACT-R (Adaptive Control of Thought, Rational) theory for analyzing pilot cognitive processes for situation elements, an SA mathematical model was improved to predict pilot SA during exposure to different display interfaces and missions. An indicator-display monitoring task was performed under different experimental conditions for SA model verification, while the SA global assessment technique (SAGAT), performance measures, 10-dimensional SA rating technique (10-D SART), and eye movement measures were adopted to comprehensively assess the operator’s SA. The experimental results revealed that theoretical prediction values calculated using the improved SA model were strongly correlated with the operation performance, and thus confirming the model validation. The SAGAT was shown to be a more effective method than SART in this research, and the overall SAGAT accuracy rate, as well as the accuracy response time, are effective indices for SA measurement. Eye-movement indices, such as the fixation/saccade ratio, which corresponds to the mode of information perception and extraction, was examined to be sensitive to operator’s SA changes. The Advances of the improved SA model have been achieved in predicting and indicating SA by using human behaviors, including operation performance, SAGAT response behaviors, and visual behaviors. Thereby, it provides a new auxiliary tool for quantitative characterization of pilot’s SA during cockpit interface design optimization and ergonomic evaluation.

A new microwave photonic signal processing structure for realising in-phase/quadrature (I/Q) mixing is presented. It is based on a dual-drive Mach Zehnder modulator (DDMZM) and an optical phase modulator inside a Sagnac loop interferometer. Two output IF signals with a quadrature phase difference are obtained by using two photodetectors to detect the upper and lower sidebands of the optical signal at the Sagnac loop output. A quadrature phase difference in the two IF signals is realised by biasing the DDMZM at the quadrature point. This has the advantage of very little quadrature phase errors as a standard modulator bias controller can accurately lock the DDMZM bias point. The optical phase modulator driven by an RF signal can be placed in a remote location for antenna remoting. Theoretical analysis and simulation show using the proposed I/Q mixer for image rejection can overcome the modulation index dependent image rejection ratio problem in the reported I/Q mixers based on a cascaded modulator structure. The Sagnac loop based I/Q mixer is experimentally verified with results show only ±1° quadrature phase error and more than 50 dB image rejection ratio over the operating frequency range of 6 to 20 GHz.

RF signal recognition is an important element toward RF situational awareness and dynamic spectrum management. In this work, machine learning-based signal recognition algorithms are proposed. Our key contribution is to engineer feature learning such that the classifiers can perform robustly even when a mixture of heterogeneous signal classes is observed, although the training is still done using non-mixture single-label samples. To achieve this, discriminative dictionary learning algorithms are developed with various feature-shaping constraints. The signal detection can then be done in a way reminiscent of the multi-user detection in wireless communication, employing linear equalizers. The algorithms are tested using real wideband RF measurement data. It is verified that the proposed algorithms can robustly classify the component signals even when their powers are widely different and their number is not known a priori.

A technique to provide independent control on the amplitude and phase of two orthogonal linearly polarised light is presented. It is based on operating a commercial dual-polarisation dual-drive Mach Zehnder modulator (DPol-DDMZM) in reverse direction that routes the input light in slow and fast axis into two different DDMZMs. The technique has the advantage of controlling the phase of the slow-axis light and the amplitude of the fast-axis light has no effect on the light travelling in the orthogonal polarisation state. It has applications in linearised microwave photonic links, frequency multipliers and microwave phase shifters. A new microwave photonic Hilbert transformer based on using the reverse operating DPol-DDMZM to alter the phase of an RF modulation sideband without affecting the orthogonally polarised optical carrier is developed. Experimental results demonstrate 24.3 dB attenuation in the fast-axis light with no change in the slow-axis light, and 0°–360° RF phase shift for only 3.3 V change in DC voltage. The new Hilbert transformer with less than 2.5° phase imbalance and 0.4 dB amplitude ripples over 4–18 GHz frequency range is also demonstrated.

This article investigates a finite-time attitude control with chattering suppression for quadrotors based on a high-order extended state observer (HESO). To improve the anti-disturbance capacity of quadrotors, a HESO capable of estimating fast time-varying disturbances is presented to enhance system robustness by regarding higher-order disturbance derivatives as extended arguments. Then, with the estimates of HESO, a finite-time attitude control policy including a double hyperbolical reaching law (DHRL) is introduced, alleviating control chattering and guaranteeing rapid response. The most significant feature is a finite time regulation for quadrotors without incurring severe oscillations can be obtained in the event of fast time-varying uncertainties. Moreover, the stability analysis is proved by a Lyapunov theorem. Finally, the effectiveness of proposed control scheme is demonstrated by numerical and experimental results.

All-solid-state 228.5 nm Deep Ultraviolet (DUV) laser has been studied with two-step second harmonious generation (SHG) of Nd:YVO4 914 nm fundamental laser, by optimizing its resonator design and SHG configurations. Adopting three-mirror folded V cavity and acousto-optical Q-switching, high peak power 457 nm laser output has been achieved by intracavity frequency-doubling of LD pumped Nd:YVO4 914 nm fundamental laser. At pump power of 41 W, the average output power for 457 nm laser has reached 600 mW at repetition frequency 10 kHz, 50 ns pulse width. With Type-I phase matching BBO crystal, external frequency doubling of 457 nm blue output was realized and optimized. Under LD pump power of 41 W, DUV laser at 228.5 nm with average output power of 35 mW has been achieved, at repetition frequency 10 kHz and pulse width of 46 ns. Under these conditions, the frequency doubling conversion efficiency is 5.8%, and the DUV laser output power instability is less than 2% in 2-hour test.

Language-modeling-based methods for story plot generation aim to generate a plot with a language model (LM). LM methods have limitations of user-assist plot generation of goal control, refinement for editing, causing the generated plots not clear sense for specific goal, lack coherence, and edit flexible. We present a control-and-edit transformer technique which uses controlled imitation learning of editing distance from dynamic programming to support deleting policy, inserting policy, a weighting-reward with prepossess of corpus statistic, and measures continues reward for the controlled goal. Automated evaluation and Haman judgement show our method is promising in comparison with the baselines.

The objective of this paper is to present a microwave photonic system that can measure the angle of arrival (AOA) of multiple microwave signals with improved measurement accuracy. It is based on a photonic mixer approach to down convert the incoming microwave signals into IF signals, which enables a low-frequency electrical spectrum analyser to be used for measuring the power of the IF signals to determine the incoming microwave signal AOAs. AOA measurement errors can be reduced by operating the optical modulator at a different transmission point for a different range of microwave signal AOA. The system also has the ability to remove the incoming microwave signal amplitude dependence in the AOA measurement. Measured results demonstrate 0°-81.5° AOA measurement with less than ±2° errors over the 0°-30° and 30°-81.5° AOA measurement range when the optical modulator is biased at the minimum and maximum transmission point respectively. The errors remain below ±2° even when there is a ±0.1 dB change in the output IF signal power. AOA measurement of two microwave signals is also demonstrated.