Automatic Modulation Recognition (AMR) plays a critical role in wireless communication systems, especially in the context of Low Earth Orbit (LEO) satellite communication automated testing. However, existing methods often rely on fixed or predefined feature combinations, leading to redundancy and suboptimal performance. This paper proposes a Double Deep Q-Network (DDQN)-based feature selector framework for AMR tasks, which adaptively selects informative features while effectively reducing the classifier’s parameter count. Experiments conducted across multiple signal-to-noise ratio (SNR) levels from -10 dB to 10 dB demonstrate stable convergence of the DDQN feature selector, with the number of selected features concentrated between 17 and 22. High-order statistical features are preferentially selected, with an average selection frequency reaching 0.7 at 0 dB. Compared to traditional methods such as Mutual Information (MI) and Principal Component Analysis (PCA), the DDQN-based approach improves recognition accuracy across various classifiers—including GRU, LSTM, CLDNN, and CGDNN— while significantly reducing model complexity. For instance, the GRU classifier combined with DDQN selection achieves a recognition accuracy of 0.61 at 6 dB SNR, outperforming the 0.524 accuracy without feature selection, with a parameter count of approximately 1.8×105. Ablation studies further validate the stability and effectiveness of the lightweight LSTM evaluator within the DDQN framework. Overall, the proposed method balances accuracy and model compactness, showing strong generalization ability and practical applicability.

With the rapid development of satellite communication and data processing technologies, satellite edge computing has emerged as a promising paradigm to enhance the performance of space information systems. However, most existing studies mainly focus on task offloading and resource allocation, while systematic research on task partitioning and multi-satellite cooperative optimization remains limited. To address this gap, this paper proposes an optimal task partitioning strategy based on game theory and optimization theory to solve the multi-satellite resource scheduling problem in satellite edge computing. The proposed method dynamically adjusts the task allocation ratios under heterogeneous computing and communication conditions, ensuring that all active satellites complete their workloads simultaneously and converge to the global optimum within a finite number of steps. Theoretical analysis and simulation results demonstrate that the proposed strategy significantly reduces end-to-end latency, improves resource utilization, and maintains stable performance under varying satellite capabilities and link conditions.

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time.

D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.

In order to improve the professionalism and accuracy of machine translation in the field of notice to air navigation,a two-way machine translation model of notice to air navigation based on transfer learning and back translation is proposed to solve the problem of the parallel corpus of machine translation in the field of air notice.On the basis of training the machine translation of People's Daily,the parameters obtained from the training are transferred to the encoding end and the decoding end of the machine translation model of the partly back-translated announcements to initialize the parameters at both ends,and adjust the parameters at both ends through experiments.The two-way machine translation model of the notice of air travel is obtained.Experiments show that the introduction of the data enhancement strategy of transfer learning and partial back translation improves the robustness of the mod-el and improves the translation quality.The BLEU value of the translation evaluation index of the Chinese-to-English model has in-creased by 2.08%.The BLEU value of the translation evaluation index increased by 3.12%.

Single-resolution homography estimation of infrared and visible images is a significant and challenging research area within the field of computing, which has attracted a great deal of attention. However, due to the large modal differences between infrared and visible images, existing methods are difficult to stably and accurately extract and match features between the two image types at a single resolution, which results in poor performance on the homography estimation task. To address this issue, this paper proposes an end-to-end unsupervised single-resolution infrared and visible image homography estimation method based on graph neural network (GNN), homoViG. Firstly, the method employs a triple attention shallow feature extractor to capture cross-dimensional feature dependencies and enhance feature representation effectively. Secondly, Vision GNN (ViG) is utilized as the backbone network to transform the feature point matching problem into a graph node matching problem. Finally, this paper proposes a new homography estimator, residual fusion vision graph neural network (RFViG), to reduce the feature redundancy caused by the frequent residual operations of ViG. Meanwhile, RFViG replaces the residual connections with an attention feature fusion module, highlighting the important features in the low-level feature graph. Furthermore, this model introduces detail feature loss and feature identity loss in the optimization phase, facilitating network optimization. Through extensive experimentation, we demonstrate the efficacy of all proposed components. The experimental results demonstrate that homoViG outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons.

Images captured from different viewpoints or devices have often exhibited significant geometric and photometric differences due to factors such as environmental variations, camera technology differences, and shooting conditions’ instability. To address this problem, homography estimation has attracted much attention as a method to describe the geometric projection relationship between images. Researchers have proposed numerous homography estimation methods for single-source and multimodal images in the past decades. However, the comprehensive review and analysis of homography estimation methods, from feature-based to deep learning-based, is still lacking. Therefore, we provide a comprehensive overview of research advances in homography estimation methods. First, we provide a detailed introduction to homography estimation’s core principles and matrix representations. Then, we review homography estimation methods for single-source and multimodal images, from feature-based to deep learning-based methods. Specifically, we analyze traditional and learning-based methods for feature-based homography estimation methods in detail. For deep learning-based homography estimation methods, we explore supervised, unsupervised, and other methods in-depth. Subsequently, we specifically review several metrics used to evaluate these methods. After that, we analyze the relevant applications of homography estimation and show the broad application prospects of this technique. Finally, we discuss current challenges and future research directions, providing a reference for computer vision researchers and engineers.

As the core professional course of computer science and technology, software testing covers a wide range of teaching contents and requires high students’ theoretical and practical ability. The software testing education in civil aviation colleges under the background of the emerging engineering is mainly oriented to engineering application, so that the students can lay a good software technology foundation for later professional course learning and engineering ability improvement based on mastering the basic theory and application of software testing technology. Taking Civil Aviation Flight University of China as an example, to adapt to the high degree of automation of Web in the smart civil aviation software industry in the new era, reasonable software testing teaching materials are selected, the theoretical and experimental teaching knowledge system of software testing is upgraded according to the needs of the civil aviation software industry, to realize the integration of curriculum knowledge, engineering practice, student cognition, and comprehensively improve the teaching effect of software testing courses.

Homography estimation is a method that describes the geometric projection relationship between images. Traditional homography estimation methods have displayed greater performance in single-source image, but it is difficult to extract accurate common features in infrared and visible images, resulting in poor performance. This paper proposed an unsupervised homography estimation module for infrared and visible images. Firstly, the network extracts the mask and feature maps of visible and infrared images using a mask generator and a feature extractor that introduces the RDB module, respectively. The feature extraction ability of the network can be enhanced by RDB's utilization of the hierarchical features present in the various convolutional layers. Then, the feature maps and masks are multiplied to get the weighted feature maps. Finally, the feature maps with assigned weights are cascaded by channel and fed into the homography estimator Res-CBAM for acquiring the homography matrix. The Res-CBAM module utilizes ResNet-34 as backbone and highlights the features that are important for homography estimation, by inserting the CBAM module after each stage (except the last one). The experimental results show that the ACE of the proposed method reduces significantly from 5.25 to 5.12.

The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects.

Homography estimation for infrared and visible images is a critical and fundamental task in multimodal image processing. Recently, the coarse-to-fine strategy has been gradually applied to the homography estimation task and has proved to be effective. However, current coarse-to-fine homography estimation methods typically require the introduction of additional neural networks to acquire multi-scale feature maps and the design of complex homography matrix fusion strategies. In this paper, we propose a new unsupervised homography estimation method for infrared and visible images. First, we design a novel coarse-to-fine strategy. This strategy utilizes different stages in the regression network to obtain multi-scale feature maps, enabling the progressive refinement of the homography matrix. Second, we design a local correlation transformer (LCTrans), which aims to capture the intrinsic connections between local features more precisely, thus highlighting the features crucial for homography estimation. Finally, we design an average feature correlation loss (AFCL) to enhance the robustness of the model. Through extensive experiments, we validated the effectiveness of all the proposed components. Experimental results demonstrate that our method outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons.

In light of the low accuracy of the numerical model system in predicting sea surface temperature (SST) on an hourly timescale, this paper thoroughly examines the spatiotemporal relationships inherent in the SST error data and delves deeply into the realm of machine learning, with a particular focus on the application of the Transformer deep learning model for this challenge. By engaging in profound learning of the error data from the past three days, the Transformer model offers an in-depth perception and understanding of the evolutionary patterns within the SST error data. Utilizing this advanced machine learning approach, the model successfully predicts the SST errors for the subsequent three days, significantly enhancing the prediction capability of the numerical model at the hourly scale. Experimental results further corroborate that the Transformer model demonstrates exceptional correction performance in machine learning across different time intervals such as 3 hours, 6 hours, and 24 hours.

In computer vision, the homography estimation of infrared and visible multi-source images based on deep learning is a current research hotspot. Existing homography estimation methods ignore the feature differences of multi-source images, which leads to poor homography performance in infrared and visible image scenes. To address this issue, we designed an infrared and visible image homography estimation method using a Multi-scale Generative Adversarial Network, called HomoMGAN. First, we designed two shallow feature extraction networks to extract fine features of infrared and visible images, respectively, which extract important features in source images from two dimensions: color channel and imaging space. Second, we proposed an unsupervised generative adversarial network to predict the homography matrix directly. In our adversarial network, the generator captures meaningful features for homography estimation at different scales by using an encoder–decoder structure and further predicts the homography matrix. The discriminator recognizes the feature difference between the warped and target image. Through the adversarial game between the generator and the discriminator, the fine features of the warped image in the homography estimation process are closer to the fine features of the target image. Finally, we conduct extensive experiments in the synthetic benchmark dataset to verify the effectiveness of HomoMGAN and its components. We conduct extensive experiments and the results show that HomoMGAN outperforms existing state-of-the-art methods in the synthetic benchmark datasets both qualitatively and quantitatively.

针对红外与可见光图像灰度差异较大、 配准精度低等问题,提出了一种基于生成对抗网络(Generative Adversarial Network,GAN)的红外与可见光图像单应性估计方法.主要步骤包括:利用浅层特征提取网络提取红外与可见光图像的精细特征;将精细特征进行通道级联输入到生成器中,以预测得出单应性矩阵;对单应性矩阵变换后的扭曲图像提取精细特征,送入判别器进行判断,从而建立一个对抗博弈过程.大量实验结果表明,所提方法可有效提升单应性估计性能,为后续图像融合工作提供技术支撑.

The proliferation of IoT applications has pushed the horizon of edge computing, which provides processing ability at the edge of networks. Task offloading is one of the most important issues in edge computing and has attracted continuous research attention in recent years. With task offloading, end devices can offload the entire task or only subtasks to the edge servers to meet the delay and energy requirements. Most existing offloading schemes are limited by the increasing complexity of task topologies, as considerable time is wasted for local/edge subtasks to wait for their precedent subtasks being executed at the edge/local device. This problem becomes even worse when the dependencies among subtasks become complex and the number of end-users increases. To address this problem, our key methodology is to exploit subtask duplications to reduce the inter-subtask delay and shorten the task completion time. Based on this, we propose a Duplication-based and Energy-aware Task Offloading scheme (DETO), which duplicates critical subtasks that have a large impact on the completion time and thus enhances the parallelism between local and edge computing. In addition, among numerous choices of subtask duplications, DETO evaluates the gain/cost ratio for each possible duplication and chooses the most efficient ones. As a result, the extra resource for duplications is greatly reduced. We also design a distributed DETO algorithm to support multi-user, multi-server edge computing. Extensive evaluation results show that DETO can effectively reduce the task completion time (by 12.22%) and improve the resource utilization (by 15.17%), in particular for multi-user edge computing networks.

With the rapid development of smart civil aviation, the demand for electronic flight pack (EFB) in the navigation field is increasing, but the research on its visual interface, content, view and data is insufficient. This paper designs and implements the navigation EFB data visualization based on SwiftUI. Firstly, this paper puts forward the overall design idea of EFB data visualization, and establishes the navigation EFB data visualization framework according to the overall idea; Secondly, based on the data visualization framework, the visualization structure of the system functions is designed; Finally, according to the design requirements, the specific realization of visual interface, content, view and data is completed. The results show that the visualization system meets the requirements of EFB data visualization in the navigation field, and provides ideas for the development of China''s navigation EFB.

航行通告信息是保障飞行安全所需的重要情报.针对航行通告信息难以采用统一格式处理特点,通过令牌化实现分词,并通过词嵌入方法,实现了航行通告中命名实体抽取.针对航行通告无标注数据集而无法进行机器学习的问题,采用改进的KMP算法结合实体间距离进行关系抽取.实验结果表明,采用此方法抽取航行通告信息,实现了航信通告信息中实体与关系的提取,得到了格式化的数据,解决了航信通告领域无标注数据集的问题.

针对难以提取红外与可见光图像中有效特征的问题,提出一种基于残差密集网络的红外与可见光图像配准方法.首先,利用基于残差密集网络设计的特征提取网络来提取图像对的分层特征.其次,利用掩码预测网络产生的掩码来对特征进行加权映射,从而突出图像中的重要特征.最后,这些重要特征通过单应性网络得到变换矩阵,从而实现图像配准.实验结果表明,残差密集网络的引入可提升配准效果,且评估指标ACE提升了21.5％.

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset.

随着时代的进步以及互联网的普及,手机等移动设备上网率已经超过电脑.针对这一现象如何在移动平台做好数据分析、可视化、数据挖掘具有重要的现实意义.分析了数据处理、数据可视化、可视化算法及相关库等关键技术,基于Android图表设计原理,设计动态展示的柱状图与圆形图图表,通过实例展示在Android平台下数据可视化流程.