Table of Content

30 March 2026, Volume 44 Issue 2

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Advanced Communications

A Position Optimization Method for Dual-UAV Double-Assisted Covert Communication

LIU Xuedong, TIAN Wen, ZHANG Shuang, DAI Yuewei, SHI Huaifeng

2026, 44(2):  181-197.  doi:10.3969/j.issn.0255-8297.2026.02.001

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Due to their high mobility and rapid deployment, unmanned aerial vehicles (UAVs) can significantly expand the coverage of covert communication by relay-assisted methods in complex military confrontation areas. However, traditional UAV relay-assisted covert communication suffers from low transmission rates over long distances due to covert constraints. To this end, this paper proposed a position optimization method for dual-UAV double-assisted covert communication. While employing one UAV as a relay to forward covert signals, this method introduces an additional UAV as a friendly jammer to increase the uncertainty at the detector. Specifically, a dual-UAV double-assisted covert communication model was built, the detection error probability of the detector under this model was analyzed, and the covert constraint was derived. Then, the block coordinate descent (BCD)method was employed to jointly optimize the positions of the jamming UAV and the relay UAV under covert and transmission power constraints, aiming to maximize the system’s covert transmission rate. Simulation results demonstrate that the proposed method can significantly improve the covert transmission rate compared to existing methods.

Dynamic Simulation of Heat Treatment Process in Core-Cladding Junction Region of Aluminum-Doped Germanium-Core Optical Fibers

ZHONG Shuangqi, DU Yifan, MA Zecheng, XU Sitao, ZHAO Ziwen

2026, 44(2):  198-207.  doi:10.3969/j.issn.0255-8297.2026.02.002

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For the heat treatment preparation and modification of pn junction devices in germanium-core fibers, this study selected the core-cladding junction region as the research focus and adopted a typical p-type germanium material with the host element aluminum as the doping element. An aluminum (Al)-doped germanium (Ge)/silicon dioxide (SiO₂) interface model was established by using Materials studio software. Dynamic simulations of the heat treatment process for pn junctions in germanium-core fibers were conducted at different temperatures, specifically 500 ℃, 600 ℃, 660 ℃, 700 ℃, 727 ℃, and 827 ℃. The mean square displacement of the dopant atoms Al, diffusion coefficients, and cellular parameter changes of the Al-doped Ge/SiO₂ structure were analyzed at different temperatures. It was observed that the diffusion of Al atoms in the structure decreased progressively at 500 ℃, 600 ℃, and 660 ℃, while it increased at 700 ℃, 727 ℃, and 827 ℃. This phenomenon preliminarily indicates the displacements of dopant atoms induced by the temperature increase lead to changes of atomic positions in the crystals and ultimately result in the alteration of the properties of pn junctions within germanium-core fibers. Moreover, the stress variations and stress-strain relationship of the Al-doped Ge/SiO₂ structure were investigated. It was found that the internal stress in germanium-core optical fiber is manifested as tensile stress and the trend of tensile stress with temperature is consistent with the changes in structural cell parameters. Additionally, the stress-strain relationship was observed to be proportional at different temperatures. For temperatures of 500 ℃, 600 ℃, 660 ℃, 700 ℃, and 727 ℃, the elastic modulus decreases as the temperature rises; however, when the temperature reached 827 ℃, the elastic modulus increased. These findings provide important insights for optimizing the heat treatment modification of junction-type devices in semiconductor-core fibers.

Routing Optimization for LEO Satellite Networks Based on MAODV algorithm

KANG Yujie, SHI Jianfeng, LI Baolong

2026, 44(2):  208-223.  doi:10.3969/j.issn.0255-8297.2026.02.003

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To address the challenges of frequent link disruptions, long route recovery delays, and poor reliability of traditional single-path routing protocols in low earth orbit satellite networks, this paper constructs a network model that integrates orbital dynamics with link-state awareness and proposes an adaptive routing optimization method based on the multipath ad hoc on-demand distance vector (MAODV) protocol. The proposed method employs an on-demand route discovery mechanism to simultaneously acquire and maintain multiple feasible paths during distance-vector propagation, thereby forming a candidate path set. By further considering link availability fluctuations induced by orbital dynamics, cross-layer parameters—including hop count, link quality, node load, and residual energy—are collected in real time to construct a comprehensive path cost metric, which is used to select both primary and backup paths that are better suited to highly dynamic topologies. A joint simulation environment is established using MATLAB and STK (Satellite Tool Kit), and comparative experiments with representative routing protocols are conducted to evaluate the performance of the proposed method. Simulation results demonstrate that, under identical constellation and traffic conditions, the proposed method achieves average throughput improvements of approximately 4.29%, 4.77%, and 3.82% over the ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), and optimized link state routing (OLSR) protocols, respectively. Moreover, the average end-to-end delay is significantly reduced compared with these baseline protocols, thereby confirming the effectiveness of the proposed approach in dynamic LEO satellite network scenarios.

Intelligent Information Processing

Reversible Data Hiding Algorithm Using Structural Similarity Index Measure

GUO Kexin, XIANG Shijun

2026, 44(2):  224-233.  doi:10.3969/j.issn.0255-8297.2026.02.004

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The distortions in reversible data hiding (RDH) include pixel distortion and structural distortion. With the high sensitivity of the human visual system to structural distortions in images considered, this paper adopted the structural similarity index measure (SSIM) as the evaluation metric for RDH. First, by analyzing the theoretical gain relationship between peak signal-to-noise ratio (PSNR) and SSIM, the dynamic and simultaneous evaluation of the two metrics was realized. Subsequently, a high-SSIM RDH method based on texture region prioritization was proposed. Image preprocessing was performed by dividing the carrier image into four independent pixel sets, and texture regions were accurately located. Data was then embedded in descending order of background complexity. Experimental results show that the strategy of prioritizing data embedding in texture regions reduces the structural distortion of images. At the same embedding rate, the SSIM value is improved, and the visual quality of images is enhanced.

Multi-view Joint Adjustment Registration Method for Images and Point Clouds with Line Feature Constraints

CHEN Lu, WANG Anni, LAN Ziyu, XU Hui, ZHANG Penglin

2026, 44(2):  234-249.  doi:10.3969/j.issn.0255-8297.2026.02.005

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The registration of images and laser point clouds is a key technology for 3D scene reconstruction, providing critical scientific support for origin analysis of explosion accidents and holding broad application prospects in fields such as autonomous driving and disaster origin tracing. However, the spatial scale and geometric feature differences between 2D and 3D data collected by heterogeneous sensors pose challenges to the refined registration of image and point cloud modalities. To this end, this paper proposed a multi-view joint adjustment registration method based on line feature constraints. Firstly, line features were extracted from both images and point cloud data for coarse registration. Then, based on the standard perspective-n-point (PnP) model, constraints of directional consistency and orthogonal consistency of line features were introduced. By aiming to minimize the error function across multiple views, the transformation parameter solution was transformed into a nonlinear least squares problem for iterative optimization, ultimately achieving the accurate registration of images and point clouds. This process does not require 2D-3D projection transformations or scale conversions, thus preventing the introduction of projection errors. Comparative experiments show that the proposed multi-view joint adjustment method with line feature constraints can significantly improve the registration accuracy of image and point cloud modalities, reducing registration errors by over 60% compared to the single-view standard PnP model.

Real-Time Semantic Segmentation Based on Composite Three-Branch and Deep Feature Encoding

LEI Xiaochun, PAN Yiwei, ZHANG Yongya, JIANG Zetao, LI Mengtong

2026, 44(2):  250-265.  doi:10.3969/j.issn.0255-8297.2026.02.006

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To address the problems of small-object segmentation errors and holes in large-object segmentation results in scenes with significant differences in object sizes in real-time semantic segmentation, this paper proposed a real-time semantic segmentation algorithm based on a composite three-branch and deep feature encoding, consisting of a composite three-branch module (CTBM), a deep feature encoding module (DFEM), and a dual-branch multi-layer perceptron (DBMLP). The CTBM used a dual-layer multi-scale feature extraction and fusion strategy to comprehensively extract information from different perspectives, enabling the model to perceive the global relationships between features better and reduce the holes in the large-object segmentation results. The DFEM enhanced the model’ s ability to express deep features through encoding methods, better perceived the semantic information of small objects, and improved the segmentation accuracy of small objects. The DBMLP effectively integrated multi-scale semantic information by utilizing both global and local features, resulting in smoother edges and more accurate contours in segmentation results. Evaluation results on the Cityscapes and ADE20K datasets have shown that the algorithm not only meets real-time speed requirements but also achieves mIoU of 74.2% and 40.4% at 42.6 FPS and 45.3 FPS, respectively, significantly outperforming other real-time semantic segmentation algorithms.

Human Action Recognition in Infrared Images Based on Median-Guided Multi-scale Feature Fusion

YUAN Shuai, YU Lei, YAO Tian, XIONG Bangshu

2026, 44(2):  266-281.  doi:10.3969/j.issn.0255-8297.2026.02.007

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Conventional deep learning models exhibit limited recognition performance in infrared images, primarily because the lack of discriminative features makes them difficult to effectively distinguish similar behaviors. To solve this problem, a novel infrared image-oriented human action recognition method based on median-guided multi-scale feature fusion was proposed. First, an information modeling mechanism that integrated median-enhanced attention and multi-scale feature comparison was constructed. This mechanism finely modeled the differences between feature hierarchies, guiding the network to focus on the key feature regions that distinguished different action categories, therefore breaking through the limitation of traditional methods that relied on global features for classification. Second, a median-enhanced spatial and channel attention module was designed, which solved the problem that traditional Siamese networks were difficult to accurately focus on the key regions of the human body in infrared action images due to the lack of explicit positional information in deep features. Finally, a multi-scale feature fusion module was proposed, which could effectively fuse multi-scale features, enhance the expression ability of action details and structural information in infrared images, strengthen the model’s ability to capture subtle action changes, and reduce the misjudgment rate caused by information loss and background interference. Experimental results show that the recognition accuracy of the proposed method is superior to that of existing mainstream methods in multiple datasets such as infrared splicing, PUB, and VAIS, which fully demonstrates the effectiveness and advancement of this method.

Artificial Intelligence Technology and Applications

Multi-Dimensional Parallel Blockchain Suitable for IoT Environment

SI Xueming, MA Shuosen, YAO Zhongyuan

2026, 44(2):  282-296.  doi:10.3969/j.issn.0255-8297.2026.02.008

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As the application of blockchain in the internet of things scenario continues to gain popularity, it becomes increasingly difficult to meet the real-time transaction processing requirements of IoT devices, and the issue of low blockchain throughput becomes increasingly prominent. Existing solutions often rely on technologies such as blockchain sharding, block pipeline, and concurrency control to enhance throughput, but these solutions do not significantly improve throughput in some conflict scenarios. This paper analyzed the characteristics of smart contract transactions of hotspots and the applicable scenarios of various concurrency control technologies. Relying on blockchain sharding technology, it constructed shards for smart contracts of hotspots with high conflict rates and shards for ordinary contracts with low conflict rates. It adapted corresponding concurrency control technologies for the two types of shards, fully leveraging the advantages of each technology to ultimately achieve efficient multi-dimensional parallelism between shards and transactions. Additionally, this paper proposed a formula for evaluating the performance of trusted sensors, assigning adaptive data transmission tasks based on different sensor capabilities, thereby enhancing overall data transmission efficiency. Experimental results have demonstrated that the improvement of throughput of the proposed solution is significant. The solution improves performance more efficiently compared to other single-dimensional parallelism solutions.

A Fast Algorithm for Matrix Multiplication Based on “Regularization-Filtering-Resampling”

DING Guangtai, LIU Tong, ZHI Xiaoli, WU Pin, TONG Weiqin

2026, 44(2):  297-315.  doi:10.3969/j.issn.0255-8297.2026.02.009

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Focusing on the trade-off in terms of speed, accuracy and efficiency between the exact and approximate algorithms for large-scale matrix multiplication, this paper proposed a fast algorithm for dense matrix multiplication employing regularization, filtering, and resampling techniques. Based on the sampling theorem, a regularization relationship between the matrix and its corresponding analog function was established, and then filtering and resampling stages were introduced to achieve the trade-off mechanism between the exact algorithm and the approximate algorithm. In pursuit of higher algorithmic efficiency, the applicable scope and conditions of the algorithm were investigated, especially the relationship between the algorithm accuracy and the statistical characteristics of the matrix data. Data experiments were conducted using matrices generated by methods such as independent and identically distributed random number generators. The results indicate that the algorithm achieves its trade-off objectives.

Breeding Score Algorithm for Succulent Leaves based on Multi-channel Feature Fusion of Area, Contour, and Color

ZHANG Lei, LI Taoyuan, LI Mingfei, DENG Haimin, XIE Cheng

2026, 44(2):  316-329.  doi:10.3969/j.issn.0255-8297.2026.02.010

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To enhance the accuracy of image scoring and consistency in screening for the propagation potential of succulent leaves, we propose a breeding scoring algorithm based on multi-channel feature fusion of “surface-outline-color”. This algorithm first employs an image segmentation model to automatically detect and locate areas of succulent leaves with breeding potential within the original image, completing preliminary screening. Subsequently, it extracts leaf area, contour, color, and high-level semantic features to construct a unified multidimensional fusion vector, which is then input into a scoring model to generate breeding scores. Among these, the area feature reflects developmental fullness, the contour feature indicates morphological regularity, and the color feature measures health status. Meanwhile, semantic features extracted by a pre-trained visual model supplement deeper growth patterns and semantic associations that traditional metrics struggle to capture, further enhancing the model’s discriminative power and generalization capability. Experimental results demonstrate that this method significantly improves the efficiency and accuracy of leaf screening. Compared to traditional methods, the Pearson correlation coefficient and area under the curve improved by an average of 0.093 8 and 0.065 3, respectively, across multiple succulent datasets. The mean squared logarithmic error decreased by approximately 0.012 1, demonstrating enhanced accuracy and robustness. This effectively supports the intelligent breeding of succulent plants.

Parallel TimesNet-Informer Model for Process Quality Prediction Using STL Decomposition and Crested Porcupine Optimizer

LIANG Xinyan, SUN Jingyun, CAI Guojing, CHEN Hailong

2026, 44(2):  330-344.  doi:10.3969/j.issn.0255-8297.2026.02.011

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In process manufacturing, quality prediction is particularly challenging due to high levels of noise, complex nonlinear dynamics, and multiscale temporal dependencies in process data. To address these issues, this paper proposes a parallel TimesNet-Informer deep learning prediction model that integrates seasonal-trend decomposition using loess (STL) with the crested porcupine optimizer (CPO). First, the STL method is employed to decompose the original time series into trend, seasonal, and residual components, thereby extracting multiscale temporal features. Second, the CPO algorithm is utilized to auto-matically optimize decomposition parameters and model hyperparameters in a data-driven manner. A parallel architecture is designed that combines the strength of TimesNet in capturing periodic and local features with Informer’s superior capability in modeling long-sequence dependencies, enabling accurate fitting and prediction of complex process quality. The model is validated on real-world data from a process manufacturing production line. Experimental results demonstrate that the proposed model outperforms other comparative models across all evaluation metrics, achieving a prediction accuracy (R²) of 0.979 8. This provides an effective solution for accurate quality prediction in process manufacturing.