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学者姓名：陈日清
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Correspondence pruning aims to identify inliers from an initial set of correspondences with a low inlier ratio. Current Graph Neural Networks (GNNs) based correspondence pruning approaches suffer from feature over-smoothing during information propagation, making it difficult to distinguish inliers from outliers. In addition, Transformer-based methods can model long-range dependencies, but their quadratic complexity limits computational efficiency. To address these issues, we propose MatchMamba, a dual-view correspondence pruning network based on a selective state space model, Mamba. MatchMamba combines the strengths of GNNs and Mamba, enhancing local feature extraction while modeling global context with appropriate complexity. Specifically, to overcome Mamba's limitations in correspondence pruning, such as the lack of local context and unidirectional modeling, we introduce the Cluster Sampling Spatial Mamba (CSSM) block and Correspondence Flip Bidirectional Mamba (CFBM) block. CSSM captures fine-grained local context through the implicit soft assignment and mitigates GNN's over-smoothing using Mamba's selective mechanism. CFBM block leverages Mamba's efficient long-sequence modeling by constructing a pseudo-sequential structure through clustering. It applies forward and backward scanning to enable each correspondence to fully capture contextual information from others, achieving global context modeling with appropriate computational cost. Extensive experiments demonstrate that MatchMamba outperforms current state-of-the-art methods on several challenging tasks. The code is available at https://github.com/Mrwyb/MatchMamba
Keyword ：
Complexity theory Complexity theory Computational efficiency Computational efficiency Computational modeling Computational modeling Context modeling Context modeling Correspondence pruning Correspondence pruning Data models Data models Deep learning Deep learning Feature extraction Feature extraction Forestry Forestry graph neural networks (GNNs) graph neural networks (GNNs) image matching image matching Mathematical models Mathematical models selective state space model selective state space model Transformer Transformer Transformers Transformers
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| GB/T 7714 | Wu, Yubin , Li, Xiaojie , Chen, Hao et al. MatchMamba: Correspondence Pruning via Selective State Space Model [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2026 , 36 (1) : 161-174 . |
| MLA | Wu, Yubin et al. "MatchMamba: Correspondence Pruning via Selective State Space Model" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 36 . 1 (2026) : 161-174 . |
| APA | Wu, Yubin , Li, Xiaojie , Chen, Hao , Yang, Changcai , Wei, Lifang , Chen, Riqing . MatchMamba: Correspondence Pruning via Selective State Space Model . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2026 , 36 (1) , 161-174 . |
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Movable antennas (MAs) are a promising technology to achieve a significant enhancement in rate for future wireless networks. The pioneering investigation on near-field directional modulation design for a reconfigurable intelligent surface (RIS)-assisted MA system is presented, with the base station equipped with a MA array. To maximize the secrecy sum rate (Max-SSR) with hardware impairments (HWIs) and imperfect channel state information (CSI), which involves a joint optimization of beamforming vectors for confidential messages and artificial noise (AN), power allocation factors, phase shift matrices, MA positions, and receive beamforming vectors. Firstly, the transmit beamforming vectors and phase shift matrices are iteratively optimized, leveraging leakage theory and phase alignment techniques. Then, two novel algorithms for discrete MA positioning are proposed, respectively, employing uniform and compressed sensing (CS)-based non-uniform grouping strategies. Subsequently, the AN is considered and designed as the additional energy required for zero-space projection, and the receive beamforming vector is derived using the minimum mean square error (MMSE) method. The proposed algorithms have low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms. Under HWIs and imperfect CSI, the proposed algorithm can achieve a 28% enhancement in SSR performance while reducing the number of antennas by 37.5% compared to traditional fixed-position antenna (FPA) systems.
Keyword ：
Array signal processing Array signal processing Broadband antennas Broadband antennas Compressed sensing Compressed sensing Costs Costs directional modulation directional modulation Directive antennas Directive antennas Hardware Hardware movable antenna movable antenna near-field near-field Noise measurement Noise measurement reconfigurable intelligent surface reconfigurable intelligent surface Reconfigurable intelligent surfaces Reconfigurable intelligent surfaces Security Security Symbols Symbols Vectors Vectors
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| GB/T 7714 | Li, Maolin , Shu, Feng , Si, Yuan et al. Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems With Hardware Impairments [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2026 , 13 : 3944-3959 . |
| MLA | Li, Maolin et al. "Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems With Hardware Impairments" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 13 (2026) : 3944-3959 . |
| APA | Li, Maolin , Shu, Feng , Si, Yuan , Chen, Riqing , Pan, Cunhua , Wu, Yongpeng . Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems With Hardware Impairments . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2026 , 13 , 3944-3959 . |
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Addressing the challenges of variable target morphology, small critical regions, and complex background interference in eggplant picking point detection within complex agricultural scenarios, this study proposes MDAD-YOLO (Multi-dimensional Attention and DySample YOLO), a detection model improved based on the YOLOv10n-pose framework. First, the model's cross-dimensional perception ability for fruits and picking points is enhanced by integrating the collaborative mechanism of regional receptive field attention with channel-space joint attention. Next, within the Neck structure, coordinate attention is incorporated to optimize the spatial localization accuracy of fine-grained features, enhancing sensitivity to minute regions such as the fruit stem apex. Additionally, dynamic pixel reorganization is applied to enhance feature map reconstruction details, addressing the detail loss caused by traditional interpolation methods. Finally, cascading adaptive fine-grained channel attention with position-sensitive attention enables multi-level modeling of channel dependencies and collaborative spatial context enhancement. Through a seven-tier validation framework, the model's effectiveness, robustness, and generalizability have been comprehensively demonstrated. Experimental results show that the model achieves 93.6% mAP@50 for object detection, 94.7% mAP@50 and 92.1% mAP for keypoints detection, and an average pixel Euclidean distance error of 19.41 on the self-built eggplant dataset, outperforming YOLOv12 and other high-performance models. Additionally, cross-crop experiments on the pepper dataset showed a 2.1% and 2.7% improvement in mAP for object and picking point detection, respectively, compared to the baseline model, confirming its cross-crop robustness. This study reveals the synergistic enhancement of dynamic upsampling and attention mechanisms in agricultural object detection, providing new insights for lightweight model design in complex scenarios.
Keyword ：
Dynamic upsampling Dynamic upsampling MDAD-YOLO MDAD-YOLO Multi-dimensional attention mechanism Multi-dimensional attention mechanism Object detection Object detection Picking point detection Picking point detection
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| GB/T 7714 | Huang, Yikun , Li, Gang , Li, Jinghu et al. Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2026 , 240 . |
| MLA | Huang, Yikun et al. "Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 240 (2026) . |
| APA | Huang, Yikun , Li, Gang , Li, Jinghu , Chen, Hao , Lin, Hefei , Yang, Changcai et al. Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2026 , 240 . |
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In this paper, channel estimation (CE) for uplink hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub blocks is derived, and the optimal number of sub blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramér-Rao lower bound is derived to evaluate the proposed algorithm’s performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Channel estimation Channel estimation Errors Errors Mean square error Mean square error Optimization Optimization
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| GB/T 7714 | Wang, Yan , Shu, Feng , Wang, Xianpeng et al. AI-empowered Channel Estimation for Block-based Active IRS-enhanced Hybrid-field IoT Network [J]. | arXiv , 2025 . |
| MLA | Wang, Yan et al. "AI-empowered Channel Estimation for Block-based Active IRS-enhanced Hybrid-field IoT Network" . | arXiv (2025) . |
| APA | Wang, Yan , Shu, Feng , Wang, Xianpeng , Chen, Minghao , Chen, Riqing , Yang, Liang et al. AI-empowered Channel Estimation for Block-based Active IRS-enhanced Hybrid-field IoT Network . | arXiv , 2025 . |
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Movable antennas (MAs) are a promising technology to achieve a significant enhancement in rate for future wireless networks. The pioneering investigation on near-field directional modulation design for a reconfigurable intelligent surface (RIS)-assisted MA system is presented, with the base station equipped with a MA array. To maximize the secrecy sum rate (Max-SSR) with hardware impairments (HWIs) and imperfect channel state information (CSI), which involves a joint optimization of beamforming vectors for confidential messages and artificial noise (AN), power allocation factors, phase shift matrices, MA positions, and receive beamforming vectors. Firstly, the transmit beamforming vectors and phase shift matrices are iteratively optimized, leveraging leakage theory and phase alignment techniques. Then, two novel algorithms for discrete MA positioning are proposed, respectively, employing uniform and compressed sensing (CS)-based non-uniform grouping strategies. Subsequently, the AN is considered and designed as the additional energy required for zero-space projection, and the receive beamforming vector is derived using the minimum mean square error (MMSE) method. The proposed algorithms have low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms. Under HWIs and imperfect CSI, the proposed algorithm can achieve a 28% enhancement in SSR performance while reducing the number of antennas by 37.5% compared to traditional fixed-position antenna (FPA) systems. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Beamforming Beamforming Beam forming networks Beam forming networks Channel capacity Channel capacity Channel state information Channel state information Communication channels (information theory) Communication channels (information theory) Compressed sensing Compressed sensing Computational complexity Computational complexity Directive antennas Directive antennas Interlocking signals Interlocking signals Iterative methods Iterative methods Matrix algebra Matrix algebra Mean square error Mean square error MIMO systems MIMO systems Phase shift Phase shift Signal receivers Signal receivers Vectors Vectors Vector spaces Vector spaces
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| GB/T 7714 | Li, Maolin , Shu, Feng , Chen, Riqing et al. Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments [J]. | arXiv , 2025 . |
| MLA | Li, Maolin et al. "Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments" . | arXiv (2025) . |
| APA | Li, Maolin , Shu, Feng , Chen, Riqing , Pan, Cunhua , Wu, Yongpeng . Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments . | arXiv , 2025 . |
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Nuclear segmentation and classification play a pivotal role in pathological image analysis. However, it is often hindered by blurred nuclear boundaries and complex structures in digital pathology slides, caused by factors such as staining techniques and imaging methods, presenting a significant challenge for accurate segmentation and classification. To address this issue, we propose a novel and efficient approach for nuclear identification, termed Information Propagation with Multi-Granularity Morphology-Guided Network (IPMMG). IPMMG progressively captures edge morphology information from different network layers while simultaneously incorporating structural morphology features at multiple granularities. By explicitly propagating features related to both the edge and structure, our approach constrains semantic features to focus on the region of interest's contour, thereby alleviating the challenge of blurred morphology. Experiments on public datasets demonstrate that IPMMG achieves state-of-the-art performance in segmentation, as measured by Dice and IoU scores, while also attaining competitive results in classification with DQ, SQ, and PQ metrics. In particular, it excels in handling nuclei with blurred edges and complex structures. © 2025, The Authors. All rights reserved.
Keyword ：
Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Fan, Dawei , Li, Jun , Cai, Chengfei et al. Ipmmg: Information Propagation with Multi-Granularity Morphology-Guided for Nuclear Segmentation and Classification [J]. | SSRN , 2025 . |
| MLA | Fan, Dawei et al. "Ipmmg: Information Propagation with Multi-Granularity Morphology-Guided for Nuclear Segmentation and Classification" . | SSRN (2025) . |
| APA | Fan, Dawei , Li, Jun , Cai, Chengfei , Lin, Lihui , Chen, Riqing , Chen, Yanping et al. Ipmmg: Information Propagation with Multi-Granularity Morphology-Guided for Nuclear Segmentation and Classification . | SSRN , 2025 . |
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The accurate identification of high-quality correspondences is a prerequisite task in feature-based point cloud registration. However, it is extremely challenging to handle the fusion of local and global features due to feature redundancy and complex spatial relationships. Given that Gestalt principles provide key advantages in analyzing local and global relationships, we propose a novel Gestalt-guided Parallel Interaction Network via orthogonal geometric consistency (GPI-Net) in this paper. It utilizes Gestalt principles to facilitate complementary communication between local and global information. Specifically, we introduce an orthogonal integration strategy to optimally reduce redundant information and generate a more compact global structure for high-quality correspondences. To capture geometric features in correspondences, we leverage a Gestalt Feature Attention (GFA) block through a hybrid utilization of self-attention and cross-attention mechanisms. Furthermore, to facilitate the integration of local detail information into the global structure, we design an innovative Dual-path Multi-Granularity parallel interaction aggregation (DMG) block to promote information exchange across different granularities. Extensive experiments on various challenging tasks demonstrate the superior performance of our proposed GPI-Net in comparison to existing methods. The code will be released at https://github.com/gwk/GPI-Net. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Geometry Geometry
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| GB/T 7714 | Gu, Weikang , Han, Mingyue , Xue, Li et al. GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration [J]. | arXiv , 2025 . |
| MLA | Gu, Weikang et al. "GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration" . | arXiv (2025) . |
| APA | Gu, Weikang , Han, Mingyue , Xue, Li , Dong, Heng , Yang, Changcai , Chen, Riqing et al. GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration . | arXiv , 2025 . |
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Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface (IRS) has garnered significant attention. In this paper, an amplify-and-forward (AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio (SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance (HP) method and a low-complexity (LC) method. The HP method is based on the semidefinite relaxation and fractional programming (SDR-FP) algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter, generalized power iteration, and generalized Rayleigh-Ritz (WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0% rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.
Keyword ：
active elements active elements AF relay AF relay double-fading double-fading hybrid IRS hybrid IRS intelligent reflecting surface intelligent reflecting surface passive elements passive elements
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| GB/T 7714 | Wang, Xuehui , Li, Qingbo , Zhu, Wen et al. Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks [J]. | SCIENCE CHINA-INFORMATION SCIENCES , 2025 , 68 (4) . |
| MLA | Wang, Xuehui et al. "Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks" . | SCIENCE CHINA-INFORMATION SCIENCES 68 . 4 (2025) . |
| APA | Wang, Xuehui , Li, Qingbo , Zhu, Wen , Shu, Feng , Huang, Mengxing , Zhou, Fuhui et al. Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks . | SCIENCE CHINA-INFORMATION SCIENCES , 2025 , 68 (4) . |
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Incident response plays a pivotal role in mitigating the impact of cyber attacks. In recent years, the intensity and complexity of global cyber threats have grown significantly, making it increasingly challenging for traditional threat detection and incident response methods to operate effectively in complex network environments. While Large Language Models (LLMs) have shown great potential in early threat detection, their capabilities remain limited when it comes to automated incident response after an intrusion. To address this gap, we construct an incremental benchmark based on real-world incident response tasks to thoroughly evaluate the performance of LLMs in this domain. Our analysis reveals several key challenges that hinder the practical application of contemporary LLMs, including context loss, hallucinations, privacy protection concerns, and their limited ability to provide accurate, context-specific recommendations. In response to these challenges, we propose IRCopilot, a novel framework for automated incident response powered by LLMs. IRCopilot mimics the three dynamic phases of a real-world incident response team using four collaborative LLM-based session components. These components are designed with clear divisions of responsibility, reducing issues such as hallucinations and context loss. Our method leverages diverse prompt designs and strategic responsibility segmentation, significantly improving the system’s practicality and efficiency. Experimental results demonstrate that IRCopilot outperforms baseline LLMs across key benchmarks, achieving sub-task completion rates of 150%, 138%, 136%, 119%, and 114% for various response tasks. Moreover, IRCopilot exhibits robust performance on public incident response platforms and in real-world attack scenarios, showcasing its strong applicability. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Automation Automation Benchmarking Benchmarking Complex networks Complex networks Computer crime Computer crime Distributed computer systems Distributed computer systems Intrusion detection Intrusion detection Network security Network security
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| GB/T 7714 | Lin, Xihuan , Zhang, Jie , Deng, Gelei et al. IRCopilot: Automated Incident Response with Large Language Models [J]. | arXiv , 2025 . |
| MLA | Lin, Xihuan et al. "IRCopilot: Automated Incident Response with Large Language Models" . | arXiv (2025) . |
| APA | Lin, Xihuan , Zhang, Jie , Deng, Gelei , Liu, Tianzhe , Liu, Xiaolong , Yang, Changcai et al. IRCopilot: Automated Incident Response with Large Language Models . | arXiv , 2025 . |
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With the rapid development of the Internet of Things (IoT), robust and secure authentication among interconnected devices has become increasingly significant. Existing cryptographic methods, despite their effectiveness, face challenges in scalability, quantum vulnerability, and high computational demands, which are particularly problematic for resource-constrained IoT devices. This paper proposes a novel and lightweight authentication scheme for IoT devices that combines the decentralization of blockchain with the efficiency of lattice-based cryptography to address these security concerns. The proposed scheme employs a decentralized identity management model built on blockchain, eliminating vulnerable central points and enhancing system resilience. For user and device authentication, an efficient lattice-based protocol is introduced, utilizing simplified hash operations and matrix-vector multiplication for key negotiation and authentication. This approach significantly reduces both computational complexity and communication overhead compared to traditional methods such as ECC-based schemes. Specifically, at a 100-bit security level, our scheme achieves authentication and key agreement in approximately 257.401 mu s and maintains a communication cost of 1052 bits per authentication session. Comprehensive performance analyses demonstrate that the proposed scheme can withstand typical cryptographic attacks and offers advantages in quantum computing resistance. Additionally, the blockchain-based design ensures high scalability, making the scheme ideal for large-scale IoT deployments without performance degradation. Experimental results further validate the scheme's practical applicability in resource-constrained IoT environments, highlighting its superior computational response times and lower communication costs compared to existing IoT authentication solutions.
Keyword ：
Blockchain Blockchain Internet of things (ioT) Internet of things (ioT) Lattice-based cryptography Lattice-based cryptography Lightweight authentication Lightweight authentication
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| GB/T 7714 | Kuang, Yingpan , Wu, Qiwen , Chen, Riqing et al. Blockchain based lightweight authentication scheme for internet of things using lattice encryption algorithm [J]. | COMPUTER STANDARDS & INTERFACES , 2025 , 93 . |
| MLA | Kuang, Yingpan et al. "Blockchain based lightweight authentication scheme for internet of things using lattice encryption algorithm" . | COMPUTER STANDARDS & INTERFACES 93 (2025) . |
| APA | Kuang, Yingpan , Wu, Qiwen , Chen, Riqing , Liu, Xiaolong . Blockchain based lightweight authentication scheme for internet of things using lattice encryption algorithm . | COMPUTER STANDARDS & INTERFACES , 2025 , 93 . |
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