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学者姓名：陈日清
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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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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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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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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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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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Two-view correspondence learning is a fundamental task in computer vision for locating the same object across two different images, and its essence lies in capturing the context from the images. Recent studies employ the standard Convolutional Neural Network (CNN) as the core architecture to capture the context. However, the inherent nature of the CNN's local receptive field and pooling operations can result in the loss of certain semantic context. This can result in the CNN-based correspondence learning methods having an insufficient understanding of the global context, especially on image pairs including challenges like significant viewpoint changes, repetitive structures and weak textures. To address this issue and these challenges, we propose a novel correspondence learning method called Parallel Reinforcement Network (PRNet). Firstly, we design a reinforcement injection block, not only to dynamically refine feature weights by using channel attention mechanism, but also to preserve important details for alleviating over-smoothing issue by strengthening the network's capacity. Secondly, to alleviate the potential issue of overlooking the weak local context by CNN, we propose a parallel fusion block to integrate both shallow and deep features, preserving local details and enhancing global context. We evaluate the performance of the proposed PRNet on an outlier rejection task and a relative pose estimation task. The experimental results demonstrate the proposed PRNet exceeds several existing state-of-the-art methods in various challenging scenarios.
Keyword ：
Channel attention mechanism Channel attention mechanism Outlier rejection Outlier rejection Parallel fusion Parallel fusion Pose estimation Pose estimation Two-view correspondence Two-view correspondence
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| GB/T 7714 | Kang, Zheng , Lai, Taotao , Li, Zuoyong et al. PRNet: Parallel Reinforcement Network for two-view correspondence learning [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
| MLA | Kang, Zheng et al. "PRNet: Parallel Reinforcement Network for two-view correspondence learning" . | KNOWLEDGE-BASED SYSTEMS 310 (2025) . |
| APA | Kang, Zheng , Lai, Taotao , Li, Zuoyong , Wei, Lifang , Chen, Riqing . PRNet: Parallel Reinforcement Network for two-view correspondence learning . | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
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Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the It & ocirc; stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.
Keyword ：
Blockchain Blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data models Data models Games Games Interference Interference Internet of Vehicles Internet of Vehicles Security Security Sensors Sensors spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack stochastic evolutionary game stochastic evolutionary game Stochastic processes Stochastic processes Throughput Throughput Wireless sensor networks Wireless sensor networks
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| GB/T 7714 | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng et al. Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) : 2233-2250 . |
| MLA | Li, Fushuai et al. "Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game" . | IEEE INTERNET OF THINGS JOURNAL 12 . 2 (2025) : 2233-2250 . |
| APA | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng , Wang, Jun , Shu, Feng , Chen, Riqing . Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) , 2233-2250 . |
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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 development of smart agriculture, fruit picking robots have attracted widespread attention as one of the key technologies to improve agricultural productivity. Visual perception technology plays a crucial role in fruit picking robots, involving precise fruit identification, localization, and grasping operations. This paper reviews the research progress in the visual perception technology for fruit picking robots, focusing on key technologies such as camera types used in picking robots, object detection techniques, picking point recognition and localization, active vision, and visual servoing. First, the paper introduces the application characteristics and selection criteria of different camera types in the fruit picking process. Then, it analyzes how object detection techniques help robots accurately recognize fruits and achieve efficient fruit classification. Next, it discusses the picking point recognition and localization technologies, including vision-based 3D reconstruction and depth sensing methods. Subsequently, it elaborates on the adaptability of active vision technology in dynamic environments and how visual servoing technology achieves precise localization. Additionally, the review explores robot mobility perception technologies, focusing on V-SLAM, mobile path planning, and task scheduling. These technologies enhance harvesting efficiency across the entire orchard and facilitate better collaboration among multiple robots. Finally, the paper summarizes the challenges in current research and the future development trends, aiming to provide references for the optimization and promotion of fruit picking robot technology.
Keyword ：
agricultural robotics agricultural robotics intelligent fruit harvesting robots intelligent fruit harvesting robots object detection object detection visual perception visual perception visual servoing visual servoing V-SLAM V-SLAM
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| GB/T 7714 | Huang, Yikun , Xu, Shuyan , Chen, Hao et al. A review of visual perception technology for intelligent fruit harvesting robots [J]. | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
| MLA | Huang, Yikun et al. "A review of visual perception technology for intelligent fruit harvesting robots" . | FRONTIERS IN PLANT SCIENCE 16 (2025) . |
| APA | Huang, Yikun , Xu, Shuyan , Chen, Hao , Li, Gang , Dong, Heng , Yu, Jie et al. A review of visual perception technology for intelligent fruit harvesting robots . | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
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In digital pathology, nuclei segmentation is a critical task for pathological image analysis, holding significant importance for diagnosis and research. However, challenges such as blurred boundaries between nuclei and background regions, domain shifts between pathological images, and uneven distribution of nuclei pose significant obstacles to segmentation tasks. To address these issues, we propose an innovative Causal inference inspired Diversified aggregation convolution Network named CDNet, which integrates a Diversified Aggregation Convolution (DAC), a Causal Inference Module (CIM) based on causal discovery principles, and a comprehensive loss function. DAC improves the issue of unclear boundaries between nuclei and background regions, and CIM enhances the model's cross-domain generalization ability. A novel Stable-Weighted Combined loss function was designed that combined the chunk-computed Dice Loss with the Focal Loss and the Causal Inference Loss to address the issue of uneven nuclei distribution. Experimental evaluations on the MoNuSeg, GLySAC, and MoNuSAC datasets demonstrate that CDNet significantly outperforms other models and exhibits strong generalization capabilities. Specifically, CDNet outperforms the second-best model by 0.79% (mIoU) and 1.32% (DSC) on the MoNuSeg dataset, by 2.65% (mIoU) and 2.13% (DSC) on the GLySAC dataset, and by 1.54% (mIoU) and 1.10% (DSC) on the MoNuSAC dataset. Code is publicly available at https://github.com/7FFDW/CDNet.
Keyword ：
Causal inference Causal inference Digital pathology Digital pathology Feature fusion Feature fusion Nuclei segmentation Nuclei segmentation Spurious correlation Spurious correlation
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| GB/T 7714 | Fan, Dawei , Chen, Zhuo , Gao, Yifan et al. Multiplex aggregation combining sample reweight composite network for pathology image segmentation [J]. | ARTIFICIAL INTELLIGENCE IN MEDICINE , 2025 , 169 . |
| MLA | Fan, Dawei et al. "Multiplex aggregation combining sample reweight composite network for pathology image segmentation" . | ARTIFICIAL INTELLIGENCE IN MEDICINE 169 (2025) . |
| APA | Fan, Dawei , Chen, Zhuo , Gao, Yifan , Yu, Jiaming , Li, Kaibin , Wei, Yi et al. Multiplex aggregation combining sample reweight composite network for pathology image segmentation . | ARTIFICIAL INTELLIGENCE IN MEDICINE , 2025 , 169 . |
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