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LKVHAN: Multi-Scale Large Kernel Vertical-Horizontal Attention Network for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12328-12342页

作者： Liu, Xun Ng, Alex Hay-Man Liao, Xuejiao Lei, Fangyuan Ren, Jinchang Ge, Linlin Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong University of Technology Department of Surveying Engineering Guangzhou510006 China Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property Big Data Guangzhou510665 China Robert Gordon University National Subsea Centre AberdeenAB21 0BH United Kingdom School of Civil and Environmental Engineering Sydney2052 Australia

Among deep learning-based hyperspectral image (HSI) classification models, convolutional neural networks (CNNs), Transformers, Mamba, and large kernel CNNs (LKCNNs) models have been widely explored for HSI classification. Nonetheless, these models suffer from several challenges: for example, 1) CNNs have a weak learning ability in capturing global information between land covers, due to their limited receptive field derived from small kernel convolutions;2) Transformers face quadratic computational complexity introduced by their self-attention mechanisms;and 3) LKCNNs require further enhancement in extracting global features, owing to the insufficient size of their receptive fields. To tackle these limitations, we propose a novel multi-scale large kernel vertical-horizontal attention network (LKVHAN) for HSI classification. The proposed LKVHAN consists of a 1 x 1 convolution module and a multi-scale large kernel vertical-horizontal attention-based convolution (MSLKVHAC). The 1 x 1 convolution module is designed to facilitate band reduction, noise suppression, and spectral feature learning. Furthermore, the MSLKVHAC, leveraging a large vertical kernel size of 17 x 1 and a large horizontal kernel size of 1 x 17, extracts both local and global spatial features by incorporating a vertical attention-based convolution module (VACM) and a horizontal attention-based convolution module (HACM). Extensive experimental results demonstrate that the proposed LKVHAN significantly outperforms ten state-of-the-art approaches across four widely used HSI datasets. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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PyionNet: Pyramid Progressive Cross-Fusion Network for Joint Classification of Hyperspectral and LiDAR data

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12042-12058页

作者： Pan, Haizhu Zhang, Quanxiu Ge, Haimiao Liu, Moqi Shi, Cuiping Qiqihar University College of Computer and Control Engineering Qiqihar161000 China Qiqihar University Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar161000 China Xi'an Jiaotong University School of Information and Communications Engineering Shanxi Xi'an710049 China Huzhou University College of Information Engineering Huzhou313000 China

Joint hyperspectral (HS) and Light Detection and Ranging (LiDAR) data for land cover classification have received widespread attention. Nevertheless, existing methods often struggle with multi-scale feature extraction and multi-source feature fusion. To address these issues, this paper proposes a pyramid progressive cross-fusion network, named PyionNet, for joint classification of HS and LiDAR data. Technically, the proposed PyionNet incorporates a pyramid multi-scale extraction (PME) module cascaded with a progressive cross-fusion (PCF) module, which are designed to extract multi-scale features and fuse multi-source features, respectively. Specifically, the PME module employs pyramid spectral and spatial convolutional blocks to extract and aggregate features from HS and LiDAR data, respectively, then uses an expansion-compression strategy to eliminate redundant information, and thus achieve a compact multi-scale feature representation. Moreover, the PCF module utilizes the transformer architecture and introduces two innovative feature fusion blocks, i.e., the discrepancy feature fusion (DFF) block and the common feature fusion (CFF) block, to progressively integrate multi-source and multi-scale features extracted by the PME module. The DFF block is designed to integrate discrepancy features between HS and LiDAR data to enhance complementary information. Subsequently, the CFF block focuses on fusing common features to progressively enrich the fused representation of the DFF block and further enhance the representation power. Experiments on three real-world datasets demonstrate that the proposed PyionNet outperforms state-of-the-art methods in both qualitative and quantitative results. © 2008-2012 IEEE.

关键词： Feature Selection

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou University The Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues such as driving safety and multi-vehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model's ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. Firstly, we propose a Curiosity Proximal Policy Optimisation (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical GRU and future GRU as input layers. The target's historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle's behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based Dynamic Group Practical Byzantine Fault Tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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Transformer-based multi-level attention integration network for video saliency prediction

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Multimedia Tools and Applications 2025年第13期84卷 11833-11854页

作者： Tan, Rui Sun, Minghui Liang, Yanhua Software College Jilin University Qianjin Street Jilin Changchun130012 China College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

Most existing models for video saliency prediction heavily rely on 3D convolutional operations to extract spatio-temporal features. However, it is worth noting that 3D convolution produces a local receptive field, which may struggle to capture long-range spatio-temporal dependencies effectively. To compensate for such shortage, this paper introduces a novel approach called the Transformer-based Multi-level Attention Integration Network (TMAI-Net) for video saliency prediction. TMAI-Net is designed as a two-stream encoder-decoder model, carefully integrating multi-level features of semantic information. Our model incorporates a Multi-level Interactive Attention(MLIA) module and a Transformer, both implemented based on self-attention mechanism, which are placed at different levels of the model to capture long-range spatio-temporal feature dependencies. Additionally, our model operates on input video frames and attentional patches, allowing the Transformer module to capture structural similarities between related objects in global features and attention features. This, in turn, enables the model to allocate increased attention to salient areas. The efficacy of our proposed approach is validated through extensive experiments conducted on three widely recognized benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Research on Spares Optimization Method of the Complex Electronic Information System Based on Monte Carlo Simulation

Research on Spares Optimization Method of the Complex Electr...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Lu, Yanchun Yang, Hongqi Lei, Ting Pan, Yong CEPREI Guangzhou510610 China Key Laboratory of Industrial Equipment Quality Big Data MIIT Guangzhou510610 China Guangdong Provincial Engineering Technology Research Center for Quality Big Data of High-end Equipment Guangzhou510610 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In order to solve the problems such as high maintenance cost and difficult spare parts optimization of the complex electronic information system, considering advantages of the generalized stochastic Petri Net (GSPN) in characterization and analysis of dynamic change process of complex system, the process and method of spare parts optimization based on Monte Carlo simulation are studied to achieve the best balance between the rate of mission success and the cost of maintenance. © 2025 SPIE.

关键词： Stochastic systems

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Efficiency Boosting of Secure Cross-Platform Recommender Systems Over Sparse data

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2146-2160页

作者： Ren, Hao Xu, Guowen Zhang, Tianwei Ning, Jianting Huang, Xinyi Li, Hongwei Lu, Rongxing School of Cyber Science and Engineering Sichuan University Chengdu610017 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education Chengdu610017 China Cyber Science Research Institute Sichuan University Chengdu610017 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Cyber Science and Engineering Wuhan University Wuhan430072 China Faculty of Data Science City University of Macau 999078 China Colledge of Cyber Security Jinan University Guangzhou510632 China School of Computer Science University of New Brunswick New BrunswickE3B 5A3 Canada

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the problem of secure sparse matrix multiplication for cross-platform RSs. Two fundamental and critical issues are addressed: preserving the training data privacy and breaking the data silo problem. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (HE) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the HE acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10×10× and 2.8×2.8×. They also attain up to 15×15× and 2.3×2.3× communication reduction without accuracy loss. © 2024 IEEE.

关键词： Differential privacy

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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Chirp-free hundred-picosecond pulse fiber laser based on carbon nanotubes mode-locker

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Optics Express 2025年第7期33卷 14850-14857页

作者： Li, Weixi Jiang, Kailin Dai, Lilong Huang, Qianqian Zou, Meng Luo, Siyu Chen, Siyu Song, Yingxiong Liu, Yunqi Yan, Zhijun Mou, Chengbo Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access System Huazhong University of Science and Technology Wuhan430074 China

We demonstrate a narrow bandwidth passively erbium-doped mode-locked fiber laser based on single-wall carbon nanotubes (SWCNTs). The center wavelength is 1546.61 nm. The strong filtering effect and low saturable absorber (SA) modulation depth provide advantages for a long pulse duration of 323 ps. An overall spectral bandwidth of 1.3 GHz (0.01 nm) is measured by the optical heterodyne method, representing the narrowest spectral width based on SWCNT under a single filter structure. The obtained spectral width is very close to the transform-limited bandwidth of 8 pm, indicating that the pulse is chirp-free. Meanwhile, the generation of beat signals reveals the strong coherence between pulses. The repetition frequency of the laser is 7.8 MHz, and the number of longitudinal mode locks is only 166, which is the minimum number of longitudinal mode-locking in passively mode-locked laser based on material SA. At the same time, numerical simulations further support our experimental results and reveal the pulse evolution, providing evidence that SA and filter are the key factors in the generation of chirp-free long pulse. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Saturable absorbers

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Distributed predefined-time robust adaptive control design for attitude consensus of multiple spacecraft

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Advances in Space Research 2025年第10期75卷 7473-7486页

作者： Yao, Qijia Li, Qing Xie, Shumin Jahanshahi, Hadi School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing100083 China School of Mechanics and Civil Engineering and Architecture Northwestern Polytechnical University Xi'an710072 China Institute of Electrical and Electronics Engineers TorontoM5V3T9 Canada

This article investigates the distributed predefined-time attitude consensus of multiple rigid spacecraft under inertia uncertainties and external perturbations over directed communication topology. First, a distributed predefined-time observer is constructed for each follower spacecraft to estimate the virtual leader's attitude, velocity, and acceleration. Then, based on the recovered information, a predefined-time nonsingular terminal sliding mode (NTSM) controller is developed for each follower spacecraft to fulfill the local attitude and velocity tracking. The predefined-time NTSM controller is strongly robust against uncertainties and perturbations but owns the undesirable chattering phenomena. To overcome such problem, an adaptive version of the predefined-time NTSM controller is carried out by incorporating with the parametric adaptation mechanism to identify the total disturbance. In this manner, the predefined-time adaptive NTSM controller has no chattering phenomenon and can maintain the high tracking accuracy simultaneously. Lastly, comparative simulations verify and highlight the main results. © 2025 COSPAR

关键词： Sliding mode control

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Transcriptome analysis reveals PTBP1 as a key regulator of circRNA biogenesis

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BMC Biology 2025年第1期23卷 1-17页

作者： Wang, Mohan Zheng, Shanshan Zhang, Yan Zhang, Jingwen Lai, Fuming Zhou, Cong Zhou, Qiangwei Li, Xingwang Li, Guoliang National Key Laboratory of Crop Genetic Improvement Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan 430070 China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming Technology for Agricultural Animals 3D Genomics Research Center College of Informatics Huazhong Agricultural University Wuhan 430070 China

Background: Circular RNAs (circRNAs) are a class of non-coding RNAs generated through back splicing. High expression of circRNAs is often associated with numerous abnormal cellular biological processes. However, the regulatory factors of circRNAs are not fully understood. Results: In this study, we identified PTBP1 as a crucial regulator of circRNA biogenesis through a comprehensive analysis of the whole transcriptome profiles across 10 diverse cell lines. Knockdown of PTBP1 led to a significant decrease in circRNA expression, concomitant with a distinct reduction in cell proliferation. To investigate the regulatory mechanism of PTBP1 on circRNA biogenesis, we constructed a minigene reporter based on SPPL3 gene. The results showed that PTBP1 can bind to the flanking introns of circSPPL3, and the mutation of PTBP1 motif impedes the back splicing of circSPPL3. Subsequently, to demonstrate that this observation is not an exception, the comprehensive regulatory effects of PTBP1 on circRNAs were confirmed by miniGFP, reflecting the necessity of the binding site in the flanking introns. Analysis of data from clinical samples showed that both PTBP1 and circRNAs exhibited substantial upregulation in acute myeloid leukemia, further demonstrating a potential role for PTBP1 in promoting circRNA biogenesis under in vivo conditions. Competitive endogenous RNA (ceRNA) network revealed that PTBP1-associated circRNAs participated in biological processes associated with cell proliferation. Conclusions: In summary, our study is the first to identify the regulatory effect of PTBP1 on circRNA biogenesis and indicates a possible link between PTBP1 and circRNA expression in leukemia. © The Author(s) 2025.

关键词： Biogenesis Circular RNAs PTBP1 RNA binding proteins Transcriptome

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