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MCFTNet: Multimodal Cross-layer Fusion Transformer network for Hyperspectral and LiDAR Data Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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Adaptive Sampling and Joint Semantic-Channel Coding under Dynamic Channel Environment

arXiv

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arXiv 2025年

作者： Qi, Zhiyuan Feng, Yulong Qin, Zhijin Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China ZTE Corporation Nanshan District Shenzhen518055 China

Deep learning enabled semantic communications are attracting extensive attention. However, most works normally ignore the data acquisition process and suffer from robustness issues under dynamic channel environment. In this paper, we propose an adaptive joint sampling-semantic-channel coding (Adaptive-JSSCC) framework. Specifically, we propose a semantic-aware sampling and reconstruction method to optimize the number of samples dynamically for each region of the images. According to semantic significance, we optimize sampling matrices for each region of the most individually and obtain a semantic sampling ratio distribution map shared with the receiver. Through the guidance of the map, high-quality reconstruction is achieved. Meanwhile, attention-based channel adaptive module (ACAM) is designed to overcome the neural network model mismatch between the training and testing channel environment during sampling-reconstruction and encoding-decoding. To this end, signal-to-noise ratio (SNR) is employed as an extra parameter input to integrate and reorganize intermediate characteristics. Simulation results show that the proposed Adaptive-JSSCC effectively reduces the amount of data acquisition without degrading the reconstruction performance in comparison to the state-of-the-art, and it is highly adaptable and adjustable to dynamic channel environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel coding

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Trust-Region-based Multi-Branch Machine Learning-Assisted Optimization for Ultra-Wideband Antenna

Trust-Region-based Multi-Branch Machine Learning-Assisted Op...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Qi Wu Weiqi Chen Haiming Wang Xin Xu Wei Hong Weishuang Yin SKL of mmWaves Southeast University Nanjing China R&D Department National Center of Technology Innovation for EDA Nanjing China Purple Mountain Laboratories Pervasive Communication Research Center Nanjina China SKL of Mobile Network and Mobile Multimedia Tech. ZTE Shanghai China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, a trust-region-based multi-branch machine learning-assisted optimization (TRMB-MLAO) method is proposed for ultra-wideband antenna design. By introducing techniques including trust-region design space constraint strategy, multi-branch lower confidence bound acquisition function and multi-point acquisition function composition into machine learning-assisted optimization scheme, the proposed method achieves better convergence speed and antenna performance compared with conventional algorithm. An ultra-wideband antenna is introduced to validate the effectiveness of the proposed method.

关键词： Machine learning algorithms Design methodology Optimization methods Europe Machine learning Ultra wideband antennas Antennas Convergence

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A Harmonic Current Self-Compensation Control Strategy of DC Active Power Filter

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Gaoxiang Wu, Huanchao Chen, Jining Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical En-gineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Guangxi Uni-versity Nanning530004 China

Due to the diversity of nonlinear loads, the harmonic frequency of the DC microgrid is unpredictable, which poses a challenge to traditional DC active power filters (DC-APF). Firstly, the harmonic compensation mechanism and characteristics of the DC-APF with classical control strategy are analyzed. For the classical DC-APF, the voltage control and harmonic extraction are independent of each other, which may lead to their conflict and reduce the harmonic compensation performance. To address this issue, a harmonic current self-compensation control strategy of DC-APF is proposed, which uses a first-order inertia controller in the voltage loop and can simultaneously achieve voltage control and harmonic extraction. Then, the mechanism, characteristics, and parametric design method of the proposed harmonic current self-compensation control are introduced in detail. Compared with the classical DC-APF, the proposed DC-APF does not require any harmonic extraction filter and has better harmonic current compensation performance. Finally, the proposed control strategy and theoretical analysis are verified by simulation and experiment. © 2025 IEEE. All rights reserved.

关键词： Active filters

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NBgossip:An Energy-Efficient Gossip Algorithm for Wireless Sensor networks

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Journal of Computer Science & technology 2008年第3期23卷 426-437页

作者：陆峰谢良镇郑国良钟伟豪 Center for Multimedia and Network Technology School of Computer EngineeringNanyang Technological University

Gossip-based algorithms for information dissemination have recently received significant attention for sensor and ad hoc network applications because of their simplicity and robustness. However, a common drawback of many gossip-based protocols is the waste of energy in passing redundant information over the network. Thus gossip algorithms need to be re-engineered in order to become applicable to energy constrained networks. In this paper, we consider a scenario where each node in the network holds a piece of information （message） at the beginning, and the objective is to simultaneously disseminate all information （messages） among all nodes quickly and cheaply. To provide a practical solution to this problem for ad hoc and sensor networks, NBgossip algorithm is proposed, which is based on network coding and neighborhood gossip. In NBgossip, nodes do not simply forward messages they receive, instead, the linear combinations of the messages are sent out. In addition, every node exchanges messages with its neighboring nodes only. Mathematical proof and simulation studies show that the proposed NBgossip terminates in the optimal O（n）-order rounds and outperforms the existing gossip-based approaches in terms of energy consumption incurred in spreading all the information.

关键词： data dissemination gossip network coding sensor networks

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UniUIR: Considering Underwater Image Restoration as An All-in-One Learner

arXiv

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arXiv 2025年

作者： Zhang, Xu Zhang, Huan Wang, Guoli Zhang, Qian Zhang, Lefei Du, Bo The Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China The Hubei Luojia Laboratory Wuhan China The National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The Horizon Robotics Beijing100083 China

Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes. To address this limitation, we propose a Universal Underwater Image Restoration method, termed as UniUIR, considering the complex scenario of real-world underwater mixed distortions as an all-in-one manner. To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module. This module enables each expert to identify distinct types of degradation and collaboratively extract task-specific priors while maintaining global feature representation based on linear complexity. Building upon this foundation, to enhance degradation representation and address the task conflicts that arise when handling multiple types of degradation, we introduce the spatial-frequency prior generator. This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts based on image content, thereby improving the accuracy of image restoration. Finally, to more effectively address complex, region-dependent distortions in UIR task, we incorporate depth information derived from a large-scale pre-trained depth prediction model, thereby enabling the network to perceive and leverage depth variations across different image regions to handle localized degradation. Extensive experiments demonstrate that UniUIR can produce more attractive results across qualitative and quantitative comparisons, and shows strong generalization than state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image reconstruction

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MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

arXiv

引用

arXiv 2025年

作者： Liu, Yepeng Sun, Zhichao Yu, Baosheng Zhao, Yitian Du, Bo Xu, Yongchao Cheng, Jun National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430070 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315211 China 1 Fusionpolis Way #21-01 Connexis South Tower Singapore138632 Singapore

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pretrained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Semantic analysis of basketball video using motion information

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3331卷 65-72页

作者： Liu, Song Yi, Haoran Chia, Liang-Tien Rajan, Deepu Chan, Syin Center for Multimedia and Network Technology School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

This paper presents a new method for extracting semantic information from basketball video. Our approach consists of three stages: shot and scene boundary detection, scene classification and semantic video analysis for event detection. The scene boundary detection algorithm is based on both visual and motion prediction information. After the shot and scene boundary detection, a set of visual and motion features are extracted from scene or shot. The motion features, describing the total motion, camera motion and object motion within the scene respectively, are computed from the motion vector of the compressed video using an iterative algorithm with robust outlier rejection. Finally, the extracted features are used to differentiate offensive/defensive activities in the scenes. By analyzing the offensive/defensive activities, the positions of potential semantic events, such as foul and goal, are located. Experimental results demonstrate the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Iterative methods

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High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

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China Communications 2017年第9期14卷 32-41页

作者： Tingzhao Wu Ruimin Hu Xiaochen Wang Shanfa Ke Jinshan Wang National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Collaborative Innovation Center of Geospatial Technology

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

关键词： object-based audio coding non-negative matrix factorization audio scenecoding

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