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Proceedings of the 2024 2nd Asia Symposium on Image and Graphics

作者： Heng Dong Jiahao Li Bin Lan Ming Xu Liang Chen Sichuan TianFu New Area Beijing Institute of Technology Innovation Equipment Research Institute Chengdu Sichuan China Beijing Institute of Technology Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-BornIntelligent Information Processing(SBlP) Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798400709906

Remote sensing scene classification plays an important role in many fields, but the traditional remote sensing classification methods face the problems of computational complexity and energy consumption, and it is difficult to meet the real-time requirements. Brain-inspired computing, especially spike neural networks (SNNs), has become an effective way to solve this problem due to its low power consumption and high reliability. In this paper, we propose an FPGA-Based SNN accelerator to implement an SNN-Based Remote sensing scene classification model to improve the real-time and resource efficiency of remote sensing image processing. We use the LeNet-5 network architecture as the core, build SNN based on the LIF model, and realize parallel processing and pipeline design by optimizing the data flow and hardware architecture, so as to improve the data throughput and processing speed. On the FPGA platform, our SNN accelerator achieves a high processing speed of 4690 frames/s while maintaining low power consumption (0.887W), which is approximately 3.75x faster than existing processors. At the same time, the overall system was evaluated using the adopted network architecture, and our architecture can achieve large-scale SNN network model inference with low power, low overhead, and high throughput.

关键词： FPGA

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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Overcoming user-rate limit of quantum network

arXiv

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arXiv 2025年

作者： Xu, Yuehan Zhang, Qijun Zhang, Junpeng Liao, Xiaojuan Shen, Ziyi Liu, Xu Zhang, Beibei Tan, Zicong Zhou, Zehao Dai, Jisheng Jiang, Xueqin Huang, Peng Wang, Tao Zeng, Guihua State Key Laboratory of Advanced Optical Communication Systems and Networks Center of Quantum Sensing and Information Processing Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Technology Donghua University Shanghai201620 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Hefei National Laboratory Hefei230088 China

Quantum networks revolutionize the way of information transmission and are an essential step in building a quantum internet. Generally, the information capacity per user-channel in a quantum network drastically decreases with the increase of network capacity, making it difficultly scale to large-user scenarios. To break this limit, we develop a quantum network architecture in which the information capacity per user-channel is independent of the network capacity (NCI-QN), and all previous quantum networks can be regarded as special examples. Three aspects are investigated. Firstly, a quantum network scheme formulated in a comprehensive multi-mode time-frequency representation is presented. Then, information characteristics of the proposed quantum network are delineated by expanding the well-known Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound and the Holevo bound from the linear combination of point-to-point links to a complex network architecture, demonstrating clear proofs of the network capacity independence. Finally, a practical NCI-QN with a network capacity of 19 is experimentally demonstrated using optical frequency comb in quantum key distribution network scenarios, in which the security under the asymptotic case, finite-size effect, composable security, and composable finite-size security are verified, with a secret key rate up to 8.75 Gbps. This achievement overcomes the user-rate limit of quantum network, which is the keystone for the development of the quantum internet. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum cryptography

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A Bi-selection Method Based on Consistent Matrix for Large-Scale Datasets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Biselection consistent matrix (CM) fuzzy sets granular computing rough sets

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MCFTNet: multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

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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Flexible three-dimensional integrated circuits (3D ICs) based on liquid metal

Wearable Electronics

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Wearable Electronics 2025年 2卷 1-17页

作者： Ruiwen Tian Jianhui Chen Ming Zhu Dianpeng Qi Xiaodong Chen MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage National and Local Joint Engineering Laboratory for Synthesis Transformation and Separation of Extreme Environmental Nutrients School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 PR China Innovative Center for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore Key Laboratory of Science and Engineering for the Multi-modal Prevention and Control of Major Chronic Diseases Ministry of Industry and Information Technology Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou 450000 PR China

The rapid advancement of flexible electronics has unlocked opportunities across diverse fields, including medical science and device engineering. Developing flexible three-dimensional integrated circuits (3D ICs) is a key strategy for driving the miniaturisation, wearability and multifunctionality of flexible devices. Among these innovations, liquid metal-based flexible electronics have elicited significant attention due to their exceptional conductivity, high stretchability and low toxicity. This review highlights recent research breakthroughs in the application of liquid metal for flexible 3D ICs, providing a detailed discussion of the design concepts and strategies for patterning and interconnection structures that harness the unique properties of liquid metal. Using 3D printing technology to fabricate high-resolution, durable 3D ICs from liquid metal represents a major research direction. Moreover, advanced fabrication methods diverging from conventional 3D printing systems are explored. Finally, we examine persistent challenges and offer a forward-looking perspective on the potential of liquid metal-based flexible 3D ICs to revolutionise human–machine interactions and enable intelligent living.

关键词： Liquid metal Gallium–indium alloy Flexible electronics Three-dimensional Integrated circuit 3D printing

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Electric Field Manipulation of Rydberg States for Very Low Frequency Fields Detection

arXiv

引用

arXiv 2025年

作者： Chen, Minze Feng, Haonan Gao, Ge Zhu, Zhiao Li, Zhongxiang Wu, Zhonghuai Xiao, Wei Dai, Weidong Peng, Peng Zheng, Dezhi State Key Laboratory of Environment Characteristics and Effects for Near-Space Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Zhuhai519088 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Taizhou Meteorological Bureau Taizhou318000 China Chongqing Caeri Robot Technology Co. Ltd. Chongqing400722 China

The very low frequency(VLF) band is widely used in submarine communication and geophysical exploration for its strong penetration and long-distance propagation. This paper theoretically and experimentally investigates Rydberg EIT in 133Cs vapor under VLF and DC fields. A model is established to describe the EIT spectral response under dual-field conditions, with theoretical predictions showing agreement with experimental results. We propose a novel calibration-free method to measure VLF electric fields, bypassing traditional Stark shift measurements. This method detects additional splitting intervals of Stark sublevels, separated from the degenerate energy level under a DC field. This phenomenon arises from the averaging effect of sublevel sinusoidal oscillations in the spectrum induced by the VLF field. The splitting interval is proportionally dependent on the VLF field amplitude. The VLF electric field sensor is enhanced by increasing the strength of the DC field, extending the traceable measurement limit for weak VLF electric fields by more than an order of magnitude. This work highlights the potential for precise VLF electric field measurements, significantly advancing the calibration-free detection capabilities of Rydberg atom sensors for low-frequency applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Rydberg states

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Semantic Segmentation Algorithm of Remote Sensing Images Based on Improved Panoptic

Semantic Segmentation Algorithm of Remote Sensing Images Bas...

引用

2022 International Conference on Automation, Robotics and Computer Engineering, ICARCE 2022

作者： Zheng, Tong Liu, Liu Chen, Qimeng Chen, Zhong Yu, Longji Zhao, Zeyu Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan China

ISBN: (纸本)9781665475488

Semantic segmentation of images can play an important role in land use, building extraction, road extraction and vehicle detection using remote sensing images. In the scenario of applying Panoptic FPN algorithm for remote sensing images, we found that the decoder of this algorithm is not robust in feature extraction and cannot do weighted enhancement of key spaces and channels, and its encoder loses a lot of high-dimensional semantic features when simply fusing semantic information at all levels by simple addition. To address these two problems, we propose the Se-Resnext encoder based on the attention mechanism and the Concat-based feature fusion mechanism, respectively, and verify the effectiveness of the methods through experiments. The accuracy of semantic segmentation is improved in both suichang dataset and posdam dataset. © 2022 IEEE.

关键词： Semantics

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Pulsar/CNS integrated navigation based on federated UKF

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Journal of Systems Engineering and Electronics 2010年第4期21卷 675-681页

作者： Jin Liu Jie Ma Jinwen Tian State Key Laboratory for Multi-spectral Information Processing Technologies Institue for Pattern Recognition and artifical Intelligence Huazhong University of Science and Technology Wuhan 430074 P. R. China

In order to improve the autonomous navigation capability of satellite,a pulsar/CNS（celestial navigation system） integrated navigation method based on federated unscented Kalman filter（UKF） is *** celestial navigation is a mature and stable navigation ***,its position determination performance is not satisfied due to the low accuracy of horizon *** pulsar navigation is a new navigation method,which can provide highly accurate range *** major drawback of single pulsar navigation is that the system is completely *** two methods are complementary to each other,the federated UKF is used here for fusing the navigation data from single pulsar navigation and *** to the traditional celestial navigation method and single pulsar navigation,the integrated navigation method can provide better navigation *** simulation results demonstrate the feasibility and effectiveness of the navigation method.

关键词： autonomous navigation celestial navigation system(CNS) pulsar federated unscented Kalman filter(UKF).

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Modeling and Coverage Analysis of RIS-assisted Integrated Sensing and Communication Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Gan, Xu Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Bader, Faouzi Zhang, Zhaoyang Yuen, Chau Guan, Yong Liang Debbah, Merouane Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xian710071 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nord Europe Lille France Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Khalifa University of Science and Technology KU6G Research Center PO Box 127788 Abu Dhabi United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km-2 BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs. © 2025 IEEE.

关键词： Network topology

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