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学者姓名：郑海峰
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Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi-domain uniform sampling method (MDU-sampling) for large-scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains, reducing the computational resources for registration. The proposed method preserves more effective information compared to other algorithms. Points are only considered on the same ring in LiDAR data as neighbouring points, eliminating the need for additional neighbouring point search. This makes it efficient and suitable for large-scale outdoor LiDAR point cloud registration. image
Keyword ：
artificial intelligence artificial intelligence robot vision robot vision signal processing signal processing SLAM (robots) SLAM (robots)
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| GB/T 7714 | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration [J]. | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| MLA | Ou, Wengjun 等. "MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration" . | ELECTRONICS LETTERS 60 . 5 (2024) . |
| APA | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration . | ELECTRONICS LETTERS , 2024 , 60 (5) . |
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Decentralized federated learning (DFL) is a novel distributed machine-learning paradigm where participants collaborate to train machine-learning models without the assistance of the central server. The decentralized framework can effectively overcome the communication bottleneck and single-point-of-failure issues encountered in federated learning (FL). However, most existing DFL methods may ignore the communication resource constraints of the system. This may result in these methods unsuitable for many practical scenarios because the given resource constraints cannot be guaranteed. In this article, we propose a novel DFL, called DFL with adaptive compression ratio (AdapCom-DFL), that can adaptively adjust the compression ratio of transmission data to keep the communication latency within the constraint. Furthermore, we propose a communication network topology pruning approach to reduce communication overhead by pruning poor links with low data rates while ensuring the convergence. Additionally, a power allocation approach is presented to improve the performance by reallocating the power of communication links while complying with the communication energy constraint. Extensive simulation results demonstrate that the proposed AdapCom-DFL with network pruning and power allocation approach achieves better performance and requires less bandwidth under the given resource constraints compared with some existing approaches.
Keyword ：
Decentralized federated learning (DFL) Decentralized federated learning (DFL) network topology pruning network topology pruning power allocation power allocation
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| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Gao, Min et al. Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) : 10739-10753 . |
| MLA | Du, Mengxuan et al. "Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks" . | IEEE INTERNET OF THINGS JOURNAL 11 . 6 (2024) : 10739-10753 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Gao, Min , Feng, Xinxin . Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) , 10739-10753 . |
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We consider the problem of hyperspectral image (HSI) reconstruction, which aims to recover 3D hyperspectral data from 2D compressive HSI measurements acquired by a coded aperture snapshot spectral imaging (CASSI) system. Existing deep learning methods have achieved acceptable results in HSI reconstruction. However, these methods did not consider the imaging system degradation pattern. In this article, based on observing the initialized HSIs obtained by shifting and splitting the measurements, we propose a dynamic Fourier network based on degradation learning, called the degradation-aware dynamic Fourier-based network (DADF-Net). We estimate the degradation feature maps from the degraded hyperspectral images to realize the linear transformation and dynamic processing of the features. In particular, we use the Fourier transform to extract the HSI non-local features. Extensive experimental results show that the proposed model outperforms state-of-the-art algorithms on simulation and real-world HSI datasets.
Keyword ：
Convolution Convolution Deep learning Deep learning Degradation Degradation Feature extraction Feature extraction fourier transform fourier transform Heuristic algorithms Heuristic algorithms hyperspectral images hyperspectral images Image reconstruction Image reconstruction Imaging Imaging Mathematical models Mathematical models snapshot compressive imaging snapshot compressive imaging
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| GB/T 7714 | Xu, Ping , Liu, Lei , Zheng, Haifeng et al. Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 2838-2850 . |
| MLA | Xu, Ping et al. "Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 2838-2850 . |
| APA | Xu, Ping , Liu, Lei , Zheng, Haifeng , Yuan, Xin , Xu, Chen , Xue, Lingyun . Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 2838-2850 . |
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Federated Learning (FL), as a privacy-enhancing distributed learning paradigm, has recently attracted much attention in wireless systems. By providing communication and computation services, the base station (BS) helps participants collaboratively train a shared model without transmitting raw data. Concurrently, with the advent of integrated sensing and communication (ISAC) and the growing demand for sensing services, it is envisioned that BS will simultaneously serve sensing services, as well as communication and computation services, e.g., FL, in future 6G wireless networks. To this end, we provide a novel integrated sensing, communication and computation (ISCC) system, called Fed-ISCC, where BS conducts sensing and FL in the same time-frequency resource, and the over-the-air computation (AirComp) is adopted to enable fast model aggregation. To mitigate the interference between sensing and FL during uplink transmission, we propose a receive beamforming approach. Subsequently, we analyze the convergence of FL in the Fed-ISCC system, which reveals that the convergence of FL is hindered by device selection error and transmission error caused by sensing interference, channel fading and receiver noise. Based on this analysis, we formulate an optimization problem that considers the optimization of transceiver beamforming vectors and device selection strategy, with the goal of minimizing transmission and device selection errors while ensuring the sensing requirement. To address this problem, we propose a joint optimization algorithm that decouples it into two main problems and then solves them iteratively. Simulation results demonstrate that our proposed algorithm is superior to other comparison schemes and nearly attains the performance of ideal FL. IEEE
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink Federated learning Federated learning integrated sensing and communication integrated sensing and communication Optimization Optimization over-the-air computation over-the-air computation Radar Radar Task analysis Task analysis Uplink Uplink
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| GB/T 7714 | Du, M. , Zheng, H. , Gao, M. et al. Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks [J]. | IEEE Internet of Things Journal , 2024 : 1-1 . |
| MLA | Du, M. et al. "Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks" . | IEEE Internet of Things Journal (2024) : 1-1 . |
| APA | Du, M. , Zheng, H. , Gao, M. , Feng, X. , Hu, J. , Chen, Y. . Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE Internet of Things Journal , 2024 , 1-1 . |
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Abstract ：
Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi-domain uniform sampling method (MDU-sampling) for large-scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains, reducing the computational resources for registration. The proposed method preserves more effective information compared to other algorithms. Points are only considered on the same ring in LiDAR data as neighbouring points, eliminating the need for additional neighbouring point search. This makes it efficient and suitable for large-scale outdoor LiDAR point cloud registration. image
Keyword ：
artificial intelligence artificial intelligence robot vision robot vision signal processing signal processing SLAM (robots) SLAM (robots)
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| GB/T 7714 | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration [J]. | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| MLA | Ou, Wengjun et al. "MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration" . | ELECTRONICS LETTERS 60 . 5 (2024) . |
| APA | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration . | ELECTRONICS LETTERS , 2024 , 60 (5) . |
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The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark. IEEE
Keyword ：
Decoding Decoding Deep Learning for Visual Perception Deep Learning for Visual Perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR Mapping Mapping Occupancy Grid Occupancy Grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic Scene Understanding Semantic Scene Understanding Three-dimensional displays Three-dimensional displays
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| GB/T 7714 | Lai, Q. , Zheng, H. , Feng, X. et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE Robotics and Automation Letters , 2024 , 9 (10) : 1-8 . |
| MLA | Lai, Q. et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE Robotics and Automation Letters 9 . 10 (2024) : 1-8 . |
| APA | Lai, Q. , Zheng, H. , Feng, X. , Zheng, M. , Chen, H. , Chen, W. . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE Robotics and Automation Letters , 2024 , 9 (10) , 1-8 . |
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Traffic flow is considered as a critical feature of intelligent transportation systems (ITS). Accurately forecasting future vehicular volumes is an effective means of mitigating traffic congestion. However, the nonlinear and complex traffic flow characteristics make the traditional approaches unable to achieve satisfactory prediction performance. Although existing methods based on deep learning models have improved the accuracy of traffic flow prediction, the spatio-temporal features of traffic flow data are still not fully explored. Moreover, existing methods pay little attention to the task of training models in a decentralized environment where data are distributed across multiple clients. To solve the problems mentioned above, we propose a novel network model called Graph Transformer Attention Network (GTAN) for traffic flow prediction, which can effectively extract traffic flow's temporal and spatial characteristics by considering all node locations' information in the traffic networks. Then, we propose a training strategy called Graph Federated Meta-learning (FedGM), solving the problem of topological heterogeneity by combining meta-learning and federated learning, to achieve an optimal initialization model which can quickly adapt to different traffic networks under low communication cost. Finally, the experimental results on a real data set show that the GTAN model has better prediction performance and faster meta-training speed. The model trained by FedGM can quickly adapt to different graph-structured data and achieve high accuracy. IEEE
Keyword ：
Adaptation models Adaptation models Correlation Correlation Data models Data models Federated learning Federated learning Federated meta-learning Federated meta-learning Graph transformer networks Graph transformer networks Predictive models Predictive models Topological heterogeneity Topological heterogeneity Traffic flow prediction Traffic flow prediction Training Training Transformers Transformers
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| GB/T 7714 | Feng, X. , Sun, H. , Liu, S. et al. Federated Meta-Learning on Graph for Traffic Flow Prediction [J]. | IEEE Transactions on Vehicular Technology , 2024 : 1-13 . |
| MLA | Feng, X. et al. "Federated Meta-Learning on Graph for Traffic Flow Prediction" . | IEEE Transactions on Vehicular Technology (2024) : 1-13 . |
| APA | Feng, X. , Sun, H. , Liu, S. , Guo, J. , Zheng, H. . Federated Meta-Learning on Graph for Traffic Flow Prediction . | IEEE Transactions on Vehicular Technology , 2024 , 1-13 . |
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The development of the extremely large-scale antenna array (ELAA) for the upcoming 6G technology indicates the significance of near-field communication. This work performs a near-field analysis to improve covertness when maximum ratio transmission (MRT) is employed with ELAA to send messages to the legitimate user. Specifically, we first derive the covertness constraint of the system by analyzing the beampattern. Based on this constraint, we introduce the concept of the vulnerable region, which is the region where covert communication is not achievable if a potential warden resides there. Furthermore, determining the vulnerable region involves deriving the range of distances by initially fixing the angle dimension, and then utilizing the covertness and the minimum effective throughput constraints to obtain the range of angle. The simulation results illustrate the efficacy of the determined vulnerable region in both distance and angle dimensions. As the azimuth angle or the distance between the legitimate user and the transmitter decreases, the area of the vulnerable region decreases. Additionally, increasing the number of warden's antennas or requiring a higher signal-to-noise ratio for legitimate user will expand the vulnerable region. IEEE
Keyword ：
Antennas Antennas Array signal processing Array signal processing Covert communication Covert communication near-field communication near-field communication Signal to noise ratio Signal to noise ratio Throughput Throughput Transmitting antennas Transmitting antennas Vectors Vectors vulnerable region vulnerable region Wireless communication Wireless communication
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| GB/T 7714 | Hu, J. , Zhou, Y. , Zheng, H. et al. Minimizing Vulnerable Region for Near-Field Covert Communication [J]. | IEEE Transactions on Vehicular Technology , 2024 : 1-6 . |
| MLA | Hu, J. et al. "Minimizing Vulnerable Region for Near-Field Covert Communication" . | IEEE Transactions on Vehicular Technology (2024) : 1-6 . |
| APA | Hu, J. , Zhou, Y. , Zheng, H. , Chen, Y. , Shu, F. , Wang, J. . Minimizing Vulnerable Region for Near-Field Covert Communication . | IEEE Transactions on Vehicular Technology , 2024 , 1-6 . |
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Cooperative perception is an advanced strategy within traffic systems designed to enhance the environmental perception capabilities of vehicles, where participants exchange cooperative perception messages (CPMs) through Vehicle-to-Everything (V2X) technology. However, most existing cooperative perception methods may ignore the communication bandwidth constraints of the system, potentially resulting in connected autonomous vehicles (CAVs) receiving outdated CPMs. In this paper, we propose a novel cooperative perception framework that enhances the accuracy of CAVs perception while reducing the transmission data size to meet the transmission delay requirements of CPMs under limited bandwidth. Furthermore, we propose a strategy for selecting cooperative partners and CPMs based on the Double Deep Q-Network (DDQN) algorithm. Additionally, an invalid action masking approach is presented to address the dynamic changes in the action space over time and reduce the size of the DDQN action space. Simulation results demonstrate that the proposed cooperative perception method consumes less data compared to some existing methods. Moreover, under limited communication bandwidth constraints, it achieves higher perception accuracy due to its ability to avoid transmission delay. © 2024 IEEE.
Keyword ：
Connected automated vehicles Connected automated vehicles cooperative perception cooperative perception deep reinforcement learning deep reinforcement learning invalid action masking invalid action masking
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| GB/T 7714 | Xu, F. , Chen, C. , Zheng, H. et al. Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
| MLA | Xu, F. et al. "Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks" [未知]. |
| APA | Xu, F. , Chen, C. , Zheng, H. , Feng, X. . Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
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Predicting traffic flow effectively alleviates congestion. However, traditional methods tend to rely solely on historical traffic flow data, overlooking the correlation between multimodal traffic data, such as speed and occupancy collected by sensors placed on the road. This limitation results in low tolerance for abnormal situations. Moreover, the decentralization of multimodal data on edge devices may pose data anomalies or partial modal missing due to equipment damage or absence. To address these challenges, we propose a Block-Term tensor decomposition-based multimodal data feature fusion algorithm for traffic prediction. This approach enhances the accuracy and robustness of traffic flow prediction by considering correlations between various modal data, such as speed and occupancy rate. In response to the issues of scattered multimodal data anomalies and missing data on edge devices, and to ensure address privacy and security issues, we employ federated learning methods to achieve adaptive extraction and fusion of multi-modal data at the edges. Our method is tested on a real highway dataset, demonstrating superior prediction performance and robustness compared to traditional methods, particularly in the context of data anomalies or missing modalities. © 2024 IEEE.
Keyword ：
Federated learning Federated learning Multimodal data Multimodal data Robustness Robustness Tensor decomposition Tensor decomposition Traffic flow Traffic flow
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| GB/T 7714 | Feng, S. , Feng, X. , Xu, L. et al. BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
| MLA | Feng, S. et al. "BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies" [未知]. |
| APA | Feng, S. , Feng, X. , Xu, L. , Zheng, H. . BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
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