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学者姓名：郑海峰
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针对反向散射系统中的隐蔽通信问题,提出一种基于频谱共享的反向散射隐蔽通信方案.在该方案中,次发射机(secondary transmitter,ST)将隐蔽信息调制到主发射机(primary transmitter,PT)的信号上,并通过反向散射以实现隐蔽传输.首先,给出了ST复反射系数的表达式.其次,推导出监测者(Willie)二元假设检验的最优检测阈值以及对应的最小检测错误概率.考虑瑞利衰落信道,对 ST 的隐蔽传输性能进行了分析,得到有效隐蔽传输速率(effective covert rate,ECR)表达式.然后,给出隐蔽传输过程中的误码率表达式.最后,在满足约束条件的前提下,通过联合优化PT的发射功率以及ST的复反射系数以最大化ECR.实验结果表明,利用瑞利衰落信道的信道不确定性,可以在该反向散射系统中实现隐蔽传输.在隐蔽传输过程中,Willie 的最小检测错误概率仅与 ST 的复反射系数相关.此外,所提出的优化方案能够有效提升该系统的隐蔽通信性能.
Keyword ：
二元假设检验 二元假设检验 反向散射通信 反向散射通信 隐蔽通信 隐蔽通信 频谱共享 频谱共享
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| GB/T 7714 | 胡锦松 , 罗龙发 , 李鸿炜 et al. 面向共享频谱的反向散射隐蔽通信设计与实验分析 [J]. | 实验技术与管理 , 2025 , 42 (6) : 112-118 . |
| MLA | 胡锦松 et al. "面向共享频谱的反向散射隐蔽通信设计与实验分析" . | 实验技术与管理 42 . 6 (2025) : 112-118 . |
| APA | 胡锦松 , 罗龙发 , 李鸿炜 , 陈由甲 , 郑海峰 . 面向共享频谱的反向散射隐蔽通信设计与实验分析 . | 实验技术与管理 , 2025 , 42 (6) , 112-118 . |
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In autonomous driving, accurately predicting the future trajectories of surrounding vehicles is essential for reliable navigation and planning. Unlike previous approaches that relied on high-definition maps and vehicle coordinates, recent research seeks to predict the future trajectories of both surrounding and ego vehicles from a bird's-eye view (BEV) perspective, leveraging data from multiple sensors on the vehicle in an end-to-end manner. A key challenge in this context is effectively modeling the spatiotemporal interactions between vehicles. In this paper, we propose a multi-scale spatiotemporal Transformer network that extracts multi-scale features from images and aligns them using a dedicated feature alignment module. We develop a divided space-time attention mechanism to capture spatiotemporal dependencies in the feature sequence. Extensive experiments on the nuScenes dataset demonstrate that the proposed framework achieves superior prediction accuracy compared to prior methods, with further performance gains as more historical information is incorporated. © 2025 IEEE.
Keyword ：
Autonomous vehicles Autonomous vehicles Behavioral research Behavioral research Forecasting Forecasting Intelligent systems Intelligent systems Intelligent vehicle highway systems Intelligent vehicle highway systems
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| GB/T 7714 | Han, Haoxuan , Zheng, Haifeng , Feng, Xinxin . Multi-Scale Spatiotemporal Transformer Networks for Trajectory Prediction of Autonomous Driving [C] . 2025 : 155-160 . |
| MLA | Han, Haoxuan et al. "Multi-Scale Spatiotemporal Transformer Networks for Trajectory Prediction of Autonomous Driving" . (2025) : 155-160 . |
| APA | Han, Haoxuan , Zheng, Haifeng , Feng, Xinxin . Multi-Scale Spatiotemporal Transformer Networks for Trajectory Prediction of Autonomous Driving . (2025) : 155-160 . |
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Multimodal information plays an important role in the advanced Internet of Things (IoT) in the era of 6G, which provides reliable and comprehensive assistance for downstream tasks through further fusion and analysis via federated learning (FL). One of the primary challenges in FL is data heterogeneity, which may lead to domain shifts and sharply different local long-tailed category distribution across nodes. These issues hinder the large-scale deployment of FL in IoT applications equipped with multiple various multimodal sensors due to performance deterioration. In this paper, we propose a novel multimodal fusion framework to tackle the aforementioned coupled problems arising during the cooperative fusion of multimodal information without privacy exposure among decentralized nodes equipped with diverse sensors. Specifically, we introduce a flexible global logit alignment (GLA) method based on multi-view domains. This method enables the fusion of diverse multimodal information with the consideration of domain shifts caused by modality-based data heterogeneity. Furthermore, we propose a novel local angular margin (LAM) scheme, which dynamically adjusts decision boundaries for locally seen categories while preserving global decision boundaries for unseen categories. This effectively mitigates severe model divergence caused by significantly different category distributions. Extensive simulations demonstrate the superiority of the proposed framework, which exhibits significant merits in tackling model degeneration caused by data heterogeneity and enhancing modality-based generalization for heterogeneous scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Federated learning Federated learning
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| GB/T 7714 | Gao, Min , Zheng, Haifeng , Feng, Xinxin et al. Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning [C] . 2025 : 16736-16744 . |
| MLA | Gao, Min et al. "Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning" . (2025) : 16736-16744 . |
| APA | Gao, Min , Zheng, Haifeng , Feng, Xinxin , Tao, Ran . Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning . (2025) : 16736-16744 . |
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As an advanced spectral imaging technology, hyperspectral has critical applications in remote sensing. Unfortunately, hyperspectral images (HSIs) are frequently contaminated by diverse noise interference during capture. It is desirable to remove these mixed noises and recover clean HSIs accurately. Current approaches struggle to deliver great performance because they fail to effectively utilize the spectral correlations in hyperspectral data. This paper introduces an innovative hyperspectral image denoising algorithm based on the tensorial weighted Schatten-p norm and graph Laplacian regularization named TWSPGLR. Firstly, to improve the accuracy of low-rank tensor recovery, the tensorial weighted Schatten- p norm is introduced to recover clean hyperspectral data. Secondly, we introduce a spectral constraint to enhance restoration accuracy by efficiently exploiting the spectral correlations of hyperspectral data. Finally, experimental results demonstrate the superiority of TWSPGLR compared with the state-of-the-art methods for HSI denoising. © 2025 IEEE.
Keyword ：
Hyperspectral imaging Hyperspectral imaging Image denoising Image denoising Laplace transforms Laplace transforms Recovery Recovery Remote sensing Remote sensing Spectrum analysis Spectrum analysis Tensors Tensors
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| GB/T 7714 | Zhang, Yufang , Feng, Xinxin , Zheng, Haifeng . Hyperspectral Image Denoising Using Tensorial Weighted Schatten-p Norm with Graph Laplacian Regularization [C] . 2025 : 420-425 . |
| MLA | Zhang, Yufang et al. "Hyperspectral Image Denoising Using Tensorial Weighted Schatten-p Norm with Graph Laplacian Regularization" . (2025) : 420-425 . |
| APA | Zhang, Yufang , Feng, Xinxin , Zheng, Haifeng . Hyperspectral Image Denoising Using Tensorial Weighted Schatten-p Norm with Graph Laplacian Regularization . (2025) : 420-425 . |
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Single disperser coded aperture spectral imaging (SD-CASSI) is well-known for its simple optical path that efficiently acquires spectral images. However, reconstructing hyperspectral images from their measurement scenes is an ill-posed and challenging problem. By applying deep learning methods to solve this ill-posed issue, it becomes possible to reconstruct high-quality hyperspectral images from measurement images in real time. However, mainstream models typically use an encoder-decoder structure, connecting the output of the encoder and the input of decoder only along the channels. This limits the ability of network to learn detailed image information. In addition, since the planar image sensor array causes varying wavelengths to experience different optical path differences after dispersion, the actual mask cannot be derived solely from a single known mask through different dispersion steps. To address these issues, this paper proposes a deep unfolding method called the channel-wise mask learning based mixing Transformer network (CML-MT). We design a denoising model based on window attention and a dual block, using the dual block as the decoder to fully utilize information from the encoder layers. Additionally, we introduce a channel-wise degradation mask learning module that implicitly learns to approximate the latent real mask under the constraint of multi-stage reprojection loss. Experimental results demonstrate that with these solutions, our model, extended to only three stages, is competitive with state-of-the-art models and excels in reconstructing details and textures in real-world scenarios.
Keyword ：
Deep learning Deep learning Degradation aware Degradation aware Hyperspectral images Hyperspectral images Snapshot compressive imaging Snapshot compressive imaging Transformer Transformer
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| GB/T 7714 | Xie, Wenyu , Xu, Ping , Zheng, Haifeng et al. Channel-wise mask learning based mixing transformer for spectral compressive imaging [J]. | JOURNAL OF THE FRANKLIN INSTITUTE , 2025 , 362 (8) . |
| MLA | Xie, Wenyu et al. "Channel-wise mask learning based mixing transformer for spectral compressive imaging" . | JOURNAL OF THE FRANKLIN INSTITUTE 362 . 8 (2025) . |
| APA | Xie, Wenyu , Xu, Ping , Zheng, Haifeng , Liu, Yian . Channel-wise mask learning based mixing transformer for spectral compressive imaging . | JOURNAL OF THE FRANKLIN INSTITUTE , 2025 , 362 (8) . |
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Recently, human activity recognition (HAR) has gained significant attention as a research field, leading to the development of diverse technologies driven by its broad range of application scenarios. Radar technology has attracted much attention because of its unique advantages such as not being limited by environmental conditions such as light, shadow, and occlusion. In this article, a continuous HAR system based on multidomain radar data fusion (CMDN) is proposed. Firstly, in order to capture more detailed motion features of the human body, we apply the short-time fractional Fourier transform (STFrFT) to map radar data into the fractional domain, yielding a novel representation of human motion. Secondly, we develop an activity detector based on variable window length short-time average/long-time average (VW-STA/LTA) to accurately identify the start/end points of continuous human actions, addressing the challenge of difficult sequence segmentation in continuous activity recognition tasks. Finally, based on the multi-input multitask (MIMT) recognition network, the features of each domain are processed in parallel, and multiple input representations are fused to obtain the continuous activity classification results with high precision.
Keyword ：
Accuracy Accuracy Doppler radar Doppler radar Feature extraction Feature extraction Fractional Fourier transform (FrFT) Fractional Fourier transform (FrFT) frequency modulated continuous wave (FMCW) radar frequency modulated continuous wave (FMCW) radar human activity recognition (HAR) human activity recognition (HAR) Radar Radar Radar detection Radar detection Radar imaging Radar imaging Radar signal processing Radar signal processing Sensors Sensors Time-frequency analysis Time-frequency analysis
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| GB/T 7714 | Feng, Xinxin , Chen, Pengcheng , Weng, Yuxin et al. CMDN: Continuous Human Activity Recognition Based on Multidomain Radar Data Fusion [J]. | IEEE SENSORS JOURNAL , 2025 , 25 (6) : 10432-10443 . |
| MLA | Feng, Xinxin et al. "CMDN: Continuous Human Activity Recognition Based on Multidomain Radar Data Fusion" . | IEEE SENSORS JOURNAL 25 . 6 (2025) : 10432-10443 . |
| APA | Feng, Xinxin , Chen, Pengcheng , Weng, Yuxin , Zheng, Haifeng . CMDN: Continuous Human Activity Recognition Based on Multidomain Radar Data Fusion . | IEEE SENSORS JOURNAL , 2025 , 25 (6) , 10432-10443 . |
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With the exponential advancement of vehicle networking applications and autonomous driving technology, the demand for efficient and secure autonomous vehicles (AVs) is increasing. AVs require the ability to gather information to navigate complex and ever-changing traffic environments, making effective communication with other vehicles or roadside units (RSUs) crucial for achieving co-awareness. Integrated Sensing and Communication (ISAC) technology emerges as a promising solution for the future of autonomous driving. However, in the dynamic and uncertain real-world road environment, the selection of sensing and communication (SC) functions becomes paramount in enhancing performance. Moreover, ambient noise often disrupts the interaction between vehicles and roadside units, leading to a partial loss of environmental states. To address this challenge, we propose a novel approach for selecting sensing and communication functions, even in the presence of partial loss of environmental states. Specifically, we approximate a partially observable Markov decision process (POMDP) to a complete Markov decision process (MDP) through matrix completion and subsequently utilize deep reinforcement learning (DRL) to solve it. Additionally, we propose a matrix completion algorithm based on the alternating direction method of multipliers (ADMM) with deep unfolding to accurately complete the missing environmental states. Finally, we demonstrate that the proposed method outperforms the other POMDP-based approaches for SC function selection in an ISAC-enabled vehicular network.
Keyword ：
Deep matrix factorization Deep matrix factorization deep unfolding deep unfolding integrated sensing and communication integrated sensing and communication partially observable Markov decision process partially observable Markov decision process vehicular network vehicular network
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| GB/T 7714 | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan et al. Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) : 4933-4945 . |
| MLA | Shen, Xiangyu et al. "Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 74 . 3 (2025) : 4933-4945 . |
| APA | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan , Feng, Xinxin . Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) , 4933-4945 . |
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Video prediction is a fundamental task in computer vision, aiming to predict future frames based on a series of historical frames. It is a dense pixel-level prediction task with broad application value in fields such as autonomous driving, traffic flow prediction, and weather forecasting. Traditional video prediction methods typically rely on autoregressive model architectures, which use a cyclical strategy, taking the output of the previous frame as the input for the next frame, and recursively predicting in a loop. However, current models still face unresolved challenges. In particular, many existing approaches perform down sampling through strided convolution when reducing the dimensionality of video data, which inevitably leads to pixel loss and neglect of local details, thereby compromising the clarity of the predicted results. To mitigate this issue, non-autoregressive models have been proposed, featuring a multi-frame input and multi-frame output architecture that generates future frames in parallel, breaking away from the cyclic framework and effectively avoiding the accumulation of prediction errors. However, existing models still face pressing issues that need to be addressed. Objects in videos often exhibit irregular motion, and the variability in video content along with multiple possible motion trajectories make it challenging for network models to predict image motion accurately, resulting in blurred image details in the predicted frames. To tackle this challenge, this paper introduces a novel research approach that leverages the characteristics of wavelet transforms through the separated learning of feature domain structure and texture to enhance the quality of video prediction. Under this separated structure, the low-frequency structural information, after detail removal, has stronger temporal correlation, which aids in more accurate spatiotemporal prediction of image regions. High-frequency detail features are learned through an independent enhancement module to improve the local quality of video prediction. Additionally, by using a two-level wavelet transform, down sampling operations can be performed to reduce image resolution without losing pixel information, and corresponding up sampling operations can be achieved through inverse wavelet transform. This symmetrical structure maximizes the retention of image information and allows for more accurate prediction of subsequent images. Furthermore, this paper designs a multi-scale 3D decoupled convolution module that uses convolutional kernels of different sizes to learn regional features at various scales. This module decouples traditional 3D convolution into 2D and 1D convolutions. This decoupling method focuses on learning the spatial and temporal characteristics of low-frequency structures, which not only improves predictive performance but also reduces the model's parameters and memory consumption. This design enables the model to more effectively capture both short-term and long-term temporal dependencies, thereby enhancing the accuracy and coherence of video prediction. Finally, a high-frequency detail enhancement module on a small scale is designed to learn the decomposed high-frequency information and predict image details and textures, enhancing the local quality of video prediction. The experimental results on synthetic data and real-world datasets show that the algorithm designed in this paper has higher prediction accuracy than existing algorithms. It has more accurate prediction performance in local details and overall prediction morphology. Among them, the MSE on the Moving MNIST dataset is 15. 7, which is 34%, 20. 7%, 11. 3%, and 4. 8% lower than the existing advanced algorithms SimVP, TAU, SwinLSTM, and VMRNN respectively. © 2025 Science Press. All rights reserved.
Keyword ：
Computer vision Computer vision Convolution Convolution Image compression Image compression Image enhancement Image enhancement Image resolution Image resolution Inverse problems Inverse problems Inverse transforms Inverse transforms Pixels Pixels Prediction models Prediction models Textures Textures Video analysis Video analysis Video recording Video recording Wavelet transforms Wavelet transforms Weather forecasting Weather forecasting
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| GB/T 7714 | Zheng, Ming-Kui , Wu, Kong-Xian , Qiu, Xin-Tao et al. Multi-Scale 3D Decoupled Convolutional Video Prediction Method Based on Structure and Texture Decomposition [J]. | Chinese Journal of Computers , 2025 , 48 (8) : 1832-1847 . |
| MLA | Zheng, Ming-Kui et al. "Multi-Scale 3D Decoupled Convolutional Video Prediction Method Based on Structure and Texture Decomposition" . | Chinese Journal of Computers 48 . 8 (2025) : 1832-1847 . |
| APA | Zheng, Ming-Kui , Wu, Kong-Xian , Qiu, Xin-Tao , Zheng, Hai-Feng , Zhao, Tie-Song . Multi-Scale 3D Decoupled Convolutional Video Prediction Method Based on Structure and Texture Decomposition . | Chinese Journal of Computers , 2025 , 48 (8) , 1832-1847 . |
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In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods.
Keyword ：
Accuracy Accuracy Computational modeling Computational modeling Costs Costs Data models Data models Deep learning Deep learning Distributed databases Distributed databases distributed learning distributed learning edge computing edge computing Edge computing Edge computing Graph-tensor Graph-tensor Imputation Imputation tensor completion tensor completion Tensors Tensors traffic data recovery traffic data recovery Training Training
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| GB/T 7714 | Deng, Lei , Liu, Xiao-Yang , Zheng, Haifeng et al. Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (4) : 2835-2847 . |
| MLA | Deng, Lei et al. "Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 12 . 4 (2025) : 2835-2847 . |
| APA | Deng, Lei , Liu, Xiao-Yang , Zheng, Haifeng , Feng, Xinxin , Zhu, Ming , Tsang, Danny H. K. . Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (4) , 2835-2847 . |
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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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