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A Novel Sleep Modulation and Quality Classification Strategy Based EEG Signals and Multi-Scale Attention Deep Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17377-17386页

作者： Lou, Yajun Zhou, Lu Song, Fanlei Xu, Fanyao Wang, Xuejing Li, Weitao Qian, Zhiyu Nanjing University of Aeronautics and Astronautics Key Laboratory of Multimodal Brain-Computer Precision Drive Ministry of Industry and Information Technology the Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Department of Biomedical Engineering College of Automation Engineering Nanjing210016 China

Sleep disorders have become the second most prevalent mental health condition globally, posing a significant challenge to public health. Consequently, there is an imperative need for the development of effective, non-pharmacological interventions to enhance sleep quality. In this study, a novel sleep modulation technique utilizing transcutaneous electrical nerve stimulation (TENS) is introduced. A specialized stimulation mode is designed by integrating sine waves of varying frequencies, including spindle waveform components. Additionally, to facilitate an accurate evaluation of sleep quality and enable individualized interventions, a multi-scale attention-based deep learning model (MSA-Net) is proposed for sleep quality classification. It consists of a two-branch convolution block (TBC-block), an adaptive feature recalibration (AFR) module, three dense blocks, three attention transition blocks. By extracting the deep features of the electroencephalography (EEG) raw data, the sleep quality classification is realized. Experimental results indicate that the proposed stimulation effectively enhances and stabilizes slow-wave activity, significantly extending the duration of deep sleep and enhancing overall sleep stability. In addition, the MSA-Net demonstrates robust performance, achieving exceptional performance with an overall accuracy of 0.881, F1 score of 0.903, and Kappa score of 0.845, outperforming baseline models across all evaluation metrics. These results demonstrate the potential of combining TENS-based sleep modulation with deep learning techniques, offering an effective framework for enhancing sleep quality and advancing personalized sleep modulation strategies. © 2025 IEEE.

关键词： Sleep research

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A Low-Profile Tri-Port Multimode Metasurface-Based Antenna Using Characteristic Mode Analysis

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1119-1123页

作者： Jiang, Zhaoneng Meng, Shuai Nie, Liying Qi, Jiquan Ji, Fangzheng Hefei University of Technology School of Computer Science and Information Hefei230009 China Southeast University National Mobile Commnications Research Laboratory Jiangsu210096 China Hefei University of Technology School of Computer Science Hefei China Huzhou Key Laboratory of Information-Energy Convergence and Interconnection Huzhou313001 China Hefei University of Technology School of Microelectronics Hefei China

In this letter, a metasurface (MTS)-based antenna is proposed for low-profile and multimode operation. An Omnidirectional radiation pattern is excited through a central coaxial cable. A pair of e-TM20 modes with broadside radiation patterns are excited via slot coupling. The size and shape of the peripheral patches of the MTS were adjusted by using characteristic mode analysis (CMA) to increase the bandwidth of the omnidirectional mode and decrease the resonant frequency of the e-TM20 mode. Three modes can operate independently without interfering due to the properly designed feeding networks for each mode. The isolation between ports is greater than 25 dB. The optimized size of the antenna is 0.96 λmin × 0.96 λmin × 0.07 λmin. By enabling multiple modes to operate in different frequency bands on a single MTS, antenna cost, and occupied space can be significantly reduced. © 2002-2011 IEEE.

关键词： Antennas Antenna radiation patterns Bandwidth Scattering parameters Broadband antennas Gain Current distribution Resonant frequency Impedance matching Impedance

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A Multi-Granularity Deep Signal Shrinkage Network for Noise-Robust Specific Emitter Identification

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IEEE Transactions on information Forensics and Security 2025年 20卷 4256-4264页

作者： Han, Guangjie Wang, Weitao Xu, Zhengwei Hohai University Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou213022 China Nanjing Research Institute of Electronics Technology Nanjing210039 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

Wireless network security is a significant issue in wireless communication systems. Specific emitter identification (SEI) technology, as an effective physical layer authentication method, has been extensively studied. Methods based on deep learning (DL) for SEI have emerged as the predominant approach, attributed to their end-to-end recognition framework and enhanced capability for feature extraction. However, the training of DL models relies on high-quality data, and the data collection in real-world scenarios is often in low signal-to-noise ratio (SNR) environments, leading to poor model training performance. This paper presents a novel solution, the Multi-Granularity Deep Signal Shrinkage Network (MGDSSN), for the challenging task of SEI in low SNR environments. To this end, the proposed MGDSSN incorporates soft thresholding processing and employs subnetworks for adaptive thresholding, effectively eliminating noise-related features and achieving robust SEI in low SNR environments. Additionally, MGDSSN incorporates a multi-granularity deep signal network architecture that improves the recognition accuracy and stability of the model. This is achieved by capturing the interrelated attributes of in-phase/quadrature-phase (I/Q) signals and features at multiple levels of granularity. Experiments conducted with real-world dataset reveal that the proposed MGDSSN surpasses the current state-of-the-art SEI methods in low SNR environments, demonstrating robust SEI and verifying the superiority of the proposed method. © 2005-2012 IEEE.

关键词： Signal to noise ratio

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More Accurate Constraints for Self-Supervised Learning in Remote Sensing Images based Object Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12303-12314页

作者： Zheng, Shangdong Wu, Zebin Xu, Yang Liu, Qian Wei, Zhihui Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense China Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China

Self-supervised learning (SSL) automatically generates internal labels by exploring the potential auxiliary task of the network itself, and trains the same model through these annotations to learn the latent representation of the data, which greatly improves the accuracy of object detection in remote sensing images (RSIs). However, most existing methods suffer from guaranteeing the quality of the generated SSL pseudo annotations, and the constructed auxiliary tasks are not detectionoriented, which is difficult to enhance the feature representations that are beneficial for object detection. In this paper, we focus on generating more accurate constraints by excavating the intercorrelation between fully and weakly-supervised learning to improve the performance of object detection in RSIs. Initially, weakly-supervised learning (WSL) assigns the pseudo instancelevel annotations for the high-scoring positive bags to model the detector, which can be regarded as a weakened version of the region proposal network (RPN). Fortunately, RPN can be constrained by the ground truth of bounding boxes in fullysupervised learning (FSL), and the high-quality supervisions it provides are unavailable in any WSL methods. Moreover, we construct a proposal generation module (PGM), which further filters the unreliable bounding boxes, predicted by RPN, to supplement high-quality constraints into the ground truth and SS generated candidate boxes to supervise the optimization of WSL branch. By constructing an interactive learning paradigm of WSL and FSL, the former has more accurate constraints to learn an efficient auxiliary task, while the latter enjoys a richer representation form of data provided by WSL, which is undoubtedly a win-win process. Finally, we cascade the losses of WSL and FSL to further explore the intrinsic correlation between them by sharing the same feature extraction network. Experimental comparisons on DOTA and DIOR datasets demonstrate that our method achieves superior per

关键词： Self-supervised learning

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Combining decomposition and graph capsule network for multi-objective vehicle routing optimization

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Intelligent Data Analysis 2025年第1期29卷 116-140页

作者： Zhang, Haifei Ge, Hongwei Li, Ting Zhou, Lujie Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education Jiangsu Wuxi China College of Automobile and Transportation Tianjin University of Technology and Education Tianjin China School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan China Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia PA United States

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering. © 2024 – IOS Press. All rights reserved.

关键词： attention mechanism decomposition strategy deep reinforcement learning graph capsule network multi objective optimization Urban logistics distribution

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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Traffic Agents Trajectory Prediction Based on Enhanced Bidirectional Recurrent Network and Adaptive Social Interaction Model

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12182-12196页

作者： Chen, Xiaobo Liang, Yuwen Hu, Chuan Wang, Hai Ye, Qiaolin Shandong Technology and Business University School of Computer Science and Technology Yantai264000 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China Shanghai Jiao Tong University Intelligent Vehicle Institute School of Mechanical Engineering Shanghai200240 China Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Nanjing210037 China

Accurate prediction of the future trajectory of traffic agents is imperative to the effective motion planning of autonomous vehicles and mobile robots. Despite enormous progress that has been made toward trajectory prediction, dynamic and crowded traffic scenarios pose major challenges to the understanding and forecasting of traffic agents’ motion behavior. In this paper, we propose a novel trajectory prediction method from the perspective of temporal modeling and social interaction. Specifically, we first put forward a recurrent modeling approach to learn temporal features in favor of capturing long-range and short-range temporal dependencies of individual agents. Then, we construct a social feature learning module to capture the sparse and directional interactions among agents while suppressing the spurious connections. Finally, to reduce the accumulated error during prediction, a coordinated bidirectional decoding module is developed where temporal and social features can be properly integrated into the forward and backward prediction processes. Extensive experiments are performed on four real-world trajectory prediction benchmarks, and the results demonstrate the superiority of our method compared with other competing approaches. Detailed ablation studies are also performed to evaluate the effectiveness of each model component. © 2004-2012 IEEE.

关键词： Prediction models

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A Novel Partial Domain Adaptation Method for Cross-Domain Specific Emitter Identification

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Han, Guangjie Zhang, Yu Xu, Zhengwei Wang, Weitao Hohai University Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou213022 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

In wireless communication networks (WCN), specific emitter identification (SEI) technology is significant for guaranteeing cybersecurity and can effectively defend against network attacks. Compared to traditional SEI methods that depend on specific training data or environmental conditions, cross-domain recognition study has attracted widespread attention for dynamic WCN. However, in a more general scenario, the target domain (TD) sometimes represents a subset of source domain (SD), i.e., in partial domain adaptation (PDA), which has not been well addressed. To solve this concern, we present a novel selective partial domain adaptation method based on complex-valued convolutional block attention module (CSPDA) for SEI, which tackles the domain shift challenge caused by the inconsistency in emitter categories between SD and TD. The proposed method includes a mixed feature extraction module that efficiently extracts representative features from both source and target raw signals at multiple levels, reducing feature information loss. In addition, by using the maximum of cosine (MoC) assessment mechanism and a data information filtering strategy, we can dynamically select the appropriate source signal samples to match each target signal sample. Experiment results on real-world data show that the CSPDA method achieves a recognition accuracy of 73.46% at -5 dB and 89.28% at 20 dB, outperforming other transfer learning and domain adaptation methods in PDA tasks. © 2015 IEEE.

关键词： information filtering

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space

arXiv

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arXiv 2025年

作者： Liang, Yuheng Liu, Feng Liu, Mingzhou Yao, Yu Wang, Zheyu Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology China Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence China

Continuous Emotion Recognition (CER) plays a crucial role in intelligent human-computer interaction, mental health monitoring, and autonomous driving. Emotion modeling based on the Valence-Arousal (VA) space enables a more nuanced representation of emotional states. However, existing methods still face challenges in handling long-term dependencies and capturing complex temporal dynamics. To address these issues, this paper proposes a novel emotion recognition model, Mamba-VA, which leverages the Mamba architecture to efficiently model sequential emotional variations in video frames. First, the model employs a Masked Autoencoder (MAE) to extract deep visual features from video frames, enhancing the robustness of temporal information. Then, a Temporal Convolutional Network (TCN) is utilized for temporal modeling to capture local temporal dependencies. Subsequently, Mamba is applied for long-sequence modeling, enabling the learning of global emotional trends. Finally, a fully connected (FC) layer performs regression analysis to predict continuous valence and arousal values. Experimental results on the Valence-Arousal (VA) Estimation task of the 8th competition on Affective Behavior Analysis in-the-wild (ABAW) demonstrate that the proposed model achieves valence and arousal scores of 0.5362 (0.5036) and 0.4310 (0.4119) on the validation (test) set, respectively, outperforming the baseline. The source code is available on GitHub: https://***/FreedomPuppy77/Charon. Copyright © 2025, The Authors. All rights reserved.

关键词： Regression analysis

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