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Facial micro-expression recognition based on dual-stream fusion network

Multimedia Tools and Applications 2025年 1-14页

作者： Sun, Jiacheng Chen, Changhong Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications No.66 Xin Mofan RD Nanjing210003 China

Micro-expressions(MEs) have emerged as a viable strategy for affective estimation due to their high reliability in emotion detection. In recent years, deep learning methods have been successfully applied to the field of micro-expression recognition. However, extracting and learning features from MEs presents challenges due to their brief duration and subtle intensity. To address these challenges, we propose the dual-stream fusion network (DSFNet). Specifically, we design shallow tokens-to-token vision transformers (T2T-ViT) to effectively capture comprehensive spatial position information. We also fine-tuned the number of ViT encoders and heads to enhance overall model performance. Additionally, the proposed multiscale convolution block (MCB) and attention mechanism modules (AMM) facilitate the effective extraction of detailed and valuable multiscale features from MEs. By employing various sizes of convolutional kernels and attention mechanisms, our approach captures higher-level image information, thereby improving MER accuracy. Finally, we integrate the information obtained from both branches. Performance evaluations on three mainstream ME datasets-SMIC, CASME II, and SAMM-demonstrate that the proposed framework significantly outperforms other advanced methods in micro-expression classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Model-Driven Deep Learning-Based Optimization of Downlink Precoding and Fronthaul Compression in Cell-Free MIMO Systems

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1804-1817页

作者： Chen, Yijie Xia, Wenchao Cai, Shu Zheng, Gan Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications and Internet of Things Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China

Cell-free multiple-input multiple-output (MIMO) system is a promising solution for wireless communications. However, the potential in improving system performance is restricted due to the capacity-limited fronthaul links. To tackle this problem, we utilize the compress-and forward approach, which leverages various compression and encoding techniques to reduce the fronthaul overhead. Thus, jointly optimizing precoding and fronthaul compression presents a significant challenge. However, current optimization algorithms frequently entail high computational complexity because of their iterative processes, rendering impractical for real-time use. To overcome this challenge, we present a model-driven deep learning-based framework to characterize the structure of precoding vectors and the compression matrix via low-dimensional parameters, leveraging the uplink-downlink duality as the expert knowledge. A deep neural network can learn these low-dimensional "key features" from channel state information. Simulation results indicate that our approach achieves performance comparable to the optimal algorithm and substantially reduces complexity. © 2013 IEEE.

关键词： Channel state information

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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AMA-Net: Adaptive Masking Attention Network for Agricultural Crop Classification From UAV Images

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

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Beamforming Design for RIS-Aided ISCC in Internet of Vehicles Systems

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IEEE Internet of Things Journal 2025年第9期12卷 12153-12165页

作者： Yang, Ruihang Wang, Dezhi Zhu, Shiyin Zhu, Chen Bao, Jianrong Yang, Zhaohui Huang, Chongwen Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Science and Technology of China Hefei230026 China New H3C Technologies Company Ltd. Hangzhou310052 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China

With the development of communication technology, the Internet of Vehicles (IoV) is becoming increasingly important, enabling vehicle-to-everything communication for real-time information exchange and processing, thereby significantly enhancing traffic efficiency and safety. In this article, we consider a joint beamforming design problem in IoV, where the objective is to minimize transmission power, computation rate, and communication rate within the integrated sensing, communication, and computation (ISCC) framework. Moreover, reconfigurable intelligent surfaces (RISs) can provide additional spatial degrees of freedom to enhance the performance of ISCC systems in IoV within limited spectrum, energy resources, and complex interference management. To address the joint beamforming design problem, we present a cooperative beamforming algorithm called weight performance optimization (WPO), which explores three single-objective optimization problems in sensing, computation, and communication within the IoV context, using alternating optimization (AO) to simplify and solve these foundational elements of the WPO framework within limited resources and vehicle mobility, enhancing resource distribution while maintaining a balance between power efficiency and system performance. Numerical results demonstrate the efficiency and potential advantages of our proposed algorithms. Specifically, the results show that the sensing error of the WPO algorithm is reduced by up to 92.2% compared to existing popular algorithms, while the computation rate and communication rate are increased by more than 29.5% and 23.9%, respectively. © 2014 IEEE.

关键词： Array signal processing Sensors Interference Heuristic algorithms Reconfigurable intelligent surfaces Real-time systems Optimization Resource management Reliability Complexity theory

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Regulation of the sintering trajectory of Ho,Pr:Y_(2)O_(3) ceramics for 2.9μm mid-infrared lasers by atmospheric sintering

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Journal of Advanced Ceramics 2025年第1期14卷 81-89页

作者： Qing Li Fei Liang Jun Wang Yan Ling Xue Jie Ma Peng Liu Shiyu Sun Zhan Hui Jian Zhang Changhua Zhang Lili Hu Haohai Yu Huaijin Zhang Dingyuan Tang State Key Laboratory of Crystal Materials and Institute of Crystal Materials Shandong UniversityJinan 250100China Jiangsu Key Laboratory of Advanced Laser Materials and Devices School of Physics and Electronic EngineeringJiangsu Normal UniversityXuzhou 221116China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronics EngineeringEast China Normal UniversityShanghai 200241China Shanghai Branch Hefei National Laboratoryand Shanghai Research Center for Quantum SciencesShanghai 201315China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China Shanghai Institute of Ceramics Chinese Academy of SciencesShanghai 201899China Future Technology School Shenzhen Technology UniversityShenzhen 518118China

The sintering trajectory of the Ho,Pr:Y_(2)O_(3) ceramics could be effectively adjusted by sintering in a flowing oxygen atmosphere instead of *** final-stage grain growth was significantly suppressed by the use of oxygen atmosphere presintering,resulting in smaller average grain sizes than those of samples sintered under vacuum,while the same relative density was *** hot isostatic pressing(HIP),the oxygen presintered Ho,Pr:Y_(2)O_(3) ceramics achieved excellent optical quality,with transmittance exceeding 80%at a wavelength of 680 *** codoping of Pr^(3+) as deactivating ions effectively depopulated the lower energy level 5I7 during the Ho^(3+):^(5)I_(6)→^(5)I_(7) transition,thereby making the Ho,Pr:Y_(2)O_(3) ceramics more conducive to promoting population inversion in the 2.9μm laser wavelength range.

关键词： Y_(2)O_(3) ceramics sintering trajectory Ho^(3+)/Pr^(2+) mid-infrared laser fluorescence properties

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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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communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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Cross-Modal Haptic Compression Inspired by Embodied AI for Haptic communications

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IEEE Transactions on Multimedia 2025年

作者： Lu, Hang Tan, Xinmeng Chen, Mingkai Zhang, Zhe Zhang, Xuguang Chen, Jianxin Wei, Xin Zhao, Tiesong Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Science & Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China

Haptic data compression has gradually become a key issue for emerging real-time haptic communications in Tactile Internet (TI). However, it is challenging to achieve a tradeoff between high perceptual quality and compression ratio in haptic data compression scheme. Inspired by the perspective of embodied AI, we propose a cross-modal haptic compression scheme for haptic communications to improve the perception quality on TI devices in this paper. Since multimodal fusion is routinely employed to improve the ability of system in cognition, we assume that haptic codec is guided by visual semantics to optimize parameter settings in the coding process. We first design a multi-dimensional tactile feature fusion network (MTFFN) relying on multi-head attention mechanism. The MTFFN extracts the multi-dimensional features from the material surface and maps them to infer the coding parameters. Secondly, we provide second-order difference and linear interpolation to establish an criterion for the determination of optimal codec parameters, which are customized by the material categories so as to give high robustness. Finally, the simulation results reveal that our compression scheme can efficiently make a personalized codec procedure for different materials, obtaining more than 17% improvement in terms of compression ratio with high perceptual quality at the same time. © 2025 IEEE.

关键词： Data compression ratio

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FaceGCN: Structured Priors Inspired Graph Convolutional Networks for Blind Face Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yan, Weidan Shao, Wenze Zhang, Dengyin Xiao, Liang Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Facial image restoration has gained a tremendous progress since the increasing boom of the deep learning methods. Owing to its nature of strong ill-posedness, different categories of a-priori constraints have been harnessed or embedded in the existing deep architectures. While, as it turns to blind face restoration with more complicated degradations, the challenge becomes greater. In this paper, a further insightful step is taken by exploring the potentials of the graph convolutional networks (GCN) in conjunction with the structured priors for the blind problem. Specifically, a lightweight yet physically more intuitive model termed FaceGCN is proposed. On the one hand, a dynamic generator of facial adjacency matrices is constructed assisted by two self-supervised losses, allowing a sparse, accurate, and adaptive construction of case-specific face graphs with facial feature components as nodes. On the other hand, to model well the joint local-nonlocal correlations among various facial feature components, a kind of novel strip-attention GCN modules is correspondingly developed by splitting facial feature maps into intra- and inter-strips in both horizontal and vertical orientations, respectively. Extensive experimental results show that FaceGCN has achieved comparable or even superior performance to state-of-the-art methods, yet at a considerably less computational cost. © 1991-2012 IEEE.

关键词： Convolution

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