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

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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Task Offloading Scheduling and Privacy Protection Optimization in Vehicular Edge Computing Based on Double Deep Q-Network 18th

Task Offloading Scheduling and Privacy Protection Optimizat...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Weifeng, Lu Saijun, Yang Jia, Xu Lijie, Xu Lingyun, Jiang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Xianlin Street Jiangsu Nanjing210023 China

ISBN: (纸本)9789819621859

As the applications and services within vehicular networking systems become increasingly diverse, vehicles with limited computing resources face challenges in handling these computationally intensive and latency-sensitive tasks. In this paper, we propose an improved Task Offloading Scheduling Strategy based on a Double Deep Q-Network (TOSDDQN) for a dynamic multi-vehicle, multi-edge unit task offloading environment in vehicular networking. This strategy designs communication models and computational models aimed at minimizing the total system overhead in processing tasks. To further protect the privacy of vehicle users, a Distributed Training Algorithm based on Federated Learning (DTAFL) is proposed. Comparative experimental results indicate that, compared to three other task offloading schemes, our approach reduces the average system overhead in processing tasks by 11%–36%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computation offloading

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Exploring the Robustness: Hierarchical Federated Learning Framework for Object Detection of UAV Cluster

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Xingyu Zhang, Wenzhe Liu, Linfeng Xu, Jia Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

The deployment of Unmanned Aerial Vehicle (UAV) cluster is an available solution for object detection missions. In the harsh environment, UAV cluster could suffer from some significant threats (e.g., forest fire hazards, electromagnetic interference, and ground-to-air attacks), which could lead to the destruction of UAVs and loss of data. To this end, we propose a Hierarchical Federated Learning Framework for Object Detection (HFL-OD) to enhance the robustness of UAV cluster conducting object detection missions. In HFL-OD, UAVs are grouped through a Three-Dimensional (3D) graph coloring method, and an intragroup backup mechanism is provided to prevent the data loss caused by the destruction of UAVs. Besides, a dynamic server selection mechanism deals with the potential destruction of servers (cluster server and group servers) by adaptively reassigning the server roles. To further improve the robustness and mission efficiency of UAV cluster, a twotier federated learning framework is introduced to make a proper trade-off between object detection accuracy and communication/computational overhead. This framework is built on the concept of hierarchical federated learning by implementing both intragroup parameter aggregation and global parameter aggregation. Extensive simulations and comparisons demonstrate the superior performance of our proposed HFL-OD, i.e., the robustness of UAV cluster conducting object detection missions can be significantly improved, and the communication/computational overhead is effectively reduced. © 2002-2012 IEEE.

关键词： Fire hazards

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Hu, Chuanqi Wang, Jin Chen, Yikai Chen, Yonghang Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China National Elite Institute of Engineering Chongqing University Chongqing401135 China School of Mechanical Engineering Zhejiang University Hangzhou310058 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments. © 2025 IEEE.

关键词： Flight paths

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A Frequency and Polarization Reconfigurable Transparent Water Antenna

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Progress in Electromagnetics Research C 2025年 152卷 177-186页

作者： Li, Lei Gao, Jing Nan, Jingchang School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing transparent liquid ground. Water enhances outstanding transparency, excellent aesthetics, and high optical stealth performance for a wider range of application scenarios. The entire structure is made with polyvinyl chloride material and distilled water, except for the feed structure. By filling different cavities with liquid water, five different operating states are obtained in 1.924–2.5 GHz (26.1%), 1.67–2.33 GHz (33%), 0.644–2.288 GHz (112.1%), 1.975–2.54 GHz (25%), and 1.748–2.108 GHz (18.7%), achieving frequency reconfigurability. The antenna can be flexibly switched between linear polarization (LP) and two right-handed circular polarization (RHCP) states. The results show that the 3 dB axial ratio (AR) bandwidth covers 1.93–2.08 GHz (7.5%) and 2.06–2.132 GHz (3.5%). The antenna achieves high optical transparency of 100% and a peak gain of 7.97 dBi. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

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T3 Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics China National Engineering Laboratory for Big Data Analytics Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Peking University China Chinese University of HongKong Xi'an Jiaotong University China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46:6% cost, and supports 3:12× more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micro Base Stations

LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micr...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Mingming Gao Xinyu Wang Congying Wang Zhongxiang Zhang Shiquan Shan Yonghui Song Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications College of Electronics and Information Engineering Liaoning Technicial University Huludao Liaoning China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications.

关键词： Base stations Power measurement 5G mobile communication Power amplifiers Energy efficiency Power electronics Distance measurement Transistors Power generation Optimization

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Multi-UAV air combat cooperative game based on virtual opponent and value attention decomposition policy gradient

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Xu, Xiaojie Wang, Yunfan Guo, Xian Huang, Kuihua Zhang, Xuebo Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin Peoples R China Natl Univ Def Technol Coll Syst Engn Lab Big Data & Decis Changsha 410073 Peoples R China

In the multi-unmanned aerial vehicle (UAV) air combat confrontation environment, deriving the cooperative policy of friendly aircraft is still a challenge, owing to the higher-order differential dynamics model of aircraft and the confidence assignment problem in multi-UAV air combat with conflict and cooperation. In this paper, a novel reinforcement learning method that combines virtual opponent and value attention decomposition is proposed. In particular, to reduce the difficulty in training induced by the higher order differential dynamics model, the actions of aircraft are abstracted into actions of the game layer and maneuvering actions of the bottom layer, in which the actions of the game layer are modeled as the pose of the virtual opponent. In the training process, only the policy of the game layer is trained, and the maneuvering policy of the bottom layer is the default policy or the rule-based policy. To address the confidence assignment problem encountered during multi-UAV cooperative training, the total value function of the team is decomposed into individual value functions based on the attention mechanism, and the policy of the game layer is optimized by integrating the individual value into the gradient computation as the baseline. Finally, the algorithm is verified on the dynamic high-fidelity training platform. The results indicate that the algorithm outperforms the state-of-the-art method in typical multi-UAV air combat scenarios such as 4V4, 5V5, and 6V6.

关键词： Air combat cooperative game Reinforcement learning Value attention decomposition

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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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