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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Wang, Zixu Lei, Lei Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China College of Information Science and Engineering Jiaxing University China

ISBN: (纸本)9781510689251

Affine registration is a pivotal task in medical image processing, especially when automated methods are needed to handle large-scale datasets. Despite its importance, there remains a notable gap in assessing the standalone performance of affine registration networks. Traditional optimization-based methods often suffer from inefficiency and limited generalizability. CNN-based approaches, while useful, struggle with receptive field limitations in managing long-range dependencies. Similarly, pure Vision Transformer (ViT)-based methods grapple with high computational demands due to their quadratic scaling of time complexity with the number of image patches, resulting in significant training and inference time. This paper introduces the Coarse-to-Fine Swin Vision Transformer (C2FSwinT), a model designed for fast and robust image affine registration. Central to our model is the Swin Transformer Block, which confines self-attention to local windows, markedly reducing computational complexity. Our architecture further incorporates a multi-stage progressive approach to enhance feature extraction. Additionally, the model integrates techniques such as pre-processing for centroid registration, non-overlapped patch embedding, and decoupled atomic transformation to refine the learning of affine transformation matrices. Experimental evaluations demonstrate that C2FSwinT surpasses other state-of-the-art methods, including conventional optimization-based and ViT-based approaches, in terms of registration accuracy, generalizability, and efficiency in training and inference times. © 2025 SPIE.

关键词： Image registration

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Novel Adaptive Control for Flexible-Joint Robots with Unknown Measurement Sensitivity

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IEEE Transactions on Automation science and engineering 2024年第2期21卷 1445-1456页

作者： Diao, Shuzhen Sun, Wei Su, Shun-Feng Zhao, Xudong Xu, Ning Liaocheng University School of Mathematics Science Liaocheng252000 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Bohai University College of Information Science and Technology Jinzhou121013 China

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method. Note to Practitioners - This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dyn

关键词： Lyapunov functions

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A Transparent Ultra-wideband Antenna Fed by CPW Based on Characteristic Mode Theory

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Applied Computational Electromagnetics Society Journal 2024年第11期39卷 987-998页

作者： Ren, Wanying Wang, Zhonggen Nie, Wenyan Mu, Weidong Li, Chenlu Wang, Mingqing School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Mechanical and Electrical Engineering Huainan Normal University Huainan232001 China School of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Electrical and Information Engineering Hefei Normal University Hefei230061 China

This paper proposes a high isolation transparent ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna based on characteristic mode theory. The antenna consists of four coplanar waveguide (CPW)-fed UWB monopoles, each of which is hollowed out into a mesh structure guided by feature mode theory with metal and substrates, with an excitation added at the feed point to successfully excite modes 1, 3, 5, and 7. These excited modes cover a frequency range of 1.7-13 GHz. An orthogonal layout is adopted to make the antenna structure more compact. Ultimately, a transparency of 76.3% and a radiation efficiency of over 85% are achieved, with an effective frequency band coverage of 1.32-12.15 GHz. A fan-shaped metal mesh (MM) decoupling structure is printed on the mesh structure of the antenna substrate, achieving isolation greater than 20 dB between any two components without reducing the antenna’s transparency. Across the entire operating frequency band, the gain of the antenna varies from 1.3 to 5.5 dBi within the effective bandwidth, and the envelope correlation coefficient (ECC) is less than 0.001. Additionally, the antenna exhibits good radiation characteristics. It can be easily manufactured at a low cost using traditional printed circuit board (PCB) and laser cutting techniques. © ACES.

关键词： Coplanar waveguides

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HEGD-FL: A Privacy-Preserving Decentralized Federated Learning Framework Based on Homomorphic Encryption 22

HEGD-FL: A Privacy-Preserving Decentralized Federated Learni...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Yao, Pengyu Zhang, Di Guo, Min Shao, Xun Beijing Jiaotong University School of Software Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan

ISBN: (纸本)9798331509712

Decentralized Federated Learning (DFL) has been proposed to address the potential single point of failure issues in traditional federated learning. However, existing DFL frameworks face challenges such as significant computational and communication overhead, along with low node collaboration efficiency, which limits their applicability in lightweight scenarios. Moreover, privacy-preserving mechanisms in DFL frameworks further increase these inefficiencies, adding delays and resource consumption. In this paper, we propose a group-based decentralized federated learning framework (HEGD-FL) that integrates homomorphic encryption to enhance privacy protection. Considering the computationally demanding nature of the Paillier homomorphic encryption method, we optimize its exponentiation and modular operations. To strengthen privacy preservation, we introduce a client selection mechanism and a contribution mechanism, dynamically adjusting client roles to ensure fairness and security throughout the federated learning process. Experimental results show that HEGD-FL effectively ensures data privacy without compromising the model's accuracy. At the same time, the group-based DFL framework design and encryption algorithm optimizations enhance system efficiency and broaden the framework's applicability. © 2024 IEEE.

关键词： Federated learning

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

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Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy 10

A Privacy-Preserving Decentralized Federated Learning Framew...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Lin Zhang, Di Guo, Min Shao, Xun Beijing Jiaotong University School of Software Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan

ISBN: (纸本)9798331520861

Federated learning allows machine learning models to be trained by local clients without sharing data directly, providing a degree of privacy protection. However, existing studies reveal that traditional federated learning is prone to issues like gradient leakage and model poisoning, which can compromise user privacy and negatively impact the performance of the global model. In this paper, we propose a Decentralized Differential Privacy Federated Learning (DDPFL) framework that strengthens privacy protection in federated learning by integrating decentralized design with differential privacy. First, we incorporate differential privacy noise through the Laplace mechanism, applying noise via a hierarchical processing scheme. Additionally, we control the noise addition by employing an adaptive gradient pruning strategy. Then, we develop a decentralized federated learning framework that employs a committee scoring mechanism. The core of this mechanism is a scoring system that determines which clients are eligible to contribute to the aggregation of the global model. Theoretical analysis confirms that the proposed DDPFL framework meets differential privacy requirements, with the level of privacy protection adjustable by selecting different values. Experimental results show that DDPFL exhibits strong resilience to model poisoning attacks, and that performance degradation can be managed effectively by selecting an appropriate privacy protection budget. © 2024 IEEE.

关键词： Federated learning

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

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Computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

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Effect of neodymium doping on structure,magnetic properties and microwave absorption performance of SrMnO_(3)

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Journal of Rare Earths 2024年第2期42卷 354-363,I0005页

作者： Jixiang Yue Lichun Cheng Jilei Xiong Qingrong Yao Man Wang Yucheng Chen Qianxin Long Xiaojian Guo School of Materials Science and Engineering Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratoryof Information Materials Guilin University of Electronic Technology Guilin541004China Anhui Jihua New Materials Co. Ltd.Anqing246300China

A simple composite Sr_(1-x)Nd_(x)MnO_(3)(x=0,0.05,0.1,0.15,0.2) nano powders were successfully prepared by the sol-gel method as a light-weight broadband electromagnetic wave *** effect of Nd^(3+)doping on the structure,microstructure and properties of SrMnO_(3) was *** is found that with the doping of Nd^(3+),the space group of the SrMnO_(3) changes from p63/mmc to pm3m,and the crystalline shape starts to change from hexagonal to cubic *** electron microscopy(SEM) and Brunauer-Emmett-Teller(BET) tests confirm that the particle sizes of the sample decrease and the specific surface area increases,which are attributed to the inhibition of grain growth after Nd^(3+)is doped.X-ray photoelectron spectroscopy(XPS) shows that after Nd^(3+)doping,the content of oxygen vacancy increases,and Mn^(4+) converts to Mn^(3+).Due to the defects of the materials,the polarization effect is enhanced,thus,the dielectric and magnetic properties of the doped samples are *** maximum reflection loss of the Sr_(0.85)Nd_(0.15)MnO_(3) is-33.41 dB at 7.84 GHz for a thickness of 2.4 mm,while Sr_(0.9)Nd_(0.1)MnO_(3) has the best bandwidth performance at 2.32 GHz with a reflection loss below-10dB.

关键词： Sr_(1-x)Mn_xO_(3) Crystal structure Microwave absorption Magnetic properties Nano powder Rare earths

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Generalized Orthogonal Chirp Division Multiplexing in Doubly Selective Channels

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IEEE Internet of Things Journal 2024年第12期12卷 18495-18507页

作者： Liu, Yun Zhao, Hao Yao, Huazhen Hu, Zeng Cui, Yinming Wan, Dehuan Guangdong University of Finance School of Internet Finance and Information Engineering Guangzhou510521 China Guangzhou Maritime University Department of Electronic Engineering Guangzhou510725 China Zhongkai University of Agriculture and Engineering School of Information Science and Technology Guangzhou510225 China South China University of Technology School of Electronics and Information Engineering Guangzhou510640 China Guangdong University of Finance School of Data Science and Artificial Intelligence Guangzhou510521 China

In recent years, orthogonal chirp division multiplexing (OCDM) has gained attention as a robust communication waveform due to its strong resistance to both time-domain and frequency-domain interference. However, similar to orthogonal frequency division multiplexing (OFDM), OCDM suffers from a high peak-to-average power ratio (PAPR), resulting in increased hardware costs and reduced energy efficiency of the transmitter's power amplifiers. In this work, we introduce a novel unitary transform called the Generalized Discrete Fresnel Transform (GDFnT) and propose a new waveform based on this transform, named Generalized Orthogonal Chirp Division Multiplexing (GOCDM). In GOCDM, data symbols from the constellation diagram are independently placed in the Generalized Fresnel (GF) domain. We derive the system's GF-domain channel matrix under a class of time-frequency doubly selective channels. These channels are characterized by multiple lags and multiple Doppler shifts (MLMDS), making them suitable for application scenarios such as vehicular mobile communication and narrowband underwater acoustic communication. We leverage the sparsity of the GF-domain channel matrix to design an iterative receiver based on the message-passing algorithm. Simulation results demonstrate that GOCDM achieves better PAPR performance than OCDM without compromising bit error rate (BER) performance. © 2014 IEEE.

关键词： Bit error rate

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