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Zero-sum game-based formation control for multi-USVs under hybrid irregular output constraints

Nonlinear Dynamics 2025年 1-15页

作者： Tang, Daocheng Pang, Ning Wang, Xin Lei, Yan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Westa Southwest University Chongqing400715 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400044 China

This article investigates the finite-time leader-following formation control (LFC) problem for multiple unmanned surface vehicles (USVs) subject to disturbances and hybrid irregular (HI) output constraints. Here, the HI output constraint refers to a generalized output constraint that alternates irregularly among unilateral (only upper or lower) constraints, bilateral (both upper and lower) constraints, and free-constraint over time. To handle HI output constraints in multi-USV scenarios, a novel barrier function (BF) is proposed to uniformly transform both bilateral and unilateral constrained systems into unconstrained ones for controller designs. Subsequently, a zero-sum game (ZSG)-based controller is developed for multi-USVs to guarantee robust optimal control (ROC) against the worst-case disturbance without extra anti-disturbance module designs. Furthermore, the identifier-actor-critic reinforcement learning (IAC-RL) framework is designed in the ZSG-based control scheme, where the identifier, critic, and actor are utilized to estimate unknown system dynamics, evaluate system performance, and implement control policy, respectively. Finally, the effectiveness of the proposed control scheme is demonstrated through simulations of the multi-USV model. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Robust control

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Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges

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IEEE Access 2025年 13卷 80637-80665页

作者： Ali, Asad Jianjun, Huang Jabbar, Ayesha Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China

This review paper systematically explores the use of Federated Learning (FL) in Connected Autonomous Vehicles (CAVs) to improve vehicle performance, safety and user experience. FL presents a decentralized infrastructure that allows collaborative learning, while also ensuring data privacy, as CAVs increasingly rely on machine learning to process large amounts of sensor data. FL has been demonstrated to contribute to cooperative perception, vehicle trajectory prediction, traffic flow optimization, predictive maintenance, personalized in vehicle experiences, and advanced driver assistance systems (ADAS) through recent advances. Secondly, FL speeds up anomaly and intrusion detection, critical safety and security matters in CAV ecosystems. However, despite all these advancements, there are still challenges of data heterogeneity, communication overhead, security vulnerabilities, model convergence as well as regulatory compliance that constitute major barriers to FL deployment. This paper synthesizes recent contributions on this topic, identifies some gaps in the current research, and proposes future directions to fill those gaps. Federated Learning is a promising technology path towards driving innovations and improving the power and effectiveness of Autonomous Transportation Systems. © 2013 IEEE.

关键词： Differential privacy

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Identification of Non-Restorative Sleep Associated with Depression Using Ambulatory Electrocardiogram and Triaxial Acceleration

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IEEE Transactions on Biomedical Engineering 2025年第6期72卷 1909-1919页

作者： Liu, Li Wang, Chongyang Liu, Guangyuan Wen, Wanhui Southwest University Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Sichuan University Key Laboratory of Rehabilitation Medicine in Sichuan Province Department of Rehabilitation Medicine Institute of Rehabilitation Medicine West China Hospital Chengdu610041 China

Objective: Non-restorative sleep is prevalent among individuals with depression and is strongly associated with the severity of the condition. Therefore, identifying non-restorative sleep can aid in the early screening of depression. Investigating non-restorative sleep in depression necessitates long-term monitoring under naturalistic conditions. Methods: In this study, we recruited 149 participants and collected electrocardiogram and triaxial acceleration from them, resulting in a total of 761 nights of data. The period from midnight to 6:30 AM was segmented into 78 five-minute intervals, from which 40 physiological features were extracted for each interval. To deal with variations in sleep patterns across participants and dates, we reordered the sleep data based on levels of parasympathetic nervous system (PNS) activation to explore the underlying neural mechanisms of non-restorative sleep in individuals with depressive symptoms. Results: We developed a model that integrated convolutional neural networks with an attention mechanism to identify nonrestorative sleep in individuals with depressive symptoms. The model demonstrated impressive performance on an independent test set, achieving an accuracy of 81.25% and an F1 score of 77.85%. Additionally, Bayes' theorem was used to compute the posterior probability indicating nonrestorative sleep in this population, assessing abnormal PNS activation. Conclusion: Finally, we designed a system capable of automatically evaluating nighttime sleep status and quantifying changes in non-restorative sleep associated with depression. Significance: This system offers a novel tool and method for the early identification of individuals at risk of depression. © 1964-2012 IEEE.

关键词： Sleep research

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RECONSTRUCTION-BASED A POSTERIORI ERROR ESTIMATES FOR THE L1 METHOD FOR TIME FRACTIONAL PARABOLIC PROBLEMS

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Journal of Computational Mathematics 2025年第2期43卷 345-368页

作者： Jiliang Cao Aiguo Xiao Wansheng Wang Department of Mathematics Shanghai Normal UniversityShanghai 200234China Hunan Key Laboratory for Computation and Simulation in Science and Engineering&Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan UniversityXiangtan 411105China

In this paper,we study a posteriori error estimates of the L1 scheme for time discretizations of time fractional parabolic differential equations,whose solutions have generally the initial *** derive optimal order a posteriori error estimates,the quadratic reconstruction for the L1 method and the necessary fractional integral reconstruction for the first-step integration are *** using these continuous,piecewise time reconstructions,the upper and lower error bounds depending only on the discretization parameters and the data of the problems are *** numerical experiments for the one-dimensional linear fractional parabolic equations with smooth or nonsmooth exact solution are used to verify and complement our theoretical results,with the convergence ofαorder for the nonsmooth case on a uniform *** recover the optimal convergence order 2-αon a nonuniform mesh,we further develop a time adaptive algorithm by means of barrier function recently *** numerical implementations are performed on nonsmooth case again and verify that the true error and a posteriori error can achieve the optimal convergence order in adaptive mesh.

关键词： Time fractional parabolic differential equations A posteriori error estimates Ll method Fractional integral reconstruction Quadratic reconstruction

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Visual Prompt Flexible-Modal Face Anti-Spoofing

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2597-2606页

作者： Yu, Zitong Cai, Rizhao Cui, Yawen Liu, Ajian Chen, Changsheng Great Bay University School of Computing and Information Technology Dongguan523000 China Nanyang Technological University ROSE Lab School of EEE 639798 Singapore Hong Kong Polytechnic University Kowloon Hong Kong Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen518060 China

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS (Yu et al. 2023) has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose Visual Prompt flexible-modal FAS (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training. © 2004-2012 IEEE.

关键词： Alignment

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A method of embedding a high-resolution image into a large field-of-view image

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Multimedia Tools and Applications 2025年第14期84卷 13037-13056页

作者： Dong, Yanmei Pei, Mingtao Jia, Yunde Key Laboratory of Intelligent Information Processing and Graph Processing Tus College of Digit Guangxi University of Science and Technology Liuzhou545006 China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China

Images captured by wide-angle cameras or fisheye cameras are with large Field-of-View (FOV) but low resolution, while images captured by conventional cameras are with high-resolution but limited FOV. To handle this contradiction, in this paper, we propose to improve the resolution of a large FOV image by embedding a high-resolution image into it. We present an image embedding method based on non-rigid transformations and mesh optimization. Without any radial distortion correction on large FOV images, we utilize non-rigid transformations to model the mathematical transformation relationship between the high-resolution image and the large FOV image. Meanwhile, we exploit mesh optimization to generate evenly distributed control points for maintaining the shape and content of the large FOV image. To verify the effectiveness of the proposed method, we capture some image pairs from a conventional camera and a large FOV camera, and exploit several classic image alignment methods to implement four baseline methods. The experimental results show that our method preserves the shape and content of the original FOV image from 58% to 82%, which significantly outperforms all baseline methods. The results indicate that the proposed method can effectively embed a high-resolution image into a large FOV image as well as preserve the shape and content of the large FOV image. In other words, the resolution of a large FOV image can be improved via high-resolution image embedding. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Metalens-based hybrid probe near-infrared endoscope objective

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Optics Express 2025年第3期33卷 5757-5768页

作者： Song, Yichen Mao, Shan Tang, Yufeng Zhao, Jianlin Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University Xi’an710129 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi’an710119 China

Rapid large-area deep tissue imaging at long working distances is important for clinical diagnosis. A near-infrared metalens-based hybrid probe con-focal micro-endoscope (pCM) objective with a large field of view of 500 µm, a long working distance of 300 µm, and a small caliber of 2.2 mm was designed, which is compatible with conventional endoscopes to produce cell-resolution images of deep tissues. The performance of the metalens-based hybrid objective optical system was analyzed. Results show that the metalens-based hybrid pCM significantly improves the imaging quality of such optical systems under the premise of meeting the design accuracy requirements, which provides a new solution and an effective realization for the design of such optical systems. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Infrared devices

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Hybrid meta-optics facilitate an athermal continuous zoom optical system

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Optics Express 2025年第8期33卷 17121-17135页

作者： Tang, Yufeng Mao, Shan Song, Yichen Zhao, Jianlin Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University Xi’an710129 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi’an710119 China

To meet the demand for continuous zoom capabilities in compact lenses over a wide temperature range, we designed a compact continuous zoom optical system based on hybrid meta-optics. This system can achieve thermal stability without additional heating or cooling across a broad temperature range, utilizing commonly available optical glass. It features a total length of just 9∼12 mm and comprises only 4 lenses. By incorporating metalens, the system effectively corrects chromatic aberration and off-axis distortion, while leveraging the thermal stability of these lenses to achieve passive thermal management. Analytical results demonstrate that the system is structurally compact, capable of smooth continuous zooming from -20∼+60 ℃ with a focal length range of 4.9∼9.8 mm, and maintains excellent imaging quality throughout (with a modulation transfer function greater than 0.5 at 100 lp/mm). Additionally, the zoom and tolerances are highly feasible. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Aberrations

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Positioning Error Compensation via Channel Knowledge Map for UAV Communication

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IEEE Internet of Things Journal 2025年第11期12卷 16977-16989页

作者： Zhang, Chiya Wang, Ting He, Chunlong Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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Computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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