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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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LSU-NET: LIGHTWEIGHT automatic ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

引用

arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehiclesin multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. © 2025 IEEE.

关键词： Network function virtualization

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Layer normalized OpenGAN framework for human intrusion activity recognition in a φ-OTDR optical fiber sensing system

引用

Optics Express 2025年第10期33卷 20272-20295页

作者： Zhou, Zhengxian Wang, Xiankun Xie, Tianchang Qu, Jun Shi, Jianping Zhang, Dawei Peng, Jiansheng Zhuang, Songlin Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Anhui Normal University Wuhu 241000 China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Artificial Intelligence and Manufacturing Hechi University Guangxi 546300 China

Distributed optical fiber vibration sensing systems (DVS) are widely employed in perimeter security for their high sensitivity, simplicity, and strong immunity to electromagnetic interference. However, these systems are facing with two serious challenges: accurately classifying closed-set signals (known events) and detecting open-set signals (unknown events). To address this, we propose an open-set recognition framework, ResEff-OpenGAN-LN. By integrating layer normalization into the OpenGAN architecture, this framework mitigates instability caused by input feature variations while leveraging ResEff for efficient feature extraction to enhance closed-set classification. Experimental results show that ResEff achieves 99.92% accuracy on closed-set tasks, and ResEff-OpenGAN-LN obtains an AUROC of 0.9900 with 96.63% overall accuracy on mixed datasets containing open-set and closed-set signals, validating its potential to improve intrusion detection and reduce false alarms. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Deep learning Fiber Bragg grating sensors Fiber optic sensors Machine learning Neural networks Optical time domain reflectometry

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Fe2O3-Functionalized MoS2 Nanostructure Sensor for High-Sensitivity and Low-Level SO2 Detection

引用

ACS Sensors 2025年

作者： Gond, Rahul Barala, Suraj Shukla, Prajjwal Bassi, Gaurav Kumar, Suresh Kumar, Mukesh Kumar, Mahesh Rawat, Brajesh Department of Electrical Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India IDRP-Space Science and Technology Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Physics Indian Institute of Technology Ropar Punjab Rupnagar140001 India Department of Physics Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Real-time sulfur dioxide (SO2) monitoring is essential to mitigate its severe health and environmental impacts while ensuring compliance with industrial safety and emission regulations. Two-dimensional MoS2 stands out as a promising material for developing low-temperature-operated gas sensors due to its exceptionally high surface-to-volume ratio and ease of surface functionalization. However, monitoring the SO2 level faces challenges, including limited selectivity, sensitivity, and detection range, with high operating temperatures (200-600 °C) or external light source requirements. To address these issues, we present a highly sensitive SO2 sensor based on Fe2O3 nanoparticle-functionalized vertically aligned MoS2 nanostructure material, which is fabricated using a scalable sputtering process. The Fe2O3-MoS2 sensor exhibits a broad detection range from 100 ppb to 100 ppm, with a theoretical detection limit of around 22.8 ppb. When exposed to 5 ppm of SO2, the sensor achieves a response of around 32.2%, with response and recovery times of approximately 104 and 141 s, respectively. The fabricated sensor demonstrated impressive sensitivity (4.9%/ppm) for SO2 concentration in the range of 0.1 to 5 ppm, coupled with excellent reproducibility and stability at 150 °C. This enhanced performance is attributed to the catalytic effect of Fe2O3 and modulation of the heterojunction barrier at the interface. This study introduces a highly scalable, reliable, and stable Fe2O3-MoS2 sensor, which paves the way for developing energy-efficient and miniaturized SO2 sensors. © 2025 American Chemical Society.

关键词： Layered semiconductors

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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Clustering Strategy for Megaconstellation with Synergistic Energy and Size Considerations

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengxi Yin, Tianle Wang, Wenshuo Wu, Jin Wang, Jihe Qi, Yuhua He, Wei Jiangnan University Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Wuxi214122 China Beijing Institute of Technology Mechanical Engineering China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong SAR Hong Kong University of Science and Technology Beijing School of Intelligent Science and Technology Beijing China School of Aeronautics and Astronautics Shenzhen518107 China Sun Yat-sen University No. 135 Xingang West Road Guangzhou China

This paper proposes an energy- and size-aware clustering (ESC) strategy for megaconstellation systems. The methodology constructs a hierarchical architecture with designated cluster heads and member satellites through role differentiation. Cluster heads serve as relay nodes, reducing the communication load on ground infrastructure by interacting with ground stations and other entities. This hierarchical approach enables efficient constellation control through interactions primarily with cluster heads. The ESC strategy begins with a cluster head election process, incorporating orphan handling management to select cluster heads, ensuring full coverage of the entire constellation. Then, a two-stage multi-objective optimization framework is used to deal with division of members to clusters and intra-cluster multi-hop connectivity configurations. This dual-phase approach optimizes energy consumption while maintaining balanced cluster sizes. Simulations on the OneWeb system validate the ESC strategy as a viable solution in that it achieves 55.9% energy consumption reduction compared to traditional discrete gradient optimization methods while maintaining more balanced cluster sizes. © 2025 IEEE.

关键词： Multiobjective optimization

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Humic Acid Mediated Self-Signal Molecularly Imprinted Electrochemical Sensor for Cortisol Detection

SSRN

引用

SSRN 2025年

作者： Ma, Jiao Fang, Rong Li, Xiaoting Wang, Meiling Sun, Huijun Guo, Kunpeng Key Laboratory of Interface Science and Engineering in Advanced Materials Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China College of Materials Science and Engineering Taiyuan University of Technology Taiyuan030024 China Institute of Scientific and Technical Information of Shanxi NO.129 South Section of Binhe West Rd Wanbailin Dist Shanxi Taiyuan030024 China

When detecting cortisol through molecularly imprinted electrochemical sensors, it is essential to add additional redox probes to the detection solution. However, this method complicates the detection process, impedes real-time monitoring, and moreover it may cause serious interference during body fluid analysis. To address the issue, this study innovatively proposed a self-signal sensor based on humic acid (HA)-mediated redox nanoprobe. The amphiphilic HA, featuring a polycyclic aromatic core and numerous hydrophilic multifunctional groups, can effectively coat multi-walled carbon nanotubes through π-π interactions and meanwhile exhibited an exceptionally high adsorption capacity towards various types of redox molecules, particularly cationic redox species. Thus, a conductive redox nanoprobe could be obtained in a facile way with a strong response signal and acted as a bottom layer to prepare an electrochemical sensor in combination with cortisol-imprinted polydopamine as an upper layer. Finally, a self-signal sensor for cortisol was successfully fabricated with a detection limit of 1.45 × 10-9 M, as well as a wide detection range from 1 nM to 1 mM and moreover a good recovery in artificial human sweat. In all, we believe the HA-mediated strategy proposed in this study could inspire more innovation in the field of self-signal sensing. © 2025, The Authors. All rights reserved.

关键词： Multiwalled carbon nanotubes (MWCN)

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