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Achieving high-security and massive-capacity optical communications based on orbital angular momentum configured chaotic laser

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Advanced Photonics Nexus 2024年第4期3卷 92-100页

作者： Yanwei Cui Jianguo Zhang Zhongquan Nie Anbang Wang Yuncai Wang Taiyuan University of Technology Ministry of EducationCollege of Electronic Information and Optical EngineeringKey Laboratory of Advanced Transducers and Intelligent Control SystemTaiyuanChina National University of Defense Technology College of Advanced Interdisciplinary StudiesChangshaChina Guangdong University of Technology School of Information EngineeringGuangzhouChina Guangdong Provincial Key Laboratory of Photonics Information Technology GuangzhouChina

Secure and high-speed optical communications are of primary focus in information *** it is widely accepted that chaotic secure communication can provide superior physical layer security,it is challenging to meet the demand for high-speed increasing communication *** theoretically propose and experimentally demonstrate a conceptual paradigm for orbital angular momentum(OAM)configured chaotic laser(OAM-CCL)that allows access to high-security and massivecapacity optical *** 11 OAM modes and an all-optical feedback chaotic laser,we are able to theoretically empower a well-defined optical communication system with a total transmission capacity of 100 Gb∕s and a bit error rate below the forward error correction threshold 3.8×10^(-3).Furthermore,the OAM-CCL-based communication system is robust to 3D misalignment by resorting to appropriate mode spacing and beam ***,the conceptual paradigm of the OAM-CCL-based communication system is *** contrast to existing systems(traditional free-space optical communication or chaotic optical communication),the OAM-CCL-based communication system has threein-one characteristics of high security,massive capacity,and *** findings demonstrate that this will promote the applicable settings of chaotic laser and provide an alternative promising route to guide high-security and massive-capacity optical communications.

关键词： chaotic secure communication orbital angular momentum configured chaotic laser high security massive capacity misalignment

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Deployment of Heterogeneous Multi-Agent systems via a PDE Approach

Deployment of Heterogeneous Multi-Agent Systems via a PDE Ap...

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Chinese Automation Congress (CAC)

作者： Jingtao Man Zhigang Zeng Qiang Xiao Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China School of Artificial Intelligence and Automation (of Huazhong University of Science and Technology) Wuhan China

Spatial deployment of large-scale heterogeneous multi-agent systems (HMASs) over desired 2D or 3D curves is investigated in this paper. With assumption that HMASs consist of numerous first-order agents (FOAs) and second-order agents (SOAs) that could obtain local information of desired curves and their positions relative to their closest neighbors, the collective dynamics of large-scale HMASs are modeled as heterogeneous partial differential equations (PDEs). In particular, this paper introduces series-dependent topological weights between neighboring agents, which are more versatile and practical than constant topological weights commonly used in previous studies. A novel single-point control scheme is proposed, where an informed agent is situated between the last FOA and first SOA. This operation could not only ensure successful implementation of spatial deployment, but also guarantee well-posedness of the constructed heterogeneous error PDEs. By utilizing inequality techniques, sufficient conditions for exponential convergence of error system are derived. A numerical example is presented to demonstrate effectiveness of the proposed approaches.

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Accurate MTPA control Based on Ferrari Method for IPMSM with Partial Derivative Term Calculation 27

Accurate MTPA Control Based on Ferrari Method for IPMSM with...

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27th International Conference on Electrical Machines and systems, ICEMS 2024

作者： Li, Yiyang Gu, Xin Wang, Zhiqiang Guo, Liyan Chen, Wei Li, Xinmin Li, Chen School of Electronics and Information Engineering Tiangong University Tianjin China School of Electrical Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin China National Local Joint Engineering Research Center of Electrical Machine System Design and Manufacturing China Zhejiang University Advanced Electrical Equipment Innovation Center Hangzhou China

ISBN: (纸本)9784886864406

Even if the exact motor parameters are known, the MTPA strategy cannot obtain precise maximum torque per ampere (MTPA) points under large current amplitude because the partial derivative of inductance to current is ignored in the modeling process. In view of this, the MTPA mathematical model considering the partial derivative of inductance to current is established in this paper, and the MTPA point tracking error caused by ignoring the partial derivative in the modeling process based on Ferrari method is analyzed. Then, an offline-online combined method for calculating the partial derivative term is proposed to compensate the MTPA tracking error. On this basis, an MTPA control strategy based on Ferrari method considering the partial derivative of inductance to current is proposed. Experiment to verify the feasibility and effectiveness of the proposed method. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Permanent magnets

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RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

RegFormer: An Efficient Projection-Aware Transformer Network...

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International Conference on Computer Vision (ICCV)

作者： Jiuming Liu Guangming Wang Zhe Liu Chaokang Jiang Marc Pollefeys Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University ETH Zürich MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China University of Mining and Technology Microsoft

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://***/IRMVLab/RegFormer.

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Surface Defect Detection of Wooden Boards Based on Multi-Modal Fusion

Surface Defect Detection of Wooden Boards Based on Multi-Mod...

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Chinese Automation Congress (CAC)

作者： Hongchuan Zhang Song Zheng Yuhang Zhu Ye Lin Zhijie Ai Zhezhuang Xu Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

Automated wood defect detection is of great significance for improving wood utilization. However, the stains on the wood surface resemble defect features and it is difficult to distinguish the appearance of wane from defect-free regions. Relying solely on machine vision for extracting surface features of wood makes it difficult to achieve accurate defect detection. To address these challenges, we propose MMDNet, a multimodal detection network that combines point cloud and image data. By adaptively fusing point cloud and image features at multiple stages, the network effectively highlights wood surface defect regions. Additionally, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module into the network, which expands the network's receptive field and enables a comprehensive perception of the defects. Furthermore, we employ a deep supervision strategy to encourage the network to learn discriminative feature representations, enhancing the model's ability to differentiate defect regions. Experimental results validate the effectiveness of our method in detecting defects that are similar to the background and reducing interference from stains.

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Impact signal detection and recovery based on the novel potential well model

Impact signal detection and recovery based on the novel pote...

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第40届中国控制会议

作者： Shangbin Jiao Qing Zhang Shuang Lei Ding Xin Zhang Qing Wang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology

To further improve the output effect of the stochastic resonance system driven by the impact signal,this paper uses a novel potential well model as the nonlinear model of the *** the α stable noise environment,the detection performance of the stochastic resonance system driven by the impact signal is analyzed when the characteristic coefficient is used as the measurement *** effect of system parameters on the output effect is studied,and the detection of multiple impact signal are *** view of the distortion of the output signal waveform of the stochastic resonance system,the cause of the waveform distortion is analyzed based on the principle of particle dynamics,and the signal recovery system is *** output signal of the stochastic resonance system is recovered by using the similarity as the output system measurement *** simulation results show that the stochastic resonance system based on the novel potential well model has a better detection effect,and can effectively detect the time information of the impact *** recovery system can realize the estimation of the time-domain waveform of the impact signal under the background of strong noise.

关键词： Novel potential well model stochastic resonance impact signal detection signal recovery

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Visual Place Recognition via Semantic and Geometric Descriptor for Automated Valet Parking

Visual Place Recognition via Semantic and Geometric Descript...

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IEEE International Conference on Robotics and Biomimetics

作者： Jingrui Yu Jianbo Su Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665405362

Visual place recognition refers to identifying whether the current visual observation is from a place in a pre-built map and if so, which one. Accurate place recognition improves the localization accuracy of autonomous vehicles and helps them to park at designated places. Existing methods perform unsatisfactorily in AVP tasks due to the sparse texture of the parking lot, the varying viewpoints and the moving cars. To alleviate these problem, this paper proposes a robust and discriminative descriptor by encoding semantic information and geometric arrangement. A coarse-to-fine place recognition framework is also presented. Coarse place recognition candidates are identified by semantic information. Then, spatial consistency is performed to enhance the previous coarse results. Experiments in a parking lot demonstrate that the proposed framework achieves better performance where existing state-of-the-art methods fail.

关键词： Location awareness Visualization Laser radar Conferences Semantics Robot sensing systems Encoding

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NeRF in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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Cooperative Event Triggered control for Multi-Robot systems with Collision Avoidance

Cooperative Event Triggered Control for Multi-Robot Systems ...

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第40届中国控制会议

作者： Xiaoduo Li Xiang Yin Shaoyuan Li Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University

This paper investigates the safety critical control for multi-robot systems with event triggered mechanism and presents an algorithm to ensure the collision avoidance which satisfies safety constraints in the motion of robots. This is achieved by minimizing the difference between the actual and the nominal controllers subject to safety constraints. The barrier functionbased constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance. Finally, a numerical simulation is provided to verify the effectiveness of the theoretical algorithm.

关键词： Multi-Robot systems Collision Avoidance Event Triggered control

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