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Effects of quantum noise on teleportation of arbitrary two-qubit state via five-particle Brown state

Chinese Physics B 2024年第8期33卷 204-211页

作者：汪澳魏玉震姜敏李泳成陈虹黄旭 School of Electronics&Information Engineering Soochow UniversitySuzhou 215006China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China School of Information Engineering Huzhou UniversityHuzhou 313000China

We propose a new protocol for quantum teleportation(QT)which adopts the Brown state as the quantum *** work focuses on the teleportation of a single unknown two-qubit state via a Brown state channel in an ideal *** validate the effectiveness of our proposed scheme,we conduct experiments by using the quantum circuit simulator ***,we investigate the effects of four noisy channels,namely,the phase damping noise,the bit-flip noise,the amplitude damping noise,and the phase-flip ***,we employ Monte Carlo simulation to elucidate the fidelity density under various noise *** analysis demonstrates that the fidelity of the protocol in a noisy environment is influenced significantly by the amplitude of the initial state and the noise factor.

关键词： quantum communication Brown state fidelity

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Novel traveling quantum anonymous voting scheme via GHZ states

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Chinese Physics B 2023年第2期32卷 97-102页

作者：赵文浩姜敏 School of Electronics&Information Engineering Soochow UniversitySuzhou 215006China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)*** this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting *** voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting *** improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also *** with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and ***,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.

关键词： quantum anonymous voting quantum secure communication GHZ states verifiability privacy

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Microwave Photonics-Based Small Hydrostatic Pressure Sensing Assisted by Convolutional Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20948-20955页

作者： Li, Songlin Xue, Ting Wu, Yan Li, Zhuping Wu, Bin Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China Tianjin University State Key Laboratory of Precision Measuring Technology and Instruments Tianjin300072 China

Hydrostatic pressure measurement is essential in many industries, such as oil and gas production, chemical processing, and environmental monitoring. Due to the minimal impact of small hydrostatic pressure on ordinary silica optical fibers, research on its measurement utilizing optical fiber sensing technology remains limited. In this paper, a novel microwave photonics technique, termed optical carrier-based microwave interferometry (OCMI), is utilized for small hydrostatic pressure sensing with the assistance of a convolutional neural network (CNN). The theory of OCMI-based phase demodulation is established, and numerical simulations are conducted to investigate the factors affecting the axial displacement of the fiber core. In practical experiments, the phase demodulation method is applied to small hydrostatic pressure measurements;however, the results are suboptimal. Therefore, the CNN is developed to assist in the implementation of accurate small hydrostatic pressure sensing. The small hydrostatic pressures predicted by the well-trained CNN model are in good agreement with the actual values, with an error of less than 0.25 kPa. In addition, the prediction results from multiple Fabry-Perot interferometers demonstrate the feasibility and effectiveness of utilizing CNN for OCMI-based small hydrostatic pressure sensing. The introduction of machine learning broadens the application scope of the OCMI technique, allowing it to be employed for distributed sensing of a wider range of physical, chemical, and biological quantities. © 2001-2012 IEEE.

关键词： Interferometry

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Echo state network based symbol detection in chaotic baseband wireless communication

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Digital Communications and Networks 2023年第6期9卷 1319-1330页

作者： Huiping Yin Chao Bai Haipeng Ren Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an710048China Xi'an Key Laboratory of Intelligent Equipment Xi'an Technological UniversityXi'an710021China

The Chaotic Baseband Wireless Communication system(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting ***,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold *** unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)*** contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using *** proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative *** this approach,the calculation complexity is reduced compared to the previous ESN-based *** proposed method achieves better BER performance compared to the previous *** reason for this superior result is ***,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was ***,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information *** results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

关键词： Chaotic baseband wireless communication system(CBWCS) Inter-symbol interference(ISI) Echo state network(ESN) Threshold prediction

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Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Reinforcement learning for optimal tracking of large-scale systems with multitime scales

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Science China(information Sciences) 2023年第7期66卷 5-29页

作者： Jinna LI Hao NIE Tianyou CHAI Frank L.LEWIS School of Information and Control Engineering Liaoning Petrochemical University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute University of Texas at Arlington

This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A considerable contribution of this paper is the development of a data-driven SP-based RL method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral RL algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.

关键词： reinforcement learning singular perturbation multitime scales data-driven control

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Causal reasoning in typical computer vision tasks

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Science China(Technological Sciences) 2024年第1期67卷 105-120页

作者： ZHANG KeXuan SUN QiYu ZHAO ChaoQiang TANG Yang Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China National Key Laboratory of Air based Information Perception and Fusion Luoyang 471000China Luoyang Institute of Electro Optical Equipment of Avic Luoyang 471000China

Deep learning has revolutionized the field of artificial *** on the statistical correlations uncovered by deep learning-based methods,computer vision tasks,such as autonomous driving and robotics,are growing *** being the basis of deep learning,such correlation strongly depends on the distribution of the original data and is susceptible to uncontrolled *** the guidance of prior knowledge,statistical correlations alone cannot correctly reflect the essential causal relations and may even introduce spurious *** a result,researchers are now trying to enhance deep learningbased methods with causal *** theory can model the intrinsic causal structure unaffected by data bias and effectively avoids spurious *** paper aims to comprehensively review the existing causal methods in typical vision and visionlanguage tasks such as semantic segmentation,object detection,and image *** advantages of causality and the approaches for building causal paradigms will be *** roadmaps are also proposed,including facilitating the development of causal theory and its application in other complex scenarios and systems.

关键词： causal reasoning computer vision tasks vision-language tasks semantic segmentation object detection

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three-dimensional displays Image reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics

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Global output feedback adaptive stabilization for systems with long uncertain input delay

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Science China(information Sciences) 2023年第1期66卷 326-327页

作者： Zhuping WANG Jingcheng LIU Chao HUANG Hao ZHANG Huaicheng YAN School of Electronics and Information Engineering Tongji University Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology

Dear editor,The uncertain input delay is frequently encountered in engineering control systems. Adaptation is indispensable when the uncertain input delay is significant. In existing delayadaptive controllers [1–6], the actuator state must be measured to achieve global stability. Recently, a logic-based switching delay-adaptive state-feedback controller [7] was proposed to realize global stability without measuring the actuator state.

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