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GPDP: Game-Enhanced Personalized Differentially Private Smart Community

GPDP: Game-Enhanced Personalized Differentially Private Smar...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yuping Zhang Youyang Qu Longxiang Gao Bruce Gu Lei Cui Xuemeng Zhai School of Network & Communication Chendu Technological University Chendu Sichuan China School of Information Technology Deakin University Geelong VIC Australia College of Engineering and Science Victoria University Footscary VIC Australia Key Laboratory of Advanced Control and Intelligent Information System Taiyuan University of Science and Technology Taiyuan Shanxi China College of Engineering and Science University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9781665417631

Smart community, as a key component of Internet of Things (IoT), is experiencing fast booming, which significantly enhances residents' quality of life. Using fog computing to establish a smart community is an effective way to further develop smart and sustainable cities due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging because the continuous attacks are putting sensitive information under great threats. Motivated by this, we propose a differentially private smart community model with personalized privacy protection. Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Extensive evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility.

关键词： Differential privacy Privacy Sensitivity Smart cities Computational modeling Bandwidth Data models

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Quantum gyroscopes based on double-mode surface-acoustic-wave cavities

arXiv

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arXiv 2024年

作者： Zhu, Yuting Xue, Shibei Ju, Fangfang Yuan, Haidong Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong School of Physics and Electronics Hunan Normal University Changsha410081 China

Recent progress shows that a surface-acoustic-wave (SAW) cavity can not only induce quantum acoustic dynamics but also can form optomechanical-like systems. Its operating frequencies in the microwave band make it resistant to the thermal noise of surrounding environments, while its radiation-pressure couplings make it susceptible to weak forces. Based on these advantages, we propose a gyroscope comprising coupled microwave-SAW cavities. In this paper, we systematically consider the three indices including range, signal-to-noise ratio, and sensitivity, which are the most important to gyroscopes but only partially considered in existing works. Additionally, we establish the fundamental limits of sensitivity when the quantum input is in the vacuum state and the squeezed vacuum state. We find that squeezing improves sensitivity and can surpass the standard quantum limit. However, this improvement can only reach up to √2/2 even as the squeezed parameter approaches infinity, which is rarely noted in recent works. Finally, we also offer analytical constraints for cooperativity and squeezed parameters. These constraints can be utilized to design gyroscopes based on coupled cavities in experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Gyroscopes

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Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource-Limited Platforms

Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource...

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International Conference on control and Automation (ICCA)

作者： Xin Liu Qi Guan Shibei Xue Dezong Zhao Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China James Watt School of Engineering University of Glasgow Glasgow U.K.

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Accurately estimating the six-degree-of-freedom pose of objects is essential for intelligent robotics. Although significant progress has been made in this area, most studies fail to account for specific hardware limitations for model deployment, which remains a major challenge for resource-constrained scenarios. To address this issue, we propose Lite-HRPE, a lightweight RGB-based pose estimation method, which leverages a multi-branch parallel structure to extract spatial and semantic information of key points for pose estimation. Additionally, Lite-HRPE adopts the G-block and G-neck modular structure and streamlines the original feature extraction network to realize a compact network structure. This allows Lite-HRPE to strike a balance between pose estimation accuracy, parameter count, computational load, and runtime speed. Our evaluation on public datasets shows that Lite-HRPE achieves a 95.7% accuracy with only 10.8% of the number of parameters and 11.8% of the FLOPs compared to Hybridpose.

关键词： Training Accuracy Runtime Pose estimation Semantics Feature extraction Real-time systems

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Analysis of non-Markovian passive quantum linear systems’ response to single-photon input fields*

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IFAC-PapersOnLine 2023年第2期56卷 5179-5184页

作者： Zhengyi Sun Shibei Xue Zibo Miao Zhiyuan Dong Dewei Li Lulu Pan Min Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 P. R. China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen 518055 P. R. China School of Electronics and Information Engineering Soochow University Suzhou 215006 P. R. China

In this paper, we analyze the response of passive quantum linear systems in a non-Markovian environment to single photon input fields. Based on an augmented modelling method for non-Markovian quantum systems, analytic forms of output intensity and output covariance function in steady-state are derived. These results allow us to quantitatively analyze the influence of non-Markovian environment on system response under a framework of linear system theory.

关键词： Non-Markovian quantum systems single photon state passive quantum linear system response theory

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Correction to: Visual Servoing of Unknown Objects for Family Service Robots

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Journal of intelligent & Robotic systems 2022年第4期106卷 1-1页

作者： Xin, Jing Dong, Caixia Zhang, Youmin Yao, Yumeng Gong, Ailing Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jinghao Zhang Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Department of Automation Tsinghua University Beijing P. R. China

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.

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Spirometer and sEMG respiratory patterns for clinical decision support system

Spirometer and sEMG respiratory patterns for clinical decisi...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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Generation of hexapartite entanglement in a four-wave-mixing process with a spatially structured pump: Theoretical study

引用

Physical Review A 2020年第2期102卷 022417-022417页

作者： Hailong Wang Kai Zhang Nicolas Treps Claude Fabre Jun Zhang Jietai Jing State Key Laboratory of Precision Spectroscopy Joint Institute of Advanced Science and Technology School of Physics and Electronic Science East China Normal University Shanghai 200062 China College of Optical and Electronic Technology China Jiliang University Hangzhou 310018 China Laboratoire Kastler Brossel Sorbonne Université CNRS ENS-PSL Research University Collège de France 4 place Jussieu 75252 Paris France UMich-SJTU Joint Institute and Key Laboratory of System Control and Information Processing (MOE) Shanghai Jiao Tong University Shanghai 200240 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Department of Physics Zhejiang University Hangzhou 310027 China

We have experimentally shown in a recent paper [Wang et al., Phys. Rev. A 95, 051802(R) (2017)] that a four-wave mixing (FWM) process in a Rb vapor with a spatially structured pump (SSP), consisting of a coherent combination of two tilted pump beams, can generate six well-separated bright beams that are quantum correlated. The experimental setup is compact, simple, phase insensitive, and easily scalable to a larger number of quantum correlated beams. For applications to quantum information processing, multipartite entanglement is needed in addition to quantum correlations. Very recently, we have also experimentally demonstrated the generation of the hexapartite entanglement from this SSP-based FWM process [Zhang et al., Phys. Rev. Lett. 124, 090501 (2020)]. The purpose of this theoretical paper is to extensively investigate the hexapartite entanglement properties by using different entanglement witnesses such as the van Loock-Furusawa criterion and the positivity under partial transposition criterion. We find that hexapartite entanglement is indeed present among the six beams. In addition, we also study the supermodes of this system using basis change.

关键词： Entanglement production Quantum entanglement

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

引用

arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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