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Hierarchical representation via message propagation for robust model fitting

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arXiv 2020年

作者： Lin, Shuyuan Wang, Xing Xiao, Guobao Yan, Yan Wang, Hanzi The Fujian Key Laboratory of Sensing and Computing for Smart City The School of Informatics Xiamen University Xiamen361005 China The College of Computer and Control Engineering Minjiang University Fuzhou350108 China

In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting. Instead of analyzing the information of each data point or each model hypothesis independently, we formulate the consensus information and the preference information as a hierarchical representation to alleviate the sensitivity to gross outliers. Specifically, we firstly construct a hierarchical representation, which consists of a model hypothesis layer and a data point layer. The model hypothesis layer is used to remove insignificant model hypotheses and the data point layer is used to remove gross outliers. Then, based on the hierarchical representation, we propose an effective hierarchical message propagation (HMP) algorithm and an improved affinity propagation (IAP) algorithm to prune insignificant vertices and cluster the remaining data points, respectively. The proposed HRMP can not only accurately estimate the number and parameters of multiple model instances, but also handle multi-structural data contaminated with a large number of outliers. Experimental results on both synthetic data and real images show that the proposed HRMP significantly outperforms several state-of-the-art model fitting methods in terms of fitting accuracy and speed. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

Networked Integrated Sensing and Communications for 6G Wireless Systems

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arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Label-consistency based graph neural networks for semi-supervised node classification

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arXiv 2020年

作者： Xu, Bingbing Huang, Junjie Hou, Liang Shen, Huawei Gao, Jinhua Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification. We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

Eeg Connectivity - Informed Cooperative Adaptive Line Enhancer for Recognition of Brain State

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Eeg Connectivity - Informed Cooperative Adaptive Line Enhanc...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Saeid Sanei Clive Cheong Took Delaram Jarchi Ales Prochazka School of Science and Technology Nottingham Trent University UK Department of Electrical Engineering Royal Holloway University of London UK School of Computer Science and Electrical Engineering University of Essex UK Department of Computing and Control Engineering University of Chemistry and Technology Czech Republic

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Bursts of sleep spindles and paroxysmal fast brain activity waveforms have frequency overlap whilst generally, paroxysmal waveforms have shorter duration than spindles. Both resemble bursts of normal alpha activity during short rests while awake with closed eyes. In this paper, it is shown that for a proposed cooperative adaptive line enhancer, which can both detect and separate such periodic bursts, the combination weights are consistently different from each other. The outcome suggests that for accurate modelling of the brain neuro-generators, the brain connectivity has to be precisely estimated and plugged into the adaptation process.

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Two-party quantum private comparison based on eight-qubit entangled state

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arXiv 2021年

作者： Fan, Peiru Ur Rahman, Atta Ji, Zhaoxu Ji, Xiangmin Hao, Zhiqiang Zhang, Huanguo China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China College of Computer Information Science Fujian Agriculture and Forestry University Fuzhou35002 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China

The purpose of quantum private comparison (QPC) is to solve "Tiercé problem" using quantum mechanics laws, where the "Tiercé problem" is to judge whether the secret data of two participants are equal under the condition of protecting data privacy. Here we consider for the fist time the usefulness of eight-qubit entangled states for QPC by proposing a new protocol. The proposed protocol only adopts necessary quantum technologies such as preparing quantum states and quantum measurements without using any other quantum technologies (e.g., unitary operations and entanglement swapping), thus the protocol have advantages in quantum device consumption. The measurements adopted only include single-particle measurements, which is easier to implement than entangled-state measurements under the existing technical conditions. The proposed protocol takes advantage of the entanglement characteristics of the eight-qubit entangled state, and uses joint computation, decoy photon technology, the keys generated by a quantum key distribution protocol to ensure data privacy. We show that when all single-particle measurements in the proposed protocol are replaced by Bell measurements, the purpose of the protocol can also be achieved. We also show that the proposed protocol can be changed into a semi-quantum protocol with a few small changes. Copyright © 2021, The Authors. All rights reserved.

关键词： Data privacy

SuperMeshing: A new deep learning architecture for increasing the mesh density of metal forming stress field with attention mechanism and perceptual features

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arXiv 2021年

作者： Xu, Qingfeng Nie, Zhenguo Xu, Handing Zhou, Haosu Liu, Xinjun Tsinghua University Beijing100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing100084 China School of Computing and Information Systems Melbourne School of Engineering University of Melbourne Melbourne3000 Australia School of Mechatronical Engineering Beijing Institute of Technology Beijing100084 China Dyson School of Design Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom

In stress field analysis, the finite element analysis is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources, leading to curtailing efficiency during the design process. To eliminate this drawback, we propose a new data-driven mesh-density boost model named SuperMeshingNet that strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model, which consisting of Res-UNet architecture, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the attention mechanism and the perceptual features are utilized, enhancing the performance of SuperMeshingNet. Compared to the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on test data, which contains prior unseen cases. Based on the data set of metal forming, the comparable experiments are proceeded to demonstrate the high quality and superior precision of the reconstructed results generated by our model. The well-trained model can successfully show more excellent performance than the baseline and other methods on the multiple scaled mesh-density, including 2×, 4×, and 8×. With the refined result owning broaden scaling of mesh density and high precision, the FEA process can be accelerated with seldom cost on computation resources. We publicly share our work with full detail of implementation at https://***/zhenguonie/*** Codes 14J60 (Primary) 14F05, 14J26 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Finite element method

Lecture Notes in Control and Information Sciences

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Preface

Lecture Notes in control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China

SF-Net: Learning scene flow from rgb-D images with CNNs 29

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SF-Net: Learning scene flow from rgb-D images with CNNs

29th British Machine Vision Conference, BMVC 2018

作者： Qiao, Yi-Ling Gao, Lin Lai, Yu-Kun Zhang, Fang-Lue Yuan, Ming-Ze Xia, Shihong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China School of Computer Science and Informatics Cardiff University United Kingdom School of Engineering and Computer Science Victoria University of Wellington New Zealand

With the rapid development of depth sensors, RGB-D data has become much more accessible. Scene flow is one of the fundamental ways to understand the dynamic content in RGB-D image sequences. Traditional approaches estimate scene flow using registration and smoothness or local rigidity priors, which is slow and prone to errors when the priors are not fully satisfied. To address such challenges, learning based methods provide an attractive alternative. However, trivially applying CNN-based optical flow estimation methods does not produce satisfactory results. How to use deep learning to improve the estimation of scene flow from RGB-D images remains unexplored. In this work, we propose a novel learning based framework to estimate scene flow, which takes both brightness and scene flow losses. Given a pair of RGB-D images, the brightness loss is used to measure the disparity between the first RGB-D image and the deformed second RGB-D image using the scene flow, and the scene flow loss is used to learn from the ground truth of scene flow. We build a convolutional neural network to simultaneously optimize both losses. Extensive experiments on both synthetic and real-world datasets show that our method is significantly faster than existing methods and outperforms state-of-the-art real-time methods in accuracy. © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Luminance

A Survey of Error Analysis and Calibration Methods for MEMS Triaxial Accelerometers

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Computers, Materials & Continua 2020年第7期64卷 389-399页

作者： Bo Xiao Yinghang Jiang Qi Liu Xiaodong Liu Mingxu Sun Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and TechnologyNanjing210044China School of Computer&Software Nanjing University of Information Science&TechnologyNanjingChina School of Computing Edinburgh Napier UniversityEdinburghUK Centre for Health Sciences Research University of SalfordSalfordM54WTUK Shandong Beiming Medical Technology Ltd. Jinan250101China

MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application ***,the low accuracy limits its range of *** ensure data accuracy and safety we need to calibrate MEMS *** authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been *** this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful ***,the content of the accelerometer calibration method needs to be overcome.

关键词： MEMS accelerometer calibration error analysis accelerometer calibration