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A delay system method to design of event-triggered control of networked control systems

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A delay system method to design of event-triggered control o...

IEEE Conference on Decision and Control

作者： Dong Yue Engang Tian Qing-Long Han Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei 430074 China School of Electrical and Automation Engineering Nanjing Normal University 78 Bancang Street Nanjing 210042 China Center for Intelligent and Networked Systems and the School of Information and Communication Technology Central Queensland University Rockhampton Qld 4702 Australia

ISBN: (纸本)9781612848006

The event-triggered H_(infinity) control design is investigated for networked control systems with uncertainties and transmission delays. A novel event-triggering scheme is proposed, which has some advantages over traditional ones with a continuous detector. Considering the effect of the transmission delay, a delay system model for the analysis is firstly constructed. Then, based on the model and Lyapunov functional method, criteria for the stability with an H_(infinity) norm bound and criteria for the co-design of both the feedback gain and the trigger parameters are derived. In order to solve the feedback gain and the trigger parameters, the linear matrix inequality technique is employed. From the simulation example, it can be concluded that the proposed event-triggering scheme is superior to some other event-triggering schemes in some existing literature.

关键词： feedback gain Networked control systems Event trigger Delay systems Scheme Co-design control systems design control

Fractional wavelet scattering network and applications

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arXiv 2018年

作者： Liu, Li Wu, Jiasong Li, Dengwang Senhadji, Lotfi Shu, Huazhong Ministry of Education Nanjing210096 China International Joint Laboratory of Information Display and Visualization Nanjing210096 China Nanjing210096 China INSERM U1099 Rennes35000 France Université de Rennes 1 Rennes35000 France Rennes35000 France Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China

Objective: The present study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network (ScatNet). Methods: In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this work, an application example of FrScatNet is provided in order to assess its performance on pathological images. Firstly, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location. Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is significantly improved in fractional scattering domain. We also compare the FrScatNet based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results. Conclusion: The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this work. Significance: The added fractional order parameter is able to analyze the image in the fractional scattering domain. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

MARL-based calibration for cameras with large FOV

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Optica Acta: International Journal of Optics 2024年第1-3期71卷

作者： Xinyu Yang Fuhan Chen Jingwen Jiang Hui Xie Qiaofeng Ou Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang People’s Republic of China

Achieving high-precision measurements in a large field of view (FOV) is a challenging task. The accuracy of vision measurements is determined by the quality of camera calibration, which is influenced by the pose of the target. To obtain suitable target pose, a target pose optimization method based on multi-agent reinforcement learning (MARL) is proposed. Firstly, the target pose optimization problem is modelled as a Markov decision process (MDP). Secondly, a multi-agent proximal policy optimization (MAPPO) algorithm for target pose optimization is designed by parameter sharing mechanism. Finally, optimization algorithm is adopted to camera calibration process. The calibration experiment was carried out under the large FOV of 4600 mm × 2300 mm. The results show that the back-projection error was 0.198 mm, the relative error of the diagonal length of target (505.847 mm) was 0.789 mm, and the success rate of large FOV camera calibration was 98.5%.

关键词： Camera calibration large FOV target pose optimization MARL MDP MAPPO

CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

Low-Cost and Portable Physiological Signal Monitor Using Physrate Model

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SSRN

SSRN 2023年

作者： Tan, Xudong Zhu, Yan Cheng, Zenghui Hu, Menghan Zhang, Xinxin Pei, Gan Yu, Chao Li, Qingli Li, Wenfang Wang, Jia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai200025 China Department of Emergency and Critical Care Shanghai Changzheng Hospital Shanghai200003 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

In this work, we propose a lightweight non-contact physiological signal monitor. The proposed system is equipped with the modified DeepPhys model and the rate estimation model PhysRate that enable non-contact physiological signal measurements without markers using the low-cost color camera. We introduce the constraint term guided by scene information into the original DeepPhys model to mitigate the overfitting problem of the original DeepPhys model. The results of validation experiments in ICU observation ward show that the modified DeepPhys model can effectively counteract the overfitting problem of the original DeepPhys model. The experimental results of PhysRate indicate that it exhibits certain performance improvements compared to other rate prediction models. In terms of system integration, the proposed physiological signal detection monitor includes Raspberry Pi 4B, RGB camera, 4G module, mobile power, and mobile terminal. With the help of above five modules, the system can analyze the captured signals at the edge and then transmit the processed data to the cloud via 4G network. The edge side is equipped with physiological signal detection model and the other data processing algorithms. On mobile devices, the users can view the relevant data through the developed Mini App. The PhysRate model code is available at https://***/ShawnTan86/PhysRate. © 2023, The Authors. All rights reserved.

关键词： Video signal processing

Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography

Improved rice algorithm of lossless compression for on-board images

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Improved rice algorithm of lossless compression for on-board...

International Conference on Multimedia Technology

作者： Wei, Yong-Wang Ding, Qing-Hai Luo, Hai-Bo Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China Graduate School of Chinese Academy of Science Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China Electrical and Information Engineering College Beihua University Jilin 132021 China Research Institute of General Development and Demonstration of Equipment Equipment Academy of Air Force Beijing100076 China

ISBN: (纸本)9781424478743

Aiming at the problems of the relatively lower compression ratio and the difficulty in hardware implementation regarding Rice lossless compression algorithm for the Space-borne Remote-sensing images, an improved Rice method is proposed in which the length of predictor FIFO is determined by the size of input images and the global unicode for differences is used. The improved Rice algorithm is mainly composed of two steps, MED prediction for the whole row data and Rice global uniform entropy coding. The statistical results show that it can significantly reduce the spatial redundancy of the adjacent pixels and the mean value of image differences. The compression bit rate is reduced approximately 0.4581 bpp(bit/pixel) against the original Rice algorithm. Furthermore, the new way reserves the all detail information of the input images throughout the whole encoding process. Because we split the all samples with the same mode, the proportion of the identifier of the splitting mechanic in the output data stream is reduced and the compression ratio is increased. The improvement can reduce the encoding complexity and be easily implemented for hardware application. ©2010 IEEE.

关键词： Signal encoding

Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

Few-Shot Class-Incremental Learning with Prior Knowledge

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arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment