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Research on SDN Multi Controller Deployment based on K-means++

Journal of Physics: Conference Series 2020年第1期1606卷

作者： Li Yue Chen Junyan Liang Chuxin Lei Xiaochun School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi cloud computing and big data collaborative innovation center Guilin 541001 China Guangxi key laboratory of image and graphic intelligent processing Guilin 541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: the mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated.

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Network Resilience

arXiv

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arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Universal enzymatic numerical P systems with small number of enzymatic variables

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Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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Design of intelligent Robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication

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

arXiv

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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

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Millimeter-wave radar image analysis for the traffic sensing

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Journal of Physics: Conference Series 2020年第5期1507卷

作者： H M Wu F Qi J K Wang Y Wang School of communication science and engineering Northeastern University Liaoning Province Shenyang 110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength.

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Protecting privacy of users in brain-computer interface applications

arXiv

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arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Research Square

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Research Square 2021年 rs3rs-571332页

作者： Roth, Holger R. Xu, Ziyue Diez, Carlos Tor Jacob, Ramon Sanchez Zember, Jonathan Molto, Jose Li, Wenqi Xu, Sheng Turkbey, Baris Turkbey, Evrim Yang, Dong Harouni, Ahmed Rieke, Nicola Hu, Shishuai Isensee, Fabian Tang, Claire Yu, Qinji Sölter, Jan Zheng, Tong Liauchuk, Vitali Zhou, Ziqi Moltz, Jan Hendrik Oliveira, Bruno Xia, Yong Maier-Hein, Klaus H. Li, Qikai Husch, Andreas Zhang, Luyang Kovalev, Vassili Kang, Li Hering, Alessa Vilaça, João L. Flores, Mona Xu, Daguang Wood, Bradford Linguraru, Marius George NVIDIA BethesdaMD United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Division of Diagnostic Imaging and Radiology Children’s National Hospital WashingtonDC United States Radiology and Imaging Sciences National Institutes of Health Clinical Center BethesdaMD United States School of Computer Science and Engineering Northwestern Polytechnical University China Heidelberg Germany Lynbrook High School San JoseCA United States Shanghai Jiao Tong University China Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg School of Informatics Nagoya University Japan Biomedical Image Analysis Department United Institute of Informatics Problems Belarus Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga/Guimarães Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany 2Ai – Polytechnic Institute of Cávado and Ave Barcelos Portugal School of Medicine and Health Sciences George Washington University WashingtonDC United States

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A;n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020. © 2021, CC BY.

关键词： COVID-19

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