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arXiv 2020年

作者： Zhang, Xiao Wu, Dongrui Ding, Lieyun Luo, Hanbin Lin, Chin-Teng Jung, Tzyy-Ping Chavarriaga, Ricardo Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil Engineering and Mechanics Huazhong University of Science and Technology Wuhan430074 China Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia La Jolla CA92093 United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA92093 United States ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g., amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable;however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e., they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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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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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期000卷

作者： Zhang Wenge Liu Fang Jiao Licheng Gao Xinbo School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Ganssian scale mixture (BLS-GSM).

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled secondgeneration bandelet transform Canny operator threshold shrinkage

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Unsupervised Local Discrimination for Medical images

arXiv

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arXiv 2021年

作者： Chen, Huai Wang, Renzhen Wang, Xiuying Li, Jieyu Fang, Qu Li, Hui Bai, Jianhao Peng, Qing Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science The University of Sydney SydneyNSW2006 Australia Department of Ophthalmology Shanghai Tenth People’s Hospital Tongji University Shanghai200240 China The Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China The Changchun GeneScience Pharmaceutical Co. LTD China

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified through optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performances. The source codes are publicly available at https://***/HuaiChen-1994/LDLearning. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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A Novel 3D Chaotic System With Line Equilibrium: Multistability, Integral Sliding Mode control, Electronic Circuit, FPGA Implementation and Its image Encryption

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IEEE ACCESS 2022年 10卷 68057-68074页

作者： Sambas, Aceng Vaidyanathan, Sundarapandian Zhang, Xuncai Koyuncu, Ismail Bonny, Talal Tuna, Murat Alcin, Murat Zhang, Sen Sulaiman, Ibrahim Mohammed Awwal, Aliyu Muhammed Kumam, Poom Univ Muhammadiyah Tasikmalaya Dept Mech Engn Tasikmalaya 46196 Indonesia Univ Utara Malaysia Inst Strateg Ind Decis Modelling ISIDM Sch Quantitat Sci Sintok 06010 Kedah Malaysia Vel Tech Univ Res & Dev Ctr Chennai 600062 Tamil Nadu India Zhengzhou Univ Light Ind Coll Elect Informat Engn Zhengzhou 450001 Peoples R China Afyon Kocatepe Univ Dept Elect & Elect Engn TR-03204 Afyon Turkey Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates Kirklareli Univ Tech Sci Vocat Sch Dept Elect TR-39100 Kirklareli Turkey Afyon Kocatepe Univ Fac Technol Dept Mechatron Engn TR-03204 Afyon Turkey Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Univ Utara Malaysia Coll Art & Sci Sch Quantitat Sci Sintok 06010 Kedah Malaysia King Mongkuts Univ Technol Thonburi KMUTT Fac Sci Dept Math KMUTTFixed Point Res LabCtr Excellence Theoret & Room SCL 802 Fixed Point LabSci Lab Bldg Bangkok 10140 Thailand Gombe State Univ Fac Sci Dept Math Gombe 760214 Nigeria Gombe State Univ GSU Math Innovat Res GSU MIR Grp Gombe 760214 Nigeria China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan

This paper announces a novel three-dimensional chaotic system with line equilibrium and discusses its dynamic properties such as Lyapunov exponents, phase portraits, equilibrium points, bifurcation diagram, multistability and coexisting attractors. New synchronization results based on integral sliding mode control (ISMC) are also derived for the new chaotic system with line equilibrium. In addition, an electronic circuit implementation of the new chaotic system with line equilibrium is reported and a good qualitative agreement is exhibited between the MATlab simulations of the theoretical model and the MultiSim results. We also display the implementation of the Field-Programmable Gate Array (FPGA) based Pseudo-Random Number Generator (PRNG) by using the new chaotic system. The throughput of the proposed FPGA based new chaotic PRNG is 462.731 Mbps. Randomness analysis of the generated numbers has been performed with respect to the NIST-800-22 tests and they have successfully passed all of the tests. Finally, an image encryption algorithm based on the pixel-level scrambling, bit-level scrambling, and pixel value diffusion is proposed. The experimental results show that the encryption algorithm not only shuffles the pixel positions of the image, but also replaces the pixel values with different values, which can effectively resist various attacks such as brute force attack and differential attack.

关键词： Chaotic communication Encryption Mathematical models Bifurcation Integrated circuit modeling Sliding mode control Field programmable gate arrays Chaotic system hidden attractors ISMC electronic circuit FPGA image encryption

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Dynamical network size estimation from local observations

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NEW JOURNAL OF PHYSICS 2020年第9期22卷

作者： Tang, Xiuchuan Huo, Wei Yuan, Ye Li, Xiuting Shi, Ling Ding, Han Kurths, Juergen Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China Potsdam Inst Climate Impact Res Res Domain Complex Sci 4 D-14473 Potsdam Germany Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod Russia

Here we present a method to estimate the total number of nodes of a network using locally observed response dynamics. The algorithm has the following advantages: (a) it is data-driven. Therefore it does not require any prior knowledge about the model;(b) it does not need to collect measurements from multiple stimulus;and (c) it is distributed as it uses local information only, without any prior information about the global network. Even if only a single node is measured, the exact network size can be correctly estimated using a single trajectory. The proposed algorithm has been applied to both linear and nonlinear networks in simulation, illustrating the applicability to real-world physical networks.

关键词： dynamical system network size Hankel matrix

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Robustness of interdependent networks based on bond percolation

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EPL 2020年第3期130卷 38003-38003页

作者： Chen, Shiming Gao, Yanli Liu, Xueming Gao, Jianxi Havlin, Shlomo East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 830013 Jiangxi Peoples R China Boston Univ Dept Phys 590 Commonwealth Ave Boston MA 02215 USA Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Rensselaer Polytech Inst Network Sci & Technol Ctr Troy NY 12180 USA Bar Ilan Univ Dept Phys IL-52900 Ramat Gan Israel

Understanding the robustness of interdependent networks has attracted much attention in recent years. In many real scenarios, links may fail instead of nodes and how the interdependent networks behave in this case has not been adequately addressed. In this work, we investigate the link failures propagation mechanism for both two-layer and n-layer interdependent networks by using the self-consistent probabilities method which significantly simplifies the mathematical analysis of such systems. For bond percolation in which initial link failures occur in one layer, we find, analytically and via simulations, that the critical percolation threshold, p(c), of this system is lower than that of site percolation. Furthermore, for interdependent ER networks, in contrast to site percolation, bond percolation results show that p(c) varies nonlinearly with the inverse of average degree. We also find, for the case of bond percolation where initial link failures occur in all layers, that the critical percolation threshold is the same as that of site percolation, but the behavior of the giant component above p(c) is different. Our research brings insight to better understand the vulnerability of interdependent networks due to link failures.

关键词： 89 75 Hc 89 75 Fb 64 60 ah

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Design and implementation of a clock recovery circuit for fast Ethernet applications

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Chinese Journal of Systems Engineering and Electronics 2004年第4期000卷

A circuit architechure to realize clock recovery for fast Ethernet applications is presented, whick includies system architecture, modified Mueller Muller algorithm for 100BASE-TX, phase detector for 100BASE-TX and multiple output charge pump PLL. The clock recovery circuit is verified by TSMC 0.35um 1P5M CMOS process. The results show that this clock recovery circuit exactly extracts the timing information. It has advantages over others for simple and easy implementation.

关键词： clock recovery phase locked loop adaptive equalizer

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Application of a genetic algorithm in triangulation of a 3-D object surface

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International Journal of Computers and Applications 2000年第2期22卷 73-77页

作者： Zhenyu, Chen Mbede, J.B. Yan, Zhou Dehua, Li Hanping, Hu State Commn. Res. Open Lab. Image P. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Intelligent Contr. and Robotics Lab. Dept. of Contr. Sci. and Engineering Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Second Artillery Institute Huazhong Univ. of Sci. and Technol. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Second Artillery Institute China Wuhan University China Artificial Intelligence Department University of Edinburgh Inst. of AI and Pattern Recognition Huazhong Univ. of Sci. and Technol. China Cogn. Sci. Natl. Key Found. Res. P. Douala University Cameroon Tübingen University Germany Automatic Control Laboratory Darmstadt University Department of Service Cameroon Min. of National Education Robotics Laboratory Huazhong Univ. of Sci. and Technol. Wuhan China Assoc. Connectionnistes These-ACTH Rennes France IEEE Soc. of Robotics and Automation IEEE Society of Control Systems IASTED Assoc. Jeunes Chercheurs Robotique France Navy University of Engineering China Huazhong Univ. of Sci. and Technol. China Department of Computer Science Airforce Radar Acad. of Engineering China Inst. of P.R and AI Huazhong Univ. of Sci. and Technol.

This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.

关键词： Object recognition

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Machine discovery of partial differential equations from spatiotemporal data: A sparse Bayesian learning framework

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CHAOS 2023年第11期33卷 113122-113122页

作者： Yuan, Ye Li, Xiuting Li, Liang Jiang, Frank J. Tang, Xiuchuan Zhang, Fumin Goncalves, Jorge Voss, Henning U. Ding, Han Kurths, Juergen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China KTH Royal Inst Technol Div Decis & Control Syst S-10044 Stockholm Sweden Tsinghua Univ Sch Automat Beijing 100084 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30309 USA Univ Cambridge Dept Engn Cambridge England Univ Luxembourg Luxembourg Ctr Syst Biomed L-4362 Belvaux Esch Sur Alzett Luxembourg Cornell Univ Coll Human Ecol Cornell MRI Facil Ithaca NY 10065 USA Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China Potsdam Inst Climate Impact Res Res Domain IV Transdisciplinary Concepts & Methods D-14412 Potsdam Germany

This study presents a general framework, namely, Sparse Spatiotemporal System Discovery (S(3)d), for discovering dynamical models given by Partial Differential Equations (PDEs) from spatiotemporal data. S(3)d is built on the recent development of sparse Bayesian learning, which enforces sparsity in the estimated PDEs. This approach enables a balance between model complexity and fitting error with theoretical guarantees. The proposed framework integrates Bayesian inference and a sparse priori distribution with the sparse regression method. It also introduces a principled iterative re-weighted algorithm to select dominant features in PDEs and solve for the sparse coefficients. We have demonstrated the discovery of the complex Ginzburg-Landau equation from a traveling-wave convection experiment, as well as several other PDEs, including the important cases of Navier-Stokes and sine-Gordon equations, from simulated data.

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