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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Deep learning enabled fast optical characterization of two-dimensional materials

arXiv

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arXiv 2019年

作者： Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Leong, Wei Sun Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Palacios, Tomás Yin, Jihao Kong, Jing Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry Boston University BostonMA02215 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Instituto de Ciencia Molecular Universidad de Valencia c/Catedrático José Beltrán 2 Paterna46980 Spain Division of Materials Science and Engineering Boston University BostonMA02215 United States

Characterization of nanomaterial morphologies with advanced microscopy and/or spectroscopy tools plays an indispensable role in nanoscience and nanotechnology research 1 2, 3, 4, 5, as rich information about the chemical compositions, crystallography, other physical and chemical properties, as well as the growth mechanism can be extracted from morphology analysis. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features are often unused because of difficulties in processing the data and finding the correlations. Such difficulties can be well addressed by deep learning 6 7, 8, 9. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural network based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can be used to predict physical properties of the materials. Finally, a transfer learning technique is applied to adapt the pretrained network to more optical characterization applications such as identifying layer numbers of chemically synthesized graphene domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Data handling

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An improved proxy-based sliding mode control method for a wearable wrist exoskeleton

IPPTA: Quarterly Journal of Indian Pulp and Paper Technical ...

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IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association 2018年第1期30卷 458-467页

作者： Tu, Xikai Wu, Sai Li, Jialu Huang, Ming School of Mechanical Engineering Hubei University of Technology Wuhan China School of Industrial Design Hubei University of Technology Wuhan China School of Mechatronic Engineering Jiangsu Institute of Technology Changzhou China MOE Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

In order to improve the safety and adaptability during the robot-assisted rehabilitation of wrist joint, a wearable wrist exoskeleton rehabilitation device driven by pneumatic muscles is developed, and its control method is a new modified proxy-based sliding mode control (PSMC) strategy. PSMC created by Ryo Kikuuwe and Hideo Fujimoto, combines good tracking with control safety so it is well suitable for pneumatic muscle actuators. This paper build the dynamics models and compares the two kinds of modified PSMC with PSMC methods applied to the wrist rehabilitation exoskeleton. Experimental results demonstrate that the improved PSMC method performs better on tracking and less little degradation on safety when the system lies in a condition of uncertain resistance and changeable load. © 2018 Indian Pulp and Paper Technical Association. All rights reserved.

关键词： Sliding mode control

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A Simple Method on Generating any Bi-Photon Superposition State with Linear Optics

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Communications in Theoretical Physics 2017年第4期67卷 391-395页

作者：张婷婷魏杰王琴 Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China Key Laboratory of Quantum Information CASUniversity Science and Technology of ChinaHefei 230026China

We present a simple method on the generation of any bi-photon superposition state using only linear *** this scheme, the input states, a two-mode squeezed state and a bi-photon state, meet on a beam-splitter and the output states are post-selected with two threshold single-photon detectors. We carry out corresponding numerical simulations by accounting for practical experimental conditions, calculating both the Wigner function and the state fidelity of those generated bi-photon superposition states. Our simulation results demonstrate that not only distinct nonclassical characteristics but also very high state fidelities can be achieved even under imperfect experimental conditions.

关键词： bi-photon superposition state Wigner function state fidelity

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Efficient closed-form estimator for moving source localization using TDOA-FDOA-AOA measurements 17

Efficient closed-form estimator for moving source localizati...

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6th WASE International Conference on Information Engineering, ICIE 2017

作者： Deng, Bing Sun, Zheng Bo He, Qin Zhengzhou Institute of Information Science and Technology P.O. Box 450001 China National Key Laboratory of Science and Technology on Blind Signal Processing P.O. Box 610041 China

ISBN: (纸本)9781450352109

This paper1 proposes a simple and efficient closed-form solution for a moving source localization using the time difference of arrival (TDOA), frequency difference of arrival (FDOA) and angle of arrival (AOA) measurements obtained at spatially distributed moving receivers. Comparing with the localization scenario utilizing TDOA and AOA measurements only, this hybrid localization scheme by adding FDOA measurements can improve source position estimate accuracy and gain source velocity information in the same time. Theoretical analysis and simulations verify the approximate efficiency of the developed positioning technique and its improved performance over the benchmark iterative method. © 2017 Copyright held by the owner/author(s).

关键词： Time difference of arrival

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Subaperture image segmentation for lossless compression

Subaperture image segmentation for lossless compression

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Ionut Schiopu Moncef Gabbouj Alexandres Iosifidis Bing Zeng Shuaicheng Liu Laboratory of Signal Processing Tampere University of Technology Tampere Finland Institute of Image Processing University of Electronic Science and Technology of China Chengdu China

The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

关键词： image segmentation image edge detection image coding Prediction algorithms Cameras Context modeling Detectors

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A comparative study on the accuracy of two methods for time delay estimation 17

A comparative study on the accuracy of two methods for time ...

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6th WASE International Conference on Information Engineering, ICIE 2017

作者： Zhao, Pei Yan Qin, Ji Dong Peng, Hua Feng National Key Laboratory of Science and Technology on Blind Signal Processing P.O. Box 610041 China Zhengzhou Institute of Information Science and Technology P.O. Box 450001 China

ISBN: (纸本)9781450352109

This paper1 presents an accuracy analysis of Time Delay Estimation (TDE) in the passive location system. Firstly, the signal processing model using the information of envelope and phase are presented, respectively. Then the Cramer-Rao Lower Bounds (CRLB) for phase delay estimation is developed. The bound is shown to be inversely proportional to carrier wavelength, and for narrowband signals, independent of the signals bandwidth. Simulation results show that the theoretical analysis is correct, and the use of phase information alone or in combination with the envelope information is expected to significantly improve the accuracy of time delay estimation. © 2017 Copyright held by the owner/author(s).

关键词： Timing circuits

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Publisher Correction: Brain imaging with portable low-field MRI

Nature Reviews Bioengineering

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Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

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State and unknown input estimation using proportional multiple integral observer for uncertain Takagi-Sugeno fuzzy systems

State and unknown input estimation using proportional multip...

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2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017

作者： Elleuch, Ilyes Khedher, Atef Othmen, Kamel Ben Research Laboratory in Automatic Signal Processing and Imaging NationalSchool of Engineers Monastir Tunisia Research Laboratory: LARA Automatic ENIT Higher Institute of Applied Science and Technology Gafsa Tunis Tunisia

ISBN: (纸本)9781509059874

This paper deals with the problem of state and faults estimation for nonlinear uncertain systems described by Takagi Sugeno structures. In this work, we considered in this paper the case of actuator fault, they are considered as unknown inputs. The state and faults estimation is made using a structure of sliding mode observer where multiple integral term is added. This structure of observer named proportional multiple integral sliding mode observer. An adaptive mathematical form is used for the sign function to facilitate the determination of the proportional gains of the conceived observer. The integration of multiple integrals action ensures great precision to the fault estimation. The observer convergence conditions are formulated in LMI form allowing to compute the observer gains. The proposed sliding mode observer is applied in the case of a numerical example showing the efficiency of the fault and state estimation. © 2017 IEEE.

关键词： State estimation

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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