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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Mingliang SU Ken Lu School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

Sleep is an important part of human life. Recognition of sleeping position is an important part of judging the quality of sleep, warning disease and prevention of pressure sores. At present, the body pressure is being used as a commonly method to recognize sleep gesture, and most of it is based on methods of extracting features, mainly based on local signs and global features. This paper proposes a deep learning method based on CNN deep learning method to deal with recognition of body pressure image. Compared with the previous methods, the method based on CNN neural network achieves higher accuracy of recognition at a lower cost and is suitable for complex application environments, which has good practical value.

关键词： Sleep apnea Feature extraction Convolution Kernel Image recognition Biological neural networks Sensors

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A Study on Sleep Position Recognition of Body Pressure Image based on KPCA and SVM

A Study on Sleep Position Recognition of Body Pressure Image...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Yang Le Jone Sun School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

ISBN: (纸本)9781728137216

Sleep is an important part of life, and sleep position recognition is an important indicator which reflects sleep quality, warns diseases and prevents pressure sores. Actually, sleep quality is usually related to the body pressure, which affects muscle comfort and blood circulation during sleep. However, in order to regulate the body pressure, one vital step is to recognize the sleep position. Body pressure is a common indicator of the recognition of sleep position. This investigation put forward a body pressure recognition method combining with kernel principal component analysis (KPCA) and support vector machine. The feature extraction based on KPCA can well solve the nonlinear separable problems as well as dimension reduction processing of PCA, and reduce the system's complexity. Compared with the image recognition method and computer vision recognition method, it realized a higher recognition accuracy at a lower cost. Ultimately, the recognition accuracy of six common sleep positions reached 96.5%.

关键词： Sleep apnea Support vector machines Image recognition Mathematical model Feature extraction Training Principal component analysis

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Polarization-Orthogonal Nondegenerate Plasmonic Higher-Order Topological States

arXiv

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arXiv 2021年

作者： Li, Yuanzhen Xu, Su Zhang, Zijian Yang, Yumeng Xie, Xinrong Ye, Wenzheng Liu, Feng Xue, Haoran Jing, Liqiao Wang, Zuojia Chen, Qi-Dai Sun, Hong-Bo Li, Erping Chen, Hongsheng Gao, Fei Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University 2699 Qianjin Street Changchun130012 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Haidian Beijing100084 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China

Photonic topological states, providing light-manipulation approaches in robust manners, have attracted intense attention. Connecting photonic topological states with far-field degrees of freedom (DoFs) has given rise to fruitful phenomena. Recently emerged higher-order topological insulators (HOTIs), hosting boundary states two or more dimensions lower than those of bulk, offer new paradigms to localize/transport light topologically in extended dimensionalities. However, photonic HOTIs have not been related to DoFs of radiation fields yet. Here, we report the observation of polarization-orthogonal second-order topological corner states at different frequencies on a designer-plasmonic Kagome metasurface in the far field. Such phenomenon stands on two mechanisms, i.e., projecting the far-field polarizations to the intrinsic parity DoFs of lattice modes and the parity splitting of the plasmonic corner states in spectra. We theoretically and numerically show that the parity splitting originates from the underlying inter-orbital coupling. Both near-field and far-field experiments verify the polarization-orthogonal nondegenerate second-order topological corner states. These results promise applications in robust optical single photon emitters and multiplexed photonic devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Polarization

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Gaseous molecules-mediated electrochemical exfoliation of halogenated MXenes and its boosting in wear-resisting tribovoltaic devices

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Nature communications 2025年第1期16卷 5051页

作者： Qi Fan Minghua Chen Longyi Li Minghui Li Chuanxiao Xiao Tianci Zhao Long Pan Ningning Liang Qing Huang Lijing Yu Laipan Zhu Michael Naguib Kun Liang Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 P. R. China. University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing 100049 P. R. China. Beijing Key Laboratory of Micro-nano Energy and Sensor Center for High-Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China. Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 P. R. China. Ningbo New Materials Testing and Evaluation Center Co. Ltd Ningbo 315201 P. R. China. School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing 100124 P. R. China. Key Laboratory of Advanced Metallic Materials of Jiangsu Province School of Materials Science and Engineering Southeast University Nanjing 211189 P. R. China. Qianwan Institute of CNITECH Ningbo 315201 P. R. China. Key Laboratory of Materials and Surface Technology (Ministry of Education) Sichuan Energy Equipment Intelligent Engineering Research Center School of Materials Science and Engineering Xihua University Chengdu 610039 P. R. China. ljyu@***. University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing 100049 P. R. China. zhulaipan@***. Beijing Key Laboratory of Micro-nano Energy and Sensor Center for High-Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China. zhulaipan@***. Department of Physics and Engineering Physics Tulane University New Orleans LA 70118 USA. Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sc

Two-dimensional transition metal carbides and/or nitrides (MXenes), especially their few-layered nanosheets, have triggered burgeoning research attentions owing to their superiorities including extraordinary electrical conductivity, accessible active surface, and adjustable processability. Molten salts etching route further achieves their controllable surface chemistry. However, the method encounters challenges in achieving few-layered structures due to more complex delamination behaviors. Herein, we present an efficient strategy to fabricate Cl- or Br-terminated MXene nanoflakes with few-layers, achieved by electrochemical intercalation of Li ions and concomitant solvent molecules from the electrolyte solution, with gaseous propylene molecules to disrupt interlayer forces. By controlling cut-off voltages, the optimal protocol results in nanosheets with a recovery rate of ~93% and preserved surface chemistry. The resultant MXenes dispersions were employed as lubricants to enhance tribovoltaic nanogenerators, where TiCBr displayed superior electrical output. These findings facilitate the understanding of MXenes' intrinsic physical properties and enable the nanoengineering of advanced electronic devices.

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Orbital angular momentum multiplexing in highly reverberant environments

arXiv

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arXiv 2019年

作者： Chen, Xiaoming Xue, Wei Shi, Hongyu Yi, Jianjia Sha, Wei E.I. Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an710049 China Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

Previous studies on orbital angular momentum (OAM) communication mainly considered line-of-sight environments. In this letter, however, it is found that OAM communication with high-order modulation can be achieved in highly reverberant environments by combining the OAM multiplexing with a spatial equalizer. The OAM multiplexing exhibits comparable performance of conventional multiple-input multiple-output (MIMO) system. Copyright © 2019, The Authors. All rights reserved.

关键词： Reverberation

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Transforming heat transfer with thermal metamaterials and devices

arXiv

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arXiv 2020年

作者： Li, Ying Li, Wei Han, Tiancheng Zheng, Xu Li, Jiaxin Li, Baowen Fan, Shanhui Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Physics University of Colorado BoulderCO80309 United States School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Paul M. Rady Department of Mechanical Engineering University of Colorado BoulderCO80309 United States

| The demand for sophisticated tools and approaches in heat management and control has triggered fast development of emerging fields including conductive thermal metamaterials, nanophononics, far-field and near-field radiative thermal management, etc. In this review, we cast a unified perspective on the control of heat transfer, based on which the related studies can be considered as complementary paradigms toward manipulating physical parameters and realizing unprecedented phenomena in heat transfer using artificial structures, such as thermal conductivity in heat conduction, thermal emissivity in radiation, and properties related to multi-physical effects. The review is divided into three parts that focus on the three main categories of heat flow control, respectively. Thermal conduction and radiation are emphasized in the first and second parts at both macro- and micro-scale. The third part discusses the efforts to actively introduce heat sources or tune the material parameters with multi-physical effects in both conduction and radiation, including works using thermal convection. We conclude the review with challenges in this research topic and new possibilities about topological thermal effects, heat waves, and quantum thermal effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Metamaterials

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The design and implementation of a reconfigurable convolution operator based on APU

The design and implementation of a reconfigurable convolutio...

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2017 China Semiconductor Technology International Conference, CSTIC 2017

作者： Huang, Yuqian Huang, Jipan Wang, Xin'an Yang, Zhiqiang Lu, Haifang Tian, Miren Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School China

ISBN: (纸本)9781509066940

In order to achieve fast IC design, reduce development cycle and cost, key Lab. of integrated microsystems in Peking University proposed Array Processing for Unification Architecture(APU) which consists of four kinds of operators: computation, data path, control and MEM operators to replace the configurable logic block in current FPGA fabric. In this paper, a reconfigurable convolution operator based on APU, is presented which is used to convolutional neural network computing. The reconfigurable convolution operator is with more coarse-grain and function changeable than APU. And the process of operator synthesis is verified by simulation-based verification and formal verification separately. With the verification method proposed in this paper, certainty and completeness have been achieved. The results show that compared with the hardware design at the cost of resources based APU, this methodology can obtain hardware of suitable performance with regular structure with the cost of 34.08% less area and 25.26% lower power. © 2017 IEEE.

关键词： Neural networks

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EMI prediction of packages by deep neural network

EMI prediction of packages by deep neural network

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Hang Jin Hanzhi Ma Er-Ping Li The Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems and Applications College of Information Science & Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781509039555

This paper presents advanced analysis and design methods such as the deep neural network (DNN) to predict the complex electromagnetic interference (EMI) radiation problems at the package level. Since the conventional analysis and design methods could not cope with the current EMI problems efficiently. The proposed DNN model can achieve good accuracy to predict EMI radiation.

关键词： Electromagnetic interference Neural networks Computational modeling Numerical models Design methodology Predictive models Feature extraction

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医用增材制造——从前沿技术到产品研发

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engineering 2020年第11期6卷 31-41页

作者：邱贵兴丁文江田伟秦岭赵宇张联盟吕坚陈代杰袁广银吴成铁卢秉恒杜如虚陈继明 Mo Elbestawi 顾忠伟李涤尘孙伟赵远锦赫捷金大地刘斌张凯李鉴墨 Kam W.Leong 赵德伟郝定均敖英芳邓旭亮杨惠林徐少克陈英奇李龙樊建平聂国辉陈芸曾晖陈玮赖毓霄 Department of Orthopedics Peking Union Medical College Hospital Chinese Academy of Medical Science & Peking Union Medical College National Engineering Research Center of Light Alloys Net Forming & State Key Laboratory of Metal Matrix Composite Shanghai Jiao Tong University National Engineering Research Center of Mg Materials and Applications and School of Materials Science & Engineering Shanghai Jiao Tong University Department of Spine Surgery Beijing Jishuitan Hospital Center for Translational Medicine Research and Development Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Engineering Laboratory of Biomaterials Additive Manufacturing State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Center for Advanced Structural Materials City University of Hong Kong Shenzhen Research Institute Hong Kong Branch of National Precious Metals Material Engineering Research Center Department of Material Science and Engineering City University of Hong Kong Department of Mechanical Engineering City University of Hong Kong School of Pharmacy Shanghai Jiao Tong University China State Institute of Pharmaceutical Industry Shanghai Institute of Pharmaceutical Industry State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of the Chinese Academy of Sciences Micro- and Nano-Technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Shien-Ming Wu School of Intelligent Engineering Guangzhou International Campus South China University of Technology Beijing Engineering Research Center of 3D Printing for Digital Medical Health Beijing University of Technology Beijing Engineering Research Center of Laser Technology Beijing University of Technology Key Laboratory of Trans-Scale Las

1.医用增材制造原料的研发和挑战医用增材制造的原料与其他多个领域使用的原料具有广泛通用性,这是构成三维(3D)打印领域的重要基础。这些领域的增材制造在材料类型、粉体特性、成型性和黏弹性等方面面临着相同的问题和挑战。例如,航空...

1.医用增材制造原料的研发和挑战医用增材制造的原料与其他多个领域使用的原料具有广泛通用性,这是构成三维(3D)打印领域的重要基础。这些领域的增材制造在材料类型、粉体特性、成型性和黏弹性等方面面临着相同的问题和挑战。例如,航空领域采用了多种金属材料,而生物医学领域会使用金属、聚合物和无机材料。在生物医学领域,

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Quantum Dot LEDs: Stretchable Organometal‐Halide‐Perovskite Quantum‐Dot Light‐Emitting Diodes (Adv. Mater. 22/2019)

引用

Advanced Materials 2019年第22期31卷

作者： Yun‐Fei Li Shu‐Yu Chou Peng Huang Changtao Xiao Xiaofeng Liu Yu Xie Fangchao Zhao Yilong Huang Jing Feng Haizheng Zhong Hong‐Bo Sun Qibing Pei Department of Materials Science and Engineering Henry Samueli School of Engineering and Applied Science University of California Los Angeles Los Angeles CA 90095 USA State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University 2699 Qianjin Street Changchun 130012 China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Materials Science & Engineering Micro Nano Technology Center School of Physics Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing 100081 China State Key Lab of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Haidian Beijing 100084 China

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