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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Ryo Onishi Tadatomo Sato Teruji Ide Advanced Electrical and Information Systems Engineering Course (leading to Bachelor of Engineering) National Institute of Technology Kagoshima College Kirishima-shi Kagoshima Japan Dept. of Electrical and Electronic Engineering National Institute of Technology Kagoshima College Kirishima-shi Kagoshima Japan

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

In the time- and frequency-variant mobile radio channel such as the Fifth- and Forth-Generation mobile communications systems (5G, 4G), it is very important to estimate the channel coefficients (gains). We propose an estimation method of channel coefficients using a deep learning. The high accuracy of estimation can be evaluated using the deep learning method for super-resolution (SR) Network compared with the conventional method. The estimation of channel gains using orthogonal frequency-division multiplexing (OFDM) can be replaced with the SR problem and applied to the SR Network method. We show that the accuracy of the proposed method is higher than the conventional methods.

关键词： Deep learning Time-frequency analysis Accuracy OFDM Superresolution Channel estimation Estimation

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FPGA-Based In-Vehicle Occupancy Detection Using mmWave Radar With Mexican Hat Wavelet Transform

IEEE Sensors Letters

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IEEE Sensors Letters 2025年第5期9卷

作者： Mohan, Anand Meena, Hemant Kumar Wajid, Mohd Srivastava, Abhishek Malaviya National Institute of Technology Department of Electrical Engineering Jaipur302017 India Aligarh Muslim University Department of Electronics Engineering Aligarh202002 India International Institute of Information Technology Centre for VLSI Design Embedded System Technology Hyderabad500032 India

A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this letter. For the purpose of validating applications for vehicle occupancy detection, a hardware field programmable gate array (FPGA) platform, also known as Python productivity for zynq ultrascale+ MPSoC (PYNQ-ZU), is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of millimeter wave (mmWave) radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3-D point cloud images, two filters, top-view (TV), and front-view (FV), were used to improve vehicle occupancy detection. These filters transformed 3-D images into 2-D ones. TV filter was found to be more effective than the FV filter. After filtering the 2-D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with logistic regression (LR) and support vector machine producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection. © 2017 IEEE.

关键词： Image enhancement

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Advances in human organs-on-chips and applications for drug screening and personalized medicine

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Fundamental Research 2025年第3期5卷 1258-1272页

作者： Zhou, Chenyang Li, Zhangjie Lu, Kangyi Liu, Yijun Xuan, Lian Mao, Hongju Wang, Xiaolin Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai 200240 China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai 200444 China

The limitations of conventional animal tests and two-dimensional cell culture hinder their advancement in fundamental research and clinical/translational applications. As an emerging alternative technology, organ-on-a-chip serves as a platform that faithfully simulates the key phenotypical, physiological, and functional features of human tissues/organs through the accurate regulation of the parameters such as physical and biochemical microenvironment, as well as cellular patterns. In this review, we mainly introduce the recent progress in the organ-on-a-chip field, including lung, gut, heart, liver, vasculature and multiorgan studies. Furthermore, we highlight the potential applications in drug screening and personalized medicine. Finally, we conclude the review by addressing the current challenges and future perspective in the technology and commercialization of organ-on-chips. We anticipate that the development of organ-on-a-chip technology will revolutionize the studies on biology and medicine by providing new understanding of mechanisms of diseases and insights into clinical therapeutics. © 2024

关键词： Drug screening Microfluidics Organ-on-a-chip Organoid Personalized medicine

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The First Experimental Study of Monocular Visual Localization for Train using Deep Learning Method 9

The First Experimental Study of Monocular Visual Localizatio...

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9th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2023

作者： Wang, Yong Xie, Junqing Zhang, Jingtao Li, Shu Fan, Kai Zhang, Miao Hangzhou Innovation Institute Beihang University Hangzhou310000 China The Center of National Railway Intelligent Transportation System Engineering and Technology Beijing100081 China Advanced Institute of Information Technology of Peking University Hangzhou311200 China Signal Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9798350304428

The development of a scalable and adaptable trackside and on-board coordinated control system (CCS+) is a top priority for the digital and automated train operations of future railways. One of the most important and reliable solutions in CCS+ is train positioning, which is called GLAT (Generic Location-Awareness Tools). The technologies of GLAT can rely on many methods like GNSS such as INS/GPS. In this paper we introduce a deep learning method to recognize the location through only monocular cameras. This method can be very useful when, for example, in 'satellite signal blind spots' such as tunnels and mountainous areas or even no GPS signals at all. We carried out the first visual train positioning research experiment in the China national Railway Test Center (East Suburban Branch). By using a well-trained Train Localization Network, we demonstrate that trains can successfully recognize the real-time location when it moves along its route. Compared with traditional INS/GNSS integrated navigation scheme, the computer vision method is more robust and attractive since it brings trains an 'intelligent brain', the train can deduce the localization by exploring its own 'visual memory'. This kind of capability can accelerate more intelligence utilized by modern trains. © 2023 IEEE.

关键词： Location

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The First Experimental Study of Monocular Visual Localization for Trains 23

The First Experimental Study of Monocular Visual Localizatio...

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23rd COTA International Conference of Transportation Professionals: Emerging Data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wang, Yong Xie, Junqing Zhang, Jingtao Li, Shu Zhang, Miao Fan, Kai Advanced Institute of Information Technology Peking Univ. Hangzhou China Hangzhou Innovation Institute Beihang Univ. Hangzhou China Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China Communication Research Institute China Academy of Railway Sciences Corp. Ltd. Beijing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

The development of a scalable and adaptable trackside and on-board coordinated control system (CCS+) is a top priority for the digital and automated train operations of future railways. One of the most important and reliable solutions in CCS+ is train positioning, which is called GLAT (generic location-awareness tools). The underlying technologies that GLAT can rely on many methods like GNSS. However, the GNSS precise positioning of trains in "satellite signal blind spots" such as tunnels and mountainous areas must rely on trackside equipment such as balise. For alleviating the need of trackside equipment, one way is using INS/GNSS integrated navigation scheme. However, with the development of computer vision, it is an attractive and challenging way. Therefore, we carry out the visual train positioning research in the China national Railway Test Center (east suburban branch). By using ORB descriptor and monocular camera, we successfully obtain the trajectory tracking of the train. We study the relationship between the number of feature points, feature tracking, and train speed in our experiments, and find that the impact of tunnels on feature tracking is vital. Through further analysis, we draw two import conclusions. © ASCE.

关键词： Global positioning system

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Chalcogenide integrated nanophotonics and its potential for quantum photonics

Chalcogenide integrated nanophotonics and its potential for ...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Lei, Kunhao Xu, Kai Bao, Kangjian Weng, Jiawei Li, Lan Lin, Hongtao The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

Due to the high optical nonlinearity and nonvolatile phase-changing properties, chalcogenide integrated nanophotonics are emerging for entangling photon generation and nonvolatile quantum states operation. We will rev... 详细信息

ISBN: (纸本)9798350379266

关键词： Silicon photonics

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Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction

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Journal of Physical Chemistry Letters 2024年第34期15卷 8845-8852页

作者： Luan, Wanhong Zhao, Zherui Li, Hang Zhai, Yongbiao Lv, Ziyu Zhou, Kui Xue, Shuangmei Zhang, Meng Yan, Yan Cao, Yan Ding, Guanglong Han, Su-Ting Kuo, Chi-Ching Zhou, Ye Institute for Advanced Study Shenzhen University Shenzhen518060 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China The Construction Quality Supervision and Inspection Station of Zhuhai Zhuhai519000 China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen518060 China South China Advanced Institute for Soft Matter Science and Technology School of Emergent Soft Matter South China University of Technology Guangzhou510640 China Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Hung Hom Hong Kong999077 Hong Kong Institute of Organic and Polymeric Materials National Taipei University of Technology Taipei10608 Taiwan Advanced Research Center for Green Materials Science and Technology National Taiwan University Taipei10617 Taiwan

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF3: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF3: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems. © 2024 American Chemical Society.

关键词： Organic field effect transistors

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Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

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Science China Chemistry 2024年第8期67卷 2497-2523页

作者： Yuan Zeng Shuhan Zhang Yuecong Guo Yalin Cong Xu Ding Peihua Li Yunxiang Lin Wenzhi Ren Hui Su Weigang Sun Liuxin Xu Guikai Zhang Shihua Chen Yu Chen Weiren Cheng Shengqi Chu Yong Guan Jinru Han Jie Lin Hengjie Liu Zheyi Liu Pan Luo Fanchun Meng Sicong Qiao Zongyin Song Ying Wang Zhao Wu Chenyu Yang Meng Yang Shirui Yang Zi Yin Zhibin Yin Pengjun Zhang Hongyu Zhang Pingping Zheng Jia Zhou Wanlin Zhou Pengfei An He Cheng Chunhai Fan Xingjiu Huang Yong Lei Lina Li Mu Li Qinghua Liu Shuming Peng Li Song Zhihu Sun Yangchao Tian Fangjun Wang Lihua Wang Liming Wang Shiqiang Wei Aiguo Wu Chunlei Xiao Xueming Yang Panchao Yin Jing Zhang Mingxin Zhang Yaling Wang Chunying Chen CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety New Cornerstone Science LaboratoryNational Center for Nanoscience and TechnologyBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029China Institute of Solid State Physics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology Zhejiang International Cooperation Base of Biomedical Materials Technology and ApplicationCAS Key Laboratory of Magnetic Materials and DevicesNingbo Cixi Institute of Biomedical EngineeringZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China College of Chemistry and Chemical Engineering Hunan Normal UniversityChangsha 410081China State Key Laboratory of Luminescent Materials and Devices South China Advanced Institute for Soft Matter Science and TechnologySouth China University of TechnologyGuangzhou 510640China Beijing Synchrotron Radiation Facility Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Separation Sciences for Analytical Chemistry Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China State Key Laboratory of Molecular Reaction Dynamics Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China Institute of Advanced Science Facilities Shenzhen 518107China State Key Laboratory of Coal Conversion Institute of Coal ChemistryChinese Academy of SciencesTaiyuan 030001China State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Acade

With the advancement of modern science and technology, large scientific facilities are increasingly oriented toward demand and application, and can be used for basic research as well as serving multiple disciplines. Developing large scientific facilities and related analytical technologies enhances understanding of large scientific facilities and popularizes their application in research across multiple disciplines. The combination of light or neutron sources from large scientific facilities and advanced analytical technologies can be achieved for materials structure information, dynamics study of chemical reactions, high dissociation of biomolecules, 3D visualization of energy materials or biological samples, etc. We first introduce the progress of domestic large scientific facilities of synchrotron radiation(SR) and free electron lasers(FELs) with different wavelengths and neutron *** further discuss the comparison between Chinese and typical foreign facilities in X-ray radiation from X-ray tubes, synchrotrons, X-ray FELs, and neutron sources based on physical parameters of light and neutron sources. In addition, we focus on the technological progress and perspectives combined with advanced X-ray radiation and neutron sources of large scientific facilities in China, especially in the nanoscience fields of energy catalysis and biological science. We hope that this roadmap will provide references on technology and methods to experimental users, as well as prospects for future development of technologies based on large research infrastructure facilities. Comprehensive studies and guidelines for basic research to practical application in various disciplines can be made with the assistance of large scientific facilities.

关键词： large scientific facilities synchrotron radiation FEL light sources neutron sources spectroscopic analysis methods X-ray imaging techniques structural information chemical dynamics

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Electron emission characteristics of nc-Si based planar-type electron emission devices

Electron emission characteristics of nc-Si based planar-type...

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International Symposium on Discharges and Electrical Insulation in Vacuum

作者： H. Shimawaki M. Nagao K. Murakami Department of System and Information Engineering Hachinohe Institute of Technology Hachinohe Japan Device Technology Research Institute National Institute of Advanced Industrial Science and Technology Tsukuba Japan

Planar-type electron emission devices based on nanocrystalline silicon (nc-Si) with graphene as the gate electrode were fabricated and the electron emission properties were investigated. In addition, the current condu...

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Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2023年第3期42卷 1000-1014页

作者： Shao, Hao-Chiang Ping, Hsing-Lei Chen, Kuo-Shiuan Su, Weng-Tai Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu Institute of Data Science and Information Computing National Chung Hsing University Taichung402 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Phison Electronics Corporation Miaoli350 Taiwan Faraday Technology Corporation Hsinchu300 Taiwan Industrial Technology Research Institute Electronic and Optoelectronic System Research Laboratories Hsinchu310 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan United Microelectronics Corporation Product Engineering Department Hsinchu300 Taiwan United Microelectronics Corporation Development of Smart Manufacturing Hsinchu300 Taiwan

Learning-based presimulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive and time consuming to collect the reference shape images of all layout clips for model training and updating. To address the problem, we propose a deep-learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pretrained presimulation model. We devise a global-local novelty scoring mechanism to assess the potential novelty of a layout by exploiting two subnetworks: 1) an autoencoder and 2) a pretrained presimulation model. The former characterizes the global structural dissimilarity between a given layout and training samples, whereas the latter extracts a latent code representing the fabrication-induced local deformation. By integrating the global dissimilarity with the local deformation boosted by a self-attention mechanism, our model can accurately detect novelties without the ground-truth circuit shapes of test samples. Based on the detected novelties, we further propose two active-learning strategies to sample a reduced amount of representative layouts most worthy to be fabricated for acquiring their ground-truth circuit shapes. Experimental results demonstrate: 1) the effectiveness of our layout novelty detection algorithm and 2) the ability of our active-learning strategies in selecting representative novel layouts for keeping a learning-based presimulation model updated. © 1982-2012 IEEE.

关键词： Fabrication

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