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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuanbin Liu Hongyi Zhang Jiacheng Liu Liangjun Lu Jiangbing Du Yu Li Zuyuan He Jianping Chen Linjie Zhou Andrew W. Poon Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Department of Electronic and Computer Engineering Photonic Device Laboratory The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s $\times 9$ on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and... 详细信息

ISBN: (纸本)9798350369311

关键词： Solitons Lasers and electrooptics Wavelength division multiplexing Data communication Quantum cascade lasers Electro-optical waveguides Optical fiber dispersion Fiber lasers

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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Quantum-Dot-Based Optical Fiber Temperature Sensor Fabricated by Two-Photon Polymerization

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激光与光电子学进展 2024年第23期61卷 431-437页

作者： Wang Chao Sun Shengzhi Qian Bin Peng Sida Feng Zeqi Qiu Jianrong Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211ZhejiangChina Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211ZhejiangChina State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027ZhejiangChina State Key Laboratory of Modern Optical Instrumentation Zhejiang UniversityHangzhou 310027ZhejiangChina

Temperature is a common and significant physical quantity,particularly under harsh conditions such as a high voltage or strong electric/magnetic *** this study,we developed an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs).The microstructure was fabricated on a fiber tip using twophoton polymerization *** results show that the fluorescence intensity of the QD microstructure is reduced with an increase in temperature,and the peak position of photoluminescence exhibits a linear change in the temperature range of 26-70℃.The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃.The proposed method has enormous potential for the development of fiber-end temperature sensors.

关键词： two-photon polymerization femtosecond laser temperature sensors quantum dots optical fiber

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Complementary multi-branch CNNs towards real-world 3D point classification

Complementary multi-branch CNNs towards real-world 3D point ...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Tang, Zifeng Hao, Fusheng Zhang, Qieshi Cheng, Jun Zhang, Jin Yuan, Shuai Shenzhen Institute of Advanced Technology Chinese Academy of Science CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Chinese University of Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering China Guangdong University of Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9781665436786

Autonomous driving requires precise and efficient point clouds processing techniques, where deep learning has shown great potential. Nonetheless, most of the existing works achieve high accuracy in synthetic data while performing unsatisfactorily in real-world datasets. On the other hand, though existing multi-view-based, volumetric-based, and point-based methods have achieved promising results, they still suffer from high computational complexity or low memory utilization efficiency. For example, the computational time and memory consumption of multi-view-based and volumetric-based methods increase at most cubically with the increasing of the input resolution, while half of the point-based methods's runtime is wasted due to their irregular memory access. To address these issues, we propose a novel method to fuse complementary CNNs, dubbed as Multi-Branch Convolutional Neural Networks (MBCNN), which achieves the current state-of-the-art performance while maintaining high efficiency. The underlying design of MBCNN is to utilize the point-based convolution as a local-feature branch and spherical convolution as a global-feature branch, to capture patterns in space and exploit the merit of the pointwise MLP, which is robust to the perturbation. Besides, we design an efficient voxel-based indexing technique to prevent the feature-extracting phase from being blocked, thus significantly accelerating the procedure of finding a point's neighbor. Experimental results show that MBCNN achieves state-of-the-art performance on the real-world 3D point clouds dataset while reducing 30% of runtime in neighbor indexing. © 2021 IEEE.

关键词： Convolutional neural networks

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Interactive 3D Medical Image Segmentation with SAM 2

arXiv

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arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Shi, Yuhang Wang, Xiangfeng Huang, Yifei School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Shanghai United Imaging Healthcare Advanced Technology Research Institute Co. Ltd Shanghai201807 China School of Software Engineering East China Normal University Shanghai200062 China

Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By treating sequential 2D slices of 3D images as video frames, SAM 2 can fully automatically propagate annotations from a single frame to the entire 3D volume. We propose a practical pipeline for using SAM 2 in 3D medical image segmentation and present key findings highlighting its efficiency and potential for further optimization. Concretely, numerical experiments on the BraTS2020 and the medical segmentation decathlon datasets demonstrate that SAM 2 still has a gap with supervised methods but can narrow the gap in specific settings and organ types, significantly reducing the annotation burden on medical professionals. Our code will be open-sourced and available at https://***/Chuyun-Shen/SAM_2_Medical_3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Merging Parameter Estimation and Classification Using LASSO

Merging Parameter Estimation and Classification Using LASSO

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IEEE Conference on Decision and Control

作者： Le Wang Ying Wang Yu Qiu Mian Li Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China SAIC Motor RD Innovation Headquarters Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China Centre for Advanced Bio Production Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness.

关键词： Employee welfare Systematics Parameter estimation Soft sensors Merging Prototypes Estimation Data models MISO Vehicle dynamics

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Modular Multilevel DC-DC Converter With Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

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CPSS Transactions on Power Electronics and Applications 2022年第1期7卷 37-48页

作者： Shanshan Zhao Yu Chen Shenghui Cui Jingxin Hu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Department of Electrical and Computer Engineering Seoul National University Korea Institute for Power Generation and Storage Systems E. ON Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype.

关键词： Multilevel converters Semiconductor devices Prototypes DC-DC power converters Transformers Valves Power electronics

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EMG for walkability assessment: A comparison between elderly and young adults

EMG for walkability assessment: A comparison between elderly...

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2020 Italian Workshop on Artificial Intelligence for an Ageing Society, AIxAS 2020

作者： Gasparini, Francesca Grossi, Alessandra Nishinari, Katsuhiro Bandini, Stefania Department of Computer Science Systems and Communications University of Milano Bicocca Italy RCAST - Research Center for Advanced Science and Technology The University of Tokyo Japan

Populations around the world are rapidly ageing, and the need to ensure healthy aging is one of the main priorities of modern society. Living and moving in environments that support and maintain intrinsic capacity and functional ability are important aspects related to healthy ageing. Most of the daily life actions of active elderly are related to walking activities, thus guaranteeing walking environments that are elderly-friendly are nowadays a priority. This work proposes to assess walkability, evaluating the safety perception of different walking conditions, relying on physiological responses. To this end a proper experiment has been designed in a controlled environment, considering both young adults and elderly, and adopting wearable devices. In this paper the analysis of the muscles activity acquired with Electromyography is presented as a preliminary study. © 2020 Copyright for this paper by its authors.

关键词： Physiological models

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