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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM

Enhanced Visual Feedback with Decoupled Viewpoint Control in...

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IEEE-RAS International Conference on Humanoid Robots

作者： Yang Chen Leyuan Sun Mehdi Benallegue Rafael Cisneros-Limón Rohan P. Singh Kenji Kaneko Arnaud Tanguy Guillaume Caron Kenji Suzuki Abderrahmane Kheddar Fumio Kanehiro School of Integrative and Global Majors (SIGMA) University of Tsukuba Japan CNRS-AIST JRL (Joint Robotics Laboratory) IRL National Institute of Advanced Industrial Science and Technology (AIST) Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Japan MIS lab Universite de Picardie Jules Verne Amiens France Faculty of Engineering and Center for Cybernetics Research University of Tsukuba Japan

ISBN: (数字)9798350309799

ISBN: (纸本)9798350309805

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: (i) the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these draw-backs, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows that the effectiveness of our solution.

关键词： Point cloud compression Visualization Simultaneous localization and mapping Humanoid robots Virtual reality Real-time systems Robustness

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Motion planning for a vigilant humanoid robot

Motion planning for a vigilant humanoid robot

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IEEE-RAS International Conference on Humanoid Robots

作者： Jean-Bernard Hayet Claudia Esteves Gustavo Arechavaleta Eiichi Yoshida Computer Science Group Centro de Investigación en Matemáticas Guanajuato Mexico Department of Mathematics Universidad de Guanajuato Guanajuato Mexico Robotics and Advance Manufacturing Group Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV) Saltillo Coahuila Mexico Intelligent Systems Research Institute National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (纸本)9781424445875

This paper presents a motion planner for a vigilant humanoid robot. In this context of surveillance, the robot task is to keep a distinctive point in the environment in sight during all of its motion. The method we propose consists of three main ingredients: (1) A motion planner for an appropriate simplified model of the walking robot, adapted to the particular needs of humanoid robots, that outputs an admissible path with local optimality properties. This path is guaranteed to satisfy the visibility constraints resulting both from the landmark and from the angular limits of the mechanical system; (2) A generic walking pattern generator that produces stable walking motions; (3) A generalized inverse-kinematics module to satisfy the remaining collisions and posture constraints, in particular the gaze direction. The effectiveness of this method is shown with several examples on the humanoid robot plataform HRP-2.

关键词： Motion planning Humanoid robots Legged locomotion Service robots Surveillance Monitoring Collision avoidance Robot sensing systems Mechanical systems Humans

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Developing and improving transportation systems: The structure and operation of IEEE intelligent transportation systems society

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IEEE Transactions on intelligent Transportation Systems 2005年第3期6卷 261-264页

作者： Wang, Fei-Yue Herget, Charles Zeng, Daniel IEEE ITSS Chinese Academy of Sciences Institute of Automation Beijing 100080 China University of Arizona Department of Systems and Industrial Engineering Tucson AZ 85721 United States IEEE ITSS Herget Associates Alameda CA 94501 United States IEEE ITSS University of Arizona Department of Management Information Systems Tucson AZ 85721 United States Program for Advanced Research in Complex Systems University of Arizona Tucson Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences ITS Department of the IEEE Intelligent Systems IEEE ITS Society IEEE SMC Board of Governors Herget Associates Alameda CA IEEE Standards ITS US Department of Transportation Department of Management Information Systems University of Arizona INFORMS AAAI and ACM IEEE ITS Society Technical Activities of the Chinese Association for Science and Technology United States

This editorial introduces the structure and operation of the IEEE intelligent Transportation Systems Society (ITSS). A brief history of the ITSS is presented, along with its mission, organizational structure, and a re... 详细信息

关键词： Transportation

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Simulation study of deep-learning-based gait classification of young/elderly adults using Doppler radar

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Measurement: Sensors 2021年 18卷

作者： Toshiyuki Hoshiga Kenshi Saho Keitaro Shioiri Masahiro Fujimoto Yoshiyuki Kobayashi Department of Intelligent Robotics Toyama Prefectural University Imizu Japan Human Augmentation Research Center National Institute of Advanced Industrial Science and Technology (AIST) Kashiwa Japan

Deep-learning-based gait classification of young and elderly adults using micro-Doppler radar (MDR) is presented in this paper. The MDR signal data were accurately simulated using an open motion-capture gait dataset, and deep-learning classification of the time-velocity distribution (i.e., spectrogram) images calculated with the generated data are presented. Utilizing a simulation, we also investigated the body parts deemed most efficient for classification based on their generation of good MDR data. As a result, the classification rate using whole-body data was 74%. However, this classification rate of using only leg data showed an accuracy of 91%, which indicates that the thighs and shanks are efficient target body parts for the gait classification of both young and elderly adults.

关键词： Gait classification Micro-Doppler Deep learning Convolutional neural network Spectrogram

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Super Resolution for Improved Positioning of an MRI-Guided Spinal Cellular Injection Robot

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Journal of Medical robotics research 2021年第1-2期6卷

作者： Martinez, Daniel Enrique Meinhold, Waiman Oshinski, John Hu, Ai-Ping Ueda, Jun Robotics PhD Program Georgia Institute of Technology North Ave NW Atlanta 30332 GA United States Department of Radiology Emory University 201 Dowman Dr Atlanta 30322 GA United States Intelligent Sustainable Technologies Division Georgia Tech Research Institute 640 Strong Street Atlanta 30318 GA United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology North Ave NW Atlanta 30332 GA United States

This paper presents the development of a magnetic resonance imaging (MRI)-conditional needle positioning robot designed for spinal cellular injection. High-accuracy targeting performance is achieved by the combination of a high precision, parallel-plane, needle-orientation mechanism utilizing linear piezoelectric actuators with an iterative super-resolution (SR) visual navigation algorithm using multi-planar MR imaging. In previous work, the authors have developed an MRI conditional robot with positioning performance exceeding the standard resolution of MRI, rendering the MRI resolution the limit for navigation. This paper further explores the application of SR to images for robot guidance, evaluating positioning performance through simulations and experimentally in benchtop and MRI experiments. © 2021 World Scientific Publishing Company.

关键词： cellular therapeutics magnetic resonance imaging needle steering spinal cord Super resolution surgical robots visual robot guidance

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First Demonstration of Defect Elimination for Cryogenic Ge FinFET CMOS Inverter Showing Steep Subthreshold Slope by Using Ge-on-Insulator Structure

First Demonstration of Defect Elimination for Cryogenic Ge F...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Yu, X.-R. Hsieh, C.-C. Chuang, M.-H. Chiu, M.-Y. Sun, T.-C. Geng, W.-Z. Chang, W.-H. Shih, Y.-J. Lu, W.-H. Chang, W.-C. Lin, Y.-C. Pai, Y.-C. Lai, C.-Y. Chuang, M.-H. Dei, Y. Yang, C.-Y. Lu, H.-Y. Lin, N.-C. Wu, C.-T. Kao, K.-H. Ma, W.C.-Y. Lu, D.D. Lee, Y.-J. Luo, G.-L. Chiang, M.-H. Maeda, T. Wu, W.-F. Li, Y.-M. Hou, T.-H. National Cheng Kung University Dept of Electrical Engineering Tainan Taiwan National Cheng Kung University Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan Taiwan National Yang Ming Chiao Tung University Inst. of Communications Engineering Hsinchu Taiwan National. Sun Yat-Sen University Dept. of Electrical Engineering Kaohsiung Taiwan National Institute of Advanced Industrial Science and Technology Ibaraki Japan National Yang Ming Chiao Tung University Inst. of Artificial Intelligence Innovation Hsinchu Taiwan Taiwan Semiconductor Research Institute Taiwan Intelligent Computing Industrial Program Ming Wu School of Computing National Cheng Kung University Taiwan Inst. of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798350327670

This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among them, the layer transferred Ge-on-Insulator (GeOI) FinFET significantly improves its I-V characteristic during cryogenic measurements, such as a steeper subthreshold swing at 10K and a better Ion. The developed GeOI fabrication method provides an effective way to eliminate the defects originating from misfit dislocations at the Ge/Si substrate during epitaxial growth, which would be treated as the key to device performance enhancement under 10 K. According to the measured IV at 10 K and circuit prediction, GeOI FinFETs with high Ge crystallinity are strong candidates for High-Performance-Computing (HPC) applications. © 2023 IEEE.

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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First Demonstration of Defect Elimination for Cryogenic Ge FinFET CMOS Inverter Showing Steep Subthreshold Slope by Using Ge-on-Insulator Structure

First Demonstration of Defect Elimination for Cryogenic Ge F...

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International Electron Devices Meeting (IEDM)

作者： X.-R. Yu C.-C. Hsieh M.-H. Chuang M.-Y. Chiu T.-C. Sun W.-Z. Geng W.-H. Chang Y.-J. Shih W.-H. Lu W.-C. Chang Y.-C. Lin Y.-C. Pai C.-Y. Lai Y. Dei C.-Y. Yang H.-Y. Lu N.-C. Lin C.-T. Wu K.-H. Kao W. C.-Y. Ma D. D. Lu Y.-J. Lee G.-L. Luo M.-H. Chiang T. Maeda W.-F. Wu Y.-M. Li T.-H. Hou Dept of Electrical Engineering National Cheng Kung University Tainan Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering National. Sun Yat-Sen University Kaohsiung Taiwan National Institute of Advanced Industrial Science and Technology Ibaraki Japan Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Intelligent Computing Industrial Doctorate Program Miin Wu School of Computing National Cheng Kung University Taiwan Semiconductor Research Institute Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among them, the layer transferred Ge-on-Insulator (GeOI) FinFET significantly improves its I-V characteristic during cryogenic measurements, such as a steeper subthreshold swing at 10K and a better I on . The developed GeOI fabrication method provides an effective way to eliminate the defects originating from misfit dislocations at the Ge/Si substrate during epitaxial growth, which would be treated as the key to device performance enhancement under 10 K. According to the measured IV at 10 K and circuit prediction, GeOI FinFETs with high Ge crystallinity are strong candidates for High-Performance-Computing (HPC) applications.

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