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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Tomoki Kobayashi Tomoyuki Yokogawa Nao Igawa Yoichiro Sato Satoshi Fujii Kazutami Arimoto Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama Japan Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan Information and Communication Systems Engineering National Institute of Technology Okinawa College Nago Okinawa Japan

This paper describes the compact and low power AI DNN discriminator which is mounted on the small drone like a hobby type. By adopting this AI module, the effective plant monitoring system using the drone has been ach...

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Concept for Deriving system architectures from Reference architectures

Concept for Deriving System Architectures from Reference Arc...

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IEEE International Conference on Industrial engineering and engineering Management

作者： S. Unverdorben B. Böhm A. Lüder Corporate Technology - Smart System Engineering & Architecture Siemens AG Erlangen Germany Institute of Ergonomics Manufacturing Systems and Automation (IAF) Chair for Factory Operation and Manufacturing Systems Otto von Guericke University Magdeburg Magdeburg Germany

The reuse of existing architectural solutions is a key enabler for the optimization of engineering of industrial production systems with respect to cost, time, and quality. In order to achieve systematic reuse of architectural solutions within the design process of specific systems, the standard “VDI 3695” determines the use of reference architectures as one possible option. Often, within the development of industrial production systems, individual aspects and specific requirements need to be regarded, which may not be fully represented by a reference architecture. In order to create specific system architectures based on reference architectures an approach is needed that considers a mix of existing and newly defined architecture content, which must be integrated in the process of designing system architectures. Therefore, within this paper, an approach for the creation of system architectures based on a reference architecture is presented, with the goal of supporting the engineering of such production systems.

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High-density implantable neural electrodes and chips for massive neural recordings

Brain-X

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Brain-X 2024年第2期2卷

作者： Wang, Longchun Suo, Yanxing Wang, Jiahao Wang, Xuanqi Xue, Kai An, Jingjing Sun, Xun Chen, Qinyu Tang, Xiaochen Zhao, Yang Ji, Bowen Liu, Jingquan Department of Micro/Nano Electronics Shanghai Jiao Tong University Shanghai China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China DCI Joint Team Collaborative Innovation Center of IFSA Shanghai Jiao Tong University Shanghai China Unmanned System Research Institute Northwestern Polytechnical University Shaanxi Xi'an China National Key Laboratory of Unmanned Aerial Vehicle Technology Shaanxi Xi'an China Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi'an China Institute of Guizhou Aerospace Measuring and Testing Technology Guizhou Guiyang China Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden Netherlands Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM United States

High-density neural recordings with superior spatiotemporal resolution powerfully unveil cellular-scale neural communication, showing great promise in neural science, translational medicine, and clinical applications. To achieve such, many design and fabrication innovations enhanced the electrode, chip, or both for biocompatibility improvement, electrical performance upgrade, and size miniaturization, offering several thousands of recording sites. However, an enormous gap exists along the trajectory toward billions of recording sites for brain scale resolution, posing many more design challenges. This review tries to find possible insight into mitigating the gap by discussing the latest progress in high-density electrodes and chips for neural recordings. It emphasizes the design, fabrication, bonding techniques, and in vivo performance optimization of high-density electrodes. It discusses the promising opportunities for circuit-level and architecture-level multi-channel chip design innovations. We expect that joint effort and close collaboration between high-density electrodes and chips will pave the way to high-resolution neural recording tools supporting cutting-edge neuroscience discoveries and applications. © 2024 The Author(s). Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology.

关键词： brain-computer interface high density implantable chips implantable electrodes massive neural recording

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Feng Xiao Cong Shen Yu Chen Tian Yang Shengyong Chen Zhijun Liao Jijun Tang School of Computer Science and Engineering Tianjin University of Technology Tianjin P.R.China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China School of Basic Medical Sciences Fujian Medical University Fuzhou Fujian P.R.China College of Intelligence and Computing Tianjin University Tianjin P.R.China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin P.R.China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong P.R.China University of South Carolina Columbia SC U.S.A.

ISBN: (纸本)9781665429825

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.

关键词： Deep learning Image segmentation Statistical analysis Lung Predictive models Feature extraction Generative adversarial networks

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Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

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Journal of Cardiovascular Magnetic Resonance 2025年第1期27卷 101840页

作者： Hanneman, Kate Picano, Eugenio Campbell-Washburn, Adrienne E Zhang, Qiang Browne, Lorna Kozor, Rebecca Battey, Thomas Omary, Reed Saldiva, Paulo Ng, Ming Rockall, Andrea Law, Meng Kim, Helen Lee, Yoo Jin Mills, Rebecca Ntusi, Ntobeko Bucciarelli-Ducci, Chiara Markl, Michael Department of Medical Imaging University of Toronto Toronto ON Canada University Clinical Center of Serbia Cardiology Division University of Belgrade Serbia Cardiovascular Branch Division of Intramural Research National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States RDM Division of Cardiovascular Medicine & NDPH Big Data Institute University of Oxford Oxford United Kingdom Dept of Radiology Division of Pediatric Radiology Children's Hospital Colorado University of Colorado School of Medicine United States University of Sydney and Royal North Shore Hospital Sydney Australia Department of Radiology and Medical Imaging University of Virginia Health System Charlottesville VA United States Departments of Radiology & Biomedical Engineering Vanderbilt University Nashville TN United States Department of Pathology University of Sao Paulo School of Medicine Sao Paulo Brazil Department of Diagnostic Radiology School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Dept of Surgery and Cancer Faculty of Medicine Imperial College London United Kingdom Departments of Neuroscience Electrical and Computer Systems Engineering Monash University Australia Department of Radiology University of Washington WA United States Department of Radiology and Biomedical Engineering UCSF San Francisco CA United States University of Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom Groote Schuur Hospital Department of Medicine University of Cape Town Cape Town South Africa Royal Brompton and Harefield Hospitals Guys’ & St Thomas NHS Trust London United Kingdom Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL United States Greenwell Project Nashville TN United States Department of Radiology Alfred Health Melbourne Australia School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine King's College U

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. Data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted. © 2025 The Authors

关键词： Climate change Energy consumption Greenhouse gas emission Planetary health Sustainability

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Primate Markerless Pose Estimation and Movement Analysis Using DeepLabCut

Primate Markerless Pose Estimation and Movement Analysis Usi...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Rollyn Labuguen Dean Karlo Bardeloza Salvador Blanco Negrete Jumpei Matsumoto Kenichi Inoue Tomohiro Shibata Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon Philippines System Emotional Science University of Toyama Toyama Japan Systems Neuroscience Section Kyoto University

The analysis and understanding of primate behavior play a fundamental role in fields such as neuroscience, medicine, psychology, genetics, and more. This paper demonstrates an automatic detection of primate features by using an open-source deep learning toolset, DeepLabCut. We trained the deep neural network to locate 17 features and extract the monkey pose by relating the set of features detected. The model is trained with 5,967 manually annotated monkey images, which achieved train and test set errors of 3.61 and 19.72 unit pixels respectively. We also plotted the feature trajectories across multiple frames to show that the trained model can be used for analyzing behavior.

关键词： Feature extraction Training Predictive models Wrist Task analysis Nose Pose estimation

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Collaborative learning of communication routes in edge-enabled multi-access vehicular environment

TechRxiv

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TechRxiv 2020年

作者： Wu, Celimuge Liu, Zhi Liu, Fuqiang Yoshinaga, Tsutomu Ji, Yusheng Li, Jie The Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo182-8585 Japan The Department of Mathematical and Systems Engineering Shizuoka University No5. 3-5-1 Johoku Hamamatsu432-8561 Japan The School of Electronics and Information Engineering Tongji University 4800 Cao’an Jiading Shanghai201804 China The Information Systems Architecture Research Division National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo101-8430 Japan The Department of Computer Science and Engineering Shanghai Jiaotong University 800 Dongchuan RD. Minhang District Shanghai200240 China

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the "proactive" and "preemptive" approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines. © 2020, CC BY.

关键词： Reinforcement learning

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Corrigendum to “Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey” [Renew Sustain Energy Rev 132 (2020) 110112]

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Renewable and Sustainable Energy Reviews 2022年 167卷

作者： Jiewu Leng Guolei Ruan Pingyu Jiang Kailin Xu Qiang Liu Xueliang Zhou Chao Liu Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou 510006 China Shenzhen Research Institute City University of Hong Kong Shenzhen 518057 China State Key Lab for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710064 China School of Mechanical Engineering Hubei University of Automotive Technology Shiyan 442002 China School of Mechatronics & Vehicle Engineering Chongqing Jiaotong University Chongqing 400074 China

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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation systems for Managing the Transport Activity of the Urban Population

Using the Model of a Functional Rationalizer of Consumer Beh...

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International Conference on Soft Computing and Measurements (SCM)

作者： Yaroslav A. Seliverstov Svytoslav A. Seliverstov Nikolay V. Podoprigora Alexey L. Starichenkov Roman S. Naryshkin Laboratory of Intelligent Transport Systems Solomenko Institute of Transport Problems of the Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University St. Petersburg Russian Federation Department of Ground Transportation Technological Machines Saint Petersburg State University of Architecture and Civil Engineering St. Petersburg Russian Federation Department of Ship Control Systems Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation Department of Programming Technology Saint Petersburg State University St. Petersburg Russian Federation

ISBN: (数字)9781728196923

ISBN: (纸本)9781728196930

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered.

关键词： Consumer behavior Mobile applications Sociology Statistics Logistics Analytical models Software

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