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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Blockchain-based Social Manufacturing for Customization Production

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IFAC-PapersOnLine 2020年第5期53卷 53-58页

作者： Xiuqin Shang Xi Chen Lulu Niu Gang Xiong Zhen Shen Xisong Dong Zhengchao Shen Chao Liu Bangwen Xi the Research Center of Precision Sensing and Control the state key laboratory for management and control of complex systems & Beijing engineering research center of intelligent systems and technology institute of automation Chinese Academy of Sciences Beijing 100190 China Beijing University of Posts and Telecommunications Beijing 100876 China Alstom Beizhong Power (Beijing) Co. Ltd. Beijing 100040 China the System Integration Electronics (Hangzhou)Co. LTD No. 58 No. 11 Road Foxconn Qiantang Tech-Industrial Area HETZ Hangzhou 310018 China

This paper proposes the social manufacturing based on Blockchain, which realizes precise and efficient customization through smart contract and distributed accounting. It is a new manufacturing mode of service-oriented, distributed and collaborative customization. This paper proposes the definition of SM based on Blockchain. In the process of using to Blockchain, it can form a strong trust relationship, so that multiple nodes / roles in SM can save the complete copy of the whole database, rather than a centralized permission / role. Secondly, the Blockchain-based SM system is developed for customized manufacturing. Finally, its application case and the optimization results are given. In SM, it is the key that how to transform customized manufacturing actions into socialized actions of manufacturing resources through internet communities and interconnect Socialized Manufacturing Resources (SMRs) in upstream and downstream. In this paper, the smart contract relationship is established based on Blockchain between SMRs to build credit mechanism in SM, Which can ensure the effective circulation and association of SMRs.

关键词： Social Manufacturing Blockchain Optimization SM Resources

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OpenHI2 – Open source histopathological image platform

arXiv

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arXiv 2020年

作者： Puttapirat, Pargorn Zhang, Haichuan Deng, Jingyi Dong, Yuxin Shi, Jiangbo He, Hongyu Gao, Zeyu Wang, Chunbao Zhang, Xiangrong Li, Chen Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech R&D Xi'an Jiaotong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Department of Power System and Automation School of Electrical Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi'an Shaanxi710061 China

— Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the study area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats;ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges so that OpenHI2 could be used as a platform for histopathological images. Further addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at https://***/BioAI/OpenHI. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

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Unified feature extraction framework based on contrastive learning

arXiv

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arXiv 2021年

作者： Zhang, Hongjie Qiang, Wenwen Zhang, Jinxin Jing, Ling College of Information and Electrical Engineering China Agricultural University Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China University of Chinese Academy of Sciences Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China National Innovation Center for Digital Fishery China Agricultural University Beijing100083 China College of Science China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture Beijing100083 China

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. As a popular self-supervised learning method, contrastive learning has recently garnered considerable attention. In this study, we proposed a unified framework based on a new perspective of contrastive learning (CL) that is suitable for both unsupervised and supervised feature extraction. The proposed framework first constructed two CL graph for uniquely defining the positive and negative pairs. Subsequently, the projection matrix was determined by minimizing the contrastive loss function. In addition, the proposed framework considered both similar and dissimilar samples to unify unsupervised and supervised feature extraction. Moreover, we propose the three specific methods: unsupervised contrastive learning method, supervised contrastive learning method 1,and supervised contrastive learning method 2. Finally, the numerical experiments on five real datasets demonstrated the superior performance of the proposed framework in comparison to the existing methods. © 2021, CC BY.

关键词： Feature extraction

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Aggregation capacity of wireless sensor networks: Extended network case

Aggregation capacity of wireless sensor networks: Extended n...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

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Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

arXiv

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arXiv 2022年

作者： Gong, Jibing Wan, Yao Liu, Ye Li, Xuewen Zhao, Yi Wang, Cheng Lin, Yuting Fang, Xiaohan Feng, Wenzheng Zhang, Jingyi Tang, Jie School of Information Science and Engineering The Key Lab for Computer Virtual Technology and System Integration The Key Laboratory for Software Engineering of Hebei Province Yanshan University No. 438 West Hebei Avenue Hebei Qinhuangdao066004 China School of Computer Science and Technology Huazhong University of Science and Technology Luoyu Road 1037 Hubei Wuhan430074 China Salesforce Research 556 Xixi Road Chicago United States School of Cyber Science and Technology Beihang University 37 Xueyuan Road Beijing100191 China Department of Computer Science Tsinghua University Beijing100084 China

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem © 2022, CC BY.

关键词： Information services

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Calibration of a three-dimensional photodynamic therapy illumination system and its segmentation assessment for port-wine stains

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Photonics and Lasers in Medicine 2016年第3期5卷 195-202页

作者： Feng, Yun Hu, Xiaoming Zhou, Ya Wang, Yong School of Software Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China School of Life Science Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China School of Optoelectronics Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China

The uniformity of light dosimetry is an important parameter that affects the efficacy of photodynamic therapy (PDT). Although this uniformity can be improved by a three-dimensional (3D) digital PDT illumination system, it has a low field-of-view (FOV) utilization rate. A checkerboard calibration method using color coding is proposed to calibrate both the projector and camera of the system with a broad common FOV. Experiments reveal that the proposed method increases the utilization rate by up to three times compared with noncolor-coding methods with almost the same accuracy. A fine distinction of phantom lesions in the 3D system can be obtained by clustering, which may be used to optimize the treatment and light-dosimetry evaluation. © 2016 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Calibration

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Designing and Application of Wearable Fatigue Detection system Based on Multimodal Physiological Signals

Designing and Application of Wearable Fatigue Detection Syst...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxuan Qiu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Judging mental fatigue can be guided by acquiring and analyzing various physiological data. However, the existing equipment focuses on single physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), which ignores the preponderance of multimodal physiological signals in fatigue states. Alternatively, some equipment is difficult to operate and thus unsuitable for portable applications. To this end, this paper details the design of a miniaturized multi-physiological signal acquisition system. The system not only acquires EEG and ECG based on combining wavelet transform with Kalman filter, but also uses precise temperature sensors to synchronously acquire proximal skin temperature signals which are not easily interfered with by environmental noise or other physiological signals of human body. Through a fatigue assessment experiment on ten healthy subjects, it was verified that our equipment reliably detects mental fatigue states by monitoring and analyzing EEG, ECG, and proximal skin temperature data. In actual applications, this system appears to the traits of easy operation and good stability. It provides better hardware support for the pervasive applications and concrete implementation of mental health in multi-scene, and can popularize use.

关键词： Fatigue Electrocardiography Electroencephalography Skin Temperature measurement Temperature distribution Temperature sensors

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A Portable system of Mental Fatigue Detection and Mitigation based on Physiological Signals

A Portable System of Mental Fatigue Detection and Mitigation...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Wu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mental fatigue of the brain will cause the weakening of the psychological and physiological functions of the human body, which increases the risk of mistakes and accidents. This paper designs a mental fatigue detection and mitigation system based on human physiological signals and classical music. Through the fatigue assessment and mitigation experiment on 20 healthy subjects, it is verified that the system is able to judge the mental fatigue state of the subjects and play classical music to mitigate fatigue via acquiring and analyzing (alpha+theta)/beta of electroencephalogram (EEG) and heart rate variability (HRV) of ballistocardiogram (BCG). The designed system realizes signal acquisition and fatigue analysis only through using portable device, this makes it have certain application value in office building, hospital, aircraft driving and other scenes.

关键词： Hospitals Buildings Psychology Fatigue Physiology Electroencephalography Multiple signal classification

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