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2nd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2022

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao Jin, Fangrui Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University College of Intelligence and Computing Tianjin University Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China College of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China

ISBN: (纸本)9781665442763

In order to enable the construction of virtual simulation experiment resources in colleges and universities to keep up with the development of professional technology, this paper proposes to build a 'diversified virtual simulation experiment teaching resource construction system' through exploration and research. With the help of virtual reality, AR, panoramic roaming, video and other technologies, it provides a unified development framework, development documents and case templates for professional teachers to independently develop virtual simulation experiment teaching resources in line with undergraduate teaching, and helps teachers quickly build virtual simulation experiment resources in accordance with unified standards. It effectively solves the problems that it is difficult for college teachers to independently develop virtual simulation courseware and the technology is not unified. © 2022 IEEE.

关键词： Virtual reality

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Variable layout CMOS pixels for end-to-end learning in task specific image Sensors

Variable layout CMOS pixels for end-to-end learning in task ...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Bhaskar Choubey Hendrik Sommerhoff Michael Moeller Andreas Kolb Chair of Analogue Circuits and Image Sensors Center for Sensor Systems (ZESS) University of Siegen Siegen Germany Chair of Computer Graphics and Multimedia Systems Center for Sensor Systems (ZESS) University of Siegen Siegen Germany Chair of Computer Vision Center for Sensor Systems (ZESS) University of Siegen Siegen Germany

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

CMOS image Sensors produce data used in several machine learning models. Typically, their layout is limited to rectangular grid of uniform pixels. However, their shape and size can provide another optimisation parameter in end-to-end training in specific machine learning tasks. Variable layout pixels are observed as one potential approach. An image sensor with such variable and learned layout is presented. A technique for automatic generation of such layouts is also described, along with data from a typical implementation of such pixels.

关键词： image sensors Semiconductor device modeling Training Shape Layout Data models Transistors

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Exploration and application of virtual simulation practice teaching evaluation system under the background of new engineering 16

Exploration and application of virtual simulation practice t...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Yong, Jiu Wang, Yangping Dang, Jianwu Lanzhou Jiaotong Univeristy The National Computer Experimental Teaching Demonstration Center Lanzhou China Lanzhou Jiaotong University The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control China Lanzhou Jiaotong University The School of Electronic and Information Engineering China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China

ISBN: (纸本)9781665414685

Virtual simulation experiment teaching can be carried out in any environment without the limitation of time and space, and used repeatedly for learning, more intuitive understanding of principle and method of the whole experiment, to combine the experimental content and abstract theory, and teachers can improve students' learning situation in virtual simulation teaching line tracking. The exploration and application of evaluation system of virtual simulation practice teaching under the background of new engineering is carried out. By scoring the students' preview, operation, experimental results and reports, and examination, a four-stage process evaluation method of 'preview before class - experimental operation - review after class comprehensive assessment' is constructed. The core of the evaluation system is the cultivation of virtual simulation talents' practice and innovation ability, and virtual simulation is emphasized. The exploration of real experiment teaching process evaluation system and the establishment of comprehensive evaluation index system can promote the deep integration of classroom teaching and virtual simulation experiment teaching, cultivate virtual simulation practice teaching resources and develop teachers, and make the virtual simulation experiment teaching evaluation mode more perfect. © 2021 IEEE.

关键词： Virtual reality

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Design and research on 3d interactive experiment case of virtual railway signal equipment based on hardware perception 16

Design and research on 3d interactive experiment case of vir...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Wang, Yangping Yong, Jiu Wang, Wenrun Yue, Biao Yang, Xu The National Computer Experimental Teaching Demonstration Center Lanzhou Jiaotong Univeristy Lanzhou China Lanzhou Jiaotong University The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou China Lanzhou Jiaotong University The School of Electronic and Information Engineering Lanzhou China Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China

ISBN: (纸本)9781665414685

With the continuous increase of railway operation mileage, higher requirements are put forward for professionals who can operate and maintain railway signal equipment accurately and skillfully. In the process of practical teaching for employees, due to the characteristics of railway operation, such as fast train running speed, high traffic density, scattered equipment layout and high safety requirements for on-site operation, the traditional practical teaching of railway signal equipment based on on-site observation is difficult for students to master the working principle of equipment, real-time information transmission process, equipment action status, linkage with other equipment, etc. To a certain extent, it limits the cultivation of students' engineering practice ability and innovation ability. The research has developed a 3D interactive experiment system of virtual railway signal equipment based on hardware perception. Through controlling 3D virtual signal equipment by hardware sensing component, students can know the internal components, composition and operation environment of the equipment at any time, so that they can quickly understand the function of railway signal equipment and understand the actual action principle of signal equipment in railway station. © 2021 IEEE.

关键词： Virtual reality

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Fingertip interactive tracking registration method for AR assembly system

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Advances in Computational Intelligence 2022年第2期2卷 1-26页

作者： Jiu, Yong Jianguo, Wei Yangping, Wang Jianwu, Dang Xiaomei, Lei The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Lanzhou China College of Intelligence and Computing Tianjin University Tianjin China The National Experimental Teaching Demonstration Center of Computer Science and Technology Lanzhou Jiaotong University Lanzhou China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China Gansu Meteorological Service Meteorological Information and Technological Supporting Center Lanzhou China

Aiming at the problems of single input mode and lack of naturalness in the assembly process of existing AR systems, a tracking registration method of mobile AR assembly system is proposed based on multi-quantity and multi-degree of freedom natural fingertip interaction. Firstly, the real-time and stable tracking of hand area in complex environment is realized based on the hand region tracking; secondly, the fingertip detection and recognition based on K-COS and parallel vector is used to improve the precision and stability of fingertip recognition; thirdly, the special movement track of fingertip is recognized based on improved DTW algorithm, which has strong compatibility and feature gradient transformation for complex fingertip trajectory recognition; finally, through the real-time transformation of projection relationship between fingertip and virtual object, the interaction between fingertip and virtual object is made more natural and realistic. The experimental results show that in the complex environment of background, illumination, scale and rotation, the precision of fingertip detection and recognition is about 93%, and the precision of fingertip motion template matching is about 91%. The translation error of the registration method based on visual feature recognition is reduced by about 100pix compared with fingertip tracking registration method, and the efficiency of mobile AR-guided assembly method is improved by about 24.77% compared with the traditional manual assisted assembly method. These data verifies the strong interaction and practicability of the fingertips based on the user's multi-quantity and multi-degree of freedom features in the process of mobile AR guided assembly.

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Construction of practical teaching system for integration of specialism and innovation based on virtual simulation technology 29th

Construction of practical teaching system for integration of...

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29th National Conference on computer Science Technology and Education, NCCSTE 2019

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China National Computer Science and Technology Experimental Teaching Demonstration Center Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China

ISBN: (纸本)9789811553899

The practical teaching for the majors of rail transit in colleges and universities involves a large number of high-cost, high-consumption, large-scale or comprehensive training. It will often encounter high-risk or extreme environment, inaccessible or irreversible operation, which seriously affects the cultivation of students professional engineering ability. In this paper, a practical teaching system based on virtual simulation technology is constructed for rail transit specialty. The system integrates the professional advantages and practical platform in the university, develops virtual simulation experimental teaching resources and practical teaching cases, and enables students to carry out "independent, cooperative and inquiry" learning in an open, safe, realistic and interactive virtual environment. This system will enhance students professional engineering practice ability and innovation ability, promote the training level of rail transit professionals and the overall quality of students. And for students, it can achieve the goal of strengthening the basic principles of curriculum, training design and research thinking, and enhancing innovation and entrepreneurship ability. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Students

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An effective coaxiality measurement for twist drill based on line structured light sensor

arXiv

引用

arXiv 2021年

作者： Cheng, Ailing Ye, Jiaojiao Yang, Fei Lu, Shufang Gao, Fei The Laboratory of Graphics & Image Processing College of Computer Science and Technology Zhejiang University of Technology Zhejiang Province Hangzhou310023 China The Research Center of Intelligent Computing Software Zhejiang Lab. Zhejiang Province Hangzhou311121 China

Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four core units is designed, which can obtain the 3-D point cloud data of full angle for the twist drill. Second, in the data processing stage, an improved robust Gaussian mixture model is established for accurate and rapid blade back segmentation. To improve measurement efficiency, a rapid reconstruction method of the twist drill axis based on orthogonal synthesis is provided to locate the axial position of the maximum deviation from the benchmark by utilizing the extracted blade back data. Finally, by calculating the maximum radial Euclidean distance from the benchmark, the coaxiality error of the twist drill is obtained. Comparing with other measurement methods, experimental results show that our proposed method is effective with high precision of 3 um and high efficiency of less than 3 s/pc. The result demonstrate that the proposed method is effective, robust and automatic, it can be applied in many actual industrial scene. © 2021, CC BY.

关键词： Drills

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Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction 1

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1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Dzyubachyk, Oleh Koolstra, Kirsten Pezzotti, Nicola Lelieveldt, Boudewijn P. F. Webb, Andrew Börnert, Peter Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands C.J. Gorter Center for High Field MRI Department of Radiology Leiden University Medical Center Leiden Netherlands Computer Graphics and Visualization Group Delft University of Technology Delft Netherlands Philips Research Eindhoven Eindhoven Netherlands Intelligent Systems Department Delft University of Technology Delft Netherlands Philips Research Hamburg Hamburg Germany

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences. © 2019, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Efficient MedSAMs: Segment Anything in Medical images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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