咨询与建议

限定检索结果

文献类型

  • 484 篇 会议
  • 328 篇 期刊文献

馆藏范围

  • 812 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 554 篇 工学
    • 402 篇 计算机科学与技术...
    • 359 篇 软件工程
    • 139 篇 信息与通信工程
    • 86 篇 生物工程
    • 57 篇 光学工程
    • 55 篇 生物医学工程(可授...
    • 47 篇 机械工程
    • 47 篇 控制科学与工程
    • 42 篇 化学工程与技术
    • 39 篇 电子科学与技术(可...
    • 38 篇 电气工程
    • 22 篇 仪器科学与技术
    • 22 篇 土木工程
    • 20 篇 建筑学
    • 19 篇 交通运输工程
    • 15 篇 材料科学与工程(可...
  • 298 篇 理学
    • 168 篇 数学
    • 89 篇 生物学
    • 76 篇 物理学
    • 56 篇 统计学(可授理学、...
    • 29 篇 化学
    • 22 篇 系统科学
  • 132 篇 管理学
    • 68 篇 管理科学与工程(可...
    • 63 篇 图书情报与档案管...
    • 28 篇 工商管理
  • 41 篇 医学
    • 35 篇 临床医学
    • 31 篇 基础医学(可授医学...
    • 22 篇 药学(可授医学、理...
  • 27 篇 法学
    • 23 篇 社会学
  • 15 篇 经济学
    • 15 篇 应用经济学
  • 14 篇 农学
  • 12 篇 教育学
  • 2 篇 文学
  • 2 篇 军事学

主题

  • 29 篇 semantics
  • 25 篇 feature extracti...
  • 24 篇 deep learning
  • 19 篇 training
  • 15 篇 object detection
  • 15 篇 convolution
  • 14 篇 neural networks
  • 14 篇 machine learning
  • 13 篇 accuracy
  • 12 篇 predictive model...
  • 11 篇 anomaly detectio...
  • 11 篇 knowledge graph
  • 11 篇 computational mo...
  • 11 篇 synthetic apertu...
  • 11 篇 forecasting
  • 10 篇 image enhancemen...
  • 10 篇 signal processin...
  • 10 篇 data models
  • 9 篇 remote sensing
  • 8 篇 reinforcement le...

机构

  • 145 篇 guangdong key la...
  • 92 篇 henan key labora...
  • 68 篇 school of comput...
  • 53 篇 school of comput...
  • 48 篇 school of comput...
  • 39 篇 school of data a...
  • 33 篇 shaanxi key labo...
  • 33 篇 school of artifi...
  • 31 篇 sun yat-sen univ...
  • 27 篇 shaanxi key labo...
  • 23 篇 school of comput...
  • 20 篇 college of compu...
  • 19 篇 microsoft resear...
  • 16 篇 key laboratory o...
  • 16 篇 xi'an key labora...
  • 15 篇 peng cheng labor...
  • 13 篇 henan university...
  • 13 篇 henan engineerin...
  • 13 篇 shaanxi key labo...
  • 11 篇 henan key labora...

作者

  • 49 篇 yin jian
  • 26 篇 wang zhongmin
  • 23 篇 wu di
  • 21 篇 su qinliang
  • 19 篇 yu jianxing
  • 18 篇 chen yanping
  • 16 篇 liu yang
  • 16 篇 wang lihui
  • 15 篇 zhang lei
  • 15 篇 zuo xianyu
  • 14 篇 duan nan
  • 14 篇 jian yin
  • 14 篇 zhongmin wang
  • 13 篇 tang duyu
  • 13 篇 xia hong
  • 13 篇 gao cong
  • 13 篇 lei zhang
  • 13 篇 li ning
  • 13 篇 zhou yipeng
  • 12 篇 xiaoke zhu

语言

  • 705 篇 英文
  • 103 篇 其他
  • 5 篇 中文
检索条件"机构=Key Laboratory of Data Analysis and Image Processing"
812 条 记 录,以下是801-810 订阅
排序:
A joint strength based genetic algorithm for network clustering
收藏 引用
Journal of Computational Information Systems 2014年 第14期10卷 5915-5922页
作者: Zhang, Xingyi Ding, Zhuanlian Tang, Jin Luo, Bin Key Laboratory of Industrial Image Processing and Analysis of Anhui Province School of Computer Science and Technology Anhui University Hefei China
The quality of network clustering is partially determined by its evaluation criterion. In this paper, a joint strength based genetic algorithm (JSGA) for network clustering is proposed, where the joint strength which ... 详细信息
来源: 评论
Parallel knowledge acquisition algorithm for big data using MapReduce
Parallel knowledge acquisition algorithm for big data using ...
收藏 引用
International Conference on Machine Learning and Cybernetics (ICMLC)
作者: Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China
With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of di... 详细信息
来源: 评论
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
arXiv
收藏 引用
arXiv 2018年
作者: Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot... 详细信息
来源: 评论
image decomposition using Bregman-GTV and Meyer's G-Norm
Image decomposition using Bregman-GTV and Meyer's G-Norm
收藏 引用
5th IEEE International Conference on Intelligent Networking and Collaborative Systems, INCoS 2013
作者: Lu, Chengwu Zhou, Daoqing Key Laboratory of Data Analysis and Image Processing Chongqing University of Arts and Sciences Chongqing China
In order to avoid stair casing and preserve small scale texture information for the classical total variation regularization, a new minimization energy functional model for image decomposition is proposed. We firstly ... 详细信息
来源: 评论
Adaptive control for synchronization of Chua's circuit
收藏 引用
Journal of Computational Information Systems 2013年 第14期9卷 5751-5759页
作者: Lan, Yaoyao College of Computer Science Chongqing University Chongqing 401331 China Department of Mathematics Key Laboratory of Data Analyzing and Image Processing Chongqing University of Arts and Sciences Yongchuan 402160 China
We present a systematic design procedure for selecting a proper controller based on Lyapunov stability theory in Chua's circuit. This method needs only a single controller to realize synchronization of this chaoti... 详细信息
来源: 评论
An iterative space alternative tiling parallel algorithm for 3D finite difference stencil computations
International Journal of Database Theory and Application
收藏 引用
International Journal of database Theory and Application 2014年 第6期7卷 105-120页
作者: Shen, Jing Zhang, Jilin Wan, Jian Zhou, Li Jiang, Ming School of Computer Science and Technology Hangzhou Dianzi University Hangzhou Zhejiang China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis Hangzhou China School of Information Engineering Hangzhou Dianzi University Hangzhou Zhejiang China
Stencils are finite-difference algorithms for solving large-scale and high-dimension partial differential equations. Due to the data dependences among the iterative statements in Stencils, traditional Stencil computat... 详细信息
来源: 评论
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts
收藏 引用
BMC NEUROSCIENCE 2017年 第SUPPL 1期18卷 95-176页
作者: [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ
来源: 评论
The Hi-GAL compact source catalogue. I : The physical properties of the clumps in the inner Galaxy (−71.0◦ 67.0◦)
arXiv
收藏 引用
arXiv 2017年
作者: Elia, Davide Molinari, S. Schisano, E. Pestalozzi, M. Pezzuto, S. Merello, M. Noriega-Crespo, A. Moore, T.J.T. Russeil, D. Mottram, J.C. Paladini, R. Strafella, F. Benedettini, M. Bernard, J.P. Di Giorgio, A. Eden, D.J. Fukui, Y. Plume, R. Bally, J. Martin, P.G. Ragan, S.E. Jaffa, S.E. Motte, F. Olmi, L. Schneider, N. Testi, L. Wyrowski, F. Zavagno, A. Calzoletti, L. Faustini, F. Natoli, P. Palmerim, P. Piacentini, F. Piazzo, L. Pilbratt, G.L. Polychroni, D. Baldeschi, A. Beltrán, M.T. Billot, N. Cambrésy, L. Cesaroni, R. García-Lario, P. Hoare, M.G. Huang, M. Joncas, G. Liu, S.J. Maiolo, B.M.T. Marsh, K.A. Maruccia, Y. Mège, P. Peretto, N. Rygl, K.L.J. Schilke, P. Thompson, M.A. Traficante, A. Umana, G. Veneziani, M. Ward-Thompson, D. Whitworth, A.P. Arab, H. Bandieramonte, M. Becciani, U. Brescia, M. Buemi, C. Bufano, F. Butora, R. Cavuoti, S. Costa, A. Fiorellino, E. Hajnal, A. Hayakawa, T. Kacsuk, P. Leto, P. Li Causi, G. Marchili, N. Martinavarro-Armengol, S. Mercurio, A. Molinaro, M. Riccio, G. Sano, H. Sciacca, E. Tachihara, K. Torii, K. Trigilio, C. Vitello, F. Yamamoto, H. INAF-IAPS via del Fosso del Cavaliere 100 Roma00133 Italy Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park Ic2 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Aix Marseille Univ. CNRS LAM Laboratoire d’Astrophysique de Marseille Marseille France Max-Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany Infrared Processing Analysis Center California Institute of Technology 770 South Wilson Ave. PasadenaCA91125 United States Dipartimento di Matematica e Fisica Università del Salento Lecce73100 Italy CNRS IRAP 9 Av. colonel Roche BP 44346 Toulouse cedex 4F-31028 France Université de Toulouse UPS-OMP IRAP Toulouse cedex 4F-31028 France Department of Physics Nagoya University Chikusa-ku Nagoya Aichi464-8601 Japan Department of Physics & Astronomy University of Calgary ABT2N 1N4 Canada Center for Astrophysics and Space Astronomy University of Colorado BoulderCO80309 United States Canadian Institute for Theoretical Astrophysics University of Toronto McLennan Physical Laboratories 60 St. George Street TorontoON Canada School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom School of Physics and Astronomy Cardiff University Cardiff WalesCF24 3AA United Kingdom IPAG University Grenoble Alpes Grenoble38000 France AIM Paris-Saclay CEA/IRFU - CNRS INSU - Univ. Paris Diderot Service d’Astrophysique CEA-Saclay Gif-surYvette Cedex91191 France INAF Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy I. Physik. Institut University of Cologne Zülpicher Strasse Köln50937 Germany European Southern Observatory Karl Schwarzschild str. 2 Garching85748 Germany Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany ASI Science Data centre 00044 Frascati Roma Italy ESA/ESAC PO Box 78 Villanueva de la Cañada Madrid28691 Spain
Hi-GAL is a large-scale survey of the Galactic plane, performed with Herschel in five infrared continuum bands between 70 and 500 µm. We present a band-merged catalogue of spatially matched sources and their prop... 详细信息
来源: 评论
Evaluation of MODIS vegetation indices and change thresholds for the monitoring of the Brazilian Cerrado
Evaluation of MODIS vegetation indices and change thresholds...
收藏 引用
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)
作者: L.G. Ferreira M.E. Ferreira N.C. Ferreira E.T. de Jesus E.E. Sano A.R. Huete Image Processing and GIS lab Federal University of Goiás (IESA — UFG) Goiânia Brazil Brazilian Agricultural Research Organization Planaltina Brazil Geoscience Institute University of Brasília-UnB Brasilia Brazil Graduate Program on Environmental Data Analysis Geoscience Institute Brasília Brazil Terrestrial Biophysics and Remote Sensing Laboratory (TBRS) SWES Department University of Arizona Tucson Tucson USA Embrapa Brazilian Agricultural Research Organization Brazil SWES Department University of Arizona Tucson USA
We investigated the use of the MODIS vegetation indices and the effect of distinct change thresholds for monitoring land cover change in the Cerrado biome, the largest region of neotropical savanna vegetation in the w... 详细信息
来源: 评论
Remote sensing image compression for deep space based on region of interest
收藏 引用
Journal of Harbin Institute of Technology(New Series) 2003年 第3期10卷 300-303页
作者: 王振华 吴伟仁 田玉龙 田金文 柳健 Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China major limitation for deep space communication is the limited bandwidths available. The downlink rate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However the Next Generation Space Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be reduced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image compression techniques. An very low bit rate image compression method based on region of interest(ROI) has been proposed for deep space image. The conventional image compression algorithms which encode the original data without any data analysis can maintain very good details and haven't high compression rate while the modern image compressions with semantic organization can have high compression rate even to be hundred and can't maintain too much details. The algorithms based on region of interest inheriting from the two previews algorithms have good semantic features and high fidelity and is therefore suitable for applications at a low bit rate. The proposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal local quality with bit rate control. The Result shows that our method can maintain more details in ROI than general image compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas
A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescop... 详细信息
来源: 评论