Institution
Nanjing University of Information Science and Technology
Education•Nanjing, China•
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.
Papers published on a yearly basis
Papers
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TL;DR: This paper provides a formal security model for ID-CDIC and proves the security of the construction under the RSA assumption with large public exponents in the random oracle model, which can eliminate the complex certificate management in traditional cloud data integrity checking protocols.
95 citations
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TL;DR: In this article, the authors evaluated three satellite-derived LAI products -MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003-2012 using fine-resolution (30 m) LAI data and field LAI measurements.
95 citations
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26 Dec 2009TL;DR: A new anti-Arnold transformation algorithm is used which got by solving equation groups, which will save lots of time when using its periodicity, especially when it used in the picture with big degree.
Abstract: Arnold transformation is applied widely in digital image scramble because of its periodicity. But we will waste lots of time when we used its periodicity to get the anti-Arnold transformation algorithm, especially when it used in the picture with big degree. So, in this paper, we use a new anti-Arnold transformation algorithm which got by solving equation groups. By this way, we needn't to figure out the degree, which will save lots of time.
95 citations
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TL;DR: In this paper, the authors exploit the association of distinct low-frequency radio emissions with the generation of terrestrial gamma ray flashes (TGFs) to directly measure for the first time the TGF source altitude.
Abstract: Many details of how thunderstorms generate terrestrial gamma ray flashes (TGFs) and other forms of high-energy radiation remain uncertain, including the basic question of where they are produced. We exploit the association of distinct low-frequency radio emissions with generation of terrestrial gamma ray flashes (TGFs) to directly measure for the first time the TGF source altitude. Analysis of two events reveals source altitudes of 11.8 ± 0.4 km and 11.9 ± 0.9 km. This places the source region in the interior of the thunderstorm between the two main charge layers and implies an intrinsic TGF brightness of approximately 10 18 runaway electrons. The electric current in this nontraditional lightning process is found to be strong enough to drive nonlinear effects in the ionosphere, and in one case is comparable to the highest peak current lightning processes on the planet.
95 citations
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TL;DR: A novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN) to exploit the potential of unlabeled data and demonstrate the superiority of the proposed method against other state-of-the-art approaches.
Abstract: Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.
95 citations
Authors
Showing all 14448 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Lei Zhang | 135 | 2240 | 99365 |
Bin Wang | 126 | 2226 | 74364 |
Shuicheng Yan | 123 | 810 | 66192 |
Zeshui Xu | 113 | 752 | 48543 |
Xiaoming Li | 113 | 1932 | 72445 |
Qiang Yang | 112 | 1117 | 71540 |
Yan Zhang | 107 | 2410 | 57758 |
Fei Wang | 107 | 1824 | 53587 |
Yongfa Zhu | 105 | 355 | 33765 |
James C. McWilliams | 104 | 535 | 47577 |
Zhi-Hua Zhou | 102 | 626 | 52850 |
Tao Li | 102 | 2483 | 60947 |
Lei Liu | 98 | 2041 | 51163 |
Jian Feng Ma | 97 | 305 | 32310 |