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Institution

Nanjing University of Information Science and Technology

EducationNanjing, 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Proceedings ArticleDOI
26 Dec 2009
TL;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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Ashok Kumar1515654164086
Lei Zhang135224099365
Bin Wang126222674364
Shuicheng Yan12381066192
Zeshui Xu11375248543
Xiaoming Li113193272445
Qiang Yang112111771540
Yan Zhang107241057758
Fei Wang107182453587
Yongfa Zhu10535533765
James C. McWilliams10453547577
Zhi-Hua Zhou10262652850
Tao Li102248360947
Lei Liu98204151163
Jian Feng Ma9730532310
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023173
2022552
20213,000
20202,492
20192,221
20181,822