Institution
Xidian University
Education•Xi'an, China•
About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.
Papers published on a yearly basis
Papers
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TL;DR: The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.
Abstract: A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.
103 citations
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TL;DR: The performance of SS-LapSVM is evaluated on AVIRIS image data taken over Indiana's Indian Pine, and the results show that it can achieve accurate and rapid classification with a small number of labeled data, and outperform state-of-the-art semi-supervised approaches.
Abstract: In this letter, we propose a new spatio-spectral Laplacian support vector machine (SS-LapSVM) for semi-supervised hyperspectral image classification. The clustering assumption on spectral vectors is used to formulate a manifold regularizer, and neighborhood spatial constraints of hyperspectral images are designed to construct a spatial regularizer. Moreover, a non-iterative optimization procedure is presented to solve this dual-regularized SVM, which makes rapid classification possible. By combining spatial and spectral information together, SS-LapSVM can avoid the speckle-like misclassification of hyperspectral images in the original Lap-SVM. The performance of SS-LapSVM is evaluated on AVIRIS image data taken over Indiana's Indian Pine, and the results show that it can achieve accurate and rapid classification with a small number of labeled data, and outperform state-of-the-art semi-supervised approaches.
103 citations
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TL;DR: Recent, state-of-the-art advances in the three levels (the primary sequence, the secondary structure and the function annotation) of the lncRNAs research are reviewed, as well as computational methods for lncRNA data analysis.
Abstract: Long noncoding RNAs (lncRNAs), generally longer than 200 nucleotides and with poor protein coding potential, are usually considered collectively as a heterogeneous class of RNAs. Recently, an increasing number of studies have shown that lncRNAs can involve in various critical biological processes and a number of complex human diseases. Not only the primary sequences of many lncRNAs are directly interrelated to a specific functional role, strong evidence suggests that their secondary structures are even more interrelated to their known functions. As functional molecules, lncRNAs have become more and more relevant to many researchers. Here, we review recent, state-of-the-art advances in the three levels (the primary sequence, the secondary structure and the function annotation) of the lncRNA research, as well as computational methods for lncRNA data analysis.
103 citations
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TL;DR: In this paper, the authors proposed a new paradigm called Outsourced ABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing cloud service provider (S-CSP).
Abstract: Attribute-based signature (ABS) enables users to sign messages over attributes without revealing any information other than the fact that they have attested to the messages. However, heavy computational cost is required during signing in existing work of ABS, which grows linearly with the size of the predicate formula. As a result, this presents a significant challenge for resource-constrained devices (such as mobile devices or RFID tags) to perform such heavy computations independently. Aiming at tackling the challenge above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing-cloud service provider (S-CSP). Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the complexity. As a result, we present two concrete OABS schemes: i) in the first OABS scheme, the number of exponentiations involving in signing is reduced from $O(d)$ to $O(1)$ (nearly three), where $d$ is the upper bound of threshold value defined in the predicate; ii) our second scheme is built on Herranz et al.’s construction with constant-size signatures. The number of exponentiations in signing is reduced from $O(d^2)$ to $O(d)$ and the communication overhead is $O(1)$ . Security analysis demonstrates that both OABS schemes are secure in terms of the unforgeability and attribute-signer privacy definitions specified in the proposed security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of OABS and show how to achieve accountability as well.
103 citations
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TL;DR: A novel framework is proposed to learn robust features of PolSAR data using the stacked sparse autoencoder (SSAE) to learn the deep spatial sparse features automatically for the first time.
Abstract: Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing. Among various classification techniques, the stacked sparse autoencoder (SSAE) is a kind of deep learning method that can automatically learn useful features layer by layer in an unsupervised manner. However, the scattering measurements of individual pixels in PolSAR images are affected by the speckle; hence, the performance of pixel-based classification approaches would be poor. In this situation, a novel framework is proposed to learn robust features of PolSAR data. The local spatial information is introduced into SSAE to learn the deep spatial sparse features automatically for the first time. Furthermore, the influences of the neighbor pixels on the central pixel are controlled depending on the spatial distances from the neighbor pixels to the central pixel. Experimental results with fully PolSAR data indicate that the proposed method provides a competitive solution.
103 citations
Authors
Showing all 32362 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Jie Zhang | 178 | 4857 | 221720 |
Bin Wang | 126 | 2226 | 74364 |
Huijun Gao | 121 | 685 | 44399 |
Hong Wang | 110 | 1633 | 51811 |
Jian Zhang | 107 | 3064 | 69715 |
Guozhong Cao | 104 | 694 | 41625 |
Lajos Hanzo | 101 | 2040 | 54380 |
Witold Pedrycz | 101 | 1766 | 58203 |
Lei Liu | 98 | 2041 | 51163 |
Qi Tian | 96 | 1030 | 41010 |
Wei Liu | 96 | 1538 | 42459 |
MengChu Zhou | 96 | 1124 | 36969 |
Chunying Chen | 94 | 508 | 30110 |
Daniel W. C. Ho | 85 | 360 | 21429 |