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Institution

Xidian University

EducationXi'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
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Journal ArticleDOI
TL;DR: This paper quantifies the impact of correlated fading on secure communication of multiple amplify-and-forward (AF) relaying networks and concludes that the channel correlation is always beneficial to the secrecy performance of full relay selection, however, it deteriorates the confidentiality performance if partial-relay selection is used.
Abstract: This paper quantifies the impact of correlated fading on secure communication of multiple amplify-and-forward (AF) relaying networks. In such a network, the base station (BS) is equipped with multiple antennas and communicates with the destination through multiple AF relays, while the message from the relays can be overheard by an eavesdropper. We focus on the practical communication scenario, where the main and eavesdropper’s channels are correlated. In order to enhance the transmission security, transmit antenna selection is performed at the BS, and the best relay is chosen according to the full- or partial-relay selection criterion, which relies on the dual-hop relay channels or the second-hop relay channels, respectively. For these criteria, we study the impact of correlated fading on the network secrecy performance, by deriving an analytical approximation for the secrecy outage probability and an asymptotic expression for the high main-to-eavesdropper ratio. From these results, it is concluded that the channel correlation is always beneficial to the secrecy performance of full relay selection. However, it deteriorates the secrecy performance if partial-relay selection is used, when the number of antennas at the BS is less than the number of relays.

153 citations

Journal ArticleDOI

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TL;DR: This paper proposes a secure and efficient AKA protocol, called SE-AKA, which can fit in with all of the group authentication scenarios in the LTE networks and cannot only provide strong security including privacy-preservation and KFS/KBS, but also provide a group authentication mechanism which can effectively authenticate group devices.

152 citations

Journal ArticleDOI
01 Nov 2006
TL;DR: In this correspondence, elementary siphons of Petri nets are redefined and the significance of this improvement is shown.
Abstract: The concept of elementary siphons of Petri nets is first proposed in our previous work. However, their definitions can cause confusion when there exist weakly independent siphons in a net. In this correspondence, we redefine elementary siphons and show the significance of this improvement

152 citations

Journal ArticleDOI
TL;DR: A light-weight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR), which is able to learn from raw traffic without manual intervention and private information is presented.
Abstract: With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various traffic features and attack-related characteristics by experts, and even, private information might be required. However, due to the outdated features labeling and privacy protocols, the existing approaches may not fit with the characteristics of the changing network environment anymore. In this paper, we present a light-weight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR). Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information. In such a framework, our proposed algorithms are compared with other state-of-the-art methods using two public datasets. The experimental results show that our framework not only can outperform the state-of-the-art methods by averaging 13.49% on encrypted traffic classification's F1 score and by averaging 12.15% on intrusion detection's F1 score but also require much lesser storage resource requirement.

152 citations

Journal ArticleDOI
Yakun Chang1, Cheolkon Jung1, Peng Ke1, Hyoseob Song2, Hwang Jung-Mee2 
TL;DR: Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.
Abstract: We propose automatic contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement. We automatically set the clip point for CLAHE based on textureness of a block. Also, we introduce dual gamma correction into CLAHE to achieve contrast enhancement while preserving naturalness. First, we redistribute the histogram of the block in CLAHE based on the dynamic range of each block. Second, we perform dual gamma correction to enhance the luminance, especially in dark regions while reducing over-enhancement artifacts. Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows. Moreover, automatic CLAHE is computationally efficient, i.e., more than 35 frames/s at $1024\times682$ resolution, due to the independent block processing for contrast enhancement. Experimental results demonstrate that automatic CLAHE with dual gamma correction achieves good performance in contrast enhancement and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.

152 citations


Authors

Showing all 32362 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Jie Zhang1784857221720
Bin Wang126222674364
Huijun Gao12168544399
Hong Wang110163351811
Jian Zhang107306469715
Guozhong Cao10469441625
Lajos Hanzo101204054380
Witold Pedrycz101176658203
Lei Liu98204151163
Qi Tian96103041010
Wei Liu96153842459
MengChu Zhou96112436969
Chunying Chen9450830110
Daniel W. C. Ho8536021429
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023117
2022529
20213,751
20203,816
20194,017
20183,382