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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: In this article, a graphical representation based HFR method (G-HFR) is proposed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration.
Abstract: Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.

122 citations

Journal ArticleDOI
TL;DR: A novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL) based on utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain.

122 citations

Journal ArticleDOI
TL;DR: An adaptive trust control model to specify, evaluate, establish, and ensure the trust relationships among system entities and a number of trust control modes supported by the system are proposed.
Abstract: Trust plays an important role in software systems, especially component-based systems in which components or their environments vary. This paper introduces an autonomic trust management solution for a component-based software system. We propose an adaptive trust control model to specify, evaluate, establish, and ensure the trust relationships among system entities. This model concerns the quality attributes of the entity and a number of trust control modes supported by the system. In particular, its parameters can be adaptively adjusted based on runtime trust assessment in order to reflect real system context and situation. Based on this model, we further develop a number of algorithms that can be adopted by a trust management framework for autonomic management of trust during component execution. We verify the algorithms' feasibility through simulations and demonstrate the effectiveness and benefits of our solution. We also discuss the issues for successful deployment of our solution in a component software platform.

122 citations

Journal ArticleDOI
Li Zhang1, Weida Zhou1
TL;DR: This paper proposes a framework of sparse ensembles and deals with new linear weighted combination methods for sparseEnsemble, which can be thought as implementing the structure risk minimization rule and naturally explains good performance of these methods.

122 citations

Journal ArticleDOI
TL;DR: The proposed control scheme can be guaranteed under the assumption that the unknown system functions are bounded by partly known nonlinear functions, and the designer can determine the approximation domain a priori via the bound of the reference signal, which is very important for the choice of the centers and widths of membership functions.

122 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