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Institution

National University of Defense Technology

EducationChangsha, China
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.


Papers
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Proceedings ArticleDOI
10 Dec 2015
TL;DR: A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost.
Abstract: Local Binary Patterns (LBP) are among the most computationally efficient amongst high-performance texture features. However, LBP is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper we introduce a novel descriptor for texture classification, the Median Robust Extended Local Binary Pattern (MRELBP). In contrast to traditional LBP and many LBP variants, MRELBP compares local image medians instead of raw image intensities. We develop a multiscale LBP-type descriptor by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure. A comprehensive evaluation on benchmark datasets reveals MRELBP's remarkable performance (robust to gray scale variations, rotation changes and noise) relative to state-of-the-art algorithms, but nevertheless at a low computational cost, producing the best classification scores of 99.82%, 99.38% and 99.77% on three popular Outex test suites. Furthermore, MRELBP is also shown to be highly robust to image noise including Gaussian noise, Gaussian blur, Salt-and-Pepper noise and random pixel corruption.

184 citations

Journal ArticleDOI
TL;DR: A hybrid structure which includes Convolutional Neural Network and Extreme Learning Machine, and integrates the synergy of two classifiers to deal with age and gender classification is introduced.

184 citations

Journal ArticleDOI
TL;DR: A novel extended LBP operator for texture analysis that fully uses the texture information contained in the ''nonuniform'' local patterns, which is discarded by the classical LBP operators, and then becomes more robust against noise.

184 citations

Proceedings ArticleDOI
04 Jun 2011
TL;DR: This work designs a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates and offers effective adaptivity for congestion beyond neighboring nodes, and proposes Destination-Based Adaptive Routing (DBAR).
Abstract: With the emergence of many-core architectures, it is quite likely that multiple applications will run concurrently on a system. Existing locally and globally adaptive routing algorithms largely overlook issues associated with workload consolidation. The shortsightedness of locally adaptive routing algorithms limits performance due to poor network congestion avoidance. Globally adaptive routing algorithms attack this issue by introducing a congestion propagation network to obtain network status information beyond neighboring nodes. However, they may suffer from intra- and inter-application interference during output port selection for consolidated workloads, coupling the behavior of otherwise independent applications and negatively affecting performance. To address these two issues, we propose Destination-Based Adaptive Routing (DBAR). We design a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates. Thus, DBAR offers effective adaptivity for congestion beyond neighboring nodes. More importantly, by integrating the destination into the selection function, DBAR mitigates intra- and inter-application interference and offers dynamic isolation among regions. Experimental results show that DBAR can offer better performance than the best baseline algorithm for all measured configurations; it is well suited for workload consolidation. The wiring overhead of DBAR is low and DBAR provides improvement in the energy-delay product for medium and high injection rates.

183 citations

Journal ArticleDOI
TL;DR: In this paper, a supersonic combustor with hydrogen injection upstream of a cavity flameholder was investigated, and the results showed that the cavity shear layer plays a very important role in the flameholding process.
Abstract: Flame characteristics and a plausible flameholding mechanism in a supersonic combustor, with hydrogen injection upstream of a cavity flameholder, were investigated in the present study. Instantaneous OH radical distribution of the combustion flowfield was obtained using OH planar laser-induced fluorescence. According to the similarity of experimental observations with different cavities, a typical L/D = 7 cavity was chosen, and its supersonic combustion flowfield with hydrogen injection was calculated by large-eddy simulation. The results showed that the cavity shear layer plays a very important role in the flameholding process. An approximately steady flame existed in the cavity shear layer and hot combustion products were transported into the injection jet by the vortex interaction ofthejet-with-cavity shear layer. Flame then spread gradually following the counter-rotating vortex induced by the jet until the whole injection jet was ignited. Combustion products, which generated from the cavity shear layer and the jet, were convected into the cavity by the unsteady motion of the cavity shear layer and transported with the recirculation flow to the cavity front wall. These hot products and their intermittent combustion then heated up the cavity, and the fuel that entered into the cavity shear layer was preheated. Thus, the flameholding cycle was formed.

183 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022469
20212,986
20203,468
20193,695