Institution
National University of Defense Technology
Education•Changsha, 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.
Topics: Computer science, Radar, Laser, Synthetic aperture radar, Fiber laser
Papers published on a yearly basis
Papers
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TL;DR: All-organic, flexible superhydrophobic nanocomposite coatings that demonstrate strong mechanical robustness under cyclic tape peels and Taber abrasion, sustain exposure to highly corrosive media, namely aqua regia and sodium hydroxide solutions, and can be applied to surfaces through scalable techniques such as spraying and brushing are described.
Abstract: Superhydrophobicity is a remarkable evolutionary adaption manifested by several natural surfaces. Artificial superhydrophobic coatings with good mechanical robustness, substrate adhesion and chemical robustness have been achieved separately. However, a simultaneous demonstration of these features along with resistance to liquid impalement via high-speed drop/jet impact is challenging. Here, we describe all-organic, flexible superhydrophobic nanocomposite coatings that demonstrate strong mechanical robustness under cyclic tape peels and Taber abrasion, sustain exposure to highly corrosive media, namely aqua regia and sodium hydroxide solutions, and can be applied to surfaces through scalable techniques such as spraying and brushing. In addition, the mechanical flexibility of our coatings enables impalement resistance to high-speed drops and turbulent jets at least up to ~35 m s−1 and a Weber number of ~43,000. With multifaceted robustness and scalability, these coatings should find potential usage in harsh chemical engineering as well as infrastructure, transport vehicles and communication equipment.
486 citations
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University of Ljubljana1, University of Birmingham2, Czech Technical University in Prague3, Linköping University4, Austrian Institute of Technology5, Autonomous University of Madrid6, Parthenope University of Naples7, University of Isfahan8, University of Oxford9, Superior National School of Advanced Techniques10, Middle East Technical University11, Dalian University of Technology12, Chinese Academy of Sciences13, ASELSAN14, United States Naval Research Laboratory15, National University of Defense Technology16, University of Science and Technology of China17, Electronics and Telecommunications Research Institute18, Zhejiang University19, Beijing University of Posts and Telecommunications20, Huazhong University of Science and Technology21, University of Missouri22, Carnegie Mellon University23, General Electric24, King Abdullah University of Science and Technology25, University of California, Merced26, University of Surrey27, University at Albany, SUNY28
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Abstract: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.
485 citations
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06 Jul 2017
TL;DR: In this article, a dual path network (DPN) is proposed for image classification, which shares common features while maintaining the flexibility to explore new features through dual path architectures, achieving state-of-the-art performance on the ImagNet-1k, Places365 and PASCAL VOC datasets.
Abstract: In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.
475 citations
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TL;DR: This paper proposes a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learned with sparse regression to perform feature selection.
Abstract: Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the $\ell_{2,1}$ -norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
468 citations
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TL;DR: An energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency, and an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution.
Abstract: Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices’ battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.
467 citations
Authors
Showing all 39659 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rui Zhang | 151 | 2625 | 107917 |
Jian Li | 133 | 2863 | 87131 |
Chi Lin | 125 | 1313 | 102710 |
Wei Xu | 103 | 1492 | 49624 |
Lei Liu | 98 | 2041 | 51163 |
Xiang Li | 97 | 1472 | 42301 |
Chang Liu | 97 | 1099 | 39573 |
Jian Huang | 97 | 1189 | 40362 |
Tao Wang | 97 | 2720 | 55280 |
Wei Liu | 96 | 1538 | 42459 |
Jian Chen | 96 | 1718 | 52917 |
Wei Wang | 95 | 3544 | 59660 |
Peng Li | 95 | 1548 | 45198 |
Jianhong Wu | 93 | 726 | 36427 |
Jianhua Zhang | 92 | 415 | 28085 |