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Institution

Harbin Institute of Technology

EducationHarbin, China
About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a rapid method based on an efficient gas/liquid interfacial microwave assisted process has been developed to synthesize flowerlike NiO hollow nanosphere precursors, which were then transformed to NiO by simple calcinations.
Abstract: A rapid method based on an efficient gas/liquid interfacial microwave-assisted process has been developed to synthesize flowerlike NiO hollow nanosphere precursors, which were then transformed to NiO by simple calcinations. The wall of the sphere is composed of twisted NiO nanosheets that intercalated with each other. Such hollow structure is different from widely reported flowerlike nanostructures with solid cores. An Ostwald ripening mechanism was proposed for the formation of the hollow nanostructures. The products were characterized by X-ray powder diffraction, scanning electron microscopy, transmission electron microscopy, high-resolution TEM, energy-dispersive X-ray analysis, and N2adsorption-desorption methods. These flowerlike NiO hollow nanospheres have high surface area of 176 m2 g−1. Electrochemical properties show a high specific capacitance of 585 F g−1 at a discharge current of 5 A g−1 and excellent cycling stability, suggesting its promising potentials in supercapacitors.

300 citations

Journal ArticleDOI
01 May 2012
TL;DR: This paper presents a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store that uses efficient graph exploration and massive parallel computing for query processing.
Abstract: The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.

300 citations

Journal ArticleDOI
TL;DR: The results demonstrate that the proposed micro-structure descriptor is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.

299 citations

Journal ArticleDOI
TL;DR: In this article, a novel high-efficiency visible-light sensitive Ag2CO3 semiconductor photocatalyst was prepared by a simple ion-exchange method based on a strategy incorporating of p-block C element into a narrow bandgap Ag2O.
Abstract: A novel high-efficiency visible-light sensitive Ag2CO3 semiconductor photocatalyst was prepared by a simple ion-exchange method based on a strategy incorporating of p-block C element into a narrow bandgap Ag2O. This photocatalyst exhibits universal high-efficient degradation ability for typically several RhB, MO and MB dyes. Getting insight into degradation patterns of dyes over Ag2CO3 identifies they are self-oxidation behavior of semiconductor rather than the effect of photosensitization. The reaction mechanism investigated by a series of radical trapping experiments, not only ascertains the major photoreaction approaches of dyes on the surface of Ag2CO3, but also reveals the unique universality advantage that arises from selective using one of many activated species to decompose many kinds of dyes such as RhB, MO and MB. The theoretical calculation based on first-principles provides inherently essential evidences for high-efficient oxidation performance of Ag2CO3 photocatalyst.

299 citations

Posted Content
TL;DR: Wang et al. as mentioned in this paper proposed a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input.
Abstract: Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to non-blindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.

299 citations


Authors

Showing all 89023 results

NameH-indexPapersCitations
Jiaguo Yu178730113300
Lei Jiang1702244135205
Gang Chen1673372149819
Xiang Zhang1541733117576
Hui-Ming Cheng147880111921
Yi Yang143245692268
Bruce E. Logan14059177351
Bin Liu138218187085
Peng Shi137137165195
Hui Li1352982105903
Lei Zhang135224099365
Jie Liu131153168891
Lei Zhang130231286950
Zhen Li127171271351
Kurunthachalam Kannan12682059886
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023383
20221,895
202110,083
20209,817
20199,659
20188,215