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Institution

Southeast University

EducationNanjing, China
About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: MIMO & Control theory. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.


Papers
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Journal ArticleDOI
Feng Xu1, Litao Sun1
TL;DR: In this paper, a review of recent progress in liquid-phase synthesis methods for preparing ZnO nanostructures as the photoanodes of the DSSCs is presented.
Abstract: Solution-phase derived ZnO nanostructures have triggered considerable interest and become the mainstream route to obtain low-cost and large-scale electrode materials for dye-sensitized solar cells (DSSCs). The article reviews recent progress in liquid-phase synthesis methods for preparing ZnO nanostructures as the photoanodes of the DSSCs. A few classic paradigms and new advancements in the ZnO nanostructures made by our group are demonstrated. The effects of ZnO nanostructured films with different morphologies, prepared by solution-phase approaches, on the performance of DSSCs are discussed. Finally, various liquid-phase methods of ZnO nanostructure synthesis are summarized and compared to allow further exploration of the ways to improve the photoelectric conversion efficiency of DSSCs.

257 citations

Journal ArticleDOI
TL;DR: The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.
Abstract: For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one-third of spatial pilot overhead at the cost of complexity. The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.

257 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.
Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

257 citations

Journal ArticleDOI
TL;DR: This paper aims to review the state of the art of binary relevance from three perspectives, and some of the recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced.
Abstract: Multi-label learning deals with problems where each example is represented by a single instance while being associated with multiple class labels simultaneously. Binary relevance is arguably the most intuitive solution for learning from multi-label examples. It works by decomposing the multi-label learning task into a number of independent binary learning tasks (one per class label). In view of its potential weakness in ignoring correlations between labels, many correlation-enabling extensions to binary relevance have been proposed in the past decade. In this paper, we aim to review the state of the art of binary relevance from three perspectives. First, basic settings for multi-label learning and binary relevance solutions are briefly summarized. Second, representative strategies to provide binary relevancewith label correlation exploitation abilities are discussed. Third, some of our recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced. As a conclusion, we provide suggestions on future research directions.

257 citations

Journal ArticleDOI
TL;DR: In this paper, Nanoscale α-MnO 2 with different morphologies (nanoparticles, nanoflowers and nanorods) were synthesized via a facile hydrothermal method and tested in peroxymonosulfate (PMS) activation for ciprofloxacin (CIP) degradation.

256 citations


Authors

Showing all 66906 results

NameH-indexPapersCitations
H. S. Chen1792401178529
Yang Yang1712644153049
Gang Chen1673372149819
Xiang Zhang1541733117576
Rui Zhang1512625107917
Yi Yang143245692268
Guanrong Chen141165292218
Wei Huang139241793522
Jun Chen136185677368
Jian Li133286387131
Xiaoou Tang13255394555
Zhen Li127171271351
Tao Zhang123277283866
Bo Wang119290584863
Jinde Cao117143057881
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023228
20221,302
20219,149
20208,667
20197,684
20186,464