Institution
Southeast University
Education•Nanjing, 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 published on a yearly basis
Papers
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TL;DR: This article comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave, terahertz, and optical wireless communication channels under all spectra.
Abstract: In this article, we present our vision of the application scenarios, performance metrics, and potential key technologies of 6G wireless communication networks. We then comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave (mm-wave), terahertz, and optical wireless communication channels under all spectra; satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios; and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input, multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of Things (IoT) communication channels under full application scenarios. We also provide future research challenges of 6G channel measurements, a general standard 6G channel model framework, and models for intelligent reflection surface (IRS)-based 6G technologies and artificial intelligence (AI)-enabled channel measurements and models.
187 citations
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TL;DR: In this article, synchronization control of stochastic neural networks with time-varying delays has been considered and a novel control method is given using the Lyapunov functional method and linear matrix inequality (LMI) approach.
Abstract: In this paper, synchronization control of stochastic neural networks with time-varying delays has been considered. A novel control method is given using the Lyapunov functional method and linear matrix inequality (LMI) approach. Several sufficient conditions have been derived to ensure the global asymptotical stability in mean square for the error system, and thus the drive system synchronize with the response system. Also, the estimation gains can be obtained. With these new and effective methods, synchronization can be achieved. Simulation results are given to verify the theoretical analysis in this paper.
186 citations
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TL;DR: It was found that the centralized disposal system has been constructed based on incineration technology, and the disposal cost of medical waste is about 580 US$/ton.
186 citations
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TL;DR: This paper proposes a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria.
Abstract: Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.
186 citations
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TL;DR: In this paper, an ultra-sensitive GO-based capacitive pressure sensor with graphene as electrodes was presented, which can detect a subtle pressure of ∼0.24Pa with a fast response time (∼100m) and a high sensitivity ( ∼ 0.8kPa−1).
186 citations
Authors
Showing all 66906 results
Name | H-index | Papers | Citations |
---|---|---|---|
H. S. Chen | 179 | 2401 | 178529 |
Yang Yang | 171 | 2644 | 153049 |
Gang Chen | 167 | 3372 | 149819 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Yi Yang | 143 | 2456 | 92268 |
Guanrong Chen | 141 | 1652 | 92218 |
Wei Huang | 139 | 2417 | 93522 |
Jun Chen | 136 | 1856 | 77368 |
Jian Li | 133 | 2863 | 87131 |
Xiaoou Tang | 132 | 553 | 94555 |
Zhen Li | 127 | 1712 | 71351 |
Tao Zhang | 123 | 2772 | 83866 |
Bo Wang | 119 | 2905 | 84863 |
Jinde Cao | 117 | 1430 | 57881 |