Institution
University of Electronic Science and Technology of China
Education•Chengdu, China•
About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.
Topics: Antenna (radio), Dielectric, Thin film, Radar, Artificial neural network
Papers published on a yearly basis
Papers
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TL;DR: This article incorporates local differential privacy into federated learning for protecting the privacy of updated local models and proposes a random distributed update scheme to get rid of the security threats led by a centralized curator.
Abstract: Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.
248 citations
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TL;DR: Sensitive spin-orbital effective field measurements up to 10 nm thick ferromagnetic layer are shown and it is found that this effective field persists even with the insertion of a copper spacer, suggesting that the spin- Orbital effective field does not rely on the heavy normal metal/ferromagnetic metal interface.
Abstract: The spin-orbit coupling present in certain nonmagnetic/ferromagnetic metal bilayers could enable electrical control of pure spin currents in future spintronic devices. Xiao et al. report the signatures of such coupling, even when the two layers are separated by a third copper layer.
248 citations
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TL;DR: In the past few years, the use of sequence-specific nucleases for efficient targeted mutagenesis has provided plant biologists with a powerful new approach for understanding gene function and developing new traits.
247 citations
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TL;DR: In this paper, a novel approach to realize uniform amplitude time modulated linear arrays with both suppressed sidelobes and sidebands is proposed, which utilizes the direct optimization of the "switch-on" time sequence of each array element via the simple genetic algorithm (SGA).
Abstract: A novel approach to realize uniform amplitude time modulated linear arrays with both suppressed sidelobes and sidebands is proposed. The approach utilizes the direct optimization of the "switch-on" time sequence of each array element via the simple genetic algorithm (SGA). As compared to previous time modulated linear arrays, the new array has the advantages of having low sidelobe level (SLL), low sideband level (SBL) and uniform excitations simultaneously. Experimental results on an experimental prototype of a 16-element printed dipole linear array with the SGA optimized time sequences verified the approach.
247 citations
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TL;DR: The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease and it achieved good accuracy as compared to previously proposed methods.
Abstract: Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naive bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.
247 citations
Authors
Showing all 51090 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gang Chen | 167 | 3372 | 149819 |
Frede Blaabjerg | 147 | 2161 | 112017 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Yi Yang | 143 | 2456 | 92268 |
Guanrong Chen | 141 | 1652 | 92218 |
Shuit-Tong Lee | 138 | 1121 | 77112 |
Lei Zhang | 135 | 2240 | 99365 |
Rajkumar Buyya | 133 | 1066 | 95164 |
Lei Zhang | 130 | 2312 | 86950 |
Bin Wang | 126 | 2226 | 74364 |
Haiyan Wang | 119 | 1674 | 86091 |
Bo Wang | 119 | 2905 | 84863 |
Yi Zhang | 116 | 436 | 73227 |
Qiang Yang | 112 | 1117 | 71540 |
Chun-Sing Lee | 109 | 977 | 47957 |