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Institution

University of Electronic Science and Technology of China

EducationChengdu, 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.


Papers
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Journal ArticleDOI
TL;DR: This work demonstrates the synthesis of large-area monolayer WS2 films by chemical vapor deposition (CVD) and investigates their photoresponse properties by fabricating n-type field effect transistors (FETs) with Al as the ohmic contact.
Abstract: We demonstrate the synthesis of large-area monolayer WS2 films by chemical vapor deposition (CVD) and investigate their photoresponse properties by fabricating n-type field effect transistors (FETs) with Al as the ohmic contact. Our CVD-grown monolayer WS2 shows an electron mobility of 0.91 cm2 V−1 s−1 and an ON/OFF ratio of 106, indicating its comparable electronic properties to the mechanically exfoliated flake sample. In a vacuum, by applying a gate bias (60 V), the responsivity of the monolayer WS2 phototransistor can increase up to 18.8 mA W−1 and a decent sub-second level response time can be maintained. In contrast, in air, it shows a very fast response time of less than 4.5 ms, but at the cost of responsivity reduction to 0.2 μA W−1. Such a distinctive ambient-sensitive photo-detecting performance can be well-explained by the pronounced effect of charge-acceptor-like O2/H2O molecule adsorption/desorption on the photocarrier transport. Our CVD-grown high quality monolayer WS2 may pave the way for developing industrial-scale optoelectronic devices for photo-detecting and chemical sensing applications.

207 citations

Journal ArticleDOI
TL;DR: The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability.

207 citations

Posted Content
TL;DR: Independently Recurrent Neural Network (IndRNN) as mentioned in this paper is a new type of RNN, where neurons in the same layer are independent of each other and they are connected across layers.
Abstract: Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret. To address these problems, a new type of RNN, referred to as independently recurrent neural network (IndRNN), is proposed in this paper, where neurons in the same layer are independent of each other and they are connected across layers. We have shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies. Moreover, an IndRNN can work with non-saturated activation functions such as relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs can be stacked to construct a network that is deeper than the existing RNNs. Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly. Better performances have been achieved on various tasks by using IndRNNs compared with the traditional RNN and LSTM. The code is available at this https URL.

206 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at the TeraHertz-band frequencies.
Abstract: Wireless communication in the TeraHertz band (0.1–10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

206 citations

Journal ArticleDOI
TL;DR: Numerical results have shown that the proposed cooperative content caching and delivery policy can significantly improve content delivery performance in comparison with existing caching strategies.
Abstract: To address the explosively growing demand for mobile data services in the 5th generation (5G) mobile communication system, it is important to develop efficient content caching and distribution techniques, aiming at significantly reducing redundant data transmissions and improving content delivery efficiency. In heterogeneous cellular network (HetNet), which has been deemed as a promising architectural technique for 5G, caching some popular content items at femto base-stations (FBSs) and even at user equipment (UE) can be exploited to alleviate the burden of backhaul and to reduce the costly transmissions from the macro base-stations to UEs. In this paper, we develop the optimal cooperative content caching and delivery policy, for which FBSs and UEs are all engaged in local content caching. We formulate the cooperative content caching problem as an integer-linear programming problem, and use hierarchical primal-dual decomposition method to decouple the problem into two level optimization problems, which are solved by using the subgradient method. Furthermore, we design the optimal content delivery policy, which is formulated as an unbalanced assignment problem and solved by using Hungarian algorithm. Numerical results have shown that the proposed cooperative content caching and delivery policy can significantly improve content delivery performance in comparison with existing caching strategies.

206 citations


Authors

Showing all 51090 results

NameH-indexPapersCitations
Gang Chen1673372149819
Frede Blaabjerg1472161112017
Kuo-Chen Chou14348757711
Yi Yang143245692268
Guanrong Chen141165292218
Shuit-Tong Lee138112177112
Lei Zhang135224099365
Rajkumar Buyya133106695164
Lei Zhang130231286950
Bin Wang126222674364
Haiyan Wang119167486091
Bo Wang119290584863
Yi Zhang11643673227
Qiang Yang112111771540
Chun-Sing Lee10997747957
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023159
2022980
20217,384
20207,220
20196,976