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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: The results revealed that state‐specific FNC disruptions were observed in IGE‐GTCS and the majority of aberrant functional connectivity manifested itself in default mode network and suggested that the dynamic FNC analysis was a promising avenue to deepen the understanding of this disease.
Abstract: Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra-network connectivity of multiple resting-state networks (RSNs); however, whether impairment is present in inter-network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic-clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting-state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE-GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, k-mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state-specific FNC disruptions were observed in IGE-GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE-GTCS from controls with an accuracy of 77.91% (P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE-GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957-973, 2017. © 2016 Wiley Periodicals, Inc.

237 citations

Journal ArticleDOI
TL;DR: A step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.

237 citations

Journal ArticleDOI
TL;DR: This work adopts a deep Q-learning approach for designing optimal offloading schemes and proposes an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure and evaluates the proposed schemes based on real traffic data.
Abstract: Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

237 citations

Journal ArticleDOI
TL;DR: This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position, in which the barrier Lyapunov function is employed to guarantee that the joint constraints are not violated.
Abstract: This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore–Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.

237 citations

Journal ArticleDOI
TL;DR: In this article, the feasibility of self-powered flexible micro-systems by introducing the technology of TENG around the following major categories: working principles, advanced materials, Teng-based active sensors, TENG-powered actuators, and integrated microsystems.

237 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