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: A lightweight device authentication protocol is proposed by leveraging the frequency response of a speaker and a microphone from two wireless IoT devices as the acoustic hardware fingerprint and authenticates the legitimate user by matching the fingerprint extracted in the learning process and the verification process.
Abstract: Device authentication is a critical and challenging issue for the emerging Internet of Things (IoT). One promising solution to authenticate IoT devices is to extract a fingerprint to perform device authentication by exploiting variations in the transmitted signal caused by hardware and manufacturing inconsistencies. In this paper, we propose a lightweight device authentication protocol [named speaker-to-microphone (S2M)] by leveraging the frequency response of a speaker and a microphone from two wireless IoT devices as the acoustic hardware fingerprint. S2M authenticates the legitimate user by matching the fingerprint extracted in the learning process and the verification process, respectively. To validate and evaluate the performance of S2M, we design and implement it in both mobile phones and PCs and the extensive experimental results show that S2M achieves both low false negative rate and low false positive rate in various scenarios under different attacks.
150 citations
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01 Jan 2017
TL;DR: The proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information.
150 citations
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TL;DR: This paper considers the line spectral estimation problem and proposes an iterative reweighted method which jointly estimates the sparse signals and the unknown parameters associated with the true dictionary, and achieves super resolution and outperforms other state-of-the-art methods in many cases of practical interest.
Abstract: Conventional compressed sensing theory assumes signals have sparse representations in a known dictionary. Nevertheless, in many practical applications such as line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing technique to such applications, the continuous parameter space has to be discretized to a finite set of grid points, based on which a “nominal dictionary” is constructed for sparse signal recovery. Discretization, however, inevitably incurs errors since the true parameters do not necessarily lie on the discretized grid. This error, also referred to as grid mismatch, leads to deteriorated recovery performance. In this paper, we consider the line spectral estimation problem and propose an iterative reweighted method which jointly estimates the sparse signals and the unknown parameters associated with the true dictionary. The proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given log-sum objective function, leading to a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. A simple yet effective scheme is developed for adaptively updating the regularization parameter that controls the tradeoff between the sparsity of the solution and the data fitting error. Theoretical analysis is conducted to justify the proposed method. Simulation results show that the proposed algorithm achieves super resolution and outperforms other state-of-the-art methods in many cases of practical interest.
150 citations
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TL;DR: In this paper, CoSe2 nanoparticles embedded Co-N-doped carbon nanoflake arrays (CS@CNC NAs) are grown on carbon cloth (CC) through a co-precipitation and annealing process, which can not only provide rich active sites, easy access of electrolyte and diffusion of H2 bubbles, but also guarantee its high conductivity and structural stability.
Abstract: It is still challenging to develop highly efficient and stable non-noble metal-based electrocatalysts for hydrogen evolution reaction (HER). Herein, CoSe2 nanoparticles embedded Co-N-doped carbon nanoflake arrays (CS@CNC NAs) are grown on carbon cloth (CC) through a co-precipitation and annealing process. The CS@CNC NAs/CC hybrid electrocatalyst shows superior HER performance with a small Tafel slope of 38 mV dec−1, an ultralow overpotential of 84 mV at 10 mA cm-2 and an excellent long-term stability with nearly no decay even after 72 h. The outstanding HER performance of CS@CNC NAs/CC is mainly attributed to the synergetic effects of CoSe2 nanoparticles and CNC nanoflake arrays, which can not only provide rich active sites, easy access of electrolyte and diffusion of H2 bubbles, but also guarantee its high conductivity and structural stability. This work provides a novel strategy for the rational fabrication of highly effective and stable transition metal selenide-based electrocatalysts for HER.
150 citations
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TL;DR: In this paper, a hexagonal wurtzite structure and high preferential c-axis orientation of transparent conducting ZnO thin films doped with Al have been prepared by sol-gel method, which were characterized by X-ray diffraction, atomic force microscopy and ultra-violet spectrometer.
150 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 |