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Kun Xu

Researcher at Beijing University of Posts and Telecommunications

Publications -  656
Citations -  8643

Kun Xu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Photonics. The author has an hindex of 42, co-authored 580 publications receiving 6499 citations. Previous affiliations of Kun Xu include IBM & Nanjing Medical University.

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Journal ArticleDOI

Multiwavelength figure-of-eight fiber laser with a nonlinear optical loop mirror

TL;DR: In this paper, a multiwavelength Erbium-doped fiber laser with figure-of-eight configuration is designed and demonstrated, where the intensity-depended transmission of nonlinear optical loop mirror can balance mode competition effect of homogenous broadening gain medium, and thus allows for stable room-temperature multi-wavelength generation.
Proceedings ArticleDOI

Low-complexity two-stage carrier phase estimation for 16-QAM systems using QPSK partitioning and maximum likelihood detection

TL;DR: In this paper, a feedforward carrier phase estimation algorithm using a modified QPSK partition approach followed by maximum-likelihood detection is proposed, which demonstrates complexity reduction by more than a factor of 2 compared with other techniques.
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A Simple Photonic-Assisted Microwave Frequency Measurement System Based on MZI With Tunable Measurement Range and High Resolution

TL;DR: In this article, a simple and practical photonic approach for microwave instantaneous frequency measurement has been proposed and experimentally demonstrated, where the unknown microwave signal with its frequency to be measured is carrier-suppressed modulated on the lightwave by a Mach-Zehnder modulator biased at the minimum point.
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Enhanced Spurious-Free Dynamic Range in Intensity-Modulated Analog Photonic Link Using Digital Postprocessing

TL;DR: In this paper, a post digital linearization technique is proposed and demonstrated for a conventional intensity-modulation direct-detection analog photonic link, where the key distortion information is directly acquired from hardware.
Proceedings ArticleDOI

Structural Information Preserving for Graph-to-Text Generation

TL;DR: This work introduces two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs, that can guide the model for preserving input information via multi-task training.