Kaifeng Duan

ORCID: 0009-0007-9885-2795
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About
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Research Areas
  • Optical Network Technologies
  • Industrial Vision Systems and Defect Detection
  • Photonic Crystal and Fiber Optics
  • Satellite Image Processing and Photogrammetry
  • Semiconductor Lasers and Optical Devices
  • Infrared Target Detection Methodologies
  • Radar Systems and Signal Processing
  • Robotics and Sensor-Based Localization
  • Advanced Vision and Imaging
  • Advanced Optical Sensing Technologies
  • Remote-Sensing Image Classification
  • Direction-of-Arrival Estimation Techniques
  • Advanced SAR Imaging Techniques
  • Advanced Measurement and Metrology Techniques
  • Advanced Fiber Laser Technologies
  • Advanced Fiber Optic Sensors

Xi’an University of Posts and Telecommunications
2023-2024

Sun Yat-sen University
2021

Chinese Academy of Sciences
2008

Currently, the loss of chalcogenide fibers is two or more orders magnitude higher than theoretical predictions. A major factor contributing to this difference optical scattering caused by rough interfaces between fiber core and cladding. We fabricate an preform using extrusion-assisted hot-diffusion method. The quality core-cladding interface diffusion was characterized scanning electron microscope (SEM)-energy-dispersive x-ray spectroscopy (EDS) Raman spectroscopy. depth 40.5 μm achieved in...

10.1364/ol.559151 article EN Optics Letters 2025-03-25

10.1109/icnlp60986.2024.10692518 article EN 2022 4th International Conference on Natural Language Processing (ICNLP) 2024-03-22

10.1109/icnlp60986.2024.10692813 article EN 2022 4th International Conference on Natural Language Processing (ICNLP) 2024-03-22

A simple and compact scheme for the phase-locking of two photonic crystal fibre (PCF) lasers is presented studied experimentally. In experiments, PCF are mutually injected by direct transmission coupling lasers. The interference strips high contrast ratio stability even in power operation. 39W coherent output obtained with combining efficiency 97.2%.

10.1049/el:20082176 article EN Electronics Letters 2008-11-05

In this paper, a deep learning framework for space-time adaptive processing is developed. Firstly, set of clutter covariance matrixes (CCMs) are modeled based on the prior parameters radar and navigation system with respect to all possible levels non-ideal factors, columns each CCM formulated as undersampled noisy linear measurements sparse coefficients corresponding angle-Doppler spectrum. Then original spectrum coefficients, obtained by least-square estimation from CCMs known steering...

10.1049/icp.2021.0487 article EN IET conference proceedings. 2021-06-03
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