People

Biwei Wang

International Visiting Students

Introduction

​Biwei Wang is a Visiting Student in Photonics Lab at KAUST. He is pursuing a Ph. D degree in The Hong Kong Polytechnic University. His current research focuses on the distributed optical fiber sensors based on Brillouin scattering and the application of machine learning techniques in optical fiber sensor systems.

Research Interests

  • ​Sensors
  • Photonics and optoelectronics

Selected Publications

  • B. Wang, L. Wang, N. Guo, F. N. Khan, A. K. Azad, C. Yu, and C. Lu, “Extraction of temperature distribution using deep neural networks for BOTDA sensing system,” Conference on Lasers and Electro-Optics/Pacific Rim 2017 (CLEO-PR), Paper s2027, pp. 1-4, Singapore, 31 July-4 August, 2017.
  • B. Wang, L. Wang, C. Yu, and C. Lu, "Simultaneous temperature and strain measurement using deep neural networks for BOTDA sensing system," Optical Fiber Communication Conference 2018 (OFC), Paper Th2A.66, pp. 1-3, San Diego, USA, 11-15 March, 2018.
  • B. Wang, N. Guo, L. Wang, C. Yu, and C. Lu, "Denoising and robust temperature extraction for BOTDA systems based on denoising autoencoder and DNN," 26th International Conference on Optical Fiber Sensors (OFS), Paper WF29, pp1-4, Lausanne, Switzerland, 24-28 September, 2018.
  • B. Wang, L. Wang, N. Guo, Z. Zhao, C. Yu, and C. Lu, "Deep neural networks assisted BOTDA for simultaneous temperature and strain measurement with enhanced accuracy," Optics Express, vol. 27, no. 3, pp. 2530-2543, 2019.
  • B. Wang, L. Wang, C. Yu, and C. Lu, “Video-BM3D denoising for BOTDA sensing systems,” Asia Communications and Photonics Conference 2019 (ACP), Chengdu, China, 2-5 November, 2019.
  • B. Wang, L. Wang, C. Yu, and C. Lu, "Long-distance BOTDA sensing systems using video-BM3D denoising for both static and slowly varying environment," Optics Express, vol. 27, no. 25, pp. 36100-36113, 2019.
  • B. Wang, N. Guo, L. Wang, C. Yu, and C. Lu, "Robust and fast temperature extraction for Brillouin optical time-domain analyzer by using denoising autoencoder based deep neural networks," IEEE Sensors Journal, DOI: 10.1109/JSEN.2019.2960876, 2019.

Education

Aug. 2016 – Now, Ph. D in Electronic and Information Engineering

Department of Electronic and Information Engineering

The Hong Kong Polytechnic University

Sept. 2011 – Jun. 2015, BEng in Optoelectronic Information Engineering

School of Optical and Electronic Information (OEI)

Huazhong Universi​ty of Science and Technology (HUST)

KAUST Affiliations

​Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | Electrical Engineering​