Recently, researchers began to use image processing methods with deep learning models to detect dim and small objects. A research team working with Dr. WEI Xin from the Xi'an Institute of Optics and Precision Mechanics (XIOPM) proposed a space debris detection method using feature learning of candidate regions in optical image sequences.
A research team led by Prof. YAO Baoli from the Xi'an Institute of Optics and Precision Mechanics proposed a novel deep learning network for 3D imaging of spinning-disk confocal microscopy (SDCM), named the SRRF-Deep. This network builds a 2.5D model with context information across neighboring pixels in z-scanning to facilitate the reconstruction of an optical slice in 3D imaging.
A research team led by Prof. YANG Yongfeng, and the co-first authors KUANG Zhonghua and Dr. WANG Xiaohui from the Shenzhen Institutes of Advanced Technology developed a small animal PET scanner named "SIAT aPET" with high spatial resolution and high sensitivity.
A team led by Dr. LIU Chengbo from the Shenzhen Institutes of Advanced Technology and their collaborators from the University of Texas at Austin explored the photoacoustic imaging mechanism of paramagnetic metalloporphyrins.
Inspired by the concept of programming, a research team led by Dr. WANG Hao from the Shenzhen Institutes of Advanced Technology proposed programmed-triboelectric nanogenerators (P-TENGs) using mechanical switches regulation methodology for energy manipulation.
Researchers from the Institute of Acoustics proposed a stepwise inversion method based on monopole acoustic well logging data to radially profile the near-borehole formation velocities.
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