Bistatic Radar Cooperative Imaging Based on Complementary Random Waveform.

The cooperative imaging of the bistatic radar is an important research topic for missile-borne radar detection. The existing missile-borne radar detection system is mainly based on the target plot information separately extracted by each radar for data level fusion, without considering the gain brought by the cooperative processing of the radar target echo signal. In this paper, a random frequency-hopping waveform is designed for the bistatic radar to achieve efficient motion compensation.

Deep-learning based photon-efficient 3D and reflectivity imaging with a 64 × 64 single-photon avalanche detector array.

A single photon avalanche diode (SPAD) is a high sensitivity detector that can work under weak echo signal conditions (≤1 photon per pixel). The measured digital signals can be used to invert the range and reflectivity images of the target with photon-efficient imaging reconstruction algorithm. However, the existing photon-efficient imaging reconstruction algorithms are susceptible to noise, which leads to poor quality of the reconstructed range and reflectivity images of target.

Fourier single pixel imaging reconstruction method based on the U-net and attention mechanism at a low sampling rate.

There exists the contradiction between imaging efficiency and imaging quality for Fourier single-pixel imaging (FSI). Although the deep learning approaches have solved this problem to some extent, the reconstruction quality at low sampling rate is still not enough to meet the practical requirements. To solve this problem, inspired by the idea of super-resolution, this paper proposes the paralleled fusing of the U-net and attention mechanism to improve the quality of FSI reconstruction at a low sampling rate.

Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning.

Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly.

Underwater ghost imaging based on generative adversarial networks with high imaging quality.

Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study.

Perioperative nursing for immediate breast reconstruction with deep inferior epigastric perforator flap after breast cancer resection.

Background: To investigate the value of the nursing cooperation workflow for immediate breast reconstruction with deep inferior epigastric perforator (DIEP) flap after breast cancer resection.

Methods: The clinical data of 29 patients who had undergone immediate breast reconstruction with DIEP flap after breast cancer resection in our center from January 2016 to December 2017 were retrospectively analyzed. In particular, the nursing cooperation workflow was reviewed.