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. A coherent processing algorithm for bistatic echo signals is designed to achieve band fusion and improve the signal quality and range resolution of the radar. Simulation and high-frequency electromagnetic calculation data were used to verify the effectiveness of the proposed method.

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. This paper builds a generative adversarial network structure to achieve recovery of high-resolution target images from low-resolution FSI reconstruction results under low sampling rate conditions. Compared with conventional FSI and other deep learning methods based on FSI, the proposed method can get better quality and higher resolution results at low sampling rates in simulation and experiments. This approach is particularly important to high-speed Fourier single pixel imaging applications.

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. In this paper, a non-local sparse attention encoder (NLSA-Encoder) neural network is proposed to extract the 3D information to reconstruct both the range and reflectivity images of target. The proposed network model can effectively reduce the influence of noise in feature extraction and maintain the capability of long-range correlation feature extraction. In addition, the network is optimized for reconstruction speed to achieve faster reconstruction without performance degradation, compared with other existing deep learning photon-efficient imaging reconstruction methods. The imaging performance is verified through numerical simulation, near-field indoor and far-field outdoor experiments with a 64 × 64 SPAD array. The experimental results show that the proposed network model can achieve better results in terms of the reconstruction quality of range and reflectivity images, as well as reconstruction speed.

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. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

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. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

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. RESULTS: All the 29 patients were emotionally stable before surgery and were able to cooperate well with the surgery. The surgery was smooth. In 27 patients, the flaps survived after surgery and primary healing was achieved at the wounds. The remaining two cases presented with venous vascular crisis within 24 h after the surgery, and the flaps survived after active rescue. The patients were followed up for 4 months to 3 years. Neither complication such as local tumor recurrence, incision infection, flap necrosis, or upper limb lymphedema in the surgical area nor complication such as abdominal wall bulging, abdominal wall hernia, or fat liquefaction of incision in the donor area was reported. The shape of the reconstructed breasts was natural and satisfactory. CONCLUSIONS: Immediate breast reconstruction with DIEP flap after breast cancer resection involves two disciplines: tumor resection and plastic repair. It is time-consuming and difficult to perform. Before the surgery, nurses in the operating room should carefully assess the patient's disease condition, communicate well with the operator, fully understand and be familiar with the surgical procedure and its special requirements, and formulate the surgical cooperation plan. During the surgery, the nurses should strictly implement cancer-free technique and be ready to assist for every next step, so as to effectively shorten the operative time, prevent local tumor recurrence, and thus pave the way for a successful surgery.