In the optical system of quantum communication optical ground station, two fast control mirrors, corresponding to precision and high-precision tracking, are used to form a single detection mode closed loop. In order to ensure the control accuracy and stability of the system, decoupling must be considered in the control process. However, when the SNR of the target is low, it is difficult to realize the accurate decoupling of precision and high-precision tracking loops. This paper proposes to add a position sensor in precision and high-precision tracking loops, respectively. On the one hand, the position sensor closed-loop is used to improve the certainty of the inner loop control object and facilitate parameter setting. On the other hand, the deviation of the position sensor reflects the deviation of the TV miss distance. The fine tracking adopts the correction of the deviation of the position sensor for closed-loop, so as to avoid system decoupling. This paper analyzes the object characteristics, control system design method, and robustness of the compound axis control structure based on this method. Theoretical and experimental results show that the proposed method has better robustness and higher accuracy without decoupling control when the target characteristics are poor, especially when the delay changes greatly.

For adaptive optics (AO) systems, Non-Common Path Aberration (NCPA) is considered as a critical issue to limit its diffraction-limited imaging performance and the static aberration will inevitably be introduced in the common path of the AO system inevitably at the same time, especially when it is coupled to telescopes intended for scientific observation. This paper presents an optimized focal-plane-based static aberration correction technique, which can copy a perfect point-spread function (PSF) generated by a single-mode fiber to the AO system via iteration optimization algorithm and static aberration in the AO system can be rapidly corrected. Compared with the focal-plane approach we proposed before, this optimized approach can achieve a global optimization result rapidly and deliver better performance when the AO system has a large initial static wavefront error. This technique can be implemented more conveniently in the AO system than other traditional correction methods for achieving an extremely high imaging performance in astronomy or other fields.

The microscopic image has the characteristics of complex background and overlapping cells. Due to the technical limitations, traditional image processing methods cannot accurately complete the real-time recognition task. To address the above-mentioned problems, we propose an automatic detection method for microscopic images using attention mechanism. This method improves the original DETR architecture by introducing a split-transform-merge mechanism, which reduces the dimensionality of input features and trains multiple groups of convolution kernels for feature extraction, thereby effectively improving the model's feature extraction ability for the targets and increasing the accuracy of model detection rate. The experimental results show that the mAP of the improved model was 96.3%, which is 10% higher than that of the original model DETR. Meanwhile, the proposed method has superior detection capabilities for scenarios such as cell overlap, adhesion, and complex background. Moreover, the detection time for each leucorrhea image was about 88.8 ms, which can satisfy the requirement of real-time microscopy examination.

The phase generated carrier (PGC) demodulation technique is widely used in distributed fiber-optic interferometric sensors, for its high sensitivity, good linearity, and large dynamic range. An improved PGC demodulation algorithm with single-path differential divide and the differential-self-multiplication (PGC-SDD-DSM) demodulation algorithm is proposed in this paper, the demodulation result of the PGC-SDD-DSM algorithm is not related to the carrier phase modulation depth (C) and light intensity disturbance (LID). The simulation and experiment results show that the proposed algorithm is insensitive to the C value, and compared with the single-path differential divide to PGC demodulation algorithm (PGC-SDD), the traditional differential-cross-multiplying (PGC-DCM) and PGC Arctangent (PGC-Arctan) demodulation algorithms, and the proposed algorithm has the best demodulation effect. When the proposed demodulation algorithm is applied in the optical fiber interferometric, it is found that the proposed algorithm can suppress the distortion caused by LID and C. The frequency of the signal to be demodulated is 1000 Hz, and the amplitude value is 2 rad. When the carrier modulation depth of 1.5 rad and the light intensity interference depth of 0.7 rad are introduced, the signal-to-noise and distortion ratio (SINAD) of the demodulation result using the improved PGC demodulation algorithm in the experimental system is 35.56 dB, which is 10.87 dB, 24.19 dB, and 6.38 dB higher than using traditional PGC-DCM, PGC-Arctan, and PGC-SDD demodulation algorithms, respectively. It is proved the system's stability improved effectively. This technology effectively promotes technical research in the fields of optical fiber sensors.

As unmanned aerial vehicle (UAV) image has the characteristics of complex background, high resolution, and large scale differences between targets, a real-time detection algorithm named as YOLOv5sm+ is proposed in this paper. First, the influence of network width and depth on UAV image detection performance was analyzed, and an improved shallow network based on YOLOv5s, which is named as YOLOv5sm, was proposed to improve the detection accuracy of major targets in UAV image through improving the utilization of spatial features extracted by residual dilated convolution module that could increase the receptive field. Then, a feature fusion module SCAM was designed, which could improve the utilization of detailed information by local feature self-supervision and could improve classification accuracy of medium and large targets through effective feature fusion. Finally, a detection head structure consisting with decoupled regression and classification head was proposed to further improve the classification accuracy. The experimental results on VisDrone dataset show that when intersection over union equals 0.5 mean average precision (mAP50) of the proposed YOLOv5sm+ model reaches 60.6%. Compared with YOLOv5s model, mAP50 of YOLOv5sm+ has increased 4.1%. In addition, YOLOv5sm+ has higher detection speed. The migration experiment on the DIOR remote sensing dataset also verified the effectiveness of the proposed model. The improved model has the characteristics of low false alarm rate and high recognition rate under overlapping conditions, and is suitable for the object detection task of UAV images.

Modulation and demodulation are key technologies to improve the transmission efficiency of optical wireless communication. Different modulation methods have different performances. This paper summarizes the research progress of various types of pulse position modulation at home and abroad, and then introduces the research from the Xi’an University of Technology in the area of pulse position modulation. For on-off keying, pulse position modulation, multiple pulse position modulation, differential pulse position modulation, overlapping pulse position modulation, dual duration position modulation, dual-amplitude pulse position modulation, digital pulse interval modulation, double-headed pulse interval modulation, dual-pulse interval modulation, dual-amplitude pulse interval modulation, fixed-length digital pulse interval modulation, fixed-length dual-amplitude pulse interval modulation, shorten pulse position modulation and separated double pulse position modulation, the performance of the symbol structure, bandwidth requirement, average transmit power, time slot error rate and average channel capacity are analyzed and compared. Finally, the development direction of pulse-like position modulation is prospected.