Dong Mingli

A 1.8-µm multiwavelength thulium-doped fiber laser based on a hybrid interference filter

ABSTRACT A 1.8-µm tunable multiwavelength thulium-doped fiber laser based on a hybrid filter is proposed. In the designed ring-cavity fiber laser, the filter consists of one Sagnac loop and one dual-pass Mach–Zehnder filter. In the experiment, the lasing threshold is 155 mW, and a continuously tunable and stable single-wavelength laser could be realized with a minimum tuning interval of 2.1 nm within a scope of 22.5 nm. When an 1858-nm laser is obtained, the peak power fluctuation is less than 0.83 dB within 20 minutes at room temperature. By adjusting the polarization controller, stable dual-wavelength lasers are simultaneously achieved, and the peak power shift is less than 1.24 dB within a scan time of 20 minutes at room temperature. By changing the polarization state, stable triple-wavelength lasing is obtained, and the power fluctuation is less than 1.95 nm. In the experiment, the 3-dB linewidth of the laser is less than 0.4 nm.

An auto multi-threshold segmentation approach of PCB image based on iteration

Print Circuit Board (PCB) is an information carrier integrating various electronic components. It has been applied in different fields widely. Traditional detection method can not satisfy the large production. The Automatic Optic Inspection (AOI) based on image-processing had showed its advantages in inspection methods. Combing the traditional iterative algorithm and the Otsus method, an auto multi-threshold image segmentation approach based on iteration is presented for the printed circuit board (PCB) images in this paper. The initial value for iteration is calculated by using the Otsus method. The iterative algorithm is applied twice to get the optimal threshold. Experiment is conducted on PCB images. The result shows that the algorithm is simple, efficient and it has good segmentation performance for PCB images.

Golf video tracking based on recognition with HOG and spatial–temporal vector

The hand and club movements contain golfer’s swing information, which can be obtained to provide good visualization to be shared on Internet and be summarized in golf studying. In this article, a hand and club tracking framework based on recognition with a complex descriptor combining histograms of oriented gradients and spatial–temporal vector is proposed to obtain their movement trajectories in golf video. After the hand and club are recognized in initial windows defined by the body region, a boosted classifier trained by the proposed descriptor is utilized for recognition and tracking in a searching window predicted by trajectory fitting with previous four object positions. Experiments show that the boosted classifier can have a precision and recall rate both better than 97%, and the hand and club tracking are basically correct in our testing videos.