Multi-band polarization switch based on magnetic fluid filled dual-core photonic crystal fiber

The research of high-performance polarization controllers is of great significance for expanding the application field of polarization optics. Here, a polarization switch is demonstrated by using a dual-core photonic crystal fiber(DCPCF)with four symmetrical air holes, placed above and below each core, filled with magnetic fluid(MF). The switch, which utilizes a magnetic field to change the coupling length ratio of the x and y polarization modes, enables dynamic tuning of the polarization state and extinction ratio. Numerical results show that when the working length is 36.638 mm, the magneto–optical polarization switch can operate in four communication bands, i.e., 1509 nm to 1520 nm, 1544 nm to1556 nm, 1578 nm to 1591 nm, and 1611 nm to 1624 nm. Moreover, the extinction ratio(ER) is greater than 20 d B in the fiber length range of 38.5 mm to 38.7 mm, indicating that the device has a good fault tolerance for the interception of the fiber length.

Super-resolution fluorescence polarization microscopy

Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological macromolecules.Several recent super resolution techniques have beenapplied to fluorescence polarization microscopy,achieving dipole measurement at nanoscale.In this review,we summarize both difraction limited and super resolution fluorescence polari-zation microscopy techniques,as well as their applications in biological imaging.

Attention-based efficient robot grasp detection network

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic information.Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a collision.This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers.We design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp configurations.AEGDN achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,respectively.The inference speed reaches 43.5 frames per second with only about 1.2×10^(6)parameters.The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well.Codes are available at https://github.com/robvincen/robot_gradet.