Tunable single frequency Hz-magnitude narrow linewidth Brillouin fiber laser based on parity-time symmetry.

An Hz-magnitude ultra-narrow linewidth single-frequency Brillouin fiber laser (BFL) is proposed and experimentally demonstrated. The single frequency of the laser is selected by parity-time (PT) symmetry, which consists of a stimulated Brillouin scatter (SBS) gain path excited by a 24 km single-mode fiber (SMF) and an approximately equal length loss path tuned with a variable optical attenuator (VOA). These paths are coupled through a fiber Bragg grating (FBG) into a wavelength space. Accomplishing single-frequency oscillation involves the precise adjustment of polarization control (PC) and VOA to attain the PT broken phase. In the experiment, the linewidth of the proposed BFL is 9.58 Hz. The optical signal-to-noise ratio (OSNR) reached 78.89 dB, with wavelength and power fluctuations of less than 1pm and 0.02 dB within one hour. Furthermore, the wavelength can be tuned from 1549.9321 nm to 1550.2575 nm, with a linewidth fluctuation of 1.81 Hz. The relative intensity noise (RIN) is below -74 dB/Hz. The proposed ultra-narrow single-frequency BFL offers advantages such as cost-effectiveness, ease of control, high stability and excellent output characteristics, making it highly promising for the applications in the coherent detection.

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization.

This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle's driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.