Aiming at the problem of slow convergence rate of reinforcement learning (RL), an improved RL algorithm based on A* algorithm and velocity obstacle (VO) is proposed. Firstly, a node path obtained by A* algorithm is used to initialize a part of Q table. Then, in the process of path planning by RL, the VO model is applied to output the minimum steering angle required by the obstacle-avoiding flight. The action which is greater than the angle will be taken to interact with the environment. Finally, in order to satisfy UAV performance constraints, B-spline curve approach is applied for path smoothing. The experiment results show that, compared with the standard Q-learning algorithm, the path length of improved RL algorithm is reduced by 26.37%, the success rate of algorithm planning path is increased by 30.76%, and the convergence round is advanced by 88.66%.
Ultrasound strain imaging is showing promise as a new way of imaging soft tissue elasticity in order to help clinicians detect lesions or cancers in tissues. In this paper, Barker code is applied to strain imaging to improve its quality. Barker code as a coded excitation signal can be used to improve the echo signal-to-noise ratio (eSNR) in ultrasound imaging system. For the Baker code of length 13, the sidelobe level of the matched filter output is -22dB, which is unacceptable for ultrasound strain imaging, because high sidelobe level will cause high decorrelation noise. Instead of using the conventional matched filter, we use the Wiener filter to decode the Barker-coded echo signal to suppress the range sidelobes. We also compare the performance of Barker code and the conventional short pulse in simulation method. The simulation results demonstrate that the performance of the Wiener filter is much better than the matched filter, and Baker code achieves higher elastographic signal-to-noise ratio (SNRe) than the short pulse in low eSNR or great depth conditions due to the increased eSNR with it.
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