Infrared small targets occupy few pixels and lack features such as color， texture and shape， so it is difficult to track them effectively. To solve this problem， an infrared small target tracking method based on state information was proposed. Firstly， the target， background and distractors in the local area of the small target to be detected were encoded to obtain dense local state information between consecutive frames. Secondly， feature information of the current and the previous frames were input into the classifier to obtain the classification score. Thirdly， the state information and the classification score were fused to obtain the final degree of confidence and determine the center position of the small target to be detected. Finally， the state information was updated and propagated between the consecutive frames. After that， the propagated state information was used to track the infrared small target in the entire sequences. The proposed method was validated on an open dataset DIRST （Dataset for Infrared detection and tRacking of dim-Small aircrafT）. Experimental results show that for infrared small target tracking， the recall of the proposed method reaches 96.2%， and the precision of the method reaches 97.3%， which are 3.7% and 3.7% higher than those of the current best tracking method KeepTrack. It proves that the proposed method can effectively complete the tracking of small infrared targets under complex background and interference.

The traditional Deep Learning （DL）-based multi-objective solvers have the problems of low model utilization and being easy to fall into the local optimum. Aiming at these problems， a Multi-objective Optimization model for Unmanned aerial vehicles Trajectory based on Decomposition and Trajectory search （DTMO-UT） was proposed. The proposed model consists of the encoding and decoding parts. First， a Device encoder （Dencoder） and a Weight encoder （Wencoder） were contained in the encoding part， which were used to extract the state information of the Internet of Things （IoT） devices and the features of the weight vectors. And the scalar optimization sub-problems that were decomposed from the Multi-objective Optimization Problem （MOP） were represented by the weight vectors. Hence， the MOP was able to be solved by solving all the sub-problems. The Wencoder was able to encode all sub-problems， which improved the utilization of the model. Then， the decoding part containing the Trajectory decoder （Tdecoder） was used to decode the encoding features to generate the Pareto optimal solutions. Finally， to alleviate the phenomenon of greedy strategy falling into the local optimum， the trajectory search technology was added in trajectory decoder， that was generating multiple candidate trajectories and selecting the one with the best scalar value as the Pareto optimal solution. In this way， the exploration ability of the trajectory decoder was enhanced during trajectory planning， and a better-quality Pareto set was found. The results of simulation experiments show that compared with the mainstream DL MOP solvers， under the condition of 98.93% model parameter quantities decreasing， the proposed model reduces the distribution of MOP solutions by 0.076%， improves the ductility of the solutions by 0.014% and increases the overall performance by 1.23%， showing strong ability of practical trajectory planning of DTMO-UT model.

The traditional ultrasound Vector Flow Mapping （VFM） technology has the limitation that it requires the proprietary software to obtain raw Doppler and speckle tracking data. In order to solve the problem， a universal VFM method combined with deep learning was proposed. Firstly， the velocity scale was used to obtain the velocities along the acoustic beam direction provided by the color Doppler echocardiogram as the radial velocity components. Then， the U-Net model was used to automatically identify the contour of the left ventricular wall， the left ventricular wall velocities were calculated by the retrained CNNs for optical flow using Pyramid， Warping， and Cost volume （PWC-Net） model as the boundary condition of the continuity equation， and the velocity component of each blood particle perpendicular to the acoustic beam direction （that was the tangential velocity component） was obtained by solving the continuity equation. Finally， the velocity vector map of the heart flow field was synthesized， and the visualization of the streamline chart of the heart flow field was realized. Experimental results show that， the velocity vector map and streamline chart of the heart flow field obtained by the proposed method can accurately reflect the corresponding time phases of left ventricular， the obtained visualized results are consistent with the analysis results of the VFM workstation provided by Aloka， and conform to the characteristics of left ventricular fluid dynamics. As a universal and fast VFM method， the proposed method do not need any vendor’s technical support and proprietary software， and can further promote the application of VFM in clinical workflow.