Current Video Super-Resolution （VSR） algorithms cannot fully utilize inter-frame information of different distances when processing complex scenes with large motion amplitude， resulting in difficulty in accurately recovering occlusion， boundaries， and multi-detail regions. A VSR model based on frame straddling optical flow was proposed to solve these problems. Firstly， shallow features of Low-Resolution frames （LR） were extracted through Residual Dense Blocks （RDBs）. Then， motion estimation and compensation was performed on video frames using a Spatial Pyramid Network （SPyNet） with straddling optical flows of different time lengths， and deep feature extraction and correction was performed on inter-frame information through RDBs connected in multiple layers. Finally， the shallow and deep features were fused， and High-Resolution frames （HR） were obtained through up-sampling. The experimental results on the REDS4 public dataset show that compared with deep Video Super-Resolution network using Dynamic Upsampling Filters without explicit motion compensation （DUF-VSR）， the proposed model improves Peak Signal-to-Noise Ratio （PSNR） and Structure Similarity Index Measure （SSIM） by 1.07 dB and 0.06， respectively. The experimental results show that the proposed model can effectively improve the quality of video image reconstruction.

Aspect-based sentiment analysis based on pre-trained models generally uses end-to-end frameworks， has the problems of inconsistency between the upstream and downstream tasks， and is difficult to model the relationships between aspect words and context effectively. To address these problems， an aspect-based sentiment analysis method integrating prompt knowledge was proposed. First， in order to capture the semantic relation between aspect words and context effectively and enhance the model’s perception ability for sentiment analysis tasks， based on the Prompt mechanism， a prompt text was constructed and spliced with the original sentence and aspect words， and the obtained results were used as the input of the pre-trained model Bidirectional Encoder Representations from Transformers （BERT）. Then， a sentimental label vocabulary was built and integrated into the sentimental verbalizer layer， so as to reduce search space of the model， make the pre-trained model obtain rich semantic knowledge in the label vocabulary， and improve the learning ability of the model. Experimental results on Restaurant and Laptop field datasets of SemEval2014 Task4 dataset as well as ChnSentiCorp dataset show that the F1-score of the proposed method reaches 77.42%， 75.20% and 94.89% respectively， which is increased by 0.65 to 10.71， 1.02 to 9.58 and 0.83 to 6.40 percentage points compared with the mainstream aspect-based sentiment analysis methods such as Glove-TextCNN and P-tuning. The above verifies the effectiveness of the proposed method.

With the deepening of population aging， fall detection has become a key issue in the medical and health field. Concerning the low accuracy of fall detection algorithms in complex scenes， an improved fall detection model PDD-FCOS （PVT DRFPN DIoU-Fully Convolutional One-Stage object detection） was proposed. Pyramid Vision Transformer （PVT） was introduced into the backbone network of baseline FCOS algorithm to extract richer semantic information without increasing the amount of computation. In the feature information fusion stage， Double Refinement Feature Pyramid Networks （DRFPN） were inserted to learn the positions and other information of sampling points between feature maps more accurately， and more accurate semantic relationship between feature channels was captured by context information to improve the detection performance. In the training stage， the bounding box regression was carried out by the Distance Intersection Over Union （DIoU） loss. By optimizing the distance between the prediction box and the center point of the object box， the regression box was made to converge faster and more accurately， which improved the accuracy of the fall detection algorithm effectively. Experimental results show that on the open-source dataset Fall detection Database， the mean Average Precision （mAP） of the proposed model reaches 82.2%， which is improved by 6.4 percentage points compared with that of the baseline FCOS algorithm， and the proposed algorithm has accuracy improvement and better generalization ability compared with other state-of-the-art fall detection algorithms.

The existing image super-resolution reconstruction methods are affected by texture distortion and details blurring of generated images. To address these problems， a new image super-resolution reconstruction network based on multi-channel attention mechanism was proposed. Firstly， in the texture extraction module of the proposed network， a multi-channel attention mechanism was designed to realize the cross-channel information interaction by combining one-dimensional convolution， thereby achieving the purpose of paying attention to important feature information. Then， in the texture recovery module of the proposed network， the dense residual blocks were introduced to recover part of high-frequency texture details as many as possible to improve the performance of model and generate high-quality reconstructed images. The proposed network is able to improve visual effects of reconstructed images effectively. Besides， the results on benchmark dataset CUFED5 show that the proposed network has achieved the 1.76 dB and 0.062 higher in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） compared with the classic Super-Resolution using Convolutional Neural Network （SRCNN） method. Experimental results show that the proposed network can increase the accuracy of texture migration， and effectively improve the quality of generated images.

The existing privacy preserving clustering data mining algorithms cannot meet better trade-off between efficiency and privacy. To resolve this problem, a distributed privacy preserving clustering algorithm based on Secure Multi-party Computation (SMC) combined with perturbation was proposed. Data owners utilized the wavelet to achieve both data reduction and information hiding, and rearranged the attribute columns randomly to prevent data reconstruction which has potential danger of causing information disclosure. The proposed algorithm reduced computation and communication cost because it only used reduced data in its computation. Thus the efficiency of the algorithm was improved. At the same time, the incorporation of multiple protection measures in the computation effectively preserved data privacy. The clustering accuracy was less affected because of the high dependability of wavelet transform. The theoretical analysis and experimental results indicate that the proposed algorithm is secure and highly effective, and the overall F-measure and the efficiency of the proposed algorithm outperform the DCT-H (Discrete Cosine Transform-Haar) algorithm when dealing with high-dimensional datasets. Above all, it effectively resolves the trade-off issue between efficiency and privacy.

In view of the kinematic model of mobile robot, a tracking controller of global asymptotic stability was proposed. The design of tracking controller was divided into two parts: The first part designed the control law of angular velocity by using global fast terminal sliding mode in order to asymptotically stabilize the tracking error of the heading angle; the second part designed the control law of linear velocity by using the Lyapunov method in order to asymptotically stabilize the tracking error of the planar coordinate. By combining Lyapunov stability theorem and two control laws, the mobile robot can track the desired trajectory in a global asymptotic sense when the angular velocity and the linear velocity satisfy these control laws. The experimental results show that the mobile robot can track desired trajectory effectively. It is helpful for promoting the practical application.

In this paper, a Self-Organizing Fuzzy Neural Network (SOFNN) based on Unscented Particle Filter (UPF) was designed and developed. The UPF was used to estimate the parameters of the SOFNN and better result was gotten. The generating criterion of fuzzy rules based on the pruning strategy of the error reduction ratio was introduced. The width of membership function was established as the state and the ideal output as the measurement. The UPF was used to learn parameters. The two typical simulations, nonlinear function approximation and system identification, were done to validate the UPF-SOFNN. It can be seen from the results of simulation that the UPF-SOFNN has a more compact structure and better generalization than the other algorithms.