In Hematoxylin-Eosin （HE）-stained pathological images， the uneven distribution of cell staining and the diversity of various tissue morphologies bring great challenges to automated segmentation. Traditional convolutions cannot capture the correlation features between pixels in a large neighborhood， making it difficult to further improve the segmentation performance. Therefore， a Multi-Channel Segmentation Network with gated axial self-attention （MCSegNet） model was proposed to achieve accurate segmentation of nuclei in pathological images. In the proposed model， a dual-encoder and decoder structure was adopted， in which the axial self-attention encoding channel was used to capture global features， while the convolutional encoding channel based on residual structure was used to obtain local fine features. The feature representation was enhanced by feature fusion at the end of the encoding channel， providing a good information base for the decoder. And in the decoder， segmentation results were gradually generated by cascading multiple upsampling modules. In addition， the improved hybrid loss function was used to alleviate the common problem of sample imbalance in pathological images effectively. Experimental results on MoNuSeg2020 public dataset show that the improved segmentation method is 2.66 percentage points and 2.77 percentage points higher than U-Net in terms of F1-score and Intersection over Union （IoU） indicators， respectively， and effectively improves the pathological image segmentation effect and the reliability of clinical diagnosis.

In the intelligent parking space management system， a decrease in accuracy and effectiveness of parking space prediction can be caused by factors such as illumination changes and parking space occlusion. To overcome this problem， a parking space detection method based on self-supervised learning HOG （Histogram of Oriented Gradient） prediction auxiliary task was proposed. Firstly， a self-supervised learning auxiliary task to predict the HOG feature in occluded part of image was designed， the visual representation of the image was learned more fully and the feature extraction ability of the model was improved by using the MobileViTBlock （light-weight， general-purpose， and Mobile-friendly Vision Transformer Block） to synthesize the global information of the image. Then， an improvement was made to the SE （Squeeze-and-Excitation） attention mechanism， thereby enabling the model to achieve or even exceed the effect of the original SE attention mechanism at a lower computational cost. Finally， the feature extraction part trained by the auxiliary task was applied to the downstream classification task for parking space status prediction. Experiments were carried out on the mixed dataset of PKLot and CNRPark. The experimental results show that the proposed model has the accuracy reached 97.49% on the test set； compared to RepVGG， the accuracy of occlusion prediction improves by 5.46 percentage points， which represents a great improvement compared with other parking space detection algorithms.

Focusing on the issues that the traditional image caption models cannot make full use of image information， and have only single method of fusing features， an image caption generation model with Adaptive Commonsense Gate （ACG） was proposed. Firstly， VC R-CNN （Visual Commonsense Region-based Convolutional Neural Network） was used to extract visual commonsense features and input commonsense feature layer into Transformer encoder. Then， ACG was designed in each layer of encoder to perform adaptive fusion operation on visual commonsense features and encoding features. Finally， the encoding features fused with commonsense information were fed into Transformer decoder to complete the training. Training and testing were carried out on MSCOCO dataset. The results show that the proposed model reaches 39.2， 129.6 and 22.7 respectively on the evaluation indicators BLEU （BiLingual Evaluation Understudy）-4， CIDEr （Consensus-based Image Description Evaluation） and SPICE （Semantic Propositional Image Caption Evaluation）， which are improved by 3.2%，2.9% and 2.3% respectively compared with those of the POS-SCAN （Part-Of-Speech Stacked Cross Attention Network） model. It can be seen that the proposed model significantly outperforms Transformer models using single salient region feature and can describe the image content accurately.

In order to realize the CAN bus communication with PC and remote monitoring, a design method of the embedded Ethernet-CAN communication transform card based on FPGA was proposed. The design chose the embedded soft processors Nios Ⅱ in FPGA as the main control chip, MCP2515 as the CAN bus controller and 88E1111 as the Ethernet PHY chip. A system hardware model was built with the SOPC (System-On-a-Programmable-Chip) technology, and the CAN controller, Ethernet initialization and the Ethernet-CAN conversion process were completed in the Nios Ⅱ IDE （Integrated Development Environment）.The experimental results show that the design completely meets the requirements of the Ethernet and CAN bus communication.

An improved algorithm for image restoration was proposed based on Primal-Dual Hybrid Gradient Descent (PDHGD) method. The preferences have a great impact on convergence rate of the known algorithm. The form was changed by introducing new variable, and the elements of the dual vector of the primal-dual hybrid model were separated, and then step-size was replaced by using parameter matrices. The numerical experiments show that the improved algorithm has advantage on choosing parameters compared to the known algorithm, and the iterative number and CPU' time nearly declined by 50%, and at the same time, the improved algorithm has exactly the same effect on image restoration.

The motion estimation algorithm in the MPEG-4 video encoding was studied and the potential parallelism in the algorithm was analyzed. Then the multi-core parallel computing concept was used in the directional diamond searching algorithm, and a prediction mechanism was introduced to improve the degree of parallelism. Then the directional diamond Motion estimation algorithm based on parallelism and prediction (PPDDME) was proposed. The experimental results coming from PC and the Omap5910 embedded platform show that the encoding speed is improved efficiently and the compression quality is the same as the serial algorithm.