Concerning the current lack of effective development and deployment tools for deep learning applications， a component-based development framework for deep learning applications was proposed. The framework splits functions according to the type of resource consumption， uses a review-guided resource allocation scheme for bottleneck elimination， and uses a step-by-step boxing scheme for function placement that takes into account high CPU utilization and low memory overhead. The real-time license plate number detection application developed based on this framework achieved 82% GPU utilization in throughput-first mode， 0.73 s average application latency in latency-first mode， and 68.8% average CPU utilization in three modes （throughput-first mode， latency-first mode， and balanced throughput/latency mode）. The experimental results show that based on this framework， a balanced configuration of hardware throughput and application latency can be performed to efficiently utilize the computing resources of the platform in the throughput-first mode and meet the low latency requirements of the applications in the latency-first mode. Compared with MediaPipe， the use of this framework enabled ultra-real-time multi-person pose estimation application development， and the detection frame rate of the application was improved by up to 1 077%. The experimental results show that the framework is an effective solution for deep learning application development and deployment on CPU-GPU heterogeneous servers.

Focused on the head pose flipping and the implicit spatial cues missing between image features when reconstructing human body from monocular images， a three-dimensional human reconstruction model based on High-Resolution Net （HRNet） and Graph Convolutional Network （GCN） was proposed. Firstly， the rich human feature information was extracted from the original image by using HRNet and residual blocks as the backbone network. Then， the accurate spatial feature representation was obtained by using GCN to capture the implicit spatial cues. Finally， the parameters of Skinned Multi-Person Linear model （SMPL） were predicted by using the features， thereby obtaining more accurate reconstruction results. At the same time， to effectively solve the problem of human head pose flipping， the joint points of SMPL were redefined and the definition of the head joint points were added on the basis of the original joints. Experimental results show that this model can exactly reconstruct the three-dimensional human body. The reconstruction accuracy of this model on the 2D dataset LSP reaches 92.41%， and the joint error and reconstruction error of the model are greatly reduced on the 3D dataset MPI-INF-3DHP with the average of only 97.73 mm and 64.63 mm respectively， verifying the effectiveness of the proposed model in the field of human reconstruction.

When using Immersed Boundary-Lattice Boltzmann Method （IB-LBM） to solve the flow field， in order to obtain more accurate results， a larger and denser flow field grid is often required， which results in a long time of simulation process. In order to improve the efficiency of the simulation， according to the characteristics of IB-LBM local calculation， combined with three different task scheduling methods in OpenMP， a parallel optimization method of IB-LBM was proposed. In the parallel optimization， three task scheduling modes were mixed to solve the load imbalance problem caused by single task scheduling. The structural decomposition was performed on IB-LBM， and the optimal scheduling mode of each structure part was tested. Based on the experimental results， the optimal scheduling combination mode was selected. At the same time， it could be concluded that the optimal combination is different under different thread counts. The optimization results were verified by speedup， and it could be concluded that when the number of threads is small， the speedup approaches the ideal state； when the number of threads is large， although the additional time consumption of developing and destroying threads affects the optimization of performance， the parallel performance of the model is still greatly improved. The flow field simulation results show that the accuracy of IB-LBM simulation of fluid-solid coupling problems is not affected after parallel optimization.

In order to solve the safety problems of privacy in the processes of authentication and communication of Radio Frequency IDentification (RFID) system, a mutual authenticate and encrypt scheme with low resource consume, high-level security and applicable for most of RFID systems was designed. This scheme combined the improved Elliptic Curve Diffie-Hellman (ECDH) algorithm and Advanced Encryption Standard (AES) algorithm to implement functions of key distribution, certification and communication encryption. It used dynamic key to enhance security. In addition, this scheme reduced the operation scale with original security strength, and saved the overhead of system resources. The measured results show that this scheme can resist replaying attacks, impersonation attacks, man-in-the-middle attacks and Denial of Service (DoS) attacks so as to save system resources. It can be applied in the field of Internet of Things (IOT) which has requirements on security and costs.

To extract skeleton based on ultraviolet rock fracture image using digital image processing technique, it needs to pretreat the rock fracture image first by image processing operation such as noise filtering, image segmentation, cavity filling, spur removal etc. Then an algorithm based on the structural elements of the layers thinning was proposed on the basis of the analysis of the skeleton characteristic and the skeleton of extraction algorithm. This algorithm can extract rock fractures of the skeleton very well. The experimental results show that the algorithm can extract a better skeleton of rock fracture efficiently and steadily.