Aiming at the high computational complexity and large memory consumption of the existing super-resolution reconstruction networks， a lightweight image super-resolution reconstruction network based on Transformer-CNN was proposed， which made the super-resolution reconstruction network more suitable to be applied on embedded terminals such as mobile platforms. Firstly， a hybrid block based on Transformer-CNN was proposed， which enhanced the ability of the network to capture local-global depth features. Then， a modified inverted residual block， with special attention to the characteristics of the high-frequency region， was designed， so that the improvement of feature extraction ability and reduction of inference time were realized. Finally， after exploring the best options for activation function， the GELU （Gaussian Error Linear Unit） activation function was adopted to further improve the network performance. Experimental results show that the proposed network can achieve a good balance between image super-resolution performance and network complexity， and reaches inference speed of 91 frame/s on the benchmark dataset Urban100 with scale factor of 4， which is 11 times faster than the excellent network called SwinIR （Image Restoration using Swin transformer）， indicates that the proposed network can efficiently reconstruct the textures and details of the image and reduce a significant amount of inference time.

A dynamic Cooperative Random Drift Particle Swarm Optimization (CRDPSO) algorithm assisted by evolution information was proposed in order to improve the population diversity of random drift particle swarm optimization. By using the vector information of context particles, the population diversity was increased by the dynamic cooperation between the particles, to improve the search ability of the swarm and make the whole swarm cooperatively search for the global optimum. At the same time, at each iteration during evolution, the positions and the fitness values of the evaluated solutions in the algorithm were stored by a binary space partitioning tree structure archive, which led to the fast fitness function approximation. The mutation was adaptive and nonparametric because of the fitness function approximation enhanced the mutation strategy. CRDPSO algorithm was compared with Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), continuous Non-revisiting Genetic Algorithm (cNrGA) and three improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithms through a series of standard test functions. Experimental results show that the performance of CRDPSO is optimal for both unimodal and multimodal test functions, which proves the effectiveness of the algorithm.

In view of the sensor network boundary identification in 3D environment, this paper presented a distributed algorithm for boundary node identification based on flipping finite plane. Based on three known adjacent nodes, the finite plane took each edge of triangle as axis to flip, the first node scanned is the new boundary node, this node and two nodes on the axis construct a new triangle. Above process was carried out iteratively, eventually the boundary contour was got and the boundary nodes were identified. The experimental result shows that, compared with Alpha-shape3D algorithm, the proposed algorithm can greatly reduce the redundant boundary nodes.