The purpose is to solve the problems of large positioning errors, low recognition speed, and low object recognition accuracy in industrial robot detection in a 5G environment. The convolutional neural network (CNN) model in the deep learning (DL) algorithm is adopted for image convolution, pooling, and target classification, optimizing the industrial robot visual recognition system in the improved method. With the bottled objects as the targets, the improved Fast-RCNN target detection model's algorithm is verified; with the small-size bottled objects in a complex environment as the targets, the improved VGG-16 classification network on the Hyper-Column scheme is verified. Finally, the algorithm constructed by the simulation analysis is compared with other advanced CNN algorithms. The results show that both the Fast RCN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can position and recognize the targets with a recognition accuracy rate of 82.34%, significantly better than other advanced neural network algorithms. Therefore, the improved VGG-16 classification network based on the Hyper-Column scheme has good accuracy and effectiveness for target recognition and positioning, providing an experimental reference for industrial robots' application and development.

No abstract

Space information flow is a new problem recently proposed by Li and Wu [1]. It studies the transmission of information in a geometric space, where information flows are free to propagate along any trajectories, and may be encoded wherever they meet. This work studies the wireless version of the space information flow problem, which models the planning of a wireless multihop network with fixed terminals in an Euclidean space. Additional relay nodes can be inserted at any location. The goal is to minimize the total cost of the wireless multihop network, while sustaining end-to-end unicast/multicast communication demands among terminals at known coordinates. We first formulate such cost minimization into a mathematical optimization problem, and examine its convexity. A generic solution that encompasses a resource allocation scheme, a flow routing scheme and a relay localization scheme is designed. A series of methods that can substantially reduce the complexity of solving the optimization problem are discussed. Finally, the algorithms are illustrated and verified by simulation studies.

No abstract

No abstract