NOMA-Based Cooperative Relaying Transmission for the Industrial Internet of Things

With the continuous maturity of the fifth generation(5G)communications,industrial Internet of Things(IIoT)technology has been widely applied in fields such as smart factories.In smart factories,5G-based production line monitoring can improve production efficiency and reduce costs,but there are problems with limited monitoring coverage and insufficient wireless spectrum resources,which restricts the application of IIoT in the construction of smart factories.In response to these problems,we propose a hybrid spectrum access mechanism based on Non-Orthogonal Multiple Access(NOMA)cooperative relaying transmission to improve the monitoring coverage and spectrum efficiency.As there are a large number of production lines that need to be monitored in smart factories,it is difficult to realize real-time monitoring of all production lines due to insufficient wireless resources.Therefore,we divide the production lines into high priority and low priority,and introduce cognitive radio technology to increase the number of monitoring production lines.In order to better describe the wireless fading channel environment in the factory,the two-wave with diffuse power(TWDP)channel is discussed to simulate the real factory environment and the outage probability of the secondary production line data transmission is derived in the proposed mechanism.Compared with the traditional mechanism,the proposed transmission mechanism can ensure the continuity of the secondary transmission,greatly reduce the outage probability of the secondary transmission,and improve the efficiency of the monitoring of the production lines.

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge.Due to the wide variety of skin lesions,doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy.The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention.With the development of deep learning,the field of image recognition has made long-term progress.The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology.In this work,we try to classify seven kinds of lesion images by various models and methods of deep learning,common models of convolutional neural network in the field of image classification include ResNet,DenseNet and SENet,etc.We use a fine-tuning model with a multi-layer perceptron,by training the skin lesion model,in the validation set and test set we use data expansion based on multiple cropping,and use five models’ensemble as the final results.The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.