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The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user’s subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types.

This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

With the development of hardware, deep learning, which based on high computation complexity, has become a popular tool for intelligent fault diagnosis. Compared with traditional methods, fault diagnosis method based on deep learning could approximate complex non-linear relationship and achieve very high precision under the same load. However, the performance of these deep learning-based fault diagnosis methods will be greatly reduced when the load changes, which are significant in fault diagnosis issues since the working load is changing all the time in real world industrial applications. Thus, a fault diagnosis method based on multi-scale convolutional neural network (CNN) is proposed to solve this problem. CNN is a type of neural network which can extract feature from the fault signal directly. In order to obtain sufficient fault information, the proposed method uses multi-scale convolution kernels to extract features of different scales. In addition, dropout is also added to the network to enhance the robustness and generalization capabilities of the model. The experimental results show that the proposed model not only performs well under the same load, but also achieves high fault diagnosis accuracy when the load changes.

This article introduces generalized distributed antenna architecture for China beyond 3G (B3G) TDD mobile communication system. Two kinds of structures, named fixed-window group cell structure and slide-window group cell structure, are included in this architecture. Detail definition of group cell and constructing approaches are described. System capacity analyses for both structures are also given. Finally simulation results show the capacity of this generalized distributed antenna architecture.