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Due to the continuous increase of the network bandwidth, the traditional coarse-grained congestion control mechanisms and flow scheduling schemes are difficult to provide satisfying performance. Therefore, a distributed algorithm based on an improved asynchronous back-pressure routing algorithm is proposed for joint channel congestion control, routing and power allocation in this paper. Considering the application scenario of wireless distributed networks with node power constraints and independent buffers for traffic flow, this paper studies joint congestion control, routing and power allocation when channel state information is known. In order to improve the defect of Newton method, an algorithm with second-order convergence performance is designed and matrix decomposition method is used to realize the distributed updating of traffic source rate, link rate and link power in network nodes and links so as to maximize network utility. Compared with the known existing algorithms, our proposed algorithm has faster convergence speed, which improves the network utility and energy utility by optimizing power allocation, and it can control the queue backlog at a very low level.

Mongolian is a language spoken in Inner Mongolia, China. In the recognition process, due to the shooting angle and other reasons, the image and text will be deformed, which will cause certain difficulties in recognition. This paper propose a triplet attention Mogrifier network (TAMN) for print Mongolian text recognition. The network uses a spatial transformation network to correct deformed Mongolian images. It uses gated recurrent convolution layers (GRCL) combine with triplet attention module to extract image features for the corrected images. The Mogrifier long short-term memory (LSTM) network gets the context sequence information in the feature and finally uses the decoder’s LSTM attention to get the prediction result. Experimental results show the spatial transformation network can effectively recognize deformed Mongolian images, and the recognition accuracy can reach 90.30%. This network achieves good performance in Mongolian text recognition compare with the current mainstream text recognition network. The dataset has been publicly available at https://github.com/ShaoDonCui/Mongolian-recognition.