Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms

Jingfeng Yang, Zhenkun Zhang, Nanfeng Zhang, Ming Li, Yanwei Zheng, Li Wang, Yong Li, Ji Yang, Yifei Xiang and Yu Zhang

Complexity, 2019, vol. 2019, 1-9

Abstract:

Because of the continuous progress of vehicle hardware, the condition where the vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress of vehicle hardware, the number of texts shows exponential growth in actual operation. In order to optimize the efficiency of mass data transmission in actual operation, this paper presented the text information (including position information) of the maximum entropy principle of a neural network probability prediction model combined with the optimized Huffman encoding algorithm, optimization from the exchange of data to data compression, transmission, and decompression of the whole process. The test results show that the text type vehicle information based on compressed algorithm to optimize the algorithm of data compression and transmission can effectively realize data compression. It can also achieve a higher compression rate and data transmission integrity, and after decompression it can basically guarantee no distortion. The method proposed in this paper is of great significance for improving the transmission efficiency of vehicle text information, improving the interpretability and integrity of text information, realizing vehicle monitoring, and grasping real-time traffic conditions.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8616215

DOI: 10.1155/2019/8616215

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