Paper Info
Title
Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms
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.
Year
DOI
Venue
2019
10.1155/2019/8616215
COMPLEXITY
Field
DocType
Volume
Interpretability,Data compression ratio,Data transmission,Algorithm,Huffman coding,Principle of maximum entropy,Artificial neural network,Data compression,Distortion,Mathematics
Journal
2019
ISSN
Citations 
PageRank 
1076-2787
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Jingfeng Yang1618.34
Zhenkun Zhang200.34
Nanfeng Zhang300.34
Ming Li45595829.00
Yanwei Zheng5507.62
Li Wang6707.29
Yong Li731.41
Ji Yang895.93
Yifei Xiang900.34
Yu Zhang1029498.00