Open AccessApplication of Sparse Dictionary Adaptive Compression Algorithm in Transient Signals
Author(s) -
Zhang Ailun,
Tong Han
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012048
Subject(s) - transient (computer programming) , matching pursuit , computer science , signal (programming language) , compression (physics) , compressed sensing , data compression , algorithm , sampling (signal processing) , feature (linguistics) , transmission (telecommunications) , compression ratio , artificial intelligence , pattern recognition (psychology) , computer vision , engineering , materials science , telecommunications , linguistics , philosophy , filter (signal processing) , automotive engineering , composite material , programming language , internal combustion engine , operating system
An adaptive compression algorithm based on sparse dictionary is proposed to solve the pressure on the transmission system caused by long-time high sampling rate sampling of transient signals. Due to the obvious difference between transient and non-transient information features of transient signals. According to the sparse dictionary construction characteristic that the more matching atoms and signal features are, the better sparse performance is, the transient signal feature information is extracted and compressed separately. After data compression, the principle of compressed sensing is applied to restore the transient signal, so as to reduce the amount of data transmitted in the transmission system. In summary, the adaptive sparse dictionary compression algorithm effectively compresses the transient signal data, and the compressed data can accurately recover the transient signal.