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This paper proposes an adaptive vibration signal compression scheme composed of a lifting discrete wavelet transform (LDWT) with set-partitioning embedded blocks (SPECK) that efficiently sorts the wavelet coefficients by significance. The output of the SPECK module is input to an optimized context-based arithmetic coder that generates the compressed bitstream. The algorithm is deployed as part of a reliable and effective health monitoring technology for machines and civil constructions (e.g. power generation system). This application area relies on the collection of large quantities of high quality vibration sensor data that needs to be compressed before storing and transmission. Experimental results indicate that the proposed method outperforms state-of-the-art coders, while retaining the characteristics in the compressed vibration signals to ensure accurate event analysis. For the same quality level, up to 59.41% bitrate reduction is achieved by the proposed method compared to JPEG2000.
|Title of host publication||2017 25th European Signal Processing Conference (EUSIPCO 2017)|
|Subtitle of host publication||Proceedings of a meeting held 28 August - 2 September 2017, Kos, Greece|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|Publication status||Published - Jan 2018|
|Event||25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece|
Duration: 28 Aug 2017 → 2 Sep 2017
|Conference||25th European Signal Processing Conference, EUSIPCO 2017|
|Period||28/08/17 → 2/09/17|
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