English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

—It is imperative to reduce load of the underground cable channel in coming Mining of Things for thousands of sensors. Gas data, for example, acts an essential role in mass time-continuous data sets of coalmine monitoring systems, we propose a multi-scale 1-bit compressive sensing algorithm in this paper to effectively compress data according the statistical properties 'regular pattern' of gas data sequence. The algorithm divides input into non-uniform intervals according to the prior attention of the gas monitoring information, then signal decision threshold and the compressed scales depend on the different attention in order to achieve a large scales of compression ratio on redundant data and as much as possible to maintain the sensitive information, comparing with the traditional 1-bit compressive sensing which brings overload quantization distortion during uniform quantization. Satisfactory results obtained by simulation and actual field applications show, which provides a useful reference to similar real monitoring data compression acquisition with compressed sampling.

Multi-Scale 1-Bit Compressed Sensing Algorithm and Its Application in Coalmine Gas Monitoring System