Earticle

초록

영어

For distributed optical fiber pipeline pre-warning system, the sampling rate used is very high and thus huge data will be generated, which makes it difficult to transfer and store. Compressive sensing is a new compressed sampling method in the field of signal processing which compresses and samples the signal simultaneously. In this paper, an adaptive compressive sensing method is presented for compression and reconstruction of distributed optical fiber pipeline data. First, partial reconstruction based detection method is used to detect whether a hazardous event happened, then different compression ratios are taken for different classes of signal thereby increasing the compression ratio. In signal reconstruction phase, a sparsity determination algorithm is used to determine the sparsity of different segment of the signal, and then wavelet tree combined with CoSamp algorithm is adopted to reconstruct the signal. The adaptive compression algorithm improves the compression ratio and the sparsity determination in reconstruction phase can determine the sparsity of each segment when the signal varies without prior knowledge of the sparsity of the signal. Experimental results show that, the proposed algorithm can obtain higher reconstruction accuracy at a relatively high compression ratio. Furthermore, location simulation shows that the reconstructed signal by the proposed method is effective for danger signal positioning.

목차

Abstract
 1. Introduction
 2. Theory of Compressed Sensing
 3. Adaptive Compressed Sensing for Pipeline Data
  3.1. The proposed Adaptive Compression and Reconstruction Process
  3.2. Signal Analysis by Wavelet Tree
  3.3. Signal Detection using OMP
  3.4. Tree based Recovery
  3.5. Segment Sparsity Determination
 4. Positioning of the Threatening Signal
 5. Experimental Results
 6. Conclusions
 Acknowledgement
 References

키워드

저자

• Hongjie Wan [ Information Engineering Dept, Beijing University of Chemical Technology, China ]
• Haojiang Deng [ National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, China ]
• Xiaoming Xie [ Information Engineering Dept, Beijing University of Chemical Technology, China ]
• Qiaoning Yang [ Information Engineering Dept, Beijing University of Chemical Technology, China ]
• Dan Su [ Information Engineering Dept, Beijing University of Chemical Technology, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605