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Recovery of Compressed Sensing Microarray Using Sparse Random Matrices

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Intelligent Data Analysis and Applications (ECC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 535))

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Abstract

Due to the uncertainty of elements in the random matrix, the design of composite probes on compressed sensing microarray (CSM) becomes more complexity. In this paper, we proposed a sparse random measurement matrix with ‘0/1’ binary element, and fixed the same amount of elements ‘1’ on each row, to construct the CSM composite probe. There is the same dilution for the mixed solution of target segments to ensure the consistency of gene concentration, so the composite probes which made up of the linear combination of target segments are very simple. Simulation experiment results show that the variation characteristics of the target segment can be accurately recovered by OMP algorithm under N = 96 sequence segments and variation sparsity level K ≤ 12, when M = 48 composite probes are constructed with a sparse random matrix fixed amount of non-zero elements each row.

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Acknowledgments

This work is partially supported by the National Natural Foundation Project (61304199), the Ministry of Science and Technology projects for TaiWan, HongKong and Maco (2012DFM30040), the Major projects in Fujian Province (2013HZ0002-1,2013YZ0002,2014YZ0001), the Science and Technology project in Fujian Province Education Department (JB13140/GY-Z13088), and the Scientific Fund project in Fujian University of Technology (GY-Z13005,GY-Z13125).

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Authors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, No. 3 Xueyuan Road, University Town, Minhou, Fuzhou, Fujian, China

    Zhenhua Gan, Baoping Xiong & Fumin Zou

  2. College of Electrical Engineering and Automation, Fuzhou University, No. 2 Xueyuan Road, University Town, Minhou, Fuzhou, Fujian Province, China

    Zhenhua Gan & Min Du

  3. College of Physics and Information Engineering, Fuzhou University, No. 2 Xueyuan Road, University Town, Minhou, Fuzhou, Fujian, China

    Baoping Xiong, Yueming Gao & Min Du

  4. Key Lab of Automotive Electronics and Electric Drive Technology of Fujian Province, No. 3 Xueyuan Road, University Town, Minhou, Fuzhou, Fujian, China

    Zhenhua Gan & Fumin Zou

Authors
  1. Zhenhua Gan
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  2. Baoping Xiong
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  3. Fumin Zou
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  4. Yueming Gao
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  5. Min Du
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Corresponding author

Correspondence to Fumin Zou .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology , Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava , Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Tien-Wen Sung

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Xiao Dong Wang

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Gan, Z., Xiong, B., Zou, F., Gao, Y., Du, M. (2017). Recovery of Compressed Sensing Microarray Using Sparse Random Matrices. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-48499-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48498-3

  • Online ISBN: 978-3-319-48499-0

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