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[Author] Zhen TAN(3hit)

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  • Improvement of Semi-Random Measurement Matrix for Compressed Sensing

    Wentao LV  Junfeng WANG  Wenxian YU  Zhen TAN  

     
    LETTER-Digital Signal Processing

      Vol:
    E97-A No:6
      Page(s):
    1426-1429

    In compressed sensing, the design of the measurement matrix is a key work. In order to achieve a more precise reconstruction result, the columns of the measurement matrix should have better orthogonality or linear incoherence. A random matrix, like a Gaussian random matrix (GRM), is commonly adopted as the measurement matrix currently. However, the columns of the random matrix are only statistically-orthogonal. By substituting an orthogonal basis into the random matrix to construct a semi-random measurement matrix and by optimizing the mutual coherence between dictionary columns to approach a theoretical lower bound, the linear incoherence of the measurement matrix can be greatly improved. With this optimization measurement matrix, the signal can be reconstructed from its measures more precisely.

  • Design of a Novel MOS VT Extractor Circuit

    Koichi TANNO  Okihiko ISHIZUKA  Zhen TANG  

     
    LETTER-Electronic Circuits

      Vol:
    E78-C No:9
      Page(s):
    1306-1310

    This paper describes a novel input-free MOS VT extractor circuit. The circuit consists of a bias voltage block and a novel VT extractor block. The proposed VT extractor block has the advantages of the ground-referenced output, low influence of the nonideality, few numbers of transistors and no influence of the PMOS process. The PSpice simulations show the supply voltage range and the bias voltage range of the proposed circuit are wider than those of Johnson's or Wang's.

  • An Efficient Wide-Baseline Dense Matching Descriptor

    Yanli WAN  Zhenjiang MIAO  Zhen TANG  Lili WAN  Zhe WANG  

     
    LETTER-Image Recognition, Computer Vision

      Vol:
    E95-D No:7
      Page(s):
    2021-2024

    This letter proposes an efficient local descriptor for wide-baseline dense matching. It improves the existing Daisy descriptor by combining intensity-based Haar wavelet response with a new color-based ratio model. The color ratio model is invariant to changes of viewing direction, object geometry, and the direction, intensity and spectral power distribution of the illumination. The experiments show that our descriptor has high discriminative power and robustness.