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Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing.

Hua Yan1, Yuankun Wei1, Yinggang Zhou1

  • 1School of Information Science and Engineering, Shenyang University of Technology, Shenyang, People's Republic of China.

Ultrasonic Imaging
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new compressed sensing (CS) method, CS-IMOMP, for acoustic tomography (AT) temperature reconstruction. The CS-IMOMP algorithm offers improved accuracy and efficiency over existing methods for non-contact temperature measurement.

Keywords:
acousticcompressed sensingreconstructiontemperature distributiontomography

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Area of Science:

  • * Physics
  • * Engineering
  • * Signal Processing

Background:

  • * Acoustic tomography (AT) is a non-contact method for temperature distribution measurement.
  • * The accuracy of AT heavily relies on the performance of its reconstruction algorithms.
  • * Existing reconstruction techniques may have limitations in accuracy and efficiency.

Purpose of the Study:

  • * To propose a novel temperature distribution reconstruction method for acoustic tomography using compressed sensing (CS).
  • * To develop an improved orthogonal matching pursuit (OMP) algorithm for efficient sparse signal recovery.
  • * To evaluate the performance of the proposed CS-IMOMP algorithm against traditional methods.

Main Methods:

  • * Establishment of a measurement matrix for an AT system within a CS framework.
  • * Selection of a sparse basis based on mutual coherence.
  • * Development and application of the improved orthogonal matching pursuit (IMOMP) algorithm for signal reconstruction.
  • * Validation using simulations and experiments with Gaussian sparse signals.

Main Results:

  • * The IMOMP algorithm demonstrated superior success ratio and reduced running time compared to the standard OMP algorithm.
  • * The proposed sparse basis selection method proved effective.
  • * The CS-IMOMP algorithm achieved lower reconstruction errors and more accurate temperature distribution data than least squares and Simultaneous Iterative Reconstruction Technique (SIRT) algorithms.

Conclusions:

  • * The CS-IMOMP algorithm represents a significant advancement in acoustic tomography for temperature distribution reconstruction.
  • * This method offers enhanced accuracy and efficiency for non-contact temperature measurements.
  • * The findings support the effectiveness of compressed sensing techniques in improving AT performance.