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Related Experiment Video

Updated: Jul 6, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Defects under insulation evaluation using convolutional neural network-based microwave technique.

Tan Shin Yee1, Muhammad Firdaus Akbar2, Muthukannan Murugesh1

  • 1School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia.

Scientific Reports
|July 4, 2026
PubMed
Summary

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Thermal Insulation in Masonry Walls01:22

Thermal Insulation in Masonry Walls

In hot, dry climates, the thermal mass of masonry walls can be beneficial, absorbing heat during the day and releasing it at night, thereby stabilizing indoor temperatures. However, in most other climates, additional insulation is necessary to enhance thermal resistance.
External insulation can be applied using an Exterior Insulation and Finish System (EIFS), which involves affixing panels of plastic foam to the wall and covering them with a polymeric stucco reinforced with glass fiber mesh.

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This study enhances subsurface defect detection using machine learning. An integrated microwave nondestructive testing framework with convolutional neural networks (CNNs) achieved 97.84% accuracy in identifying small delaminations.

Area of Science:

  • Materials Science
  • Electrical Engineering
  • Non-Destructive Testing

Background:

  • Subsurface defect detection is crucial for material integrity.
  • Machine learning, especially CNNs, shows promise but struggles with fine-scale defects.
  • Conventional methods often lack the sensitivity for subtle flaws.

Purpose of the Study:

  • To develop an improved method for detecting small-scale delamination in ceramic insulation.
  • To integrate microwave nondestructive testing with advanced signal processing and machine learning.
  • To enhance the accuracy and reliability of subsurface defect identification.

Main Methods:

  • Utilized Q-band open-ended rectangular waveguide sensing for signal acquisition (33-50 GHz).
  • Employed Short-Time Fourier Transform (STFT) for time-frequency feature extraction.

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Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Related Experiment Videos

Last Updated: Jul 6, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

  • Applied Z-score normalization and outlier suppression for data preprocessing.
  • Classified defects using a trained Convolutional Neural Network (CNN).

    • Main Results:

    • The integrated framework achieved a high classification accuracy of 97.84%.
    • Demonstrated significant improvement in detecting subtle delaminations compared to traditional techniques.
    • Effective extraction of localized, frequency-dependent features using STFT.

    Conclusions:

    • The proposed microwave NDT framework with CNNs offers superior performance for detecting small subsurface defects.
    • This data-driven approach enhances the reliability of ceramic insulation inspection.
    • The integration of STFT and CNNs provides a robust solution for challenging NDT applications.