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This may result in a possibly flawed detection by the optical flow.
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Split-spectrum processing combined with neural networks as post-processors can improve flaw detection.
Flaw detection in the presence of microstructure scattering noise is a challenging problem.
Ultrasonic imaging is used for nondestructive evaluation of materials and flaw detection.
These results exhibit accurate flaw detection with low false alarm rate.
Ultrasonic pulse-echo methods for flaw detection have been widely employed as an effective strategy for nondestructive evaluation, and flaw detection plays an important role due to its ability to detect localized damage in structures.
Based on this information, we propose a post-processing scheme for structure noise elimination and flaw detection.
We proposed the measurement of flaw detection, based on TFT array testing and characterization with respect to opto-electric transformation measurement.
Ultrasonic techniques are traditionally associated with the engineering-based, non-destructive testing of concrete structures and their integrity analyses (e.g., flaw detection, shear/longitudinal velocity determination, etc).
Each method has its respective strengths and weaknesses considering their characteristics such as test speed, flaw detection sensitivity, and probe structure complexity.
The study resulted in small-pixel size TFT array show the flaw detection performance and preference approaches using the voltage imaging technique.
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