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This interaction can be based on different physical principles such as scattering, dispersion, absorption, radiation force, etc., and different propagation modes such as bulk, surface or Lamb waves, giving rise to very different types of sensors, imaging modes and techniques. Front-End Electronics and System Optimization The signal-to-noise ratio (SNR) in ultrasonic imaging can be improved by operating the transducer at a higher voltage. Imaging Methods for NDT One of the most relevant parameters for ultrasound imaging in industry is the scanning speed, particularly when inspecting large components with a high spatial resolution and/or for high-volume production lines. Ultrasound imaging is a wide research field, covering areas from wave propagation physics, sensors and front-end electronics to image reconstruction algorithms and software. [Extracted from the article]

The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification. [ABSTRACT FROM AUTHOR]