EMAT noise suppression using information fusion in stationary wavelet packets
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Updated: Jun 1, 2026

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
Published on: January 17, 2025
Michal Kubinyi1, Ondrej Kreibich, Jan Neuzil
1Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Measurement, Prague, Czech Republic. michal.kubinyi@fel.cvut.cz
Abstract:
An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.
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