{'fraction_non_alphanumeric': 0.016835016835016835, 'fraction_numerical': 0.0, 'mean_word_length': 5.324468085106383, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reconstructing ultrasonic images and flaw detection in time-frequency domain by matching a-scan inspections

Time-frequency techniques are applied for mixing signals from a material inspected by a multiple sensor detection system. Ultrasonic sensors are located at the perimeter of a rectangular shaped material evaluated in a pulse-echo scheme. The resulting mixed signal becomes a high-resolution time-frequency image of the material. Different kinds of classification techniques can be applied to this image in order to obtain the defects in the material. In this paper, a simulation and experimental evaluation of the proposed approach are presented. Several time-frequency transforms and the fuzzy c-means are used. In the simulations, backscattering of the material grain is modelled by using Gaussian and K distributions with different signal to noise ratio parameters. The validity of the presented method is assessed through the detection and spatial location of artificial defects in a material with a rectangular shape. The performance of the classification technique in discerning defects buried in the backscattering from the material grain microstructure, is also discussed.