{'fraction_non_alphanumeric': 0.020022246941045607, 'fraction_numerical': 0.0, 'mean_word_length': 5.164383561643835, '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}}
Neural network data analysis for laser-induced thermal acoustics

A general, analytical closed-form solution for laser-induced thermal acoustic (LITA) signals using homodyne or heterodyne detection and using electrostrictive and thermal gratings is derived. A one-hidden-layer feed-forward neural network is trained using back-propagation learning and a steepest descent learning rule to extract the speed of sound and flow velocity from a heterodyne LITA signal. The effect of the network size on the performance is demonstrated. The accuracy is determined with a second set of LITA signals that were not used during the training phase. The accuracy is found to be better than that of a conventional frequency decomposition technique while being computationally as efficient. This data analysis method is robust with respect to noise, numerically stable and fast enough for real-time data analysis.