{'fraction_non_alphanumeric': 0.013975155279503106, 'fraction_numerical': 0.0, 'mean_word_length': 5.935483870967742, '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}}
Computationally Efficient Norm-Constrained Adaptive Blind Deconvolution using Third-Order Moments

Third-order central moments have been shown to be well suited as objective functions for blind deconvolution of impulsive signals. Online implementations of such algorithms may suffer from increasing filter norm, forcing adaptation under constrained filter norm. This paper extends a previously known efficient algorithm with self-stabilizing properties to the case of using a third-order moment objective function. New results herein use averaging analysis to determine adaptation stepsize conditions for asymptotic stability of the filter norm