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A novel technique for detecting duty cycles of noisy arbitrary waveforms by employing artificial neural networks trained with empirical moments of asynchronously sampled waveforms' amplitudes is presented. The proposed technique is software‐based and hence can be applied flexibly to numerous waveform types without requiring any hardware changes. Furthermore, in contrast to existing duty‐cycle detection methods, this technique is capable of detecting duty cycles of noisy waveforms. Results obtained for square and sawtooth waveforms demonstrate wide duty‐cycle detection range of 10–95% with mean absolute percentage errors < 0.4%. [ABSTRACT FROM AUTHOR]

A novel algorithm for simultaneous modulation format/bit‐rate classification and non‐data‐aided (NDA) signal‐to‐noise ratio (SNR) estimation in multipath fading channels by applying deep machine learning‐based pattern recognition on signals' asynchronous delay‐tap plots (ADTPs) is proposed. The results for three widely‐used modulation formats at two different bit‐rates demonstrate classification accuracy of 99.8%. In addition, NDA SNR estimation over a wide range of 0−30 dB is shown with mean error of 1 dB. The proposed method requires low‐speed, asynchronous sampling of signal and is thus ideal for low‐cost multiparameter estimation under real‐world channel conditions. [ABSTRACT FROM AUTHOR]