Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

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Many signal features of interest are time limited and not band limited in the observational context of interest, such as noise “clicks,” “spikes,” or impulses. To acquire these signal features a time‐domain finite state automaton (tFSA) is often most appropriate [116–124]. Human hearing, for example, is a nonlinear system that thereby circumvents the restrictions of the Gabor limit (to allow for musical geniuses, for example, who have “perfect pitch”), where time‐frequency acuity surpasses what would be possible by linear signal processing alone [116] , such as with Nyquist sampled linear response recording devices that are bound by the limits imposed by the Fourier uncertainty principle (or Benedick’s theorem) [117] . Thus, even when the powerful Fourier Transform or Hidden Markov Model (HMM) feature extraction methods are utilized to full advantage, there is often a sector of the signal analysis that is only conveniently accessible to analysis by way of FSAs (without significant oversampling), such that a parallel processing with both HMM and FSA methods is often needed (results demonstrating this in the context of channel current analysis [1–3] will be described in ssss1). Not all of the methods employed at the FSA processing stage derive from standard signal processing approaches, either, some are purely statistical such as with oversampling [118] (used in radar range oversampling [119, 120]) and dithering [121] (used in device stabilization and to reduce quantization error [122, 123]).