Signal Processing
Shannon entropy is a measure of uncertainty or randomness associated with a set of outcomes, originally formulated by Claude Shannon in the context of information theory. It quantifies the average amount of information produced by a stochastic source of data, playing a key role in data compression and transmission. In the context of signal processing and wavelet analysis, understanding Shannon entropy helps in evaluating the effectiveness of wavelet transforms and assessing the information content in signals.
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