Figure 1
From: Adaptive stochastic resonance for unknown and variable input signals
Principle of adaptive SR. Stochastic resonance describes how a sub-threshold signal with added noise of sufficient amplitude can result in a non-zero sensor output. If the input signal amplitude changes over time, the optimal noise level can be estimated and adjusted in a feedback loop from a resonance curve. If the probability density of the input signal levels is known, the resonance curve can be obtained from a variety of objective functions. For unknown input signals, however, the only applicable objective function is the autocorrelation of the sensor output.
