A high dimensional stochastic resonance system and its application in signal processing.

• A high-dimensional stochastic resonance (SR) model is proposed, and its dynamic characteristic is analyzed. • SR phenomenon is explained from the perspective of equilibrium point which is driven by input signal. • The key parameters of proposed SR system are derived based on the theoretical analysis. • The noise reduction of aperiodic image signal based on proposed SR model is studied, and the hardware implementation is consistent with numerical simulation. At present, the stochastic resonance (SR) theory based on the traditional model with periodic input signal has been extensively studied. To further investigate dynamic process and improve the universality of SR system, a high-dimensional SR model is proposed in this paper. By analyzing the dynamic characteristics, the SR phenomenon is explained from the perspective of equilibrium point which is driven by the input signal. The expression of some key parameters is derived, and the noise reduction of aperiodic image signal is studied. Experimental results show that the SR phenomenon can be induced by appropriate input signal, and the proposed high-dimensional SR system has obvious noise reduction effect on aperiodic image signals, which further widens the application of SR theory in signal processing. [ABSTRACT FROM AUTHOR]