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Register a new userCoupled nonlinear stochastic differential equations generating arbitrary distributed observable with 1/f noise
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Nonlinear stochastic differential equations provide one of the mathematical models yielding 1/f noise. However, the drawback of a single equation as a source of 1/f noise is the necessity of power-law steady-state probability density of the signal. In this paper we generalize this model and propose a system of two coupled nonlinear stochastic differential equations. The equations are derived from the scaling properties necessary for the achievement of 1/f noise. The first equation describes the changes of the signal, whereas the second equation represents a fluctuating rate of change. The proposed coupled stochastic differential equations allows us to obtain 1/f spectrum in a wide range of frequencies together with the almost arbitrary steady-state density of the signal.
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Internal mechanism leading to the emergence of the widely occurring 1/f noise still remains an open issue. In this paper we investigate the distinction between internal time of the system and the physical time as a source of 1/f noise. After demonstrating the appearance of 1/f noise in the earlier proposed point process model, we generalize it starting from a stochastic differential equation which describes a Brownian-like motion in the internal (operational) time. We consider this equation together with an additional equation relating the internal time to the external (physical) time. We show that the relation between the internal time and the physical time that depends on the intensity of the signal can lead to 1/f noise in a wide interval of frequencies. The present model can be useful for the explanation of the appearance of 1/f noise in different systems.
Simple analytically solvable models are proposed exhibiting 1/f spectrum in wide range of frequency. The signals of the models consist of pulses (point process) which interevent times fluctuate about some average value, obeying an autoregressive process with very small damping. The power spectrum of the process can be expressed by the Hooge formula. The proposed models reveal possible origin of 1/f noise, i.e., random increments of the time intervals between pulses or interevent time of the process (Brownian motion in the time axis).
Noise of stochastic processes whose power spectrum scales at low frequencies, $f$, as $1/f$ appears in such diverse systems that it is considered universal. However, there have been a small number of instances from completely unrelated fields, e.g., the fluctuations of the human heartbeat or vortices in superconductors, in which power spectra have been observed to cross over from a $1/f$ to a non-$1/f$ behavior at even lower frequencies. Here, we show that such crossover must be universal, and can be accounted for by the memory of initial conditions and the relaxation processes present in any physical system. When the smallest frequency allowed by the experimental observation time, $omega_{obs}$, is larger than the smallest relaxation frequency, $Omega_{min}$, a $1/f$ power spectral density is obtained. Conversely, when $omega_{obs}<Omega_{min}$ we predict that the power spectrum of any stochastic process should exhibit a crossover from $1/f$ to a different, integrable functional form provided there is enough time for experimental observations. This crossover also provides a convenient tool to measure the lowest relaxation frequency of a physical system.
We investigate the validity and accuracy of weak-noise (saddle-point or instanton) approximations for piecewise-smooth stochastic differential equations (SDEs), taking as an illustrative example a piecewise-constant SDE, which serves as a simple model of Brownian motion with solid friction. For this model, we show that the weak-noise approximation of the path integral correctly reproduces the known propagator of the SDE at lowest order in the noise power, as well as the main features of the exact propagator with higher-order corrections, provided that the singularity of the path integral associated with the non-smooth SDE is treated with some heuristics. We also show that, as in the case of smooth SDEs, the deterministic paths of the noiseless system correctly describe the behaviour of the non-smooth SDE in the low-noise limit. Finally, we consider a smooth regularisation of the piecewise-constant SDE and study to which extent this regularisation can rectify some of the problems encountered when dealing with discontinuous drifts and singularities in SDEs.
Here we present a model for a small system combined with an explicit entropy bath that is comparably small. The dynamics of the model is defined by a simple matrix, M. Each row of M corresponds to a macrostate of the system, e.g. net alignment, while the elements in the row represent microstates. The constant number of elements in each row ensures constant entropy, which allows reversible fluctuations, similar to information theory where a constant number of bits allows reversible computations. Many elements in M come from the microstates of the system, but many others come from the bath. Bypassing the bath states yields fluctuations that exhibit standard white noise; whereas with bath states the power spectral density varies as S(f)~1/f over a wide range of frequencies, f. Thus, the explicit entropy bath is the mechanism of 1/f noise in this model. Both forms of the model match Crooks fluctuation theorem exactly, indicating that the theorem applies not only to infinite reservoirs, but also to finite-sized baths. The model is used to analyze measurements of 1/f-like noise from a sub-micron tunnel junction.
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