Frequency modulated signal standing out by stochastic resonance effect

The effect of stochastic resonance is investigated. The principles of the effect are considered on the basis of a Brownian particle moving in a system with a symmetric bistable potential. Different from the widely existing noise elimination methods, stochastic resonance enables a new method to utilize the energy of noises, nonlinear system, and signal frequency to reach certain weak signal enhancement effects that noise elimination cannot perform well. The presence of noise at the input of nonlinear systems possessing the effect of the stochastic resonance allows to stand out a weak signal from an additive mixture with white Gaussian noise. The equation and structural scheme of stochastic resonance are considered The conditions for the occurrence of stochastic resonance are formulated and the determining role of noise variance in the realization of this effect is shown. For the first time, a numerical calculation of standing out of harmonic oscillation from an additive mixture with white Gaussian noise based on the stochastic resonance effect is carried out. The output signal-to-noise ratio dependence of the stochastic resonator on the intensity of input noise and the input signal frequency is investigated. The frequency dependencies of the output signal are investigated and the components of this signal are analysed. It is shown that the stochastic resonator acts as a low-pass filter and reduces the output noise level. Numerical modelling of the stochastic resonance effect also shows that the output signal of the stochastic resonator contains odd harmonics of the input oscillation, which is characteristic of nonlinear systems. It is shown that the stochastic resonance effect can be widely used in radio engineering and telecommunications to amplify the useful signal.

A simple, paradigmatic, model is used to illustrate some general properties of effects subsumed under the label "stochastic resonance." In particular, analyses of the transparent model show that 1) a small amount of noise added to a much larger signal can greatly increase the response to the signal, but 2) a weak signal added to much larger noise will not generate a substantial added response. The conclusions drawn from the model illustrate the general result that stochastic resonance effects do not provide an avenue for signals that are much smaller than noise to affect biology. A further analysis demonstrates the effects of small signals in the shifting of biologically important chemical equilibria under conditions where stochastic resonance effects are significant.

Stochastic resonance is a phenomenon in which the performance of certain non-linear detectors can be enhanced by the addition of appropriate levels of random noise. Signal detection theory offers a powerful tool for analysing this type of system, through an ability to separate detection processes into reception and classification, with the former generally being linear and the latter always non-linear. Through appropriate measures of signal detectability it is possible to decide whether a local improvement in detection via stochastic resonance occurs due to the non-linear effects of the classification process. In this case, improvement of detection through the addition of noise can never improve detection beyond that of a corresponding adaptive system. Signal detection and stochastic resonance is investigated in several integrate-and-fire neuron models. It is demonstrated that the stochastic resonance observed in spiking models is caused by non-linear properties of the spike-generation process itself. The true detectability of the signal, as seen by the receiver part of the spiking neuron (the integrator part), decreases monotonically with input noise level for all signal and noise intensities.

One major challenge of QRS wave detection is the deleterious impact of inevitable noise in electrocardiogram (ECG) signals, which can deteriorate the detection performance of algorithms. Previous research has shown a nonlinear monostable potential‐based algorithm for enhancing the QRS complex in a noisy environment. Its main mechanism is the stochastic resonance effect, which transfers noise energy to information‐carrying ECG signals in the nonlinear potential. However, the QRS detection algorithm based on monostable stochastic resonance (MSR) exhibits a limited noise margin to maintain extremely good performance. To further improve noise robustness, we propose a multi‐parameter constrained bistable SR (MCBSR) module for the QRS detection algorithm without requiring a large amount of training data. The MCBSR module can maximize the ECG signal while suppressing noise when there is a QRS complex and minimizes the ECG signal otherwise. Using four publicly available databases for testing, the MCBSR‐based QRS detection algorithm has been proven to achieve superior performance among state‐of‐the‐art algorithms. Compared to the conventional nonlinear method, the MCBSR enables the QRS detection algorithm to have significantly stronger noise robustness under various noise types. Therefore, this study successfully provides a more excellent nonlinear potential and greatly enhances the noise robustness of the QRS detection algorithm in large noise environments. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Stochastic resonance-based input circuits for SQUIDs

The paper presents a new method of fault signal feature extraction based on reinforcement cascaded multi-stable stochastic resonance (SR) system and it can extract signal fault feature from heavy background noise. The multi-stable model can further increase the noise utilization to achieve better detection effects of SR for weak signals than the bistable stochastic resonance system. On the basis of cascaded multi-stable stochastic resonance theory, the reinforcement characteristic of stochastic resonance induced by adding the second driven periodic signal is studied. Adding the second driving signals to the first and the second cascaded system, when the time scale is matched with the stochastic fluctuations, the phenomenon of frequency absorption happens and the effect of stochastic resonance in multi-stable system is reinforced. At last, an example of rolling bearing fault signal confirms that this method can efficiently extract weak fault signal feature, and has a good prospect in signal detection fields

Stochastic resonance in noisy spiking retinal and sensory neuron models

This paper contains a survey on iterative decoders of low-density parity-check (LDPC) codes made of unreliable logic gates that are capable to provide lower probability of error, when compared to their perfectly reliable counterparts. We have recently shown that the error-floor performance of message-passing decoders can be improved, if randomness that exists in unreliable logic gates is incorporated into decoding deliberately, without any complexity cost. Furthermore, we have shown that controlling the level of unreliability enable us to exploit the stochastic resonance phenomenon, previously observed in theoretical physics, electronic and magnetic systems. In contrary to common belief, we have shown that for a narrow range of gate failure probability the overall decoding performance is dramatically increased. In this paper, we show that the effect of stochastic resonance is even more noticeable for the case of the gradient descent bit-flipping (GDBF) algorithm. This decoder combines the simplest iterative decoding algorithm with gradient descent optimization, making it an attractive solution for a variety of low complexity storage systems, or code-based cryptosystems. In addition, we show that getting the most of the stochastic resonance is essentially a deep learning problem, since setting the levels of unreliability for individual parts of the decoder by a training process is a step toward incorporating the machine learning techniques into design and analysis of iterative decoders of LDPC codes.

: In this paper, we discuss the Stochastic Resonance (SR) effect for the binary-input ternary-output Discrete Memoryless Channels (DMC). The DMC is of great interest for various digital communications channels, including acoustic underwater communication. We focus on the capacity maximization, brought about by additive Gaussian noise, for the DMCs. We analyze the capacity of the threshold binary communication channels in the presence of Additive Gaussian Noise (AGN). In the particular case of binary asymmetric channels with symmetric erasures (BAC/SE) we derive the optimum noise level in order to obtain the maximum capacity. The stochastic resonance for the general binary erasure channel is compared with those of binary symmetric channels. For the general case (2, n) DMC, the Pinsker and Helgert capacity bounds were used for the study of the stochastic resonance.

The quality of speech signal needs to be enhanced for applications like teleconferencing, hearing aids, speech recognition etc. Basically, speech-enhancement techniques are responsible for the removal of additive noise, to recover the noise free speech signal. This paper deals with an effective method, stochastic resonance (SR), in which the weak speech signal can be improved by increasing its signal-to-noise ratio (SNR) through addition of white Gaussian noise. Noise, though considered obnoxious in signal processing, can also be used constructively. Pertinence of stochastic resonance in signal processing is aimed at examining the detection of a weak speech signal buried in strong noise. Realizing the effect of stochastic resonance depends on the effective nonlinear system which is mathematically optimized through fourth-order Runge–Kutta algorithm and the system parameters were adjusted adaptively to examine the stochastic resonance (SR) effect on output signal-to-noise ratio and peak of the output signal spectrum.

Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as 'noise' we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics.

In this work we theoretically investigate the stochastic resonance (SR)\neffect in an optomechanical membrane system subject to two weak signals (one\noptical field and one mechanical force). The quadratic optomechanical coupling\nallows us to find a region with tristability where the noise activated\nstochastic switching among three stable states occurs and SR phenomena are\nobserved at the cooperation of input signals and noises. We show that the\nmechanical force and the optical field respectively serve as an additive signal\nand a multiplicative signal to the membrane position and they induce completely\ndifferent SR behaviors. Moreover, when two signals coexist the SR effect can be\nenhanced and the beating effect appears in the SR synchronization process with\nunsynchronized signals.\n

In this paper, we propose a highly realistic haptic feedback method for myoelectric prosthetic hands. Although several haptic feedback methods have been studied, this study attempts to create realistic sensory feedback through prosthetic hands by not only using feedback methods but also by improving the haptic sensation based on the stochastic resonance effect. In the experiment, contact information obtained from a microphone attached to the fingertip of a prosthetic hand was transferred through a vibrotactile stimulator near the elbow fossa, and white noise vibration was also applied near the elbow fossa to verify the improvement of tactile sensitivity of the fingertips. The results demonstrated the possibility of transmitting tactile information for myoelectric prostheses through sensory enhancement of the user.

We explore numerically the impact of additive Gaussian noise on the spatiotemporal dynamics of ring networks of nonlocally coupled chaotic maps. The local dynamics of network nodes is described by the logistic map, the Ricker map, and the Henon map. 2D distributions of the probability of observing chimera states are constructed in terms of the coupling strength and the noise intensity and for several choices of the local dynamics parameters. It is shown that the coupling strength range can be the widest at a certain optimum noise level at which chimera states are observed with a high probability for a large number of different realizations of randomly distributed initial conditions and noise sources. This phenomenon demonstrates a constructive role of noise in analogy with the effects of stochastic and coherence resonance and may be referred to as chimera resonance.

Stochastic resonance in binary composite hypothesis-testing problems in the Neyman–Pearson framework