Principle Of Stochastic Resonance Research Articles

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.

Unveiling the principles of stochastic resonance and complex potential functions for bearing fault diagnosis

This paper introduces a novel and complex Unsaturated Piecewise Linear Quad-Stable Stochastic Resonance System (UPLQSR) to address the issue of output saturation in the Classical Quad-Stable Stochastic Resonance (CQSR) system. By linearizing the structure of the potential function, the constraints imposed by high-order terms are effectively eliminated, allowing Brownian particles to move more freely. Numerical simulations demonstrate that UPLQSR achieves significantly higher output signal amplitudes compared to CQSR, highlighting its remarkable signal amplification capability. By analyzing the structure of the potential function, the relationship between the height of the potential barrier and the aggressiveness of the particles jumping is determined. Utilizing adiabatic approximation theory, the paper derives the Steady-state Probability Density (SPD), Mean First Passage Time (MFPT), and Spectral Amplification (SA), revealing the specific process of the particle jumping, as well as the influence of the parameters on the performance of the UPLQSR. After optimizing parameters using the Adaptive Genetic Algorithm (GA), UPLQSR is applied to the early fault diagnosis of various bearing models under Gaussian white noise, demonstrating superior fault detection capabilities. In summary, this study pioneered the theory of non-saturation of quad-stable systems, optimized the weak signal detection technique, and provided a more accurate means of signal identification and analysis, highlighting its great value of application in engineering.

Characterising modulatory effects of high-intensity high frequency transcranial random noise stimulation using the perceptual template model

Neural noise is an inherent property of all nervous systems. However, our understanding of the mechanisms by which noise influences perception is still limited. To elucidate this relationship, we require techniques that can safely modulate noise in humans. Transcranial random noise stimulation (tRNS) has been proposed to induce noise into cortical processing areas according to the principles of stochastic resonance (SR). Specifically, it has been demonstrated that small to moderate intensities of noise improve performance. To date, however, high intensity tRNS effects on neural noise levels have not been directly quantified, nor have the detrimental effects proposed by SR been demonstrated in early visual function. Here, we applied 3 mA high-frequency tRNS to primary visual cortex during an orientation-discrimination task across increasing external noise levels and used the Perceptual Template Model to quantify the mechanisms by which noise changes perceptual performance in healthy observers. Results show that, at a group level, high-intensity tRNS worsened perceptual performance. Our computational analysis reveals that this change in performance was underpinned by an increased amount of additive noise and a reduced ability to filter external noise compared to sham stimulation. Interestingly, while most observers experienced detrimental effects, a subset of participants demonstrated improved performance. Preliminary evidence suggests that differences in baseline internal noise levels might account for these individual differences. Together, these results refine our understanding of the mechanisms underlying the influence of neural noise on perception and have important implications for the application of tRNS as a research tool.

Incipient Bearing Fault Diagnosis Based on the Two-State Theory for Stochastic Resonance Systems

The principle of stochastic resonance (SR) has been intensively used in designing algorithms for incipient bearing fault diagnosis based on stochastic simulation. In order to directly apply the semi-analytic result to the laboratory designs, an incipient bearing fault diagnosis algorithm based on the method of moments for stochastic resonant systems was proposed, with its real-time monitoring efficiency being assured by an available table of the involving stationary moments. Nevertheless, since the noise that buried the fault signal can only be estimated after sampling, calculation of such stationary moments might be an inconvenience. To fix this inconvenience, a brand-new fault diagnosis algorithm is designed in this paper based on the two-state theory for a biased underdamped bistable stochastic resonant system. The capability and the efficiency of the algorithm in identifying the characteristic frequency of the hidden fault signal are confirmed with the experimental data sets. This investigation actually opens a new way for improving the existing SR-based diagnosis algorithms.

Color image perception based on stochastic spiking neural network

Our aim is to present an interpretable algorithm for enhancing low-illuminance color image based on the principle of stochastic resonance and the fundamental biophysical process of human brain perceiving object color. To this end, the phenomenon of stochastic resonance in a conductance-based integrate-and-fire neuronal network is first explored, with the effect of firing threshold, synaptic weight and the population size on the signal-to-noise ratio revealed, and the firing threshold is recognized as the key parameter for the resonance effects. And then, a color image enhancement algorithm, where the peak signal-to-noise ratio and the natural image quality evaluator are adopted as quantifying indexes, is developed by combining the stochastic spiking neuronal network and the involved biophysical process relating to visual perception. Note that the enhanced image is aperiodic, thus in order to optimize the performance of the algorithm, an illuminance distribution based threshold strategy is given by us for the first time. The numerical tests show that the algorithm has good enhancement performance and stability. We wish this algorithm could be applied to relevant signal processing fields such as military detection and medical image preprocessing.

Novel Bearing Fault Diagnosis Algorithm Based on the Method of Moments for Stochastic Resonant Systems

The principle of stochastic resonance (SR) in the noisy Duffing oscillator model has shown benefit for designing novel mechanical fault diagnosis algorithms, where noise is utilized rather than being eliminated. However, there is a clear gap between the model progress and the experimental application. In this article, effort is made trying to narrow the gap by applying the method of moments to obtain the spectral amplification factor within the linear response range to improve the algorithm design, which avoids the conventional time-consuming direct simulations. A strategy for estimating noise, which is critical for programming the algorithm, is proposed and evaluated. Through simulation and experimental data analysis, it is confirmed that the new algorithm has advantages over the overdamped system-based methods as it does not depend on the signal preprocessing techniques such as envelope extraction and high-pass filter. Also, the new method has advantages over the existing underdamped system-based methods as it can decrease the computational time for seeking the optimal parameter by at least one order of magnitude.

Stochastic Resonance Based Visual Perception Using Spiking Neural Networks.

Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.

Stochastic resonance model of synaesthesia.

In synaesthesia, stimulation of one sensory modality evokes additional experiences in another modality (e.g. sounds evoking colours). Along with these cross-sensory experiences, there are several cognitive and perceptual differences between synaesthetes and non-synaesthetes. For example, synaesthetes demonstrate enhanced imagery, increased cortical excitability and greater perceptual sensitivity in the concurrent modality. Previous models suggest that synaesthesia results from increased connectivity between corresponding sensory regions or disinhibited feedback from higher cortical areas. While these models explain how one sense can evoke qualitative experiences in another, they fail to predict the broader phenotype of differences observed in synaesthetes. Here, we propose a novel model of synaesthesia based on the principles of stochastic resonance. Specifically, we hypothesize that synaesthetes have greater neural noise in sensory regions, which allows pre-existing multisensory pathways to elicit supra-threshold activation (i.e. synaesthetic experiences). The strengths of this model are (a) it predicts the broader cognitive and perceptual differences in synaesthetes, (b) it provides a unified framework linking developmental and induced synaesthesias, and (c) it explains why synaesthetic associations are inconsistent at onset but stabilize over time. We review research consistent with this model and propose future studies to test its limits. This article is part of a discussion meeting issue 'Bridging senses: novel insights from synaesthesia'.

Adaptive stochastic resonance for unknown and variable input signals

All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.

Broadband vibration energy harvesting by application of stochastic resonance from rotational environments

A model for energy harvesting from a rotating automotive tyre is suggested in which the principle of stochastic resonance is advantageously exploited. A bistable response characteristic is obtained by recourse a small harvester comprising a magnetically repellant configuration in which an instrumented cantilever beam can flip between two physical response states when suitably excited by the rotation of a car wheel into which it is fitted. The rotation of the wheel creates a periodic modulation which enables stochastic resonance to take place and as a consequence of this for energy to be harvested from road noise transmitted through the tyre. An optimised mathematical model of the system is presented based on a series of experimental tests and it is shown that a ten-fold increase in harvested energy over a comparable monostable case is feasible. The suggested application for this harvester is to provide electrical power for a tyre pressure monitoring system.

Feasibility of energy harvesting from a rotating tire based on the theory of stochastic resonance

Recently the use of nonlinear bi-stable micro-electro mechanical systems (MEMS) to achieve automobile tire vibration power generation has made some progress. However, the theory of stochastic resonance has not been successfully applied to automobile tires, which can produce a larger vibrational response than for a typical resonance while inputting a weak periodic force and noise excitation into a nonlinear bi-stable system. Hence, in this paper, in view of the principle of stochastic resonance, a new model is derived by positioning a magnetic end mass attached to a cantilever beam and another permanent magnet with the same polarity on the frame. Due to the road noise excitation along with the periodic force inputted to the mechanism, whether the phenomenon of stochastic resonance can happen will be discussed. Meanwhile, on the basis of Kramers rate and duffing equations the preliminary experimental device is also designed.

A Shoe Insole Delivering Subsensory Vibratory Noise Improves Balance and Gait in Healthy Elderly People

ObjectivesTo test whether subsensory vibratory noise applied to the sole of the foot using a novel piezoelectric vibratory insole can significantly improve sensation, enhance balance, and reduce gait variability in elderly people, as well as to determine the optimal level of vibratory noise and whether the therapeutic effect would endure and the user's sensory threshold would remain constant during the course of a day. DesignA randomized, single-blind, crossover study of 3 subsensory noise stimulation levels on 3 days. SettingBalance and gait laboratory. ParticipantsHealthy community-dwelling elderly volunteers (N=12; age, 65–90y) who could feel the maximum insole vibration. InterventionsA urethane foam insole with the piezoelectric actuators delivering subsensory vibratory noise stimulation to the soles of the feet. Main Outcome MeasuresBalance, gait, and timed Up and Go (TUG) test. ResultsThe vibratory insoles significantly improved performance on the TUG test, reduced the area of postural sway, and reduced the temporal variability of walking at both 70% and 85% of the sensory threshold and during the course of a day. Vibratory sensation thresholds remained relatively stable within and across study days. ConclusionsThis study provides proof of concept that the application of the principle of stochastic resonance to the foot sole sensory system using a new low-voltage piezoelectric technology can improve measures of balance and gait that are associated with falls. Effective vibratory noise amplitudes range from 70% to 85% of the sensory threshold and can be set once daily.

Electrical stimulation for difficult wounds: only an alternative procedure?

In the wound healing research, the exact mechanism of action of different modalities of electrical stimulation (ES) remains controversial and unresolved. In this study we discuss a particular ES, with a different type of waveform, corresponding to the principle of stochastic resonance. Between July 2008 and May 2010, 32 patients were enrolled and ES was applied to wounds using the bioelectrical signal therapy (BST) device (LifeWave, Petach Tiqwa, Israel). The outcome evaluated in group 1 (n = 21) was wound healing, while group 2 (n = 11) was evaluated for wound-related pain [Visual Number Scale (VNS) pain scale] during treatment. In group 1, 87% of the wounds closed in an average time of 97 days (range 10-150 days); three patients were lost to follow-up. In group 2, 45% of the patients experienced a complete pain disappearance after 7 days of treatment; 36% reported a reduction in VNS from 9·3 to 3·2 in 7 days; 19% stopped morphine-like painkillers after 2 weeks. The clinical application of the stochastic resonance enables the usage of easy-to-use, non-invasive, painless and pain-relief treatment. Our experience with ES has demonstrated the BST device to be a very good alternative in cases of small size defects, compared with other therapies such as surgery, dressing and negative pressure devices.

Experimental Research of Weak Signal Detection Method Based on the Dual-resonance

The coupled bistable system is composed of two single bistable systems by means of nonlinear coupling. One of the bistable systems is controlled system which is subjected by the weak periodic signal, and the other is controlling system which is under the action of control signal. The dual-resonance characteristics of coupled bistable system under the controlling signal action were analyzed. When the frequency of control signal is twice of the weak periodic signal's frequency, the coupled bistable system can exist resonance in controlling system and stochastic resonance in controlled system, and a method based on dual-resonance to enhance stochastic resonance is proposed. A coupled bistable system hardware circuit system was designed based on dual-resonance. Experimental results confirm that there exists dual-resonance in coupled bistable system and the dual-resonance can enhance stochastic resonance greatly. Put the hardware circuit system into the detection of bearing fault signal, the power spectrum of coupling systems' output at the eigen frequency increase greatly, and it can detect weak signal of bearing fault effectively. A novel solution to detect the weak signal Based on the principle of stochastic resonance is provided, and it has a good application prospect.

Enhancement of positron emission tomography-computed tomography image quality using the principle of stochastic resonance.

Purpose:Acquisition of higher counts improves visual perception of positron emission tomography-computed tomography (PET-CT) image. Larger radiopharmaceutical doses (implies more radiation dose) are administered to acquire this count in a short time period. However, diagnostic information does not increase after a certain threshold of counts. This study was conducted to develop a post processing method based on principle of “stochastic resonance” to improve visual perception of the PET-CT image having a required threshold counts.Materials and Methods:PET-CT images (JPEG file format) with low, medium, and high counts in the image were included in this study. The image was corrupted with the addition of Poisson noise. The amplitude of the Poisson noise was adjusted by dividing each pixel by a constant 1, 2, 4, 8, 16, and 32. The best amplitude of the noise that gave best images quality was selected based on high value of entropy of the output image, high value of structural similarity index and feature similarity index. Visual perception of the image was evaluated by two nuclear medicine physicians.Results:The variation in structural and feature similarity of the image was not appreciable visually, but statistically images deteriorated as the noise amplitude increases although maintaining structural (above 70%) and feature (above 80%) similarity of input images in all cases. We obtained the best image quality at noise amplitude “4” in which 88% structural and 95% feature similarity of the input images was retained.Conclusion:This method of stochastic resonance can be used to improve the visual perception of the PET-CT image. This can indirectly lead to reduction of radiation dose.

A Novel Frequency Locked Loop Based on Stochastic Resonance

Frequency locked loop (FLL) plays an important role in carrier synchronization because of its excellent dynamic performance. However, it performs inadequately in low signal-to-noise ratio (SNR). In this paper, the principle of stochastic resonance (SR) is briefly introduced and a SR processor is proposed. Based on traditional FLL, the SR processor is added before frequency discriminator in order to weaken the effect that thermal noise brings to FLL. The paper investigates the processing effect of SR. Simulation results show that the performance of improved FLL is greatly improved. It can tolerate rather high dynamics and tracking accuracy of frequency achieve 0.2Hz even with CNR as low as 25 dBHz, which verified the validity of above ideas.

Low SNR Singal Recovery System Based on Stochastic Resonance

Stochastic resonance (SR) enhances the nonlinear system behavior with the assistance of noise. The energy-transfer mechanism makes the weak information revealed in the output spectrum, while the time-waveform is distorted. In this article, The Principle of stochastic resonance and recovery method are proposed and based on which, The nonlinear circuit is formed.At last, some examples are given to identify the performance of this nonlinear circuit.

Theory and experiment research on a Piecewise-linear model based on stochastic resonance

We propose a new piecewise-linear model. The principle of stochastic resonance in the new piecewise-linear model is introduced, and the formula of the signal-to-noise ratio (SNR) is deduced. It is proved that stochastic resonance characteristics can be utilized to realize the conversion of noise energy into periodic signal energy. The results clearly show the enhancement of the SNR of the output signal. The model characteristic that the weak periodic signal can be detected from noise background is investigated by the numerical simulation. The stochastic resonance system based on the model is built by using a circuit. The behaviors of stochastic resonance are studied when the circuit is driven by noise and period signal. The simulation and experimental results show that the model can effectively detect weak periodic signal, and enhance the SNR prominently.

Medium-Low-Frequency Signal Detection and Simulation Based on the Principle of Stochastic Resonance

The principle of stochastic resonance in bistable system is introduced firstly. The medium-low-frequency periodic signal and multi-frequency harmonic signal (the large parameter signal) are common in mechanical failure, but it is difficult to achieve stochastic resonance in these signals detection. The signal modulation characteristic is used in this paper to transform the various frequency components into small parameter signals which satisfy the adiabatic approximation theory. On the basis of that, weak signal detection based on stochastic resonance theory is realized. Then a mixing circuit system based on stochastic resonance is designed, the circuit first makes a frequency selection processing with a mixer on the mixed signal between the measurable signal and a scanning signal, and then it is input to the nonlinear bistable system to realize signal detection based on stochastic resonance. At last, the MATLAB simulation result shows that the circuit can realize the stochastic resonance and detection of weak periodic signal in medium-low frequency from noise background.

Experimental research of weak signal detection based on the stochastic resonance of nonlinear system

Noise can be used by the nonlinear stochastic resonance system to enhance the ability of weak signal detection, which creates a new method of weak signal detection under heavy background noise. A hardware circuit system, designed based on the basic principle of stochastic resonance, is used to detect single-frequency and multi-frequency weak signals. When the signal with noise that simulates actual engineering situation is introduced into the system, the spectrum of samples of the output signal shows that single-frequency and multi-frequency weak signals under heavy background noise can be detected by the technology based on stochastic resonance effectively. The application field of weak signal detection technology based on stochastic resonance is entended by the effective detection of mutil-frequency weak signal, and the degradation of signal recognition by low-frequency noise is eliminated by digital filtering effectively. The weak signal detection technology based on stochastic resonance has potential applications in the information recognition and information processing.