Stochastic Resonance Research Articles

Robust neural networks using stochastic resonance neurons

AbstractVarious successful applications of deep artificial neural networks are effectively facilitated by the possibility to increase the number of layers and neurons in the network at the expense of the growing computational complexity. Increasing computational complexity to improve performance makes hardware implementation more difficult and directly affects both power consumption and the accumulation of signal processing latency, which are critical issues in many applications. Power consumption can be potentially reduced using analog neural networks, the performance of which, however, is limited by noise aggregation. Following the idea of physics-inspired machine learning, we propose here a type of neural network using stochastic resonances as a dynamic nonlinear node and demonstrate the possibility of considerably reducing the number of neurons required for a given prediction accuracy. We also observe that the performance of such neural networks is more robust against the impact of noise in the training data compared to conventional networks.

Rotor misalignment detecting by novel adaptive time–frequency mode decomposition and parametric stochastic resonance

Rotor misalignment detecting by novel adaptive time–frequency mode decomposition and parametric stochastic resonance

Using stochastic resonance of two-level systems to increase qubit coherence times

Using stochastic resonance of two-level systems to increase qubit coherence times

Inverse stochastic resonance in adaptive small-world neural networks.

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

One-Dimensional Lattice Potential-Based Stochastic Resonance for Robust QRS Detection in Noisy Electrocardiogram

One-Dimensional Lattice Potential-Based Stochastic Resonance for Robust QRS Detection in Noisy Electrocardiogram

CNN Intelligent diagnosis method for bearing incipient faint faults based on adaptive stochastic resonance-wave peak cross correlation sliding sampling

As a representative of deep learning networks, convolutional neural networks (CNN) have been widely used in bearing fault diagnosis with good results. However, the signal length and segmentation of the input CNN can have a significant impact on diagnostic accuracy. In addition, the signal-to-noise ratio of early bearing faults is usually very low, which makes it difficult for traditional CNNs to accurately identify and classify these faults. To solve this problem, this paper proposes an adaptive stochastic resonance wave peak cross-correlation sliding sampling method. Firstly, the adaptive stochastic resonance is used to reduce the noise of the original signal, and then the data is divided from the position of the signal wave peak, the correlation coefficient between the divided signals is calculated, and the maximum value is found to determine the size of the division window. Finally, it is converted into a 2D image by Gramian Angular Field and input into CNN for diagnostic classification. The design methodology was validated using the Case Western Reserve University bearing dataset. Subsequently, three validation strategies were established on a self-built platform, including mixed diagnosis of 10 different bearing states, variable speed diagnosis, and low sampling data diagnosis. The proposed method outperforms the conventional CNN by 10% in the Case Western Reserve University dataset test set. The variable speed test set is 24.67% and 31.17% higher, respectively. It is 30% higher in low sampling data diagnosis.

Low-dose radiographic inspection of welding by a novel aperiodic reverse stochastic resonance method

Abstract Low-dose radiographic inspection is a growing trend in industry to minimize radiation risks to humans and the environment. However, reduction in radiation dose often introduces significant noise, which affects image quality and hinders accurate identification of subtle defects. This study addresses this issue by introducing a novel phenomenon called aperiodic reverse stochastic resonance (ARSR), observed in nonlinear systems excited by aperiodic binary signals. ARSR enables simultaneous amplitude amplification and reversal of signals under specific noise conditions. Leveraging ARSR, we propose an image denoising framework for low-dose radiographic inspections. First, a set of projection data is obtained by using Radon transform to reduce the dimensionality of X-ray images from different angles. Then, the projection data is modulated based on the ARSR system. Finally, the image is reconstructed based on the inverse Radon transform. Simulations and experimental comparison results in welding applications validate the effectiveness of the framework, demonstrating significant improvements in image quality for low-dose radiographic defect detection. Unlike advanced methods such as Gaussian filtering, BM3D, and DnCNN, which operate at the pixel level, ARSR performs denoising at the projection data stage, reducing noise impact, preserving original information, and focusing on physical data processing during imaging. This approach enhances the detection of subtle defects, highlighting the potential of stochastic resonance in image processing.

Dynamical analysis and fault detection application of a time-delayed multi-stable stochastic resonance system driven by white correlated noises.

Bearing fault diagnosis is a crucial means to ensure the normal operation of machinery, reduce maintenance costs, enhance equipments safety and extend the service life of bearings. However, the noise interference in input signals often affects the effective extraction of bearing fault signals. In this paper, a time-delay multi-stable stochastic resonance (SR) system driven by white correlated noises is proposed. Firstly, the expressions of the steady-state probability density (SPD) function and the mean first passage time (MFPT) is derived. Simultaneously, we delve into the influences of various system parameters, including multiplicative noise intensity, additive noise intensity, noise correlated strength, time-delay, and time-delay feedback intensity, on the dynamical behavior exhibited by particles within the system. Then, according to the signal-to-noise ratio (SNR) formula, the paper investigates the impact of system parameters on the SNR. It is found that the ease of SR occurrence is directly related to the system parameters. Finally, the experimental results demonstrate that the proposed method exhibits superior performance in detecting faults compared with the classical bistable SR system and the quad-stable SR system.

A Novel Nonlinear Stochastic-Resonance-Enhanced Acceleration Data Processing Approach

Abstract. Smartphones are indispensable tools in modern social life, and they can be used for online shopping, electronic payment, gaming, and navigation. In particular, low-cost inertial measurement unit (IMU) sensors are widely integrated into smartphones, so pedestrian dead reckoning (PDR) positioning techniques based on smartphone IMU sensors have been applied and developed. PDR positioning techniques require acceleration data for step detection, step length estimation, and step heading estimation. However, due to the cost limitations of the built-in IMU sensor in smartphones, acceleration data contains measurement noise and interference, resulting in poor consistency in acceleration peak detection and the generation of false peaks, which is not conducive to step detection and accurate step length estimation. Therefore, this paper proposes a stochastic resonance (SR) enhancement method for smartphone IMU acceleration data. The SR-enhanced acceleration data has better peak consistency and is conducive to step detection. Finally, the algorithm is evaluated using actual measurement data collected from a smartphone. The results show that the SR-enhanced acceleration data has excellent peak consistency and higher step detection accuracy.

Host-Parasitoid Systems are Vulnerable to Extinction via P-Tipping: Forest Tent Caterpillar as an Example.

Continuous-time predator-prey models admit limit cycle solutions that are vulnerable to the phenomenon of phase-sensitive tipping (P-tipping): The predator-prey system can tip to extinction following a rapid change in a key model parameter, even if the limit cycle remains a stable attractor. In this paper, we investigate the existence of P-tipping in an analogous discrete-time system: a host-parasitoid system, using the economically damaging forest tent caterpillar as our motivating example. We take the intrinsic growth rate of the consumer as our key parameter, allowing it to vary with environmental conditions in ways consistent with the predictions of global warming. We find that the discrete-time system does admit P-tipping, and that the discrete-time P-tipping phenomenon shares characteristics with the continuous-time one: Both require an Allee effect on the resource population, occur in small subsets of the phase plane, and exhibit stochastic resonance as a function of the autocorrelation in the environmental variability. In contrast, the discrete-time P-tipping phenomenon occurs when the environmental conditions switch from low to high productivity, can occur even if the magnitude of the switch is relatively small, and can occur from multiple disjoint regions in the phase plane.

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Deep stochastic resonance array and its application in enhancing underwater weak signals

Deep stochastic resonance array and its application in enhancing underwater weak signals

Nonlinearity-enhanced continuous microwave detection based on stochastic resonance.

In practical sensing tasks, noise is usually regarded as an obstacle that degrades the sensitivity. Fortunately, stochastic resonance can counterintuitively harness noise to notably enhance the output signal-to-noise ratio in a nonlinear system. Although stochastic resonance has been extensively studied in various disciplines, its potential in realistic sensing tasks remains largely unexplored. Here, we propose and demonstrate a noise-enhanced microwave sensor using a thermal ensemble of interacting Rydberg atoms. Using the strong nonlinearity present in the Rydberg ensembles and leveraging stochastic noises in the system, we demonstrate the stochastic resonance driven by a weak microwave signal (from several microvolts per centimeter to millivolts per centimeter). A substantial enhancement in the detection is achieved, with a sensitivity surpassing that of a heterodyne atomic sensor by 6.6 decibels. Our results offer a promising platform for investigating stochastic resonance in practical sensing scenarios.

External noise-induced stochastic resonance and stochastic transitions in p53-Mdm2 regulatory network

Because noise is an inevitable attribute in gene regulatory networks, a stochastic differential equations model is constructed to study the impact of external noise on p53-Mdm2 regulatory network. Near the Hopf bifurcation of the corresponding deterministic model, external noise can induce stochastic resonance. When three stable steady states coexist in the corresponding deterministic model, external noise can lead to stochastic transition. Therefore, external noise can not only expand the parameter range of p53 regulatory network oscillations, but also increase the amplitude of p53 regulatory network oscillations, and is closely related to the outcome of cell fate. These findings deepen our understanding of the impact of external noise on gene regulatory networks and may provide new perspectives for the treatment of related diseases.

Enhanced fault diagnosis of segmented asymmetric stochastic resonance in rotating machinery under strong noise environment

Enhanced fault diagnosis of segmented asymmetric stochastic resonance in rotating machinery under strong noise environment

Multiplicative noise induced bistability and stochastic resonance

Abstract Stochastic resonance is a well established phenomenon, which proves relevant for a wide range of applications, of broad trans-disciplinary breath. Consider a one dimensional bistable stochastic system, characterized by a deterministic double well potential and shaken by an additive noise source. When subject to an external periodic drive, and for a proper choice of the noise strength, the system swings regularly between the two existing deterministic fixed points, with just one switch for each oscillation of the imposed forcing term. This resonant condition can be exploited to unravel weak periodic signals, otherwise inaccessible to conventional detectors. Here, we will set to revisit the stochastic resonance concept by operating in a modified framework where bistability is induced by the nonlinear nature of the multiplicative noise. A candidate model is in particular introduced which fulfils the above requirements while allowing for analytical progress to be made. Working with reference to this case study, we elaborate on the conditions for the onset of the generalized stochastic resonance mechanism. As a byproduct of the analysis, a novel resonant regime is also identified which displays no lower bound for the frequencies that can be resolved, at variance with the traditional setting.

Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization

Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with Lévy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.

A Hardware Circuit for Extracting Weak Damage Characteristics of Steel Wire Rope Using an Asymmetric Monostable System

ABSTRACTNon‐destructive testing of steel wire rope is significant in lifting machinery. However, it is still challenging to extract weak damage characteristics of steel wire rope under noise caused by harsh working conditions. Currently, extracting damage characteristics mostly involves secondary processing through software, which is cumbersome and inefficient. Stochastic resonance is a typical stochastic dynamic phenomenon of a nonlinear system, and it can extract weak damage characteristics through the cooperation of noise, weak signals, and nonlinear system models. Among different nonlinear system models, the asymmetric monostable system, which has only one fixed point, is advantageous for achieving a more stable response under pulse excitation. In this work, a hardware circuit based on an asymmetric monostable system is constructed to extract weak damage characteristics of steel wire rope. This hardware circuit can efficiently extract damage characteristics without secondary processing. Additionally, the denoising performance of the nonlinear circuit, focusing on weak signals with damage characteristics under a noisy background, is evaluated using the cross‐correlation coefficient and local cross‐correlation coefficient. The results demonstrate that the proposed method provides high recovery in the non‐destructive testing of steel wire rope.

Bistability in the sunspot cycle

A direct dynamical test of the sunspot cycle is carried out to indicate that a stochastically forced nonlinear oscillator characterizes its dynamics. The sunspot series is then decomposed into its eigen time-delay coordinates. The relevant analysis reveals that the sunspot series exhibits bistability, with the possibility of modeling the solar cycle as a stochastically and periodically forced bistable oscillator, accounting for poloidal and toroidal modes of the solar magnetic field. Such a representation enables us to conjecture stochastic resonance as the key mechanism in amplifying the planetary influence on the Sun, and that extreme events, due to turbulent convection noise inside the Sun, dictate crucial phases of the sunspot cycle, such as the Maunder minimum.

Novel classification algorithms inspired by firing rate stochastic resonance

Novel classification algorithms inspired by firing rate stochastic resonance