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Optical metasurfaces, artificial planar nanostructures composed of subwavelength meta-atoms, have attracted significant attention due to their ability to tailor optical nanoscale properties, making them a versatile platform for shaping light in both linear and nonlinear regimes. This paper reports on the realization of second harmonic generation (SHG) enhancement based on a dolmen-type gold metasurface containing two resonances. Nonlinear scattering theory is employed to numerically investigate the SHG enhancement phenomenon in the resonant metasurface. The periodic dolmen-type gold metasurface introduces a diffraction coupling effect between Fano resonance and surface lattice resonance (SLR), providing strong local-field enhancement and significantly enhancing the nonlinear effect. We analyze the influence of the coupling between Fano resonance and SLR on the SHG intensity and achieve a 230-fold enhancement in SHG intensity compared to the single resonance case by adjusting the periodicity of the metasurface. The SHG-enhanced gold metasurface may find applications in sensing, imaging, optical computing, and integrated nonlinear optics. [ABSTRACT FROM AUTHOR]

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging. [ABSTRACT FROM AUTHOR]

Low-intensity noisy galvanic vestibular stimulation (nGVS) is a promising non-invasive treatment for enhancing vestibular perceptual performance and postural control in patients with chronic vestibular hypofunction. However, this approach has so far been studied mainly under laboratory conditions. Evidence indicates that continuous application of nGVS in daily life is necessary for it to be effective. To address this need, we have developed a mobile nGVS stimulator and conducted a series of pilot studies to evaluate its safety, tolerability, functionality, and therapeutic effects. The device is a lightweight, compact, and portable AC stimulator featuring a user-friendly interface for the individualized adjustment of nGVS parameters. It includes an integrated motion sensor that automatically activates stimulation during body movement and deactivates it during inactivity, optimizing its practical use in real-world settings. The stimulator adheres to strict safety standards and, in initial long-term use, has exhibited only mild side effects (e.g., skin irritation and headaches), likely attributable to the current electrode placement, which requires further optimization. As expected, the device consistently elicits known vestibular sensorimotor reflex responses in healthy individuals. Importantly, further pilot studies in healthy participants demonstrate that the device can reliably replicate known facilitating effects on vestibular perception and postural control. Together, these findings suggest that this mobile stimulation device can facilitate the translation of nGVS into therapeutic everyday use. [ABSTRACT FROM AUTHOR]

Poisson white noise is a more accurate representation than ideal Gaussian white noise for simulating the noise background during early bearing failures. In the context of strong Poisson white noise interference, the failure mode of rolling bearings usually remains undetermined. This paper proposes a new method for diagnosing unknown bearing faults in bearings, diagnosis is proposed, which effectively identifies unknown faults across various components. The theoretical characteristics of Poisson white noise are initially analyzed and discussed. Then, to extract potential fault characteristic information, a false characteristic frequency of bearing is constructed in the response spectrum, and the response results at this false fault characteristic are obtained. Finally, coherent resonance (CR) is introduced, and false frequencies caused by false faults are eliminated by defining a quality factor. To verify the effectiveness of the method, the experimental and simulation results were compared with the decomposition results of the SVMD algorithm. The SNR of the experimental signals for outer and inner ring faults under variable speed conditions increased to 8.62dB and 11.74dB, respectively. Results indicate show that this method not only successfully identifies fault features, but also exhibits a strong noise reduction effect. [ABSTRACT FROM AUTHOR]

Piezoelectric needle selectors, as key weaving components in the jacquard knitting process of knitting machinery, are widely used in textile equipment. Accurate diagnostic procedures for needle selector drive faults are crucial to ensure the normal operation of the equipment. However, traditional vibration diagnosis methods cannot detect weak periodic signals, resulting in low accuracy of monitoring results. To address this issue, this paper proposes an adaptive stochastic resonance (SR) method based on an improved generative adversarial network (IGAN). Firstly, in order to solve the problem of difficulty in obtaining stochastic resonance parameters, SR is combined with IGAN, and IGAN provides the optimal SR parameters. Secondly, a soft threshold residual attention mechanism and residual network were introduced in the GAN network, and multiple generators were utilized to alleviate the problem of model collapse, in order to adapt to the actual working environment. In addition, due to the large amount of data, it is recommended to use feature parameters for training to improve the efficiency of model training. Finally, through a typical vibration data, the influence of different parameter quantities on the training accuracy of the model was studied, and the superiority of the proposed model compared to other models and the stability of the model under different environmental influences were explored. The results show that this method can effectively detect weak periodic signals of the needle selection driver, effectively alleviate the problem of model collapse in different environments, and is superior to existing methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Harvesting energy from nonlinear systems has been at the centre of research in the energy harvesting community. Many such proposed systems are single nonlinear harvester. While these systems show an increase in bandwidth of harvesting frequency, overall, they are not effective enough in power generation. This article studies power harvesting and frequency bandwidth characteristics of an array of harvesters. Multiple harvesters are considered with linear and nonlinear coupling between the harvesters. The phenomena of internal resonance (IR) and stochastic resonance (SR) are reported. The IR in multiple coupled nonlinear harvesters is explored using multiple-scale analysis. A parametric study is conducted to demonstrate the effect of coupling strength, frequency mistuning, innate nonlinearity and other parameters. The parametric study helped establish effective ways to increase bandwidth. Moreover, a stochastically loaded linearly coupled bistable harvester array is numerically analysed to find the effect of coupling strength and array size on the phenomenon of SR and on the system's harvesting performance. Through these studies, the potential of multiple coupled nonlinear harvesters in enhanced energy harvesting is demonstrated under both harmonic and stochastic excitation. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

With the rapid development of wind power generation in recent years, the demand for detecting weak signals of wind turbine faults has become more urgent. This paper introduces a novel memristor chaotic circuit constructed based on third-order magnetically memristors. The Melnikov chaotic condition of this circuit is analyzed, and its dynamical characteristics are studied through phase trajectory diagrams, bifurcation diagrams, Lyapunov exponent spectra, and Poincaré maps. Leveraging the initial value sensitivity and noise immunity of chaotic systems, the memristor chaotic circuit is employed for the detection of weak signals in wind turbine faults. Using the chaotic system state transition method, we find the threshold for the circuit state to transition from chaotic state to large-scale periodic state, adjust the parameters to make the system in a critical state, input the wind turbine fault vibration signal, and detect the fault signal based on its state transition. Subsequently, the chaotic resonance method is employed, introducing the signal under test, which contains high-intensity chaotic noise, into this novel memristive circuit. This results in chaotic resonance, causing the noise components to be concentrated toward the frequency region where the weak signal under test is located, thereby enhancing the fault signal and facilitating fault identification. The results indicate that this novel memristor chaotic circuit possesses advantages such as high accuracy, strong noise immunity, straightforward operation, and clear judgment in the field of weak signal detection. This circuit shows promising applications in the field of weak signal detection. [ABSTRACT FROM AUTHOR]

The ability to respond effectively to balance perturbations is crucial for fall prevention. Subsensory electrical stimulation (SES) applied to the skin leads to improved proactive balance control but there is limited evidence on the SES effect on reactive balance control. To test the efficiency of SES in improving reactive balance control against unpredictable support surface perturbations and to compare the effects of SES applied to the trunk and the lower legs. Twenty-three young adults stood on a treadmill while recovering from 15 forward and 15 backward surface translations of increasing magnitude to determine the backward and forward stepping thresholds (BSTh and FSTh). Then, they recovered from three repetitions of forward and backward perturbations of fixed magnitude to determine the characteristic of the compensatory step (i.e., step time, step length, step delay and Margin of Stability - MOS). Each test was conducted with no stimulation (NS), leg stimulation (LS), or trunk stimulation (TS) equal to 90 % of the sensory threshold. Repeated-measures ANOVA and Tukey post-hoc tests were used to analyze the main and interaction effects of stimulation and repetition. TS and LS increased the BSTh by 31.5 % (p=0.002) and 16.4 % (p=0.028), respectively, with greater effects of TS; (ii) during backward perturbations, TS reduced compensatory step time by 9.0 %, step length by 17.1 %, and MOS at compensatory heel strike by 17.7 % (p<0.016); and (iii) during forward perturbations, LS and TS reduced the step time by 4.5 % and 3.5 % (p<0.017), and increased the minimum MOS by 7.8 % and 4.5 %, respectively (p<0.048). This is the first study that showed how the application of SES affects reactive balance control during support surface perturbations. TS was more effective than LS during backward perturbations. TS may be an effective strategy to enhance balance control during reactive postural tasks, thus potentially reducing fall risk. • Support surface perturbations were delivered with a treadmill while standing. • Subsensory electrical stimulation increased reactive control of balance. • Trunk stimulation showed greater effects than leg stimulation. • Higher baseline instability was associated with greater effects of the stimulation. [ABSTRACT FROM AUTHOR]

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator's behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low-voltage AC distribution system in numerous loads generates a large amount of noise, which can weaken the arc fault characteristics to lead much more difficult to detect series arc faults, seriously threatening the safety of electricity consumption. Therefore, to solve the problem of insufficient arc fault detection capability in noise background, the paper proposes a series arc fault detection method based on IDOA-SR-VMD and ensemble learning. By comparing the high-order harmonic characteristics of resistive, inductive, and capacitive load arc faults before and after occurrence, the fault frequency distribution range is determined. Subsequently, adaptive SR and VMD methods are employed for noise reduction and feature enhancement, constructing a multi-layered signal processing model and outputting the reconstructed signal. KPCA algorithm is utilized for signal dimensionality reduction, generating a feature matrix used as input for the Stacking ensemble learning model to achieve accurate diagnosis and load classification of arc faults in noisy background. The method achieved significant improvements in diagnostic accuracy and load classification accuracy, reaching 99.5% and 98.25%, respectively. Comparative analysis with other methods validated the effectiveness and superiority of the proposed approach. In summary, the method provides a reliable solution for arc fault detection in noisy backgrounds, with broad prospects for practical applications. [ABSTRACT FROM AUTHOR]

ABSTRACT Non‐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. [ABSTRACT FROM AUTHOR]

Perception of voice means acoustic electric conversion in the auditory system, and changes of external magnetic field can affect the neural activities by taming the channel current via some field components including memristor and Josephson junction. Combination of two capacitors via an electric component is effective to describe the physical property of artificial cell membrane, which is often used to reproduce the characteristic of electric activities in cell membrane. Involvement of two capacitive variables for two capacitors in the neural circuit can discern the effect of field diversity in the media in two sides of the cell membrane in theoretical way. A Josephson junction is used to couple a piezoelectric neural circuit composed of two capacitors, one inductor and one nonlinear resistor. Field energy is mainly kept in the capacitive and inductive components, and it is obtained and converted into dimensionless energy function. The Hamilton energy function in an equivalent auditory neuron is verified by using the Helmholtz theorem. Noisy excitation on the neural circuit can be detected via the Josephson junction channel and similar stochastic resonance is detected by regulating the noise intensity, as a result, the average energy reaches a peak value under stochastic resonance. An adaptive law controls the bifurcation parameter, which is relative to the membrane property, and energy shift controls the mode selection during continuous growth of the bifurcation parameter. That is, external energy injection derived from acoustic wave or magnetic field will control the energy level, and then suitable firing patterns are controlled effectively. [ABSTRACT FROM AUTHOR]

An approach combining the stochastic resonance, the harmonic wavelet packet transforms and the probability density function was proposed to obtain the early fault signal of a rolling bearing. Firstly, an adaptive variable-scale stochastic resonance was employed to detect the frequency range of the rolling bearing's fault signal based on the improved signal-to-noise ratio, and then the harmonic wavelet packet transforms and the probability density function were utilized to extract and identify the fault signal of the rolling bearings from the detected signals. The case studies show that the proposed method can effectively obtain the early fault signals of the outer race, inner race and rolling element in the rolling bearings despite not knowing the frequency band distribution, and that the early fault diagnosis of the rolling bearings can be achieved. [ABSTRACT FROM AUTHOR]

Abstract 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.

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

The aim of this paper is to present a category of novel pattern classification algorithms inspired by the phenomenon of the firing rate based stochastic resonance in a noisy leaky integrate-and-fire neuron. To this end, the firing rate-based stochastic resonance phenomenon in the noisy leaky integrate-and-fire neuron model is displayed by means of the approximation of adiabatic elimination. And then, a multi-layer neural network with back-propagation learning is constructed by using the stationary firing rare for activation function. Since the intensity of the involving Gaussian white noise is taken as an independent trainable parameter, the benefit of noise can be maximally utilized. The algorithm and its improvements have been verified with binary classification and handwritten digit recognition. By further simplifying calculation of the firing rate activation, this algorithm is embedded into different network architectures of PreAct-ResNet-18 and VGG-16 for more complex tasks. It is shown that the improved version based on the stochastic gradient descent optimizer outperforms several typical artificial neural network algorithms and brain-inspired spiking neural network algorithms on the CIFAR-10 dataset, and it achieves a good accuracy on CIFAR-100, surpassing the accuracy of most of the state-of-the art models. Since the trained intensity of Gaussian white noise is nonzero in all the applications, stochastic resonance like effect has been observed. Hence it is disclosed from this study that noise can really be designed as an optimizable factor into the brain-inspired machine learning algorithms. [ABSTRACT FROM AUTHOR]

We investigate the dynamics of Landau–Zener (LZ) transitions in an anisotropic, dissipative three-level LZ model (3-LZM) using the numerically accurate multiple Davydov D2Ansatz in the framework of the time-dependent variational principle. It is demonstrated that a non-monotonic relationship exists between the Landau–Zener transition probability and the phonon coupling strength when the 3-LZM is driven by a linear external field. Under the influence of a periodic driving field, phonon coupling may induce peaks in contour plots of the transition probability when the magnitude of the system anisotropy matches the phonon frequency. The 3-LZM coupled to a super-Ohmic phonon bath and driven by a periodic external field exhibits periodic population dynamics in which the period and amplitude of the oscillations decrease with the bath coupling strength. [ABSTRACT FROM AUTHOR]

This paper proposes a novel segmented underdamped asymmetric tristable stochastic resonance (SUATSR) system. The research involves deriving the equivalent potential function for the underdamped background and analyzing the system’s output saturation. Results demonstrate that the SUATSR system effectively mitigates output saturation issues, enhancing the output amplitude. The study also employs adiabatic approximation theory to derive theoretical expressions for mean first passage time (MFPT) and spectral amplification (SA). The paper discusses the impact of varying system parameters on these performance indicators. Numerical simulations of the classical tristable stochastic resonance (CTSR), segmented underdamped tristable stochastic resonance (SUTSR) and SUATSR systems validate the theoretical derivations. Furthermore, the three systems are applied to different bearing fault tests and optimized using the adaptive genetic algorithm (AGA). The results demonstrate that the SUATSR system outperforms the other two systems, exhibiting higher SA and signal-to-noise ratio (SNR) values at the fault frequency. This highlights the SUATSR system’s superior noise resistance and its effectiveness in detecting and enhancing fault signals. [ABSTRACT FROM AUTHOR]

The spectral detection of weak frequency band signals poses a serious problem in many applications, especially when the target is within a certain frequency band under low signal-to-noise ratio (SNR) conditions. A kind of novel technique based on the pre-whitening scale transformation of stochastic resonance (SR) in a symmetric bistable system in a parallel configuration is proposed to solve the problem. Firstly, pre-whitening can ensure the Gaussian distribution of the receiving signal fits the requirements for SR processing. Secondly, scale transformation can help to effectively utilize the properties of a weak signal, especially under a low-frequency band. Thirdly, the SR in a symmetric bistable system in a parallel configuration can try to smoothly reduce the variances in the clutter and additive noise. Fourthly, by subtracting the steady state response of the SR in the selected symmetric bistable system from the parallel output, the spectral detection of a weak signal can be realized successfully. Experiment results based on actual sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an overdamped bistable system subject to Gaussian white noise and logical inputs. By solving and estimating the steady distribution of the corresponding Fokker–Planck equation and considering the two essential features of the reliable logic operations (RLOs)—initial value independence and sign invariance—we establish the sufficient condition for RLO occurrence and identify parametric resonance phenomena in the system. Our numerical simulations confirm the reliability and accuracy of the theoretical results. This work offers insights into enhancing the accuracy of stochastic systems, particularly in the realm of logical stochastic resonance, thereby contributing to advancements in understanding and controlling stochastic dynamical systems. [ABSTRACT FROM AUTHOR]

Stochastic resonance, a phenomenon that amplifies and detects weak signals, has been observed in various physical systems. However, due to the challenge of constructing controllable nonlinear damping, stochastic resonance has never been experimentally performed using van der Pol nonlinearity. Here, we report the experimental observation of stochastic resonance in a single trapped-ion phonon laser system exhibiting van der Pol nonlinearity. This experiment demonstrates the ability of the phonon laser to achieve stochastic resonance, amplifying weak signals via inputting additional noise at a single-atom level. Our experiment illustrates the single-ion phonon laser providing a platform to explore the physical properties of the nonlinear van der Pol oscillator. This work opens up a way for designing single-atomic devices for weak signal processing in real environments. [ABSTRACT FROM AUTHOR]

Weak sensory noise acts on the nervous system and promotes sensory and motor functions. This phenomenon is called stochastic resonance and is expected to be applied for improving biological functions. This study investigated the effect of electrical stimulation on grip force adjustment ability. The coefficient of variation and absolute motor error in grip force was measured during a visuomotor tracking task under different intensities of somatosensory noise. Depending on the style of force exertion, the grip movement used in the visuomotor tracking task consisted of force generation (FG), force relaxation (FR), and constant contraction (Constant) phases. The subthreshold condition resulted in significantly lower coefficient of variation in the Constant phase and motor errors in the FG and Constant phases than the no-noise condition. However, the differences among the other conditions were insignificant. Additionally, we examined the correlation between the motor error in the condition without electrical stimulation and the change in motor error induced by subthreshold electrical stimulation. Significant negative correlations were observed in all FG, FR, and Constant phases. These results indicated that somatosensory noise had a strong effect on subjects with large motor errors and enhanced the grip force adjustment ability. By contrast, subjects with small motor errors had weak improvement in motor control. Although the effect of subthreshold noise varies depending on the individual differences, stochastic resonance is effective in improving motor control ability. [ABSTRACT FROM AUTHOR]

The stochastic resonance (SR) phenomenon for a bistable system subject to signal-modulated noise and to multiplicative and additive noise is investigated. The signal is modelled as a random-phase asymmetric square-wave one. Based on adiabatic approximation condition and two-state theory, the system output signal-to-noise ratio (SNR) is deduced. It is found that double SR phenomenon occurs when the SNR varies with the asymmetry of the square-wave signal. One resonance peak appears when the SNR changes with the amplitude of the square-wave signal. Traditional SR can be observed on the curves of the SNRs vs. the strength of the signal-modulated noise and vs. the intensities of the multiplicative and additive noise. The non-monotonous dependence of the SNR on the system parameter is discussed. [ABSTRACT FROM AUTHOR]

• In stochastic resonance, optimal noise enhances the performance of a system. • The applications of stochastic resonance have been demonstrated in medical devices to improve sensory function. • Stochastic resonance can help improve existing technologies in the medical field. Noise is generally considered to be detrimental. In the right conditions, however, noise can improve signal detection or information transmission. This counterintuitive phenomenon is called stochastic resonance (SR). SR has generated significant interdisciplinary interest, particularly in physics, engineering, and medical and environmental sciences. In this review, we discuss a growing empirical literature that suggests that noise at the right intensity may improve the detection and processing of auditory, sensorimotor, and visual stimuli. We focus particularly on applications of SR in sensory biology and investigate whether SR-based technologies present a pathway to improve outcomes for individuals living with sensory impairments. We conclude that there is considerable evidence supporting the application of SR in developing sensory prosthetics. However, the progression of SR-based technologies is variable across the sensory modalities. We suggest opportunities for further advancements in each modality, considering the best approaches to maximise benefits and capitalise on progress already made. Overall, SR can offer opportunities to improve existing technologies or to motivate innovations. [ABSTRACT FROM AUTHOR]

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Introduction: Chaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronizes with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method. Method: This study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behavior. Results and discussion: Our results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments. [ABSTRACT FROM AUTHOR]

TheraBracelet is a peripheral vibrotactile stimulation applied to affected upper extremities via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine the feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently, except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with a focus on the potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy. [ABSTRACT FROM AUTHOR]

Simple Summary: Noise can positively and negatively affect the systems it interacts with. When the right amount of noise is added to a weak signal, it can make it easier to detect, a phenomenon known as stochastic resonance. Our research focused on applying noise to improve human reaction times. We observed a significant decrease in the reaction times after placing subjects in the beneficial noise branch close to the optimal point. These findings suggest a novel approach to enhancing human performance in tasks that require faster reaction times, such as sports. Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports. [ABSTRACT FROM AUTHOR]

Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices. [ABSTRACT FROM AUTHOR]

A recent theoretical study (Xu in Proc R Soc A Math Phys Eng Sci 477:20200913, 2021) has derived conditions on the coefficients of Jeffreys-type equation to predict thermal oscillations and resonance during phonon hydrodynamics in non-metallic solids. Thermal resonance, in which the temperature amplitude attains a maximum value (peak) in response to an external exciting frequency source, is a phenomenon pertinent to the presence of underdamped thermal oscillations and explicit finite speed for the thermal wave propagation. The present work investigates the occurrence condition for thermal resonance phenomenon during the electron–phonon interaction process in metals based on the hyperbolic two-temperature model. First, a sufficient condition for underdamped electron and lattice temperature oscillations is discussed by deriving a critical frequency (a material characteristic). It is shown that the critical frequency of thermal waves near room temperature, during electron–phonon interactions, may be on the order of terahertz ( 10 - 20 THz for Cu and Au, i.e., lying within the terahertz gap). It is found that whenever the natural frequency of metal temperature exceeds this frequency threshold, the temperature oscillations are of underdamped type. However, this condition is not necessary, since there is a small frequency domain, below this threshold, in which the underdamped thermal wave solution is available but not effective. Otherwise, the critical damping and the overdamping conditions of the temperature waves are determined numerically for a sample of pure metals. The thermal resonance conditions in both electron and lattice temperatures are investigated. The occurrence of resonance in both electron and lattice temperature is conditional on violating two distinct critical values of frequencies. When the natural frequency of the system becomes larger than these two critical values, an applied frequency equal to such a natural frequency can drive both electron and lattice temperatures to resonate together with different amplitudes and behaviors. However, the electron temperature resonates earlier than the lattice temperature. [ABSTRACT FROM AUTHOR]

Background: Gait impairment is a key feature in later stages of Parkinson's disease (PD), which often responds poorly to pharmacological therapies. Neuromodulatory treatment by low-intensity noisy galvanic vestibular stimulation (nGVS) has indicated positive effects on postural instability in PD, which may possibly be conveyed to improvement of dynamic gait dysfunction. Objective: To investigate the effects of individually tuned nGVS on normal and cognitively challenged walking in PD patients with mild-to-moderate gait dysfunction. Methods: Effects of nGVS of varying intensities (0–0.7 mA) on body sway were examined in 32 patients with PD (ON medication state, Hoehn and Yahr: 2.3 ± 0.5), who were standing with eyes closed on a posturographic force plate. Treatment response and optimal nGVS stimulation intensity were determined on an individual patient level. In a second step, the effects of optimal nGVS vs. sham treatment on walking with preferred speed and with a cognitive dual task were investigated by assessment of spatiotemporal gait parameters on a pressure-sensitive gait carpet. Results: Evaluation of individual balance responses yielded that 59% of patients displayed a beneficial balance response to nGVS treatment with an average optimal improvement of 23%. However, optimal nGVS had no effects on gait parameters neither for the normal nor the cognitively challenged walking condition compared to sham stimulation irrespective of the nGVS responder status. Conclusions: Low-intensity nGVS seems to have differential treatment effects on static postural imbalance and continuous gait dysfunction in PD, which could be explained by a selective modulation of midbrain-thalamic circuits of balance control. [ABSTRACT FROM AUTHOR]

Semiconductor superlattices are periodic nanostructures consisting of epitaxially grown quantum wells and barriers. For thick barriers, the quantum wells are weakly coupled and the main transport mechanism is a sequential resonant tunneling of electrons between wells. We review quantum transport in these materials, and the rate equations for electron densities, currents, and the self-consistent electric potential or field. Depending on superlattice configuration, doping density, temperature, voltage bias, and other parameters, superlattices behave as excitable systems, and can respond to abrupt dc bias changes by large transients involving charge density waves before arriving at a stable stationary state. For other parameters, the superlattices may have self-sustained oscillations of the current through them. These oscillations are due to repeated triggering and recycling of charge density waves, and can be periodic in time, quasiperiodic, and chaotic. Modifying the superlattice configuration, it is possible to attain robust chaos due to wave dynamics. External noise of appropriate strength can generate time-periodic current oscillations when the superlattice is in a stable stationary state without noise, which is called the coherence resonance. In turn, these oscillations can resonate with a periodic signal in the presence of sufficient noise, thereby displaying a stochastic resonance. These properties can be exploited to design and build many devices. Here, we describe detectors of weak signals by using coherence and stochastic resonance and fast generators of true random sequences useful for safe communications and storage. [ABSTRACT FROM AUTHOR]

The functional neurons are basic building blocks of the nervous system and are responsible for transmitting information between different parts of the body. However, it is less known about the interaction between the neuron and the field. In this work, we propose a novel functional neuron by introducing a flux-controlled memristor into the FitzHugh-Nagumo neuron model, and the field effect is estimated by the memristor. We investigate the dynamics and energy characteristics of the neuron, and the stochastic resonance is also considered by applying the additive Gaussian noise. The intrinsic energy of the neuron is enlarged after introducing the memristor. Moreover, the energy of the periodic oscillation is larger than that of the adjacent chaotic oscillation with the changing of memristor-related parameters, and same results is obtained by varying stimuli-related parameters. In addition, the energy is proved to be another effective method to estimate stochastic resonance and inverse stochastic resonance. Furthermore, the analog implementation is achieved for the physical realization of the neuron. These results shed lights on the understanding of the firing mechanism for neurons detecting electromagnetic field. [ABSTRACT FROM AUTHOR]

This paper examines the existence of spectral resonance in the Fitzhugh–Nagumo (FHN) system driven by periodical signal and unbounded noise having Gaussian distribution. It is newly revealed that if the inter-spike-interval (ISI) distribution is accumulated on a single cluster, there exists a dual relationship between stochastic resonance and spectral resonance determined by commonly used metric normalized standard deviation of ISI. Furthermore, the ISI distribution is also concentrated on more than one cluster depending on different driving signal frequency. Consequently, the apparent regular spiking behavior is observed to occur at specified driving signal frequencies which result in a local minimum in entropy function indicating spectral resonance. Therefore it is proposed that occurrence of spectral resonance strongly depends on the shape of ISI distribution tuned by the stochastic and deterministic driving signal parameters and conventional metrics may not indicate entire resonance behavior. Correspondingly, the entropy function is utilized in this paper as an alternative metric to enable the detection of the spectral resonance occurrence. The ISI distribution obtained from the FHN system is investigated to relate the real electromyography (EMG) measurements under different conditions such as myokymia and neuromyotonia. It is seen that ISI distribution observed from myokymic EMG exhibits notably close behavior as in the case of spectral resonance generated by FHN whereas a wider distribution is monitored in the case of neuromyotonia. It is contributed that the modeling and parameterization based on ISI distribution can be potentially used to identify different neural activities. [ABSTRACT FROM AUTHOR]

Currently, the focus of stochastic resonance (SR) research is primarily on bistable systems, and classical bistable SR systems have the problems of low dimension, inconvenient parameter adjustment, and high threshold for SR effects. In this paper, based on the classic bistable system, a class of multi-dimensional single potential well SR systems without transition threshold is proposed and applied to signal processing. Firstly, the mechanism of double potential well SR and single potential well SR is studied. On this basis, a kind of multi-dimensional single potential well SR system is defined and its theoretical conditions are analyzed. Then a specific four-dimensional single potential well SR system is constructed, and the dynamics of the system is analyzed. The gain range and the approximate relationship between the system input and the four-dimensional output are derived. Finally, the four-dimensional single potential well SR system is applied to the processing of various signals. The experimental results show that the constructed system has good noise reduction and feature amplification effects on noisy signals through the advantages of multi-dimensional output, and can be used to highlight the fault feature frequency in bearing fault signals. [ABSTRACT FROM AUTHOR]

To accurately detect the early deterioration of electrical insulation in power cables, an energizing system is required for offline partial discharge (PD) measurements. The damped alternating current (DAC) testing system has emerged as an effective tool for inducing PD events. However, the existing systems often suffer from limitations such as signal interference, low sensitivity, and high costs. To address these issues, this study proposes a novel dual‐resonance DAC testing system that is simple in structure and cost‐effective. The prototype was built for testing 10‐kV distribution cables, and its effectiveness was validated through experimental tests on a cable sample. The results show that this approach significantly improves PD detection sensitivity and successfully completes PD localization. This research contributes to the development of more efficient and affordable DAC generation technology for power cable testing applications. [ABSTRACT FROM AUTHOR]

The phenomenon in which the response of a neuronal network to a weak signal is significantly enhanced in moderate noise is known as stochastic resonance (SR). Most of the previous studies on the transmission of signals by networks have been based on static synaptic connections, whereas dynamic synaptic connections modified by spike-time-dependent plasticity (STDP) are the basis of learning and memory in the nervous system. In this paper, we explore the phenomenon of SR in a neuronal network consisting of different ratios of excitatory vertebral neurons and inhibitory interneurons. The equivalent circuit method was employed to assess the average energy efficiency of the network. The differences in signal response before and after the introduction of STDP were compared for purely excitatory, purely inhibitory and excitatory-inhibitory networks, respectively. It was found that excitatory STDP promotes the network's response to weak signals, while inhibitory STDP has the opposite effect. The introduction of the inhibitory STDP makes the inhibitory network insensitive to the modulation of the coupling strength and increases its robustness. Furthermore, in the excitatory-inhibitory network, we found that STDP had little effect on the overall signalling of the network, and that the network's response to weak signals was more stable. Our findings contribute to the understanding of the importance of excitatory-inhibitory balance in ensuring accurate transmission and processing of information and provide new insights into the role of STDP in neuronal information processing. [ABSTRACT FROM AUTHOR]

In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance. [ABSTRACT FROM AUTHOR]

Background: Postural imbalance and falls are an early disabling symptom in patients with progressive supranuclear palsy (PSP) of multifactorial origin that may involve abnormal vestibulospinal reflexes. Low-intensity noisy galvanic vestibular stimulation (nGVS) is a non-invasive treatment to normalize deficient vestibular function and attenuate imbalance in Parkinson's disease. The presumed therapeutic mode of nGVS is stochastic resonance (SR), a mechanism by which weak sensory noise stimulation can enhance sensory information processing. Objective: To examine potential treatment effects of nGVS on postural instability in 16 patients with PSP with a clinically probable and [18F]PI-2620 tau-PET-positive PSP. Methods: Effects of nGVS of varying intensity (0–0.7 mA) on body sway were examined, while patients were standing with eyes closed on a posturographic force plate. We assumed a bell-shaped response curve with maximal sway reductions at intermediate nGVS intensities to be indicative of SR. An established SR-curve model was fitted on individual patient outcomes and three experienced human raters had to judge whether responses to nGVS were consistent with the exhibition of SR. Results: We found nGVS-induced reductions of body sway compatible with SR in 9 patients (56%) with optimal improvements of 31 ± 10%. In eight patients (50%), nGVS-induced sway reductions exceeded the minimal clinically important difference (improvement: 34 ± 5%), indicative of strong SR. Conclusion: nGVS yielded clinically relevant reductions in body sway compatible with the exhibition of SR in vestibular sensorimotor pathways in at least half of the assessed patients. Non-invasive vestibular noise stimulation may be thus a well-tolerated treatment strategy to ameliorate postural symptoms in PSP. [ABSTRACT FROM AUTHOR]

Low-intensity noisy galvanic vestibular stimulation (nGVS) is a promising non-invasive treatment for enhancing vestibular perceptual performance and postural control in patients with chronic vestibular hypofunction. However, this approach has so far been studied mainly under laboratory conditions. Evidence indicates that continuous application of nGVS in daily life is necessary for it to be effective. To address this need, we have developed a mobile nGVS stimulator and conducted a series of pilot studies to evaluate its safety, tolerability, functionality, and therapeutic effects. The device is a lightweight, compact, and portable AC stimulator featuring a user-friendly interface for the individualized adjustment of nGVS parameters. It includes an integrated motion sensor that automatically activates stimulation during body movement and deactivates it during inactivity, optimizing its practical use in real-world settings. The stimulator adheres to strict safety standards and, in initial long-term use, has exhibited only mild side effects (e.g., skin irritation and headaches), likely attributable to the current electrode placement, which requires further optimization. As expected, the device consistently elicits known vestibular sensorimotor reflex responses in healthy individuals. Importantly, further pilot studies in healthy participants demonstrate that the device can reliably replicate known facilitating effects on vestibular perception and postural control. Together, these findings suggest that this mobile stimulation device can facilitate the translation of nGVS into therapeutic everyday use.

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system.

The time-delayed feedback term can improve the output of a system, while the two-dimensional stochastic resonance (SR) system has a stronger signal amplification capability. To improve the output signal-to-noise ratio (SNR) of the system, this paper proposes a two-dimensional time-delayed tri-stable stochastic resonance system (TDTDTSR) based on the advantages of the above two systems. First, the steady-state probability density function (SPD), the mean first-pass time (MFPT), and the output SNR are derived under adiabatic approximation theory, and the effects of different system parameters on them are investigated. Next, TDTDTSR and the classical two-dimensional tri-stable stochastic resonance system (CTDTSR) system are simulated numerically. The results show that the mean signal-to-noise gain (MSNRG) of TDTDTSR system is higher than that of the CTDTSR system. Finally, the system parameters are optimized using a genetic algorithm, and the application of TDTDTSR to bearing fault detection is compared with CTDTSR and the novel piecewise symmetric two-dimensional tri-stable stochastic resonance (NPSTDTSR) systems. The experimental results demonstrate that TDTDTSR system has better performance, providing valuable theoretical support and practical engineering applications for the system in subsequent analyses. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low quality of coherent demodulation transmission signal of digital signal in the background of Levy noise, this paper proposes a new method of 4FSK signal reception based on adaptive tristable stochastic resonance (SR) system. The bit error rate (BER) of output signals of adaptive tristable SR system model and the traditional modulation model are compared by simulation. The numerical simulation results show that compared with the traditional model, the output BER of the 4FSK signal after demodulation of the adaptive tristable SR system model is reduced by 4% compared with the traditional model. The time domain image of 4FSK signal is clearer, the burrs are significantly reduced and the spectral amplitude of 4FSK signal is greatly improved. The success of this experiment suggests a strong promise of SR systems for digital signal detection in impact noise. [ABSTRACT FROM AUTHOR]