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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]

There exist numerous factors that contribute to the amplification of errors and complexity in motor processes, among which variability and noise are particularly noteworthy. Transcranial random noise stimulation (tRNS) has been proposed as a potential means of enhancing motor performance by modulating excitability in the motor cortex. This study aimed to examine the role of the concomitant administration of tRNS with training in enhancing the performance measures of movement time, noise, covariation, and tolerance in the acquisition of a perceptual-motor task. This study enlisted a cohort of 30 healthy male adults (mean age: 22.62 ± 3.83 years) who were randomly assigned to three distinct groups. The participants executed the specified motor task during three sequential phases, namely, the pre-test, intervention, and post-test phases. Statistical analyses showed that training with tRNS has a significant effect on noise cost, co-variation, and movement tolerance (p ≤ 0.05). In addition, tRNS improved the function of the sensorimotor wave (p ≤ 0.05). Moreover, the results indicate that tRNS elicited a significant reduction in both spatial error and movement execution time, (p ≤ 0.05). The study's findings indicate that a mere three training sessions leveraging tRNS may suffice in diminishing the spatial error; nevertheless, a higher number of training sessions is required to alleviate the temporal error. [ABSTRACT FROM AUTHOR]

Vestibular dysfunction has been reported as a potential cause in adolescent idiopathic scoliosis (AIS). However, it remained unclear how stochastic galvanic vestibular stimulation (GVS) affected kinetic performance of patients with AIS. This study aimed to investigate the effect of stochastic GVS on ground reaction forces (GRF) measures during obstacle negotiation among patients with AIS. Fifteen patients with AIS and 15 age/sex-matched healthy controls (HC) participated in this study. Stochastic GVS was applied via electrodes placed over bilateral mastoid process with the intensity of 80% of individual sensory thresholds. Six walking trials including 2 types of GVS (stochastic GVS/sham stimulation) and 3 obstacle conditions (Level/Low/High) were randomly allocated to each participant, and each trial was repeated 3 times. Four AMTI force plates were used to measure GRF peaks and impulses in anterior-posterior (AP1/AP2), medial-lateral (ML1/ML2), and vertical (V1/V2) directions. Significant interactions were observed in AP1 (F2,56=3.537, p = 0.036), V1 (F2,56=4.118, p = 0.021), ML1 (F2,56=3.313, p = 0.044) and medial-lateral impulses (F2,56=4.386, p = 0.017) for the step negotiating obstacles. Post-hoc comparisons showed that in comparison to sham stimulation, the application of stochastic GVS significantly (1) increased AP1 (Low: p = 0.038) and V1 (Low: p < 0.001; High: p = 0.035) in two groups; (2) decreased ML1 of two groups (AIS: ps < 0.01; HC: ps < 0.05) and medial-lateral impulses in patients with AIS (Low: p = 0.013; High: p = 0.015) during obstacle negotiation. Additionally, the rates of change in ML1 and medial-lateral impulses among patients with AIS were significantly higher than that of HC, indicating that stochastic GVS demonstrated a greater effect of decreasing ML1 and medial-lateral impulses in AIS. Stochastic GVS ameliorated kinetic performance of patients with AIS during obstacle negotiation, and its potential mechanism may involve the induction of stochastic resonance phenomenon to enhance vestibular perception. Our study offered stochastic GVS as a novel approach to target vestibular-related postural instability in AIS. [ABSTRACT FROM AUTHOR]

Encryption techniques are becoming more robust through innovative advancements, but cryptanalysis techniques are also progressing. Although encryption techniques have one step ahead, this balance may shift as literature reports more breaches in encryption techniques. Current encryption methods are founded on innovative concepts like chaos, randomness, DNA, and neural networks; however, these alone may not provide enough protection against modern cryptography analysis. To effectively thwart cryptanalysis tools, encryption techniques should include methods with a global state to increase nonlinearity and confusion of ciphertext. This paper presents a novel encryption technique that integrates principles from chaos, neural networks, cognitive theory, and deep bit manipulation. The proposed technique can detect variations in the key and plaintext, and impose significant changes to the ciphertext. Additionally, it employs a reactive substitution space that monitors changes in the plaintext and adjusts the processing model accordingly, resulting in a highly-confused output. Experimental results show that the proposed technique meets rigorous security standards while maintaining efficient execution times, outperforming state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With the wide application of Residence Time Difference (RTD) fluxgate sensors in Unmanned Aerial Vehicle (UAV) aeromagnetic measurements, the requirements for their measurement accuracy are increasing. The core characteristics of the RTD fluxgate sensor limit its sensitivity; the high-permeability soft magnetic core is especially easily interfered with by the input noise. In this paper, based on the study of the excitation signal and input noise characteristics, the stochastic resonance is proposed to be realized by adding feedback by taking advantage of the high hysteresis loop rectangular ratio, low coercivity and bistability characteristics of the soft magnetic material core. Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. The simulation results show that optimizing the odd polynomial feedback coefficients with PSO enables the sensor to reach a resonance state, improving sensitivity by at least 23.5%, effectively enhancing sensor performance and laying a foundation for advancements in UAV aeromagnetic measurement technology. [ABSTRACT FROM AUTHOR]

Animal morphogenesis, the development of an organism's body form, is commonly perceived as a directed and almost deterministic process. However, noise and stochastic fluctuations are ubiquitous in biological systems. The questions on the role of fluctuations in morphogenesis and what ensures the robustness of this process under noisy conditions remain elusive. Here, we utilize Hydra regeneration, subjected to an external electric field, to provide unique insights into these questions. We found that during Hydra morphogenesis, a phase can be induced where fluctuations lead to stochastic morphological swings, back and forth, between a nearly spherical structure (the incipient tissue's state) and an elongated cylindrical shape (the final body form of a mature Hydra). Despite these prolonged swings, the tissue regenerates into a normal Hydra. The stochastic transitions between two well-defined shapes imply that morphological development occurs through an activation process. Indeed, by introducing a periodic perturbation through modulation of the electric field, we were able to demonstrate morphogenesis dynamics with characteristics of stochastic resonance--the tissue's response to the perturbation displayed a resonance-like behavior as a function of the noise level. Our findings add a dynamic layer to the problem of morphogenesis and offer an unconventional physical framework based on an activation transition in a slowly varying double-well potential that ensures a canalized regeneration of the body form under fluctuations. [ABSTRACT FROM AUTHOR]

The aircraft can experience complex environments during the flight. For the random actions, the traditional Gaussian white noise assumption may not be sufficient to depict the realistic stochastic loads on the wing structures. Considering fluctuations with extreme conditions, Lévy noise is a better candidate describing the stochastic dynamical behaviors on the airfoil models. In this paper, we investigated a classical two-dimensional airfoil model with the nonlinear pitching stiffness subjected to the Lévy noise. For the deterministic case, the nonlinear stiffness coefficients reshape the bistable region, which influences the size of the large limit cycle oscillations before the flutter speed. The introduction of the additive Lévy noise can induce significant inverse stochastic resonance phenomena when the basin of attraction of the stable limit cycle is much smaller than that of the stable fixed point. The distribution parameters of the Lévy noise exhibit distinct impacts on the inverse stochastic resonance curves. Our results may shed some light on the design and control process of the airfoil models. [ABSTRACT FROM AUTHOR]

Imperceptible noisy galvanic vestibular stimulation (nGVS) improves standing balance due to the presence of stochastic resonance (SR). There is, however, a lack of consensus regarding the optimal levels and type of noise used to elicit SR like dynamics. We aimed to confirm the presence of SR behavior in the vestibular system of young healthy adults by examining postural responses to increasing amplitudes of white and pink noise stimulation scaled to individual cutaneous perceptual threshold. Forty (40) healthy young participants (19 males, 25.1 ± 5.6 years) were randomly divided into a group that received nGVS with white (WHITE group) or pink noise (PINK group). Participants performed a cutaneous perceptual threshold detection task followed by 8 trials of quiet standing and eyes closure (60s) with nGVS applied during the last 30s. Balance stabilization was quantified in the ratio of the stimulus versus pre-stimulus Centre of Pressure (CoP) 90% ellipse area, Root Mean Square (RMS) and mean velocity. Cutaneous perceptual threshold was similar across groups. Group analysis confirmed that the mean CoP velocity increased across nGVS intensities, particularly for the PINK group while the other two variables remained unchanged. Single subject analysis indicated that 55% of WHITE and 30% of PINK group participants showed an SR-like response judged by three experts. Results are puzzling with respect to the presence of SR-like response dynamics in young healthy adults and highlight the need for further research using individual calibrated stimulus intensities. White noise seems more effective than pink noise in revealing an SR-like response to nGVS. [ABSTRACT FROM AUTHOR]

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]

Rub-impact is a common nonlinear fault of the rotor system, occurring in rotating machines with radial clearance between the rotor and the stator, which may lead to serious consequences. Since the vibration response of rotor rub-impact is shown as multicomponent with time-varying characteristics of undulatory instantaneous frequency, it is desired to exploit advanced signal processing methods for rub-related feature excavation and failure diagnosis under complex noise interferences, which is of crucial significance to ensure the stable and efficient operation of the whole unit. This paper concerns the processing of acceleration signals and proposes a novel intrawave frequency modulation detection approach for structural rotor rubbing diagnosis based upon targeted component extraction and stochastic resonance enhancement. First, the acquired vibratory acceleration signal is converted into displacement signal via a two-stage integration strategy. Next, to extract the rotating frequency component of high information clarity for further time–frequency analysis from the multicomponent signal, an especially designed improved variational mode decomposition method based on the modified target frequency index is put forward, and the instantaneous frequency of the objective component is estimated. Then, the optimum stochastic resonance is leveraged for intrawave frequency modulation enhancement. Finally, the rotor rub-related symptom can be distinctly revealed and the diagnostic procedure can be performed. The effectiveness and superiority of the proposed rotor rub-impact diagnosis approach are demonstrated through both simulations and experiments, indicating that it is suitable to be implemented in practical applications, with high noise-resistance ability, and can efficiently extract the potential characteristics of rotor rub-impact malfunction from multicomponent signals. [ABSTRACT FROM AUTHOR]

Objective: This study aims to propose a high-dimensional coupled system based on the FitzHugh–Nagumo (FHN) neuron model to explore the stochastic resonance (SR) phenomenon driven by both external periodic excitation and random noise, and apply it to bearing fault diagnosis to improve the ability of fault signal detection. Methods: Firstly, the equivalent potential function of the single FHN neuron model is derived to determine the range of bistable characteristics of neuron discharge dynamics. Then, the stationary probability density (SPD), transition rate, and output signal-to-noise ratio (SNR) of the single FHN neuron model are derived using the two-state theory. Next, the analysis is extended to a three-dimensional coupled FHN neuron model, and the influence of parameters on the output SNR is studied through numerical simulation. Finally, a high-dimensional bidirectional coupled FHN neuron method is proposed, and the adaptive genetic algorithm (AGA) is used to determine the optimal output of the system. Results: The coupled FHN neuron model shows a significant SR effect under the combined influence of noise and coupling; bidirectional coupling achieves the highest output mean signal-to-noise ratio (MSNR) in the FHN system, and there always exists an optimal dimension n that maximizes the MSNR of the output signal; this method outperforms the single FHN system in detecting weak fault signals, and coupling at the end yields higher SNR compared to coupling at the center. Conclusion: This research significantly enhances the ability of fault signal detection, provides a more robust method compared to the traditional single neuron system, and improves the reliability and accuracy of fault diagnosis in various applications. [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]

The zebrafish is one of the preferred species in biomedical research because it strikes a balance between systems complexity and practical simplicity. Despite its widespread use in research, no consensus on how to maintain zebrafish has been reached. The paucity of knowledge about how different maintenance parameters affect zebrafish physiology, brain function, and behavior may result in suboptimal maintenance conditions that increase phenotypical variance in the studied zebrafish population. Laboratory-specific idiosyncratic maintenance conditions also contribute to irreproducibility of results. Systematic analyses of laboratory maintenance conditions are needed. These analyses may be guided by information about the ecology and ethology of zebrafish and from studies conducted in, and about, the natural habitat of this species. Although zebrafish (Danio rerio) neuroscience research is rapidly expanding, the fundamental question of how these fish should be maintained in research laboratories remains largely unstudied. This may explain the diverse practices and broad range of environmental parameters used in zebrafish facilities. Here, we provide examples of these parameters and practices, including housing density, tank size, and water chemistry. We discuss the principles of stochastic resonance versus homeostasis and provide hypothetical examples to explain why keeping zebrafish outside of their tolerated range of environmental parameters may increase phenotypical variance and reduce replicability. We call for systematic studies to establish the optimal maintenance conditions for zebrafish. Furthermore, we discuss why knowing more about the natural behavior and ecology of this species could be a guiding principle for these studies. [ABSTRACT FROM AUTHOR]

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 issue of suboptimal demodulation performance in OFDM signal enhancement processes due to slow response speeds in first-order overdamped stochastic resonance systems, this paper designs an OFDM signal enhancement and demodulation system based on second-order underdamped unsaturated bistable stochastic resonance (SUUBSR). Firstly, a nonsaturated bistable potential function model is constructed by combining a traditional monostable potential function with a Gaussian potential function, and a damping coefficient is introduced simultaneously to build the SUUBSR system. Subsequently, the transient and steady-state output response analytical expressions of the SUUBSR system under OFDM signal excitation are derived, the energy loss of OFDM symbol waveforms caused by the transient response of the system is discussed, and the relationship between the damping coefficient and the steady-state output response of the system is explored. Finally, simulations are conducted to evaluate the enhancement and demodulation process of OFDM signals using the SUUBSR system. The simulation results show that at an input signal–noise ratio of 2 dB, compared to the first-order unsaturated bistable stochastic resonance (FUBSR) system, the proposed system reduces the system response time by 3.956% of a symbol period and decreases the demodulation bit error rate for OFDM signals by approximately 35%. [ABSTRACT FROM AUTHOR]

A logic gate is typically an electronic device with a Boolean or other type of function, e.g., adding or subtracting, including or excluding according to its logical properties. They can be used in electronic, electrical, mechanical, hydraulic, and pneumatic technology. This paper presents a new method for generating logic gates based on optical systems with an emission frequency equal to that used in current telecommunications systems. It uses an erbium-doped fiber laser in its monostable operating region, in contrast to most results published in the literature, where multistable behavior is required to induce dynamic changes, and where a DC voltage signal in the laser pump current provides the control between obtaining the different logic operations. The proposed methodology facilitates the generation of the gates, since it does not require taking the optical system to critical power levels that could damage the components. It is based on using the same elements that the EDFL requires to operate. The result is a system capable of generating up to five stable and robust logic gates to disturbances validated in numerical simulation and experimental setup. This eliminates the sensitivity to the initial conditions affecting the possible logic gates generated by the system and the need to add noise to the system (as is performed in works based on stochastic logic resonance). The experimental observations confirm the numerical results and open up new aspects of using chaotic systems to generate optical logic gates without bistable states. [ABSTRACT FROM AUTHOR]

To address the output saturation issue in classical bistable stochastic resonance systems during the enhancement of weak orthogonal frequency division multiplexing (OFDM) signals, which results in low noise utilization efficiency, this study proposes an enhancement and demodulation technique based on a segmented asymmetric bistable stochastic resonance (SABSR) system. The SABSR model is developed by integrating a classical bistable SR system with a linear function and introducing an asymmetry factor. Using the adiabatic approximation theory, the Kramers escape rate and output signal-to-noise ratio (SNR) of the SABSR system are derived and analyzed. Additionally, transient response expressions for the left and right wells, as well as steady-state response expressions under OFDM signal input, are formulated, and the influence of the asymmetry factor on transient responses is thoroughly investigated. The SABSR system is then applied to OFDM signal enhancement and demodulation, with SNR gain used as the optimization metric. The quantum particle swarm optimization algorithm is employed to fine-tune system parameters. Simulation results demonstrate that, at an input SNR of 8 dB, the SABSR system achieves a bit error rate (BER) approximately 30% lower than that of the segmented symmetric system, significantly improving OFDM signal detection and demodulation performance.

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.

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.

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]

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. [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]

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]

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]

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]

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]

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]