Weak Signal Research Articles

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

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.

Nonlinearity-enhanced continuous microwave detection based on stochastic resonance.

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

Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Unveiling Superior Solar-Blind Photodetection with a NiO/ZnGa2O4 Heterojunction Diode.

This investigation presents a self-powered, solar-blind photodetector utilizing a low-temperature fabricated crystalline NiO/ZnGa2O4 heterojunction with a staggered type-II band alignment. The device leverages the pyrophototronic effect (PPE), combining the photoelectric effect in the p-n junction and the pyroelectric effect in the non-centrosymmetric ZnGa2O4 crystal. This synergistic effect enhances the photodetector's performance parameters, thereby outperforming traditional solar-blind photodetectors. The device demonstrates an extremely low dark current of 5.39 fA, a high responsivity of 88 mA/W, and a very high specific detectivity of 2.03 × 1014 Jones under 246 nm light irradiation at 0 V bias. Significantly, due to the PPE, the impact demonstrates a much-enhanced transient response when tested under various light intensities, ranging from 18 to 122 μW/cm2. The photodetector shows a high responsivity of 338 A/W and an outstanding detectivity of 7.1 × 1018 Jones with an applied voltage of -13 V, showing its ability to detect weak signals. Single-crystalline ZnGa2O4 fabricated by MOCVD exhibits significant absorption of deep UV light, and the heterojunction's type-II band alignment with NiO is responsible for its exceptional self-powered pyrophotoelectric detecting and rectifying capabilities.

Vehicle noise characteristics in magnetotelluric data and vehicle noise removal using waveform fitting

Magnetic field fluctuations due to vehicle noise were observed in magnetotelluric (MT) time-series data measured near roads. The observed vehicle noise had magnitudes ranging from tens to thousands of μA/m, whereas the observed weak natural MT signal magnitudes were approximately tens of μA/m. A small signal-to-noise ratio made it difficult to apply robust processing for removing vehicle noise. In addition, vehicle noise severely distorts the MT response in the MT deadband from 0.01 Hz to 0.3 Hz, where the MT signal is very weak, and methods to remove it are required for deep structure imaging. In this study, magnetic field fluctuations due to moving vehicles were simulated with a magnetic dipole and attempted to be removed using a waveform fitting method. A total of 378 vehicle noises were extracted from the near-road MT data and synthesized with the remote MT data without vehicle noises to investigate the effect of vehicle noise on the MT response. Removal of vehicle noise from synthesized remote MT data resulted in substantial restoration of the apparent resistivity and phase curves around the MT deadband and below 0.001 Hz. In the MT field data, the vehicle noise was simulated and removed with two moving dipoles; the magnitude of the remaining vehicle noise was reduced by approximately half compared to a single dipole, and very stable apparent resistivity and phase curves were obtained. Although electromagnetic noise distortion remains after vehicle noise removal, the waveform fitting method significantly improves the apparent resistivity and phase curve response in the 0.01–0.3 Hz frequency band.

ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering

AbstractThe Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

Faulty section location method for high impedance grounding fault in resonant grounding system based on discrete Fréchet distance

When a High Impedance Fault (HIF) occurs in a resonant grounding system, the differentiation of the current flowing upstream and downstream of the fault point is inconspicuous due to the weak electrical signal. This paper proposes a faulty section location approach based on Discrete Fréchet Distance (DFD) to address this issue. Initially, taking the zero-sequence current as the characteristic quantity, the instantaneous value of the zero-sequence current at each sampling point and its previous sampling moments are accumulated to amplify the fault signal, and furthermore to obtain the cumulative waveform of the zero-sequence current at each monitoring point. The DFD values between the cumulative waveforms of zero-sequence currents flowing through each section are also calculated. For a branchless section, if the Coefficient of Variation (CV) of the DFD in each section does not exceed the threshold value, it is identified as an end-of-line fault, otherwise, the section with the maximum DFD is identified as a faulty section. For a section with branches, the branching coefficient is calculated to determine whether it is a faulty section or not. Finally, MATLAB/Simulink simulation results and field-recorded data demonstrate the validity and robustness of the approach under various fault conditions, despite the connection of Distributed Generators (DGs).

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.

Detecting optical frequency in weak signal light using injection-induced frequency modulation

In fiber-optic sensing, long-distance transmission and demodulation of weak signal light (SL) are critical. This study improves the system by relocating an external cavity tunable laser (ECTL) to the transmission end. Directly injecting the SL into the ECTL modulates the output light and uses the high-power ECTL light for transmission, reducing loss. At the demodulation end, an orthogonal Mach-Zehnder interferometer (MZI) and an H13C14N gas absorption cell (GAC) extract SL wavelength information. Results demonstrate effective modulation with SL intensity as low as 1 nW and a demodulation resolution of up to 10 MHz, offering significant improvements in long-distance fiber-optic sensing.

Long afterglow nanoprobes labeled image enhancement using deep learning in rapid and sensitive lateral flow immunoassay

Point-of-care testing (POCT) is currently the predominant method of in vitro diagnostics, with lateral flow immunoassay(LFIA) being commonly utilized in POCT. The performance of the probe in LFIA will directly affect the results of the detection. Therefore, the selection of probes is particularly important. However, the current commercially produced probes generally suffer from short luminescence lifetime, high detection cost, and poor anti-interference capability. Meanwhile, the traditional detection system has difficulties in detecting weakly positive samples. These ultimately lead to the problem of poor detection sensitivity. In order to address the above issues, we have synthesized an ultralong organic phosphorescence nanoprobe (UOPN). The UOPNs exhibit a long afterglow phosphorescence signal of around 300 ms after being excited, which can be used to eliminate endogenous interference. Meanwhile, focusing on the problem of poor detection of weakly positive samples in conventional detection systems, and the weak inherent signal of UOPNs, resulting in a low signal-to-noise ratio, we have developed a detection system that combines image processing and neural network-driven analysis for long afterglow image analysis. Through the analysis of the long afterglow images of serum amyloid A (SAA) in human blood, a detection limit of 0.01 μg/mL and a linear range of 0.1–251 μg/mL were achieved. The whole analysis was completed within 5 minutes. After pre-processing with the denoising algorithm, the system demonstrated an impressive classification accuracy of 99.24 %, validating the reliability of the detection system. This approach has further enhanced the sensitivity of immunoassay, contributing to the advancement of point-of-care testing.

The influence of temperature and β-1,3-glucans on the occurrence of white spot syndrome virus and white spot disease in post-larvae of Penaeus vannamei shrimp

Penaeus vannamei is the main species of shrimp farmed worldwide, and for Ecuador it is the first non-oil economic sector. However, various pathogens, including white spot syndrome virus (WSSV), threaten the sustainability of shrimp farming. P. vannamei larvae are susceptible to WSSV infection via vertical or horizontal transmission. To decrease the incidence of WSSV in PLs, a bioassay was performed by exposing P. vannamei shrimp (PL25), which were negative for WSSV and white spot disease (WSD), to two protective factors, water temperature (T = 24 °C and T = 31 °C), and immunestimulation using β-1,3-glucans (BG) (with and without BG added to the food), using a crossed two-factor design, for 20 days. The incidence of WSSV and WSD was modeled using generalized linear model (GLM). The strength of the association between the response and explanatory variables was estimated using the odds ratio [Exp (Beta)] and interpreted as the level of risk for the incidence of WSSV or WSD in one of the categories compared to the baseline category in the GLM. Odds ratios were considered significant if their 95 % confidence interval did not include the value 1. The results showed that BG had a significant negative effect on the number of WSSV-positive animals. In addition, a lower probability of WSSV infection was determined by combining 31 °C and BG. In this treatment, the WSD injuries were almost undetectable. Most larvae shrimps affected by WSD exhibited injuries to the antennal gland and connective tissue. In situ hybridization analysis revealed that 29 % of shrimp, initially negative by histology, were positive for WSSV. The virus was detected mainly in the nervous tissue of the head, epithelium, connective tissue of the head appendages, and the oral region surrounding the integumental glands. Apoptosis analysis showed a weak signal in nerve tissue but was more intense in epithelial cells of the head appendages, in association with cuticular damage. According to the findings, WSSV could disseminate among shrimp populations by infecting them through the TG and epithelium of the head appendage, followed by connective tissue and nervous tissue. However, applying BG at 31 °C may serve as a protective measure to reduce WSD injuries and restrict WSSV dissemination.

Unveiling Nanoscale Ordering in Amorphous Semiconducting Polymers Using Four-Dimensional Scanning Transmission Electron Microscopy.

We present four-dimensional (4D) scanning transmission electron microscopy (STEM) analysis to obtain a high level of detail regarding the nanoscale ordering within largely disordered organic semiconducting polymers. Understanding nanoscale molecular ordering in semiconducting polymers is crucial due to its connection to the materials' important properties. However, acquiring such information in a spatially localized manner has been limited by the lack of a nanoscale experimental probe, weak signal from ordering, and radiation damage to the sample. By collecting nanodiffraction patterns with a high dynamic range pixelated detector, we acquired statistically robust, high signal-to-noise ratio diffraction patterns from semiconducting organic materials, including poly(3-hexylthiophene-2,5-diyl) (P3HT), P3HT/[6,6]-phenyl C61 butyric acid methyl ester, and indacenodithiophene-co-benzothiadiazole (IDTBT), which largely have disordered structures. Real-space images of the ordered domains were reconstructed from the 4D-STEM data set for a variety of scattering vectors and in-plane angles to capture the different molecular stacking distances and their in-plane orientation. These were then analyzed to obtain the average size of the ordered domains within the sample. Such measurements were arranged in a two-dimensional (2D) histogram, which showed a direct relationship between the type and size of molecular ordering. Complementary analyses, such as intensity variance and angular correlation, were applied to obtain ordering and symmetry information. These analyses enabled us to directly characterize the alkyl and π-π stacking of P3HT, as well as the fullerene domains caused by donor segregation in the P3HT sample. Furthermore, the analysis also captured changes in the P3HT domains when the fullerenes are incorporated. Lastly, IDTBT showed a much lesser degree of ordering without much disinclination between the domains within the 2D histogram. The 4D-STEM analysis that we report here unveils new details of molecular ordering that can be used to optimize the properties of this important class of materials.

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

Detecting and Imaging of Magnons at Nanoscale with van der Waals Quantum Sensor

AbstractMagnonic devices are extensively studied for energy‐efficient information processing. High spatial resolution and high accuracy measurement is required to characterize the excitation and distribution of magnons. Here, sensing and imaging of magnons in the magnetic insulator (YIG) is realized with negatively charged boron vacancy () spin defects in 2D hexagonal boron nitride (hBN). Thermal magnon noise is studied through spin relaxometry, illustrating the nanometers proximity of the 2D quantum sensor over a large area. The small probe‐sample standoff distance helps to detect weak signals with diffraction‐limited spatial resolution. The uniform out‐of‐plane symmetry axis of is further utilized to study perpendicular magnetic anisotropy (PMA). It effectively extracts the stray field of microwave‐excited magnons from the direct stripline field. The distributions of propagating and localized magnons in different structures are subsequently imaged and analyzed. The work provides the strategy for utilizing the distinctive advantages of the van der Waals quantum sensor in magnetic imaging. The results will promote the development of magnonic devices for diverse applications.

Multiplicative noise induced bistability and stochastic resonance

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

Wafer-Sized CsPbBr3/CsPbCl3 Heterojunction: Breaking the Trade-Off between Sensitivity and Dark Current for Efficient X-ray Detector.

Polycrystalline lead halide perovskite finds promising use in fabricating X-ray detectors with a large lateral size, adjustable thickness, and diverse synthesis processes. However, a large dark current hinders its development for weak signal detection. Herein, we propose a multistep pressing strategy for manufacturing a CsPbBr3/CsPbCl3 heterojunction wafer for a reduced dark current X-ray detector, and the device keeps a high sensitivity value after the insertion of a barrier by heterojunction; thus, the trade-off between sensitivity and dark current can be broken. The X-ray detector with a metal-semiconductor-metal structure yields a sensitivity of 6.32 × 104 μC Gyair-1 cm-2 at a bias of 12 V, a 1/f noise of 1.02 × 10-13 A/Hz-1/2, and a detection limit of 66.58 nGy s-1. These performance parameters are considerably better than those of a similar X-ray detector based on the single-structure wafer. The improved device performance of the heterostructure X-ray detector is ascribed to the suppressed carrier recombination, enhanced carrier transportation of the heterojunction, and strong X-ray attenuation of the CsPbCl3 layer. The pixel array device is further used in imaging applications. Hence, this study provides an efficient strategy for fabricating heterostructure polycrystalline lead halide perovskite wafers for use in high-performance wafer-based X-ray detectors.

Weak signal ultrafast interrogation of a micro-nano FBG probe sensor based on the hybrid amplified dispersion Fourier-transform method

To elucidate the thermal transport mechanisms at interfaces in micro- and nanoscale electronic devices, real-time monitoring of temperature variations at the microscopic and nanoscopic levels is crucial. Micro-nano fiber Bragg grating (FBG) sensors have been demonstrated as effective in-situ optical temperature probes for measuring local temperatures. Time-stretch dispersion Fourier transform (TS-DFT) that enables fast, continuous, single-shot measurements in optical sensing has been integrated with a micro-nano FBG probe (FBGP) for local temperature sensing. However, its temperature sensitivity and interrogation resolution are limited by the detection sensitivity. In this paper, we propose a hybrid amplified dispersion Fourier transform (ADFT) method to achieve ultrafast interrogation of FBGP’s weak signal. Thanks to the combined effect of TS-DFT and hybrid optical amplification, the reflection signal of the FBGP is amplified, and the wavelength shift of the FBGP sensor is converted to a temporal spacing change between two dispersed pulses through dispersion-induced wavelength-to-time mapping. The proposed method uses a homemade dissipative soliton mode-locked laser as the light source. The hybrid optical amplification technique comprises a L-band erbium-doped fiber amplifier and a distributed Raman amplifier. Their noise figure and net gain for the FBGP are 4.81 dB and 15.93 dB, respectively. In addition, the temperature calibration experiments show that a sampling rate of 51.43 MHz and the maximum temperature measurement error of 1.98°C are achieved within the temperature range of 20.3°C to 97°C. The stability of the net gain provided by the hybrid ADFT system is demonstrated by the coefficient of variation, which ranges from 2.22% to 2.95% in the peak voltage signal of the FBGP. This approach applies to scenarios requiring the handling of weak optical signals, particularly in temperature measurement at the micro-nano scale.

Role of the inverse Čerenkov effect in the formation of ultrashort Raman solitons in silica microspheres.

We theoretically demonstrate a new regime, to the best of our knowledge, of the formation of ultrashort optical solitons in spherical silica microresonators with whispering gallery modes. The solitons are driven by a coherent CW pump at the frequency in the range of normal dispersion, and the energy is transferred from this pump to the solitons via two channels: the Raman amplification and inverse Čerenkov effect. We discuss three different regimes of soliton propagation and we also show that these Raman solitons can be controlled by weak coherent CW signals.

Fault diagnoses of a nonlinear cracked rotor-bearing system based on vibration energy space and incremental learning approach

Health services of rotor-bearing systems are directly related to safe and stable works of rotating machineries in the industrial production. To address the issues of a low impact on vibration characteristics caused by faults in the higher rotational speed region, narrow and weak excitation signals, high dimensionality related to fault induction mechanisms, and difficulty in diagnosing using artificial intelligence technology due to the small number of fault samples in rotor systems, a novel fault detection method of nonlinear rotor systems through the incremental two-dimensional principal component analysis (I2DPCA) of the machine learning on the vibration energy space is proposed. Unlike mainly analyzing dynamic characteristics of rotor-bearing systems in vibration space, the method applies signals of the vibration energy space such as energy tracks, energy-time histories and energy-frequency spectra to have more advantages in vibration amplitudes caused by cracks in the higher rotational speed region and to overcome narrow and weak signals in the early fault stage. Then, the I2DPCA is applied to extract online low dimensional fault features and to process small vibration features of the rotor system. Simulation and experiment results show that performances of between-class margins and within-class clusters of different health conditions on the vibration energy space are more perfect than that on the vibration space. Based on the I2DPCA algorithm and the energy space method, the recognition rate of crack faults within the high rotational speed region is up to 99.6%, and it has good convergence and online learning ability in the case of small samples. The achieved conclusions of the method could provide a novel reference direction for fault diagnoses of rotor systems.

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

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