Weak Signal Enhancement Research Articles

Gaussian bistable cascade double feedback stochastic resonance weak signal enhancement detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

Deep Learning–Based Denoising Improves Receiver Function Imaging Using Dense Short-Period Teleseismic Data

Abstract Receiver function (RF) imaging using seismic data from dense short-period arrays has gained increasing importance in recent years in investigating fine-scale structures of the crust and uppermost mantle. A crucial step in such studies is to remove the instrument response (IR) to enhance teleseismic signals at ∼0.01 to 5 Hz, thereby simulating broadband records. However, this procedure also amplifies noise within the same frequency band. For weak signals, distinguishing them from noise is often challenging and in some cases is even impossible with traditional denoising methods such as filtering. To address this challenge, we develop a new convolutional neural network model, NodalWaden, using decades of high-quality global broadband teleseismic body waves for training. The broadband data exhibit the characteristics we target to achieve by removing the IR from the short-period records. The applicability of NodalWaden is justified by denoising the three-component short-period records of more than 18 months from 155 nodes deployed in northern Sumatra. We find that NodalWaden substantially improves the signal-to-noise ratio (SNR), upgrading ∼50% of the teleseismic data from the “very-low-SNR” (∼1) to “very-high-SNR” (>10) categories. RFs calculated from the denoised dataset show better separation of merged phases and noticeable enhancement of weak signals, resulting in improvement in the quality of structure imaging. In particular, a positive phase is consistently detected at ~2 s throughout the dataset and interpreted as the Conrad discontinuity, which is unresolvable in the original RFs. This denoising technique would be particularly useful for short-duration (e.g., one month) deployment with limited teleseismic data, both from the past and in the future.

Observation of magnetic amplification using dark spins

Quantum amplification enables the enhancement of weak signals and is of great importance for precision measurements, such as biomedical science and tests of fundamental symmetries. Here, we observe a previously unexplored magnetic amplification using dark noble-gas nuclear spins in the absence of pump light. Such dark spins exhibit remarkable coherence lasting up to 6 min and the resilience against the perturbations caused by overlapping alkali-metal gas. We demonstrate that the observed phenomenon, referred to as "dark spin amplification," significantly magnifies magnetic field signals by at least three orders of magnitude. As an immediate application, we showcase an ultrasensitive magnetometer capable of measuring subfemtotesla fields in a single 500-s measurement. Our approach is generic and can be applied to a wide range of noble-gas isotopes, and we discuss promising optimizations that could further improve the current signal amplification up to [Formula: see text] with [Formula: see text]Ne, [Formula: see text] with [Formula: see text]Xe, and [Formula: see text] with [Formula: see text]He. This work unlocks opportunities in precision measurements, including searches for ultralight dark matter with sensitivity well beyond the supernova-observation constraints.

Enhancement of weak signals by dynamic stochastic resonance in dark-field microscopy imaging

Abstract Enhancing weak signal acquisition is pivotal for bolstering analysis and detection capabilities. Herein, a dynamic stochastic resonance (DSR) algorithm for weak signal amplification that was identified to significantly improve the imaging visibility of dark-field microscopy nanoparticles under low illumination conditions is presented. When further combined with composite filed microscopy, DSR displays signal amplification much more effectively, showing high promise for detection purposes.

A novel drum-shaped metastructure aided weak signal enhancement method for bearing fault diagnosis

Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring.

Enhancement of weak signals by applying a suppression method to high-intense methyl and methylene signals of lipids in NMR spectroscopy.

Lipids play crucial roles in human biology, serving as energy stores, cell membranes, hormone production, and signaling molecules. Accordingly, their study under lipidomics has advanced the study of living organisms. 1-Dimensional (D) and 2D NMR methods, particularly 1D 1H and 2D 1H-1H Total Correlation Spectroscopy (TOCSY), are commonly used in lipidomics for quantification and structural identification. However, these NMR methods suffer from low sensitivity, especially in cases of low concentrated molecules such as protons attached to hydroxy, esters, aliphatic, or aromatic unsaturated carbons. Such molecules are common in complex mixtures such as dairy products and plant oils. On the other hand, lipids have highly populated fractions of methyl and methylene groups that result in intense peaks that overwhelm lower peaks and cause inhomogeneities in 2D TOCSY spectra. In this study, we applied a method of suppression to suppress these intense peaks of methyl and methylene groups to detect weaker peaks. The suppression method was investigated on samples of cheese, butter, a mixture of lipids, coconut oil, and olive oil. A significant improvement in peak sensitivity and visibility of cross-peaks was observed, leading to enhanced comparative quantification and structural identification of a greater number of lipids. Additionally, the enhanced sensitivity reduced the time required for the qualitative and comparative quantification of other lipid compounds and components. This, in turn, enables faster and more reliable structural identification and comparative quantification of a greater number of lipids. Additionally, it reduces the time required for the qualitative, and comparative quantification due to the enhancement of sensitivity.

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

Stochastic resonance induced weak signal enhancement in a second-order tri-stable system with single-parameter adjusting

Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data.

A successive sources deletion method for enhancing the trackside acoustic weak signal of axle bearing

The traditional beamforming method has been a common tool for extracting the trackside acoustic weak signal of axle bearing and been applied to fault diagnosis. Nevertheless, considering the severe impact interference and the strong background noise, such as the rolling noise, the effectiveness of beamforming method would be poor. In order to overcome the issue, a new successive sources deletion approach based on the kurtosis and energy estimators (noted SSD-KE) is proposed for enhancing the trackside acoustic weak signal of axle bearing. Firstly, the time-domain beamforming is performed using the acoustic signal acquired by the microphone array. Then, the strong impact interference is iteratively removed in terms of a kurtosis estimator. Likewise, the energy is employed as a subsequent statistic estimator to further remove the strong background noise. Finally, the enhanced signal could be used for the fault diagnosis. The validity of SSD-KE is first examined by numerical simulations. Different configuration parameters and noise conditions are also simulated to demonstrate the superiority and robustness of SSD-KE. Furthermore, experiments considering two different types of axle bearing faults are carried out, and the obtained results emphasize that SSD-KE has better performance on weak signal enhancement and could be used for fault diagnosis under strong background noise, even with strong impact interference.

Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

Abstract Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

Deep time–frequency learning for interpretable weak signal enhancement of rotating machineries

Rotating machinery generates periodic impulses when weak faults occur, but the fault resonant bands (FRB) which contains rich fault information is usually seriously contaminated by random interference and background noise. With the development of deep learning (DL), the existing signal enhancement methods based on DL have the property of ”black box”, i.e., limited interpretability, by which the fault components with clear physical meaning (CPM) cannot be extracted. Aiming at the aforementioned problems, an interpretable weak signal enhancement method based on deep time–frequency learning (DTFL) is proposed. Firstly, a series of simulated template signals of different resonant bands with CPM are constructed as training samples for DTFL, besides, a resonant band time–frequency ratio mask with CPM is generated as the training target by using the time–frequency representations (TFR) of pure and noise-added simulated template signals to construct a non-linear mapping relationship between the TFR of simulated template signals and resonant bands, thereby to achieve accurate enhancement of FRB and credible mask suppression of background noise interference. Therefore, the time–frequency ratio mask with CPM obtained from the DTFL model can be adapted to enhance the FRB, which enables the results of the enhanced FRB to be interpretable, thereby the constructed DTFL model is interpretable. The DTFL method overcomes the problem that the existing signal enhancement methods rely heavily on expert experience, which can provide a CPM of the FRB to support the diagnosis of weak faults in rotating machineries.

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

Multi-mechanism collaboration enhanced photoacoustic analyzer for trace H2S detection

To realize the real-time highly sensitive detection of SF6 decomposition product H2S, a multi-mechanism collaboration enhancement photoacoustic spectroscopy analyzer (MCEPA) based on acoustic resonance enhancement, cantilever enhancement and excitation light enhancement is proposed. An SF6 background gas-induced photoacoustic cell (PAC) was used for acoustic resonance (AR) enhancement of the photoacoustic signals. A fiber-optic acoustic sensor based on a silicon cantilever is optimized and fabricated. The narrow-band acoustic signal enhancement based on cantilever mechanical resonance (MR) is realized in the optimal working frequency band of the PAC. A fiber-coupled DFB cascaded an Erbium-doped fiber amplifier (EDFA) realized the light power enhancement (LPE) of the photoacoustic signals excitation source. Experimental results show that the MR of the fiber-optic silicon cantilever acoustic sensor (FSCAS) is matched with the AR of the PAC and combined with the LPE, which realizes the multi-mechanism collaboration enhancement of weak photoacoustic signals. The Allan-Werle deviation evaluation showed that the minimum detection limit of H2S in the SF6 background is 10.96 ppb when the average time is 200 s. Benefiting from the all-optimization of photoacoustic excitation and detection, the MCEPA has near-field high-sensitivity gas detection capability immune to electromagnetic interference.

Study on weak signal enhancement method in wireless communication under electromagnetic interference environment

In order to overcome the problems of high bit error rate and high signal enhancement time in weak signal enhancement methods, this paper proposes a weak signal enhancement method in wireless communication under electromagnetic interference environment. Firstly, the transmission characteristics of electromagnetic wave are analysed and the attenuation results of electromagnetic wave signal transmission are determined. Then, the bandwidth of weak signal is determined by band-pass sampling theorem, and the key characteristics of weak signal are determined by time-domain characteristic analysis. Finally, the amplitude modulation coefficient is used to adjust the frequency offset and initial phase through a low-pass filter to enhance the weak signal in wireless communication. The experimental results show that the lowest bit error rate of this method is about 1%, the weak signal enhancement time is no more than 2.6 s, and the weak signal enhancement effect is good.

Weak Seismic Signal Enhancement Using Curvelet Transform and Compressive Sampling

Conventional curvelet-domain denoising methods suppress random noise by thresholding the amplitude of curvelet coefficients, which makes it hard to distinguish weak seismic signals from random noise because they share the same characteristic of weak amplitude in the curvelet domain. Here we put forward an innovative weak seismic signal enhancement method taht can distinguish weak seismic signals from random noise. After compressive sampling, the curvelet coefficients of weak seismic signals show significant amplitude reduction, whereas random noise does not. We take advantage of this characteristic and design a sensitivity coefficient, the absolute ratio of curvelet coefficients before and after compressive sampling. The sensitivity coefficient can distinguish weak seismic signals from random noise in the curvelet domain better than thresholding the amplitude of curvelet coefficients. The results of synthetic and field seismic data applications both indicate that our method outperforms the conventional curvelet-domain denoising method on weak seismic signal enhancement.

Study on weak signal enhancement method in wireless communication under electromagnetic interference environment

Study on weak signal enhancement method in wireless communication under electromagnetic interference environment

All Two‐dimensional Integration‐Type Optoelectronic Synapse Mimicking Visual Attention Mechanism for Multi‐Target Recognition

AbstractThe human visual attention mechanism enables them to rapidly perceive important information and objects in a complex external scene; this effectively solves the problems of data redundancy, low‐resolution images, and substantial computing resources. The process by which the attention system reconstructs the visual information can be considered as integrating internal attention signals with external visual details in the postsynaptic neuron. However, electronic devices that simulate visual attention modulation by incorporating device characteristics into neuromorphic vision systems (NVSs) to achieve visual attention behavior are rarely reported. Herein, a synapse device that integrates optical and electrical stimulation is designed using ReS2/hBN/monolayer graphene heterojunction to mimic attention regulation and integrate multiple neuron signals successfully. The synapse array can imitate perceptual learning of the human visual system (HVS) to realize visual preprocessing, such as image contrast improvement and weak signal enhancement at the sensory terminal, and overcome data redundancy. Moreover, by applying gate voltage pulses, electric‐tunable synaptic plasticity is successfully observed, attributed to the carrier trapping and de‐trapping mechanism in the floating layer. Attention stabilization, fluctuation, distraction, and reinforcement are exhibited, simulating the attention behaviors of the HVS. Thus, an NVS with attention mechanism is established depending on the optoelectronic hybrid synaptic plasticity of the device, which successfully mimics the HVS to perform a multi‐target recognition task. Furthermore, the effect of device defects on the NVS is rarely evaluated, in which a method is provided to analyze the application results of the NVS when considering uniformity and fault rate. This study may provide new inspiration for developing neuromorphic vision systems for autonomous driving and brainwave control in the future.

Weak Signal Enhancement for Passive Seismic Data Reconstruction Based on Deep Learning

In conventional passive seismic exploration, it is often necessary to make a long-period seismic record. On the one hand, the passive seismic records with long period allowed us to screen several good passive seismic records with long period for seismic interferometry reconstruction and perform piecewise stacking on them. On the other hand, a sufficiently long recording time can help us avoid noise interference generated by nonpassive sources during the recording process, such as animal activities, construction operations, industrial electrical interference, etc. Compared with the passive seismic records with short period, the passive seismic records with long period can obtain higher signal-to-noise ratio after seismic interferometry reconstruction. However, they also cause huge consumptions of manpower, material resources, and time. Based on this, this paper proposes a seismic interferometry reconstruction method using passive signals of short-period recordings. Based on deep learning technology, the effective information is extracted and enhanced, the strong coherent noise after reconstruction is suppressed and weakened, the SNR of reconstructed recording is improved, and the effective information is mined. It can effectively reduce the time of passive seismic recording required for acquisition and improve acquisition efficiency. In addition, it also has a certain monitoring effect on real-time changes in underground structures.

Feedback-based wavefront shaping for weak light with lock-in beat frequency detection.

Feedback-based wavefront shaping is a promising and versatile technique for enhancing the contrast of a target signal through highly scattering media. However, this technique can fail for low optical signals such as fluorescence and Raman signals or in a reflection setup because the trend in weak feedback signals can be easily overwhelmed by noise. To address this challenge, we develop a technique based on a single acousto-optic deflector (AOD) to create a signal with a selected beat frequency from optical signals that can serve as feedback, in which the phase distribution of various radio frequency components of the driving signal for the AOD is optimized for wavefront shaping. By shifting incident light frequency with the AOD, the feedback signal at a selected beat frequency can be measured with a high signal-to-noise ratio (SNR) by a lock-in amplifier, thus enabling the enhancement of weak target signals through highly scattering media. It is found that the method of lock-in beat frequency detection can significantly improve fluorescence imaging and Raman spectral measurements in a reflection setup, and thus could be potentially used for in vivo measurements.