Impulsive Signals Research Articles

An Agile 3 cm‐Scale Quadruped Piezoelectric Robot with a Rigid Ring‐Shaped Structure

AbstractMiniature piezoelectric robots can perform various tasks in narrow spaces, due to their small sizes and agile motions. However, there is key challenge of reconciling large load capacity with agile motions, which limits the integration of functional units. In this work, a miniature agile quadruped piezoelectric robot (AQPR) inspired by hard‐shell animals is proposed. The prominent feature of AQPR is the rigid ring structure, which can be utilized to achieve large load capacity with its high stiffness; the degeneracy of different vibration modes is used to generate multi‐dimensional trajectories at the foot, which can achieve linear and rotational motions. A prototype with size of 30 × 30 × 14.3 mm3 and weight of 6.9 g is produced. The experimental results show that the maximum linear and rotational speed is 255 mm s−1 and 1265°s−1, respectively. The load capacities reach 200 g (≈30 times self‐weight). By using an impulse signal, the resolutions of linear and rotational motions reach 0.25 µm and 32.7 µrad, respectively. Benefiting from small size, large load capacity, high resolution, agile, and fast speed, AQPR shows great potential for applying micro‐operations in narrow spaces, such as large‐scale wafer transport and detection.

An original feature retention deconvolution algorithm and its application to bearing fault diagnosis

Abstract Traditional blind deconvolution algorithms perform well in estimating the repetition rate of impulses within signals; however, they fall short in preserving the original features of the signal. In engineering applications, particularly for cyclic impulse signals, maintaining signal fidelity is as crucial as accurately estimating impulse counts, making pure impulse count estimation insufficient for practical needs. To address this limitation, we propose a novel deconvolution algorithm—maximum correlation Pearson fidelity coefficient deconvolution (MCPSFD). This method constructs an objective function based on two key metrics: the correlation Pearson coefficient (CPC), which quantifies the periodicity of impulses, and the signal fidelity coefficient (SFC), which measures the similarity between the original and recovered signals. By combining CPC and SFC, we introduce a new objective function, termed the correlated Pearson fidelity factor (CPSF), which simultaneously considers both the number of impulses and the original characteristics of the filtered signal without introducing redundant parameters. The MCPSFD algorithm is derived by maximizing the CPSF function. Extensive experiments on simulated and measured bearing signals demonstrate that the proposed method significantly outperforms existing deconvolution algorithms in recovering periodic impulses and minimizing signal distortion.

Active Vibration Control of a Cantilever Beam Structure Using Pure Deep Learning and PID with Deep Learning-Based Tuning

Vibration is a major problem that can cause structures to wear out prematurely and even fail. Smart structures are a promising solution to this problem because they can be equipped with actuators, sensors, and controllers to reduce or eliminate vibration. The primary objective of this paper is to explore and compare two deep learning-based approaches for vibration control in cantilever beams. The first approach involves the direct application of deep learning techniques, specifically multi-layer neural networks and RNNs, to control the beam’s dynamic behavior. The second approach integrates deep learning into the tuning process of a PID controller, optimizing its parameters for improved control performance. To activate the structure, two different input signals are used, an impulse signal at time zero and a random one. Through this comparative analysis, the paper aims to evaluate the effectiveness, strengths, and limitations of each method, offering insights into their potential applications in the field of smart structure control.

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

Defect quantification evaluation of a rolling element bearing based on physical modelling and instantaneous vibration energy investigation

The estimation of defect size of rolling element bearing (REB) is expected to remain a significant and challenging task in vibration-based condition monitoring of rotating machinery. The conventional approaches focus on extracting the time spacing of the double impulse signal to estimate the defect size of the REB which is subsequently found to be inaccurate and arbitrary. In this paper, a novel model is developed to estimate the defect size of the REB based on the theories of physics and kinematics, in which the multi-event excitations, i.e. the entry, destressing and collision generated by the rolling element-defect interaction, are considered and investigated. The kinematic and geometric contact mechanism, which is mapped onto the rolling element-defect interaction as the rolling element passes over the defect zone, are analyzed and modelled as a function of the time information. In view of the fact that the variation of the vibration energy contained in the multi-impact vibration response is deeply mapped to the time information, a novel idea is proposed to extract the time information by analyzing the instantaneous energy variation of the defect-induced impulse using the scheme of the autoregressive model and the smooth pseudo Wigner-Ville distribution. The proposed model and idea are validated by experiments under different defect size and rotational speed scenarios. The results calculated by the proposed model show good agreement with the real defect size. Experimental and modelling comparisons show that the proposed model has reliable effectiveness and good performance and confidence in quantifying the size of the defect area localized on the outer raceway of the REB, and can provide some theoretical guidance for damage detection and remaining useful life prediction of the REB.

Synchronization of Time-Delay Coupled Neural Networks With Stabilizing Delayed Impulsive Control.

This brief studies the distributed synchronization of time-delay coupled neural networks (NNs) with impulsive pinning control involving stabilizing delays. A novel differential inequality is proposed, where the state's past information at impulsive time is effectively extracted and used to handle the synchronization of coupled NNs. Based on this inequality, the restriction that the size of impulsive delay is always limited by the system delay is removed, and the upper bound on the impulsive delay is relaxed, which is improved the existing related results. By using the methods of average impulsive interval (AII) and impulsive delay, some relaxed criteria for distributed synchronization of time-delay coupled NNs are obtained. The proposed synchronization conditions do not impose on the upper bound of two consecutive impulsive signals, and the lower bound is more flexible. Moreover, our results reveal that the impulsive delays may contribute to the synchronization of time-delay systems. Finally, typical networks are presented to illustrate the advantage of our delayed impulsive control method.

Acoustic enhancement and weak signal detection based on quasibound states in the continuum

Acoustic signal detection is important in several fields of research. However, realizing weak-signal detection remains a challenge. Recently, the bound state in continuum (BIC) has attracted considerable attention because of its ultrahigh quality-factor (Q-factor) and strong field enhancement. In this study, we achieved two Fabry–Perot BICs (bonding and anti-bonding BICs) in a coupled-resonator system and a mirror-induced BIC in a single-resonator system. And the acoustic enhancement from quasi-BICs of the coupled-resonator system and single-resonator system was investigated in the simulation. Compared to the coupled-resonator system, the amplification performance of the single-resonator system was better and verified in the experiment. The experimental results agreed with the numerical results, and the maximum pressure magnification was approximately 28×. Furthermore, the single-resonator system enhanced the weak harmonic signal, weak periodic impulse signal, and weak modulated signal in the presence of strong background noise, making signal detection much easier. We believe that this work provides a new method to realize weak signal detection based on acoustic enhancement.

Transformer winding state diagnosis method based on short-circuit impulse response and multi-feature joint

Abstract To investigate the relationship between the physical characteristics of transformer short-circuit impulse signals and winding deformation, this paper proposes a transformer winding state diagnosis method based on the combination of short-circuit impulse response and multiple features. Firstly, based on the relative displacement model of a single degree of freedom system, a winding short-circuit impulse response spectrum is constructed, and the energy entropy characteristics of different frequency bands are extracted using the Hilbert energy spectrum. Secondly, the energy spectrum is converted into a time-frequency matrix, and its energy density distribution and short-term energy attenuation changes are quantified. Then, we perform support vector regression (SVD) decomposition and calculate the proportion of singular value vectors. Finally, multiple feature quantities are combined to construct a feature set, which is then input into the SVD model optimized by the particle swarm algorithm (PSO) for diagnosis. The experimental results show that the proposed method can accurately extract the physical characteristics of short-circuit impulse signals, and the error between the obtained transformer winding deformation diagnosis value and the actual state value is only 3.6%, which can provide guidance for online monitoring of the transformer winding state.

Comparing Kurtosis-adjusted weighted levels with other metrics to assess the risk of hearing loss from non-Gaussian noise exposures

Several studies have shown that impulsive noise can cause more damage to hearing than steady-state noise of equal energy. As a result, a large body of research has been devoted to evaluating the hazards of impulsive noise. However, work environments often have varying noise patterns, including Gaussian background noise combined with high-level transient noises contributing to the worker's daily dose. Thus, an energy-based noise metric underestimates the risk of hearing loss unless incorporating a temporal structure correction term. Kurtosis has been reported as an effective adjunct to energy for predicting hearing hazards from non-Gaussian noise exposures. In this work, recordings of real impulsive noise were conducted using a portable measuring system at a very high sampling frequency and using high-pressure microphones. Controlled synthetic non-Gaussian noise was generated using these impulsive signals and steady-state background noise. After exploring the detailed temporal structure of the noise, the daily exposure time has been determined using different metrics, including kurtosis-adjusted weighted levels, weighted peak levels, and auditory risk units. The examples presented here aim to demonstrate how kurtosis can help develop more accurate methods to prevent noise-induced hearing loss.

Moving source 2D-hologram in the presence of intensive internal waves in SWARM-95 experiment

This paper presents the holographic signal processing results from an acoustical oceanography experiment on the New Jersey continental shelf called SWARM-95. This experiment that was conducted in summer of 1995 aimed at propagation of broadband acoustic signals in the presence of intensive internal waves (IIW). Impulsive acoustic signals were from a moving source. The IIW caused modes coupling and significant 3D acoustic effects (Badiey M. et al., JASA, 117(2): 613–625). The current research is based on holographic signal processing (Pereselkov S., Kuz’kin V., JASA, 151(2): 666- 676) utilizing holographic signal processing. Here, the sound intensity distributions in frequency-time coordinates, called the source interferogram, is analyzed using a 2D Fourier transform (2D-FT). The result of the 2D-FT is called the Fourier-hologram (hologram). Two distinct sets of spectral spots were identified in the hologram of the moving source. The first set corresponds to the sound field in an unperturbed waveguide (without IIW). The second set relates to the hydrodynamic perturbation of the sound field caused by IIW. Such a hologram structure allows for the reconstruction of the parameters of the moving source. Results of this research can be used for detection and localization of Autonomous Underwater Vehicles (AUV). [This research was supported by a grant from the Russian Science Foundation (grant number 23-61-10024).]

Under-ice ambient noise and its masking effect on marine mammals in the Arctic

Climate change is changing the pattern of sea ice in the Arctic, which increases underwater ambient sound levels during the ice-covered season. The Arctic is home to multiple marine mammal species, even during the winter, which rely on the underwater acoustic environment for acoustic communication. Thus, climate change may be impacting the ability of these animals to communicate. Under-ice ambient noise is primarily generated by wind acting on the ice surface, rapid temperature changes, and impulsive transient signals resulting from ice cracking and ridging. To characterize under-ice ambient noise, acoustic data were collected from several locations in the western Canadian Arctic in this study. To determine the relationship between the received noise level, and wind speed and ice concentration, correlation analysis was used. For the detection and analysis of broadband transient events in the acoustic data, a spectrogram image processing approach was used. The spectral characteristics of transient events were generated using measurements and a random pulse train model. Furthermore, the wind and ice generated noise components were combined to predict the total ambient noise field in an ice-covered ocean using a wavenumber integral model. Finally, the model together with measurements was used to assess how ice-generated noise can cause communication masking in marine mammals such as ringed seals and bearded seals.

Implications of in-ice volume scattering for radio-frequency neutrino experiments

Over the last three decades, several experimental initiatives have been launched with the goal of observing radio-frequency signals produced by ultra-high energy neutrinos (UHEN) interacting in solid media. Observed neutrino event signatures comprise impulsive signals with duration of order the inverse of the antenna+system bandwidth (∼10 ns), superimposed upon an incoherent (typically white noise) thermal noise spectrum. Whereas bulk volume scattering (VS) of radio-frequency (RF) signals is well-studied within the radio-glaciological communities, polar ice-based neutrino-detection experiments have thus far neglected VS in their signal projections. As discussed herein, coherent volume scattering (CVS, for which the phase of the incident signal is preserved during scattering) generated by in-ice neutrino interactions may similarly produce short-duration signal-like power, albeit with a slightly extended time structure, and thereby enhance neutrino detection rates, whereas incoherent (randomized phase) volume scattering (IVS) will persist for O(100 ns), appearing similar to thermal white noise and therefore reducing the measured Signal-to-Noise Ratio (SNR) of neutrino signals. Herein, we present the expected voltage profiles resulting from in-ice volume scattering as a function of the molecular scattering cross-section, for both CVS and IVS, and assess their impact on UHEN experiments. VS contributions are currently only weakly constrained by extant data; stronger limits may be obtained with dedicated calibration experiments.

Dynamics of N-elastically longitudinal coupled rotating pendulums with smooth and discontinuous nonlinearities: Generation of chaotic bursting with many orbits as a solution

The dynamics of a mechanical network, consisting of a certain number (N) of cells, in which each unit cell consists of the irrational coupling of a simple pendulum and inclined mass-spring oscillator, is investigated. The single cell was recently introduced by Ning Han and Qingjie Cao, and it has strong irrational nonlinearity, exhibiting smooth and discontinuous characteristics due to the geometry configuration. The obtained network has a large degree of freedom and consequently has richer dynamics as compared to a single one. The set of discrete equations of this network is found, while the first equation is driven by an external sinusoidal force; the variables here are the angles between the vertical direction and the directions of mass in each unit cell. By using the non-driven version of this set of equations, the equilibrium points are found as well as their stability using the Jacobian matrix. Then, by using the multiple time scale method, the solutions of the set of the network equations are found for weak amplitude oscillations of the system, while the large amplitude solutions are found numerically, showing the ability of the network to transform the shape of some solutions at the input to other forms as the signals evolve in the network. One notices the generation of simple chaotic signals as well as the train of impulse signals. What is important here is the generation of the chaotic bursting with many orbits by the system, which are the simultaneous oscillations around several fixed points. For the case of numerous cells, the system can be used as a waveguide; this is why the set of the equation of the network is reduced to the extended complex Ginzburg Landau (ECGL) equation, with complex nonlinear dispersion and nonlocality terms, using the Tanuiti's reduction perturbation methods. The dissipative dark compacton and peakon as solutions for the ECGL equation are then found. The input-output matching of the network is next investigated via the transfer function of the system near the equilibrium points. The inverse Fourier transform of the transfer function multiplied by the Fourier transform of the input sinusoidal force gives the solution of the network equation for low amplitude oscillations. Finally, the supratransmission condition is studied, leading to the fact that the input sinusoidal signal with frequency nearly inside the forbidden gap zone gradually transforms into the train of chaotic bursting.

A Fast iterative shrinkage and threshold algorithm-based least-squares deconvolution technique with application to bearing fault signals

Bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown. The low energy level of fault-induced impulsive signals, however, renders it vulnerable to being masked by environmental noise and other disturbances present in the vibration signal. To cope with this issue, this paper investigates the application of the finite impulse response (FIR) filter for bearing fault detection. In this work, the estimation of the FIR is formulated as a least squares optimization problem, aiming to minimize the l 2 norm of the FIR and effectively suppress noise and interference sources. The solutions of the least squares optimization problem are obtained using the fast iterative shrinkage–thresholding (FISTA) algorithm. Furthermore, to better select the regularization parameter in our method, an improved gray wolf optimizer (I–GWO) is employed to conduct the parameter selection. Based on these improvements, the proposed FISTA–LSD technique enables rapid and precise detection of fault characteristics upon their occurrence, thereby mitigating the propagation of faults and preventing accidents from happening. Furthermore, with this automated selection process, engineers can save valuable time that would otherwise be spent on trial-and-error approaches. Through its rapid response capabilities and precise fault detection abilities enabled by the FISTA–LSD technique along with I–GWO integration for automatic parameter selection; this FISTA–LSD technique greatly enhances its practical value. The proposed technique is validated through the utilization of both numerical and experimental data.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.

A Wide-Bandwidth Inexpensive Current Sensor Based on the Signal Fusion of Tunneling Magnetoresistance and a Current Transformer.

In technology and industrial production, many applications require wide-bandwidth current measurements. In this paper, a signal fusion scheme for a current sensor comprising tunneling magnetoresistance and a current transformer is proposed, achieving a flat frequency response in the DC to MHz range. The measurement principles in different cases of the scheme are introduced, and the total transfer function of the entire scheme is derived by analyzing each section separately. Furthermore, the feasibility and selected parameters of the scheme are verified through a systematic simulation utilizing the MATLAB software. Based on the proposed scheme, a group of principal prototypes are built to experimentally evaluate the bandwidth, amplitude and phase flatness, accuracy, sensitivity, and impulse response. The relative amplitude variation in the passband of the fusion sensor is less than 4%, and the estimated bandwidth of the fusion sensor is close to 17 MHz. The accuracy is better than 0.6%, even when measuring the current at 1 MHz, and the relative standard deviation is 5% when measuring the impulse signal. The sensors developed using this scheme, with a low financial cost, have advantages in many wide-bandwidth current measuring scenarios.

Research on SNR enhancement method of underwater airgun impulse signal

Abstract Seismic airgun blasting is nowadays widely employed for ocean exploration and marine geophysical study. Being powerful acoustic source showing excellent repeatability and consistency, it’s potentially applicable for long-distance underwater acoustic communication. Given the sound generating mechanism of airguns, the generated signals cannot be modulated compared with sources signals that are more commonly used for communications purposes. Thus the use of time delay shift coding system, which is both necessary and advantageous under such circumstances. The detection of the impulsive signal from airgun blasting and accurate estimation of its arrival time at the receiving end is paramount for carrying out a time delay shift coding communication. Consequentially, augmentation of the signal-to-noise ratio (SNR) of received signal through enhancement is the most direct and effective approach. This manuscript presents an enhancement method of the airgun blasting signals: A recursive wavelet modulus maxima denoising method is introduced, by separating the raw signals into noise components and signal components iteratively, details information that is erroneously erased by conventional wavelet modulus maxima denoising method can thus been preserved. Coupled with the proposed use of a loop coefficient of determination, automatic balancing between denoising effectiveness and distortion of valid signals is acrhieved.

Disturbance observer-based event-triggered impulsive control for nonlinear systems with unknown external disturbances

Input-to-state practical stability (ISpS) of a kind of nonlinear systems suffering from unknown exogenous disturbances is explored in this article, where a disturbance observer is established to estimate the information of the exogenous disturbances. To achieve ISpS of the system, the impulsive controller as well as state-feedback controller are both considered to regulate the discrete and continuous dynamics of the system, respectively. Especially, a novel disturbance observer-based event-triggered mechanism is devised to decide the release of impulsive control signal. Furthermore, several adequate conditions are given for excluding the occurrence of Zeno phenomenon. To confirm the feasibility of the proposed results, two numerical instances and their corresponding simulation results are presented.

Solar flare observations with the Radio Neutrino Observatory Greenland (RNO-G)

The Radio Neutrino Observatory – Greenland (RNO-G) seeks discovery of ultra-high energy neutrinos from the cosmos through their interactions in ice. The science program extends beyond particle astrophysics to include radioglaciology and, as we show herein, solar observations, as well. Currently seven of 35 planned radio-receiver stations (24 antennas/station) are operational. These stations are sensitive to impulsive radio signals with frequencies between 80 and 700 MHz and feature a neutrino trigger threshold for recording data close to the thermal floor. RNO-G can also trigger on elevated signals from the Sun, resulting in nanosecond resolution time-domain flare data; such temporal resolution is significantly shorter than from most dedicated solar observatories. In addition to possible RNO-G solar flare polarization measurements, the Sun also represents an extremely useful above-surface calibration source.Using RNO-G data recorded during the summers of 2022 and 2023, we find signal excesses during solar flares reported by the solar-observing Callisto network and also in coincidence with ∼2/3 of the brightest excesses recorded by the SWAVES satellite. These observed flares are characterized by significant time-domain impulsivity. Using the known position of the Sun, the flare sample is used to calibrate the RNO-G absolute pointing on the radio signal arrival direction to sub-degree resolution. We thus establish the Sun as a regularly observed astronomical calibration source to provide the accurate absolute pointing required for neutrino astronomy.

Psychoacoustic model for detecting the sensation of impulsivity in acoustic signals from refrigerators

This study investigates the perception of impulsivity in audio signals specifically caused by thermal expansion (cracking noise) in domestic refrigerators. It employs a multifaceted approach encompassing an analysis of sensitivity to impulsive events, an assessment of the prominence of impulse signals, and a correlation analysis of these data. The investigation revealed different responses to various sound samples, highlighting subjective variations. Using linear regression, correlation analysis demonstrated a robust and positive relationship (Pearson correlation coefficient of 0.851) between perceived impulsivity and the Impulsivity Prediction Model (IPM). This alignment underscores the reliability of the developed IPM in capturing and predicting subjective perceptions of low-amplitude transient signals. Comparisons between groups of participants, conducted using both Analysis of Variance (ANOVA) and t-tests, explored potential disparities related to gender, age, and acoustic knowledge. The results indicated no statistically significant differences in the perception of impulsivity concerning gender, age groups, or acoustic knowledge. In conclusion, this study provides insights into the perceptual aspects of impulsivity in audio signals from home refrigerators, specifically addressing thermal expansion noises, and establishes the reliability of the Impulsivity Prediction Model (IPM) as a tool for objective assessment. The congruence between subjective judgments and objective metrics enhances the applicability of IPM in diverse fields, from acoustic engineering to psychoacoustic research.