Impulsive Signals Research Articles

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.

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.

Prescribed-time consensus of time-varying open multi-agent systems with delays on time scales

In this study, we focus on prescribed-time leader-following consensus problems for a class of time-varying open multi-agent systems (OMASs) with time delays on time scales. To achieve this objective, we propose a novel segmented state feedback control protocol which contains a time-varying scalar function. It ensures prescribed-time consensus can be achieved while the monotonicity of Lyapunov function is unknown. On this basis, impulsive signals dependent on the leader are further introduced at each opening instant to avoid the possibility of the controller norm being too large. During the process of theoretical analysis, we further innovatively extend Halanay-like inequalities on time scales to resolve theoretical analysis difficulties caused by time delays. Based on time scale theory and Lyapunov stability theory, sufficient conditions depending on system parameters and controller parameters for achieving prescribed-time consensus can be derived. Besides, considering absolute information of each agent cannot be completely obtained, we further design one observer system to reconstruct information of the original system, guaranteeing it can still reach consensus within the pre-set time. Finally, two simulation examples are used to illustrate the validity of our proposed theory.

Hydroacoustic Monitoring of Mayotte Submarine Volcano during Its Eruptive Phase

Submarine volcanoes are more challenging to monitor than subaerial volcanoes. Yet, the large eruption of the Hunga Tonga-Hunga Ha’apai volcano in the Tonga archipelago in 2022 was a reminder of their hazardous nature and hence demonstrated the need to study them. In October 2020, four autonomous hydrophones were moored in the sound fixing and ranging channel 50 km offshore Mayotte Island, in the North Mozambique Channel, to monitor the Fani Maoré 2018–2020 submarine eruption. Between their deployment and July 2022, this network of hydrophones, named MAHY, recorded sounds generated by the recent volcanic activity, along with earthquakes, submarine landslides, marine mammals calls, and marine traffic. Among the sounds generated by the volcanic activity, impulsive signals have been evidenced and interpreted as proxy for lava flow emplacements. The characteristics and the spatio-temporal evolution of these hydroacoustic signals allowed the estimation of effusion and flow rates, key parameters for volcano monitoring. These sounds are related to the non-explosive quenching of pillow lavas due to the rapid heat transfer between hot lava and cold seawater, with this process releasing an energy equivalent to an airgun source as used for active seismic exploration. Volcano observatories could hence use autonomous hydrophones in the water column to detect and monitor active submarine eruptions in the absence of regular on-site seafloor survey.

Input-to-state stability of discrete-time impulsive switched delayed systems with delay-dependent impulse under two asynchronous cases

This paper studies a class of discrete-time impulsive switched delayed systems with delay-dependent impulses, aiming to solving the input-to-state stability (ISS) problem in the case of asynchronous switching signal between subsystem and controller and asynchrony of switching signals and impulsive signals. Among them, the delay effect is handled by a new Lyapunov-Krasovskii function divided into a delay-dependent part and a delay-independent part. In contrast to admissible edge-dependent average dwell time (AED-ADT) methods that address switching signals, we propose admissible edge-dependent average impulsive interval (AED-AII) to deal with delayed impulses, thereby establishing the relationship between these methods and the decay rate. Meanwhile, a stabilizing controller for discrete impulsive switched linear delayed systems is proposed and the minimum average dwell time and controller gain are obtained. Compared with the previous work, the parameters of Lyapunov functional relation depend on the directed edges; the operation of subsystem is divided into asynchronous time and synchronous time, which are less conservative; the combination of AED-ADT and AED-AII can be applied to a wider range of impulsive switched systems. Finally, two examples are given to show the effectiveness of the results.

Atmospheric Noise Measurements in the Garden: Detecting Universalities in Inter-Spheric Waiting Time Statistics

Lightning flashes result in an instantaneous emission of electromagnetic (EM) waves that encompass a broad spectrum of frequencies in the domain of radio waves. The signature of these impulses in the region of the very low frequency (VLF) of radio waves and bellow are called spherics. These impulsive signals traverse great distances in the waveguide between the Earth’s ionosphere and its surface. Due to the abundance of lightnings the lower end of the EM spectrum is dominated by the waveforms of these emission. It is called atmospheric radio noise because of its origin. This article outlines a straightforward approach for capturing spherics activity within the VLF radio wave range. Employing data processing techniques, we pinpoint the timestamps of spherics within time series data. The distribution of inter-spheric times is analyzed across various detection threshold levels. Utilizing recordings spanning two distinct years and seasons, we replicate the established form of the inter-spheric time distribution described in the literature. Notably, we demonstrate that by rescaling the time intervals between spherics with the mean time for a specific recording and given detection threshold, the distributions collapse to a master curve. This universal pattern is accurately characterized by a single-parameter mean-scaled Gamma distribution. Additionally, we note the similarities in the distribution of inter-spheric times with patterns found in earthquake recurrence times.

Application of a Modal Parameter Identification Method Based on Variational Mode Decomposition in Flight Flutter Testing

Signals of flight flutter testing exhibit non-stationary characteristics, closely spaced modes, and low signal-to-noise ratio, presenting challenges in data processing. In recent years, the variational mode decomposition (VMD) method has emerged as a promising approach to mitigate mode mixing and exhibit robust noise resistance. Therefore, a novel time-frequency domain modal parameter identification method based on VMD is proposed to process impulse response signals in flight flutter testing. The modal frequency and damping ratio are determined through a three-step process: VMD analysis, Hilbert transform, and least square fitting. The efficacy of the proposed method in identifying closely spaced modes and resisting noise is validated through a numerical example. Furthermore, this method is applied to analyze two types of pulse excitation signals in actual flight flutter testing: one induced by the pilot’s shaking stick and the other induced by small rocket excitation. The obtained modal parameters are compared with those from ground vibration tests and specialized software, respectively, to showcase the effectiveness and superiority of the proposed method.

Optimal weight impulse extraction: New impulse extraction methodology for incipient gearbox condition monitoring

Gear faults in a transmission system generally cause impulse components in vibration signals, which is a crucial symbol for gearbox fault diagnosis. However, their related signals are often interfered or even submerged by the noisy meshing components (NMC) of gearboxes in degradation, which introduces challenges for incipient fault detection and condition monitoring. Commonly employed deconvolution-based methods attempt to design a filter to extract impulse components. However, these methods fail to address the interference issue of the NMC on deconvolution process. To overcome this limitation, this paper proposes an optimal weight impulse extraction (OWIE) methodology to suppress the NMC and highlight impulse component in vibration signal. Different from deconvolution-based methods, the proposed method locates impulses adaptively driven by waveform itself. A non-impulse part is suppressed through point-to-point removal, while impulse components are highlighted by subtracting a weighted signal from a raw signal. An iterative procedure is utilized to solve the optimal weight sequence by maximizing the kurtosis of an impulse signal. The effectiveness of the proposed OWIE is validated through a simulation case study and a run-to-failure experiment of gearboxes. Results demonstrate that the OWIE is sensitive to incipient faults and is suitable for the health condition monitoring of gearboxes.

ДО ПИТАННЯ ПОБУДОВИ АЛГОРИТМІВ ПЕРЕДАЧІ ПОТОКОВОГО ВІДЕО ДЛЯ МЕРЕЖ З НЕСТАБІЛЬНОЮ ПРОПУСКНОЮ ЗДАТНІСТЮ

Some questions of development of advanced video transmission algorithms that could robustly operate in the electronic warfare conditions are considered. The technology of adaptation of MJPEG compressed video stream for the networks with variable and unstable bandwidth is proposed. The new approach where connection control is separated from encoder basing on man-in-the-middle scenario is developed. In this case it became possible to preserve not only standard decoders on the client side, but the encoder code on the server side as well. The performance and reliability of the proposed MJPEG transmission algorithm was demonstrated in the field tests. The MJPEG transcoder based on developed algorithm demonstrated sufficiently stable video stream from the drone via WiFi network in the presence of impulse signal jamming.

A Wideband Circularly Polarized Antenna With High Impulse Signal Fidelity for IR-UWB Positioning Applications

A Wideband Circularly Polarized Antenna With High Impulse Signal Fidelity for IR-UWB Positioning Applications

Spatial Selection-Based Intelligent N-OFDM Signal Processing in Wireless Communication Systems

The method of joint processing of pulses and OFDM (N-OFDM) signals is proposed. The corresponding analytical relations for the lower Cramer-Rao boundary on the dispersion of OFDM (N-OFDM) signals amplitude ratings in the presence of sources of pulsed radiation are obtained. Using mathematical modeling properties and limitations of the demodulation method of OFDM (N-OFDM) signals in the background of impulse signals in the integrated radar and telecommunication systems are established. It is determined that the use of the angular distance between the pulsed and OFDM signals sources at a value that is not less than 0.75 widths of the secondary beam of the digital antenna array pattern does not affect the accuracy of the OFDM signal amplitudes. The same applies to the active interferences.

Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Singular value decomposition technique proves its effectiveness in mechanical signal analysis by decomposing the test signal into a series of singular spectral components of different frequency bands. Nevertheless, how to adapt this technology to the needs of cyclo-nonstationary signal and how to set the decomposition number while maintaining detailed features to obtain the optimal component containing the most fault information, remains an important issue that needs to be addressed in the field of mechanical fault diagnosis. To overcome these disadvantages, the symmetric circulant matrix decomposition (SCMD) is presented. Two main ideas structure the present technique. Firstly, symmetric circulant matrix is used to generate eigenvectors, which will better adapt to the cyclo-nonstationary signal associated with the structural symmetry of rotating machinery. Then, an impulse fluctuation measure is established to adaptively search for the decomposition number and extract the optimal component. Moreover, to better improve the impulse extraction effect of SCMD, the multivariate group sparse coding based on the multivariate correlation characteristics and intra group sparsity characteristics of impulse signals is proposed, which can enhance impulse features while preserving fault details as much as possible. The reliability and feasibility of the proposed method are verified by the experimental signals. The comparison with several classic methods shows that this method is more effectiveness in weak feature extraction.