Noise Amplitude Research Articles

Noise tailoring, noise annealing, and external perturbation injection strategies in memristive Hopfield neural networks

The commercial introduction of a novel electronic device is often preceded by a lengthy material optimization phase devoted to the suppression of device noise as much as possible. The emergence of novel computing architectures, however, triggers a paradigm shift in noise engineering, demonstrating that non-suppressed but properly tailored noise can be harvested as a computational resource in probabilistic computing schemes. Such a strategy was recently realized on the hardware level in memristive Hopfield neural networks, delivering fast and highly energy efficient optimization performance. Inspired by these achievements, we perform a thorough analysis of simulated memristive Hopfield neural networks relying on realistic noise characteristics acquired on various memristive devices. These characteristics highlight the possibility of orders of magnitude variations in the noise level depending on the material choice as well as on the resistance state (and the corresponding active region volume) of the devices. Our simulations separate the effects of various device non-idealities on the operation of the Hopfield neural network by investigating the role of the programming accuracy as well as the noise-type and noise amplitude of the ON and OFF states. Relying on these results, we propose optimized noise tailoring and noise annealing strategies, comparing the impact of internal noise to the effect of external perturbation injection schemes.

Analysis of VLF Signal Noise Changes in the Time Domain and Excitations/Attenuations of Short-Period Waves in the Frequency Domain as Potential Earthquake Precursors

In this paper, we complete pioneering research that indicates the very low frequency (VLF) signal amplitude and phase noise reductions, and short-period wave excitations and attenuations as new potential earthquake precursors. We consider changes in the VLF signal broadcast in Italy by the ICV transmitter and recorded in Serbia that start a few tens of minutes before earthquakes. The sampling interval of the analyzed data is 0.1 s. The main objectives of this study are (1) to complete this research in the time and frequency domains during the periods of the four earthquakes analyzed in the previous studies, and (2) to define the parameters of the VLF signal amplitude and phase in both domains that should be further examined in statistical analyses of the aforementioned potential earthquake precursors. In the first part of this study, we analyze the ICV signal amplitude in the frequency domain during the period around three earthquakes that occurred in November 2010 near the considered signal propagation path. Here, we apply the Fourier transform to the relevant recorded data. In the second part, we compare characteristics of the signal amplitude and phase noise reductions in the time domain, and wave excitations and attenuations in the frequency domain. The results of these comparisons indicate the parameters that should be analyzed in subsequent studies to confirm the connection of the considered VLF signal changes with seismic activity before earthquakes, and potentially establish procedures for their detection are: (a) the start and end times of the noise reductions in the time domain and the excited/attenuated waves in the frequency domain, (b) the differences in the corresponding times, and (c) the wave periods of wave excitations of both the signal amplitude and phase.

Synchronizing Renewable Energy Sources in Distributed Generation Systems.

In renewable and distributed generation systems the grid voltage quality can be poor. So, the connection of power converters to the grid requires accurate control of synchronism between the two sources. The paper discusses issues on synchronization of power converters connected to the mains and related control methods. The design and experimentation of a prototype of a 200 kW power converter that connects a wind generator to the grid help to validate the adopted approach. In this paper a new zero crossing synchroniser is proposed having zero error in steady state, moderate speed response to grid frequency changes and amplitude voltage variations, and harmonics and noise immunity. The speed response, and harmonics and noise immunity are a trade-off. Simulation results with strong disturbances in the grid voltage show the robustness of the proposed method.

A promising method for breaking the logjam of time-frequency analysis in astronomy

Abstract Time-frequency analysis could provide detailed dynamic information of celestial bodies and is critical for comprehension of astronomical phenomena. However, it is far from being well-developed in astronomy. Hilbert–Huang transform (HHT) is an advanced time-frequency method but has two problems in analysing astronomical signals. One is that many astronomical signals may be composed of multiple components with various amplitudes and frequencies, while HHT uses assisted noises with the same amplitude to extract all components. The other is that HHT is an empirical method requiring tunable parameters to be optimized using experimental results or known facts, which are challenging to obtain in astronomy and it is therefore hard to determine whether the signal decomposition is right or not. In this study, we adjust the noise amplitude to optimize the decomposition based on the orthogonality of the obtained components and discard the decompositions with non-physical results. Three experiments show that this new extension of HHT is an effective method suitable for high-resolution time-frequency analysis in astronomy. It can be used to dig out valuable pieces of information which are inaccessible with other methods, and thus has the potential to open up new avenues for astronomy research.

Aircraft noise modifies acoustic signals and social interactions of a microendemic frog from Mexico City

Research on urban noises affecting acoustic communication of animals is a growing area of study and conservation concern. However, the consequences of aircraft noise on anuran acoustic communication have been explored on a restricted basis to date. In this study, we investigated the impact of aircraft overflights on the individual and chorus performance of acoustic signalling in the Pedregal frog (Eleutherodactylus grandis), a microendemic and threatened species from Mexico City. During the summer of 2021, we recorded acoustic signals from male individuals and conducted acoustic monitoring to analyse how chorus social interactions change due to aircraft overflights. For this purpose, we evaluated acoustic properties of the call, including call duration, dominant frequency, call amplitude, and call repetition rate. Additionally, we measured the amplitude of environmental noises and signals by means of two types of complementary methodological approaches. Our results show changes in the individual performance of acoustic signals with longer call duration, lower dominant frequency, and higher call amplitude due to aircraft overflights, while individual call repetition rate did not vary. Furthermore, a decrease in the number of calls within the chorus was observed during aircraft overflight. Overall, these results show that aircraft noise causes plastic changes in the acoustic signals of the Pedregal frog and that social interactions between conspecifics decrease, thus adding a new disturbance to this species that already suffers from other anthropogenic noises, and habitat fragmentation.

Analysis of line spectral fluctuation characteristics for source depth discrimination in shallow water

The difference in intensity fluctuation between a surface signal and a submerged signal can be used to discriminate the source depth in the shallow water waveguide. However, the properties of fluctuation distinction in intensity between surface source and submerge source are rarely studied, resulting in the poor performance of fluctuation-based methods sometimes. In this work, the intensity fluctuations caused by source depth fluctuations is analyzed by differential method and variance statistics method to figure out the physics of intensity fluctuations, and to present the suitable quantity for depth discrimination. Firstly, the derivative expression of real part, power and amplitude of the pressure field are respectively derived from the normal mode theory, hence their fluctuation quantities are specified. Then, the numerical examples, including the factor of receiver depth, source frequency and source range, are treated to verify the derivation and summarize the characteristic of fluctuations. The property of the intensity fluctuations is also compared with the distribution of normalized quantity on the depth dimension. Finally, the SWellEx-96 experimental data processing results validate the conclusions. The processing results of derivation, simulation data and experimental data show that the interactions between modes excited by the source give rise to the fluctuations. Meanwhile, the property of the interactions is affected by the received depth, source range and source frequency. Amplitude is capable of building a quantity based on fluctuations for source depth discrimination. For surface and submerged sources with lower frequency, their distinction in their fluctuations is stably represented by amplitude. In the SWellEx-96 experiment, the normalized values calculated from the amplitude of surface noise and submerged sound sources show the lowest difference of 0.5 and the highest difference of 2.5, indicating the effectiveness of using amplitude fluctuations to discriminate the sound source depth.

GPU-enabled ensemble data assimilation for mesh-refined lattice Boltzmann method

We implemented the ensemble data assimilation (DA) method, the local ensemble transform Kalman filter (LETKF), into the mesh-refined lattice Boltzmann method (LBM) for turbulent flows. Both the LETKF and the mesh-refined LBM were fully implemented on GPUs, so that they are efficiently computed on modern GPU-based supercomputers. We examined the DA accuracy against the flow around a cylinder. The result showed that our method enabled accurate DA with spatially- and temporarily-sparse observation data; the error of the assimilated velocity field with the observation interval of τK/2 and the observation resolution D/16 (1.56% of the total computational grids) was smaller than the amplitude of the observation noise, where τK is the period of the Kármán vortex and D is diameter of the square cylinder.

A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system.

With the evolution of wearable systems, more and more people tend to wear wearable devices for health monitoring during sports. However, a large amount of motion artifact noise is introduced at this time, which is difficult to filter out due to its stochasticity. The amplitude and characteristics of motion artifact noise vary with changes in motion intensity. In order to filter out the motion artifact noise, the paperproposes a new particle algorithm, which can detect the intensity of the motion artifact for adaptive filtering, especiallysuitable for wearable health monitoring systems. In this algorithm, variational mode decomposition was first introduced to analyze the noisy electrocardiogram (ECG) signal in order to find the clean components. Then, the Laguerre estimation technique was applied to obtain an accurate ECG polar model. Taking this model as the state equation, a particle filter algorithm was defined to filter out the motion artifact noise. In the particle filter algorithm, we defined a parameter γ whose values were obtained from the six-axis data of motion sensor MPU6050 in our wearable device. This parameter γ could reflect the current noise levels and adaptively update the particle weights. Finally, some exercise experiments proved that the parameter γ could map the motion artifacts in real time and also demonstrated the superiority of the algorithm in terms of signal-to-noise ratio improvement and error reduction compared to other algorithms. The new particle filter algorithm proposed in this paper combines the six-axis data (three-axis accelerometer and three-axis gyroscope) with the ECG signal to effectively eliminate a large amount of motion artifact noise, thus solving the problem of excess noise from wearable devices when people are exercising, allowing them to accurately obtain real-time ECG health information.

Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction.

Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes. It has previously been shown that combining data-driven regularization with iterative reconstruction is promising for tackling challenging inverse problems in medical imaging. In this work, we present an algorithm that allows seamless combination of data-driven regularization with quasi-Newton solvers, which can better deal with ill-conditioned problems compared to gradient descent-based optimization algorithms. Contrary to most available algorithms, our method applies regularization in the gradient domain rather than in the image domain. This comes with a crucial advantage when applied in conjunction with quasi-Newton solvers: the Hessian is approximated solely based on denoised data. We apply the proposed method, which we call GradReg, to both conventional breast CT and GI-CT and show that both significantly benefit from our approach in terms of dose efficiency. Moreover, our results suggest that thanks to its sharper gradients that carry more high spatial-frequency content, GI-CT can benefit more from GradReg compared to conventional breast CT. Crucially, GradReg can be applied to any image reconstruction task which relies on gradient-based updates.

Application of an improved Minimum Noise Amplitude Deconvolution for bearing fault diagnosis

Application of an improved Minimum Noise Amplitude Deconvolution for bearing fault diagnosis

ANALYSE DES SERIES TEMPORELLES DES COLONNES VERTICALES DU DIOXYDE D’AZOTE ET DU FORMALDEHYDE ATMOSPHERIQUES A KINSHASA PAR LA DETECTION SYNCHRONE

This study concerns the analysis of atmospheric nitrogen dioxide and formaldehyde vertical column data in Kinshasa. The methodology uses the principle of synchronous detection. To do this, an algorithm was implemented and applied to search for the different harmonics in the spectra and to determine their respective characteristics. A multitude of harmonics was found in all frequency domains for each time series of the abundances of the considered atmospheric pollutants. The results of this study reveal an analysis technique capable of extracting weak signals embedded in a very high amplitude noise. In addition, apart from the information on the frequency, the analysis by synchronous detection makes it possible to obtain the phase and the amplitude of these signals extracted from the noise. Additionally, synchronous detection is able to find a particular oscillation buried in the noise and to determine its characteristics. It is therefore possible to study the phenomena associated with this oscillation (the rhythm of human activities, the alternation of seasons, the various tides, the Madden Julian or EL NINO oscillations) as well as the forces that maintain it (the various gravitational forces). The knowledge of the characteristics of a particular oscillation could also allow the exploration of the atmospheric environment of Kinshasa (emission site, altitude and relative abundance of atmospheric pollutants)

A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning

This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning. We construct a surrogate model to simulate the turbulent combustion process in real time, based on a state-of-the-art spatiotemporal forecasting neural network. To address the issue of shifted distribution in autoregressive long-term prediction, two training techniques are proposed: unrolled training and injecting noise training. These techniques significantly improve the stability and robustness of the model. Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner (Cabra burner) have been generated for model validation. The effects of model architecture, unrolled time, noise amplitude, and training dataset size on the long-term predictive performance are explored. The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady.

Noise-induced, ac-stabilized sine-Gordon breathers: Emergence and statistics

Noisy and ac forcing can cooperatively lead to the emergence of sine-Gordon breathers robust to dissipation. This phenomenon is studied, for both Neumann and periodic boundary conditions, at different values of the main system parameters, such as the noise intensity and the ac frequency-amplitude pair. In all the considered cases, nonmonotonicities of the probability of generating only breathers versus the noise strength are observed, implying that optimal noise ranges for the breather formation process exist. Within the latter scenarios, the statistics of the breathers’ number, position, and amplitude are analyzed. The number of breathers is found to grow, on average, with the noise amplitude. The breathers’ spatial distribution is sharply peaked at the system’s edges for Neumann boundary conditions, whereas it is essentially uniform for periodic boundary conditions. The average breather amplitude is dictated by the ac frequency-amplitude pair. Finally, a size analysis shows that the minimum system length for the generation mechanism is given by the typical breather half-width (width) in the case of Neumann boundary conditions (periodic boundary conditions).

A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and resolution. Mode-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance, providing a promising solution to overcome the limitations of traditional sensing technologies. Amplitude noise plays a key role in determining the resolution of mode-localized sensors, as the output metric is derived from the measured AR (amplitude ratio) within the weakly coupled resonator system. However, the amplitude noise originating from the weakly coupled resonator’s closed-loop circuit has not yet been fully investigated. This paper presents a decouple-decomposition (DD) noise analysis model, which is applied to achieve high resolution in a mode-localized tilt sensor based on a weakly coupled resonator closed-loop circuit. The DD noise model separates the weakly coupled resonators using the decoupling method considering the nonlinearity of the resonators. By integrating the decoupled weakly coupled resonators, the model decomposes the weakly coupled resonator’s closed-loop circuit into distinct paths for amplitude and phase noise analyses. The DD noise model reveals noise effects at various circuit nodes and models the system noise in the closed-loop circuit of the weakly coupled resonators. MATLAB/Simulink simulations verify the model’s accuracy when compared to theoretical analysis. At the optimal operating point, the mode-localized tilt sensor achieves an input-referred instability of 3.91 × 10-4° and an input-referred AR of PSD of 2.01 × 10-4°⁄√Hz using the closed-loop noise model. This model is also applicable to other varieties of mode-localized sensors.

Consistent traffic noise impacts few fitness-related traits in a field cricket

BackgroundAnthropogenic habitat change is occurring rapidly, and organisms can respond through within-generation responses that improve the match between their phenotype and the novel conditions they encounter. But, plastic responses can be adaptive or maladaptive and are most likely to be adaptive only when contemporary conditions reasonably mimic something experienced historically to which a response has already evolved. Noise pollution is a ubiquitous anthropogenic stressor that accompanies expanding urbanization. We tested whether the amplitude of traffic noise influences a suite of fitness-related traits (e.g. survival, life history, reproductive investment, immunity) and whether that depends on the life stage at which the noise is experienced (juvenile or adult). Our treatments mimic the conditions experienced by animals living in urban roadside environments with variable vehicle types, but continuous movement of traffic. We used the Pacific field cricket, an acoustically communicating insect that was previously shown to experience some negative behavioral and life history responses to very loud, variable traffic noise, as a model system.ResultsAfter exposing crickets to one of four traffic noise levels (silence, 50dBA, 60dBA, and 70dBA which are commonly experienced in their natural environment) during development, at adulthood, or both, we measured a comprehensive suite of fifteen fitness-related traits. We found that survival to adulthood was lower under some noise treatments than under silence, and that the number of live offspring hatched depended on the interaction between a female’s juvenile and adult exposure to traffic noise. Both of these suggest that our noise treatments were indeed a stressor. However, we found no evidence of negative or positive fitness effects of noise on the other thirteen measured traits.ConclusionsOur results suggest that, in contrast to previous work with loud, variable traffic noise, when noise exposure is relatively constant, plasticity may be sufficient to buffer many negative fitness effects and/or animals may be able to habituate to these conditions, regardless of amplitude. Our work highlights the importance of understanding how the particular characteristics of noise experienced by animals influence their biological responses and provides insight into how commensal animals thrive in human-dominated habitats.

Noise analysis of irregularly sampled VLBI position time series using maximum likelihood estimation

Identification of the optimal noise model for space geodetic time series is crucial for various studies in geodynamics and geodesy. However, the position time series of Very Long Baseline Interferometry (VLBI) are typically irregularly sampled. There is no clear evidence from previous studies to support that the Maximum Likelihood Estimate (MLE) method can allow for stable and reliable noise estimates for irregularly sampled data. To address this issue, we first conducted simulation studies. Results indicate that when the number of data points in the VLBI position time series reaches 300, reliable noise estimations result can be obtained using the MLE. We also found that the higher proportion of white noise a station has, the less influences the data gaps would be on noise estimations. Second, we apply the MLE to investigate the noise properties in the 32 VLBI position time series. About 12 (37.5%) stations exhibit pure white noise, and 15 (46.9%) stations display a combination of white noise and flicker noise. For these stations, the median amplitudes of white noise in the East, North, and Up components are 3.9, 4.6, and 11.9 mm, respectively. Moreover, white and flicker noise amplitudes show a latitude-dependent pattern, with stations located in the southern hemisphere and low latitudes (0-30°N) in the northern hemisphere exhibiting higher noise amplitudes. The remaining approximately 5 stations (15.6%) have the optimal noise model of power law noise with the mean spectral index of −0.32, −0.36, and −0.53 for the East, North, and Up components, respectively. Simply assuming pure white noise can significantly affect velocity uncertainty estimates when compared to using a noise model that includes colored noise. The velocity uncertainty will be underestimated by a factor of 2.9 on average. The effects of a small proportion of colored noise in VLBI data on velocity uncertainty estimation can be ignored only when the time series length exceeds 32.6 years. Additionally, the choice of different noise model can lead to a maximum difference of 0.84 mm for the annual signal of the Up component.

Suppression of Transients From Continuous Seismic Records Without Modifying the Amplitude of Ambient Noise

Suppression of Transients From Continuous Seismic Records Without Modifying the Amplitude of Ambient Noise

Analysis of direct and indirect noise in a next-generation aviation gas turbine combustor

A large-eddy simulation (LES) of a next-generation combustor is performed to examine effects of this combustor concepts on direct and indirect combustion noise characteristics. Direct noise is computed considering the unsteady heat release predictions while entropy fluctuations in the downstream part of the combustor are used to estimate indirect noise through the transfer function of the outlet nozzle. A low-order acoustic reconstruction technique, which utilizes the Green’s function of the configuration, is developed to compute the acoustics inside the combustor. Comparisons to experimental results are reported, showing the reasonable accuracy of the LES and combustion noise computations. The direct noise spectrum peaks at frequencies around 3 kHz because of the fast timescales associated with the chemical reactions inside the combustor. In addition, the indirect noise is dominant at low frequencies, i.e., less than 400 Hz, because of the characteristics of the entropy spectrum at the outlet nozzle. Compared to a conventional rich-quench-lean (RQL) combustor configuration, significant differences in the acoustics are observed, and are explained by two specific technological novelties. Firstly, the direct noise spectrum peaks at significantly higher frequencies due to the combustor’s compactness. Secondly, the entropy inhomogeneities downstream of the combustion region are an order of magnitude smaller in amplitude due to the lack of dilution with cold air. This leads to a significant reduction in the indirect noise amplitude at low frequencies, where it is the dominant source of noise. These results suggest opportunities for advanced combustion technologies to achieve substantial reductions in combustion-related noise.

Application of Short Time Fourier Transform (STFT) For Diagnosing Rolling Bearing Faults

A fan is crucial for maintaining airflow in industries. Bearings in fans prevent friction and must be robust to function effectively. Damage to the bearings can diminish machine performance. Predictive maintenance is essential for early detection of faults. One way to analyze bearing faults is by using the Short Time Fourier Transform (STFT), as it excels in analyzing non-stationary signals. Experiments were conducted under normal conditions and with inner race faults in bearings at a shaft speed of 1162.5 Hz. Vibration detection was done using an accelerometer sensor, and Matlab analysis was employed. The data was processed using the Fourier Transform (FT) method through both time and frequency domains, as well as the STFT through spectrograms. In the spectrum plot, there is still a significant amount of noise present. This high amplitude of noise from other frequencies obscures the bearing fault amplitudes. Furthermore, the Fourier Transform (FT) is only suitable for analyzing stationary signals. To address this, an envelope analysis was used to filter out the noise. The STFT analysis method provides simultaneous frequency and time information. This reveals that the spectrogram results for inner race faults depict three high amplitude peaks at harmonic frequencies. This indicates that the signal is non-stationary due to fluctuating amplitudes, making bearing fault analysis more accessible.

Effects of auditory white noise stimulation on sustained attention and response time variability.

"The moderate brain arousal model" claims that white noise improves attention by optimizing brain arousal. We analyze Conners' Continuous Performance Test-3 (CCPT-3) performance, expecting to find reduced reaction time variability with noise mediated by decrease under long event-rates and in later parts of the test, indicating that noise reverse fall in phasic and tonic arousal. Sixty-five children with high or lower ADHD-symptoms from a child psychiatric unit, succeeded to complete the CCPT-3 with and without white noise. Noise reduced overall variability, improved performance in later parts of the test, and reduced response variability under the longest event rate particularly in the high symptoms group. No overall change in omissions and commissions, but the high symptoms group made fewer omissions during noise compared the low symptom group. The study indicates an arousal effect of noise but should be replicated with other noise variants and amplitudes to improve effect and compliance.