Noise Fluctuations Research Articles

Methods in fluctuation (noise) spectroscopy and continuous analysis for high-throughput measurements

Abstract Fluctuation (noise) spectroscopy is widely used to investigate the low-frequency dynamics of charge carriers in condensed-matter systems, aiming to (i) improve the performance of micro- and nanoscale electronic devices and sensors, and (ii) use ‘noise as a signal’ in order to fundamentally investigate the microscopic motion and transitions of charge carriers coupled to the low-lying excitations in solids. Here, we focus on measurements of the ubiquitous 1 / f -type fluctuations, often composed by a superposition of (independent or correlated) two-level systems each giving rise to a Lorentzian spectrum, denoted random telegraph noise in the time domain. We briefly review the basic concepts of noise and fluctuations and give a comprehensive overview of state-of-the-art measuring techniques, using both commercially available signal analyzers and custom software in order to perform the spectral analysis of the recorded time signals, and critically evaluate their advantages and drawbacks. We describe how to use fast data acquisition devices in a cross-correlation setup providing additional quantitative information on the magnitude of uncorrelated instrument noise, and demonstrate the mathematical intricacies of extracting the noise spectra. We introduce a new method for high-throughput measurements by automated spectral analysis based on the concept of Continuous Analysis. We demonstrate the potential of this technique, working towards a FAIR data workflow, by measurements of magnetic flux noise within the hysteresis loop of ferromagnetic nanostructures.

Impact of easy-care and softener treatments on fabric frictional noise properties: a study on two protective fabrics

This research aimed to reduce fabric frictional noise to enhance wearing comfort, as such noise can be bothersome. This goal was achieved through chemical surface treatments, specifically cross-linker treatment and silicone softener treatment. Frictional noise from the treated fabrics was generated, recorded, and analyzed using a frictional noise generator, fast Fourier transform, and sixth-band analysis to determine the total noise level. A novel aspect of this work is the use of psychoacoustic characterization to evaluate the fabric frictional noise by calculating Zwicker’s parameters using the Brüel and Kjær application. In addition, the mechanical properties were tested using the Kawabata compression and surface testers, with data comparison achieved using Student’s t-test. The results showed that softeners are more effective than cross-linkers in reducing the total frictional noise level, loudness, and fluctuation strength. However, cross-linkers are better at reducing sharpness. Neither treatment affected the roughness of the frictional noise. In summary, both easy-care and softener treatments help reduce discomfort and produce less loud and sharp fabric frictional noise.

Noise hindering sound complexity in urban environments: Towards a novel evaluation criterion for urban sustainability

Urban environments are complex adaptive systems, wherein changes in one area can lead to unforeseen consequences in others. Environmental noise emerges as a multifaced issue influencing various receivers across different spatial and temporal scales. The perception of noise as a high-intensity or unwanted sound has defined the concept of quietness accordingly overlooking sustainability co-benefits. This study proposes an alternative evaluation criterion for urban planning and design, focusing on the sound complexity of public spaces that are vulnerable to environmental noise fluctuations. To determine the sensitivity of sound complexity to environmental noise, sound level measurements and sound recordings were conducted along the waterfront of a Mediterranean metropolis during the pandemic lockdowns (April 2021). Additionally, the bird dawn chorus phenomenon was studied. The analysis of collected data revealed an inverse relationship between complexity and noise, highlighting an additional impact on the urban sound environment. The promotion of sound complexity as a strategy for livable and sustainable cities can guide urban planners and designers in developing quiet and ecologically resilient urban environments.

Improvement of noise calculation method considering aerodynamic noise condition during aircraft landing

Recently, while aircraft engine noise has become quieter, aerodynamic noise such as airframe noise, gear, and flaps have become relatively louder. In addition, the WHO environmental noise guidelines for the European region require consideration of small noise effects, and it has become necessary to increase the validity of calculations at distant airports, where aerodynamic noise fluctuations are large. Therefore, Nakazawa et al. reported a method to improve the validity of noise prediction for distant airports by correcting the existing noise calculation method to take aerodynamic noise into account. However, the predicted results deviated to a smaller extent than the measured results, indicating that the correction method needs to be improved. This method was improved, and using the results of highly accurate microphone array measurements with 195 microphones, NPD data for correction was prepared from aircraft noise by component using J-FRAIN, taking aerodynamic noise

Evaluating attentional responses to electric vehicle driving noise through event-related potentials: a frequency bands analysis

The advent of electric vehicles (EVs), known for their quieter operation compared to Internal Combustion Engine (ICE) vehicles, has shifted auditory attention towards wind and road noise. This shift is further accentuated by the anticipated future dominance of autonomous vehicles, which is expected to reduce the cognitive demands of driving, potentially heightening sensitivity to external noise and leading to increased annoyance and discomfort. This research investigates the impact of driving noise on selective attention by identifying sounds that significantly disrupt adaptation to driving noise through the analysis of event-related potentials (ERPs). Unlike previous studies that concentrated on the peak high-frequency driving noise fluctuations around 1000 Hz, our study explores a broader spectrum of higher frequencies. By recording brain responses to driving-related auditory stimuli, we detected variations in ERPs across different frequency bands, thereby pinpointing the acoustic characteristics most disruptive to concentration. Our findings aim to contribute towards enhancing driver focus and improving the acoustic comfort inside vehicle cabins.

Numerical study on aerodynamic and aeroacoustic characteristics of sinusoidal wavy square cylinders

This paper numerically investigates the influences of amplitude and wavelength of sinusoidal wavy square cylinders on aerodynamic performance and noise reduction by large eddy simulation along with the Ffowcs Williams–Hawkings equation. The results show that the mean drag, lift fluctuation, and far-field noise of wavy cylinders are all reduced compared to the straight counterpart. The far-field noise of wavy cylinders varies monotonically with amplitude in a specific range but not with wavelength. The case with the largest amplitude demonstrates a significant tonal noise reduction of 47 dB/Hz, while a tonal noise reduction of 23 dB/Hz is observed for the case with the largest wavelength. To explore the mechanisms of noise reduction, the characteristics of a flow field are analyzed. It is found that wavy cylinders attenuate the transverse oscillation of a shear layer and produce more three-dimensional coherent structures in the wake. The wake region is significantly extended due to the delayed vortex shedding, and the mutual interaction between shear layers is remarkably weakened along the entire span. The spanwise coherence is attenuated in a similar way. These lead to the suppression of wall pressure fluctuations and turbulence fluctuations in the wake, which are closely related to far-field noise radiation.

Nonlinear Langevin functionals for a driven probe.

When a probe particle immersed in a fluid with nonlinear interactions is subject to strong driving, the cumulants of the stochastic force acting on the probe are nonlinear functionals of the driving protocol. We present a Volterra series for these nonlinear functionals by applying nonlinear response theory in a path integral formalism, where the emerging kernels are shown to be expressed in terms of connected equilibrium correlation functions. The first cumulant is the mean force, the second cumulant characterizes the non-equilibrium force fluctuations (noise), and higher order cumulants quantify non-Gaussian fluctuations. We discuss the interpretation of this formalism in relation to Langevin dynamics. We highlight two example scenarios of this formalism. (i) For a particle driven with the prescribed trajectory, the formalism yields the non-equilibrium statistics of the interaction force with the fluid. (ii) For a particle confined in a moving trapping potential, the formalism yields the non-equilibrium statistics of the trapping force. In simulations of a model of nonlinearly interacting Brownian particles, we find that nonlinear phenomena, such as shear-thinning and oscillating noise covariance, appear in third- or second-order response, respectively.

A time patch dynamic attention transformer for enhanced well production forecasting in complex oilfield operations

In the field of oil production, accurately predicting well production is of great significance for optimizing production processes and enhancing economic benefits. However, oil production data generally exhibit characteristics such as high noise, complex distribution, and severe temporal fluctuations, which pose significant challenges to traditional prediction models, thereby limiting their accuracy. To address this issue, this study proposes an innovative Time Patch Dynamic Attention Transformer (TPDAT) model. This model segments the time series data into multiple time patches, extracts local features, and integrates a dynamically fused causal temporal attention mechanism to enhance the recognition of transient events and short-term fluctuations. The TPDAT model comprises the Temporal Patch Feature Extraction (TPF) module and the Dynamic Fusion Causal Temporal Attention (DFCTA) mechanism, effectively capturing multi-scale temporal features and enhancing the modeling of causal relationships during oilfield production. Experiments on the North Sea Volve oilfield dataset show that the TPDAT model significantly outperforms traditional deep learning models in well production prediction, achieves the best performance in model evaluation metrics, and demonstrates strong adaptability to complex data. The results confirm that the TPDAT model improves the accuracy and stability of well production prediction in complex oilfield environments, providing reliable support for oilfield production optimization.

The Effect of Noisy Magnetic Fields on Electrons

Abstract In this work, we study the behavior of an electron in the presence of a constant and uniform magnetic field, which is subjected to white noise fluctuations with an autocorrelation of magnitude Δ B . Starting from the Schwinger representation for a propagator in the presence of a constant magnetic field, we use the method of replicas to determine the effect of the magnetic noise on the shape of the renormalized quasi-particle parameters. Leading to an effective charge and an effective refraction index that depend not only on the energy scale, as usual, but also on the magnitude of the noise ΔB and the average field B.

Smoothing of the Noisy Lithium-ion Battery Surface Temperature Data Based on Savitzky–Golay (SG) Filter

As the charging current and surrounding temperatures rise concurrently in an electric vehicle's (EV) battery pack, the battery temperature increases abruptly beyond safe limits. The battery is susceptible to triggering thermal runaway at higher temperatures, leading to battery damage and even an explosion. The control system depends on these measurement data from the sensors to prevent dangerous situations and maximize the performance and cycle life of batteries. However, traditional noisy temperature measurement sensors in a real application, such as thermistors and thermocouples, are extensively used. In this paper, the experimental setup, the heat pipe-based battery thermal management system (BTMS) was designed and experimented with high input power. The battery was sandwiched with heat pipes, and heated at 30, 40, 50, and 60 W. The Savitzky-Golay filter technique is applied to the noisy temperature data of the surface temperature of the lithium-ion batteries (LIBs) used to estimate the condition of the battery. According to this study, the start-up time parameter in battery thermal management can be controlled by the Savitzky-Golay filter. This will attenuate random temperature fluctuations of battery temperature noise and avoid the cooling system from being falsely triggered. The results are measured by the signal-to-noise (SNR) to demonstrate the ability of the Savitzky-Golay filter to eliminate noise

Design of a optimal robust adaptive neural network-based fractional-order PID controller for H-bridge single-phase inverter

Adaptive Neural Network- based Fractional-order PID Control (NN-FO-PID) approach is designed for H-bridge inverter. This inverter has an LC filter to decrease the level of Total Harmonic Distortion (THD) that can affect the efficiency of the system. In addition, the reduction of THD and stability insurance of the filter are challenging performances. To fulfill this function, a fractional order proportional integrated derivative (FO-PID) controller is developed for the inverter. A few merits of the Fractional-Order notion as a useful technique include reduced sensitivity to noise and parametric fluctuation; however, for a wider range of disturbances, such as noise, this approach shows an unsuitable practical application based on its fixed gain values. Moreover, having parameters uncertainties including parametric variations, load uncertainty, supply voltage variation uplifts this challenging condition severely, and the parameters need to be adjusted once more for more dependable operations. As a result, the control parameters must be optimized once more to provide ideal operations. Here, an adaptive mechanism is proposed based on neural network structure to optimize the gains of the FO-PID controller for better performances. In real applications, this approach has some benefits, since it uses the Black-box technique, which does not necessitate a precise mathematical model of the system, resulting in a reduced computational burden, simple implementation, and reduced dependence on the model’s states. Even under extremely difficult circumstances, the artificial neural network structure effectively optimizes the FO-PID gains in real-time. This benefit can reduce the amount of THD-level for the inverter, properly. Additionally, to have a proper response in the first step condition, and trying to reduce the level of dangerous conditions, based on benefits of the ant lion optimization method, it is considered to select the initial values of the FO-PID controller gains. Here to verify the superiority of the proposed controller, PID and FO-PID controllers are offered to drive a comparison with this method, which are optimized using the PSO optimization algorithm. The analysis of simulation data shows that the suggested control strategy is appropriate for maintaining stability as well as adequately compensating for disturbances and uncertainties in the inverter. Furthermore, with the help of this controller, THD level decreased lower than 2 percent.

A space-borne ultra-stable laser system with an excellent long-term frequency stability for gravitational wave detection

Abstract Ultra-stable lasers are pivotal in various scientific applications, notably in space gravitational wave detection projects. We develop a space-borne ultra-stable laser system based on a home-made non-planar ring oscillator (NPRO) laser and an ultra-stable cavity laser stabilization system. The ultra-stable cavity is a vertically mounted 8 cm long cavity, with tunable zero-crossing temperature and low vibrational sensitivity. To make a cavity with any standard grade ultra-low expansion glass (ULE) material, and tune the zero-crossing temperature to the satellite platform temperature, we design three ultra-stable cavities with different configurations to unambiguously explore their thermal properties. The measurement results meet the design goals well, and the zero-crossing temperature of the cavity can be tuned from − 19 ∘ C to 16.0 °C. We measure the temperature fluctuation noise through modulation experiment, and it agrees well with the theoretical simulations. The vibrational sensitivities in three directions are measured to be around 10−11 /g–10−10 /g. The total weight of the system is 14.0 kg, with a volume of about 18 L, and the power dissipation of the electrical system is 18.6 W. Finally, the prototype of the space-borne laser shows a frequency instability of 9.5 × 10 − 16 at 0.2 s, and the frequency noise is measured to be 3.6 Hz/Hz1/2 at 6 mHz over three months, satisfying the mission targets of all current space gravitational wave detection programs.

Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering

One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret information from these videos. One-photon imaging videos’ low resolution, high noise, and high background fluctuation pose significant challenges. Here, we develop a software pipeline to address the challenges of processing one-photon calcium imaging videos. We extend our previous two-photon active neuron segmentation algorithm, Shallow U-Net Neuron Segmentation (SUNS), to better suppress background fluctuations in one-photon videos. We also develop additional neuron extraction (ANE) to locate small or dim neurons missed by SUNS. To train our segmentation method, we create ground truth neurons by developing a manual labeling pipeline assisted with semi-automatic refinement. Our method is more accurate and faster than state-of-the-art techniques when processing simulated videos and multiple experimental datasets acquired over various brain regions with different imaging conditions.

Buffering effects of nonspecifically DNA-bound RNA polymerases in bacteria

RNA polymerase (RNAP) is the workhorse of bacterial gene expression, transcribing rRNA and mRNA. Experiments found that a significant fraction of RNAPs in bacteria are nonspecifically bound to DNA, which is puzzling as these idle RNAPs could have produced more RNAs. Whether nonspecifically DNA-bound RNAPs have any function or are merely a consequence of passive interaction between RNAP and DNA is unclear. In this work, we propose that nonspecifically DNA-bound RNAPs buffer the free RNAP concentration and mitigate the crosstalk between rRNA and mRNA transcription. We verify our theory using mean-field models and an agent-based model of transcription, showing that the buffering effects are robust against the interaction between RNAPs and sigma factors and the spatial fluctuation and temporal noise of RNAP concentration. We analyze the relevant parameters of and find that the buffering effects are significant across different growth rates at a low cost, suggesting that nonspecifically DNA-bound RNAPs are evolutionarily advantageous. Published by the American Physical Society 2024

Dynamic quantitative phase microscopy: a single-shot approach using geometric phase interferometry

There is a significant gap in cost-effective quantitative phase microscopy (QPM) systems for studying dynamic cellular processes while maintaining accuracy for long-term cellular monitoring. Current QPM systems often rely on complex and expensive voltage-controllable components like Spatial Light Modulators or two-beam interferometry. To address this, we introduce a QPM system optimized for time-varying phase samples using azobenzene liquid crystal as a Zernike filter with a polarization-sensing camera. This system operates without input voltage or moving components, reducing complexity and cost. Optimized for gentle illumination to minimize phototoxicity, it achieves a 1 Hz frame rate for prolonged monitoring. The system demonstrated accuracy with a maximum standard deviation of ±42 nm and low noise fluctuations of ±2.5 nm. Designed for simplicity and single-shot operations, our QPM system is efficient, robust, and precisely calibrated for reliable measurements. Using inexpensive optical components, it offers an economical solution for long-term, noninvasive biological monitoring and research applications.

Experimental investigation on aerodynamic noise of a small-scale multi-blade centrifugal fan

In this study, the aerodynamic noise of a small-scale centrifugal fan was experimentally investigated using acoustic testing techniques and time-resolved stereoscopic particle image velocimetry (SPIV). During the acoustic experiments, both far-field noise and near-field pressure fluctuations of the test fan were measured. The overall far-field noise towards the fan inlet side was found to be higher than that of the back side. The pressure fluctuations on the fan upper casing exceeded those on the side wall due to the uncontracted volute tongue, indicating pronounced flow-to-wall interactions. Moreover, based on a simultaneous measurement, the coherence between the near-field pressure fluctuations and far-field noise highlighted the significant contributions of impeller rotation to noise radiation. SPIV measurements uncovered the time-averaged and transient flow fields at the fan's inlet and outlet. The time-averaged results demonstrated the concentrated inlet flow and outlet flow separation, leading to high flow unsteadiness. Transient flow fields displayed an asymmetric jet-wake region characterized by both quasi-steady flow and rotational flow behaviours. The instantaneous flow results were analyzed using the dynamic mode decomposition (DMD) method, which clearly recognized the jet-wake patterns with frequencies corresponding to the rotational frequency. The observed consistency in frequency characteristics among noise, pressure fluctuations, and unsteady flow affirms that flow dynamics are crucial to the primary noise mechanisms.

Device and Noise Performances of AlGaN/GaN High Electron Mobility Transistors with Various GaN Channel Layers Grown on AlN Buffer Layer

AlGaN/GaN high electron mobility transistors (HEMTs) with different GaN channel layers grown on AlN buffer layer are fabricated and investigated in order to optimize the device performances and to study the noise properties. To investigate the strain effect of the GaN channel layer grown on the AlN buffer layer, the positive shift of Raman peak is observed as the GaN channel becomes thinner. The threshold voltages (Vth) of the fabricated devices shift to positive direction according to the decreased GaN channel layer due to the decreased 2‐dimensional electron gas (2DEG) and deteriorated crystal quality of GaN channel layer. All devices demonstrated 1/f noise properties and the dominance of the carrier number fluctuations (CNF) noise mechanism. The largest trap density (Nt) value in the narrowest GaN channel device is because of the degraded crystal quality and the enhanced strain effect of the GaN channel layer. However, the lowest noise levels at the drain current (Id) > 10−6 A for the device with the GaN channel thickness of 30 nm grown on AlN buffer layer are observed to be due to the fully depleted GaN channel layer although its poor crystal quality.

Electronic Keyboard Performance based multimodal data fusion and noise fluctuation analysis using machine learning

Electronic Keyboard Performance based multimodal data fusion and noise fluctuation analysis using machine learning

Optical network noise fluctuation analysis using Markov Reinforcement Attention Fuzzy model

Optical network noise fluctuation analysis using Markov Reinforcement Attention Fuzzy model

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives.

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.