White Noise Research Articles

Speckle denoising based on Swin-UNet in digital holographic interferometry

Speckle noise, mechano-physical noise, and environmental noise are inevitably introduced in digital holographic coherent imaging, which seriously affects the quality of phase maps, and the removal of non-Gaussian statistical noise represented by speckle noise has been a challenging problem. In the past few years, deep learning methods based on convolutional neural networks (CNNs) have made good progress in removing Gaussian noise. However, they tend to fail when these deep networks designed for Gaussian noise removal are used to remove speckle noise. Recently, numerous studies have employed CNNs to address the issue of degraded speckle images, yielding encouraging results. Nevertheless, the degradation of speckle noise that is simulated in isolation is limited and insufficient to encompass the increasingly complex DHI noise environment. This paper presents what we believe to be a novel approach to simulating complex noise environments by multiplexing simulated Gaussian noise and speckle noise. The noise resulting from aliasing does not adhere to the statistical laws of the noise prior to aliasing, which poses a more challenging task for the noise-reduction algorithms utilized in neural networks. Consequently, in conjunction with the capacity of the Swin Transformer to model multi-scale features, this paper proposes a DHI speckle denoising approach based on Swin-UNet. In this paper, Gaussian, speckle, and blending noise datasets with different noise densities are constructed for training and testing by numerical simulation, and generalizability tests are performed on 1,100 randomly selected open-source holographic tomography (HT) noise images at Warsaw University of Technology and 25 speckle images selected from DATABASE. All test results are quantitatively evaluated by three evaluation metrics: mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). All convolutional neural network (CNN) algorithms are evaluated qualitatively based on the number of parameters, floating point operations, and denoising time. The results of the comparison demonstrate that the denoising algorithm presented in this paper exhibits greater stability, accuracy, and generalizability.

Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.

Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistency of neural responses to stimuli that are presented repeatedly is enhanced implicitly in an unsupervised way, and results in improved perceptual consistency. Here, we propose the term "selective consistency" to describe this input-dependent consistency and hypothesize that it will be acquired in a self-organizing manner by plasticity within the neural system. To test this, we investigated whether a reservoir-based plastic model could acquire selective consistency to repeated stimuli. We used white noise sequences randomly generated in each trial and referenced white noise sequences presented multiple times. The results showed that the plastic network was capable of acquiring selective consistency rapidly, with as little as five exposures to stimuli, even for white noise. The acquisition of selective consistency could occur independently of performance optimization, as the network's time-series prediction accuracy for referenced stimuli did not improve with repeated exposure and optimization. Furthermore, the network could only achieve selective consistency when in the region between order and chaos. These findings suggest that the neural system can acquire selective consistency in a self-organizing manner and that this may serve as a mechanism for certain types of learning.

Enhancing the sensitivity of spin-exchange relaxation-free magnetometers using phase-modulated pump light with external Gaussian noise

An optical pumping scheme is proposed for reducing the gradient of electron spin polarization and suppressing light source noise in a spin-exchange relaxation-free magnetometer. This is achieved by modulating only the phase of a narrow-linewidth pump light field with external Gaussian noise. Compared to the absence of phase modulation, the uniformity of electron spin polarization was improved by over 40%, and the light-frequency noise suppression ratio of the magnetometer was enhanced by 4.3 times. Additionally, the response of the magnetometer was increased by 54%, resulting in a sensitivity of 0.34 fT/Hz1/2 at 30 Hz. The applicability of this scheme can extend to other optical pumping experiments involving large atom ensembles requiring uniform electron spin polarization distribution, which is beneficial for developing ultra-high sensitivity and high stability magnetometers essential for magneto-cardiography and magneto-encephalography research applications.

Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility

Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility.

Extension of the characterization of non-Gaussianity in gravitational wave detectors with a statistical hypothesis test

Abstract In gravitational wave astronomy, non-Gaussian noise, such as scattered light noise disturbs stable interferometer operation, limiting the interferometer's sensitivity, and reducing the reliability of the analyses. In scattered light noise, the non-Gaussian noise dominates the sensitivity in a low frequency range of less than a few hundred Hz, which is sensitive to gravitational waves from compact binary coalescence. This non-Gaussian noise prevents reliable parameter estimation, since several analysis methods are optimized only for Gaussian noise. Therefore, identifying data contaminated by non-Gaussian noise is important. In this work, we extended the conventional method to evaluate non-Gaussian noise, the Rayleigh statistic, by using a statistical hypothesis test to determine a threshold for non-Gaussian noise. First, we estimated the distribution of the Rayleigh statistic against Gaussian noise, called the background distribution, and validated that our extension serves as the hypothetical test. Moreover, we investigated the detection efficiency by assuming two non-Gaussian noise models. For example, for the model with strong scattered light noise, the true positive rate was always above 0.7 when the significance level was 0.05. The results showed that our extension can contribute to an initial detection of non-Gaussian noise and lead to further investigation of the origin of the non-Gaussian noise.

Determining the maximum of solar cycle 25 with a memory modulated white noise

The exact determination of the solar maximum of a solar cycle before it occurs remains an open problem in solar physics. Using a memory modulated white noise framework, a procedure is presented to determine the still unknown solar maximum of solar cycle 25. We investigate the first thirty months of each of the past 24 solar cycles from 1755 to 2019 using a second order structure function derived from white noise analysis. Specifically, the mean square deviation (MSD) of the fluctuating monthly sunspot numbers for the first 30 months of a solar cycle is matched with the theoretical MSD to obtain a value for the memory parameter μ. By similarly determining the value of μ for the first 30 months of solar cycle 25, a forecast is made for its solar maximum.

Efficient and Robust Arabic Automotive Speech Command Recognition System

The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources.

Optical soliton solutions in birefringent fibers with multiplicative white noise: an analysis for the perturbed Chen–Lee–Liu model

Optical soliton solutions in birefringent fibers with multiplicative white noise: an analysis for the perturbed Chen–Lee–Liu model

Defensive behaviors and c-fos expression in the midbrain.

Pavlovian fear conditioning serves as a valuable method for investigating species-specific defensive reactions (SSDRs) such as freezing and flight responses. The present study examines the role of white noise under different experimental conditions. Given that white noise has been shown to elicit both conditional (associative) and unconditional (nonassociative) defensive responses, we compared the response to noise following three separate training conditions: shock-only, white noise paired with shock, and context-only. Results showed that baseline freezing level significantly changed across groups: Both the shock-only group and the white noise paired with shock group froze more than the context-only group on the test day. White noise evoked differential freezing between groups on day 2: The shock-only group froze more than the context-only group although both groups were never exposed to white noise during training. Further, an activity burst triggered by white noise was similar for the shock-only and white noise paired with shock groups during testing, although shock-only group was never exposed to white noise stimuli during training. This aligned with c-fos data, indicating similar c-fos activity levels across different periaqueductal gray (PAG) regions for both shock-only and white noise paired with shock groups. However, the driving force behind c-fos activation-whether freezing, activity burst, or a combination of both-remains uncertain, warranting further analysis to explore specific correlations between SSDRs and c-fos activity within the PAG and related brain areas.

Sufficient condition for reliable logic operations in an over damped bistable system driven by Gaussian white noise.

In this paper, we investigate an overdamped bistable system subject to Gaussian white noise and logical inputs. By solving and estimating the steady distribution of the corresponding Fokker-Planck equation and considering the two essential features of the reliable logic operations (RLOs)-initial value independence and sign invariance-we establish the sufficient condition for RLO occurrence and identify parametric resonance phenomena in the system. Our numerical simulations confirm the reliability and accuracy of the theoretical results. This work offers insights into enhancing the accuracy of stochastic systems, particularly in the realm of logical stochastic resonance, thereby contributing to advancements in understanding and controlling stochastic dynamical systems.

Distributed target detection based on gradient test in deterministic subspace interference

This paper investigates the problem of distributed target detection in the presence of interference and Gaussian noise, where the target signal and interference are assumed to lie in different deterministic subspaces. Building upon this assumption, we propose several adaptive detectors resorting to the gradient criterion tailored for homogeneous environment and partially homogeneous environment. Simulation results indicate that the proposed gradient-based detectors outperform their competitors in some scenarios. Furthermore, all of these Gradient-based detectors exhibit the constant false alarm rate (CFAR) property.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

New Fault Diagnosis Method for Rolling Bearings Based on Improved Residual Shrinkage Network Combined with Transfer Learning

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance.

Convergence analysis of a fully discrete scheme for diffusion-wave equation forced by tempered fractional Brownian motion

In this paper, we investigate a fully discrete approximation of stochastic diffusion-wave equation driven by additive tempered fractional Gaussian noise. This additive noise exhibits semi-long range dependence. The model involves two nonlocal terms in time, i.e., a Caputo fractional derivative and a tempered fractional Brownian motion. The space discretization is achieved via the spectral Galerkin method. The Caputo fractional derivative of order α∈(1,2) is approximated by using the Grünwald–Letnikov formula. Combining Mittag-Leffler function, Laplace transform and z-transform, we establish the error estimates of the full discretization in the sense of mean-squared L2-norm. Numerical experiments are presented to confirm the strong convergence rates.

Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties

Precise physical properties of the known transiting exoplanets are essential for their precise atmospheric characterization using modern and upcoming instruments. Leveraging the large volume of high-signal-to-noise-ratio photometric follow-up data from TESS, highly precise physical properties can be estimated for these systems, especially for those discovered using ground-based instruments prior to the TESS mission. In this work, I have used the publicly available TESS follow-up data for 28 transiting systems with 10 < V mag < 10.5, with an aim to update their known physical properties. The observed lightcurves have been analyzed by implementing a state-of-the-art critical noise treatment algorithm to effectively reduce both time-correlated and uncorrelated noise components, using sophisticated techniques like wavelet denoising and Gaussian-process regression. Compared with the previous studies, the estimated transit parameters are found to be more precise for most of the targets, including a few cases where a larger space-based instrument like Spitzer, Kepler, or CHEOPS has been used in the previous study. The large volume of transit observations used for each target has also resulted in a more accurate estimation of the physical properties, as this overcomes any error in parameter estimations from bias present in a smaller volume of data. Thus, comparing with the literature values, statistically significant improvements in the known physical properties of several targeted systems have been reported from this work. The large volume of transit-timing information from the analyses was also used to search for transit-timing variation trends in these targets, which has resulted in no significant detection.

Competitive exclusion and coexistence in a general two-species stochastic chemostat model with Markov switching

In this paper, a stochastic framework with a general nonmonotonic response function is formulated to investigate the competition dynamics between two species in a chemostat environment. The model incorporates both white noise and telegraph noise, the latter being described by Markov process. The existence of a unique global positive solution for the stochastic chemostat model is established. Subsequently, by using the ergodic theory of Markov process and utilizing techniques of stochastic analysis, the critical value differentiating between persistence in mean and extinction for the microorganism species is explored. Moreover, the existence of a unique stationary distribution is proved by using stochastic Lyapunov analysis. Finally, numerical simulations are introduced to support the obtained results.

Noise Reduction Using Sparsity Constrained and Regularized Iterative Thresholding Algorithm and Dictionary

Dictionary-based methods are recognized for their ability to estimate clear speech from speech contaminated with noise. However, these techniques face a limitation in distinguishing between speech and noise components. The presented algorithm addresses this challenge by introducing an innovative solution through an iterative thresholding method. In this method, the thresholding is based on noise characteristics and remains independent of variations in noise power. To determine the stopping criteria for thresholding, the algorithm leverages the structure of the speech signal in time–frequency domains using the Gini Index. Remarkably, this technique adeptly extracts reliable signal components from a noisy time–frequency magnitude spectrum, performing well under non-varying and varying Signal-to-Noise Ratio (SNR) conditions. Importantly, it achieves this without the need for mask functions, voice activity detection techniques, noise or mixture dictionaries, or SNR information, which are essential components in other dictionary-based methods. Following the thresholding process, the clean speech is assessed using a dictionary-based approach to restore perceptual loss. In evaluating its performance, the algorithm is compared with traditional enhancement techniques concerning perceptual evaluation of speech quality and short-time objective intelligibility. The assessment is conducted under various background noise conditions, including babble, white, factory, and Volvo noises. The proposed algorithm exhibits superior performance, particularly in low and varying SNR scenarios.

SAR Image Despeckling Based on Denoising Diffusion Probabilistic Model and Swin Transformer

The speckle noise inherent in synthetic aperture radar (SAR) imaging has long posed a challenge for SAR data processing, significantly affecting image interpretation and recognition. Recently, deep learning-based SAR speckle removal algorithms have shown promising results. However, most existing algorithms rely on convolutional neural networks (CNN), which may struggle to effectively capture global image information and lead to texture loss. Besides, due to the different characteristics of optical images and synthetic aperture radar (SAR) images, the results of training with simulated SAR data may bring instability to the real-world SAR data denoising. To address these limitations, we propose an innovative approach that integrates swin transformer blocks into the prediction noise network of the denoising diffusion probabilistic model (DDPM). By harnessing DDPM’s robust generative capabilities and the Swin Transformer’s proficiency in extracting global features, our approach aims to suppress speckle while preserving image details and enhancing authenticity. Additionally, we employ a post-processing strategy known as pixel-shuffle down-sampling (PD) refinement to mitigate the adverse effects of training data and the training process, which rely on spatially uncorrelated noise, thereby improving its adaptability to real-world SAR image despeckling scenarios. We conducted experiments using both simulated SAR image datasets and real SAR image datasets, evaluating our algorithm from subjective and objective perspectives. The visual results demonstrate significant improvements in noise suppression and image detail restoration. The objective results demonstrate that our method obtains state-of-the-art performance, which outperforms the second-best method by an average peak signal-to-noise ratio (PSNR) of 0.93 dB and Structural Similarity Index (SSIM) of 0.03, affirming the effectiveness of our approach.

Identification of anomaly of tunnel segment strain using an adaptive machine learning model

ABSTRACT Anomaly identification is a crucial issue for preventing diseases in tunnel structures. However, distinguishing whether the structure is abnormal from outliers is challenging, as the monitoring dataset composed of time series may contain non-structural abnormal data caused by operational environmental pollution. Therefore, this study aims to propose a structural anomaly identification model to distinguish the structural anomaly from the polluted dataset based on an improved autoencoder model with adaptive loss function modification (AAE). To solve the problem of insufficient abnormal samples in practice, the proposed model is applied to a time series dataset obtained from numerical simulation, where extreme conditions are introduced to generate structural abnormal data, and Gaussian noises are overlaid to pollute the raw data and generate non-structural abnormal data. Then, the anomaly identification capability of the AAE model under different intensities of noise pollution conditions is discussed, along with its reliability compared to other common methods. Experimental results demonstrate that the AAE model can effectively identify structural anomaly information from heavily polluted datasets. The accuracy of the AAE model improved by at least 4% to other models even on a serious polluted dataset. Therefore, the structural anomaly identification model presented in this study is reliable.

Multi-method piecewise low-frequency identification

Inter-area frequency modes of electromechanical oscillations are decisive factors that influence the performance of electrical power systems. However, the accurate estimation of these frequencies remains a challenge due to the non-linear behavior of the response system following a disturbance and the strong coupling among the frequencies. This work describes a curve fitting and electromechanical frequency estimation algorithm, called Multi-method Piecewise Identification (MPI), based on a combination of the Matrix Pencil Method (MPM), the Prony method, and Subspace Identification (SSI), choosing the best approximation of an interval basis according to the model accuracy index (MAI) and the mean square error (MSE). The advantages and limitations of MPI were analyzed by computational analyses that were carried out with Python-based code that estimates the low frequencies for several ringdown signals, including oscillating signals from Australian and Brazilian systems that arise after large disturbances. To ensure robustness to random and noisy conditions, the method was tested with a synthetic signal with Gaussian white noise and time-variant frequencies, and compared to the methods mentioned above, the Hilbert–Huang Transform (HHT), and Eigensystem Realization Algorithm (ERA). The MPI has shown robust results and is capable of obtaining a better fitting for the signal than the other methods.