Gaussian Background Noise Research Articles

Computer diffraction tomography: a comparative analysis of the use of controlled and wavelet filters for image processing

The paper provides digital processing of 2D X-ray projection images of a Coulomb-type point defect in a Si(111) crystal recorded by a detector against the background of statistical Gaussian noise. A managed filter and a wavelet filter with a 4th-order Daubechies function are used. The efficiency of filtering 2D images is determined by calculating the relative quadratic deviations of the intensities of filtered and reference (noiseless) 2D images averaged over all points. A comparison of the calculated values of the relative deviations of the intensities shows that the considered methods work quite well and both, in principle, can be effectively used in practice for noise processing of X-ray diffraction images used for 3D reconstruction of nanoscale defects of crystal structures.

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.

Quasi-likelihood estimation of the arrival time of an ultra-wideband quasi-radio signal with unknown amplitude and initial phase

The problem of estimating the time of arrival of a signal is relevant in the fields of radio and hydrolocation, radio astronomy, remote observation and sensing, etc. The useful signal is often described by a quasi-deterministic function of time, which can be regular or discontinuous. In recent years, problems of processing ultra-wideband signals have been of significant interest. Among the many UWB signals, it is customary to distinguish a separate class of ultra-wideband quasi-radio signals, the structure of which is similar to the structure of narrowband radio signals, but the condition of relative narrowband for which may not be met. Algorithms for estimating the arrival time of regular ultra-wideband quasi-radio signals have been studied in the existing literature. In this case, the synthesis and analysis of an algorithm for quasi-plausible estimation of the arrival time of a discontinuous signal of finite duration and arbitrary shape is of practical interest. The purpose of the work is to synthesize and analyze a quasi-likelihood algorithm for estimating the time of arrival of a discontinuous ultra-wideband quasi-radio signal with an unknown amplitude and initial phase against a background of white Gaussian noise. In this work, closed asymptotically exact expressions for the bias and scattering of the estimate are obtained, which make it possible to study the effectiveness of the synthesized algorithm, as well as compare its effectiveness with special cases of signal models - narrowband and wideband radio and video signals. The results of the study make it possible to determine losses in the accuracy of estimation the signal arrival time due to a priori ignorance of the initial phase and shape of the modulating function of the signal. Depending on the required accuracy of estimate the time of arrival of an ultra-wideband quasi-radio signal, it is possible to optimize the parameters of the transmitted signals and determine the limits of applicability of the designed radio system.

Detection of phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise

In conditions of a complex electromagnetic environment in communication and data transmission systems, a change in the type of modulation occurs when one or more signal parameters change abruptly, therefore the task of determining the type of signal modulation can be reduced to the task of detecting a disorder, that is, detecting the fact of a change in the type of modulation of the signal and estimation of the disorder time. The purpose of the work is to synthesize an algorithm for detecting phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals with unknown amplitude and initial phase against a background of white Gaussian noise and to analyze the effectiveness of the resulting algorithm based on the results of statistical modeling. The work synthesizes an algorithm for detecting a phase modulation disorder of a received sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise. To analyze the effectiveness of the resulting algorithm, statistical modeling of algorithm for detecting a phase modulation disorder is carried out. The dependences of the probability of detection of a disorder on the signal-to-noise ratio and the length of the sequence of received signals at a fixed moment of disorder are obtained. A comparison of the detection characteristics of phase modulation disorder of ultra-wideband quasi-radio signals and narrowband radio signals is made. It is shown that when a low-order phase modulation disorder is detected, the required values of the probability of detection are achieved at low signal-to-noise ratios, while with an increase in the modulation order and the use of its modifications, the efficiency indicators of the algorithm will deteriorate. The introduction of algorithms for detecting a disorder in radio engineering applications will allow optimizing synchronization algorithms and adaptation to interference conditions with a concomitant change in the type of modulation, as well as reducing the amount of transmitted service information in telecommunication systems.

Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering

Introduction. The use of non-equidistant pulse sequences as probing radar signals makes it possible to eliminate blind spots in speed and range. However, the implementation of multi-channel Doppler filtering (MDF) based on the classical fast Fourier transform (FFT) algorithm of non-equidistant signal samples in the signal detection problem is associated with energy losses. The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. To eliminate this drawback, the authors previously proposed using combined classical and modified FFT algorithms. However, the use of the combined method does not lead to an optimal solution in terms of MDF efficiency.Aim. Optimization of weight processing of non-equidistant signals to improve the efficiency of MDF.Materials and methods. An MDF synthesis was carried out using optimization procedures, and the effectiveness of the algorithms was assessed using computer calculations.Results. The results show that the Kaiser Bessel window with a window parameter of 4.42 provides the highest signal-(clutter+noise) ratio improvement coefficient averaged over frequency channels equal to 30.06 dB and the highest probability of correct signal detection averaged over MDF channels equal to 0.5 at processing of non-equidistant pulse sequences. Optimization of the weight processing of MDF under the specified conditions increased the average efficiency characteristics used of up to 53.18 dB and 0.92, respectively.Conclusion. Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. However, these advantages are achieved at the cost of not using the FFT, i.e., implemented within the framework of the discrete Fourier transform (DFT) algorithm.

Fluctuation-response relations for integrate-and-fire models with an absolute refractory period.

We study the problem of relating the spontaneous fluctuations of a stochastic integrate-and-fire (IF) model to the response of the instantaneous firing rate to time-dependent stimulation if the IF model is endowed with a non-vanishing refractory period and a finite (stereotypical) spike shape. This seemingly harmless addition to the model is shown to complicate the analysis put forward by Lindner Phys. Rev. Lett. (2022), i.e., the incorporation of the reset into the model equation, the Rice-like averaging of the stochastic differential equation, and the application of the Furutsu-Novikov theorem. We derive a still exact (although more complicated) fluctuation-response relation (FRR) for an IF model with refractory state and a white Gaussian background noise. We also briefly discuss an approximation for the case of a colored Gaussian noise and conclude with a summary and outlook on open problems.

Asymptotic normality of the LSE for chirp signal parameters

A time continuous statistical model of chirp signal observed against the background of stationary Gaussian noise is considered in the paper. Asymptotic normality of the LSE for parameters of such a sinusoidal regression model is obtained.

Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging.

Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method's detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% ( F1 ) for detection accuracy.

Adaptive double subspace target detection based on gradient test: Designs and comparisons

The issue of adaptive detection for double subspace (DOS) signals under the Gaussian noise background is investigated in this paper. The DOS signal has the special structure where the rows and columns of the matrix-valued signal lie in known subspaces with unknown coordinates. To deal with the DOS signal detection problem, two kinds of Gradient tests are proposed in homogeneous environments and partially homogeneous environments. Remarkably, the Durbin test, as well as its two-step version, is also used for the detector design, which is consistent with the existing methods. To evaluate the performance of the proposed methods, both simulated data and real data collected by IPIX radar are used. The results indicate that the Gradient tests ensure the constant false alarm rate property. Furthermore, the proposed detectors obtain relatively robust characteristics and achieve superior performance to existing competitors in some scenarios.

Consistency of the LSE for Chirp Signal Parameters in the Models with Strongly and Weakly Dependent Noise

A time continuous statistical model of multiple chirp signal observed against the background of strongly-dependent stationary Gaussian noise is considered in the paper. Strong consistency of the least squares estimates for such a trigonometric regression model parameters is proved.

Robust and Energy Efficient Sparse-Coded OFDM-DCSK System via Matrix Recovery

In this paper, we devise a sparse-coded orthogonal frequency division multiplexing (OFDM) differential chaos shift keying (DCSK) communication system based on low-rank matrix recovery which can handle Gaussian background noise and outlier-contaminated symbols simultaneously. As the noise-free OFDM-DCSK symbol matrix has rank 1, we exploit the vector outer product for its modeling, while sparse coding is also applied to reduce the transmission energy. To demodulate information bits from the sparse-coded signal, we formulate an objective function which consists of a sum of Frobenius norm for rank-1 matrix recovery and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm for identifying the possibly outlier-contaminated symbols, with a self-adaptive weight parameter. The resultant optimization problem is solved iteratively via block coordinate descent, and the Laplacian kernel with the Silverman’s rule is adopted for outlier detection. Theoretical analysis including convergence of the objective function, bit error rate (BER), energy efficiency and computational complexity, are provided. Simulation results show that the proposed system has comparable mean square error and BER performance with the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>p</i></sub> -norm minimization based matrix recovery approach at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> = 2 in additive white Gaussian noise, and is superior to that of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> = 1 in Middleton class A noise, even when sparse coding is applied. Moreover, compared with other binary DCSK systems, our system achieves higher energy efficiency thanks to the sparse coding.

Performance Evaluation of Environmental Sound Classification: A Machine Learning Stacking and Multi-Criteria Metrics Based Approach

This study proposes an Environment Sound Classification Task (ESC) model that includes numerous element channels given as a contribution to Machine learning with an Attention instrument. ESC is a significant testing issue. The interest in the paper lies in utilizing different part channels involving the MFCCs-Mel Frequency Cepstral Coefficients a mutual module in speaker detection and artificial speech systems. LPCs-Linear Prediction Coefficients and Linear Prediction Cepstral Coefficients were the most commonly used types in ASR- Automated speech recognition. The paper also discusses some basic features of MFCCs and how to put them into practice. The techniques used for this project are Background Gaussian Noise and Time Shifting, by observing that every technique is implemented with a provided probability, however, when at the time of generation of a new sample the same spectrogram input may have several combinations. We go through the blend information expansion method to additional lift execution. Our model can accomplish cutting-edge execution on three benchmark climate sound characterization datasets, for example, the UrbanSound8K.

IDBD-Based Beamforming Algorithm for Improving the Performance of Phased Array Radar in Nonstationary Environments

Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the tap weights is fixed, leading to errors in the beam pattern and a reduced output signal-to-noise ratio (SNR). In this paper, we use the incremental delta-bar-delta (IDBD) algorithm, which has been widely used for system identification problems in nonstationary environments, to control the time-varying learning rates of the tap weights. The designed iteration formula for the learning rate ensures that the tap weights adaptively track the Wiener solution. The results of numerical simulations show that in a nonstationary environment, the traditional gradient descent algorithm with a fixed learning rate has a distorted beam pattern and reduced output SNR; however, the IDBD-based beamforming algorithm, in which a secondary control mechanism is used to adaptively update the learning rates, showed a similar beam pattern and output SNR to a traditional beamformer in a Gaussian white noise background; that is, the main beam and null satisfied the pointing constraints, and the optimal output SNR was obtained. Although the proposed algorithm contains a matrix inversion operation, which has considerable computational complexity, this operation could be replaced by the Levinson-Durbin iteration due to the Toeplitz characteristic of the matrix; therefore, the computational complexity could be decreased to O(n), so additional computing resources are not required. Moreover, according to some intuitive interpretations, the reliability and stability of the algorithm are guaranteed.

Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis

The vibration signal of a faulty rolling bearing exhibits typical non-stationarity – often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of envelope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods.

A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement

Improving the line-spectrum detection capability of a single hydrophone is of great significance for the passive detection of small underwater platforms. In this paper, we propose a single-hydrophone cross-power spectrum (SHCS) method based on time-domain coherence. This method uses the coherence of the line spectrum and the non-coherence of the continuous spectrum noise to obtain coherent gain and improve the signal-to-noise ratio (SNR) of the line spectrum. The effects of the input SNR, number of averaging operations, and overlap ratio on the performance of the SHCS method under a background of Gaussian white noise are simulated and analyzed. The results show that when the overlap ratio is 0 and the number of averaging operations reaches saturation, the SHCS method can achieve the best performance and about 15 dB coherence gain is obtained. The performance of the SHCS method was verified by sea experiments. Under the extremely low input SNR, in which the line spectrum was almost completely submerged in the marine environmental noise, the SHCS method can obtain about 10 dB coherence gain. Under the conventional input SNR, in which the line spectrum could be observed, the SHCS method can obtain about 13 dB coherence gain. The results of processing the radiated noise from an actual cargo ship also demonstrate the effectiveness of the SHCS method.

Identifying glitches near gravitational-wave signals from compact binary coalescences using the Q-transform

We present a computational method to identify glitches in gravitational-wave data that occur nearby gravitational-wave signals from compact binary coalescences. The Q-transform, an established tool in LIGO-Virgo-KAGRA data analysis, computes the probability of any excess in the data surrounding a signal against the assumption of a Gaussian noise background, flagging any significant glitches. Subsequently, we perform validation tests on this computational method to ensure self-consistency in colored Gaussian noise, as well as data that contains a gravitational-wave event after subtracting the signal using the best-fit template. Finally, a comparison of our glitch identification results from real events in LIGO-Virgo’s third observing run against the list of events which required glitch mitigation shows that this tool will be useful in providing precise information about data quality to improve astrophysical analyses of these events.

Robust Denoising of Phonocardiogram Signals using Time-Frequency Analysis and U-Nets

Cardiovascular diseases are the cause of many deaths worldwide every day. Automated cardiac auscultation is a promising diagnosis method; however, one of its main disadvantages is that it is prone to receiving too much noise during sound recording, which hinders diagnosis. In the available literature, most phonocardiogram (PCG) denoising methods have been evaluated using only synthetic sources such as white noise; this work proposes a more realistic approach. In this paper, the denoising process occurs in the time-frequency domain. More specifically, we compute the Short-Time Fourier Transform (STFT) of the contaminated PCG signal and train a U-Net to recognize normal and pathological cardiac sounds from noise. We are unaware of previous attempts to develop a robust PCG-denoising algorithm capable of simultaneously removing noise signals from four different sources: additive white Gaussian noise (AWGN), additive pink Gaussian noise (APGN), speech, and real PCG background noise. Since we are limited by the relatively small number of clean PCG signals available, we also propose a method for high-quality phonocardiogram data augmentation. In our tests, the proposed method exhibits high performance even in unfavorable scenarios since it can denoise a PCG signal contaminated with a signal-to-noise ratio (SNR) of −5 dB with average improvements ranging from 11.85 dB up to 17.60 dB, depending on the noise type used to degrade the cardiac signal. This method could significantly improve the performance of automatic cardiac sound classification algorithms in noisy environments but could also be used in electronic stethoscopes.

EXTRACTION OF A COMPLEX TREND OF SIGNALS IN SYSTEMS OF INTELLIGENT SUPPORT OF THE MOVEMENT OF OBJECTS

The article is devoted to the study of problems of intellectualization of control systems of moving objects to ensure their reliable operation in difficult conditions in various physical environments. Intelligent traffic support systems are developed based on the integration of measurement, computing, communication and control technologies and are designed to ensure the collection, accumulation and processing of information, determination of navigation parameters, formation of control influences, visualization of the positioning and movement trajectories of objects, monitoring of thefunctional state of objects and the state of the operating environment, etc. When restoring the lost trajectory of a moving object in the conditions of its complex movement, when approximating complex algorithms in adaptive control systems, with sudden changes in the trajectory or conditions of movement, there is a need to extract a complex trend from noiselike signals. The existing filtering algorithms do not provide acceptable efficiency, especially in conditions of limited a priori information about the nature of changes in the useful signal component.The article proposes and substantiates the use of wavelet filtering to suppress interference and extract the initial signal of a complex shape. A study of the effectiveness of extracting a complex useful signal against the background of Gaussian noise and harmonic disturbances was conducted. To process the simulated signals, wave functions of various orders from the families of Dobechies, symlets, and coiflets were used. The effectiveness of wavelet filtering was evaluated by the mean square deviation value of the selected trend and the model of the useful signal. The comparative analysis of the obtained results showed the feasibility of using the wave function of the family of symlets, which ensured the minimum value of the root mean square deviation of the selected trend from the model of the useful signal.

Coincidence detection simulations of the electromagnetic response signals generated by stochastic high-frequency gravitational waves

Gravitational wave (GW) detection is a typical open problem for extracting a significantly weak signal from a strong noise background. Therefore, in addition to building extremely sensitive detection installations to achieve the desired signal detection, a highly efficient data processing method is particularly important for identifying the detected signal. Different from the detection of mid and low frequency astrophysical source gravitational waves using laser interferometers, here we focus on the electromagnetic response detection of high-frequency gravitational waves using the modulated strong magnetic field technique. We discuss how to identify the stochastic gravitational wave signals from a strong background of Gaussian white noise by simulating the generation of a series of high-frequency stochastic gravitational wave detection data in the time domain. It is shown that, based on cross-correlation between the detection data obtained by two geosynchronous detection transpositions, the simulated high-frequency stochastic gravitational wave signals can be effectively identified. The current work should be useful in the future for constructing coincidence detection installations to probe the relevant high-frequency stochastic gravitational waves.

Adaptive Robust Radar Target Detector Based on Gradient Test

The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian noise whose covariance matrix is unknown. To improve robustness to mismatched signals, a random perturbation that follows the complex normal distribution is added under the alternative hypothesis. Since traditional detectors that divide complex parameters into real parts and imaginary parts are sometimes difficult to obtain, a new robust, complex parameter gradient test is derived directly from the complex data. Moreover, the CFAR property of the new detector is proven. The performance assessment indicates that the gradient detector exhibits suitable robustness to the mismatched signals.