Noise Envelope Research Articles

Detectable Radius of Investigation for One Flow Period with Bourdet Derivative

Summary A new definition of the radius of investigation (ROI) is proposed to overcome the ambiguity present in the results from conventional ROI quantification methods. The term ROI is commonly used to quantify the minimum reservoir size or the distance to a potential boundary evaluated through pressure transient testing. However, the various methods available in the literature to quantify ROI often provide different answers stemming from varying assumptions and thus often lead to confusion in terms of the appropriate definition to choose. Although the ROI method developed by Van Poolen is well recognized in the industry, there is still debate about its general applicability because it is limited to a constant-rate flow period and is insensitive to flow rate, flow sequence, gauge resolution, and measurement noise level. This contrasts with operational experience, where a higher flow rate, higher gauge precision, and lower level of measurement noise lead to higher quality pressure transient testing data from which reservoir boundaries, or other features, can be identified farther away from the wellbore. In other words, higher flow rates, better gauges, and lower noise levels can lead to a larger achievable ROI. We propose a new definition of ROI, which is the detectable ROI for each drawdown or buildup flow period. The detectable ROI is derived from the actual pressure derivative response and not from a generic model assumption. By defining a derivative noise envelope, the new method clearly identifies the time when the derivative deviates from an unbounded model due to the presence of a boundary and thus provides an estimate of the detectable ROI for the analyzed period. This method overcomes the limitations of most conventional methods and provides ROI predictions that depend on flow rate and gauge noise while maintaining a consistent result with the current pressure transient interpretation. While detectable ROI is applicable for general drawdown/buildup pressure transient tests, the concept was developed with deep transient testing (DTT) in mind.

A novel underwater weak signal detection method based on parameter optimized VMD and 3D chaotic system

Based on chaos theory, combined with improved variational mode decomposition, a novel method for underwater weak signal detection and frequency extraction is proposed. The aim of this research is to address the challenge of detecting underwater signals and extracting their frequencies under ultra-low Signal-to-Noise Ratio (SNR). A novel three-dimensional (3D) chaotic system with infinitely shrinking attractors is proposed, which incorporates a cosine function to achieve chaos control and enhancement. The nonlinear characteristics of the system, such as Lyapunov exponent and bifurcation, are analyzed in detail. Then, we design a frequency array and perform scale transformation on the chaotic system to construct a detection model that covering all frequencies. The model can transition from critical chaos to large period or intermittent chaos in the presence of external periodic signals by adjusting the parameters of the cosine function. Considering the relationship between the generation of intermittent chaos and the frequency of target signal, a frequency extraction algorithm based on chaos sparrow search algorithm optimization variational mode decomposition (CSSA-VMD) and Hilbert transform is designed. The algorithm can estimate the real frequency of underwater signals by performing noise reduction and envelope analysis on intermittent chaotic signals. The experimental results demonstrate that the proposed system can detect weak signals in complex marine backgrounds and extract their frequency features, with a detection SNR of -48 dB and a frequency error of less than 0.01%. Importantly, the circuit of the detection system has been implemented, verifying its feasibility for detecting and tracking underwater signals.

Analysis of the Microstructure and Hardness of Flake Graphite Cast Iron Using the Barkhausen Noise Method and Conventional Techniques

The new brake disc was evaluated for microstructure and hardness by the conventional destructive tests and non-destructive Barkhausen noise method (BNM). Ten non-destructive measurements were carried out in different areas of a brake disc, which were then cut out and made into metallographic test samples. Qualitative and quantitative analysis of graphite precipitates was performed to assess their volume in material matrix, anisotropy and size. Subsequently, graphs showing the relationships between selected stereological parameters of graphite precipitates and parameters determined from the RMS envelope of Barkhausen noise were elucidated. Similar relationships between hardness and parameters coming from non-destructive tests were carried out. Magnetic parameters that specified the size of a graphite precipitate was selected. In addition, repeatability studies using BNM were carried out in the areas of the material with the smallest and largest average size of graphite precipitates. A linear relationship between amplitude of BN and length of graphite flakes was found. The paper presents the possibilities of assessing the volume and size of graphite precipitates, as well as cast iron hardness using BNM.

Fatigue Behaviour of Medium Carbon Steel Assessed by the Barkhausen Noise Method

Abstract In this paper, an attempt to estimate the stage of the fatigue process using the Barkhausen noise method is studied. First, microstructural and static tensile tests were carried out and, subsequently, fatigue tests up to failure were conducted. After determination of the material behaviour in the assumed static and dynamic conditions, the interrupted fatigue tests were performed. Each specimen was stressed up to a different number of cycles corresponding to 10%, 30%, 50%, 70% and 90% of fatigue lifetime for the loading conditions considered. In the next step of the experimental programme, the specimens were subjected to the Barkhausen magnetic noise measurements. Various magnetic parameters coming from the rms Barkhausen noise envelopes were determined. The linear relationship betweenthe full-width at half-maximum (FWHM) of the Barkhausen noise envelope and the number of loading cycles to fracture was found. Specimens loaded up to a certain number of cycles were also subjected to a tensile test to assess an influence of fatigue on the fracture features.

Correlation function of the logarithmic derivative of the Gaussian noise envelope

Correlation function of the logarithmic derivative of the Gaussian noise envelope

Medically related speech: Impacts of age, familiarity, and noise on word recognition

Verbal communication between patients and healthcare providers is an important piece of successful healthcare delivery. Less familiar medical terminology coupled with noisy hospital environments can lead to speech intelligibility challenges between healthcare providers and patients. Prior results showed lower word recognition accuracy for lower familiarity and frequency words and in the presence of noise in younger adults (Bent et al., 2022). The current study expands on the previous work with the inclusion of older adults and an additional noise masking condition. Online listening tests were conducted to study the impacts of word familiarity and background noise on speech perception in younger (age 18–35) and older adults (age 60–85). Participants were presented with a corpus of 160 medically related sentences with varying word familiarity and frequency characteristics, and word recognition accuracy was recorded. Sentences were presented in quiet, hospital noise, speech-shaped noise, and speech-shaped noise modulated by the hospital noise envelope, a novel condition from previous work. Results highlight a comparison of speech recognition performance between the two age groups. Effects of sentence familiarity/frequency rating and type of noise masker on speech intelligibility are discussed. [Work supported by the James S. McDonnell Foundation https://doi.org/10.37717/2021-3028.]

ВЕРОЯТНОСТНОЕ ОПИСАНИЕ ПОРЫВОВ ВЕТРА В ЗОНЕ ВЗЛЁТНО-ПОСАДОЧНОЙ ПОЛОСЫ, ВЛИЯЮЩИХ НА БЕЗОПАСНОСТЬ ПОЛЁТОВ ВОЗДУШНЫХ СУДОВ

The influence of wind gusts on the safety of aircraft flights is described. The impulse nature of wind gusts is substantiated. Experimental studies of wind gusts carried out with the help of a parametric recording device on an ultrasonic beam and an anemometer are analyzed, and it is shown that wind gusts have an impulsive nature, similar to atmospheric noise envelope emissions. The results of the analytical description of the probabilistic characteristics of atmospheric noise envelope emissions known from the literature are applied to wind gusts on the runway. Characteristics are given for the average duration of wind gusts, intervals between wind gusts and the average duration of intervals, the distribution of the duration of wind gusts, the average number and distribution of maxima of wind gusts.

Gini Indices II and III: Two new Sparsity Measures and Their Applications to Machine Condition Monitoring

Machine condition monitoring (MCM) uses signal processing and machine learning methods to analyze monitoring data and perform timely condition-based maintenance. Since monitoring data usually have a sparsity property, sparsity measures (SMs) are naturally considered to quantify the sparsity of signals and they serve as the objective functions of many signal processing and machine learning methods. Although Gini index, kurtosis, smoothness index, negative entropy, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lq</i> norm have been considerably investigated for MCM, the design of new SMs for enhancing MCM is rarely reported. In this article, based on the ratio of different quasi-arithmetic means (RQAM), two new SMs, coined as Gini index Ⅱ (GI2) and Gini index Ⅲ (GI3), are designed. New proofs show that the GI2 and GI3 satisfy all six sparsity attributes. Subsequently, the GI2 and GI3 of the square envelope of Gaussian white noise are theoretically investigated and their theoretical values are, respectively, equal to 2/3 and 1/3, which can be used as baselines for machine abnormality detection. Once GI2 and GI3 exceed the baselines, abnormal health conditions can be detected without needing historical data and prior fault knowledge. Finally, simulated and experimental case studies showed that the proposed GI2 and GI3 have competitive performance with Gini index and that they are better than kurtosis, negative entropy, and smoothness index, in characterizing the sparsity of signals. This article demonstrates that the RQAM is a potential framework to design new SMs.

Generalized Gini indices: Complementary sparsity measures to Box-Cox sparsity measures for machine condition monitoring

Sparsity measures that can quantify the sparsity of signals are often used as objective functions of signal processing and machine learning algorithms (e.g., sparse filtering, compressive sensing, blind deconvolution, and the fast Kurtogram, etc.). Classic sparsity measures include kurtosis, Gini index, negative entropy, the ratio of Lp norm to Lq norm, smoothness index, L1 norm, L0 norm, etc. To enrich the library of sparsity measures, this paper aims to generalize a classic Gini index to construct generalized Gini indices (GGIs). Firstly, inspired by the ratio of different quasi-arithmetic means, investigations on the GGIs showed that the GGIs satisfy all six properties of good sparsity measures. The GGIs only have one parameter a⩾0. When a=1, the GGIs are reduced to the classic Gini index. Secondly, investigations reveal that: (1) the GGIs can monotonically quantify the sparsity changes of Bernoulli coefficients. Sparsity quantification curves given by the GGIs are complementary to those generated by our recently proposed Box-Cox sparsity measures (BCSMs). Especially, when the unique parameters of the GGIs and the BCSMs are equal to zero, these sparsity quantification curves of the GGIs and BCSMs are reduced to that of negative entropy; (2) the GGIs are convergent with an increase of signal lengths; (3) when the squared envelope of white Gaussian noise is quantified by the GGIs, theoretical values of the GGIs are derived as a/(a+1), which can be used as baselines for machine abnormality detection. At last, two bearing run-to-failure datasets are used to validate the effectiveness of the GGIs for machine condition monitoring and their results showed that the GGIs are effective in detecting incipient faults and indicating informative frequency bands. In future, the proposed GGIs can be used in any algorithms that need sparsity measures.

Nonlinear state estimation by Extended Parallelotope Set-Membership Filter

In this paper, we propose a state estimation method called the Extended Parallelotope Set-Membership Filter that provides a higher estimation accuracy than existing methods for discrete-time nonlinear systems. The Extended Parallelotope Set-Membership Filter is motivated by the fact that the iteration operations in existing methods generate much redundancy, and will deteriorate the accuracy of the state estimation. To account for this issue, an innovative parallelotope envelope method is proposed for the purpose of reducing the redundancy arising from the process of the noise envelope. In addition, a cofactor separation method is designed for nonlinear systems to obtain a tight envelope of the parallelotope set. Furthermore, we develop a novel parallelotope intersection method suitable for the parallelotope envelope to update the state set. The simulation results verified the effectiveness of the proposed method as well as its superiority over conventional methods in terms of both the maximum and average accuracies of the state estimation.

Usage of Barkhausen Noise for Assessment of Corrosion Damage on Different Low Alloyed Steels

This study deals with corrosion damage of low alloyed feritic steels of variable strength. Three different steels of nominal yield strength 235, 700 and 1100 MPa were subjected to the variable degree of corrosion attack developed in the corrosion chamber under a neutral salt spray (NSS) atmosphere. The corrosion damage was investigated by the use of conventional metallographic observations when the thickness of corroded layer was quantified. Moreover, non-destructive magnetic technique, based on Barkhausen noise, was also employed. It was found that the rate of corrosion damage decreases along with the increasing number of days in the chamber. The similar evolution can be also found for Barkhausen noise emission and the extracted parameters from the emission. It can be reported that conventional rms value of Barkhausen noise signal as well as FWHM (full width at half maximum of Barkhausen noise envelope) can be linked with the corrosion extent, especially in the early phases of corrosion attack. The PP (peak position of Barkhausen noise envelope) values exhibit poor sensitivity.

Assessment of Noise Signature for a Cavitating Centrifugal Pump

AbstractIn recent days, sophisticated instruments have emerged to obtain an online measurement of performance parameters from centrifugal pump of different kinds and the signals can be directed to the hands of pump users through mobile applications. With this in mind, a centrifugal pump of low specific speed was chosen for cavitation studies from 80% to 120% of nominal flowrate and for three different speeds. An assessment was carried out for cavitation noise signature from those operating conditions of that pump. The result of cavitation noise based on peak magnitude as well as average revealed nature in relation to cavitation coefficient, and it greatly depends on the flowrate with respect to nominal flowrate. The noise envelope for the flowrate at best efficiency and above was having a similar trend whereas at flows less than the nominal, it was totally different. So the criteria for finding the deviation in noise cannot be uniform for all flowrates. In this paper, the method adapted was to impose a trend line to the measured cavitation noise information and to find out the deviation with respect to normal operating conditions. It was concluded that detection of abnormality in pumps due to cavitation effects requires the current operating condition to be diagnosed first and then proper criteria for deviation in noise has to be imposed.

Radar Target Detection via GAMP: A Sparse Recovery Strategy Off the Grid

Sparse recovery (SR) is a promising tool for radar signal processing. However, SR-based target detection is still an open problem in the challenging scenarios of grid mismatch, very high dimensionality and constant false-alarm rate (CFAR) requirement. To address the above challenges, an efficient approach termed as knowledge-aided generalized approximate message passing (KA-GAMP) is proposed. Firstly, traditional signal processing (TSP) is performed to obtain prior knowledge about targets of interest. Based on this prior knowledge, dimensionality reduction is carried out, and a new approximate observation model of the received signal is established. Then, considering the grid-mismatch problem, target parameter estimations are carried out before SR, and an estimate of the measurement matrix is obtained. Finally, by exploiting the sparsity of the received signal, GAMP is adopted to perform target recovery. Based on recovery results, target detection is implemented. Interestingly, it is shown that the noise envelope outputted by GAMP approximately follows an i.i.d Gaussian distribution, and the proposed detector is CFAR. Numerical results via both Monte Carlo simulations and the measured data show that the proposed approach is superior to the TSP-based method in terms of target detection performance.

Barkhausen Noise Emission in AISI 321 Austenitic Steel Originating from the Strain-Induced Martensite Transformation

This paper investigates the sensitivity of the Barkhausen noise technique against strain-induced martensite in AISI 321 austenitic stainless steel. Martensite transformation was induced by the uniaxial tensile test, and a variable martensite fraction was obtained at different plastic strains. It was found that Barkhausen noise emission progressively increases with plastic straining, while its evolution is driven by the martensite fraction in the deformed matrix. This study also demonstrates that the uniaxial tensile stressing produced a certain level of stress and magnetic anisotropy in the samples. The number of strong Barkhausen pulses increased for more developed strains, whereas the position of the Barkhausen noise envelope remained less affected. This study clearly demonstrates the good sensitivity of the Barkhausen noise technique against the degree of martensite transformation in austenitic stainless steel. Moreover, this technique is sensitive to the direction of the exerted load.

Radio Station Background Noise Detection Based on Time‐Frequency Domain Electromagnetic Spectrum

The electromagnetic spectrum resource is one of the important national resources. It is a physical channel for wireless communication between ships and between ships and radio stations. Good communication quality must be guaranteed, so it is urgent to monitor and analyze the environmental background noise of the electromagnetic spectrum. The estimation of the radio frequency signal coverage in the target area during the monitoring process is of great significance to the study of electromagnetic spectrum resource management and control. This paper estimates the upper envelope and lower envelope of the background noise of the target frequency band based on the electromagnetic spectrum data in the time‐frequency domain and combines the forward difference algorithm to estimate the background noise envelope curve. We set up fixed detection nodes and mobile detection nodes for specific construction areas and collect time‐frequency spectrum data of electromagnetic spectrum in multiple locations. The instantaneous frequency spectrum and the collected data of a specific frequency point are compared, and it is difficult to judge whether there is a valid signal. This paper is based on the time‐frequency domain electromagnetic spectrum data in the construction area of the project and estimates the background noise of the coast station frequency band in the current environment. It is based on the energy gradient estimation of the time‐frequency domain spectrum, and the effective signal of the target frequency band is obtained and combines the noise envelope and the effective signal location to improve the estimation result of the background noise envelope. The experimental results show that the background noise estimation algorithm can reflect the changes in the noise floor of different target frequency bands.

A Noise-Shaped Signaling Method for Vehicle-to-Everything Security

This paper presents a method to improve the Vehicle-to-Everything (V2X) security. With the recent rapid development of communication technology and traffic applications, V2X is recently commercialized and has been growing as a fundamental system for future applications. Because of the high mobility of the vehicles, V2X requires a low latency and high-reliability. However, previous security methods demand a large computational burden and generate high latency owing to complex operations and long additional data bits for ensuing security. To resolve such constraints, an advanced security method ensuring lower latency and higher reliability is required. We propose a noise-shaped signaling method that provides high-level security with low latency for reliable V2X communication. The proposed method encrypts original data symbols to noise-like symbols by applying a noise envelope that consists of Chaotic Random Magnitude Sequence (CRMS) and Chaotic Random Phase Sequences (CRPS). Our method simplifies the sequence sharing process between a sender and an intended receiver by adapting the characteristics of a chaotic dynamic system. Moreover, the proposed method does not demand additional data bits and generate delay because the method only uses simple multiplication and division procedure for data encryption in the physical layer. We analyze our method in depth using extensive simulations and various viewpoints such as error rate, probability of modulation identification. From the simulations, we demonstrate that a malicious adversary cannot comprehend the transmitted symbols and always has the maximum error rate under various network environments and conditions. We also demonstrate how the adversary cannot infer the modulation scheme from the symbols applying the proposed method. After these analyses, we confirm that the noise-shaped signaling method is high-level of secure method with a low latency for V2X communication.

Statistical Signatures of Nanoflare Activity. II. A Nanoflare Explanation for Periodic Brightenings in Flare Stars Observed by NGTS

Abstract Several studies have documented periodic and quasi-periodic signals from the time series of dMe flare stars and other stellar sources. Such periodic signals, observed within quiescent phases (i.e., devoid of larger-scale microflare or flare activity), range in a period from 1 to 1000 s and hence have been tentatively linked to ubiquitous p-mode oscillations generated in the convective layers of the star. As such, most interpretations for the observed periodicities have been framed in terms of magnetohydrodynamic wave behavior. However, we propose that a series of continuous nanoflares, based upon a power-law distribution, can provide a similar periodic signal in the associated time series. Adapting previous statistical analyses of solar nanoflare signals, we find the first statistical evidence for stellar nanoflare signals embedded within the noise envelope of M-type stellar lightcurves. Employing data collected by the Next Generation Transit Survey (NGTS), we find evidence for stellar nanoflare activity demonstrating a flaring power-law index of 3.25 ± 0.20, alongside a decay timescale of 200 ± 100 s. We also find that synthetic time series, consistent with the observations of dMe flare star lightcurves, are capable of producing quasi-periodic signals in the same frequency range as p-mode signals, despite being purely composed of impulsive signatures. Phenomena traditionally considered a consequence of wave behavior may be described by a number of high-frequency but discrete nanoflare energy events. This new physical interpretation presents a novel diagnostic capability, by linking observed periodic signals to given nanoflare model conditions.

NESZ Estimation and Calibration for Gaofen-3 Polarimetric Products by the Minimum Noise Envelope Estimator

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than -20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

Stable Automatic Envelope Estimation for Noisy Doppler Ultrasound

Doppler ultrasound technology is widespread in clinical applications and is principally used for blood flow measurements in the heart, arteries, and veins. A commonly extracted parameter is the maximum velocity envelope. However, current methods of extracting it cannot produce stable envelopes in high noise conditions. This can limit clinical and research applications using the technology. In this article, a new method of automatic envelope estimation is presented. The method can handle challenging signals with high levels of noise and variable envelope shapes. Envelopes are extracted from a Doppler spectrogram image generated directly from the Doppler audio signal, making it less device-dependent than existing image-processing methods. The method's performance is assessed using simulated pulsatile flow, a flow phantom, and in vivo ascending aortic flow measurements and is compared with three state-of-the-art methods. The proposed method is the most accurate in noisy conditions, achieving, on average, for phantom data with signal-to-noise ratios (SNRs) below 10 dB, bias and standard deviation of 0.7% and 3.3% lower than the next-best performing method. In addition, a new method for beat segmentation is proposed. When combined, the two proposed methods exhibited the best performance using in vivo data, producing the least number of incorrectly segmented beats and 8.2% more correctly segmented beats than the next best performing method. The ability of the proposed methods to reliably extract timing indices for cardiac cycles across a range of signal quality is of particular significance for research and monitoring applications.

Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion

As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.