Significant Noise Component Research Articles

An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia

Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias.

Aeroacoustic analysis of the tip-leakage flow of an ultrahigh bypass ratio fan stage

A detailed aeroacoustic analysis of the flow induced by the clearance between the fan tip and the shroud is performed in a scale-model fan stage of an ultrahigh bypass ratio turbofan engine, which was designed to operate at transonic regimes. A wall-modeled large eddy simulation is performed at approach condition, which corresponds to a fully subsonic operating point. The contributions of the tip-gap noise to the total fan noise are investigated using the Ffowcs Williams and Hawkings analogy. The surface is split into two parts: the tip region and the rest of the blade in order to analyze the acoustic contributions of these two regions separately. It is shown that the tip-gap region generates a significant noise component above 2 kHz, which corresponds to approximately 1.2 times the blade passing frequency. Two separate tip-leakage vortices are identified in the vicinity of the fan tip. The dominant noise sources in the tip-gap region are observed at the trailing edge of the fan blade. The wall pressure spectra in the tip-gap region and the coherence of pressure fluctuations between monitor points at different positions show an acoustic contribution of the tip-leakage flow at two different frequency ranges. The first range corresponds to medium frequencies between 2 and 9 kHz, and the second range corresponds to high frequencies between 10 and 25 kHz. The analysis of dynamic mode tracking, fluctuating pressure and velocity spectra, and instantaneous flow fields relates specific vortices in the tip-gap flow to their spectral signature and paves the way for further analytical modeling of tip-gap noise sources.

INTERFERENCE RESISTANCE OF A CORRELATION RECEIVER AT A LIMITED OBSERVATION INTERVAL

The article considers the possibility of increasing the capability of detecting a useful radio signal against the background of noise and simultaneously reducing the possibility of mistaking noise fluctuations for a signal. Moreover, if it is absent then the Neumann-Pearson statistical test should be used. This criterion is a partial case from the class of criteria consisting in the calculation of the likelihood ratio and does not require knowledge of a priori probabilities of the presence or absence a useful signal while constructing an optimal receiver and Bayesian methods are used.
 The effectiveness of the detection procedure according to the Neumann-Pearson test is characterized by the probability of correct detection at a fixed false alarm probability. These probabilities depend on the signal-to-noise ratio at the output of the detection device. So, an important preparatory stage of the reception immunity analysis is the study of this parameter which is very important in the section of statistical radio engineering.
 It is known that the optimal detection of a signal against a background of white noise is reduced to the correlation integral calculation. This calculation can be done directly by using a multiplier and integrator (correlation method), by using a filter that is optimal for this signal (filter method) or by using a correlation-filter method.
 The presence of a priori uncertainty during signal reception makes it impossible to create an optimal (consistent) receiving path and therefore makes it impossible to obtain the required value of the signal-to-noise ratio at the output of the detection device. In this case, an autocorrelation algorithm is used which is resistant to a priori uncertainty of signal parameters and is “optimal” while detecting signals of unknown shape. The most general character for determining the signal/noise ratio has an expression for the case of an incoherent single-channel autocorrelation algorithm for detecting a noisy signal against a background of stationary white noise (interference).
 For example, quite a large number of scientific works are devoted to the interference immunity issue of the correlation reception method. They mainly consider the interference immunity of correlation receivers when a mixture of harmonic signal and noise passes through them. The circuits with the same average frequencies of multiplying processes and etc. are analyzed. There are also known works in which formulas relating the value of the signal-to-noise ratio at the output of the receiving path for a complex signal to the input signal-to-noise ratio are given.
 In the above works, idealized cases are considered, namely, when the observation is conducted on a fairly long observation interval but the real observation interval is quite limited. In this case, the averaging is carried out over a limited time interval and it is necessary to take into account the intercorrelation relations between the signal and noise, therefore, along with the signal-signal, noise-noise, signal-noise and noise-signal components, we will have the following mutual correlation moments between the combinations of signal and noise component.
 The issue of an autocorrelation receiver when passing an additive mixture of signal and white noise at a limited observation interval is investigated in which the immunity index is considered and a general solution of the signal/noise ratio based on the deviation criterion is obtained. Therefore, obtaining an analytical expression that establishes the relationship between the signal-to-noise ratio at the input and output of the receiver is very important and relevant.
 The purpose of the article is to obtain an analytical expression of the signal-to-noise ratio value at the threshold device input of an incoherent autocorrelation receiver at a limited observation interval.

Dual-microphone active noise cancellation paved with Doppler assimilation for TADS

An improved dual-microphone adaptive Active Noise Cancellation (ANC) method called IM-RM-LMS is proposed to solve the problem of the bearing sound signal being interfered with by wheel-track contact noise in the TADS. Two microphones, the main microphone and the reference microphone, are used to collect the bearing acoustic signal and the wheel-track contact noise signal, respectively. Due to the different relative spatial positions of the two microphones and the noise source, the noise signal components collected by the two microphones obey different Doppler distortion laws, which makes the LMS-based ANC method ineffective. Therefore, the main research content of this paper is the ANC of two moving sound source signals that obey different Doppler distortion laws. An Inverse Modulation and Re-Modulation (IM-RM) processing is innovatively performed to assimilate the Doppler distortion law of the noise signal components collected by the two microphones. The effectiveness of IM-RM-LMS is verified by simulation and experiment. Compared with the traditional LMS algorithm without considering Doppler assimilation, it can achieve a significantly better denoising effect. This method provides a new theoretical basis for noise cancellation of two moving sound sources obeying different Doppler distortion laws and it is expected to be applied to the noise cancellation of TADS.

WAVELET DE-NOISING FOR AUTONOMOUS LATITUDE DETERMINATION

Autonomous determination of the latitude of the place of movable and immovable objects is used as an independent task, as well as the task of determination of the initial value of latitude for operation of both platform and platform-free navigation systems. To solve these problems, it is necessary to have an inertial measurement unit (IMU) with at least three gyroscopes and three accelerometers. When using the IMU, executed by MEMS technology, the output signals of micromechanical gyroscope and accelerometers have significant noise components. Kalman filter is usually used to filter such signals. However, for this purpose it is necessary to know, besides the exact mathematical model of sensitive elements, many of their initial random characteristics.
 In the article, the research was conducted in order to investigate the use of wavelet transformation for the filtering of output signals of micromechanical accelerometers and gyroscopes for autonomous determination of the latitude of the place. The peculiarity of using wavelet transform for noisy signals is that due to changing scale, wavelets can detect differences in process characteristics on different scales, and with help of the shift we can analyze process properties at different points on the whole investigated interval. Due to the properties of this system's fullness that it is possible to restore the process by means of inverse wavelet transform. The efficiency of the developed method of increasing the accuracy of the autonomous determination of the latitude of the IMU on the basis of micromechanical gyroscope and accelerometers has been experimentally confirmed. The projections of the angular velocity of Earth rotation and gravitational acceleration were obtained from the IMU made by MEMS technology. After that, the signals of the gyroscopes and accelerometers of the inertial measuring unit were filtered, using the wavelet ‘Daubechies 10’ in decomposition, and averaged. These signals were used in a computational algorithm to determine the latitude. The results showed that, unlike the well-known Kalman filter, which almost did not increase the accuracy of the latitude calculation, wavelet denoising and further averaging reduced calculation error by almost twice.

The measurement of chromatic dispersion of liquids based on rainbow technology with empirical mode decomposition

An acquisition system is designed to collect the rainbow signals at two different wavelengths simultaneously. The empirical mode decomposition (EMD) method was used to decompose and reconstruct the first-order rainbow signal. Airy, ripple and noise signal components were completely separated from the signal of the first-order rainbow by the EMD, and the refractive index of the droplet can be obtained by analyzing the separated airy structures. In our experiments, the rainbows from droplets of water, NaCl and C6H12O6 aqueous solutions were collected and the rainbow signals were analyzed by EMD. Based on the Cauchy dispersion model, the coefficients and the dispersion relation of water, NaCl and C6H12O6 aqueous solutions at different concentrations were obtained by measuring the refractive index at the wavelengths of 632.8 and 473 nm. We demonstrate an EMD-rainbow method to detect the chromatic dispersion of liquids.

Suitability of fibre-optic distributed temperature sensing for revealing mixing processes and higher-order moments at the forest–air interface

Abstract. The suitability of a fibre-optic distributed temperature sensing (DTS) technique for observing atmospheric mixing profiles within and above a forest was quantified, and these profiles were analysed. The spatially continuous observations were made at a 125 m tall mast in a boreal pine forest. Airflows near forest canopies diverge from typical boundary layer flows due to the influence of roughness elements (i.e. trees) on the flow. Ideally, these complex flows should be studied with spatially continuous measurements, yet such measurements are not feasible with conventional micrometeorological measurements with, for example, sonic anemometers. Hence, the suitability of DTS measurements for studying canopy flows was assessed. The DTS measurements were able to discern continuous profiles of turbulent fluctuations and mean values of air temperature along the mast, providing information about mixing processes (e.g. canopy eddies and evolution of inversion layers at night) and up to third-order turbulence statistics across the forest–atmosphere interface. Turbulence measurements with 3D sonic anemometers and Doppler lidar at the site were also utilised in this analysis. The continuous profiles for turbulence statistics were in line with prior studies made at wind tunnels and large eddy simulations for canopy flows. The DTS measurements contained a significant noise component which was, however, quantified, and its effect on turbulence statistics was accounted for. Underestimation of air temperature fluctuations at high frequencies caused 20 %–30 % underestimation of temperature variance at typical flow conditions. Despite these limitations, the DTS measurements should prove useful also in other studies concentrating on flows near roughness elements and/or non-stationary periods, since the measurements revealed spatio-temporal patterns of the flow which were not possible to be discerned from single point measurements fixed in space.

Improved Gaussian Process Acquisition for Targeted Bayesian Optimization

A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ (LCB) acquisition function is proposed. The expression used for the confidence bound is squared in order to better comply with the target requirement. With this modification, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained.

SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR

Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.

Suppression of surface waves with the ensemble empirical mode decomposition to enhance ultrasound images of reinforced concrete elements

The surface waves were suppressed to enhance ultrasound images of reinforced concrete elements with no hard assumption about the surface waves’ time-domain characteristics. The surface waves yield artifacts that degrade image quality and generally hinder the recognition of shallow inclusions and defects. The Ensemble Empirical Mode Decomposition (EEMD) was used to adaptively decompose the signals into their Intrinsic Mode Functions (IMFs). The signals were acquired in a concrete element with rebars at 54, 74, and 94 mm from the concrete element’s surface. The surface waves were generally decomposed to their own IMFs. The IMFs which comprised noise signal components, including the surface waves, were labeled as such based on empirical data. The remaining IMFs were used to construct new signals that generally comprised signal components from reflection at a rebar and the backwall and other low-frequency phenomena. The new signals yielded images in which the surface waves artifacts were not present and the rebars were represented correctly. The Synthetic Aperture Focusing Technique was used for imaging. The method has two main drawbacks. The EEMD performs according to parameters which require extensive ad hoc testing to be selected. The IMFs comprising the surface waves suffered from mode mixing with the backwall at times and hence the backwall was not fully represented in the new images.

Beamforming Method for Extracting the Broadband Noise Sources of Counter-Rotating Open Rotors

Counter-rotating open rotors (CRORs) are known to have advantageous propulsive efficiency properties but, at the same time, many noise emission issues related to this technology still need to be investigated. The noise they generate consists of tonal and broadband components, of which the tonal components often appear in narrow frequency bands with large amplitudes. During beamforming investigations, this often makes sorting them out from among other noise sources in the same frequency bin rather straightforward. On the other hand, the broadband noise sources can be characterized as having small amplitudes: in general, they are not the dominant noise sources in many frequency bins; but, overall, a significant noise component that needs to be investigated. The literature has provided a single microphone signal preprocessing method for removing the tonal components, hence isolating the broadband noise of a CROR: the signal of which has been shown to be appropriate for the investigation of broadband spectra. In this paper, this preprocessing method is further developed into a beamforming method that can be used in order to localize the broadband noise sources of CRORs. The resulting beamforming maps have been compared to the results of earlier broadband noise CROR studies, showing the validity of the new method and the advantages of using it as compared to methods used in other investigations.

Improved Gaussian Process Acquisition for Targeted Bayesian Optimization

A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ acquisition function is proposed. With this change, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained. Additional modifications result in further improvement. An intuitive explanation and a formal proof are given to explain the reduction in the number of lengthy function evaluations needed to reach the target.

Allan variance in dynamically tuned inertial-unit gyro error analysis

The purpose of the study was to consider an algorithm for obtaining the measurement information from a dynamically tuned gyroscope in the mode of an angular velocity sensor and output signal noise component estimate, the algorithm being based on the Allan variance method. The results obtained were evaluated

An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

Scale Factor Nonlinearity of Hemispherical Resonator Gyro

В статье рассматривается измерительный прибор - твердотельный волновой гироскоп, работающий в режиме свободных колебаний. Повышение точности твердотельного волнового гироскопа всегда остается актуальной задачей. Твердотельный волновой гироскоп - измерительная система, в состав которой входит измерительная компонента. Несовершенство измерительной компоненты сказывается на точностных характеристиках прибора в целом. Погрешности в измерительной компоненте приводят к появлению нелинейности масштабного коэффициента. В статье исследуется данный вид погрешности, предлагается подход повышения точности выходного сигнала за счет использования информации о нелинейности масштабного коэффициента. Исследуются возможные причины нелинейности масштабного коэффициента, такие как разные коэффициенты усиления датчиков перемещения системы съема, отклонения от необходимого углового размещения датчиков, нелинейность преобразования самих датчиков. Выявляется основной фактор погрешности - угловая ориентация волны. Анализируется влияние нелинейности масштабного коэффициента на систему управления. Приводятся несколько способов измерения нелинейности и оценки ее точности. Даются рекомендации, в каких случаях какие способы измерения удобнее и эффективнее. Описывается влияние дрейфа волны и шумовой составляющей сигналов на точность оценки нелинейности масштабного коэффициента. Предлагается методика повышения точности выходного сигнала за счет подстройки в вычислительной компоненте ТВГ фазовых переменных волновой картины. Остаточное влияние нелинейности предлагается уменьшать за счет компенсации погрешности. Анализируются вопросы реализации компенсации нелинейности. Рассматривается другой возможный фактор нелинейности - температура. Описывается способ измерения данного фактора. Предлагается унификация моделей нелинейности и дрейфа для повышения эффективности компенсации погрешностей выходного сигнала ТВГ.

Passive Localization for Mixed-Field Moving Sources

Abstract Due to the mobility of underwater equipment, high-precision underwater positioning technology will face two technical challenges: dealing with mixed-field signals composed of near-field signals and far-field signals; adapting to variable component of mixed-field signals considering the mobility of equipment. Under this condition, an effective method based on MUSIC is addressed in this paper. After distinguishing far-field signal subspace from mixed-field signal subspace, estimations of DOAs and powers of far-field sources are carried out. Then the corresponding far-field and noise signal components can be eliminated from the signal subspace. After that, based on path-following algorithm, modified 2D-MUSIC is performed for DOA and range estimations of near-field sources. The performance of the proposed method is verified and compared with the other methods through computer simulations. Reasonable classification of source types and accurate localization estimation can be achieved by using the proposed method.

FPGA Implementation of a Phase-Aware Single-Channel Speech Enhancement System

This paper presents a real-time architecture of an improved single-channel speech enhancement system based on phase-aware multi-band complex spectral subtraction. Using the proposed technique, the short-time spectral magnitude of the clean speech signal is estimated by considering the spectral phase of the speech and noise signal components. Moreover, the estimated spectral phase of the clean speech signal is also utilized for signal reconstruction in the time domain. The proposed system is made of the basic preprocessing module followed by an short-time Fourier transform analyzer, a noise power estimator based on improved minima controlled recursive array, a phase estimator unit and an overlap-add synthesis unit. The proposed architecture is implemented on a Field Programmable Gate Array (FPGA) using the Xilinx ISE tool. The overall resource utilization and the maximum operating frequency are also computed for a Virtex-6 FPGA chip. It has been experimentally shown that the proposed speech enhancement framework performs better than the other existing standard benchmark methods in terms of various quality and intelligibility assessment metrics.

Improved single channel phase‐aware speech enhancement technique for low signal‐to‐noise ratio signal

In the state-of-the-art single channel speech enhancement techniques, the short-time spectral amplitude is modified while the effect of the phase corruption due to the contamination of additive noise is neglected. This study introduces an improved speech enhancement algorithm based on a phase-aware multi-band spectral subtraction technique which estimates the spectral amplitude of the clean speech signal by considering the phase of the speech and noise signal components, and uses the estimated phase of the clean speech signal for signal reconstruction in the time domain. Experimental results show that the proposed algorithm yields better performance in terms of various objective and composite quality measures and other intelligibility assessment metrics while compared with other existing spectral subtraction methods. Using the composite objective measure quality evaluation technique, it is observed that the overall signal quality of the enhanced speech signal is improved on an average by 70% at 0 dB global input signal-to-noise ratio by using the proposed approach.

Photoacoustic Cell with Digital Differential Detection

Solutions which reduce the impact of the external acoustic noise on the photoacoustic signal rely often on the appropriate modification of the photoacoustic cell structure. The goal is to obtain a frequency response of the cell which suppresses the external noise as much as possible. Another approach is differential detection, in which two microphones are used, and assuming that the external noise signal components from both microphones are identical, their subtraction should result in canceling the external noise. The main difficulty of that solution is that both microphone signal paths should be calibrated to have virtually identical characteristics. The paper presents a differential photoacoustic cell with digital differential detection.

ENHANCEMENT OF PROPULSION PERFORMANCE THROUGH JET NOISE REDUCTION TECHNOLOGIES: A REVIEW

ABSTRACTJet noise remains a significant noise component in modern aero-engines. A high-speed flow mixing with the surrounding air constitutes noise sources behind the nozzle. One noise-reduction technology is expected to enhance mixing within a limited region downstream of the nozzle. The enhanced mixing leads to the suppression of broadband peak components of jet noise.The main goal of this paper is to provide an overview of, the aircraft noise generating sources with emphasis on the jet noise, the main technologies employed for control and reduction of aircraft noise, the effect of different techniques and a comparison between them on the flow field and acoustic performance for subsonic and supersonic jets, and finally a survey of the current applications of large-eddy simulation(LES) for predicting of the noise from single stream turbulent jets, including numerical methods for simulation of near and far field of a jet nozzle.