Noise Components Research Articles

Blind deep-learning based preprocessing method for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a technique for tackling the trade-off between the resolution and the imaging field of view by combining the techniques from aperture synthesis and phase retrieval to estimate the complex object from a series of low-resolution intensity images captured under angle-varied illumination. The captured images are commonly corrupted by multiple noise, leading to the degradation of the reconstructed image quality. Typically speaking, the noise model and noise level of the experimental images are unknown, and the traditional image denoising methods have limited effect. In this paper we model the FPM forward imaging process corrupted by noise and divide the noise in the captured images into two parts: the signal-dependent part and the signal-independent part. Based on the noise model we propose a novel blind deep-learning based Fourier ptychographic microscopy preprocessing method, termed BDFP, for removing these two components of noise. First, from a portion of the captured low-resolution images, a set of blocks corresponding to the smooth area of the object are extracted to model signal-independent noise. Second, under the assumption that the signal-dependent noise follows a Poisson distribution, we add Poisson noise and signal-independent noise blocks to clean images to form a paired training dataset, which is then used for training a deep convolutional neural network (CNN) model to reduce both signal-dependent noise and signal-independent noise. The proposed blind preprocessing method, combining with typical FPM reconstruction algorithms, is tested on simulated data and experimental images. Experimental results show that our preprocessing method can significantly reduce the noise in the captured images and bring about effective improvements in reconstructed image quality.

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging.

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient's dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

Subspace tracking for phase noise source separation in frequency combs.

It is widely acknowledged that the phase noise of an optical frequency comb primarily stems from the common mode (carrier-envelope) and the repetition rate phase noise. However, owing to technical noise sources or other intricate intra-cavity factors, residual phase noise components, distinct from the common mode and the repetition rate phase noise, may also exist. We introduce a measurement technique that combines subspace tracking and multi-heterodyne coherent detection for the separation of different phase noise sources. This method allows us to break down the overall phase noise sources associated with a specific comb-line into distinct phase noise components associated with the common mode, the repetition rate and the residual phase noise terms. The measurement method allow us, for the first time, to identify and measure residual phase noise sources of a frequency modulated mode-locked laser.

On the Hardness of Learning from Censored and Nonstationary Demand

We consider a repeated newsvendor problem in which the inventory manager has no prior information about the demand and can access only censored/sales data. In analogy to multiarmed bandit problems, the manager needs to simultaneously “explore” and “exploit” with inventory decisions in order to minimize the cumulative cost. Our goal is to understand the hardness of the problem disentangled from any probabilistic assumptions on the demand sequence—importantly, independence or time stationarity—and, correspondingly, to develop policies that perform well with respect to the regret criterion. We design a cost estimator that is tailored to the special structure of the censoring problem, and we show that, if coupled with the classic exponentially weighted forecaster, it achieves optimal scaling of the expected regret (up to logarithmic factors) with respect to both the number of time periods and available actions. This result also leads to two important insights: the benefit from “information stalking” as well as the cost of censoring are both negligible, at least in terms of the regret. We demonstrate the flexibility of our technique by combining it with the fixed share forecaster to provide strong guarantees in terms of tracking regret, a powerful notion of regret that uses a large class of time-varying action sequences as benchmark. Numerical experiments suggest that the resulting policy outperforms existing policies (that are tailored to or facilitated by time stationarity) on nonstationary demand models with time-varying noise, trend, and seasonality components. Finally, we consider the “combinatorial” version of the repeated newsvendor problem, that is, single-warehouse, multiretailer inventory management of a perishable product. We extend the proposed approach so that, again, it achieves near-optimal performance in terms of the regret. Funding: G. Lugosi was supported by the Spanish Ministry of Economy, Industry and Competitiveness [Grant MTM2015-67304-P (AEI/FEDER, UE)]. M. G. Markakis was supported by the Spanish Ministry of Economy and Competitiveness [Grant ECO2016-75905-R (AEI/FEDER, UE)] and a Juan de la Cierva fellowship as well as the Spanish Ministry of Science and Innovation through a Ramón y Cajal fellowship. G. Neu was supported by the UPFellows Fellowship (Marie Curie COFUND program) [Grant 600387]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoo.2022.0017 .

FBG strain monitoring data denoising in wind turbine blades based on parameter-optimized variational mode decomposition method

Herein, we aimed to solve the problem of difficulty in filtering the noise components in the monitoring of strain on wind turbine blades using fiber bragg grating, a denoising method based on parameter-optimized variational mode decomposition (VMD) is proposed. This method uses the minimum envelope entropy as the fitness function and the slime mould algorithm for self-adaptive optimization to find the optimal combination of modal decomposition components K and the quadratic penalty factor α of VMD. The optimized VMD was used to decompose the strain data of wind turbine blades over time into K intrinsic mode components, and the noise mode was removed using the sample entropy to obtain the effective signal. The proposed method was compared to ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition using the simulated signals and engineering data. The experimental results show that the proposed method can effectively remove noise from the strain data of wind turbine blades and has better denoising performance than the other two methods, which provides a reliable basis for analyzing the strain data of wind turbine blades.

Data-informed statistical finite element analysis of rail buckling

In this paper, the statistical finite element method is developed further to synthesize distributed rail response data with nonlinear finite element model predictions within and outside the measured loading range. In the data-generating model of the statistical finite element method, the distributed sensing data is decomposed into a finite element model component, a model-reality mismatch component, and a noise component. Each component is considered uncertain and is represented as a Gaussian random vector with a corresponding prior density. The finite element prior density is updated using the Bayesian statistical framework in light of the distributed fiber optic sensing data. The calculated posterior density enables one to infer the true structural response. The required finite element prior density is determined by solving a conventional stochastic forward problem using a polynomial chaos expansion of random fields and a non-intrusive pseudo-spectral projection approach. In this paper, a lab test involving a section of a rail (i.e., a beam-column structural member) instrumented with distributed fiber optic sensors and subjected to axial load causing progressive lateral buckling is considered to demonstrate the use of the statistical finite element method to improve rail response prediction. The results show improved prediction of true rail strain in linear and nonlinear regimes compared with a pure finite element model-based prediction.

Long-term measurement study of urban environmental low frequency noise.

Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.)VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.)LFN was minimally affected by high wind speeds at either Site. Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.

Evaluation of passive acoustic methods for ambient noise baseline and gas flow rate quantification at a proposed nearshore carbon capture and storage site in Australia

Measurement, monitoring, and verification (MMV) is an integral component of carbon capture and storage (CCS) projects, providing valuable feedback for management practices and assuring integrity. Within an operational MMV equipment, hydrophone-based passive acoustic techniques are used to establish ambient noise baseline and flow rate quantification at short range, specifically to facilitate ‘detect-attribute-quantify’ sequence of an MMV program. But nearshore environments are acoustically complex with overlapping components of biophony, geophony, anthropophony, and deployment noise resulting from multiple instruments used for MMV. These soundscape components can disproportionately dominate ambient noise levels, potentially masking acoustic signatures of bubbles used to quantify seabed gas seeps. Therefore, a robust baseline describing ambient noise variability across the range of frequencies associated with acoustic emissions of gas seeps is required, from which changes can be detected and monitored. In this context, multi-year hydrophone measurements (20 Hz to 24 kHz) from a proposed nearshore CCS site in Australia are analysed to establish a temporally resolved baseline, identifying key drivers causing overall ambient noise variability. These results are compared with acoustic bubble spectrum features and flow rate estimates from a controlled in situ gas release experiment. The experiment was performed using a custom-made bubble generator at 1, 10, and 25 m range from the hydrophone to understand the likelihood of detecting bubbles and quantifying flow rate at the proposed CCS site. Despite the complexities of nearshore environment, the evaluation highlights that low-cost hydrophone-based passive acoustic methods can provide a practical solution to complement quantification component of operational MMV programs.

Noise cancellation in distributed feedback laser based interrogation system for twin grating accelerometer

In this paper, we demonstrate a noise cancellation method for suppressing laser frequency noise in distributed feedback laser based system for interrogating a twin fiber Bragg grating based accelerometer. A simple method has been developed by studying the noise characteristics of the sensing photodiode and the reference photodiode, used in standard intensity based wavelength demodulation system. Proposed method could be able to reduce deterministic noise components to about 20% of the original noise components without using any complex feedback mechanism to stabilize the laser.

Transient-extracting wavelet transform for impulsive-like signals and application to bearing fault detection

Being able to characterize impulsive-like signals and extract their transitory features is difficult due to the presence of noise and irrelevant signal components in real signals. To address these problems, a brand-new time-frequency (TF) analysis technique called the transient-extracting wavelet transform is developed. This method is put forth by first investigating which TF coefficients can represent the fundamental TF properties of impulsive signals, and then designing an extraction operator to get the most related TF coefficients while simultaneously removing the unrelated ones. The signal reconstruction of this method is also analyzed. Additionally, a transient feature extraction approach is suggested for pinpointing the impulse’s occurrence timing, which is essential for correctly identifying the fault type. The analysis shows that the suggested method is more able to analyze impulsive-like data and is an effective bearing defect detector.

Propeller-shaft-hull coupling acoustic radiation characteristics induced by friction self-excitation of the stern bearing

This paper focuses on the practical engineering problem of excessive propeller-shaft-hull coupling vibration noise, which is caused by poor lubrication contact of water-lubricated stern bearings in the ship propulsion shaft system. Firstly, based on the three-dimensional sono-elasticity theory of ships, calculation models for the propeller-shaft-hull coupling acoustic radiation transfer function are established separately, considering the propeller excitation and friction excitation of the stern bearing. Secondly, by analyzing the acoustic radiation contribution of each mode, the vibration mode with the highest contribution to the typical acoustic radiation transfer function peaks of the propeller-shaft-hull coupling under the friction excitation of the stern bearing is separated. The spatial distribution and pointing characteristics of the sound field corresponding to the propeller-shaft-hull coupling acoustic under friction excitation of the stern bearing are given. Finally, a quantitative evaluation model of propeller-shaft-hull coupling acoustic radiation caused by friction self-excitation of the stern bearing is established. The mechanism behind the typical acoustic radiation peaks of propeller-shaft-hull coupling under friction excitation of the stern bearing is revealed, which provides methodological guidance for the quantitative evaluation of friction vibration noise and the separation of vibration noise components in ship stern structures.

A vital signsignal noise suppression method for wearable piezoelectric devices.

This paper tackles the problem of noise suppression during vital signsignal monitoring. Physiological signal monitoring is a significant and promising medical monitoring method, and wearable medical monitoring devices based on piezoelectric polymer sensors are a trending way for their advantages of being flexible in the shape, portable to use, and comfortable to wear. However, this raises the question that the measured signal contains much more noise components. To avoid the following shortcoming of low signal to noise ratio (SNR), a noise suppression method based on improved wavelet threshold and empirical mode decomposition combined with singular value decomposition (SVD) screening the intrinsic mode function (IMF) components is proposed. A wavelet transform is first used under the combination of hard and soft thresholds to focus the target range in the low-frequency region where the energy of the physiological signal is concentrated. Then, a complete ensemble empirical mode decomposition is used to decompose the signal effectively, which can resist the influence of random noises. Meanwhile, a SVD decomposition procedure was used to filter out the lower correlated IMF components to retain the validity of the original signal. We verified the effectiveness of the proposed method through simulated and measured experiments as well as the advantages and disadvantages of the algorithm compared with other physiological signal denoising algorithms through SNR filtering results, power spectrum distribution, and other perspectives. The results proved that the proposed method could effectively remove more detailed noise and improve the SNR of the signal efficiently, which is more conducive to the demand for auxiliary medical diagnosis in the future.

IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions

Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this research work include: 1) designing a multiscale denoising branch to extract multi-level information and reduce noise information. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; 2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios.

Noise Reduction Method for Fiber Optical Current Transformer Based on Mode Decomposition-Sample Entropy

When a fiber optic current transformer (FOCT) malfunctions, its output signal is usually accompanied by a large noise, which affects the subsequent fault diagnosis results of the transformer. In response to the above noise issues, this paper constructs a new noise reduction algorithm by combining Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) and Sample Entropy (SE). Above all, the output signal of FOCT is decomposed into multiple fault components using the CEEMDAN algorithm. Then, by comprehensively considering the frequency domain characteristics and sample entropy calculation value of each component, the characteristics of each component are determined, so the power frequency and low-frequency components are selected as the feature vectors representing the fault mode. Finally, the noise components with high sample entropy are eliminated, and the power frequency current components with medium sample entropy and the low-frequency fault feature components with low sample entropy are recombined and superposed to build a fault signal dataset after noise reduction.

Research and analysis on noise characteristics of low voltage power lines carrier communication

Noise is one of the most important factors affecting the reliability of low-voltage power line carrier communication. In order to fully grasp the noise characteristics, a channel analysis equipment has been designed to collect and analyze noise. The analysis results show that power line noise is mostly manifested as a combination of colored background noise and pulse noise, mainly concentrated in the frequency band below 700kHz. Compared with the low peak period of electricity consumption, the peak period has stronger time-varying noise, larger amplitude of background noise, richer components of pulse noise, and there is also jitter in the duration of periodic pulse noise.

BIT ERROR PERFORMANCE ENHANCEMENT FOR UNDERWATER ACOUSTIC NOISE CHANNEL BY USING CHANNEL CODING

The harm that underwater noise pollution poses to aquatic ecosystems and the resources that support it are being acknowledged on a worldwide scale. Fisheries and ecotourism are only two of the important businesses that are impacted by noise pollution. Reducing underwater impact noise is a major challenge for underwater acoustic communication systems. However, the implementation of noise reduction measures (noise abatement) remains limited. Most communication systems assume that the noise is both additive and Gaussian. Underwater Acoustic Noise (UWAN) systems generally perform poorly because of the often large non-Gaussian components in intermittent noise in the ocean. This study presents an experimental model (Dolphin-EAR DE200 Series) sound channel noise underwater at Lake Diyala Hamrin, Iraq, using a hydrophone model. Low data volume, multipath propagation, low bandwidth, and higher bit error rate (BER) of received data are major issues for underwater communication systems. In this paper, the Underwater Acoustic Channel (UWAC) aspect is evaluated and an error performance term is determined from the noise in the Student's t-distribution. In addition, Signals using binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) are used to generate error power analysis.

A Spatial Delay Domain-Based Prony Channel Prediction Method for Massive MIMO LEO Communications

Massive multiple-input–multiple-output (massive MIMO)-based low Earth orbit (LEO) intersatellite link communications is a promising research topic for the fifth generation (5G) and forthcoming sixth generation (6G) wireless networks. However, due to the fast relative movement between the transmitter and receiver in LEO satellites, intersatellite link communication is facing serious Doppler shifts and delays, which makes it difficult to obtain the accurate channel state information (CSI). Therefore, this article proposes an improved channel prediction method, that is, spatial-delay domain-based Prony (SDD-Prony) prediction, which not only realizes the accurate CSI acquisition for massive MIMO-based LEO intersatellite link systems but also substantially reduces the computational complexity. In particular, it is shown that the prediction error of the proposed method can converge to zero with the increase in antenna number and bandwidth. In addition, we combine the SDD-Prony prediction method with the total least squares method to reduce the influence of the noise and error components on the prediction accuracy, which further improves the prediction performance. The effectiveness of the proposed method is demonstrated by a theoretical proof and simulation results.

A multivariate statistical analysis of the noise emitted by an installed propeller

Novel-aircraft concepts consider the possibility of placing the propulsor very close to the fuselage to ingest the incoming airframe boundary layer. In this configuration, the engine takes the inflow at a reduced velocity, consuming less fuel in the combustion process. However, this induces a series of noise consequences that alter tonal and broadband noise components. The present work reports an experimental investigation to analyse the sound emitted by a propeller ingesting a turbulent boundary layer. Experiments have been performed in the anechoic wind tunnel at the University of Bristol and the set-up consisted of a two-bladed propeller close to a tangential flat plate to simulate the installed effects. A tripping device was placed 1 m upstream of the propeller and was used to generate a turbulent boundary layer at the propeller location. The wind tunnel velocity was fixed at 33 m/s keeping the advance ratio at J = 0.65. Far-field noise has been acquired using a microphone array positioned parallel to the plate, directly overhead of the propeller. The data were analysed in the frequency domain, providing a characterisation of spectral quantities. Furthermore, wavelet analysis was performed to investigate the time evolution of the identified pressure features. Results show evident haystacking humps close to higher BPF harmonics, due to the ingestion of the boundary layer. Moreover, the wavelet analysis revealed the intermittent nature of the haystacking humps, clearly visible at higher BPF harmonics with low-frequency energy content.

Development of a high-voltage stabilization system for precision ion beams

A new system to precisely control the accelerating voltage of an electrostatic accelerator has been developed. With this development, the acceleration voltage stability ΔV/V in the low-frequency range was successfully controlled to 6.0 × 10−5. In this development, the cause of the voltage fluctuation of the acceleration voltage was investigated. As a result, it was found that noise components due to mechanical vibration existed. Therefore, the HVD (High-Voltage Divider) feedback method, which is less susceptible to mechanical vibration, was adopted and the effect of vibration was successfully eliminated. Furthermore, by adding a high-sensitivity CPO feedback system in place of the previously used slit feedback system, low-frequency voltage fluctuations were eliminated, and high acceleration voltage stability could be obtained even in beamlines without a slit system.

Feasible Resolution of Angular Displacement Measurement by an Optical Angle Sensor Based on Laser Autocollimation

The feasible resolution of angular displacement measurement by an optical angle sensor based on laser autocollimation is investigated. Improving the sensor sensitivity while maintaining the noise level of the sensor signal as low as possible is necessary to achieve high-resolution angular displacement measurement. In this paper, the contribution of each component, such as a photodiode, a trans-impedance amplifier, and an analog-to-digital converter in the optical angle sensor, to the noise level of the sensor signal is first estimated on the basis of theoretical equations. The feasible sensitivity of the optical angle sensor is also estimated in numerical calculations. The sensitivity of a photodiode element at the edge of its photosensitive area is evaluated in experiments to realize the estimation of the angle sensor sensitivity. Experimental results are applied to the numerical calculations. The influences of the measurement laser beam diameter, the spot diameter of the focused laser beam on the photosensitive area, and the focal length of the collimator objective of the optical angle sensor are also considered in the numerical calculations. Finally, a prototype optical setup is developed. Experiments are performed to demonstrate that a compact optical angle sensor based on laser autocollimation with a collimator objective having a focal length shorter than 100 mm can achieve a resolution beyond 0.001 arc-second with a bandwidth of 1 kHz. This resolution is better than those achieved by commercial autocollimators employing an image sensor or a position-sensitive detector. The industrial contribution of this paper lies in the detailed breakdown of noise components in the readout signal of an angle sensor in a practical condition and the systematic estimation of its feasible resolution as well as its sensitivity.