Noise Correction Research Articles

A review on fault diagnosis in TCSC compensated transmission lines using various techniques

This study presents a comparative analysis of fault detection and categorization methods in TCSC (Thyristor Controlled Series Capacitor)-compensated power systems. The analysis explores different techniques for extracting fault features from current data obtained from TCSC-compensated transmission lines. Machine learning algorithms, namely kNearest Neighbor (k-NN) and Support Vector Machine (SVM) are employed to categorize the extracted features, enabling fault detection and identification. To evaluate the efficacy of the proposed method, simulation tests were conducted on a test system, considering various fault scenarios. The simulations accounted for the presence of noise and TCSC correction, reflecting real-world operating conditions. The results demonstrate the efficiency of multiple approaches in accurately and swiftly detecting and classifying faults. By leveraging the capabilities of machine learning algorithms and extracting relevant features from current data, the proposed method offers a promising solution for fault detection and categorization in TCSC-compensated power systems. The ability to operate effectively even in the presence of noise and TCSC correction highlights the robustness and reliability of the proposed approach.

Multitype Label Noise Modeling and Uncertainty-Weighted Label Correction for Concealed Object Detection

Recently, plenty of millimeter-wave image concealed object detection models have achieved superior performance on benchmark datasets. The success of these models heavily depends on a large amount of fully annotated data. However, noisy-label data often exist in a large-scale dataset, which deteriorate the performance of models. In this article, we delve into the training of the concealed object detector with label noise, where we recognize divergent types of noisy annotations. To tackle the multitype label noise problem, we propose a unified framework, including two stages: label noise modeling and correction training. We design the region-level label noise modeling, which analyzes and models label noise via the classification branch loss curves. To correct the labels during training, we design the uncertainty-weighted loss for label correction. The experiments on a millimeter-wave security image dataset demonstrate the effectiveness of our proposed training framework, which can alleviate the negative influence of noisy annotations on network training. By introducing our framework, the object detection network can achieve a prominent performance improvement on mean average precision (0.176 on average) compared to the one trained with noisy labels.

Unpaired Speckle Extraction for SAR Despeckling

Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since speckle-free SAR images are inaccessible, supervised denoising methods are not suitable for this task. To exploit the strong capabilities of convolutional neural networks (CNNs), we propose Unpaired Speckle Extraction (SAR-USE), an unsupervised method for SAR despeckling. Our method utilizes unpaired SAR and clean optical images to extract “real” speckle for learning despeckling. First, a CNN that has never seen clean SAR images is employed to extract speckle from the SAR image. Then, the extracted speckle is multiplied with a random optical image to synthesize paired data for learning speckle removal. Through a Siamese network, speckle extraction and learning despeckling are performed alternately and promote each other. To make the extracted speckle more visually and statistically realistic, it is constrained by a noise correction module to be unit mean while maintaining spatial correlation. After convergence, the CNN is a good denoiser that can effectively extract speckle from SAR images. Experiments on synthetic datasets show that the denoising ability of the proposed method is as good as its supervised counterpart. More importantly, SAR-USE is very efficient for removing the spatially correlated speckle in real data that supervised learning methods cannot.

Central Autonomic Network Alterations in Anorexia Nervosa Following Peripheral Adrenergic Stimulation

BackgroundAnorexia nervosa (AN) is characterized by low body weight, disturbed eating, body image disturbance, anxiety, and interoceptive dysfunction. However, the neural processes underlying these dysfunctions in AN are unclear. This investigation combined an interoceptive pharmacological probe, the peripheral β-adrenergic agonist isoproterenol, with resting-state functional magnetic resonance imaging to examine whether individuals with AN relative to healthy comparison participants show dysregulated neural coupling in central autonomic network brain regions. MethodsResting-state functional magnetic resonance imaging was performed in 23 weight-restored female participants with AN and 23 age- and body mass index–matched healthy comparison participants before and after receiving isoproterenol infusions. Whole-brain functional connectivity (FC) changes were examined using central autonomic network seeds in the amygdala, anterior insular cortex, posterior cingulate cortex, and ventromedial prefrontal cortex after performing physiological noise correction procedures. ResultsRelative to healthy comparison participants, adrenergic stimulation caused widespread FC reductions in the AN group between central autonomic network regions and motor, premotor, frontal, parietal, and visual brain regions. Across both groups, these FC changes were inversely associated with trait anxiety (State-Trait Anxiety Inventory-Trait), trait depression (9-item Patient Health Questionnaire), and negative body image perception (Body Shape Questionnaire) measures, but not with changes in resting heart rate. These results were not accounted for by baseline group FC differences. ConclusionsWeight-restored females with AN show a widespread state-dependent disruption of signaling between central autonomic, frontoparietal, and sensorimotor brain networks that facilitate interoceptive representation and visceromotor regulation. Additionally, trait associations between central autonomic network regions and these other brain networks suggest that dysfunctional processing of interoceptive signaling may contribute to affective and body image disturbance in AN.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics.

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Dark Current Noise Correction Method Based on Dark Pixels for LWIR QWIP Detection Systems

The long-wave infrared (LWIR) quantum-well photodetector (QWIP) operates at low temperatures, but is prone to focal plane temperature changes when imaging in complex thermal environments. This causes dark current changes and generates low-frequency temporal dark current noise. To address this, a dark current noise correction method based on dark pixels is proposed. First, dark pixels were constructed in a QWIP system and the response components of imaging pixels and dark pixels were analyzed. Next, the feature data of dark pixels and imaging pixels were collected and preprocessed, after which a recurrent neural network (RNN) was used to fit the dark current response model. Target data were collected and input into the dark current response model to obtain dark level correction values and correct the original data. Finally, after calculation and correction, temporal noise was reduced by 49.02% on average. The proposed method uses the characteristics of dark pixels to reduce dark current temporal noise, which is difficult using conventional radiation calibrations; this is helpful in promoting the application of QWIPs in LWIR remote sensing.

Deep Learning for Multiwell Automatic Log Correction

Researchers have dedicated numerous applications of machine-learning (ML) techniques for fi eld-scale automated interpretation of well-log data. A critical prerequisite for automatic log processing is to ensure that the log characteristics are reasonably consistent across multiple wells. Manually correcting logs for consistency is laborious, subjective, and error prone. For some wellbore logs, such as gamma ray and neutron porosity, borehole effects and miscalibration can cause systematic inconsistencies or errors that might be present even after the application of wellbore and environmental corrections. Biased or consistently inaccurate data in the logs can confound ML approaches into learning erroneous relationships, leading to misinterpretations, such as wrong lithology prediction, reservoir estimation, and incorrect formation markers. To overcome such difficulties, we have developed a deep learning method to provide petrophysicists with a set of consistent logs through the multiwell automatic log correction (MALC) workflow. Presently, the corrections we target are systematic errors on the standard logs, especially gamma ray and neutron logs, random noises, and to a lesser extent, local formation property misreading due to washouts. We applied the proposed method in multiple fi elds worldwide containing different challenges, and in this paper, we include the results in two fi eld examples. The first one covers the correction of synthetic coherent noise added to fi eld data, and the second example covers the correction applied to original measurements.

Efficiency rectification of predictive machine learning models by data centric optimization

There are two ways to optimize the performance of machine learning models, one is model centric, and another is data centric. Model centric approaches sometimes cannot yield expected results, so researchers and developers are now focusing on data centric optimization. Main steps of data centric approaches are – noise detection and correction, effective feature selection, feature transformation and data augmentation in case of limited datasets. Be it image, audio, text or numerical data, the mentioned approaches are always beneficial irrespective of model architecture. This paper is devoted to developing new preprocessing techniques for numerical data. The developed algorithm is tested with different artificial and real datasets for linear regression, k-means clustering, SVM classifier and deep learning networks. The algorithm is easy to implement and computationally less expensive. Experimental results show that proposed methods work well to improve model efficiency.

Using Seafloor Geodesy to Detect Vertical Deformation at the Hikurangi Subduction Zone: Insights From Self‐Calibrating Pressure Sensors and Ocean General Circulation Models

AbstractSeafloor pressure sensor data is emerging as a promising approach to resolve vertical displacement of the seafloor in the offshore reaches of subduction zones, particularly in response to slow slip events (SSEs), although such signals are challenging to resolve due to sensor drift and oceanographic signals. Constraining offshore SSE slip distribution is of key importance to understanding earthquake and tsunami hazards posed by subduction zones. We processed seafloor pressure data from January to October 2019 acquired at the Hikurangi subduction zone, offshore New Zealand, to estimate vertical displacement associated with a large SSE that occurred beneath the seafloor array. The experiment included three self‐calibrating sensors designed to remove sensor drift, which, together with ocean general circulation models, were essential to the identification and correction of long‐period ocean variability remaining in the data after applying traditional processing techniques. We estimate that long‐period oceanographic signals that were not synchronous between pressure sensors and reference sites influenced our inferred displacements by 0.3–2.6 cm, suggesting that regionally deployed reference sites alone may not provide sufficient ocean noise correction. After incorporating long‐period ocean variability corrections into the processing, we calculate 1.0–3.3 cm of uplift during the SSE offshore Gisborne at northern Hikurangi, and 1.1–2.7 cm of uplift offshore the Hawke's Bay area at central Hikurangi. Some Hawke Bay displacements detected by pressure sensors near the trench were delayed by 6 weeks compared to the timing of slip onset detected by onshore Global Navigation Satellite System sites, suggesting updip migration of the SSE.

Label confidence-based noise correction for crowdsourcing

In crowdsourcing scenarios, each instance obtains multiple noisy labels from different crowd workers and then gets its integrated label via a label aggregation method. In spite of the effectiveness of label aggregation methods, a certain level of noise still remains in the integrated labels. To solve this problem, several noise correction methods have been proposed in recent years. However, to our best knowledge, these methods seldom take the label confidence of each instance into account. Therefore, we propose a label confidence-based noise correction (LCNC) method. At first, LCNC calculates the label confidence of each instance using its multiple noisy labels to filter all instances and obtains an original clean set and noise set. Second, LCNC builds multiple random trees on the original clean set to recalculate the label confidence of each instance using its predicted labels generated by these random trees and then refilters all instances to obtain a new clean set and noise set. Finally, LCNC builds two heterogeneous classifiers on the new clean set to correct the noise instances in the new noise set according to a consensus voting strategy. The experimental results on 34 simulated and two real-world crowdsourced datasets show that LCNC significantly outperforms all the other state-of-the-art noise correction methods used for comparison.

Biquaternion-based DOA estimation of noncircular signals with time-modulated antenna arrays

A novel direction of arrival (DOA) estimation method of noncircular signals based on biquaternion with time-modulated antenna arrays is proposed in this paper. Time-modulated antenna arrays (TMAA) is introduced into the DOA estimation of noncircular signals for the first time, which makes the receiver only need a single RF processing channel, which effectively saves hardware resources. The noncircular features of the signal are fully exploited for virtual array expansion with the biquaternion framework. Aiming at the problem of nonuniform noise in the array output after time modulation, the nonuniform noise correction is realized by using the theoretical analysis of the time sequence scheme. The simulation illustrates that the proposed method has superior performance than conventional DOA estimation method with TMAA, in terms of resolution and numbers of localizable sources.

Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR).

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7T.

Multi-Task Learning with High-Dimensional Noisy Images

Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image regression models that fit each dataset separately are not equipped to leverage information across inter-related images, and existing multi-task learning approaches are compromised by the inability to account for the noise that is often observed in images. We propose a novel joint scalar-on-image regression framework involving wavelet-based image representations with grouped penalties that are designed to pool information across inter-related images for joint learning, and which explicitly accounts for noise in high-dimensional images via a projection-based approach. In the presence of nonconvexity arising due to noisy images, we derive nonasymptotic error bounds under nonconvex as well as convex grouped penalties, even when the number of voxels increases exponentially with sample size. A projected gradient descent algorithm is used for computation, which is shown to approximate the optimal solution via well-defined nonasymptotic optimization error bounds under noisy images. Extensive simulations and application to a motivating longitudinal Alzheimer’s disease study illustrate significantly improved predictive ability and greater power to detect true signals, that are simply missed by existing methods without noise correction due to the attenuation to null phenomenon. Supplementary materials for this article are available online.

A multi-view-based noise correction algorithm for crowdsourcing learning

Crowdsourcing services provide a way to obtain large amounts of labeled data, which is inexpensive and effective. In crowdsourcing scenarios, the integrated labels of instances can be deduced by implementing ground truth inference algorithms. However, those labels often contain substantial noise and, to mitigate the effects of noise, label noise handling techniques are needed. This paper proposes a novel multi-view-based noise correction algorithm (MVNC). MVNC introduces the idea of multi-view learning to make better use of the information from crowdsourced data. It adds a new view composed of multiple noise labels and then trains classifiers on two views respectively to correct noise instances. In this process, different information between the views is fully utilized to generate the disagreement between two classifiers, so that the classifiers can complement each other and make more reliable predictions for noise instances. Experimental results on 38 benchmark data sets and 6 real-world data sets show that the new view significantly enhances the effect of noise correction.

A Waveform Sampling Prototype ASIC for Picosecond-Level Time Measurement

This article presents the design and test results of a waveform sampling application-specific integrated circuit (ASIC) based on the switched capacitor array (SCA). It integrates eight differential-input channels, and each channel consists of a 256-cell capacitor array, 256 12-bit Wilkinson analog-to-digital converters (ADCs), and a serial data readout. A sampling rate of up to 5 Gsps is achieved with a low-jitter delay-locked loop (DLL). Two shared coarse counters running at the rising and falling edges of the reference clock are used to extend the time measurement range. Several crosstalk reduction strategies were adopted in the circuit design, especially in the clock generation circuit and the digitization circuit. The ASIC has been fabricated in 180-nm CMOS process. The test results show that the random noise is approximately 0.7-mV rms after the fixed-pattern noise correction and that the precision of waveform timing is better than 7-ps rms with the sampling interval correction.

Diversified Shifts in the Cross Talk between Members of the Gut Microbiota and Development of Coronary Artery Diseases.

Coronary artery disease (CAD) is one of leading causes of mortality worldwide. Studies on roles that the gut microbiota plays in development of atherosclerosis or acute myocardial infarction (AMI) have been widely reported. However, the gut microbiota is affected by many factors, including age, body mass index (BMI), and hypertension, that lead to high CAD risk. However, the associations between gut microbiota and CAD development or other CAD risk factors remain unexplored. Here, we performed a 16S RNA gene sequencing analysis of 306 fecal samples collected from patients with mild coronary stenosis (MCS; n = 36), stable angina (SA; n = 91), unstable angina (UA; n = 48), and acute myocardial infarction (AMI; n = 66) and 65 non-CAD controls. Using a noise-corrected method based on principal-component analysis (PCA) and the random forest algorithm, we identified the interference with gut microbial profiling of multiple factors (including age, gender, BMI, and hypertension) that potentially contributed significantly to the development of CAD. After correction of noise interference from certain interfering factors, we found consistent indicator microbiota organisms (such as Vampirovibrio, Ruminococcus, and Eisenbergiella) associated with the presence of MCS, SA, and AMI. Establishment of a diagnostic model revealed better performance in early CAD than clinical indexes with indicator microbes. Furthermore, indicator microbes can improve the accuracy of clinical indexes for the diagnosis of AMI. Additionally, we found that the microbial indicators of AMI Sporobacter and Eisenbergiella showed consistent positive and negative correlations to the clinical indexes creatine kinase (CK) and hemoglobin (Hb), respectively. As a control indicator of AMI, Dorea was negatively correlated with CK but positively correlated with Hb. IMPORTANCE Our study discovered the effect of confounding factors on gut microbial variations and identified gut microbial indicators possibly associated with the CAD development after noise correction. Our discovered indicator microbes may have potential for diagnosis or therapy of cardiovascular disorders.

Joint few-shot registration and segmentation self-training of 3D medical images

Medical image segmentation and registration are very important related steps in clinical medical diagnosis. In the past few years, deep learning techniques for joint segmentation and registration have achieved good results in both segmentation and registration tasks through one-way assisted learning or mutual utilization. However, they often rely on large labeled datasets for supervised training or directly use pseudo-labels without quality estimation. We propose a joint registration and segmentation self-training framework (JRSS), which aims to use segmentation pseudo-labels to promote shared learning between segmentation and registration in scenarios with few manually labeled samples while improving the performance of dual tasks. JRSS combines weakly supervised registration and semi-supervised segmentation learning in a self-training framework. Segmentation self-training generates high-quality pseudo-labels for unlabeled data by injecting noise, pseudo-labels screening, and uncertainty correction. Registration utilizes pseudo-labels to facilitate weakly supervised learning, and as input noise as well as data augmentation to facilitate segmentation self-training. Experiments on two public 3D medical image datasets, abdominal CT and brain MRI, demonstrate that our proposed method achieves simultaneous improvements in segmentation and registration accuracy under few-shot scenarios. Outperforms the single-task fully-supervised training state-of-the-art model in the metrics of Dice similarity coefficient and standard deviation of the Jacobian determinant.

Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions.

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

Multi-temporal InSAR tropospheric delay modelling using Tikhonov regularization for Sentinel-1 C-band data

The increased availability of satellite SAR data coupled with improved InSAR processing algorithms has led to higher accuracy of InSAR-derived displacements. However, obtaining millimeter-level accuracy still remains a challenge due to the inability to accurately model the tropospheric delay, which is sometimes larger than the actual deformation. We propose to estimate the relative daily SAR tropospheric delay by double differencing interferograms that share a common image by leveraging Sentinel-1’s short revisit time and the interchanging role of the common image. The resulting double-differenced interferograms consist of tropospheric delay and processing noise. This combination leads to an inverse problem as a result of the differential nature of the interferograms. The unknown SAR tropopsheric delay is solved by Tikhonov regularization and the processing errors are accounted for by covariance modelling. In cases of rapid localized displacement, the deforming region is masked and the tropospheric delay for the deforming region is determined through kriging. Validation is performed using simulated SAR data and GNSS tropospheric delays. We find a good correlation between estimated SAR tropospheric delays and GNSS tropospheric delays, with an average root mean square error (RMSE) of 1.93 radians across six GNSS locations. In addition, we examine the performance of the correction on interferograms through variogram modelling, which indicates improved correction both for short-wavelength and long-wavelength tropospheric noise. SAR tropospheric delay estimates are integrated into a multitemporal InSAR workflow through either interferometric subtraction or stochastic modelling. Validation using GNSS measurements indicates that SAR tropospheric delay estimates significantly improve the accuracy of the InSAR-derived time-series displacement.

Study on Noise Correction Algorithm of Infrared Emissivity of Rock under Uniaxial Compression

In the process of uniaxial loading of rocks, the original temperature information of infrared radiation is easily submerged in the noise signal, which leads to distortion of the obtained infrared radiation response information. In this paper, we propose a multi-band pseudo-emissivity denoising algorithm. Based on the basic theory of infrared radiation, by separating the infrared temperature measurement from the emissivity of the measured object, we constructed an infrared multi-band temperature measurement vector group that does not involve the emissivity to reduce the noise interference caused by the infrared temperature measurement results and the emissivity. Under a loading experiment of rock under uniaxial compression, the change of infrared radiation (IR) characteristics with loading was observed. The research results show that the multi-band pseudo-emissivity algorithm could effectively denoise infrared images and, using the denoised rock surface MIRT, AIRT and IRV as indicators, the characteristics of infrared radiation change in the process of uniaxial compression loading and fracturing of real rocks were analyzed.