Noise Errors Research Articles

Moving target localization in distributed MIMO radar systems with sensor position errors in the presence of a calibration object

This paper investigates the moving target localization problem in distributed multiple-input multiple-out (MIMO) radars with sensor position errors. Unlike the previous work, by invoking a moving calibration object, we develop a closed-form solution to determine the position and velocity of the unknown target for alleviating the performance degradation caused by sensor position errors in traditional methods. The proposed method has three stages. The calibration object is utilized in the first stage to estimate the sensor position errors and improve the sensor parameters, and these parameters are devoted to establishing the pseudo-linear equations by introducing nuisance variables in the second stage. After solving the pseudo-linear equations, the target parameters are refined by exploiting the relationships between the extra variables and target parameters in the final stage. The proposed method is shown analytically to reach the Cramér-Rao lower bound (CRLB) performance under sufficiently small measurement noise and sensor localization error conditions, and it can be easily extended to general cases where exist imprecise calibration object parameters or multiple calibration objects. Simulation results corroborate the theoretical analysis and show that the proposed method has a significant performance improvement over the state-of-the-art methods.

Retrieving Water Quality Parameters from Noisy-Label Data Based on Instance Selection

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi’an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data.

Grid Adaptive Bucketing Algorithm Based on Differential Privacy

With the popularity of location-based services, the restricted relationship between the availability of big location data and user’s privacy security has become a challenging issue. The spatial division is an effective measure for statistical location data characteristics. This paper proposes a grid adaptive bucketing algorithm based on differential privacy (GAB) to solve the problem that the existing differential privacy location data division method does not fully consider the distribution characteristics of spatial data points and noise superimposition. The algorithm first divides the mobile app location spatial data set into two layers of grids. It then performs adaptive binning judgment according to the square sum error value in the divided area. It puts areas with similar distributions into the same bucket, reducing the uniform assumption error and the noise error caused by many blank areas. Finally, a noise allocation strategy based on Hopkins statistic is adopted to achieve a reasonable noise allocation. The experimental results on two real large-scale location data sets, Checkin and Beijing, show that, compared with other classical algorithms based on differential privacy space partitioning, the GAB algorithm performs better in common evaluation indicators such as relative error and absolute error, which means GAB can obtain better range query effects and privacy protection effects.

A model-free method for attitude estimation and inertial parameter identification of a noncooperative target

Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes.

Upper limb orientation assessment as an articulated body chain.

Upper limb orientation estimation based on Magnetic, Angular Rate, and Gravity sensors considering multiple body segments is presented in this work. The proposal allowed assessing the activity of two or more body segments individually and jointly, regardless of their spatial relationship. A custom-made system was developed incorporating a complementary filter and a proportional-integral control for data sensor merging and, noise and instrumentation error reduction. Two controlled tests were carried out to assess the performance of the system. The former evaluated the response of the method in motionless conditions, while the latter assessed the feasibility to follow trajectories in 3D space. Ten volunteers were recruited to evaluate the system performance in three semi-controlled and daily life task tests. The system was evaluated using the common parameters in motion tracking methods and relied on a digital motion processor. The system's outcome presented a root mean square error in the range of 2.65°-3.98° for the semi-controlled tests and 0.48°-1.389° for the daily life task test. The system tests analysis proved that the proposal permitted obtaining the articulated body chain information of multiple segments when three or more MARG sensors are used.

Tracking and Regulation Performance Limitations of Networked Control Systems Over Erasure Channel With Input Quantization

Limitations in communication networks, such as delays, packet dropouts, and insufficient bandwidth, create great challenges in the output tracking control and regulation performance of networked control systems. In this article, we propose some novel performance indexes for achieving the optimal tracking and regulation performance. By considering the influences of noise, packet dropouts, and logarithmic quantization on the channel input, the performance limitations of the networked systems are investigated by applying a two-degree-of-freedom (TDOF) control technique. The results obtained prove that the tracking and regulation performance is dependent not only on the position of the plant’s nonminimum phase zeros and unstable poles, but also on the directions. Furthermore, it is demonstrated that noise and quantization error have a negative influence on the control performance, and the design of the TDOF controller can eliminate the effect of packet dropouts on control performance. To validate the innovation results, several simulations are conducted.

Nonlinear wavelet-based estimation to spectral density for stationary non-Gaussian linear processes

Nonlinear wavelet-based estimators for spectral densities of non-Gaussian linear processes are considered. The convergence rates of mean integrated squared error (MISE) for those estimators over a large range of Besov function classes are derived, and it is shown that those rates are identical to minimax lower bounds in standard nonparametric regression model within a logarithmic term. Thus, those rates could be considered as nearly optimal. Therefore, the resulting wavelet-based estimators outperform traditional linear methods if the degree of smoothness of spectral densities varies considerably over the interval of interest, such as sharp spike, cusp, bump, etc., since linear estimators are not able to attain these rates. Unlike in classical nonparametric regression with Gaussian noise errors where thresholds are determined by normal distribution, we determine the thresholds based on a Bartlett type approximation of a quadratic form with dependent variables by its corresponding quadratic form with independent identically distributed (i.i.d.) random variables and Hanson-Wright inequality for quadratic forms in sub-gaussian random variables. The theory is illustrated with some numerical examples, and our simulation studies show that our proposed estimators are comparable to the current ones.

Probabilistic damage detection and identification of coupled structural parameters using Bayesian model updating with added mass

Damage detection inevitably involves uncertainties originated from measurement noise and modeling error. It may cause incorrect damage detection results if not appropriately treating uncertainties. To this end, vibration-based Bayesian model updating (VBMU) is developed to utilize vibration responses or modal parameters to estimate structural parameters and the associated uncertainties of those estimates. However, traditional VBMU often assumes that mass is well known and invariant because simultaneous identification of mass and stiffness may yield an unidentifiable problem due to the coupling effect of the mass and stiffness. In addition, the posterior PDF in VBMU is usually approximated by single Markov Chain Monte Carlo (MCMC), leading to a low acceptance rate and limited capability for complex structures. This paper proposed a novel VBMU to address the coupling effect and identify mass and stiffness by adding known mass. Two vibration data sets are acquired from original and modified systems with added mass, giving the new characteristic equations. Then, the posterior PDF is reformulated by measured data and predicted counterparts from new characteristic equations. For efficiently approximating the posterior PDF, Differential Evolutionary Adaptive Metropolis (DREAM) algorithm is adopted to draw samples by running multiple Markov chains parallelly to enhance acceptance rate and sufficiently explore possible solutions. Finally, a numerical example with a ten-story shear building and a laboratory-scale three-story frame structure are utilized to demonstrate the efficacy of the proposed VBMU framework. The results show that the proposed method can successfully identify both mass and stiffness, and their uncertainties. Reliable probabilistic damage detection can also be achieved.

Study on egg sorting model based on visible-near infrared spectroscopy

To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate () and the rejection rate () are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.

Deep Bayesian inference for seismic imaging with tasks

We use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking. Seismic imaging is an ill-posed inverse problem because of bandwidth and aperture limitations, which are hampered by the presence of noise and linearization errors. Many regularization methods, such as transform-domain sparsity promotion, have been designed to deal with the adverse effects of these errors; however, these methods run the risk of biasing the solution and do not provide information on uncertainty in the image space and how this uncertainty impacts certain tasks on the image. A systematic approach is developed to translate uncertainty due to noise in the data to the confidence intervals of automatically tracked horizons in the image. The uncertainty in the seismic image is characterized by a convolutional neural network (CNN) that is used to reparameterize the image. To assess these uncertainties, samples are drawn from the posterior distribution of the CNN weights. Compared with traditional priors, it is argued in the literature that these CNNs introduce a flexible inductive bias that is a surprisingly good fit for a diverse set of problems, including medical imaging, compressive sensing, and diffraction tomography. The method of stochastic gradient Langevin dynamics is used to sample from the posterior distribution. This method is designed to handle large-scale Bayesian inference problems with computationally expensive forward operators as in seismic imaging. Aside from offering a robust alternative to the maximum a posteriori estimate that is prone to overfitting, access to these samples allows us to translate uncertainty in the image, due to noise in the data, to uncertainty on the tracked horizons. For instance, it admits estimates for the pointwise standard deviation on the image and for confidence intervals on its automatically tracked horizons.

Dynamic Deformation Monitoring of Offshore Oil Platforms with Integrated GNSS and Accelerometer

Dynamic deformation monitoring is a crucial component of the structural health monitoring of an offshore oil platform. Given the insensitivity of the Global Navigation Satellite System (GNSS) to high-frequency vibration information, a combination of GNSS and accelerometer is used for vibration monitoring of platform structures. A hybrid filter based on complementary ensemble empirical mode decomposition (CEEMD) combined with a Chebyshev filter is proposed to process the monitoring data. The extracted GNSS low-frequency displacement is fused with the high-frequency displacement obtained from acceleration integration to obtain the overall dynamic displacement of the platform. The experimental analysis shows that the combination of t-test and correlation coefficient can select the required intrinsic mode function (IMF), the proposed hybrid filter can reduce the noise error to a certain extent, and the quadratic frequency domain integration of the acceleration data can avoid the influence of the integration trend term. The correlation coefficient between the overall reconstructed displacement and the original GNSS monitoring data was 0.8576, and the signal correlation after denoising and refactoring was more than 85%, thereby preserving the essential information components. Integrated GNSS and accelerometer monitoring systems complement each other’s advantages, compensating for their shortcomings in monitoring the dynamic deformation of a structure.

Microearthquake location and uncertainty analysis using a Kirchhoff wavefront imaging method: A comparison with traveltime inversion and full wavefield imaging methods

The uncertainty and resolution of microearthquake location are an important topic for reservoir characterization and early warning of induced earthquakes. Many independent studies have either improved certain source location methods or applied a certain method to a data set without perhaps studying the full limitations compared with other methods. We have developed a P- and PS-wavefront imaging (WI) method to estimate the source origin time sequentially, which overcomes the unknown variable of source origin time in Kirchhoff-type imaging and helps to reduce the location uncertainties originating from the source origin time. We develop a 3D synthetic example by finite-difference modeling the realistic elastic 3C data using the SEG advanced modeling (SEAM) 4D earth model, and jointly compare the reservoir and basement microearthquake source location uncertainties based on traveltime inversion (TI), WI, and full wavefield imaging (FWIMG) methods. For each method, we also investigate the source location uncertainties between the P- and PS-wave results. Our results indicate that, using P waves and typical surface receiver arrays, TI has larger depth location uncertainties and cannot separately resolve the reservoir and basement events, whereas WI has larger horizontal uncertainties; FWIMG has the smallest horizontal location uncertainty, its depth uncertainty is similar to the WI method, and it is much smaller than the TI. Using PS waves can significantly reduce the location uncertainty compared with using only P waves for the three methods, and the full wavefield method has the smallest location uncertainty. The WI method has a moderate computation cost between TI and full wavefield methods. Data noise and velocity scaling errors change the source location uncertainties based on different imaging methods.

Analysis of noise and bias errors in intelligence information systems.

An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team-oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team-oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

Background and purposeTo investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.Materials and methodsHigh-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen’s kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists.ResultsOverall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm3) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men.ConclusionImage quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.

Vehicle trajectory prediction considering aleatoric uncertainty

Trajectory prediction is imperative in the operation of autonomous vehicles because it aids in understanding the surrounding environment through perception fusion of multiple sensors and high-accuracy localisation. Furthermore, the output of algorithms used for trajectory prediction is used in decision making and path planning. However, the accuracy of trajectory prediction is dependent on the perception system particularly when low-cost sensors are used or severe weather conditions are encountered. Data-dependent and task-dependent aleatoric uncertainty affects the trajectory prediction task. Therefore, in this study, we proposed a network for trajectory prediction that integrates the temporal pattern attention and the graph convolutional sequence encoding to address the data-dependent uncertainty caused by observation noise and machine-perceptual errors. Furthermore, an adaptive weight loss function was developed to address the training difficulties caused by loss-dominated tasks (task-dependent aleatoric uncertainty) in a trajectory prediction network. The results of our experiment indicate a 20% improvement in predictive performance over existing methods under a 5-s prediction horizon. Furthermore, an experiment comparing robustness to heteroscedastic uncertainty confirmed that the proposed method improves trajectory prediction robustness against observation noise and perceptual errors.

Mosaicism of common pathogenic MECP2 variants identified in two males with a clinical diagnosis of Rett syndrome.

Rett (RTT) syndrome, a neurodevelopmental disorder caused by pathogenic variation in the MECP2 gene, is characterized by developmental regression, loss of purposeful hand movements, stereotypic hand movements, abnormal gait, and loss of spoken language. Due to the X-linked inheritance pattern, RTT is typically limited to females. Recent studies revealed somatic mosaicism in MECP2 in male patients with RTT-like phenotypes. While detecting mosaic variation using Sanger sequencing is theoretically possible for mosaicism over ~15%-20%, several variables, including efficiency of PCR, background noise, and/or human error, contribute to a low detection rate using this technology. Mosaic variants in two males were detected by next generation sequencing (NGS; Case 1) and by Sanger re-sequencing (Case 2). Both had targeted digital PCR (dPCR) to confirm the variants. In this report, we present two males with classic RTT syndrome in whom we identified pathogenic variation in the MECP2 gene in the mosaic state (c.730C > T (p.Gln244*) in Patient 1 and c.397C > T (p.Arg133Cys) in Patient 2). In addition, estimates and measures of mosaic variant fraction were surprisingly similar between Sanger sequencing, NGS, and dPCR. The mosaic state of these variants contributed to a lengthy diagnostic odyssey for these patients. While NGS and even Sanger sequencing may be viable methods of detecting mosaic variation in DNA or RNA samples, applying targeted dPCR to supplement these sequencing technologies would provide confirmation of somatic mosaicism and mosaic fraction.

Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks

This paper investigates the long memory properties of the prices series of two major precious metals (gold and silver) and six non-precious metals (aluminium, copper, lead, zinc, tin and nickel) by using a fractional integration modelling framework, while controlling for structural breaks and non-linearities. We use daily data in a range from November 01, 2007 to March 20, 2020. From the results, Copper and Tin exhibit a very small degree of mean reversion. However, if autocorrelation is permitted, the unit root null hypothesis cannot be rejected in any of the series. We also account for structural breaks and non-linearities. We observe that all the series under investigation are exposed to multiple breaks. Results obtained under white noise errors shows some evidence of mean reversion for silver aluminium, copper, tin and zinc in some of the subsamples. However, under the assumption of Bloomfield autocorrelated errors the confidence intervals are so wide that we cannot confirm this evidence in any single case.

Physics-guided deep neural network for structural damage identification

The physics-driven method via finite element model and the data-driven methods via supervised learning is commonly used in the analysis of structural damage identification. The finite element model can be susceptible to environmental noise and modeling errors, which leads to model identification results deviating from the real damage. The supervised learning method faces the challenge of insufficient labeled damage data, which limits the learned model in identifying damage scenarios that are not contained in the training data. In this study, a novel physics-guided data-driven method is proposed to incorporate physical perception into the data-driven method for identifying structural damage. Specifically, the physics from the finite element model (e.g., structural dynamic characteristics) is incorporated into the neural network model to guide the damage feature learning from scarce measured data. In addition, a loss function with physical significance is developed to evaluate the discrepancy between the finite element model and the measured data, which guides the neural network to learn the damage features of the real structure. The learned neural network model has the potential to improve the damage detection results. The proposed methodology is validated by a numerical case study of a suspension bridge model and an experimental study of a frame structure.

QAPP: A quality-aware and privacy-preserving medical image release scheme

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (DCT) with differential privacy (DP). Specifically, QAPP is composed of three phases. First, DCT is applied to each medical image to obtain its cosine coefficients matrix. Second, the original cosine coefficients matrix is compressed into k*k cosine coefficients matrix, which can retain the main features of each image. Third, the appropriate Laplace noise is injected into the formed k*k matrix to achieve differential privacy, and these noise-added coefficients are used to reconstruct the noise-added medical images through inverse DCT. Especially, considering there two error sources affecting the image quality in our work: the compression error caused by DCT, and the injected noise error caused by DP, Therefore, a selection function is proposed to determine the optimal compression dimension k, which can minimize the influence of these two errors to improve the visualization quality of the medical image. Subjective and objective image quality evaluation, and extensive experiments of image classification and segmentation using the real medical image dataset demonstrate that the proposed method QAPP can better balance medical image quality and privacy than other similar DP-based methods.

2D Generalized Optical Spatial Modulation for MIMO-OWC Systems

In this paper, a novel two-dimensional (2D) generalized optical spatial modulation (GOSM) scheme is proposed for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. By grouping multiple successive time slots as one time block, 2D GOSM mapping can be performed not only in the space domain but also in the time domain. Specifically, two types of 2D GOSM mapping schemes are designed, including 2D-1 and 2D-2 GOSM mappings. Moreover, to address the high complexity issue of optimal joint maximum-likelihood (ML) detection and the noise amplification and error propagation issues of zero-forcing-based ML (ZF-ML) detection, a deep neural network (DNN)-aided detection scheme is further designed for 2D GOSM systems. Simulation results demonstrate the superiority of the proposed 2D GOSM scheme with deep learning-aided detection for high-speed and low-complexity MIMO-OWC systems. More specifically, a remarkable 3.4-dB signal-to-noise ratio (SNR) gain can be achieved by 2D GOSM in comparison to the conventional one-dimensional (1D) GOSM, when applying the DNN-aided detection.