Levels Of Random Noise Research Articles

Super-resolved reconstruction method for spatiotemporally encoded magnetic resonance imaging based on deep neural network

Single-shot spatiotemporally-encoded magnetic resonance imaging (SPEN MRI) is a novel ultrafast MRI technology. The SPEN MRI possesses great resistance to inhomogeneous <i>B</i><sub>0</sub> magnetic field and chemical shift effect. However, it has inherently low spatial resolution, and the super-resolved reconstruction is required to improve the spatial resolution of SPEN MRI image without additional signal acquisition. Several super-resolved reconstruction methods have been proposed, but they all suffer the problems of long iterative solution time and/or aliasing artifacts residue in the reconstructed results. In this paper, a super-resolved reconstruction method is proposed for single-shot SPEN MRI based on deep neural network. In this method the simulation samples are used to train the deep neural network, and then the trained network model is adopted to reconstruct the real sampled signals. Experimental results of numerical simulation, water phantom and in vivo rat brain show that this method can quickly reconstruct a super-resolved SPEN image with no residual aliasing artifacts, and clear texture information. An appropriate number of training samples and an appropriate random noise level for training samples contribute to improving the reconstruction results.

Robust bacterial co-occurence community structures are independent of r- and K-selection history

Selection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.

Uncovering the microseismic signals from noisy data for high-fidelity 3D source-location imaging using deep learning

Localizing the microseismic event plays a key role in microseismic monitoring. However, microseismic data usually suffer from a low signal-to-noise ratio (S/N), which could affect the resolution of the microseismic source location. We have developed an unsupervised deep learning approach based on variational autoencoder (VAE) and squeeze-and-excitation (SE) networks for enhancing microseismic signals, as well as suppressing noise. First, the microseismic data are divided into several overlapped patches. Second, the VAE encodes the data, extracting the significant features related to the useful signals. Finally, the extracted latent features are decoded to uncover the useful signals and discard the others. The SE network is used to guide the VAE to preserve the useful information related to the clean signal by scaling the extracted features from the encoder part and concatenating them with the features of the decoder part. Our algorithm is evaluated using several synthetic and field examples. As a result, a robust denoising performance is shown despite the existence of a high level of random and coherent noise, for example, with an S/N as low as −32.45 dB. Then, the denoised signal can be used as input data to image the source location using a reverse time migration method, leading to better location accuracy. Our algorithm performs the best when compared to benchmark methods such as f- x deconvolution and the damped multichannel singular spectrum analysis methods.

Krylov subspace iterative methods for time domain equivalent sources method based nearfield acoustical holography

This paper studies the acoustic field reconstruction on an arbitrarily shaped vibrating surface using the time-domain equivalent source method (ESM) based near-field acoustic holography (NAH) from measurements of the pressure field on a nearby conformal surface. For the classical frequency-dependent ESM, we represent the acoustic field by a set of frequency-dependent monopole sources strategically distributed over a close and conformal surface to the vibrating structure. We demonstrated that in this setup, the ESM representation approximates the classical integral representations, which justifies the ESM as an extension to the classical integral theory. For time-domain ESM in a similar manner, we demonstrate that a set of time-dependent monopole sources approximates the single layer retarded potential and then characterize the acoustic field. The discretization of time-domain ESM produces a matrix system with a particular sparse structure that can be efficiently solved using Krylov subspace iterative methods. The iterative methods that will be of our interest are Conjugate Gradients Normal equation Residual, Conjugate Gradients Normal equation Error and Least Squares QR. To reduce the amplification of measurement in the numerical reconstructions, we combine the proposed iterative methods with heuristic stopping rules like L-curve, Quasi-Optimality, Hanke–Raus, Brezinski–Rodriguez–Seatzu and a posteriori stopping rule. To determine the best combination of reconstruction technique, iteration and stopping rule, we develop a statistical study that uses multiple samples of numerically generated data by Gaussian pulses over a sphere. We produce these samples using a random source position and different noise levels. Finally, we validate our proposed methodology using scattering data from an elastic spherical shell.

Synthesis of a robust stochastic regulator for the model of electrohydraulic brake system

Three new algorithms for synthesizing control for the model of an electrohydraulic disk brake system are presented, which are based on the synergetic control theory. The first algorithm is developed relying on the classical method of analytical design of aggregated regulators in an assumption of a completely defined object. The second algorithm represents an algorithm of nonlinear adaptation on a target manifold and is designed for an object with a nonrandom disturbance in the control channel. The third algorithm takes account of the random disturbance in the discrete description of this object and rests on the strategies minimizing the dispersion of the output macrovariable. The results of a comparative numerical simulation of the three control algorithms are presented and the recommendations concerning the selection of the regulator parameters are formulated depending on the level of systematic disturbances and random noise.

Rapid ultrasonic array assessment framework method for evaluating ultrasonic array transducer performance

Ultrasonic array modelling is widely used to understand the performance of new transducer materials or geometries in a range of application scenarios. However, array modelling from first principles is both time-consuming and challenging due to the required knowledge of accurate acoustic and piezoelectric properties. This paper presents an alternative approach in which two elements are manufactured and characterised in the desired application scenario through a set of experimentally measured parameters. This experimental information is then used to construct a frequency domain model of an array of these elements. In this approach, the more challenging transducer modelling aspects are replaced by an experiment, and wave propagation modelling is used to simulate how those elements would perform in an array assembly. In order to characterise the elements and the application scenario the input pulse, attenuation coefficient, and both coherent and random noise amplitudes are measured. These experimental parameters are then used in a model to simulate the expected array image, from which the array performance, such as the signal-to-noise ratio, coherent noise scattering level, random noise level, and imaging artefacts, in the given application can be quantified. The approach is demonstrated with two commercial ultrasonic arrays and three test structure materials with differing levels of coherent grain scattered noise. Results across these various conditions show agreement within 4.2 dB between the simulations and their experimental counterparts in terms of image signal-to-noise ratio. Simulations with defects are also conducted and compared with experimental data for different array element geometries.

A novel transient balancing technology of the rotor system based on multi modal analysis and feature points selection

In this paper, a novel transient balancing technology for identifying the unbalance parameters of the rotor system is proposed. Through basic coordinate transformation and multi modal analysis of the dynamical equations of rotor system, the calculation method for transient excitation forces is proposed. The transient excitation forces are calculated by the transient responses and modal parameters at several special positions of the rotor model. Then the “feature points” are introduced in the identification procedure and the unbalance parameters are identified by selecting the “feature points” of the calculated excitation forces. The accuracy of the proposed method is demonstrated by numerical simulations and experiments of the rotor system. Identified results of the proposed method are presented for different unbalance parameters and rotor models. Effects of different random noise levels to the displacement signals, varieties in damping and support stiffness are also presented to verify the adaptability and efficiency of the proposed method. The results show that the unbalance parameters estimations are robust with respect to the above influence factors.

REQUIREMENTS FOR AERIAL IMAGES QUALITY, OBTAINED FOR MAPPING PURPOSES

Abstract. The quality of cartographic products, obtained on base of aerial images, depends on their quality. The image‘s quality can be defined in terms of measurement characteristics and in it’s visual properties. The object of the paper is an aerial image’s quality, concerning its visual perception. The process of topographic aerial images quality assessing is currently not regulated, therefore, this problem becomes particularly relevant today. The article is devoted to development the quality criteria system for aerial images, obtained for mapping purposes, and definition the requirements for them. The analysis of factors affecting the quality of aerial images is carried out, they include natural conditions, technical and technological parameters of aerial surveying and images post-processing. The main imperfections of image quality, appearing as a result of these factors influence are determined – blurring, high level of random image noise, haze, color imbalance, loss of information in shadows and illumination. Methods of identifying these imperfections and assessing their influence on aerial images quality are shown.

Determining model parameter from self-potential data using quantum-behaved particle swarm optimization

A new approach for quantitative analysis of self-potential (SP) data is introduced. In this paper, anomaly of SP is associated with simple geometric models such as a vertical cylinder, a horizontal cylinder and a sphere object. Then, in order to estimate the depth, the electric dipole moment, the anomaly body’s centre, the geometrical form factor and polarization of the anomaly, the method was developed and implemented. The development and implementation of the method is based on the global optimization concept. This method uses Quantum-behaved Particle Swarm Optimization (QPSO) algorithm to overcome the inversion problem on SP anomaly modelling. The QPSO algorithm was randomly tested on synthetic data which consist of different random noise levels. The result shows a close agreement between the assumed and the measured parameters. At last, the validity of the method was tested on real SP anomaly data and compared to the results given by other advanced inversion approaches.

Prediction of Heat-Generation and Electromagnetic Parameters from Temperature Response in Porous Fins

The application of combined electric and magnetic fields is proposed for better heat transfer enhancement in a porous fin system. An inverse estimation technique is established to simultaneously determine the interior thermal energy production and strengths of electrical and magnetic fields, by solely using the surface temperature field. At first, verified direct solutions based on the fourth-order Runge–Kutta method are obtained for the computation of the temperature field, and then three unidentified parameters are estimated using the inverse procedure supported by the Artificial Bee Colony (ABC) algorithm. The corresponding analytical solution is evaluated using the differential transformation method. The existing investigation demonstrates that even though various parametric groups sustain a particular thermal field, amongst them, the nearly unique value of the thermomagnetic field governs the heat transport phenomena. Additionally, the joint interaction between the electric field and thermal-energy-generation parameter is accountable for the observed temperature field. In spite of the effect of random noise levels within +/− 11%, the ABC-based inverse solution technique is found to establish the thermal criteria and to excellently reconstruct the given thermal field within a less than 1% error margin with respect to the ideal situation. For any case, 50 iterations of ABC are observed to be satisfactory. For fulfilling the desired rate of thermal energy transfer from porous fins, the present prediction scheme is suggested to be beneficial in properly adjusting the electrical and magnetic fields along with the unknown state of thermal energy production.

Smart sampling and probing

Sampling (collecting samples) and probing (measuring variables) are crucial when accessing key information from any system that must be characterized and understood. In this process, the tendency is to collect as many samples or signals as possible and to obtain information using all available methods. To avoid excessive, usually expensive and time-consuming analytical effort, in this work, we describe how to use simple unsupervised multivariate analysis tools (PCA and POA) to estimate the number of objects and factors in a given multivariate dataset. With this information, one may easily determine if the numbers of analyzed samples and variables are satisfactory in terms of whether key information has been extracted from the investigated system. By simulating several analytical possibilities, we tested the presence of different levels of random noise, sample restrictions and variable restrictions to detect the information contained in the system. After this validation process, the same methodologies were applied to verify different available datasets selected from previously published research papers (a total of 41 datasets) and draw a conclusion about sampling and probing. Finally, some useful “rules of thumb” were developed to simplify the analysis of complex multivariate systems, especially for the characterization of complex environmental cases.

Методика численной оценки показателей изобразительного качества аэрофотоснимков для целей картографирования

The task of assessing the quality of aerial imagery, obtained for mapping, in terms of vision properties, is very ambiguous due to the lack of objective criteria and evaluation methods. A system of indicators for aerial images quality and methods of their numerical assessment is presented. The fine aerial image’s quality is characterized by a set of its structural and gradation properties. The structural properties of the image are determined by the actual spatial resolution and photographic sharpness. Gradation properties of an image are characterized by the correct color rendering, the level of random noise and information completeness indicators – haze, radiometric resolution and the percentage of information loss in illumination and shadows.Methods of evaluating these indicators are formulated, and their recommended and acceptable numerical values are determined analytically. To clarify and correct the obtained analytical recommended and acceptable numerical values of the image quality indicators of their practical application possibility and further experimental studies are necessary with materials, obtained through various airborne imaging sensors for mapping.

Sufficiency of a Gaussian power spectrum likelihood for accurate cosmology from upcoming weak lensing surveys

ABSTRACT We investigate whether a Gaussian likelihood is sufficient to obtain accurate parameter constraints from a Euclid-like combined tomographic power spectrum analysis of weak lensing, galaxy clustering, and their cross-correlation. Testing its performance on the full sky against the Wishart distribution, which is the exact likelihood under the assumption of Gaussian fields, we find that the Gaussian likelihood returns accurate parameter constraints. This accuracy is robust to the choices made in the likelihood analysis, including the choice of fiducial cosmology, the range of scales included, and the random noise level. We extend our results to the cut sky by evaluating the additional non-Gaussianity of the joint cut-sky likelihood in both its marginal distributions and dependence structure. We find that the cut-sky likelihood is more non-Gaussian than the full-sky likelihood, but at a level insufficient to introduce significant inaccuracy into parameter constraints obtained using the Gaussian likelihood. Our results should not be affected by the assumption of Gaussian fields, as this approximation only becomes inaccurate on small scales, which in turn corresponds to the limit in which any non-Gaussianity of the likelihood becomes negligible. We nevertheless compare against N-body weak lensing simulations and find no evidence of significant additional non-Gaussianity in the likelihood. Our results indicate that a Gaussian likelihood will be sufficient for robust parameter constraints with power spectra from stage IV weak lensing surveys.

Seismic ground roll attenuation in DWT domain by split Bregman iteration algorithm

Noise reduction of seismic data is one of the most substantial steps in seismic signal processing. Goudarzi and Riahi presented wavelet domain ground roll analysis (WDGA), a technique based on the data. The results of this method showed a good improvement of the signal-to-noise ratio, but the WDGA did not work well in the data with a high level of random noise because this method uses a comparison-based search algorithm. This problem requires additional research to find a way to overcome this limitation. The wavelet domain split Bregman (WDSB) method presented in this paper is based on the use of the split Bregman algorithm in a discrete wavelet domain. The goal is to simultaneously use the capabilities of the F-K filter and the split Bregman iteration algorithm in the discrete wavelet domain, to overcome the problem of high levels of random noise in ground roll attenuation. The results of using WDSB for synthetic and real seismic data show improvement in ground roll attenuation while protecting seismic signals. The new algorithm is studied in time and wavelet domains.

New Inversion Approach for Interpreting Gravity Data Caused by Dipping Faults

AbstractA new inversion algorithm of gravity data utilizing the particle swarm optimization (PSO) algorithm is used to interpret dipping faults models. The PSO technique is stochastic in nature; its development was motivated by the common in‐flight performance of birds looking for food. Particles or models represent birds. Individual particles have a location and a velocity vector. The parameter value represents the position vectors. PSO is adjusted by random particles or models and searches for targets by updating generations. This algorithm determines the dipping faults different parameters (amplitude factor, depth to the center of the upper part of the layer, depth to the center of the lower part of the layer, fault dip angle, and the origin of the fault trace). Herein, the PSO algorithm is applied to noise‐free synthetic data, synthetic data contaminated with different random noise levels (5%, 10%, and 15%) and real field gravity data from Egypt. The applicability and efficiency of the PSO inversion algorithm are well demonstrated for synthetic and field gravity data. The errors of the different estimated parameters are calculated for synthetic data, also, the root mean square error is calculated for synthetic and real data. The parameters estimated from real data matches well with that resulted from different published techniques. From the results obtained by using the present technique, we can apply the proposed technique in different applications, like mining and ore exploration.

A-posteriori Estimation of Random Uncertainty for the Reflection Type Q-factor Measurements

A frequently used Q factor measurement procedure consists of determining the values of the input reflection coefficient vs. frequency with the use of a network analyzer, and processing the measured values with a data-fitting procedure to evaluate the location and the size of the corresponding Q-circle. That information is then used to compute the value of the loaded and unloaded Q factors and the coupling coefficient of the resonator being tested. This paper describes a novel method of post-processing the measured data, which also provides information on the uncertainty of the obtained results. Numerical examples show that this a-posteriori procedure can not only provide the uncertainty estimates but also improve the accuracy of results, even in the presence of a significant level of random measurement noise.

Structural damage detection and localization using decision tree ensemble and vibration data

AbstractThis paper explores the capabilities of decision tree ensembles (DTEs) for detecting and localizing damage in structural health monitoring (SHM). Unlike research on many other learning models, the goal of this study is to identify damage with a localization accuracy down to the single structural element, rather than limiting the evaluation to the story scale. The SHM methodology herein discussed, denoted as D2‐DTE, is based on decision trees ensemble and belongs to the class of vibration‐based approaches, being the health assessment of the structure obtained by analyzing dynamic properties of the structural system, namely, mode shapes and natural frequencies. The proposed damage detection method is validated for three different test cases, including both numerical simulations and experimentally recorded data, which consider a wide array of damage configurations, including single and multiple damages; different damage types and severities; and the presence of random noise levels associated with dynamic properties acquisition. The performances of the D2‐DTE are evaluated in terms of accuracy, confidence of probabilistic predictions, and measurements of physical distances in localization errors. Additionally, two of the investigated test cases are based on available benchmarks, thus allowing a direct comparison with a state‐of‐the‐art methodology. This comparative analysis evidences competitive performances of the DTE learning method.

Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks

The inverse problem of damage identification involves real-time, continuous observation of structures to detect any undesired, abnormal behavior and ultrasonic guided waves are considered as one of the preferred candidates for this. A parallel implementation of a reduced-order spectral finite element model is utilized to formulate the forward problem in an isotropic and a composite waveguide. In this work, along with a time-series dataset, a 2D representation of continuous wavelet transformation based time–frequency dataset is also developed. The datasets are corrupted with several levels of Gaussian random noise to incorporate different kinds of uncertainties and noise present in the real scenario. Deep learning networks like convolutional and recurrent neural networks are utilized to numerically approximate the solution of the inverse problem. A hybrid strategy of classification and regression in a supervised setting is proposed for combined damage detection and localization. The performance of the networks is compared based on metrics like accuracy, loss value, mean absolute error, mean absolute percentage error, and coefficient of determination. The predictions from conventional machine learning algorithms, trained on feature engineered dataset are compared with the deep learning algorithms. The generalization of the trained deep networks on different excitation frequencies and a higher level of uncertainties is also highlighted in this work.

A Comparative Analysis of Statistical Methods to Estimate the Reproduction Number in Emerging Epidemics, With Implications for the Current Coronavirus Disease 2019 (COVID-19) Pandemic.

BackgroundAs the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding the pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the basic reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts.MethodsUsing simulated epidemic data, we assess the performance of 7 commonly used statistical methods to estimate R0 as they would be applied in a real-time outbreak analysis scenario: fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015–2016 epidemic in Latin America and the Caribbean.ResultsWe find that most methods considered here frequently overestimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts.ConclusionsWe show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision in the early stages of epidemic growth, particularly for data with significant over-dispersion. As localized epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.

Extended use of receiver groups: Theory, synthetic example, and noise considerations

Receiver grouping is commonly used in marine towed-streamer seismic acquisition. Measurements from several receivers in a group are stacked to increase the signal-to-noise ratio of the resulting data and form an analog spatial antialiasing filter. I propose a method for extracting inline derivatives of the wavefield as additional measurements from the groups. This is achieved by multiplying the signal from the individual receivers in a group with predefined weights that corresponds to a finite-difference (FD) operator. The inline derivative(s) makes it possible to use multichannel sampling theorems to reconstruct the signal on a denser grid. Extraction of FD data from clusters of receivers is not a new concept, but I find that, by using the geometry of conventional streamer groups, it is possible to obtain FD data which are well suited for multichannel interpolation. The key to finding suitable FD operators is to recognize that it is not the ideal differentiation response we seek, but the impulse response of the group multiplied with the ideal differentiation response. Furthermore, under a Gaussian noise assumption, I derive formulas for the resulting noise level from sinc and higher order sinc interpolations. I find that the random noise level in the reconstructed data, when using higher order sinc interpolation, is expected to be higher than when using conventional sinc interpolation and will vary with respect to the distance from the original sampling points. The statistical analysis shows that it is beneficial to find FD operators with as small an [Formula: see text] norm as possible. A synthetic example shows that the proposed method of extracting FD operators and subsequent interpolation works very well. I foresee that the proposed method can be used to reduce the density of receivers (hydrophones or geophones) when designing new streamers or with existing equipment to improve the inline sampling.