Noise Level Of Data Research Articles

Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence

The purpose of the paper is to investigate the impact of physiological and environmental factors on milk productivity by using artificial intelligence (AI). The model will be useful for the user to decide the best cow treatment in order to gain the best milk production. The research starts with a literature review and an early survey of cattle physiological, environment factors and milk productivity. The next step is measuring the environment data (temperature, wind speed, noise level and relative humidity) and measuring the physiological aspect (heart rate, body temperature) correlated with the milk productivity in 500 pairs of data. All the data are collected and stored into the database and then trained and validated using Back Propagation Neural Network (BPNN) with Genetic Algorithm (GA) optimization. The initial BPNN architectures are selected in 2 hidden layers, delta bar delta learning rule, sigmoid transfer function and epoch 10000. The sensitivity analysis of all independent factors with temperature, relative humidity, core body temperature and heart rate in milk production are successfully presented. Finally, the research successfully increases cow milk production at an average = 0.96 kg/day.

A numerical reconstruction method in inverse elastic scattering

In this paper a new numerical method for the shape reconstruction of obstacles in elastic scattering is proposed. Initially, the direct scattering problem for a rigid body and the mathematical setting for the corresponding inverse one are presented. Inverse uniqueness issues for the general case of mixed boundary conditions on the boundary of our obstacle, which are valid for a rigid body as well are established. The inversion algorithm based on the factorization method is presented into a suitable form and a new numerical scheme for the reconstruction of the shape of the scatterer, using far-field measurements, is given. In particular, an efficient Tikhonov parameter choice technique, called Improved Maximum Product Criterion (IMPC) and its linchpin within the framework of the factorization method is exploited. Our regularization parameter is computed via a fast iterative algorithm which requires no a priori knowledge of the noise level in the far-field data. Finally, the effectiveness of IMPC is illustrated with various numerical examples involving a kite, an acorn, and a peanut-shaped object.

Optimization of experiment settings in ferromagnetic resonance measurements

In a ferromagnetic resonance experiment, the frequency vs resonance field data is fitted to the resonance condition formula for extraction of the effective magnetization and gyromagnetic ratio of the ferromagnetic material under test. We found that the fitting may result in significant error in some cases. We studied the influence of the number of data points, frequency range and noise level on the precision of the fittings by means of numerical simulation. The results suggest that the fitting is sensitive to the frequency range but an optimal frequency range can be estimated. The precision of the fitting is found to be proportional to the uncertainty in field measurements. As one increases the number of data points, the precision increases sharply in the beginning but the effect vanishes when the number of data points exceeds 40. The findings offer practical guidance for the design of ferromagnetic resonance experiments, which is a popular technique for ferromagnetic material characterization.

A new noise reduction method for airborne gravity gradient data

Airborne gravity gradient (AGG) measurements offer an increased resolution and accuracy compared to terrestrial measurements. But interpretation and processing of AGG data are often challenging as levelling errors and survey noise affect the data, and these effects are not easily recognised in the gradient components. We adopted the classic method of upward continuation in the noise reduction using the noise level estimates by the AGG system. By iteratively projecting the survey data to a lower level and upward continuing the data back to the survey height, parts of the high-frequency signal are suppressed. The filter, which is defined by this approach, is directly dependent on the noise level of the AGG data, the maximum number of iterations and the iterative step. We demonstrate the method by applying it to both synthetic data and real AGG data over Karasjok, Norway, and compare the results to the directional filtering method. The results show that the iterative filter can effectively reduce high-frequency noise in the data.

Detection of damage in truss structures using Simplified Dolphin Echolocation algorithm based on modal data

Nowadays, there are two classes of methods for damage detection in structures consisting of static and dynamic. The dynamic methods are based on studying the changes in structure\'s dynamic characteristics. The theoretical basis of this method is that damage causes changes in dynamic characteristics of structures. The dynamic methods are divided into two categories: signal based and modal based. The modal based methods utilize the modal properties consisting of natural frequencies, modal damping and mode shapes. As the modal properties are sensitive to changes in the structure, these can be used for detecting the damages. In this study, using dynamic method and modal based approach (natural frequencies and mode shapes), the objective function is formulated. Then, detection of damages of truss structures is addressed by using Simplified Dolphin Echolocation algorithm and solving inverse optimization problem. Many scenarios are used to simulate the damages. To demonstrate the ability of the algorithm, different truss structures with several multiple elements scenarios are tested using a few runs. The influence of the two different levels of noise in the modal data for these scenarios is also considered. The last example of this article is investigated using a different mutation. This mutation obtains the exact answer with fewer loops and population by limited computational effort.

The feasibility of standardless molecular spectral analysis of mixtures under conditions of photochemical transformations of molecules

The problem of standardless molecular spectral analysis of multicomponent mixtures with allowance for photochemical reactions of their molecules has been formulated and its formal procedure has been developed. The case when the time dependence of the spectrum of the mixture is determined only by the change in the concentrations of the components with time and the spectra of the individual components remain constant has been considered. The corresponding requirements for the physicochemical properties of the molecules have been identified and shown to be met for quite a wide range of polyatomic molecules and their reactions. The exception is molecules with metastable excited electronic states. A large set of model calculations relevant to the actual molecular entities and their experimentally observed photochemical transformations have shown the practicability of the approach. The solution of the problem converges to exact values and is resistant to uncertainty in setting the initial spectral data due to the peculiarities of the experimental procedure, the choice of theoretical models, and other factors. The resulting theoretical values of the concentrations meet the least squares test and their errors are measured by the variance per unit weight. The variance makes it possible to evaluate the noise level of the spectral data used in the analysis and reliability of the result and can serve as a criterion for the correctness of the initial hypotheses about the qualitative composition of a multicomponent mixture.

Spectral algorithm for non-destructive damage localisation: Application to an ancient masonry arch model

Structural monitoring and vibration-based damage identification methods are fundamental tools for condition assessment and early-stage damage identification, especially when dealing with the conservation of historical constructions and the maintenance of strategic civil structures. However, although the substantial advances in the field, several issues must still be addressed to broaden the application range of such tools and to assert their reliability. This study deals with the experimental validation of a novel method for non-destructive damage identification purposes. This method is based on the use of spectral output signals and has been recently validated by the authors through a numerical simulation. After a brief insight into the basic principles of the proposed approach, the spectral-based technique is applied to identify the experimental damage induced on a masonry arch through statically increasing loading. Once the direct and cross spectral density functions of the nodal response processes are estimated, the system's output power spectrum matrix is built and decomposed in eigenvalues and eigenvectors. The present study points out how the extracted spectral eigenparameters contribute to the damage analysis allowing to detect the occurrence of damage and to locate the target points where the cracks appear during the experimental tests. The sensitivity of the spectral formulation to the level of noise in the modal data is investigated and discussed. As a final evaluation criterion, the results from the spectrum-driven method are compared with the ones obtained from existing non-model based damage identification methods.

Parametric analysis of RNA-seq expression data.

Various methods had been introduced for normalization and comparison of RNA-seq count data. However, they lacked objectivity because they based on ad hoc assumptions that were never verified their appropriateness. Here, we introduced a method that assumes parsimony models on data distribution; the assumptions were verified according to exploratory data analysis. As was expected, count data were lognormally distributed. The level of noise in recent data appeared to be much higher than those of microarrays. Still, the appropriate distribution model would improve certainty and accuracy of normalization, by finding out the reliable range of data. Primary cause of noise was not the principle of the methodology; that is, each read is a trial that which transcript is read. Rather, the cause would be overlooking of transcripts, and the overlooking occurred more often among lower range of data. To find out genes likely to be overlooked, number of replications would be more important than read depth, which will not prevent overlooking. Both signal and noise in the reliable range of data were distributed normally, showing the suitability to use generalized linear model to evaluate differences in expression levels. In the framework, normalized data can be compared and combined freely beyond studies.

Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform

According to the compressive sensing (CS) theory in the signal-processing field, we proposed a new CS approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain. The seislet transform appears to be the sparest among the state-of-the-art sparse transforms. The FPOCS can obtain much faster convergence than conventional POCS (about two thirds of conventional iterations can be saved), while maintaining the same recovery performance. The FPOCS can obtain faster and better performance than FISTA for relatively cleaner data but will get slower and worse performance than FISTA, which becomes a reference to decide which algorithm to use in practice according the noise level in the seismic data. The seislet transform based CS approach can achieve obviously better data recovery results than f−k transform based scenarios, considering both signal-to-noise ratio (SNR), local similarity comparison, and visual observation, because of a much sparser structure in the seislet transform domain. We have used both synthetic and field data examples to demonstrate the superior performance of the proposed seislet-based FPOCS approach.

Estimation of the density distribution from small-angle scattering data

The one-dimensional density distribution for symmetrical scatterers is estimated from small-angle scattering data. The symmetry of the scatterers may be one dimensional (lamellar), two dimensional (cylindrical) or three dimensional (spherical). Previously this problem has been treated either by a two-step approach with the distance distribution as an intermediate [Glatter (1981).J. Appl. Cryst.14, 101–108] or in a single step using spherical harmonics [Svergun, Feigin & Schedrin (1982).Acta Cryst.A38, 827–835]. A combination of these two methods is presented here, where the density distribution is estimated using constraints without the explicit use of an intermediate distribution. A maximum entropy constraint is introduced for this problem and the results are compared with the results of the conventional smoothness constraint. Bayesian methods are used for estimation of the overall noise level of the experimental data and for the maximum dimension of the density distribution. The method described is tested on both simulated and experimental data and shown to provide reliable estimates for the Guinier radius and maximum dimension. In both cases the effects of minor deviations from the assumed symmetry as well as incorrect background subtraction are studied.

Ocean acoustic reverberation tomography

Seismic wide-angle imaging using ship-towed acoustic sources and networks of ocean bottom seismographs is a common technique for exploring earth structure beneath the oceans. In these studies, the recorded data are dominated by acoustic waves propagating as reverberations in the water column. For surveys with a small receiver spacing (e.g., <10 km), the acoustic wave field densely samples properties of the water column over the width of the receiver array. A method, referred to as ocean acoustic reverberation tomography, is developed that uses the travel times of direct and reflected waves to image ocean acoustic structure. Reverberation tomography offers an alternative approach for determining the structure of the oceans and advancing the understanding of ocean heat content and mixing processes. The technique has the potential for revealing small-scale ocean thermal structure over the entire vertical height of the water column and along long survey profiles or across three-dimensional volumes of the ocean. For realistic experimental geometries and data noise levels, the method can produce images of ocean sound speed on a smaller scale than traditional acoustic tomography.

A comprehensive study of sensitivity in measuring oscillatory magnetic fields using rotary saturation pulse sequences

Detecting the oscillatory currents with a specific frequency distribution may have the potential to make neuronal current MRI (ncMRI) come true. The phase shift or dephasing induced by both positive and negative episodes of oscillatory neuronal currents is likely to be canceled out over the echo time in typical BOLD-contrast fMRI experiments. Based on the contrast of rotary saturation, both of the recently developed spin-locked oscillatory excitation (SLOE) and stimulus-induced rotary saturation (SIRS) pulse sequences have been demonstrated to be able to detect weak oscillatory magnetic fields in phantoms with 3T MR scanners. In this report, through Bloch equation simulation as well as water phantom and anesthetic rats experiments, we comprehensively evaluate and compare the sensitivities of these two methods (SLOE and SIRS) in detecting the oscillatory magnetic fields for both high (100Hz) and low (10Hz) oscillation frequencies, while using their respective optimal imaging parameters. In agreement with the theoretical predications, both the simulated and experimental results showed that the SLOE method features a much higher detection sensitivity of weak magnetic fields than that of the SIRS method. SLOE was able to detect applied oscillatory magnetic fields as low as 0.1nT in a water phantom and 0.5nT in rat brains and the deteriorated noise levels in rat data may account for the reduced sensitivity in vivo. These promising results form the foundation for direct detection of in vivo neuronal currents using MRI.

A BEM formulation in conjunction with parametric equation approach for three-dimensional Cauchy problems of steady heat conduction

This study documents the first attempt to apply a nonsingular indirect boundary element method (BEM) for the solution of three-dimensional (3D) inverse heat conduction problems. The present BEM formulation avoids the calculation of hyper-singular integrals. Furthermore, the exact geometrical representation of computational domain is adopted by parametric equations to eliminate the errors in traditional approaches of polynomial shape functions. Due to its boundary-only discretizations and semi-analytical nature, the proposed method can be viewed as a competitive candidate for the solution of inverse problems. Four benchmark numerical examples indicate that the proposed method, in conjunction with proper regularization techniques, is accurate, computationally efficient and numerically stable for the solution of 3D inverse problems subjected to various levels of noise in input data.

Computing interior eigenvalues of domains from far fields

Interior eigenvalues of bounded scattering objects can be rigorously characterised from multistatic and multi-frequency far eld data, that is, from the behavior of scattered waves far away from the object. This characterisation, the so-called inside-outside duality, holds for various types of penetrable and impenetrable scatterers and is based on the behavior of a particular eigenvalue of the far eld operator. It naturally leads to a numerical algorithm for computing interior eigenvalues of a scatterer that does not require shape or physical properties of the scatterer as input. Since the non-linear inverse problem to compute such interior eigenvalues from far eld data is ill-posed, we propose a regularising algorithm that is shown to converge as the noise level of the far eld data tends to zero. We illustrate feasibility and accuracy of our algorithm by numerical experiments where we compute interior transmission eigenvalues and Robin eigenvalues of the Laplacian in three-dimensional domains from scattering data of these domains due to plane incident waves.

An analysis of wavelet frame based scattered data reconstruction

In real world applications many signals contain singularities, like edges in images. Recent wavelet frame based approaches were successfully applied to reconstruct scattered data from such functions while preserving these features. In this paper we present a recent approach which determines the approximant from shift invariant subspaces by minimizing an ℓ 1 -regularized least squares problem which makes additional use of the wavelet frame transform in order to preserve sharp edges. We give a detailed analysis of this approach, i.e., how the approximation error behaves dependent on data density and noise level. Moreover, a link to wavelet frame based image restoration models is established and the convergence of these models is analyzed. In the end, we present some numerical examples, for instance how to apply this approach to handle coarse-grained models in molecular dynamics.

Vessel extraction from non-fluorescein fundus images using orientation-aware detector

The automatic extraction of blood vessels in non-fluorescein eye fundus images is a tough task in applications such as diabetic retinopathy screening. However, vessel shapes have complex variations, and accurate modeling of retinal vascular structures is challenging. We have therefore developed a new approach to accurately extract blood vessels in non-fluorescein fundus images using an orientation-aware detector (OAD). The detector was designed according to the intrinsic property of vessels being locally oriented and having linearly elongated structures. We employ the OAD to extract vessel shapes with no assumptions on parametric orientations of vessel shapes. The orientations of vessels can be efficiently modeled by the energy distribution of Fourier transformation. Accordingly, both wide and thin vessels can be extracted with two-scale segmentation in which line operators are applied in large scale and the Gabor filter bank is applied in small scale. A post-processing technique, based on the path opening operation, is applied to eliminate false responses to nonvascular areas, such as retinal structures (optic disc and macula) and pathologies (exudates, hemorrhages,and microaneurysms). This makes the detector robust and structure-aware. By achieving a competitive CAL measurement of 80.82% for the DRIVE database and 68.94% for the STARE, the experimental results demonstrated that the OAD approach outperforms existing segmentation methods. Furthermore, the proposed approach effectively works with non-fluorescein fundus images and proves highly accurate and robust in complicated regions such as the central reflex, close vessels, and crossover points, despite a high level of illumination noise in the original data.

Non-parametric efficiency estimation using Richardson–Lucy blind deconvolution

We propose a non-parametric, three-stage strategy for efficiency estimation in which the Richardson–Lucy blind deconvolution algorithm is used to identify firm-specific inefficiencies from the residuals corrected for the expected inefficiency μ. The performance of the proposed algorithm is evaluated against the method of moments under 16 scenarios assuming μ=0. The results show that the Richardson–Lucy blind deconvolution method does not generate null or zero values due to wrong skewness or low kurtosis of inefficiency distribution, that it is insensitive to the distributional assumptions, and that it is robust to data noise levels and heteroscedasticity. We apply the Richardson–Lucy blind deconvolution method to Finnish electricity distribution network data sets, and we provide estimates for efficiencies that are otherwise inestimable when using the method of moments and correct ranks of firms with similar efficiency scores.

Feasibility of CO2 plume detection using 4D seismic: CO2CRC Otway Project case study — Part 2: Detectability analysis

A key objective of stage 2 of the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) Otway Project is to explore the ability of the seismic reflection method to detect and monitor injection of a small amount of greenhouse gas into a saline formation. Development of a seismic monitoring program requires an understanding of expected time-lapse (TL) seismic signals. Hence, before such an injection experiment is undertaken, we assessed the feasibility of seismic monitoring in a modeling study. Considering realistic gas distributions inferred from reservoir simulations, we analyzed the influence of various factors (injection volume, time after injection, and realizations of the reservoir flow model) on the TL seismic signal. However, the applicability of seismic monitoring depends not only on the strength of the TL seismic signal but also on the noise level of the seismic data. Hence, to estimate the detectability of gas in the subsurface, we have developed a workflow that integrated actual data repeatability observed at the Otway test site into the seismic feasibility study. Although we observed differences between the considered scenarios, all of the scenarios indicated a high likelihood of successful plume detection with the observed noise level and surface 4D seismic acquisition geometry used in stage 1 of the CO2CRC Otway Project at the same site. However, a thin layer of gas spreading out from the edges of the main plume below the seal in all scenarios would be a challenge for surface seismic monitoring.

On the measurability of change in Amazon vegetation from MODIS

The Amazon rainforest is a critical hotspot for bio-diversity, and plays an essential role in global carbon, water and energy fluxes and the earth's climate. Our ability to project the role of vegetation carbon feedbacks on future climate critically depends upon our understanding of this tropical ecosystem, its tolerance to climate extremes and tipping points of ecosystem collapse. Satellite remote sensing is the only practical approach to obtain observational evidence of trends and changes across large regions of the Amazon forest; however, inferring these trends in the presence of high cloud cover fraction and aerosol concentrations has led to widely varying conclusions. Our study provides a simple and direct statistical analysis of a measurable change in daily and composite surface reflectance obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) based on the noise level of data and the number of available observations. Depending on time frame and data product chosen for analysis, changes in leaf area need to exceed up to 2 units leaf area per unit ground area (expressed as m2m−2) across much of the basin before these changes can be detected at a 95% confidence level with conventional approaches, roughly corresponding to a change in NDVI and EVI of about 25%. A potential way forward may be provided by advanced multi-angular techniques, such as the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), which allowed detection of changes of about 0.6–0.8units in leaf area (2–6% change in NDVI) at the same confidence level. In our analysis, the use of the Enhanced Vegetation Index (EVI) did not improve accuracy of detectable change in leaf area but added a complicating sensitivity to the bi-directional reflectance, or view geometry effects.

Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in measurement space

Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.