Presence Of Random Errors Research Articles

The accuracy of measuring the coefficients of transmission and reflection of materials in the presence of random errors in the formation of fields

Background. One of the characteristic features of the current stage in the development of military equipment is the widespread use of special structural materials and coatings with specified properties of interaction with electromagnetic fields, which are subject to measurement during production and operation. Most often, measurements are carried out in quasi-flat radiation and reception fields. At the same time, the regularities of the influence of random errors in the formation of flat fields on the accuracy of measuring the characteristics of special structural materials and coatings have not been studied enough. Aim. The purpose of this work is to establish quantitative patterns of the influence of random phase errors in the formation of flat fields on the errors in measuring the transmission and reflection coefficients of materials and coatings. Methods. Studies of the influence of random phase errors in the formation of flat fields of irradiation and signal reception on the accuracy of measuring the transmission and reflection coefficients of objects were carried out using the methods of mathematical modeling and statistical radio engineering. Results. Calculation relations are obtained for estimating the magnitude of errors in measuring the transmission and reflection coefficients. Conclusion. With small errors and a small interval of spatial correlation, the errors of the measured average values of the transmission and reflection coefficients are proportional to the errors in the formation of the irradiation and reception fields and the relative value of the spatial correlation interval must exceed 22 degrees.

Association of Hospitalization Rate, Mortality, and CD4 T Cell Count with Comorbidity of COVID-19 and HIV: A Systematic Review and Meta-Analysis.

Epidemiological data demonstrate the greater severity of SARS-CoV-2 infection in HIV patients along with the more hospitalization, and mortality rates. Thus, this meta-analysis aimed to assess the possible differences in hospitalization, mortality, and the CD4 T cell counts between COVID-19/HIV co-infected patients and the control group. The relevant studies were obtained from online databases such as Science Direct, PubMed, Scopus, Web of Science, and Google Scholar using Mesh and Non-Mesh keywords and the meta-analysis was conducted according to the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols checklist. Then, the Newcastle-Ottawa scale (NOS) checklist was used to assess the quality of selected studies. According to the random effect models, the odds ratios of hospitalization, mortality, and CD4 T cell counts were estimated. The odds ratios of hospitalization and mortality rates in COVID-19 patients with HIV were 1.67 (confidence interval [CI]: 0.76 to 3.71) and 0.80 (CI: 0.57 to 1.11), respectively, compared to that of the COVID-19 group. In this meta-analysis, there was no statistically significant difference in the rates of hospitalization, mortality, and CD4 T cell counts between COVID-19 patients with HIV and the control group. The similarity between the studied groups could be attributed to factors such as the rarity of COVID-19/HIV co-infection patients and the presence of random error, administration of antiretroviral therapy in HIV patients, and early hospitalization time in COVID-19/HIV co-infected patients.

A method to improve accuracy of definition and promptness of forecasting of spacecraft motion parameters

The subject of the presented paper is the methods used to define spacecraft motion parameters. The goal of the paper is to develop approaches to improve accuracy of definition and promptness of forecasting of motion parameters. The tasks are 1) to determine the aspects influencing the result accuracy at defining initial conditions of the spacecraft motion, 2) to reveal the drawbacks of traditional methods and 3) to suggest some possible ways that can be used to improve accuracy and promptness at defining the spacecraft motion parameters. During the research it is revealed that the result accuracy at defining the initial conditions of the spacecraft motion is influenced by three aspects – a random component caused by presence of random errors in trajectory measurements, a dynamic component due to dynamic errors of the used model of the spacecraft motion and the error defined by the size of convergence region of the minimization method that is used to solve the multi-point boundary problem. To receive the least inaccuracy at defining the spacecraft motion parameters it is necessary to process some optimal quantity of measuring information which should ensure sufficient compensation of random errors and at the same time prevent the dynamic error from influencing considerably. At it it should be taken into account that the amount of the measured information should not be less than it is necessary to ensure convergence of the algorithm used to define those motion parameters. The main results are as follows. For solving the task of increasing accuracy of spacecraft motion parameters definition the paper suggests a new method based on minimization with definition of initial approximation region. The suggested method implements a non-local approach to minimization of the goal function, which ensures better as compared to traditional methods convergence. To increase the promptness of spacecraft motion forecasting it is suggested to use the mathematical apparatus of differential transformations. It allows decreasing the computational expenditures 3 to 4 times as compared to the regular ballistic and navigational algorithms while the given accuracy is preserved. On the basis of the performed research the following conclusions can be made. To increase the accuracy of the spacecraft motion parameters definition it is necessary to optimize the required number of measurement orbit passes, for that it is appropriate to use the Nelder-Mead search method. At implementing the advanced coordinate methods of the spacecraft control, to increase the promptness of the spacecraft motion forecasting, it is necessary to considerably decrease the computational expenditures for the motion parameters definition. To achieve that it is reasonable to use the mathematical apparatus of the differential transformations.

Handling uncertainty in eHealth sensors using fuzzy system modeling

In remote health multiple sensors are attached to a patient and collected data is transferred to the cloud, which is used by the physician for diagnosis. These sensors are prone to random errors. Challenge is to provide correct information since any inaccuracy in the information leads to the incorrect diagnosis of the patient. Any abnormal condition recorded by a single sensor can be cross-checked by matching it against another sensor recording different vital signs. The decisions of the doctors rely upon whatever the sensor is streaming currently, because matching against other vitals has to be done at real-time. Since fuzzy-based system works with the imprecise dataset and merges them in ranges, it favours the decision of a medical practitioner in remote health. Therefore, in this paper, we discuss how to reduce uncertainty from the remote health sensors using fuzzy modelling system. We discuss some use cases to simulate remote health scenario with fuzzy inferencing system and obtain acceptable output in presence of random errors. We also compare our proposed model with basic (statistical) and advanced (context-aware) models to show its performance exceeds the other two. First, the statistical model needs more data set than our proposed model and second context-aware model may not correctly detect random error from the current context.

Hybrid Profile\u2013Gradient Approaches for the Estimation of Surface Fluxes

The Monin–Obukhov similarity theory-based wind speed and potential temperature profiles are inherently coupled to each other. We have developed hybrid approaches to disentangle them, and as a direct consequence, the estimation of Obukhov length (and associated turbulent fluxes) from either wind-speed or temperature measurements becomes an effortless task. Additionally, our approaches give rise to two easily measurable indices of atmospheric stability. We compare these approaches with the traditional gradient and profile methods that require both wind-speed and temperature profile data. Using Monte-Carlo-type numerical experiments we demonstrate that, if the input profiles are free of any random errors, the performance of the proposed hybrid approaches is almost equivalent to the profile method and better than the gradient method. However, the proposed hybrid approaches are less competitive in comparison to their traditional counterparts in the presence of random errors.

Reconstruction theorem for bandlimited signals from noisy samples in the offset linear canonical transform domain

As the offset linear canonical transform has became a novel and effective method in signal processing and optics, the sampling and reconstructing theorems for bandlimited signals associated with the OLCT have been widely researched in the literature. However, all these theorems are derived under the condition of noiseless. In this work, the reconstruction theorem of bandlimited signals by using its noisy samples associated with the OLCT has been well studied. We first show that the well-known reconstruction theorem for the OLCT bandlimited signals is invalid in the presence of random errors. Then, the oversampling theorem of the OLCT bandlimited signals is obtained based on the orthogonal complete set of the OLCT bandlimited signals. Subsequently, the reconstruction theorem for the OLCT bandlimited signals from samples in the presence of noise is derived by utilizing the oversampling theorem. Lastly, the simulations are provided to indicate the advantages and correctness of the derived results.

Non-parametric estimation of particle size distribution from spectral extinction data with PCA approach

Non-parametric estimation of particle size distribution (PSD) is carried out with the help of spectral extinction method and Tikhonov regularization method. Meanwhile, a selection principle of optimal measurement wavelengths on the basis of principle component analysis (PCA) approach is proposed to improve the retrieval accuracy. Results show that the optimally selected wavelengths can ensure retrieval accuracy, compared with randomly selected wavelengths. Then, four common PSDs, namely Log-Normal (L-N), Rosin-Rammer (R-R), Normal (N-N) and Gamma distributions, are estimated under different random measurement errors. Numerical tests show that the present methodology can be successfully used to reconstruct the PSDs with high stability in the presence of random errors and low susceptibility to the shape of distributions. Finally, the PSD of aerosol measured over Harbin in China and the PSD of microalgae cell are retrieved, respectively. The study shows that increasing the number of subintervals of the particle size range will improve the retrieval accuracy as well as increase the cost of experiment, so the number of subintervals should be selected reasonably. Moreover, to obtain more reliable results, the measurement sample size should be as large as possible. As a whole, the present methodology provides a reliable and effective technique to estimate the PSDs non-parametrically from the spectral extinction signals.

Research integrated identification method

Considered a new method to identify objects in the presence of random errors in measurementsof input and output signals. Confirmed the possibility not displaced efficient parameter estimatesfacility proposed method

Algorithms of increasing the calculation accuracy for an aircraft’s orientation angle

Algorithms for calculating an aircraft's orientation angles based on the results of the numerical integration of Poisson's and quaternion equations are proposed. The algorithms in the presence of random errors of the matrix elements of direction cosines and quaternions are characterized by a significantly higher accuracy in comparison to the formulas for solving the formulated problem. The results of testing the mathematical modeling data under random errors, which confirm the increased accuracy of the calculation of the orientation angles, are given.

Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation

Land surface models (LSMs) are often applied to predict the one-way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground-based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one-way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground-based observations. Results suggest that LSMs do not generally overestimate the strength of one-way surface SM/LH coupling.

Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm

The particle size distribution (PSD) plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the logarithmic normal (L-N) distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

Cramér-Rao Bounds for Polynomial Signal Estimation Using Sensors With AR(1) Drift

We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information matrix and subsequently Cram\'er-Rao bounds (upto reasonable approximation) for the estimation accuracy of drift-corrupted signals. We assume a polynomial time-series as the representative signal and an autoregressive process model for the drift. When the Markov parameter for drift \rho<1, we show that the first-order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. For \rho=1, i.e., when the drift is non-stationary, we show that the constant part of a signal can only be estimated inconsistently (non-zero asymptotic variance). Practical usage of the results are demonstrated through the analysis of 1) networks with multiple sensors and 2) bandwidth limited networks communicating only quantized observations.

Prediction of gravity anomalies for geophysical exploration

Interpretation of gravity anomalies (determined on the earth’s surface) reveals information on mineral resources beneath the earth. The density of gravity stations (where gravity anomalies are determined) is critical to the successful interpretation of these anomalies. Where the density of the available gravity anomalies is not enough, for a particular purpose of geophysical exploration, more gravity stations can be established within the surveyed area and the gravity anomalies observed for these stations. In some cases, where observations of gravity anomalies are not possible due, probably, to inaccessibility of the newly chosen gravity stations, the required gravity anomalies for such stations can be estimated (predicted). Currently, classical least squares technique is used to accomplish such task. However, the technique does not produce optimum results because its formulation assumes that the observed gravity anomalies, used for the prediction, are error free, whereas, all observed quantities are affected by random errors. Therefore, in this study, an attempt is made to carry out prediction of gravity anomalies for geophysical exploration using least squares collocation technique. This is considered to be a better alternative because its formulation takes the presence of random errors of observations in the observed quantities into consideration and makes provision for filtering out these errors while predicting the signals of interest at the required number of stations.

Determination of optimum residual gravity anomalies for mineral exploration: a least squares collocation approach

Analysis of gravity anomalies, measured on the earth's surface, can reveal information on sub-surface structures of the earth. The first step in this analysis is to extract residual gravity anomalies (needed for mineral exploration) from the observed gravity anomalies. Currently, a classical least squares technique is used to achieve this aim. However, this technique does not produce optimum results because it assumes that the observed gravity anomalies used for the computation of residual gravity anomalies are error free, whereas, all observed quantities are affected by random errors. In this study, an attempt was made to extract the residual gravity anomalies from the observed gravity anomalies using a least squares collocation technique. The least squares collocation technique was considered to be a better alternative because its formulation takes the presence of random errors of observations in the observed quantities into consideration and makes provision for filtering out the errors from the observed quantities while predicting the needed signals at the required number of points.

Field Statistics of Acoustic Array of the Equipment for Remote Sensing of the Atmosphere

In the article, there are given simple formulas for estimation of statistical effects in acoustic arrays of the equipment for remote sensing of the atmosphere in the presence of random errors in excitation of their radiators. It is shown on an example of two concrete arrays that even small phase errors in array radiators noticeably influence a side radiation level and a directivity of the arrays. It is noted that the amplitude errors usually are less dangerous than the phase ones. Possible ways of weakening the influence of randomness on array parameters are discussed. In conclusion, expedient directions of further investigations are formulated.

Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution

The accuracy of a source location estimate is very sensitive to the accurate knowledge of receiver locations. This paper performs analysis and develops a solution for locating a moving source using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements in the presence of random errors in receiver locations. The analysis starts with the Crameacuter-Rao lower bound (CRLB) for the problem, and derives the increase in mean-square error (MSE) in source location estimate if the receiver locations are assumed correct but in fact have error. A solution is then proposed that takes the receiver error into account to reduce the estimation error, and it is shown analytically, under some mild approximations, to achieve the CRLB accuracy for far-field sources. The proposed solution is closed form, computationally efficient, and does not have divergence problem as in iterative techniques. Simulations corroborate the theoretical results and the good performance of the proposed method

Exploring Fine-Grained Fault Tolerance for Nanotechnology Devices With the Recursive NanoBox Processor Grid

Advanced molecular nanotechnology devices are predicted to have exceedingly high transient fault rates and large numbers of inherent device defects compared to conventional CMOS devices. We describe and evaluate the Recursive NanoBox Processor Grid as an application specific, fault-tolerant, parallel computing system designed for fabrication with unreliable nanotechnology devices. In this study we construct hardware description language models of a NanoBox Processor cell and evaluate the effectiveness of our recursive fault masking approach in the presence of random errors. Our analysis shows that complex circuits constructed with encoded lookup tables can operate correctly despite 2% of the nodes being in error. The circuits operate partially correct with up to 4% of the nodes being in error.

Equation-based TCP-friendly congestion control under lossy environment

A major problem in supporting multimedia streaming in the Internet is that the streaming protocol used tends to take bandwidth away from competing TCP traffic streams. Thus it is important to devise rate based protocols that are TCP-friendly. In the paper we examine the effectiveness of two important models in devising TCP-friendly rate based protocols in lossy environment such as wireless networks. Using simulation for a variety of network scenarios, it is shown that the rate based protocols based on one model underestimates while the other overestimates TCP bandwidth in the presence of errors. A modification is made to the models which allows us to construct a rate based protocol that can track TCP bandwidth better in the presence of random error and hence exhibit behavior that is more TCP-friendly.

Robust Damage Location in Structures Using Taguchi Method

A robust damage estimation technique is presented such that the location and severity of damages can be estimated in the presence of random errors in measured data as well as systematic errors in the analytical model. In order to estimate damage efficiently, the concept of design of experiment using orthogonal array is used for screening the main effects of each parameter which corresponds to possible damage location in the finite element (FE) model. Then, the Taguchi method, which has been widely used as effective design tools in many areas of engineering, is applied to parameter updating in the analytical FE model in an iterative way. The numerical simulations of truss structures and cantilever beam show that damages in structure can be located in the presence of various noises in measurement as well as systematic errors in the analytical model.

Robust Data Compression: Variable Length Codes and Burst Errors

When variable length codes are used in the presence of error, slippage can occur. The number of symbols decoded before synchronization is regained can differ from the number of encoded symbols, causing misinterpretation of data. In previous work certain synchronization schemes have been shown to be satisfactory in preventing permanent loss of synchronization and slippage in the presence of random error. In this paper we demonstrate that these schemes can also be satisfactory in the presence of burst errors, and that their relative merits remain the same.