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The paper concerns the study of the Rice statistical distribution’s peculiarities which cause the possibility of its efficient application in solving the tasks of high precision phase measuring in optics. The strict mathematical proof of the Rician distribution’s stable character is provided in the example of the differential signal consideration, namely: it has been proved that the sum or the difference of two Rician signals also obey the Rice distribution. Besides, the formulas have been obtained for the parameters of the resulting summand or differential signal’s Rice distribution. Based upon the proved stable character of the Rice distribution a new original technique of the high precision measuring of the two quasi-harmonic signals’ phase shift has been elaborated in the paper. This technique is grounded in the statistical analysis of the measured sampled data for the amplitudes of the both signals and for the amplitude of the third signal which is equal to the difference of the two signals to be compared in phase. The sought-for phase shift of two quasi-harmonic signals is being calculated from the geometrical considerations as an angle of a triangle which sides are equal to the three indicated signals’ amplitude values having been reconstructed against the noise background. Thereby, the proposed technique of measuring the phase shift using the differential signal analysis, is based upon the amplitude measurements only, what significantly decreases the demands to the equipment and simplifies the technique implementation in practice. The paper provides both the strict mathematical substantiation of a new phase shift measuring technique and the results of its numerical testing. The elaborated method of high precision phase measurements may be efficiently applied for solving a wide circle of tasks in various areas of science and technology, in particular — at distance measuring, in communication systems, in navigation, etc.

The paper provides a solution of the two-parameter task of joint signal and noise estimation at data analysis within the conditions of the Rice distribution by the techniques of mathematical statistics: the maximum likelihood method and the variants of the method of moments. The considered variants of the method of moments include the following techniques: the joint signal and noise estimation on the basis of measuring the 2-nd and the 4-th moments (MM24) and on the basis of measuring the 1-st and the 2-nd moments (MM12). For each of the elaborated methods the explicit equations’ systems have been obtained for required parameters of the signal and noise. An important mathematical result of the investigation consists in the fact that the solution of the system of two nonlinear equations with two variables — the sought for signal and noise parameters — has been reduced to the solution of just one equation with one unknown quantity what is important from the view point of both the theoretical investigation of the proposed technique and its practical application, providing the possibility of essential decreasing the calculating resources required for the technique’s realization. The implemented theoretical analysis has resulted in an important practical conclusion: solving the two-parameter task does not lead to the increase of required numerical resources if compared with the one-parameter approximation. The task is meaningful for the purposes of the rician data processing, in particular — the image processing in the systems of magnetic-resonance visualization. The theoretical conclusions have been confirmed by the results of the numerical experiment.

The paper provides a review of the existing methods of signals’ processing within the conditions of the Rice statistical model applicability. There are considered the principle development directions, the existing limitations and the improvement possibilities concerning the methods of solving the tasks of noise suppression and analyzed signals’ filtration by the example of magnetic-resonance visualization. A conception of a new approach to joint calculation of Rician signal’s both parameters has been developed based on the method of moments in two variants of its implementation. The computer simulation and the comparative analysis of the obtained numerical results have been conducted.

A hierarchical method of mathematical and computer modeling of interval-stochastic thermal processes in complex electronic systems for various purposes is developed. The developed concept of hierarchical structuring reflects both the constructive hierarchy of a complex electronic system and the hierarchy of mathematical models of heat exchange processes. Thermal processes that take into account various physical phenomena in complex electronic systems are described by systems of stochastic, unsteady, and nonlinear partial differential equations and, therefore, their computer simulation encounters considerable computational difficulties even with the use of supercomputers. The hierarchical method avoids these difficulties. The hierarchical structure of the electronic system design, in general, is characterized by five levels: Level 1 — the active elements of the ES (microcircuits, electro-radio-elements); Level 2 — electronic module; Level 3 — a panel that combines a variety of electronic modules; Level 4 — a block of panels; Level 5 — stand installed in a stationary or mobile room. The hierarchy of models and modeling of stochastic thermal processes is constructed in the reverse order of the hierarchical structure of the electronic system design, while the modeling of interval-stochastic thermal processes is carried out by obtaining equations for statistical measures. The hierarchical method developed in the article allows to take into account the principal features of thermal processes, such as the stochastic nature of thermal, electrical and design factors in the production, assembly and installation of electronic systems, stochastic scatter of operating conditions and the environment, non-linear temperature dependencies of heat exchange factors, unsteady nature of thermal processes. The equations obtained in the article for statistical measures of stochastic thermal processes are a system of 14 non-stationary nonlinear differential equations of the first order in ordinary derivatives, whose solution is easily implemented on modern computers by existing numerical methods. The results of applying the method for computer simulation of stochastic thermal processes in electron systems are considered. The hierarchical method is applied in practice for the thermal design of real electronic systems and the creation of modern competitive devices.

We consider a model of spontaneous formation of a computational structure in the human brain for solving a given class of tasks in the process of performing a series of similar tasks. The model is based on a special definition of a numerical measure of the complexity of the solution algorithm. This measure has an informational property: the complexity of a computational structure consisting of two independent structures is equal to the sum of the complexities of these structures. Then the probability of spontaneous occurrence of the structure depends exponentially on the complexity of the structure. The exponential coefficient requires experimental determination for each type of problem. It may depend on the form of presentation of the source data and the procedure for issuing the result. This estimation method was applied to the results of a series of experiments that determined the strategy for solving a series of similar problems with a growing number of initial data. These experiments were described in previously published papers. Two main strategies were considered: sequential execution of the computational algorithm, or the use of parallel computing in those tasks where it is effective. These strategies differ in how calculations are performed. Using an estimate of the complexity of schemes, you can use the empirical probability of one of the strategies to calculate the probability of the other. The calculations performed showed a good match between the calculated and empirical probabilities. This confirms the hypothesis about the spontaneous formation of structures that solve the problem during the initial training of a person. The paper contains a brief description of experiments, detailed computational schemes and a strict definition of the complexity measure of computational structures and the conclusion of the dependence of the probability of structure formation on its complexity.

The paper develops a new mathematical method of the joint signal and noise parameters determination at the Rice statistical distribution by method of moments based upon the analysis of data for the 1-st and the 3-rd raw moments of the random rician value. The explicit equations’ system have been obtained for required parameters of the signal and noise. In the limiting case of the small value of the signal-to-noise ratio the analytical formulas have been derived that allow calculating the required parameters without the necessity of solving the equations numerically. The technique having been elaborated in the paper ensures an efficient separation of the informative and noise components of the data to be analyzed without any a-priori restrictions, just based upon the processing of the results of the signal’s sampled measurements. The task is meaningful for the purposes of the rician data processing, in particular in the systems of magnetic-resonance visualization, in ultrasound visualization systems, at the optical signals’ analysis in range measuring systems, in radio location, etc. The results of the investigation have shown that the two parameter task solution of the proposed technique does not lead to the increase in demanded volume of computing resources compared with the one parameter task being solved in approximation that the second parameter of the task is known a-priori There are provided the results of the elaborated technique’s computer simulation. The results of the signal and noise parameters’ numerical calculation have confirmed the efficiency of the elaborated technique. There has been conducted the comparison of the accuracy of the sought-for parameters estimation by the technique having been developed in this paper and by the previously elaborated method of moments based upon processing the measured data for lower even moments of the signal to be analyzed.

The paper studies the determination for one of the dynamic quality parameter (PDK) of railway vehicles — car body lateral acceleration — by using of computer simulation system for railway vehicles dynamic Simpack Rail. This provide the complex simulation environment with variable velocity depending on the train schedule. The rail vehicle model of typical 1520 mm gauge fright locomotive section used for simulation has been verified by means of the chair “Electric multiple unit cars and locomotives” in the Russian University of Transport (RUT (MIIT)). Due to this homologation the questions of model creating and verification in preprocessor are excluded in this paper. The paper gives the detail description of cartographic track modeling in situation plane, heights plane and superelevation plane based on the real operating data. The statistic parameters (moments) for the rail related track excitation and used cartographic track data of the specified track section in this simulation are given as a numeric and graphical results of reading the prepared data files. The measurement of the car body residual lateral acceleration occur under consideration of the earth gravity acceleration part like the accelerometer measurement in the real world. Finally the desired quality parameter determined by simulation is compared with the same one given by a test drive. The calculation method in both cases is based on the middle value of the absolute maximums picked up within the nonstationary realizations of this parameter. Compared results confirm that this quality factor all the first depends on the velocity and track geometry properties. The simulation of the track in this application uses the strong conformity original track data of the test ride track section. The accepted simplification in the rail vehicle model of fright electric locomotive section (body properties related to the center of gravity, small displacements between the bodies) by keeping the geometric and force law characteristics of the force elements and constraints constant allow in Simpack Rail the simulation with necessary validity of system behavior (reactions).

The currently performed mathematical and computer modeling of thermal processes in technical systems is based on an assumption that all the parameters determining thermal processes are fully and unambiguously known and identified (i.e., determined). Meanwhile, experience has shown that parameters determining the thermal processes are of undefined interval-stochastic character, which in turn is responsible for the intervalstochastic nature of thermal processes in the electronic system. This means that the actual temperature values of each element in an technical system will be randomly distributed within their variation intervals. Therefore, the determinative approach to modeling of thermal processes that yields specific values of element temperatures does not allow one to adequately calculate temperature distribution in electronic systems. The interval-stochastic nature of the parameters determining the thermal processes depends on three groups of factors: (a) statistical technological variation of parameters of the elements when manufacturing and assembling the system; (b) the random nature of the factors caused by functioning of an technical system (fluctuations in current and voltage; power, temperatures, and flow rates of the cooling fluid and the medium inside the system); and (c) the randomness of ambient parameters (temperature, pressure, and flow rate). The interval-stochastic indeterminacy of the determinative factors in technical systems is irremediable; neglecting it causes errors when designing electronic systems. A method that allows modeling of unsteady interval-stochastic thermal processes in technical systems (including those upon interval indeterminacy of the determinative parameters) is developed in this paper. The method is based on obtaining and further solving equations for the unsteady statistical measures (mathematical expectations, variances and covariances) of the temperature distribution in an technical system at given variation intervals and the statistical measures of the determinative parameters. Application of the elaborated method to modeling of the interval-stochastic thermal process in a particular electronic system is considered.

A cluster method of mathematical modeling of interval-stochastic thermal processes in complex electronic systems (ES), is developed. In the cluster method, the construction of a complex ES is represented in the form of a thermal model, which is a system of clusters, each of which contains a core that combines the heat-generating elements falling into a given cluster, the cluster shell and a medium flow through the cluster. The state of the thermal process in each cluster and every moment of time is characterized by three interval-stochastic state variables, namely, the temperatures of the core, shell, and medium flow. The elements of each cluster, namely, the core, shell, and medium flow, are in thermal interaction between themselves and elements of neighboring clusters. In contrast to existing methods, the cluster method allows you to simulate thermal processes in complex ESs, taking into account the uneven distribution of temperature in the medium flow pumped into the ES, the conjugate nature of heat exchange between the medium flow in the ES, core and shells of clusters, and the intervalstochastic nature of thermal processes in the ES, caused by statistical technological variation in the manufacture and installation of electronic elements in ES and random fluctuations in the thermal parameters of the environment. The mathematical model describing the state of thermal processes in a cluster thermal model is a system of interval-stochastic matrix-block equations with matrix and vector blocks corresponding to the clusters of the thermal model. The solution to the interval-stochastic equations are statistical measures of the state variables of thermal processes in clusters - mathematical expectations, covariances between state variables and variance. The methodology for applying the cluster method is shown on the example of a real ES.

This paper presents a model mixture of probability distributions of signal and noise. Typically, when analyzing the data under conditions of uncertainty it is necessary to use nonparametric tests. However, such an analysis of nonstationary data in the presence of uncertainty on the mean of the distribution and its parameters may be ineffective. The model involves the implementation of a case of a priori non-parametric uncertainty in the processing of the signal at a time when the separation of signal and noise are related to different general population, is feasible.