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The paper develops a theory of a new so-called two-parametric approach to the random signals' analysis and processing. A mathematical simulation and the task solutions’ comparison have been implemented for the Gauss and Rice statistical models. The applicability of the Rice statistical model is substantiated for the tasks of data and images processing when the signal’s envelope is being analyzed. A technique is developed and theoretically substantiated for solving the task of the noise suppression and initial image reconstruction by means of joint calculation of both statistical parameters — an initial signal’s mean value and noise dispersion — based on the maximum likelihood method within the Rice distribution. The peculiarities of this distribution’s likelihood function and the following from them possibilities of the signal and noise estimation have been analyzed.

The generalization of Margolus’s block cellular automaton on a hexagonal grid is formulated. Statistical analysis of the results of probabilistic cellular automation for vast variety of this scheme solving the test task of diffusion is done. It is shown that the choice of the hexagon blocks is 25% more efficient than Y-blocks. It is shown that the algorithms have polynomial complexity, and the polynom degree lies within 0.6÷0.8 for parallel computer, and in the range 1.5÷1.7 for serial computer. The effects of embedded into automaton’s field defective cells on the rate of convergence are studied also.

The paper demonstrates the efficiency of asymptotically most powerful test of statistical hypotheses about the number of mixture components in the adding and splitting component models. Test data are the samples from different finite normal mixtures. The results are compared for various significance levels and weights.

Comets and asteroids are recognized by the scientists and the governments of all countries in the world to be one of the most significant threats to the development and even the existence of our civilization. Preventing this threat includes studying the motion of large meteors through the atmosphere that is accompanied by various physical and chemical phenomena. Of particular interest to such studies are the meteors whose trajectories have been recorded and whose fragments have been found on Earth. Here, we study one of such cases. We develop a model for the motion and destruction of natural bodies in the Earth’s atmosphere, focusing on the Benešov bolid (EN070591), a bright meteor registered in 1991 in the Czech Republic by the European Observation System. Unique data, that includes the radiation spectra, is available for this bolid. We simulate the aeroballistics of the Benešov meteoroid and of its fragments, taking into account destruction due to thermal and mechanical processes. We compute the velocity of the meteoroid and its mass ablation using the equations of the classical theory of meteor motion, taking into account the variability of the mass ablation along the trajectory. The fragmentation of the meteoroid is considered using the model of sequential splitting and the statistical stress theory, that takes into account the dependency of the mechanical strength on the length scale. We compute air flows around a system of bodies (shards of the meteoroid) in the regime where mutual interplay between them is essential. To that end, we develop a method of simulating air flows based on a set of grids that allows us to consider fragments of various shapes, sizes, and masses, as well as arbitrary positions of the fragments relative to each other. Due to inaccuracies in the early simulations of the motion of this bolid, its fragments could not be located for about 23 years. Later and more accurate simulations have allowed researchers to locate four of its fragments rather far from the location expected earlier. Our simulations of the motion and destruction of the Benešov bolid show that its interaction with the atmosphere is affected by multiple factors, such as the mass and the mechanical strength of the bolid, the parameters of its motion, the mechanisms of destruction, and the interplay between its fragments.

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.

For modeling and statistical analysis of data characterized by cyclicity (periodicity) in various areas of science are used circular or wrapped distribution models. The phase distribution function of a harmonic and phase-shift-keying signal in case additive white Gaussian noise is considered. Algorithms for modeling random phases sample of harmonic and modulated signals with specified parameters and correlation function are presented. Expressions for the phase distribution density of the phase-shift-keying signal are given. It is shown that the phase probability density function of the phase-shift-keying signal becomes multimodal. In addition, the probability density function under consideration is a periodic function, which means that the trigonometric Fourier basis can be used to decompose it into a series. In paper for the first time, analytical expressions for the coefficients of the Fourier series when decomposing the density under consideration into a harmonic basis are obtained, and the derivation of the corresponding expressions are presented. Examples of computer modeling and corresponding graphical materials of calculating Fourier coefficients of the phase probability density function for harmonic and phase-shift-keying signals are presented. A formula for the cumulative distribution function and its decomposition into a Fourier series are also obtained. Based on the representation of the phase probability density function in the form of a Fourier series, a comparison is made with other circular distributions often used in practical problems, the Mises distribution and the wrapped normal distribution. The results obtained in this work are of theoretical and practical interest for modeling and statistical analysis of signal phases in various applied problems in area radio engineering, digital communication, radar, etc. In particular, in the problems of estimating the signal-to-noise ratio, the bit error rate, as well as the reliability of demodulator solutions, i. e. soft demodulation of phase-shift-keying signals. Analytical expressions for the Fourier series coefficients can be used to estimate the empirical probability density function.