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In this paper we consider the properties of the random iterated function systems (RIFS) obtained using a generalization of the Chaos game algorithm. Used for the RIFS simulation R is a free software environment for statistical computing and graphics. The similarity dimension by the polygonal protofractals Z = {z_j}, j = 1, 2, . . . , k nonmonotonically depends on the RIFS parameters d_S(μ|k) with an extreme value max d_S(μ|k)=−ln k/ln(1/(1+μ)).

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 problem of online convex optimization naturally occurs in cases when there is an update of statistical information. The mirror descent method is well known for non-smooth optimization problems. Mirror descent is an extension of the subgradient method for solving non-smooth convex optimization problems in the case of a non-Euclidean distance. This paper is devoted to a stochastic variant of recently proposed Mirror Descent methods for convex online optimization problems with convex Lipschitz (generally, non-smooth) functional constraints. This means that we can still use the value of the functional constraint, but instead of (sub)gradient of the objective functional and the functional constraint, we use their stochastic (sub)gradients. More precisely, assume that on a closed subset of $n$-dimensional vector space, $N$ convex Lipschitz non-smooth functionals are given. The problem is to minimize the arithmetic mean of these functionals with a convex Lipschitz constraint. Two methods are proposed, for solving this problem, using stochastic (sub)gradients: adaptive method (does not require knowledge of Lipschitz constant neither for the objective functional, nor for the functional of constraint) and non-adaptivemethod (requires knowledge of Lipschitz constant for the objective functional and the functional of constraint). Note that it is allowed to calculate the stochastic (sub)gradient of each functional only once. In the case of non-negative regret, we find that the number of non-productive steps is $O$($N$), which indicates the optimality of the proposed methods. We consider an arbitrary proximal structure, which is essential for decisionmaking problems. The results of numerical experiments are presented, allowing to compare the work of adaptive and non-adaptive methods for some examples. It is shown that the adaptive method can significantly improve the number of the found solutions.

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 presents the results of theoretical investigation of the peculiarities of the quasi-harmonic signal’s phase statistical distribution, while the quasi-harmonic signal is formed as a result of the Gaussian noise impact on the initially harmonic signal. The revealed features of the phase distribution became a basis for the original technique elaborated for estimating the parameters of the initial, undistorted signal. It has been shown that the task of estimation of the initial phase value can be efficiently solved by calculating the magnitude of the mathematical expectation of the results of the phase sampled measurements, while for solving the task of estimation of the second parameter — the signal level respectively to the noise level — the dependence of the phase sampled measurements variance upon the sough-for parameter is proposed to be used. For solving this task the analytical formulas having been obtained in explicit form for the moments of lower orders of the phase distribution, are applied. A new approach to quasi-harmonic signal’s parameters estimation based on the method of moments has been developed and substantiated. In particular, the application of this method ensures a high-precision measuring the amplitude characteristics of a signal by means of the phase measurements only. The numerical results obtained by means of conducted computer simulation of the elaborated technique confirm both the theoretical conclusions and the method’s efficiency. The existence and the uniqueness of the task solution by the method of moments is substantiated. It is shown that the function that describes the dependence of the phase second central moment on the sough-for parameter, is a monotonically decreasing and thus the single-valued function. The developed method may be of interest for solving a wide range of scientific and applied tasks, connected with the necessity of estimation of both the signal level and the phase value, in such areas as data processing in systems of medical diagnostic visualization, radio-signals processing, radio-physics, optics, radio-navigation and metrology.

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.

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.