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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 propagation of stable coherent entities of an electromagnetic field in nonlinear media with parameters varying in space can be described in the framework of iterations of nonlinear integral transformations. It is shown that for a set of geometries relevant to typical problems of nonlinear optics, numerical modeling by reducing to dynamical systems with discrete time and continuous spatial variables to iterates of local nonlinear Feigenbaum and Ikeda mappings and nonlocal diffusion-dispersion linear integral transforms is equivalent to partial differential equations of the Ginzburg–Landau type in a fairly wide range of parameters. Such nonlocal mappings, which are the products of matrix operators in the numerical implementation, turn out to be stable numerical- difference schemes, provide fast convergence and an adequate approximation of solutions. The realism of this approach allows one to take into account the effect of noise on nonlinear dynamics by superimposing a spatial noise specified in the form of a multimode random process at each iteration and selecting the stable wave configurations. The nonlinear wave formations described by this method include optical phase singularities, spatial solitons, and turbulent states with fast decay of correlations. The particular interest is in the periodic configurations of the electromagnetic field obtained by this numerical method that arise as a result of phase synchronization, such as optical lattices and self-organized vortex clusters.

The main statements and inferences of the Dynamic Theory Information (DTI) are considered. It is shown that DTI provides the possibility two reveal two essentially important types of information: objective (unconventional) and subjective (conventional) informtion. There are two ways of obtaining information: reception (perception of an already existing one) and generation (production of new) information. It is shown that the processes of generation and perception of information should proceed in two different subsystems of the same cognitive system. The main points of the Natural-Constructivist Approach to modeling the cognitive process are discussed. It is shown that any neuromorphic approach faces the problem of Explanatory Gap between the “Brain” and the “Mind”, i. e. the gap between objectively measurable information about the ensemble of neurons (“Brain”) and subjective information about the human consciousness (“Mind”). The Natural-Constructive Cognitive Architecture developed within the framework of this approach is discussed. It is a complex block-hierarchical combination of several neuroprocessors. The main constructive feature of this architecture is splitting the whole system into two linked subsystems, by analogy with the hemispheres of the human brain. One of the subsystems is processing the new information, learning, and creativity, i.e. for the generation of information. Another subsystem is responsible for processing already existing information, i.e. reception of information. It is shown that the lowest (zero) level of the hierarchy is represented by processors that should record images of real objects (distributed memory) as a response to sensory signals, which is objective information (and refers to the “Brain”). The next hierarchy levels are represented by processors containing symbols of the recorded images. It is shown that symbols represent subjective (conventional) information created by the system itself and providing its individuality. The highest hierarchy levels containing the symbols of abstract concepts provide the possibility to interpret the concepts of “consciousness”, “sub-consciousness”, “intuition”, referring to the field of “Mind”, in terms of the ensemble of neurons. Thus, DTI provides an opportunity to build a model that allows us to trace how the “Mind” could emerge basing on the “Brain”.

Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

We study excitation of oscillations in the stochastic gene systems with time-delayed feedback loop during transcription. The oscillations arise due to interaction noise and time delay even when deterministic counterpart of the system exhibits stationary behaviour. This effect becomes important when degree-of-freedom of a system is not high, and role of fluctuations becomes principal. The analytical solution of master-equation is obtained. The results of numerical simulations are presented.

The Goodwin dynamical model under the random external disturbances is considered. A full parametrical analysis for equlibria and cycles of deterministic model is developed. We study probabilistic properties of stochastic attractors using stochastic sensitivity functions technique and numerical methods. A phenomenon of the generation of stochastic business cycles in the zones of stable equilibria is discussed.

This paper is devoted to the analysis of the effect of additive and parametric noise on the processes occurring in the nerve cell. This study is carried out on the example of the well-known Morris – Lecar model described by the two-dimensional system of ordinary differential equations. One of the main properties of the neuron is the excitability, i.e., the ability to respond to external stimuli with an abrupt change of the electric potential on the cell membrane. This article considers a set of parameters, wherein the model exhibits the class 2 excitability. The dynamics of the system is studied under variation of the external current parameter. We consider two parametric zones: the monostability zone, where a stable equilibrium is the only attractor of the deterministic system, and the bistability zone, characterized by the coexistence of a stable equilibrium and a limit cycle. We show that in both cases random disturbances result in the phenomenon of the stochastic generation of mixed-mode oscillations (i. e., alternating oscillations of small and large amplitudes). In the monostability zone this phenomenon is associated with a high excitability of the system, while in the bistability zone, it occurs due to noise-induced transitions between attractors. This phenomenon is confirmed by changes of probability density functions for distribution of random trajectories, power spectral densities and interspike intervals statistics. The action of additive and parametric noise is compared. We show that under the parametric noise, the stochastic generation of mixed-mode oscillations is observed at lower intensities than under the additive noise. For the quantitative analysis of these stochastic phenomena we propose and apply an approach based on the stochastic sensitivity function technique and the method of confidence domains. In the case of a stable equilibrium, this confidence domain is an ellipse. For the stable limit cycle, this domain is a confidence band. The study of the mutual location of confidence bands and the boundary separating the basins of attraction for different noise intensities allows us to predict the emergence of noise-induced transitions. The effectiveness of this analytical approach is confirmed by the good agreement of theoretical estimations with results of direct numerical simulations.

The article proposes a method of joint analysis of multidimensional financial time series based on the evaluation of the set of properties of stock quotes in a sliding time window and the subsequent averaging of property values for all analyzed companies. The main purpose of the analysis is to construct measures of joint behavior of time series reacting to the occurrence of a synchronous or coherent component. The coherence of the behavior of the characteristics of a complex system is an important feature that makes it possible to evaluate the approach of the system to sharp changes in its state. The basis for the search for precursors of sharp changes is the general idea of increasing the correlation of random fluctuations of the system parameters as it approaches the critical state. The increments in time series of stock values have a pronounced chaotic character and have a large amplitude of individual noises, against which a weak common signal can be detected only on the basis of its correlation in different scalar components of a multidimensional time series. It is known that classical methods of analysis based on the use of correlations between neighboring samples are ineffective in the processing of financial time series, since from the point of view of the correlation theory of random processes, increments in the value of shares formally have all the attributes of white noise (in particular, the “flat spectrum” and “delta-shaped” autocorrelation function). In connection with this, it is proposed to go from analyzing the initial signals to examining the sequences of their nonlinear properties calculated in time fragments of small length. As such properties, the entropy of the wavelet coefficients is used in the decomposition into the Daubechies basis, the multifractal parameters and the autoregressive measure of signal nonstationarity. Measures of synchronous behavior of time series properties in a sliding time window are constructed using the principal component method, moduli values of all pairwise correlation coefficients, and a multiple spectral coherence measure that is a generalization of the quadratic coherence spectrum between two signals. The shares of 16 large Russian companies from the beginning of 2010 to the end of 2016 were studied. Using the proposed method, two synchronization time intervals of the Russian stock market were identified: from mid-December 2013 to mid- March 2014 and from mid-October 2014 to mid-January 2016.

The study of the properties of seismic noise in Kamchatka is based on the idea that noise is an important source of information about the processes preceding strong earthquakes. The hypothesis is considered that an increase in seismic hazard is accompanied by a simplification of the statistical structure of seismic noise and an increase in spatial correlations of its properties. The entropy of the distribution of squared wavelet coefficients, the width of the carrier of the multifractal singularity spectrum, and the Donoho – Johnstone index were used as statistics characterizing noise. The values of these parameters reflect the complexity: if a random signal is close in its properties to white noise, then the entropy is maximum, and the other two parameters are minimum. The statistics used are calculated for 6 station clusters. For each station cluster, daily median noise properties are calculated in successive 1-day time windows, resulting in an 18-dimensional (3 properties and 6 station clusters) time series of properties. To highlight the general properties of changes in noise parameters, a principal component method is used, which is applied for each cluster of stations, as a result of which the information is compressed into a 6-dimensional daily time series of principal components. Spatial noise coherences are estimated as a set of maximum pairwise quadratic coherence spectra between the principal components of station clusters in a sliding time window of 365 days. By calculating histograms of the distribution of cluster numbers in which the minimum and maximum values of noise statistics are achieved in a sliding time window of 365 days in length, the migration of seismic hazard areas was assessed in comparison with strong earthquakes with a magnitude of at least 7.

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.