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Application of stochastic approximation in an infinite-dimensional Hilbert space to the problem of object detection on one observed image distorted by noise and to the problem of fast detection of object arrival in a sequence of noisy images is considered. No a priori information is assumed to be available. The observer is supposed to have two stochastically independent sequences of independent "exact" noisy images, one containing and one not containing the object that has to be (repeatedly) detected by the observer in the nearest time.

A method of detecting boundary points in brightness images, based on subpixel calculation of the brightness difference, is proposed. In a fragment 4 × 4 pixels in size, this method allows calculating 12 directions of the jump in brightness; the algorithm complexity is ~34N atomic operations. The method considered is compared with available methods of detection of boundary points in the image. The algorithm proposed is demonstrated to be more stable to the "salt and pepper" noise, ensures more stable determination of the brightness jump direction, and provides a more intense response to the signal. An application of the method is noted.

A problem of automated retouching of point artifacts in the pre-press process is considered. A new algorithm of detection and localization of multiple point flares is proposed. The algorithm is based on using the so-called conjugate indicator. A scheme for constructing learning rules for tuning the system for different types of artifacts is developed. An example illustrating the proposed algorithm performance on a real image is given.

This paper presents a method for recognizing natural objects on air photographs based on a two-level segmentation procedure and a complex calculation of statistical and fractal parameters using artificial neural networks to determine the category of a natural object and then the type of the object in the category determined. This method is effective for recognizing tree species on air photographs. The basic formulas for calculating the characteristics of natural textures are given, and the topology of the artificial neural networks used is substantiated.

We propose a new method for reconstructing the internal structure of a three-dimensional body using four tomograms constructed on the basis of interflation of functions of three variables, and a new method for describing changes that occur in the object using the tomograms obtained at different times on a system of planes crossing the object.

The ensemble clustering algorithm ECCA (Ensemble of Combined Clustering Algorithms) for processing large datasets is proposed and theoretically substantiated. Results of an experimental study of the algorithm on simulated and real data proving its effectiveness are presented.

Several equalization algorithms utilizing the rotationally variant nature of the received signals are presented in this paper to combat the detrimental effect of intersymbol interference (ISI) introduced by frequency selective channels. Their adaptive implementations and application to a time-reversal space-time block coded (TR-STBC) system are also considered. In addition, a turbo equalization algorithm is derived for systems employing the error correction code. The proposed equalizers and turbo equalizer are evaluated over broadband fixed wireless access channels, and are shown to yield superior performance compared to the conventional equalization schemes.

A method of constructing supervisory control systems for major hazard facilities is proposed.
The hardware architecture and software based on these principles are described. An automated system for controlling the motion of trains of the Novosibirsk subway is presented.

This paper presents a new approach to solving the problem of automatic extremum seeking for a nonlinear one-channel object with inaccurately known parameters. Its distinguishing feature is the implementation of a two-loop system with a sliding-mode inner loop which includes the dynamic part of the object. Using a special dynamic subsystem in this loop as a device for estimating state variables leads to self-oscillations in it. The latter are proposed to be used as search oscillations to estimate the gradient of the static quality function, as is done in the synchronous detection method. Automatic motion to an extremum is provided by the integral control of the outer loop of the system. Features of the proposed approach are illustrated by simulation.

This paper considers the stabilization of a dynamic system with variable uncontrolled perturbations using a controller containing additive adjustment. The variable parameters are perturbations in the form of piecewise continuous functions. The parameters change at arbitrary and unknown times. The adaptive controller and adapter are synthesized using the reference equation method and the velocity vector method, respectively. The closed-loop system has feedback on derivatives of state variables, which leads to different time-scale motions. The stability of the control system is investigated using a common Lyapunov function and the method of separation of motions. The properties of the control system are illustrated by a numerical example.