V. S. Kirichuk, V. P. Kosykh, T. Kurmanbek uulu
Keywords: detection of objects, small-scale objects, adaptive filtration, multichannel filtration, accumulation of images
Pages: 14-22
An algorithm is proposed for detection, in a sequence of images, of small-scale objects moving with a known velocity vector, the initial positions of the objects being unknown. Random noise is suppressed by four-channel filtration based on estimating subpixel coordinates of the objects in the sequence of images. Results of numerical experiments are presented as a dependence of the detection probability on the false alarm level for different object sizes and input noise levels.
Methods are proposed to generate thredimensional surface images with improved angular resolution for low-altitude airborne multichannel radars based on the optimal reconstruction of reflection fields and narrow-band Doppler filtering.
A. P. Trifonov, R. V. Kutsov
Keywords: applicative model, detection, unknown velocity vector of object motion, unknown intensities, maximum likelihood algorithm
Pages: 34-45
Synthesis and analysis of the maximum likelihood algorithm of detecting an object extended in space are performed. The influence of the absence of a priori information on the velocity vector of object motion and image and background intensities on detection efficiency is studied.
A method based on invariants of in-plane motion of an object is developed for compensation of systematic errors of measurements in goniometric systems. Exact equations are derived for systematic errors of measurement of the object latitude with ignored fluctuating errors of measurement. Analytical expressions are also derived to calculate the standard deviation of the error in estimating systematic errors being taken into account, which allow us to impose grounded requirements to tactical and technical characteristics of goniometric systems designed for observation of various objects.
V. V. Savchenko, D. A. Ponomarev
Keywords: stochastic time series, autoregression model, forecast estimate, whitening filter, minimum information divergence criterion
Pages: 56-64
The problem of automatic segmentation of a stochastic time series into homogeneous data segments one cluster long is posed and solved according to the general formulation of the disorder problem. A new algorithm with cluster normalization by the variance of the generating noise is developed using an autoregression model and the minimum information divergence criterion. It is shown that the main advantage of the proposed algorithm over existing analogs is high dynamic properties. The algorithm was tested by analyzing stock market dynamics in the United States and Russia. Estimates of the admissible (threshold) level of disorder in a time series within one cluster are obtained using the Kullback-Leibler information measure.
V. G. Alekseev
Keywords: probability density function, nonparametric (kernel) estimator, uniform consistency of estimate with increasing sample size
Pages: 65-72
Nonparametric (kernel) estimation of a probability density function f(x) for a sample of finite size is considered using the -approach. The smoothness parameter β of the estimated probability density is introduced. For the case β > 2, it is shown that the convergence of the density estimate fn(x) to the function f(x) can be improved by using alternating-sign weight functions (higher-order weight functions). Estimation of the derivatives of a function is briefly considered using the same approach.
The properties and areas of application of images formed by an array of light emitting diodes with modulated brightness observed in an oscillating mirror are considered. It is shown that these properties can be used to immerse large groups of spectators into virtual environment.
A new iterative algorithm of tomographic reconstruction of objects on the basis of projection data available in a limited range of angles only is proposed. The algorithm is based on calculating artificial projections in those directions where projection data are unavailable. By means of numerical simulations, it is verified that the algorithm developed ensured high quality of reconstruction up to the angular interval of 45-60
A. B. Bogdanov, I. A. Borisova, V. V. Dyubanov, N. G. Zagoruiko, O. A. Kutnenko, A. V. Kuchkin, M. A. Meshcheryakov, N. G. Milovzorov
Keywords: data mining, pattern recognition, informativeness of attributes, function of rival similarity
Pages: 91-101
This paper describes the Spektran software system intended for automated analysis of object-attribute data tables which implements data mining algorithms based on a function of rival similarity (FRiS). The Spektran system is used to analyze a set of objects (microparticles of a substance) described by spectral characteristics. The following basic problems of data mining are solved: particle clustering by similarity of their spectra, selection of the subset of the most informative spectrum channels, identifying the classes to which particles and their mixes belong and some others.
M. M. Vekshin, A. V. Nikitin, V. A. Nikitin, N. A. Yakovenko
Keywords: microlenses, integrated-optic coupler, glass substrate, channel waveguide, field-induced ion migration, directed and radiation modes
Pages: 102-108
A multichannel microlens integrated-optic coupler is designed. The coupler outputs the radiation through the substrate surface, thus, the signal detectors can be located on the surface. The procedures of coupler design, fabrication, and investigation are described.