Vladimir Geppener

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—Empirical mode decomposition (EMD) is a principally new technique, intended to process various types of non-stationary signals by means of decomposing them into a set of certain functions, called " Intrinsic mode functions " (IMFs) or Empirical modes. This paper is dedicated to a newly developed EMD application to Data Mining, namely, to segmentation and(More)
The present paper is concerned with a new technique intended for the spectral density estimation of telemetric signals with the help of the wavelet transform. We briefly revise basic information on classical spectral estimates based on the calculation of non-modified and modified Fourier periodogram and estimates obtained via parametric modeling(More)
This paper describes a method developed by the authors for estimating the state of the Earth's magnetic field. The method is based on the combination of wavelet transform with radial basis neural networks. The method includes decomposing of recorded geomagnetic field variations on different scale components, estimating their disturbance degree and forming(More)
The present paper is devoted to the development of new techniques intended for multichannel signal processing. We study multichannel filter banks and their implementations for wideband monitoring tasks. The key point of the paper is a multichannel modification of the single-channel WOLA-algorithm, which can be considered as a generalization of a(More)
This paper discusses the main aspects of geomagnetic data processing using the wavelet transform. The wavelet transform is shown to be efficient for automatic extraction of unperturbed level of the horizontal component of the Earth’s magnetic field. As a result, it becomes possible to significantly reduce the errors arising during automatic calculations of(More)
The paper considers a multicomponent model (MCM) of ionospheric parameter time variations which was developed by the authors. The suggested model describes the parameter regular variations and detects the periods of anomalous changes in data. MCM identification is based on the combination of autoregressive-integrated moving average method with orthogonal(More)
The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce(More)