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)
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 is devoted to handling wideband monitoring tasks by discrete Fourier transform (DFT) modulated filter banks. Filter bank implementation is considered using CPU (Central Processing Unit) and CUDA (Compute Unified Device Architecture) based on GPUs (Graphics Processing Units). We show that CUDA is more efficient for big signal sets due to low(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)
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)
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