Model of random field with piece-constant values and sampling-restoration algorithm of its realizations

Yuri Goritskiy; Vladimir Kazakov; Olga Shevchenko; Francisco Mendoza

doi:10.3390/e21080792

Model of random field with piece-constant values and sampling-restoration algorithm of its realizations

Yuri Goritskiy, Vladimir Kazakov, Olga Shevchenko, Francisco Mendoza

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Zacatenco

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels can be arbitrary. Realizations of a random field model of the desired shape are created by appropriate selection of parameters for formative realization of Markov processes. For the proposed field model, we investigated the sampling and restoration algorithm of any selected realizations. As a result, we determined the optimal sampling and recovery algorithms. The resulting sampling is fundamentally non-periodic. Recovery errors are calculated. Two examples are considered.

Original language	English
Article number	792
Journal	Entropy
Volume	21
Issue number	8
DOIs	https://doi.org/10.3390/e21080792
State	Published - Aug 2019

Keywords

Non-gaussian model of a random field with an arbitrary number of states
Reconstruction algorithm
Reconstruction error algorithm
Sampling-reconstruction procedure of such model

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10.3390/e21080792

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title = "Model of random field with piece-constant values and sampling-restoration algorithm of its realizations",

abstract = "We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels can be arbitrary. Realizations of a random field model of the desired shape are created by appropriate selection of parameters for formative realization of Markov processes. For the proposed field model, we investigated the sampling and restoration algorithm of any selected realizations. As a result, we determined the optimal sampling and recovery algorithms. The resulting sampling is fundamentally non-periodic. Recovery errors are calculated. Two examples are considered.",

keywords = "Non-gaussian model of a random field with an arbitrary number of states, Reconstruction algorithm, Reconstruction error algorithm, Sampling-reconstruction procedure of such model",

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AU - Goritskiy, Yuri

AU - Kazakov, Vladimir

AU - Shevchenko, Olga

AU - Mendoza, Francisco

PY - 2019/8

Y1 - 2019/8

N2 - We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels can be arbitrary. Realizations of a random field model of the desired shape are created by appropriate selection of parameters for formative realization of Markov processes. For the proposed field model, we investigated the sampling and restoration algorithm of any selected realizations. As a result, we determined the optimal sampling and recovery algorithms. The resulting sampling is fundamentally non-periodic. Recovery errors are calculated. Two examples are considered.

AB - We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels can be arbitrary. Realizations of a random field model of the desired shape are created by appropriate selection of parameters for formative realization of Markov processes. For the proposed field model, we investigated the sampling and restoration algorithm of any selected realizations. As a result, we determined the optimal sampling and recovery algorithms. The resulting sampling is fundamentally non-periodic. Recovery errors are calculated. Two examples are considered.

KW - Non-gaussian model of a random field with an arbitrary number of states

KW - Reconstruction algorithm

KW - Reconstruction error algorithm

KW - Sampling-reconstruction procedure of such model

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VL - 21

JO - Entropy

JF - Entropy

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ER -

Model of random field with piece-constant values and sampling-restoration algorithm of its realizations

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Keywords

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