Multidimensional Scattered Time-varying Scattered Data Meshless Interpolation for Sensor Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43971599" target="_blank" >RIV/49777513:23520/23:43971599 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-36805-9_7" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-36805-9_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-36805-9_7" target="_blank" >10.1007/978-3-031-36805-9_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multidimensional Scattered Time-varying Scattered Data Meshless Interpolation for Sensor Networks

Popis výsledku v původním jazyce

Interpolation and approximation of scattered scalar and vector data is a part of a solution of many engineering problems. The methods are based mostly on some triangulation of the data domain, usually limited to 2D or 3D data, followed by an interpolation or an approximation to obtain a smooth result. This contribution presents a meshless approach based on the Radial Basis Functions (RBF). It is nearly dimensionless and it enables interpolation of time varying data, i.e. interpolation of scattered spatio-temporal varying data, i.e. interpolation in space-time domain without “time-frames”. The meshless methods for scattered spatio-temporal data can be used for interpolation, approximation and evaluation of data acquired from buoys, sensor networks, sensors for tsunami, chemical and radiation detectors, ships and submarines detection, weather forecast, 3D vector fields compression and visualization, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Multidimensional Scattered Time-varying Scattered Data Meshless Interpolation for Sensor Networks

Popis výsledku anglicky

Interpolation and approximation of scattered scalar and vector data is a part of a solution of many engineering problems. The methods are based mostly on some triangulation of the data domain, usually limited to 2D or 3D data, followed by an interpolation or an approximation to obtain a smooth result. This contribution presents a meshless approach based on the Radial Basis Functions (RBF). It is nearly dimensionless and it enables interpolation of time varying data, i.e. interpolation of scattered spatio-temporal varying data, i.e. interpolation in space-time domain without “time-frames”. The meshless methods for scattered spatio-temporal data can be used for interpolation, approximation and evaluation of data acquired from buoys, sensor networks, sensors for tsunami, chemical and radiation detectors, ships and submarines detection, weather forecast, 3D vector fields compression and visualization, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational Science and Its Applications – ICCSA 2023. Lecture Notes in Computer Science

ISBN

978-3-031-36804-2

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

99-112

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Athens

Datum konání akce

3. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001166618800007