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Optimizations for Passive Electric Field Sensing
Passive electric field sensing can be utilized in a wide variety of application areas, although it has certain limitations. In order to better understand what these limitations are and how countervailing measures to these limitations could be implemented, this paper contributes an in-depth discussio...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415647/ https://www.ncbi.nlm.nih.gov/pubmed/36015989 http://dx.doi.org/10.3390/s22166228 |
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author | von Wilmsdorff, Julian Kuijper, Arjan |
author_facet | von Wilmsdorff, Julian Kuijper, Arjan |
author_sort | von Wilmsdorff, Julian |
collection | PubMed |
description | Passive electric field sensing can be utilized in a wide variety of application areas, although it has certain limitations. In order to better understand what these limitations are and how countervailing measures to these limitations could be implemented, this paper contributes an in-depth discussion of problems with passive electric field sensing and how to bypass or solve them. The focus lies on the explanation of how commonly known signal processing techniques and hardware build-up schemes can be used to improve passive electric field sensors and the corresponding data processing. |
format | Online Article Text |
id | pubmed-9415647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94156472022-08-27 Optimizations for Passive Electric Field Sensing von Wilmsdorff, Julian Kuijper, Arjan Sensors (Basel) Article Passive electric field sensing can be utilized in a wide variety of application areas, although it has certain limitations. In order to better understand what these limitations are and how countervailing measures to these limitations could be implemented, this paper contributes an in-depth discussion of problems with passive electric field sensing and how to bypass or solve them. The focus lies on the explanation of how commonly known signal processing techniques and hardware build-up schemes can be used to improve passive electric field sensors and the corresponding data processing. MDPI 2022-08-19 /pmc/articles/PMC9415647/ /pubmed/36015989 http://dx.doi.org/10.3390/s22166228 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article von Wilmsdorff, Julian Kuijper, Arjan Optimizations for Passive Electric Field Sensing |
title | Optimizations for Passive Electric Field Sensing |
title_full | Optimizations for Passive Electric Field Sensing |
title_fullStr | Optimizations for Passive Electric Field Sensing |
title_full_unstemmed | Optimizations for Passive Electric Field Sensing |
title_short | Optimizations for Passive Electric Field Sensing |
title_sort | optimizations for passive electric field sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415647/ https://www.ncbi.nlm.nih.gov/pubmed/36015989 http://dx.doi.org/10.3390/s22166228 |
work_keys_str_mv | AT vonwilmsdorffjulian optimizationsforpassiveelectricfieldsensing AT kuijperarjan optimizationsforpassiveelectricfieldsensing |