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ISSN:
2468-5925
eISSN:
2352-8648
Disciplines
(Field of Science):
- automation, electronics, electrical engineering and space technologies (Engineering and Technology)
- information and communication technology (Engineering and Technology)
- social communication and media studies (Social studies)
- computer and information sciences (Natural sciences)
(Field of Science)
Ministry points: Help
| Year | Points | List |
|---|---|---|
| Year 2024 | 100 | Ministry scored journals list 2024 |
| Year | Points | List |
|---|---|---|
| 2024 | 100 | Ministry scored journals list 2024 |
| 2023 | 100 | Ministry Scored Journals List |
| 2022 | 100 | Ministry Scored Journals List 2019-2022 |
| 2021 | 100 | Ministry Scored Journals List 2019-2022 |
| 2020 | 100 | Ministry Scored Journals List 2019-2022 |
| 2019 | 100 | Ministry Scored Journals List 2019-2022 |
Model:
Open Access
Points CiteScore:
| Year | Points |
|---|---|
| Year 2023 | 12.8 |
| Year | Points |
|---|---|
| 2023 | 12.8 |
| 2022 | 9.9 |
| 2021 | 14.1 |
| 2020 | 12.6 |
| 2019 | 8.8 |
| 2018 | 7 |
| 2017 | 4.2 |
| 2016 | 3.1 |
Impact Factor:
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Sherpa Romeo:
Papers published in journal
Filters
total: 1
Catalog Journals
Year 2023
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LOS and NLOS identification in real indoor environment using deep learning approach
PublicationVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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