Impact of the Pre-Processing in AI-Based Classification at Mm-Waves
| Autor: | Zidane, Flora, Lanteri, Jerôme, Marot, Julien, Migliaccio, Claire |
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| Přispěvatelé: | Laboratoire d'Electronique, Antennes et Télécommunications (LEAT), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Jul 2022, denver, United States. pp.203-204, ⟨10.1109/AP-S/USNC-URSI47032.2022.9886369⟩ |
| DOI: | 10.1109/ap-s/usnc-ursi47032.2022.9886369 |
| Popis: | International audience; Based on various applications involving millimeter- wave (mm-wave) imaging, we highlight the importance of processing the measurements prior to their classification with Artificial Intelligence (AI) algorithms. The key point for enabling a good classification accuracy is to obtain the same structure for the training and the test datasets. Throughout the paper, we discuss a set of pre-processing methods, ranging from 2-DimensionalFast Fourier Transform (2D-FFT) with or without segmentation to 3-Dimensional Fast Fourier Transform (3D-FFT), and their influence on the final classification results. |
| Databáze: | OpenAIRE |
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