Leidiane C. M. M. Fontoura
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Leidiane C. M. M. Fontoura has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Adaildo Gomes D'ASsuncao
Federal University of Rio Grande do Norte, Brazil
1 shared publication - Alfredo Gomes Neto
Federal Institute of Paraíba, Brazil
1 shared publication - Arthur S. Bertuleza
Department of Communication Engineering, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
1 shared publication - Hertz Wilton De Castro Lins
Universidade Federal do Rio Grande do Norte
1 shared publication
Latest Publications
- Open Access
- 7 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 9, 85785-85794
Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm
This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm. The proposed FSS structure is composed of an array of metallic patches printed on a dielectric substrate for stopband spatial filtering microwave applications. The shapes of the metallic patches are based on the sunflower (helianthus annus) geometry. In the first step, a parametric analysis is performed to investigate the use of different FSS geometries, including those with circular, annular and corolla integrated patch elements, to compose the sunflower geometry, regarding multiband and polarization independent performances with size reduction. Two bioinspired FSS geometries are synthesized using supervised machine learning with the decision tree algorithm. The random forest (RF) algorithm is used to validate the decision tree algorithm and to confirm the obtained results. The numerical analysis of the proposed FSS geometries is performed using Ansoft Designer software. Prototypes are fabricated and measured. The good agreement observed between simulated and measured results has validated the proposed approach. The use of supervised machine learning with the decision tree algorithm resulted in a particularly efficient and accurate synthesis procedure due to its intuitive implementation and simplified and effective data analysis modelling.
- 7 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 9, 85785-85794
Open AccessSynthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm
This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm. The proposed FSS structure is composed of an array of metallic patches printed on a dielectric substrate for stopband spatial filtering microwave applications. The shapes of the metallic patches are based on the sunflower (helianthus annus) geometry. In the first step, a parametric analysis is performed to investigate the use of different FSS geometries, including those with circular, annular and corolla integrated patch elements, to compose the sunflower geometry, regarding multiband and polarization independent performances with size reduction. Two bioinspired FSS geometries are synthesized using supervised machine learning with the decision tree algorithm. The random forest (RF) algorithm is used to validate the decision tree algorithm and to confirm the obtained results. The numerical analysis of the proposed FSS geometries is performed using Ansoft Designer software. Prototypes are fabricated and measured. The good agreement observed between simulated and measured results has validated the proposed approach. The use of supervised machine learning with the decision tree algorithm resulted in a particularly efficient and accurate synthesis procedure due to its intuitive implementation and simplified and effective data analysis modelling.