Banca de DEFESA: LEIDIANE CAROLINA MARTINS DE MOURA FONTOURA
Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.STUDENT : LEIDIANE CAROLINA MARTINS DE MOURA FONTOURA
DATE: 09/07/2021
TIME: 09:00
LOCAL: Sala virtual
TITLE:
A Novel Synthesis Method of Multiband FSS Based on Machine Learning for Wireless Communication Systems
KEY WORDS:
Bioinspired FSS, multiband FSS, frequency filter, FSS synthesis, machine learning, decision tree.
PAGES: 165
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Telecomunicações
SPECIALTY: Teoria Eletromagnetica, Microondas, Propagação de Ondas, Antenas
SUMMARY:
Frequency selective surfaces, or simply FSS, play a fundamental role in optimizing telecommunications systems by reducing undesirable signals, among other applications. Combining the dimensions and arrangement of the elements and defining the physical characteristics of these devices, such as thickness and permittivity of the substrate, can cause conflict of objectives and make more complex the analysis and synthesis of the FSS. In this context, the present work is a study on the application of supervised machine learning with the decision tree algorithm in the synthesis of frequency selective surfaces. For this, the sunflower (Helianthus annuus) was used as a base element, being an original and simplified geometry, with frequency response characteristics similar to those of fractal structures. The thesis work is thus divided in two parts: the proposed element characterization and synthesis of the multiband FSS. Initially, the evolution of geometry and design equations are presented. The intermediate and the proposed structures are numerically characterized using the commercial software Ansoft Designer, manufactured, and experimentally characterized, with good agreement between the simulated and measured results. In the second step, the sunflower geometry is partially modified to define parameterization variables. The Ansoft Designer numerically characterizes the value of each variable of the new geometry, and it generates the frequency responses without repetition. The decision tree algorithm performs the dataset classification and evaluation, and the random forest algorithm validates and confirms the results. This process and the synthesis of the FSS using the decision tree algorithm occur in less than 10 seconds, with accuracy greater than 90\%, meeting the desirable criteria, under two different scenarios. Based on these scenarios, two FSS are manufactured and experimentally characterized, obtaining results with good agreement. Moreover, it is observed that the agility and precision of this classification algorithm make the synthesis of the structures particularly interesting. Intuitive implementation, simplicity in training and validation, and an efficient data analysis model are highlighted.
BANKING MEMBERS:
Presidente - 6345784 - ADAILDO GOMES D ASSUNCAO
Interno - 349732 - LAERCIO MARTINS DE MENDONCA
Interno - 3921178 - VALDEMIR PRAXEDES DA SILVA NETO
Externo ao Programa - 1422699 - HERTZ WILTON DE CASTRO LINS
Externo à Instituição - CUSTÓDIO JOSÉ OLIVEIRA PEIXEIRO - IT
Externo à Instituição - ADAILDO GOMES D ASSUNCAO JUNIOR - IFPB
Externo à Instituição - ALFREDO GOMES NETO - IFPB