Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Neto, Miércio Cardoso de Alcântara; Ferreira, Hélio Renato Oeiras; Araújo, Jasmine Priscyla Leite de; Barros, Fabrício José Brito; Neto, Alfrêdo Gomes; Alencar, Marina de Oliveira; Cavalcante, Gervásio P. S.

doi:10.1049/iet-map.2019.0821

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…In Ref. 24, an ultra‐wide band framework was obtained with the most suitable dimensions for operating in the frequency ranges of the X and Ku bands at the same time, making it applicable for operation in satellite communications systems. The FSS is optimized through fast and accurate hybrid bio‐inspired multiobjective technique.…”

Section: Introductionmentioning

confidence: 99%

Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

Şahin,

Serinken,

Vural

et al. 2024

Int J Numerical Modelling

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High‐performance frequency selective surfaces (FSSs) have gained attention for their spatial filtering characteristics in 5G communication systems. In this work, we propose an efficient and accurate design methodology for the FSS. Three different artificial neural network methods (ANN) are employed, and their performances are compared for analysis and synthesis purposes. Results show that GRNN has the highest performance for both training and test phase of ANN based FSS analysis and synthesis. A novel, compact, low‐profile triple band FSS unit cell is introduced, and the working mechanism is described. By applying ANN based design procedure, the unit cell dimensions to resonate at the 5G mm‐wave frequency band is extracted. A unit cell with the extracted physical dimensions is simulated with a full‐wave analysis tool. The simulation results show that the FSS has the filtering feature at the predetermined mm‐wave frequencies of the 5G communication. The prototype of the FSS is fabricated, as well. The simulations are verified experimentally with measurement results. The results show that proposed ANN based analysis and synthesis method can be an effective tool for the design of FSS band‐pass filter for 5G applications.

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Section: Introductionmentioning

confidence: 99%

Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

Şahin,

Serinken,

Vural

et al. 2024

Int J Numerical Modelling

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“…Recently, in [10], authors used GRNN (General Regression Neural Network) with Multi-Objective Flower Pollination Algorithm (MOFPA) to obtain the physical dimensions from the desired resonance frequencies. In [11], authors used PSO (Particle Swarm Optimization) to determine the value of air gap in the bandwidth optimization of two coupled FSSs.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm

et al. 2021

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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.

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“…Isolating electronic devices is crucial not only in terms of electronic device-to-device interaction but also in terms of protecting the body tissue from electromagnetic pollution. Due to the incredible penetration of electronic devices in our daily life, electromagnetic compatibility became a central issue that can be solved by using additional designs such as frequency selective surfaces (FSS), electronic bandgap structures (EBG), left-handed materials, double negative materials (DNG), single negative materials (SNG), and artificial magnetic conductors (AMC) [1,2,[12][13][14][15][16][17][18][19][20]. FSSs are one of the goal-directed components that researchers pay significant attention in order with isolation problems [2].…”

Section: Introductionmentioning

confidence: 99%

Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM)

Can

Soysal

2021

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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In this study Support Vector Machine (SVM) based estimation technique is proposed for estimating the bandstop frequency and bandwidth of square-split ring resonators. Artificially engineered surfaces especially the planar frequency selective surfaces like the SRRs have narrowband properties so that estimating the filtering frequencies and the bandwidth is essential in a cost and design-effective way. The proposed method, which is superior to optimization methods and 3D electromagnetic solvers in terms of cost and computational burden, achieved accurate results via SVMs generalization capability. This study represents two SVM regression models one for predicting frequency and the other for predicting bandwidth having fast response and accuracy. Results of the proposed model reveal that resonance frequency estimation error, in terms of percentage, is bounded in the interval [0.0542, 3.5938], with an overall error of 0.89 % for the test data. The mean and standard deviation of the percentage error is obtained as 0.9861 and 0.9376, respectively. In addition to that -10dB bandwidth is estimated with the bounded error where estimation error in terms of percentage would be lie in the interval [0.068925, 6.876800] with an overall error of 3.68% for the test data.

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Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Cited by 11 publications

References 21 publications

Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm

Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM)

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