Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4389
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dc.contributor.authorMazumder, Lopamudra-
dc.contributor.authorGhosal, Sandip-
dc.contributor.authorDe, Arijit-
dc.date.accessioned2024-02-15T04:32:04Z-
dc.date.available2024-02-15T04:32:04Z-
dc.date.issued2023-11-
dc.identifier.citationIEEE 3rd International conference on Applied Electromagnetics, Signal Processing & Communication (AESPC), KIIT University, 24-26 November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4389-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis work provides a solution for the forward design problem of leaky wave antennas to predict its directivity using a novel deep neural network architecture. Considering the nonlinear relation of the angle of maximum directivity and frequency at leaky wave resonance, this paper leverages the advantage of deep learning framework for such non-linear regression problem. Using the Maxwell’s equations, the dataset has been generated for different permittivity and thickness values of three-layered leaky wave antenna. For training purpose, it considers 80% of total data. Remaining 20% data are used to obtain an accurate prediction of the maximum directivity within less than 1% error. The proposed method can be suitable replacement for rigorous full-wave simulation in terms of time and computational resource.en_US
dc.subjectLeaky wave antennaen_US
dc.subjectMulti-layered mediumen_US
dc.subjectDirectivityen_US
dc.subjectDeep learningen_US
dc.titleDeep Learning Approach for Directivity Prediction of Multi-Layered Leaky Wave Antennaen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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