Wideband corrugated horn design based on machine learning technique
| dc.contributor.author | Ibrahim, Aminah | |
| dc.contributor.author | Mohamed, Saphia | |
| dc.contributor.author | Gadelrab, Mahmoud | |
| dc.contributor.author | Elsaadany, Mahmoud | |
| dc.contributor.author | Shams, Shoukry I. | |
| dc.date.accessioned | 2026-02-02T21:41:50Z | |
| dc.date.available | 2026-02-02T21:41:50Z | |
| dc.date.issued | 2025 | |
| dc.description | Presented on July 21, 2025, at the IEEE 20th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), in St. John's, Newfoundland, Canada. | |
| dc.description.abstract | Corrugated horn antennas are essential in satellite and space communication systems due to their wide bandwidth, low cross-polarization, low side lobes, and excellent return loss. In this paper, several machine learning algorithms are used and trained on CST Microwave Studio data to predict antenna design parameters. The result values achieve a wideband response below 10 dB and a gain error within ±2dBi. These methods offer an efficient initial point for antenna design and reduce development time. | |
| dc.description.uri | https://macewan.primo.exlibrisgroup.com/permalink/01MACEWAN_INST/d1nmsu/cdi_ieee_primary_11114264 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14078/4177 | |
| dc.language.iso | en | |
| dc.rights | All Rights Reserved | |
| dc.subject | corrugated horn antenna | |
| dc.subject | learning data set | |
| dc.subject | machine learning | |
| dc.subject | simulation tools | |
| dc.title | Wideband corrugated horn design based on machine learning technique | en |
| dc.type | Presentation |