MARC details
| 000 -LEADER |
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| fixed length control field |
03445ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260727153245.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260727t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009494 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKEE .M34 2026 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Md Mahabub Alam, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine learning-based predictive modelling for bandwidth optimization of circular siw antennas in the K/KA band / |
| Statement of responsibility, etc. |
Md Mahabub Alam |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2026 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2026 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 155 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-COM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Electrical & Electronics Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Machine Learning (ML) has emerged as a powerful tool in antenna engineering, enabling efficient optimization, accurate electromagnetic (EM) modelling, and rapid performance prediction with significantly reduced computational cost. This thesis presents the implementation and validation of an efficient ML-based framework for predicting the impedance bandwidth of circular substrate-integrated waveguide (SIW) antennas operating in the millimetre-wave(mm-wave) spectrum. The primary objective is to overcome the high computational cost and time-consuming nature of conventional fullwave EM simulation-based antenna design, particularly for K- and Ka-band applications requiring extensive parametric optimization. A triple-band circular SIW antenna was designed and optimized using CST Microwave Studio, achieving resonant frequencies at 26.176 GHz, 31.6 GHz, and 33.59 GHz, with an impedance bandwidth of 2.52 GHz covering 24.885-27.405 GHz. The antenna’s robustness was verified through crossplatform EM simulations using CST, HFSS, and ADS. A comprehensive simulationbased dataset was then generated by systematically varying key geometrical parameters affecting bandwidth. Statistical analysis confirmed uniform data distribution and suitability for ML training. Six classical regression models and an artificial neural network (ANN) were developed to predict antenna bandwidth directly from geometrical parameters. The ANN outperformed all other models, achieving a testing coefficient of determination (R²) of 93.95%. Further validation using 5-fold cross-validation yielded average metrics of MAE = 0.0411, MSE = 0.00448, RMSE = 0.0631, and R² = 0.8527, demonstrating strong generalization and robustness. Additional testing on 15 unseen antenna configurations showed close agreement between ML predictions and full-wave EM simulation results. The proposed ML-based framework significantly reduces computational overhead and design iteration time, enabling rapid bandwidth estimation and efficient exploration of the antenna design space. These results confirm the framework’s effectiveness as a practical tool for bandwidth optimization and parametric analysis of circular SIW antennas in K/Ka-band applications. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Electrical & Electronics Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Thesis |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |