Printed Ridge Gap Waveguide Synthesis Approach Based on Genetics Programming
Bibliographic record
Abstract
This article introduces, for the first time, a scalable genetic programming (GP)-based approach for the synthesis of a Printed Ridge Gap Waveguide (PRGW) unit cell. The proposed approach is applicable for any given stop-band frequencies ranging from 3 to 300 GHz. GP is used to generate a straightforward mathematical expression to predict the dimensions of the PRGW unit cell in terms of a predefined stop band and used substrate materials. Thus, reliability is improved and computational time is reduced. The proposed approach shows better performance compared to test-and-trial and traditional machine learning techniques in terms of MSE and MAE values as well as computational time. Using the generated mathematical equation, we conducted an experimental validation by designing a two-PRGW-based waveguide specifically for the Internet of Space (12-16 GHz) and mid-band 5G (3-4 GHz) applications. The two waveguides are fabricated and measured. The obtained results confirm the effectiveness of our proposed GP-based design approach in successfully meeting the predefined objectives. The proposed GP-based approach highlights the potential of using GP as an efficient and reliable component and subsystem design process and thus contributes to the various methods used for automatic design and synthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".