Bandwidth improvement of parasitic coupled patch antenna with Genetic Algorithm
Bibliographic record
Abstract
Patch antennas with parasitic subarrays are used to improve the bandwidth of the patch antennas. Parasitic subarrays are coupled to radiating edges, nonradiating edges or all four edges of the patch. These subarrays create new resonance frequencies which interfere with the main resonance frequency and improve the bandwidth. Gupta et al. [1] used segmentation method to optimize the gap dimensions and parasitic patch dimensions to obtain bandwidth as large as 10%. In this paper we set gap coupling and parasitic dimensions fixed and use genetic algorithm combined with FDD method for simulation and optimization of the problem. Our goal is to increase return loss bandwidth. Genetic Algorithm (GA) is performed on the parasitic subarrays with no change on the main patch. At each iteration of GA we remove some defined cells form the parasitic patches and simulate the structure to monitor bandwidth width enhancement, Such iteration is performed several times to obtain good bandwidth. Main patch is designed at 9 GHz, and parasitic patches have the same dimension as the main patch. Primary simulations have shown bandwidth improvement more than 10%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".