Application of Invasive Weed Optimization to Design a Broadband Patch Antenna With Symmetric Radiation Pattern
Bibliographic record
Abstract
In this letter, we present a patch antenna over a high impedance surface (HIS) substrate, using Jerusalem cross-shaped frequency selective surfaces (JC-FSSs). The objective in this design is to obtain the enhancement in bandwidth (BW) while achieving the symmetric radiation pattern over the frequency band of interest. In order to derive optimal dimensions of the patch antenna and JC-FSS parameters, a hybrid optimization algorithm that originates from invasive weed optimization (IWO) empowered with the analytical lumped circuit model has been employed. In general, we utilized the IWO features while proposing additional contributions in terms of efficient design and computational efficiency. The optimization benefits from the use of circuit model as a powerful tool to find specific limits for its variables. Therefore, it provides a reasonable starting point for the optimization procedure. For the most efficient design, the antenna and FSS ground plane are optimized simultaneously. In this case, the optimization time can be noticeably reduced. The simulations compared very well with measured results. This antenna shows relative bandwidth 10.44% with the radiation efficiency of better than 85% over the entire bandwidth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".