Gain and efficiency enhancement of compact and miniaturised microstrip antennas using multi-layered laminated conductors
Bibliographic record
Abstract
When microstrip antennas are miniaturised, because of high ohmic losses, their gain and efficiency drop significantly. In this study, a new technique is presented to improve these two important parameters of such antennas, where multiple laminated conductors are used to form the conducting patch. The technique essentially reduces the ohmic losses, and consequently improves the gain and efficiency. At first, a planar conductor is studied to show that using multiple laminated conductors, instead of a single conductor of the same thickness, losses can be reduced. The same technique is then used in several miniaturised microstrip antennas, in order to reduce their ohmic losses. It is shown that this process enhances the gain and efficiency of these antennas progressively, by increasing the number of layers in the lamination. Simulation studies on two miniaturised antennas have provided about 1.5 and 2.4 dB improvement in the gain, and from 30 to 40.7% improvement in the efficiency. Experimental investigations are also conducted and confirmed the simulated results. For a microstrip square ring antenna, a measured gain enhancement of 2.16 dB at 2.2 GHz is obtained, by laminating only the ring with four conducting layers.
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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 | 0.000 | 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".