Low Complexity Irregular Clusters Tiling through Quarter Region Rotational Symmetry
Bibliographic record
Abstract
In order to reduce the complexity and cost of an N M large planar array from a practical point of view, firstly, the array matrix is divided into four equal N 4 M 4 quarter regions, and then only one quarter is selected to be optimized. After that, this selected quarter region is tiled with a few irregular polyomino clusters (IPCs) and then rotating it to the other three-quarter regions. This method is called Quarter Region Rotational Symmetry (QRRS). The copy from the selected region is rotated by three angles 90, 180, and 270 degrees respectively until the main planar array is filled. Two methods of feeding clusters based on amplitude only and phase only were used to reduce the complexity further. In addition, the complexity can be reduced more by applying the thinning technique with clusters or building clusters for a part of the planar array. A genetic algorithm (GA) is used to implement these ideas until a radiation pattern (RP) useful for modern applications. An additional constraint is included in the optimization process represented by a mask to cover the pattern according to the desired shape. The simulation results showed that the RP can be fully controlled by applying the QRRS technique successfully while reducing the complexity of the feeding network to only 2.25% in the amplitude-only and phase-only cases, and 1.75% and 1.5% in the thinning and partially tiling cases, respectively. Moreover, a detailed design of the feeding network circuit of the main planar array based on IPC is given for practical implementation.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".