Frequency‐diverse aperture imaging using an open cavity with a rough base
Bibliographic record
Abstract
Abstract This paper proposes a single‐shot computational imaging using a new frequency‐diverse aperture, an open‐ended cavity with a rough surface base. First, it shows that scattering from a conducting rough surface made of conducting cones placed at random positions on a conducting ground plane, normally illuminated by an ultra‐wideband horn antenna (working in the 2–20 GHz range) provides random patterns with a frequency correlation function (FCF) width of Δ f , about hundreds of MHz. Next, by introducing four conducting walls placed around the rough surface, it obtains a higher number of spatially uncorrelated radiation patterns and a narrower FCF width of about Δ f /10, tens of MHz. To approximate the radiation patterns and measurement matrices in the numerical simulations, the geometrical optics (GO) approximation is used taking into account multiple interactions. On the other hand, to estimate them in practice, a trihedral corner reflector installed on an XYZ positioning table is employed. Finally, the image of a planar object with the shape of plus is reconstructed using the minimum least‐squares technique. The paper shows that for a 0.81 square metre image size, a decent‐focused image with a pixel size of about 0.81/400 square metres (about 5 cm × 5 cm) is realisable by using 400 frequency samples within the frequency range of 2–20 GHz.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".