The Use of Metasurfaces to Enhance Microwave Imaging: Experimental Validation for Tomographic and Radar-Based Algorithms
Bibliographic record
Abstract
Over the last two decades, metamaterials (MMs) and metasurfaces (MTSs) have been used to fabricate innovative antenna designs, offering cost-effective solutions compared to conventional radiating systems. This paper investigates the feasibility of combining MM design concepts and imaging techniques to create innovative microwave imaging systems. In particular, we present an experimental study with the aim of enhancing microwave imaging for haemorrhagic stroke detection using a new MTS design. First, we show the improvement in performance for a stand-alone MTS-loaded antenna, by studying its operating characteristics in the near and far fields. Then, we assess the performance of the MTS on the reconstruction results from simulations and measurements on two tissue-mimicking gel-based brain phantoms with a cylindrical target representing the bleeding in haemorrhagic stroke. The brain phantom was immersed inside an imaging tank filled with 90% glycerol matching liquid. To perform the image reconstructions, we used both a Huygens based radar algorithm and a DBIM-TwIST tomography algorithm. Our simulation and measurement results indicate that the proposed MTS design improves target localization and decreases image artefacts for the tomographic algorithm and enables target’s detection through our radar technique, paving the way for a hybrid microwave imaging prototype with MTS enhanced antennas.
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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 | 0.001 | 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".