On the Use of Focused Incident Near-Field Beams in Microwave Imaging
Bibliographic record
Abstract
We consider the use of focused incident near-field (NF) beams to interrogate the object of interest (OI) in NF microwave imaging (MWI). To this end, we first discuss how focused NF beams can be advantageously utilized to suppress scattering effects from the neighbouring objects whose unknown dielectric properties are not of interest (i.e., undesired scatterers). We then discuss how this approach can also be helpful in reducing the required measured data points to perform imaging. Driven by the relation between the electromagnetic inverse source and inverse scattering problems, our approach emphasizes the importance of tailoring the induced contrast sources in the imaging domain through the utilized incident NF beams. To demonstrate this idea, we consider two recently-proposed NF beams, and simulate them for imaging applications. The first one is a subwavelength focused NF beam generated by a passive NF plate, and the other is a Bessel beam generated by a leaky radial waveguide. Simple imaging examples are considered to explore the potential advantages of this approach, in particular, toward mainly seeing the object of interest, and not the unknown undesired scatterers. The scope of this paper is limited to homogeneous dielectric objects for which the induced total field distributions in the interrogated objects are similar to the incident field distributions (e.g., those that satisfy the Born approximation). Simple inversion results for focused and non-focused beams are presented accompanied by discussions comparing the achieved reconstructed values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".