Electromagnetic inverse scattering based object imaging and characterization
Bibliographic record
Abstract
Summary form only given. Object imaging and object characterization based on electromagnetic inverse scattering involves retrieving the equivalent source from the scattered field measured outside the source region due to a known incident field. The solution to the inverse equivalent source problem is not unique. In (S. Shahir, M. Mohajer, A. Rohani, and S. Safavi-Naeini, “Permittivity profile estimation based on nonradiating equivalent source,” Progress in Electromagnetic Research B, Vol. 50, pp. 157-175, 2013), we estimated uniquely the scatterer permittivity profiles by minimizing the non-radiating objective function and applying the information of the scatterer locations and boundaries.Object imaging and object characterization of one and two dielectric cylinders are reported by Zakaria (A. Zakaria, I. Jeffrey, M. Ostadrahimi, M. Asefi and J. LoVetri, "A Novel 3D Near-field Microwave Imaging System," IEEE international symposium on Antennas and Propagation, pp. 816-817, 2013). Zakaria has been able to reconstruct the scatterers' cross sections. Some artifacts can be observed in the results. The reconstructed cross section of the circular cylinder is not circular. Ostadrahimi has done initial calibration by using a PEC circular cylinder to work around the issue (M. Ostadrahimi, A. Zakaria, J. LoVetri, and L. Shafai, "A Near-Field Dual Polarized Microwave Imaging System," IEEE Transactions on Microwave Theory and Techniques, Vol. 61, No. 3, 2013). In the aforementioned experiments, the mutual coupling between the adjacent probes was unavoidable, and the objects under test were small in terms of wavelength. We are going to present our experimental results for object imaging and characterization at 75-110 GHz. By calibrating the electromagnetic inverse scattering system accurately, we have successfully been able to reconstruct the large-size object images. The reconstructed objects are also to be characterized by minimizing the non-radiating objective function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.030 |
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".