NOVEL OPTIMIZATION METHODS IN MICROWAVE ENGINEERING: APPLICATIONS IN IMAGING AND DESIGN
Bibliographic record
Abstract
In this thesis, inverse problems related to microwave imaging and microwave component design are investigated. Our contribution in microwave imaging for breast tumor detection can be divided into two parts. In the first part, a vectorial 3D near-field microwave holography is proposed which is an improvement over the existing holography algorithms. In the second part, a simple and fast post-processing algorithm based on the principle of blind de-convolution is proposed for removing the integration effect of the antenna aperture. This allows for the data collected by the antennas to be used in 3D holography reconstruction. The blind deconvolution algorithm is a well-known algorithm in signal processing and our contribution here is its adaptation to microwave data processing. Second, a procedure for accelerating the space-mapping optimization process is presented. By exploiting both fine- and surrogate-model sensitivity information, a good mapping between the two model spaces is efficiently obtained. This results in a significant speed-up over direct gradient-based optimization of the original fine model and enhanced performance compared with other space-mapping approaches. Our approach utilizes commercially available software with adjoint-sensitivity analysis capabilities.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".