A dielectric filled ultra-wideband antenna for breast cancer detection
Bibliographic record
Abstract
X-ray mammography, the most prevalent screening process for breast cancer, has limitations, thus generating interest in alternative detection methods One method of interest is microwave breast cancer detection which exploits the contrasts in dielectric properties between healthy and malignant tissue. Tissue sensing adaptive radar (TSAR) imaging has been proposed for microwave breast cancer detection (Fear, E.C. et al., IEEE Trans. Microwave Theory and Technique, 2003). This paper presents an antenna and its feeding structure for TSAR. The slotline bowtie hybrid (SBH) is selected as the basic design. The SBH has an integrated ultra-wideband balun to allow it to be easily connected via coaxial cable. The SBH is a hybrid of a slotline circuit board antenna and bowtie horn. A broad bandwidth match is achieved by widening the slot with a Vivaldi profile. The SBH achieves a flat main beam over the bandwidth by additionally employing triangular bowtie plates on the slot profile. A rolled edge at the bowtie aperture reduces edge diffraction. The introduction of an ambient dielectric in place of free space allows its size to be scaled down. Simulation results for the balun show that an acceptable insertion loss of less than 2 dB can be achieved, even when the balun is immersed in an ambient dielectric. Antenna simulations show that it meets the design objectives.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".