Ultrawideband radar imaging system for biomedical applications
Bibliographic record
Abstract
Ultrawideband (UWB) (3–10GHz) radar imaging systems offer much promise for biomedical applications such as cancer detection because of their good penetration and resolution characteristics. The underlying principle of UWB cancer detection is a significant contrast in dielectric properties, which is estimated to be greater than 2:1 between normal and cancerous tissue, compared to a few-percent contrast in radiographic density exploited by x rays. This article presents a feasibility study of the UWB imaging of liver cancer tumors, based on the frequency-dependent finite difference time domain method. The reflection, radiation, and scattering properties of UWB pulses as they propagate through the human body are studied. The reflected and back-scattered electromagnetic energies from cancer tumors inside the liver are also investigated. An optimized, ultrawideband antenna was designed for near field operation, allowing for the reduction of the air-skin interface. It will be placed on the fat-liver tissue phantom with a malignant tumor stimulant. By performing an incremental scan over the phantom and removing early time artifacts, including reflection from the antenna ends, images based on the back-scattered signal from the tumor can be constructed. This research is part of our effort to develop a UWB cancer detection system with good detection and localization properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".