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Record W1699873136 · doi:10.1109/aps.2003.1217438

A dielectric filled ultra-wideband antenna for breast cancer detection

2004· article· en· W1699873136 on OpenAlexaff
C.J. Shannon, M. Okoniewski, Elise Fear

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBalunBandwidth (computing)Ultra-widebandVivaldi antennaWidebandSlot antennaMicrowave imagingMicrowaveHorn antennaDielectricMaterials scienceAntenna (radio)OpticsComputer sciencePhysicsOptoelectronicsDipole antennaTelecommunicationsRadiation pattern

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.206
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2004
Admission routes1
Has abstractyes

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