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Record W2190834234 · doi:10.1155/2015/751739

A Directional Antenna in a Matching Liquid for Microwave Radar Imaging

2015· article· en· W2190834234 on OpenAlexafffund
Saeed I. Latif, Daniel Flores Tapia, Diego Rodriguez Herrera, Mario Solis Nepote, Stephen Pistorius, L. Shafai

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
FundersCanadian Cancer SocietyUniversity of ManitobaCancerCare Manitoba Foundation
KeywordsAntenna measurementHorn antennaAcousticsCoaxial antennaImpedance matchingMicrowave imagingOpticsAntenna factorAntenna (radio)Dipole antennaMicrowavePhysicsSlot antennaEngineeringElectrical impedanceElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The detailed design equations and antenna parameters for a directional antenna for breast imaging are presented in this paper. The antenna was designed so that it could be immersed in canola oil to achieve efficient coupling of the electromagnetic energy to the breast tissue. Ridges were used in the horn antenna to increase the operating bandwidth. The antenna has an exponentially tapered section for impedance matching. The double-ridged horn antenna has a wideband performance from 1.5 GHz to 5 GHz (3.75 GHz or 110% of impedance bandwidth), which is suitable for breast microwave radar imaging. The fabricated antenna was tested and compared with simulated results, and similar bandwidths were obtained. Experiments were conducted on breast phantoms using these antennas, to detect a simulated breast lesion. The reconstructed image from the experiments shows distinguishable tumor responses indicating promising results for successful breast cancer detection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.012
GPT teacher head0.245
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2015
Admission routes2
Has abstractyes

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