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Record W1972114438 · doi:10.1109/tap.2013.2287288

Estimating the Efficiency of Antennas Used as Sensors in Microwave Tissue Measurements

2013· article· en· W1972114438 on OpenAlexaff
Kaveh Moussakhani, Reza K. Amineh, Natalia K. Nikolova

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMicrowaveDirectional antennaAntenna (radio)Slot antennaComputer scienceAcousticsFree spaceElectronic engineeringPhysicsOpticsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A measurement-based method to evaluate the efficiency of antennas used in microwave tissue imaging is proposed. The method is versatile and, in principle, applicable to antennas operating in free space as well. Two identical antennas are employed, one transmitting and the other one receiving. A signal flow graph (SFG) is developed for the two-port network formed by the antennas and the medium between them. The medium's electrical properties mimic those of tissue and can vary according to the application. Several measurements are performed with one of the antennas being loaded with known loads. A system of equations is constructed from the acquired data. The solution of this system produces the efficiency of the antenna in addition to other unknown parameters of the SFG. The efficiencies of two antennas are estimated from measured and simulated data and are verified through comparisons.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.018
GPT teacher head0.234
Teacher spread0.217 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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