MétaCan
Menu
Back to cohort
Record W2728271144 · doi:10.1109/tap.2017.2723085

Reliable RF B/E-Field Probes for Time-Domain Monitoring of EM Exposure During Medical Device Testing

2017· article· en· W2728271144 on OpenAlexafffund
Ali Attaran, William B. Handler, Krzysztof Wawrzyn, Ravi S. Menon, Blaine A. Chronik

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2017
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsField (mathematics)Radio frequencyTime domainElectromagnetic fieldComputer scienceElectronic engineeringPhysicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper presents electric and magnetic probes to measure radio frequency (RF) electric and magnetic fields for measuring the time-varying RF fields used in magnetic resonance imaging (MRI). A small single loop (2 cm) and short dipole antenna (2 cm) were developed to monitor the near field magnetic and electric exposure during the medical device testing. Theoretical analysis for each designed probe was performed to convert the real time receive signals to the exposed electric and magnetic field. Probes in this paper were tuned and matched at center frequency of 127.6 MHz for 3T MRI scanners to improve the accuracy and sensitivity. Different cables were designed to reduce the E-field pick up and achieving the most accurate measurement setup. Probes were fabricated on a single sided printed circuit board, FR4 of thickness 1.57 mm and a copper thickness of 35 μm. The measured S-parameters of the magnetic and electric field probes show less than -44 and -45 dB return loss at 127.6-MHz center frequency of the RF Birdcage.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Antennas and PropagationSame topicElectromagnetic Compatibility and MeasurementsFrench-language works237,207