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Record W2052914062 · doi:10.1109/tap.2014.2367536

Design of Near-Field Synthesis Arrays Through Global Optimization

2014· article· en· W2052914062 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsNear and far fieldComputer scienceField (mathematics)Antenna (radio)Relation (database)Set (abstract data type)Task (project management)TelecommunicationsMathematicsPhysicsEngineeringSystems engineeringOpticsData mining

Abstract

fetched live from OpenAlex

We provide a study of the relation between the near- and far-fields in antenna systems by working with a concrete design problem. The task of finding an antenna array capable of synthesizing a desired near-field distribution is tackled within the general framework of near-field theories recently proposed in literature. We use a genetic algorithm to search for a set of small antennas by working only with far-field data. It is shown, as predicted theoretically, that such information in the far zone are sufficient for reconstructing the entire near-field in the exterior region. We provide a set of examples demonstrating the procedure and validating the proposals. The methodology can be applied to arbitrary antennas and any desired near-field pattern, and we hope it will help automating design methods in the emerging research area of near-field array synthesis.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.536

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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