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Electromagnetic Metasurface Beam Shaping Using Far Field Masks

2022· article· en· W4296911598 on OpenAlexaff
Stewart Pearson, Sean V. Hum

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNear and far fieldBeam (structure)OpticsEnhanced Data Rates for GSM EvolutionPhased arrayElectromagnetic fieldMethod of moments (probability theory)Field (mathematics)Surface (topology)PhysicsCoupling (piping)Computer scienceAcousticsElectronic engineeringEngineeringMathematicsGeometryTelecommunicationsMechanical engineeringAntenna (radio)

Abstract

fetched live from OpenAlex

Electromagnetic metasurfaces (EMMSs) have been an area of intense research in recent years owing to their low profile and reduced cost compared to existing phased-array solutions. EMMSs relate the tangential fields on one side of the surface to the other through their surface parameters. Unfortunately, there is no analytical relationship between the required tangential fields on each side of the EMMS and desired far field criteria in the form of upper and lower masks. Here we present a method for optimizing the EMMS surface parameters to satisfy a given far field upper and lower mask. This involves using the alternating direction method of multipliers to optimize a two-dimensional EMMS model constructed using the method of moments. This model incorporates important inter-cell mutual coupling along with edge effects. An example performing cosecant beam shaping is presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

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