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Record W4415471517 · doi:10.1002/adom.202502356

Cloaking and Antennas: From Theoretical Paradigms to Next‐Generation Intelligent Systems

2025· article· en· W4415471517 on OpenAlexafffund
Xun Li

Bibliographic record

VenueAdvanced Optical Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCloakingTransformation (genetics)Coupling (piping)ScatteringAntenna (radio)System integrationTransformation optics

Abstract

fetched live from OpenAlex

Abstract Electromagnetic cloaking techniques have evolved significantly over the past two decades, transitioning from mere concepts to functional implementations. Though their applications abound, those involving antennas are among the most widely investigated, spurred by the potential to render radiating/receiving components invisible or tailor their scattering properties—a pursuit of interest since the earliest metamaterial‐based cloaks. This review maps the development of leading cloaking methodologies, from the pioneering theories of transformation optics, scattering cancellation, and transmission‐line networks to the rise of dynamic, self‐adaptive systems fueled by recent advances in artificial intelligence. Each approach is examined in terms of its underlying mechanisms, prevailing limitations, and antenna integration capacity, particularly for addressing mutual coupling and blockage effects in dense array configurations. Finally, a comparative analysis reveals that intelligent cloaking systems combining deep learning with transformation optics and reconfigurable metasurfaces offer particularly promising solutions for modern stealth, sensing, and communication systems.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.557

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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