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Record W4413424199 · doi:10.1109/access.2025.3599112

Planar Microwave Sensors: State of the Art and Applications

2025· article· en· W4413424199 on OpenAlexafffund
Carlos G. Juan, Katia Grenier, Mohammad H. Zarifi, Amir Ebrahimi, Ferran Martı́n

Bibliographic record

VenueIEEE Access · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersEuropean Social FundAgencia Estatal de InvestigaciónAgència de Gestió d'Ajuts Universitaris i de RecercaUniversity of TabrizUniversity of British ColumbiaEuropean CommissionUniversity of TokyoInstitució Catalana de Recerca i Estudis AvançatsCentre National de la Recherche ScientifiqueEuropean Regional Development FundUniversity of Alberta
KeywordsPlanarComputer scienceMicrowaveState (computer science)OptoelectronicsMaterials scienceTelecommunicationsComputer graphics (images)

Abstract

fetched live from OpenAlex

This review paper focuses on the latest advances and applications of planar microwave sensors contributed by the most prominent researchers in the field. The paper presents the different working principles, design approaches, fabrication technologies, materials, and applications of a wide variety of planar sensors operating at microwave and millimeter-wave frequencies, including contact and contactless sensors, wired and wireless sensors, microfluidic sensors, “green” sensors, wearables, biosensors, physical sensors, chemical sensors, and more. Advanced techniques for sensor performance optimization (e.g., sensitivity, resolution, selectivity, etc.), based on artificial intelligence, active feedback loops, microwave spectroscopy, losses engineering, etc., will also be discussed in the paper.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2025
Admission routes2
Has abstractyes

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