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Dual-Band Split Ring Resonator Sensor for Independent Simultaneous Multi-Material Identification

2025· article· en· W4413321499 on OpenAlexaff
Mehri Ziaee Bideskan, Amirhossein Yazdanicherati, Zahra Abbasi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIdentification (biology)ResonatorDual (grammatical number)Ring (chemistry)Optical ring resonatorsComputer scienceSplit-ring resonatorMaterials scienceOptoelectronicsElectronic engineeringAcousticsPhysicsEngineeringChemistry

Abstract

fetched live from OpenAlex

This paper presents a novel microwave reader-tag-based sensor designed for the real-time, simultaneous measurement of the properties of two different materials under test. Conventional techniques for evaluating multiple materials typically depend on individual sensors for each material, which increases complexity and limited data integration. Our proposed sensor addresses these challenges by integrating both sensing capabilities into a single device. Utilizing a transmission line (TL) as the reader and two split ring resonator (SRR)-based tags, this sensor achieves two distinct resonances at specified frequencies, which are completely isolated from each other. Each resonance is responsible for sensing changes in the electrical properties of its surrounding environment. We highlight the advantages of employing this sensor for real-time monitoring and analysis, emphasizing its promising potential in various applications such as precision agriculture, biomedical engineering, and more. The integration of independent dual-material sensing in a single planar microwave sensor not only simplifies the measurement process but also enhances data accuracy and reliability.

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 score0.348
Threshold uncertainty score0.756

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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 routes1
Has abstractyes

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