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Record W2997767399 · doi:10.1109/jsen.2019.2963451

An Angular Displacement Sensor Based on Microstrip Wideband Impedance Transformer With Quasi-Chebyshev Frequency Response

2020· article· en· W2997767399 on OpenAlexaff
Hong‐Xu Zhu, Pedro Cheong, Kam‐Weng Tam, Sut‐Kam Ho, Wai‐Wa Choi

Bibliographic record

VenueIEEE Sensors Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsPolytechnique Montréal
FundersUniversidade de Macau
KeywordsChebyshev filterImpedance matchingMicrostripWidebandAcousticsBandwidth (computing)Stub (electronics)Electronic engineeringAngular displacementElectrical impedanceMaterials sciencePhysicsOpticsEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, the design methodology of a microstrip angular displacement sensor based on an impedance transformer with a quasi-Chebyshev frequency response is proposed. This sensor with impedance transforming function can realize the angular displacement detection by bandwidth sensing, and matching sensing simultaneously. The insertion loss in stopband achieves 24.9 dB to 32.6 dB with an average bandwidth selectivity of 2.25 MHz/degree of the sensor centered at 2.4 GHz with two poles of 1.71 GHz and 2.81 GHz. Meanwhile, the average matching level of return loss for displacement sensitivity are 0.18 dB/degree and 0.15 dB/degree, respectively. It is the first time to apply an impedance transformer into the angular displacement sensor design for both bandwidth and matching level sensing among reviewed literatures. In this design, the load-to-source impedance transformation is designed for 250-to-50 Ω, and by adding a rotated stub on the proposed microstrip transformer, the sensing function is developed with angular displacement.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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