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Six-port Technology for Passive Millimeter-wave Identification Systems

2023· article· en· W4388094743 on OpenAlexafffund
C. Hannachi, Matthieu Egels, Philippe Pannier, Serioja Ovidiu Tatu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersPolytechnique Montréal
KeywordsPhase-shift keyingExtremely high frequencyPort (circuit theory)Electronic engineeringKeyingMillimeterComputer scienceElectrical engineeringEngineeringTelecommunicationsPhysicsOpticsBit error rate

Abstract

fetched live from OpenAlex

This paper presents a novel six-port-based millimeter-wave passive reader front-end configuration for emerging millimeter-wave identification (MMID) systems. The performance of the proposed architecture is investigated through Quadrature Phase Shift Keying (QPSK) demodulations and error vector magnitude (EVM) analyses. This is achieved thanks to a Keysight ADS EM device-level co-simulation using the S-parameters measurements of an MHMIC six-port circuit to achieve a realistic simulation environment. The obtained results show that the proposed reader configuration exhibits a maximum read range of 10 meters at an EVM of 14.2%.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.035
GPT teacher head0.240
Teacher spread0.205 · 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 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
Published2023
Admission routes2
Has abstractyes

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