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Record W2116403279 · doi:10.1109/iscas.2009.5117805

Remote frequency calibration of passive wireless microsensors and transponders using injection-locked phase-locked loop

2009· article· en· W2116403279 on OpenAlexafffund
Nima Soltani, Fei Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoltage-controlled oscillatorRelaxation oscillatorPhase-locked loopSIGNAL (programming language)CMOSCalibrationOscillation (cell signaling)Electronic engineeringFrequency synthesizerRadio frequencyComputer scienceElectrical engineeringCadencePhase noiseEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

This paper proposes a new remote frequency calibration method that allows a passive wireless microsensor to adjust the oscillation frequency of its local oscillator using a calibrating reference signal sent by its reader. The reference signal is transmitted wirelessly by the reader in the form of a modulated tone. Frequency calibration is achieved by employing an injection-locking phase-locked loop that adjusts the oscillation frequency of the relaxation oscillator of the microsensor to that of the reference signal. The hysteresis of the relaxation oscillator is created by a novel mono-stable current pulse generating circuit also proposed in this paper. The proposed remote frequency calibration system has been designed in TSMC-0.18µm 1.8V 6-metal 1-poly CMOS technology and analyzed using SpectreRF from Cadence Design Systems with BSIM3V3 device models. The power consumption of the VCO is 525 nW while that of the system is 912 nW.

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 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 score0.252
Threshold uncertainty score1.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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