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Record W4367663466 · doi:10.1109/tcsii.2023.3271745

An FLL Providing Real-Time Frequency Calibration for OOK Power Oscillator Transmitters

2023· article· en· W4367663466 on OpenAlexaff
Yadong Yin, Zehui Zhang, Xiao Wei-ming, Ximing Fu, Kamal El‐Sankary, Sio Hang Pun

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTransmitterFrequency modulationVoltage-controlled oscillatorFrequency driftLocal oscillatorFrequency deviationModulation (music)Power (physics)Electronic engineeringPhysicsDetectorInterference (communication)Electrical engineeringRadio frequencyComputer scienceAcousticsOpticsAutomatic frequency controlTelecommunicationsEngineeringVoltage

Abstract

fetched live from OpenAlex

A frequency-locked loop (FLL) is proposed to provide a real-time frequency calibration for OOK power oscillator transmitters. This FLL incorporates a pulse-width detector (PWD) based on Time-Registers (TRs) to discriminate accurately between the carrier frequency and the preset target frequency during the data modulation. As a result, frequency calibration can be performed simultaneously with data modulation without interruption. A prototype of an OOK power oscillator transmitter integrated with the proposed FLL is implemented with onboard discrete electronic elements for demonstration. The measurement indicates that the FLL can accurately lock to a carrier frequency of 160MHz despite electromagnetic (EM) interference in the loop antenna of the transmitter. In contrast, with EM interference and charge leakage, the carrier frequency differs from the expected value by about 1 MHz when the FLL is disabled.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 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 routes1
Has abstractyes

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