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Record W2134120744 · doi:10.1109/iscas.1997.608751

Amplitude modulated signal generation using a third-order delta-sigma oscillator

2002· article· en· W2134120744 on OpenAlexafffund
B.R. Veillette, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDelta-sigma modulationResonatorDelta modulationSIGNAL (programming language)Modulation (music)Electronic engineeringAmplitudeSigmaAnalog transmissionVoltage-controlled oscillatorThird orderPhysicsAnalog signalElectrical engineeringComputer scienceEngineeringPulse-amplitude modulationAcousticsCMOSDigital signal processingOptics

Abstract

fetched live from OpenAlex

A major challenge for the realization of mixed-signal built-in self-test is the generation of analog signals. Not only must the analog sources be reliable and tunable but they should also require the smallest area possible. Delta-sigma oscillators which use a delta-sigma modulator in the feedback loop of a digital resonator were introduced to provide complex communication signals for this purpose. Circuits generating sinewaves and frequency modulated carriers have already been introduced. These designs were based on second-order digital resonators. A third-order highpass delta-sigma oscillator capable of generating amplitude modulated signals is presented. To the best of our knowledge, this is the first reported use of highpass delta-sigma modulation for signal generation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.052
GPT teacher head0.245
Teacher spread0.192 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2002
Admission routes2
Has abstractyes

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