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Record W1914871464 · doi:10.1109/test.1995.529820

Arbitrary-precision signal generation for bandlimited mixed-signal testing

2002· article· en· W1914871464 on OpenAlexafffund
X. Haurie, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBandlimitingSIGNAL (programming language)Computer scienceElectronic engineeringSignal-to-noise ratio (imaging)Noise (video)Field-programmable gate arrayRange (aeronautics)Analog signalChipSignal processingDigital signal processingEngineeringComputer hardwareTelecommunicationsMathematicsArtificial intelligenceFourier transform

Abstract

fetched live from OpenAlex

This paper presents significant improvements in the generation of analog signals for on-chip analog circuit testing. In particular the novel oscillators proposed here can achieve signal-to-noise ratios far greater than previous designs, while remaining area-efficient. One particular example illustrates a 30 dB improvement in the SNR. Alternatively, signals can be generated with the same SNR as with older designs but over a wider range of frequencies. Multitone signal generation is enhanced in the same fashion. Prototypes were built and satisfactorily tested on FPGA technology.

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)0.002
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.106
GPT teacher head0.247
Teacher spread0.141 · 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

Citations24
Published2002
Admission routes2
Has abstractyes

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