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Record W2020390578 · doi:10.1109/4.890298

30-100-MHz NPN-only variable-gain class-AB instantaneous companding filters for 1.2-V applications

2000· article· en· W2020390578 on OpenAlexafffund
Mourad N. El-Gamal, R.A. Baki, A. Bar-Dor

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2000
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsMcGill University
FundersMcGill University
KeywordsCompandingCutoff frequencyTotal harmonic distortionCutoffFilter (signal processing)HarmonicMaterials scienceDistortion (music)PhysicsAcousticsElectrical engineeringOptoelectronicsVoltageEngineeringAmplifier

Abstract

fetched live from OpenAlex

Two variations of a continuous-time instantaneous companding filter were integrated in a 25 GHz bipolar process. Their -3-dB frequencies are tunable in the ranges of 1-30 and 30-100 MHz. The DC gains are controllable up to 10 dB. The measured dynamic ranges for a 1% total harmonic distortion are 62.5 and 50 dB, for the 30 and 100 MHz filters, respectively. At maximum cutoff frequencies, the filters dissipate 6.5 mW from a 1.2 V supply.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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

Citations26
Published2000
Admission routes2
Has abstractyes

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