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Record W2107408684 · doi:10.1109/iscas.1988.15094

Switched-capacitor filter networks derived from general parameter bandpass LC ladder networks

2003· article· en· W2107408684 on OpenAlexaff
Gianluca Roberts, A.S. Sedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBand-pass filterCapacitorFilter (signal processing)Electronic engineeringSwitched capacitorElectronic filter topologyComputer scienceCapacitanceTopology (electrical circuits)Electrical impedanceElectronic circuitPrototype filterLow-pass filterEngineeringElectrical engineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

Extensions to two previously published switched-capacitor (SC) bandpass filter synthesis techniques are presented. These techniques are the impedance scaling method of R.B. Datar and A.S. Sedra (IEEE Trans. Circuits Syst., vol.CAS-30, p.888-898, Dec. 1983) and the mode simulation method for bandpass filter design of K. Martin and A.S. Sedra (IEEE Trans. Circuits Syst., vol.CAS-27, June 1980). The main focus of the present study is on the SC circuit simulation of a set of circuit describing equations derived from arbitrary general parameter bandpass LC ladder networks. An eight-order design example has been used to illustrate the extension to the two design methods. Although this example requires more op amps than the filter order, both designs have been shown to possess low sensitivity to component variations and a relatively low total capacitance.>

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.010
GPT teacher head0.189
Teacher spread0.178 · 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
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

Citations1
Published2003
Admission routes1
Has abstractyes

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