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Record W2140989385 · doi:10.1109/iscas.1992.230314

Multi-rate switched capacitor filter design with aggressive sampling-rates: filtorX in action

2003· article· en· W2140989385 on OpenAlexaff
C. Ouslis, M. Snelgrove, A.S. Sedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassbandSampling (signal processing)Filter (signal processing)Filter designSwitched capacitorBand-pass filterPrototype filterCapacitorComputer scienceLow-pass filterDigital filterAnalogue filterElectronic engineeringControl theory (sociology)EngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A methodology to accurately design multirate switched-capacitor (SC) filters is derived. Multirate SC filter design is an effective means of minimizing silicon area for bandpass systems. The theory derived allows designs to achieve maximal area savings by employing sampling rates approaching the minimum allowed by sampling theory. Designing filters to simultaneously operate at different sampling rates can require numerical optimization to correct for the effects of multirate operation, filtorX, a computer-aided filter design tool, was employed to design a CCITT V.22 high-band modem filter as a test of the derived design methodology. The result was an improvement of the uncorrected multirate filter, which had exceeded passband tolerances, to one which was within tolerances. The multirate filter, operating at sampling rates of 128 kHz and 8 kHz, required 70% less capacitor area than the equivalent single-rate filter operating at a sampling rate of 128 kHz.>

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.299
Teacher spread0.207 · 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
Published2003
Admission routes1
Has abstractyes

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