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Record W2759485015 · doi:10.1109/iscas.2017.8050691

The analytic expression of the output spectrum of ΔΣ ADCs with nonlinear binary-weighted DACs and Gaussian input signals

2017· article· en· W2759485015 on OpenAlexaff
Ghyslain Gagnon, François Gagnon, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsMcGill UniversityÉcole de Technologie Supérieure
Fundersnot available
KeywordsQuantization (signal processing)Delta-sigma modulationGaussianBinary numberAnalog signalAnalytic signalNonlinear systemGaussian noiseNoise shapingAlgorithmExpression (computer science)MathematicsComputer scienceElectronic engineeringSignal processingPhysicsBandwidth (computing)Digital signal processingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper derives the equations leading to the analytic expression of the frequency spectrum at the output of multi-bit delta-sigma modulators afflicted by component mismatch in the digital-to-analog converter used in the feedback path. The effect of the mismatch is modeled as an error signal added to an ideal digital-to-analog converter. The frequency content of this error signal is derived from the probability density functions of the input signal and the shaped quantization noise. The analysis is applied to band-limited Gaussian input signals. Several simulation results are reported, showing a 0.3 dB accuracy of the analytic expressions whenever the number of quantization bits is higher than two.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.211
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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