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Noise analysis of translinear circuits

2021· preprint· en· W18654702 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCompandingIntermodulationNoise (video)Electronic circuitElectronic engineeringSIGNAL (programming language)Noise powerComputer sciencePower (physics)Electrical engineeringEngineeringTelecommunicationsPhysicsCMOSArtificial intelligence

Abstract

fetched live from OpenAlex

<p>This thesis presents noise analysis of translinear circuits, or in general, log-domain filters. Due to the inherit companding [sic] behaviour and nonstationary nature of the translinear noise sources, a nonlinear noise analysis method is proposed. Based on the large-signal calculation, the first-order noise and signal-noise intermodulation terms are considered. Overall, two important noise specifications, power spectral density and signal-to-noise ratio, of both static and dyamic translinear circuits are computed. For dynamic translinear circuits, two methods, either combining noise sources and moving them to output, or process them in situ and individually, are elaborated. A number of generic examples are illustrated to demonstrate the effectiveness and applications of the methods.</p>

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.862
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.262
Teacher spread0.217 · 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