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Record W2326065176 · doi:10.1109/wartia.2014.6976178

New PLL based signal conditioning circuitry for capacitive sensors

2014· article· en· W2326065176 on OpenAlexaff
Issa Jaafar, Bo Vincent Wenger

Bibliographic record

Venue2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsAssiniboine Community College
Fundersnot available
KeywordsCapacitive sensingCapacitanceSignal conditioningMaterials sciencePhase-locked loopElectrical engineeringSIGNAL (programming language)VoltageLC circuitElectronic engineeringCapacitorOptoelectronicsEngineeringPower (physics)Computer scienceElectrodePhase noisePhysics

Abstract

fetched live from OpenAlex

This paper presents a new capacitive signal-conditioning interface, employing a Phase Locked Loop (PLL) circuit. The entire circuit design is based on the principle of capacitance-frequency-voltage conversion. The CD4046 digital phase-locked loop (Fairchild Semiconductor) was used for the circuit implementation since it offers approximately 1% linearity, which is suitable for most applications [1]. The functional sensor material used was Polyvinylidene Fluoride (PVDF) and this was mixed with 7wt.% Ethyl Cellulose and 1wt.% Lecithin. Terpinol-α was used as the solvent to form the thick film paste. The circuit sensitivity has been tested with the developed thick film sensor in terms of output circuit voltage versus capacitance change. It was found that the developed circuit has lower power consumption when compared to standard frequency-to-voltage converter configurations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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