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Driver circuit design based on NI-6363 data acquisition card for the magnetic fluid deformable mirror

2024· article· en· W4400284042 on OpenAlexaff
Xiang Li, Jiyuan Zhao, Dongsheng Zeng, Azhar Iqbal, Zhizheng Wu

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsActuatorWavefrontComputer scienceDeformable mirrorSIGNAL (programming language)Data acquisitionDiffractionChannel (broadcasting)Printed circuit boardComputer hardwareAcousticsOpticsPhysicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Abstract Commonly used methods for generating diffraction-free beams are prone to signal loss or coding errors. Therefore, a method of generating a diffraction-free beam based on a magnetic fluid deformation mirror (MFDM) with dual-layer actuators is proposed. It can produce the desired wavefront in real-time and has the advantages of a large magnitude of mirror deformation, low manufacturing cost, continuous and smooth surface, and easy expansion of the actuator. In this paper, a driver circuit is designed based on Simulink and NI-6363 data acquisition card for the MFDM with its multi-channel control ability. The experiment result shows that the driver circuit can accurately produce the signal for each channel, and has the advantages of stable output, simple operation, and strong expandability.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.055
GPT teacher head0.268
Teacher spread0.214 · 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
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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