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Record W1994466028 · doi:10.1063/1.2956974

Multichannel digital phase sensitive detection using a field programmable gate array development platform

2008· article· en· W1994466028 on OpenAlexaff
Steven J. Lascos, Daniel T. Cassidy

Bibliographic record

VenueReview of Scientific Instruments · 2008
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsMcMaster University
Fundersnot available
KeywordsField-programmable gate arrayDemodulationGate arrayComputer scienceProgrammable logic deviceComputer hardwareScalabilityDigital signal processingSIGNAL (programming language)Data acquisitionDetectorElectronic circuitDigital electronicsElectronic engineeringElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper describes the development and performance of digital phase sensitive detectors (PSDs) based on a field programmable gate array (FPGA) logic device. A FPGA development system combined with a custom analog data acquisition board provides a powerful platform for the development of digital signal processing systems. Conventional performance metrics for digital PSDs are determined by the analog front-end devices and do not represent improvements to the PSD itself. FPGAs offer a scalable platform on which digital PSDs can be implemented with enhanced capabilities including simultaneous demodulation of multiple independent analog signals at multiple frequencies with arbitrary demodulation functions. Circuits for providing dynamic frequency tracking and ultrafine (<0.001 degrees ) phase resolution are presented.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.251
Teacher spread0.225 · 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".

Quick stats

Citations21
Published2008
Admission routes1
Has abstractyes

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