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A Hybrid Optical Correlator Used as an Intelligent Instrument

2005· article· en· W1988546448 on OpenAlex
S. Chang, Chander P. Grover

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

VenueKey engineering materials · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptical correlatorComputer scienceSpatial light modulatorFlexibility (engineering)Spatial filterFilter (signal processing)Optical filterArtificial intelligenceElectronic engineeringComputer hardwareComputer visionEngineeringFourier transformOptics

Abstract

fetched live from OpenAlex

A hybrid correlation system incorporates an optical correlator, spatial light modulators(SLM), digital cameras and a computer. Spatial light modulators and cameras are used to dynamically update the input and the spatial filter. The hybrid correlation system integrates the parallel processing capability of the optical correlator and the flexibility of the digital system. It can be used as a high-speed multi-function information processor. This paper focuses on the design and fabrication of a hybrid optical correlator and how it is used as an intelligent instrument. We address the issues of rigorous requirements for filter registration and matched filtering by proposing practical approaches. We include the analysis and use of intensity filters based on commercial SLMs for real-time pattern recognition. We present the engineering details of a specific hybrid optical correlator for applications to the real-time identification of an aircraft.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.225
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