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Record W2164152047 · doi:10.1109/cleoe.2005.1568284

Improved methods for generating dynamic computer generated holograms for realising adaptive optical cross connects

2006· article· en· W2164152047 on OpenAlex
Jamie L. Ramsey, Ravi Shankar, Trevor J. Hall

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
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHolographyComputer scienceWavefrontComputer-generated holographySpatial light modulatorOptical engineeringAdaptive opticsProcess (computing)Optical computingVolume hologramInterconnectionElectronic engineeringComputer hardwareOpticsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Diffractive optical elements (DOE) designed by computer, i.e. computer generated holograms (CGH), offer precise control over optical wavefronts that is difficult to attain using classical optical elements and that can be of significant benefit to optical interconnection systems. Typically a CGH is implemented in a fixed medium via standard methods of micro- fabrication which may either be used directly or as a master for volume manufacture via process of replication. The computer time required to design the CGH is then not a significant issue and it is standard practice to employ computationally expensive algorithms such as simulated annealing (SA) to optimize the design. However, advances in spatial light modulator technology now offer the prospect of programmable computer generated holograms that have particularly promising application in reconfigurable interconnections systems e.g. holographic beam steering switches.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.528
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.330
Teacher spread0.296 · 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