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Record W1918818101 · doi:10.1109/ccece.1995.526611

A numerical study of a triple product optical processor

2002· article· en· W1918818101 on OpenAlexaff
Branimir Tasic, V. M. Ristić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical and Acousto-Optic Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvelope (radar)Computer scienceRadarPulse repetition frequencySIGNAL (programming language)Modulation (music)Electronic engineeringFourier transformPulse compressionDigital signal processorSignal processingOpticsAcousticsDigital signal processingTelecommunicationsPhysicsComputer hardwareEngineering

Abstract

fetched live from OpenAlex

An analytical and numerical study of the triple product acousto-optic processor output was performed. The processor is capable of detecting the carrier frequency and modulation envelope of both coherent and incoherent signals. This triple product acousto-optic based optical processor could ultimately be used to determine the carrier and pulse repetition (PRF) frequencies of radar signals. The processor utilizes the time and space integrating capabilities of acousto-optic cells to determine the desired frequencies. The advantage of the processor in question compared to the existing ones is its ability to determine the PRF of incoherent radar signals. This is accomplished by performing a Fourier transform of the envelope signal alone, rather than of the composite signal consisting of the carrier and envelope signals. At the same time the processor can perform the operation on a number of radar signals of different carrier frequencies simultaneously.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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 designSimulation or modeling
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
Published2002
Admission routes1
Has abstractyes

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