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Record W4392603362 · doi:10.1002/9781394205318.ch9

Photonic‐Assisted Instantaneous Frequency Measurements

2024· other· en· W4392603362 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer sciencePhysics

Abstract

fetched live from OpenAlex

In the field of electronic warfare, it is of critical importance to analyze an intercepted radio frequency signal from a hostile radar or communication system. This chapter presents a comprehensive review of the techniques for photonics-assisted instantaneous frequency measurement (IFM) with an emphasis on frequency measurement based on an optical channelizer and frequency measurement based on optical or microwave power monitoring. It briefly reviews other techniques for photonics-assisted IFM, including frequency measurement based on an optical scanning receiver, a photonic Hilbert transformer, a monolithically integrated echelle diffractive grating, and a frequency-to-time mapper. The IFM system based on optical channelizers can estimate the frequencies of multiple simultaneous signals. The techniques based on power monitoring are more promising for frequency measurement of a single-channel signal since they can cover a large frequency measurement range, and can provide better accuracy. The chapter discusses the challenges in implementing these techniques for practical IFM.

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 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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
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)0.0070.005

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 head0.041
GPT teacher head0.268
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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