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Record W2467829111 · doi:10.1109/jlt.2016.2587580

Photonics-Based Broadband Microwave Measurement

2016· article· en· W2467829111 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Lightwave Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersNanjing University of Aeronautics and AstronauticsGovernment of Jiangsu ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBroadbandMicrowavePhotonicsBandwidth (computing)Electronic engineeringMicrowave engineeringComputer scienceElectronicsSignal processingMicrowave imagingEngineeringElectrical engineeringTelecommunicationsOpticsPhysicsDigital signal processing

Abstract

fetched live from OpenAlex

Microwave measurement refers to the acquisition of parameters of a microwave signal or the identification of properties of an object via microwave-based approaches. Thanks to the broad bandwidth and high speed provided by modern photonics, microwave measurement in the optical domain can provide better performance in terms of bandwidth and speed which may not be achievable using traditional, even state-of-the-art electronics. In this tutorial, techniques for photonics-based broadband and high-speed microwave measurement are discussed with an emphasis on the system architectures for microwave signal parameter measurement and object property identification. Emerging technologies in this area and possible future research directions are also discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.578
Threshold uncertainty score0.413

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 science0.0010.000
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 head0.017
GPT teacher head0.223
Teacher spread0.207 · 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