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Record W1631093874 · doi:10.1109/jlt.2015.2477336

Ultrafast Three-Dimensional Serial Time-Encoded Imaging With High Vertical Resolution

2015· article· en· W1631093874 on OpenAlexafffund
Jiejun Zhang, Weifeng Zhang, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsOpticsWaveformMicrowave imagingInterferometryMicrowaveUltrashort pulsePhase (matter)Image resolutionPhase modulationMaterials scienceResolution (logic)PhysicsRadarPhase noiseComputer scienceTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

A three-dimensional (3-D) serial time-encoded imaging system with a high vertical resolution based on microwave phase or frequency detection is proposed and experimentally demonstrated. A regular serial time-encoded imaging system can perform ultrafast two-dimensional imaging in which the reflectivity of a sample surface is represented by the intensity change of a microwave waveform. By adding one reference channel to form a Mach-Zehnder interferometer structure, the depth information of the sample surface is encoded as the microwave phase or frequency change, thus, 3-D imaging is implementable by extracting the phase or frequency information. In the proposed approach, the intensity and phase or frequency information are extracted based on Hilbert transform from the microwave waveform. The approach is experimentally evaluated. The imaging of a silicon chip as a sample is performed. A vertical resolution better than 130 nm and a depth measurement range greater than 2 mm are demonstrated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.575

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 integrity0.0000.001
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 head0.009
GPT teacher head0.229
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2015
Admission routes2
Has abstractyes

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