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Record W2161550487 · doi:10.1109/tmtt.2004.827018

Time-Domain Measurement of Negative Group Delay in Negative-Refractive-Index Transmission-Line Metamaterials

2004· article· en· W2161550487 on OpenAlexaff
Omar Siddiqui, S.J. Erickson, George V. Eleftheriades, Mo Mojahedi

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2004
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransmission lineMetamaterialGroup delay and phase delayPhysicsNegative refractionOpticsPulse (music)Time domainGroup velocityClassification of discontinuitiesElectromagnetic pulseWaveformTransmission (telecommunications)Mathematical analysisMathematicsTelecommunicationsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

We have simulated and constructed a one-dimensional metamaterial composed of a periodically loaded transmission line that exhibits both negative and positive group velocities in a band of effective negative index of refraction. The negative group velocity or, equivalently, the negative group delay, is demonstrated theoretically and experimentally in the time domain using modulated Gaussian pulses. Due to this negative delay, we can show an output pulse peak emerging from the loaded transmission line prior to the input peak entering the line, i.e., the output pulse precedes the input pulse. The fact that this surprising behavior does not violate the requirements of relativistic causality is illustrated with time-domain simulations, which show that discontinuities in the pulse waveforms are traveling at exactly the speed of light in vacuum. The pulse-reshaping mechanism underlying this behavior is also illustrated using time-domain simulations.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.262
Teacher spread0.245 · 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 designBench or experimental
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

Citations102
Published2004
Admission routes1
Has abstractyes

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