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Record W2548261807 · doi:10.1109/ccece.2016.7726731

Diffraction: A critical propagation mechanism

2016· article· en· W2548261807 on OpenAlexaff
Roshanak Zabihi, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsSierra Wireless (Canada)
Fundersnot available
KeywordsDiffractionRadiosity (computer graphics)Computer scienceUniform theory of diffractionContext (archaeology)TransmitterMultipath propagationRay tracing (physics)Radio propagation modelOpticsElectronic engineeringPhysicsRadio propagationTelecommunicationsChannel (broadcasting)EngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

With nearly all of our billions of wireless links having no line-of-sight between the transmitter and receiver, multipath propagation mechanisms, and their accurate modelling, are coming under revitalized scrutiny. This paper reviews diffraction and how it models transmission around blind corners even when there are no supporting surface currents on the corner structure. From the context of defining diffraction as the difference between geometric optics and reality, the basic equations are reviewed and compared to results from numerical experiments (simulations) and physical experiments, to test the theory. The simulations are from a finite-difference time-domain type of approach, which can be expected to work reasonably well, whereas the method of moments, because it uses surface current sources (electric or magnetic), cannot be expected to demonstrate diffraction for many situations. The applications range from radiosity and simulation tools for architectural lighting and animation, to accurate coherent propagation prediction around multiple, canonical baffles.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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