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Record W2164934818 · doi:10.1109/aps.2006.1710921

Real-time S-MRTD simulation of electrically large indoor wireless channels with commodity GPUs

2006· article· en· W2164934818 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.

Bibliographic record

Venue2006 IEEE Antennas and Propagation Society International Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceGraphicsFidelityHigh fidelityComputational scienceScalingWirelessTime domainFinite-difference time-domain methodGeneral-purpose computing on graphics processing unitsSimulationReal-time computingElectronic engineeringParallel computingAlgorithmComputer graphics (images)Computer engineeringElectrical engineeringTelecommunicationsPhysicsComputer visionOpticsEngineeringMathematics

Abstract

fetched live from OpenAlex

Asymptotic and statistical models have been the only practical means, in terms of cost, performance and accuracy, for simulating electrically large environments. We show, in practice, how the combination of commodity graphics processing units (GPUs), and higher-order scaling function based multi-resolution time-domain (S-MRTD) techniques realize an unprecedented high-fidelity full-wave simulator that is orders of magnitude faster (134x) than otherwise previously possible

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.675

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)0.0000.000

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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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