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Record W4288696755 · doi:10.36227/techrxiv.20363892.v1

On the Theory and Experiments of Electromagnetic Time-Reversal Method

2022· preprint· en· W4288696755 on OpenAlexaff
Xiaoyao Feng, Jun Cai, ZHIZHANG CHEN

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectromagneticsTime domainBandwidth (computing)Computer scienceInverseInverse problemWork (physics)Computational electromagneticsAlgorithmElectronic engineeringComputer engineeringMathematicsTelecommunicationsElectromagnetic fieldPhysicsEngineeringMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

Time-reversal (TR) is an effective method of solving inverse problems. However, the theory about the TR is still not clear. This work starts by examining fundamental laws in wave physics and electromagnetics and presents a revisit of the electromagnetic TR method. Then a cavity is employed, and the hardware experiments are performed to validate the TR as a robust and straightforward method for source reconstruction. Since the testing equipment has a limited working bandwidth, special algorithms are developed to transform the band-limited testing data into causal time-domain signals for the TR operations. The results verify the effectiveness of the TR method in real-world situations. The work presented in this paper paves the way for extensions and applications of the TR method toward solving complex, realistic problems.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.250
Teacher spread0.241 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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