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Record W4416859891 · doi:10.1049/icp.2025.3361

On the theory, simulation, testing and applications of electromagnetic time-reversal method

2025· article· en· W4416859891 on OpenAlexaff
Xiaoyao Feng, Yunqi Fu

Bibliographic record

VenueIET conference proceedings. · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectromagneticsWork (physics)Inverse problemComputational electromagneticsInverseElectromagnetic testingElectromagnetic pulse

Abstract

fetched live from OpenAlex

Time-reversal (TR) is an effective method for solving inverse problems. However, theoretical controversies on the mechanism of TR still exist. This work starts by examining fundamental laws in wave physics and electromagnetics and presents a new view 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 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: none
Teacher disagreement score0.918
Threshold uncertainty score0.316

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 head0.010
GPT teacher head0.244
Teacher spread0.235 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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