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Record W4400525477 · doi:10.1109/mmm.2024.3403047

Fourier-Space Image Reconstruction Using Microwave Measurements: The Path Toward Real-Time Microwave and Millimeter-Wave Imaging

2024· article· en· W4400525477 on OpenAlexaff
Reza K. Amineh, Natalia K. Nikolova

Bibliographic record

VenueIEEE Microwave Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcMaster University
FundersNational Science Foundation
KeywordsMicrowaveMicrowave imagingExtremely high frequencyFourier transformIterative reconstructionPath (computing)Opticsk-spaceMillimeterPhysicsComputer scienceComputer visionTelecommunications

Abstract

fetched live from OpenAlex

The use of electromagnetic waves at microwave and millimeter-wave (mm-wave) frequencies in imaging has been growing rapidly in the last two decades with applications in security screening, biomedical imaging, nondestructive testing, and the inspection of goods and packages. The nonionizing nature of the radiation renders microwave and mm-wave imaging (MMI) safe for humans and, thus, attractive, especially for frequent imaging of living tissue and humans. At the same time, the radiation penetrates many materials, which are optically opaque: e.g., fog and foliage, soil and living tissue, brick and drywall, wood, fabrics, and plastics. Importantly, modern MMI systems offer compact and relatively low-cost hardware due to advancements in high-frequency microelectronics.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.023
GPT teacher head0.230
Teacher spread0.207 · 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 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

Citations7
Published2024
Admission routes1
Has abstractyes

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