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Record W2099877411 · doi:10.1109/tmtt.2012.2237181

A Near-Field Dual Polarized (TE–TM) Microwave Imaging System

2013· article· en· W2099877411 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

VenueIEEE Transactions on Microwave Theory and Techniques · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingOpticsTransverse planePhysicsPolarization (electrochemistry)MicrowaveMagnetic fieldElectric fieldEngineering

Abstract

fetched live from OpenAlex

In this paper, we introduce a novel dual polarized microwave imaging system. The system is comprised of a circular array of multiplexed antennas, distributed evenly around an object-of-interest (OI), along with a novel plurality of probes located at the antennas' apertures. Each probe consists of several p-i-n diodes biased in two different states (open and short). The probes are used to measure field scattered by an OI based on the modulated scatterer technique. Half of the probes are oriented vertically with the second half oriented horizontally. The presence of the two probe-orientations enables the imaging system to collect two orthogonal field polarizations, transverse electric (TE) and transverse magnetic (TM), without the need for mechanical rotation. In order to illuminate the object with all possible polarizations of the electromagnetic field, the transmitting antennas are placed at a slant angle with respect to the longitudinal plane of the imaging chamber. Near-field data are collected using each probe set, then calibrated. We show that the calibrated data for each polarization can be used to reconstruct the dielectric profile of various objects using either two-dimensional TE or TM inversion algorithms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.004
GPT teacher head0.200
Teacher spread0.195 · 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