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Record W1993944879 · doi:10.1109/tmtt.2014.2333497

A 3-D Dual-Polarized Near-Field Microwave Imaging System

2014· article· en· W1993944879 on OpenAlexaff
Mohammad Asefi, Majid Ostadrahimi, Amer Zakaria, Joe LoVetri

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOpticsMicrowaveMicrowave imagingNear and far fieldPhysicsElectric fieldScatteringMaterials science

Abstract

fetched live from OpenAlex

A novel 3-D dual-polarized microwave imaging system based on the modulated scattering technique (MST) is presented. The system collects the magnitude and phase of the scattered field using 120 MST probes and 12 transmitter/collector antennas distributed around an object-of-interest in the near-field region. The 12 antennas form a middle circumferential layer while the printed MST probes are arranged on three circumferential layers including the middle layer. The antennas are linearly polarized double-layer Vivaldi antennas, each fixed inside its own cylindrical conducting cavity and slanted with respect to the vertical axis of the imaging chamber. The MST probes are etched on both sides of a thin substrate and loaded with five evenly distributed p-i-n diodes along their length. These are positioned vertically and horizontally so that the z- and φ-components of the electric field is measured. Field data are collected using MST, calibrated, and then inverted using a multiplicatively regularized finite-element contrast source inversion algorithm. The system performance is evaluated by collecting and inverting data from different 3-D targets.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designBench or experimental
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

Citations15
Published2014
Admission routes1
Has abstractyes

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