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Record W4393188276 · doi:10.1109/tmtt.2024.3378680

Real-Time Millimeter-Wave Imaging With Linear Frequency Modulation Radar and Scattered Power Mapping

2024· article· en· W4393188276 on OpenAlexafffund
Romina Kazemivala, Aaron D. Pitcher, Jimmy Nguyen, Natalia K. Nikolova

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceFrequency domainRadarRadar imagingExtremely high frequencyTime domainIterative reconstructionMicrowave imagingContinuous-wave radarElectronic engineeringMicrowaveComputer visionTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We present a novel real-time image reconstruction method processing linear frequency modulated (LFM) signals. The method exploits the principles of the Fourier-space scattered power mapping (F-SPM). We show that F-SPM, originally developed for frequency-domain signals, can be easily modified to process time-domain data with the same reconstruction speed and image quality. To facilitate validation, we have developed an in-house time-domain radar simulator, which generates synthetic LFM data much faster than full-wave time-domain simulations, which are prohibitively slow. The new image-reconstruction method is validated through synthetic data generated by the radar simulator as well as experimental data acquired with off-the-shelf millimeter-wave (77 to 81 GHz) LFM radar. Comparisons in terms of reconstruction speed and accuracy are carried out with the method of microwave holography, which is deemed the fastest image-reconstruction method for LFM radar.

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 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 score0.621
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 integrity0.0000.000
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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