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Record W4388507408 · doi:10.1145/3628357.3629709

MARS: a mmWave Rotating Synthetic Aperture Radar System for Indoor Imaging

2023· article· en· W4388507408 on OpenAlexaff
Wei Zhao, Rong Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSynthetic aperture radarMars Exploration ProgramComputer scienceInverse synthetic aperture radarRadar imagingSide looking airborne radarRadarRemote sensingAccelerationBack projectionExtremely high frequencyComputer visionArtificial intelligenceRadar engineering detailsGeologyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we develop, MARS, a Millimeter wAve (mmWave) Rotating Synthetic aperture radar (ROSAR) platform that can scan a 360° view of the environment. The platform consists of a radar attached to the edge of a rotating plate. As the plate spins, the radar transmits signals that reflect off the targets in the surroundings. By applying the Back-Projection Algorithm (BPA) on the collected data, we can reconstruct high-resolution images of the target area. However, BPA is computationally intensive. To speed up the imaging process, we propose two methods: range-FFT and GPU acceleration. Experiments show that MARS can successfully generate images of the indoor environments, and that GPU acceleration can reduce the time cost up to 98% compared to the conventional BPA.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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