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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 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

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.726

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 head0.009
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

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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