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Record W2807034788 · doi:10.1186/s13638-018-1054-0

`Through-wall human being detection using UWB impulse radar

2018· article· en· W2807034788 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

VenueEURASIP Journal on Wireless Communications and Networking · 2018
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Victoria
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsComputer scienceFast Fourier transformRadarUltra-widebandArtificial intelligenceTelecommunicationsPattern recognition (psychology)Algorithm

Abstract

fetched live from OpenAlex

Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
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 studies0.0020.000
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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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