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Record W2123155584 · doi:10.1109/health.2011.6026747

Bandwidth allocation in view of EMI on medical equipments in healthcare monitoring systems

2011· article· en· W2123155584 on OpenAlex
Di Lin, Fabrice Labeau

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
TopicWireless Body Area Networks
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceBandwidth (computing)Bandwidth allocationQuality of serviceWirelessComputationComputer networkEMIGenetic algorithmDynamic bandwidth allocationChannel allocation schemesElectromagnetic interferenceTelecommunicationsAlgorithmMachine learning

Abstract

fetched live from OpenAlex

To enhance the capacity of patients supported by in-hospital wireless monitoring systems, a bandwidth allocation scheme for the transmission of medical data in the WLAN is proposed. The problem of bandwidth allocation, subject to limited wireless bandwidth, quality of service (QoS) requirements of medical data transmission, and electromagnetic interference is modeled as an optimization problem. To solve this problem, we propose an algorithm based on genetic theory and analyze the computation time of this algorithm. Finally, based on this algorithm, we analyze the capacity of patients supported by the monitoring system.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.355

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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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
Published2011
Admission routes1
Has abstractyes

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