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Record W2116849219 · doi:10.1109/health.2011.6026771

Robust license-free Body Area Network access for reliable public m-health services

2011· article· en· W2116849219 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
TopicWireless Body Area Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer networkComputer scienceRobustness (evolution)LicenseComputer securityService (business)Channel (broadcasting)TelecommunicationsBusiness

Abstract

fetched live from OpenAlex

Public m-health service will enable care-givers to continuously monitor patients' vital signals wherever they are, using Body Area Networks (BANs). To make this service available to a large number of people everywhere, public m-health service should be implemented in a license-free frequency band. However, the coexistence problem inherent in such a medium that may be shared by many co-located systems may cause disruption of BAN transmissions. This paper addresses the above problem by proposing a Centralized BAN Access Scheme (CBAS) to improve service availability and robustness of BANs. CBAS puts most of the complexities in the gateway, which has less energy and resource constraints than the body nodes. The gateway monitors all the available channels and assigns an interference-free channel to each scheduled node. Extensive simulation results show the effectiveness of CBAS in reducing durations of service interruption and thus improving service reliability of a BAN.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.238
Teacher spread0.167 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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