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Record W2087703515 · doi:10.5555/2442691.2442730

Robust access for wireless body area networks in public m-health

2012· article· en· W2087703515 on OpenAlexaff
Narjes Torabi, Victor C. M. Leung

Bibliographic record

VenueInternational Conference on Body Area Networks · 2012
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRobustness (evolution)WirelessComputer scienceComputer networkBody area networkWireless sensor networkWireless networkSoftware deploymentWireless intrusion prevention systemKey distribution in wireless sensor networksTelecommunications

Abstract

fetched live from OpenAlex

Recent advances in very-low-power wireless communications have stimulated great interest in the development and application of wireless technology in biomedical applications, including wireless body area networks (BANs). A BAN consists of multiple sensor nodes capable of sampling, processing, and communicating one or more vital signs (e.g., heart rate, brain activity, blood pressure, oxygen saturation) and/or environmental parameters (location, temperature, humidity, light) over extended periods via wireless transmissions over short distances. Low cost implementation and ubiquitous deployment calls for the use of license-exempt ISM bands, in which coexistence of other license-exempt devices, particular WiFi radios, negatively impacts on the robustness of BANs. We present proposals to increase the robustness of wireless access in BANs by identifying and taking advantages of spectrum holes that are unused by co-existing devices. Simulation and experimental results are presented to show the effective of our proposals in increasing the robustness of channel access in BANs.

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 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.112
GPT teacher head0.301
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations2
Published2012
Admission routes1
Has abstractyes

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