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Record W2089764762 · doi:10.1109/icbbt.2010.5479009

Reliability modeling for wireless Ultra Wideband biomedical radar sensing network

2010· article· en· W2089764762 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 Saskatchewan
Fundersnot available
KeywordsWireless sensor networkUltra-widebandComputer scienceRadarMean time between failuresReliability (semiconductor)WirelessReal-time computingElectronic engineeringPower (physics)EngineeringComputer networkReliability engineeringTelecommunicationsFailure rate

Abstract

fetched live from OpenAlex

Impulse Radio Ultra Wideband technology is a newly emerged technology suitable for low-power, low-complex, and low-cost biomedical radar sensing network. Fault tolerance and reliability, and power-saving perform a critical role in the operation of the IR-UWB human bio-sensing network designed for real-time human body health monitoring. In this paper, an IR-UWB bio-sensing network is proposed and the continuous Markov process is applied to model the proposed UWB bio-sensor network. Two different models are investigated, one is sensor with three transmitting power levels, and the other is sensor with six power levels. Both of them consume same total amount of power. The radar sensor and the sink node Markov model with repair rates taken into account are modeled as well. The paper is a contributing effort to develop an analytical model and explore the trade-offs in wireless IR-UWB bio-sensor network in terms of predicted reliability, operation time (MTTF), and power consumption.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.578
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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