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Record W2095728036 · doi:10.1109/iembs.2003.1279569

An architecture of a scalable wireless monitoring system

2004· article· en· W2095728036 on OpenAlexaff
Doron Nussbaum, Xiaoyang Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsScalabilityComputer scienceWirelessTelemedicineRemote patient monitoringWireless sensor networkReal-time computingArchitectureWireless transmissionEmbedded systemWireless networkData acquisitionTransmission (telecommunications)Health careComputer networkTelecommunicationsOperating systemMedicine

Abstract

fetched live from OpenAlex

Automatic patient monitoring is becoming an import part of any health care system. The challenge is to create a patient monitoring that is capable of providing a continuous, reliable and real time monitoring and data acquisition services while removing any restrictions on the patient whereabouts. We present a scalable wireless telemedicine system that is capable of simultaneously monitoring large number of patients (acquiring patient's ECG data). We present a simple but useful technique for overcoming temporary "surges" in the environment where large number of patients (beyond the normal capacity of the monitoring system) must be monitored. The solution is based on modification to data transmission over the wireless network.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.392

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)0.0000.000

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2004
Admission routes1
Has abstractyes

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