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Record W1963509883 · doi:10.1109/ccece.2013.6567769

FPGA based reconfigurable body area network using Nios II and uClinux

2013· article· en· W1963509883 on OpenAlex
A. D. Voykin, Francis M. Bui, R.J. Bolton

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
KeywordsReconfigurabilityField-programmable gate arrayComputer scienceIdentification (biology)Embedded systemNios IIReal-time computingComputer hardwareOperating system

Abstract

fetched live from OpenAlex

This paper presents a reconfigurable Body Area Network (BAN) system that can be used to monitor human vital signs and identify abnormalities. The identification of clinically significant patterns in electrocardiogram (ECG) data is the application used to verify the system operation as well as to demonstrate reconfigurability of the system. Data files from the MIT-BIH Arrhythmia database were used for this purpose. As built, the system demonstrates the ability to record raw ECG data and detect and record R-R intervals as well as premature ventricular contractions. Moreover, the overall system was designed to be highly reconfigurable, allowing it to be used for other BAN applications besides pattern recognition in ECG data signals.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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