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Record W2096653575 · doi:10.1186/1687-3963-2012-10

An advanced physiological data logger for medical imaging applications

2012· article· en· W2096653575 on OpenAlexafffund
Tareq Khan, Khan A. Wahid

Bibliographic record

VenueEURASIP Journal on Embedded Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUSBComputer scienceData loggerBluetoothComputer hardwareWirelessEmbedded systemInterface (matter)TelemetrySoftwareOperating systemTelecommunications

Abstract

fetched live from OpenAlex

The interest of physiological data sensing and recording using wireless body sensor network has increased in recent years due to the advancement of miniature and portable electronic devices. In this study, the design of a portable and rechargeable data logger with high data rate multiple wireless connectivity (Bluetooth and 2.4-GHz radio frequency) is discussed. The data are logged in micro secure digital (SD) cards and can be transferred to PC or Smartphone using SD card reader, USB interface, or Bluetooth wireless link. Analog signals can also be logged through an 8-channel analog-to-digital interface. A graphical LCD with touch screen is added for control and diagnosis. The hardware is generic and targeted for various medical imaging and data collection applications. The functionality of the prototype is later tested for wireless capsule endoscopy and skin temperature logging application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.033
GPT teacher head0.317
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2012
Admission routes2
Has abstractyes

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