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A sub-mW Ultra-Low Power Low-Voltage LED Driver for a Patch Pulse Oximetry

2023· article· en· W4385624995 on OpenAlexaff
Mahziar Serri Mazandarani, Gabriel Gagnon-Turcotte, Mohamad Sawan, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsPolytechnique MontréalUniversité Laval
Fundersnot available
KeywordsDuty cycleLED circuitPulse-width modulationVoltageElectrical engineeringLight-emitting diodeSIGNAL (programming language)Driver circuitCMOSElectronic circuitCurrent mirrorPower (physics)EngineeringComputer scienceTransistorPhysicsShort circuit

Abstract

fetched live from OpenAlex

This paper presents a low-power wide-swing current mirror light emitting diode (LED) driver designed for an ultra-low power pulse oximeter. The presented driver can regulate red (Forward Voltage= 2.2 V) and infrared (Forward Voltage = 1.55 V) LEDs with a low supply voltage battery. The proposed new design relies on a clock-based strategy along with a wide-swing current mirror topology providing an improved dynamic range. The proposed LED driver allows the independent control of the driving current of each LED. By varying the duty cycle of a pulse wave modulation (PWM) signal, the operating and idle times of the circuits can be modulated to regulate the current. Using a 1% duty cycle and a 100 Hz PWM signal frequency, the estimated power consumption is 0.92 mW with a typical driving current of 14 mA for the red LED and a typical driving current of 20 mA for the infrared LED. The circuit is designed using a CMOS TSMC 130 nm process.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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