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Hybrid Power Electronic Converter for Wearable Electric Stimulators

2024· article· en· W4407951806 on OpenAlexaff
Kumar Joy NagiD, Aleksandar Prodić iD

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWearable computerElectrical engineeringPower (physics)Computer scienceElectronic engineeringEngineeringPhysicsEmbedded system

Abstract

fetched live from OpenAlex

A GaN based hybrid switch-mode power electronic converter and a complementary mixed-signal controller suitable for battery-powered portable electric stimulators are introduced. Lithium-ion coin cell battery voltage is first efficiently stepped-up using a quasi-resonant (QR) boost converter, and then an efficient downstream switched-capacitor (SC) stage generates high voltage stimulation pulses. High efficiency of the QR-boost converter and high slew rate of the SC stage (reducing the pulse energy required for stimulation) extends the battery life. The SC stage stage generates pulses with (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex>) zero-net charge, thus eliminating the need of bulky dc blocking capacitors and, (b) high slew rate, relaxing the need of high voltage stimulation which makes the stimulation less painful. The regulation of the converter is performed with a mixed-signal controller, which can be easily configurable to generate a variety of electric pulses, with variable voltage amplitude, pulse duration and frequency. The operation of the introduced two-stage ES is verified using a discrete prototype with input voltages of 3 V - 3.6 V and output voltages of 100 V - 200 V. The SC stage generates bi-phasic pulses with slew rates as high as 2 kV/us.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.705

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)0.0000.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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