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Sub-threshold Current Conveyer for Current-Mode Processing Bio-Analog Front Ends

2024· article· en· W4402726966 on OpenAlexaff
Shahab Mahmoudi Sadaghiani, Han Cat Nguyen, Sharmistha Bhadra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsCurrent (fluid)Analog front-endElectrical engineeringMode (computer interface)Computer scienceElectronic engineeringCurrent conveyorEngineeringCMOSVoltageCapacitor

Abstract

fetched live from OpenAlex

Current-mode analog front ends for reading biosensors have emerged as a prominent design approach in recent times. This method involves processing input current signals directly with current-mode blocks instead of converting them to voltage and utilizing voltage-mode blocks. The primary advantages include reduced power consumption, minimized area usage, and increased dynamic range, particularly at low voltage levels. In current-mode design, the current conveyor plays a pivotal role. Therefore, this work focuses on the design and fabrication of a second generation current conveyor (CCII) to serve as a current buffer in low-LED photoplethysmography (PPG) or any bio-potential readout analog front end with input currents ranging from 1.5nA to 1000nA. Our fabricated CCII consumes 1.79µW and the$\beta$dependency to the load is improved 33% in compared to previous design. Also, X node follows Y node voltage with 10mv variation for full input current range. The area usage of our CCII is$3400 um^{2}$.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.072
GPT teacher head0.351
Teacher spread0.279 · 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
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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