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Record W2546165732 · doi:10.1109/icm-02.2002.1161534

Low power, low voltage, 10bit-50MSPS pipeline ADC dedicated for front-end ultrasonic receivers

2004· article· en· W2546165732 on OpenAlexafffund
Kamal El‐Sankary, Abdallah Kassem, R. Chebli, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSAnalog front-endPipeline (software)Electrical engineeringAnalog-to-digital converterUltrasonic sensorVoltageFront and back endsElectronic engineeringDynamic rangeOffset (computer science)Low-power electronicsComputer scienceLow voltageEngineeringPower (physics)Power consumptionPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper concerns the design and the implementation of a low power, low voltage 10bit-50MS/s pipeline analog to digital converter (ADC) dedicated to ultrasonic receivers. The ADC is used in the front-end stage to convert the signals coming from the time gain compensator (TGC) of the handheld ultrasonic apparatus. The proposed architecture is based on 1.5 bits per stage pipeline structure followed by a digital offset compensation to relax the constraints on the analog circuitry. The converter is implemented in digital CMOS 0.18 /spl mu/m technology, the circuit occupies an active area of 1.2 mm/sup 2/, the input differential voltage dynamic range is chosen to be 1.6 Vpp and the power consumption is found to be 31 mW from 1.8 V supply.

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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.203
Teacher spread0.195 · 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

Citations14
Published2004
Admission routes2
Has abstractyes

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