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Record W2545152094 · doi:10.1109/nssmic.2004.1462701

Front-end electronics for the RatCAP mobile animal PET scanner : timing discriminator and 32 line address priority serial encoder

2005· article· en· W2545152094 on OpenAlexaff
J.‐F. Pratte, S. Junnarkar, P. O’Connor, C. Woody, S. P. Stoll, A. Villanueva, A. Kandasamy, V. Radeka, Bo Yu, S. Robert, Roger Lecomte, Réjean Fontaine

Bibliographic record

VenueIEEE Symposium Conference Record Nuclear Science 2004. · 2005
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDiscriminatorComputer hardwareScannerEncoderCMOSJitterElectrical engineeringComputer scienceApplication-specific integrated circuitTransceiverLine (geometry)DetectorEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

We report on the development of the integrated front-end electronic for the RatCAP (Rat Conscious Animal PET). The RatCAP is a head-mounted, APD-based portable positron emission tomography scanner intended to perform brain imaging and behavioral studies of the awake rat. This paper focuses on the development and characterization of the zero-crossing discriminator (ZCD) and the 32 line address serial encoder for the miniature scanner. The ZCD, used as a time pick-off circuit for each APD detector, has a power consumption of only 300 /spl mu/W. The 32 line address serial encoder is used to multiplex the timing edge of every channel together with its address into a single output. The ASIC, realized in a CMOS 0.18 /spl mu/m process, has a maximum power dissipation of 125 mW. The electronic timing jitter, the time walk and the coincidence timing resolution of the ZCD measured at the encoder output are presented.

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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.018
GPT teacher head0.246
Teacher spread0.227 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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