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Record W2545231430 · doi:10.1109/nssmic.1991.259184

Front-end processing electronics for a PET tomograph based on BGO-avalanche photodiode detectors

2002· article· en· W2545231430 on OpenAlexaffabout
P. Richard, D. Rouleau, S. Rodrigue, J. Cadorette, M. Neon, Roger Lecomte

Bibliographic record

VenueConference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAvalanche photodiodeDetectorNuclear electronicsDiscriminatorPhotodiodeAnalog signal processingPhysicsFront and back endsSIGNAL (programming language)TomographyElectronicsAPDSComputer hardwareAnalog signalComputer scienceElectrical engineeringOpticsEngineeringDigital signal processing

Abstract

fetched live from OpenAlex

Summary form only given, as follows. The front-end analog and digital signal processing electronics for the Sherbrooke animal PET (positron emission tomography) tomograph, based on BGO-avalanche photodiode detectors, is discussed. The system is implemented with high-density dual-side surface mount printed circuit boards mounted directly behind the detector arrays in the tomograph, and FASTBUS format boards housed in external cabinets. The signals from each detector are processed using fast/slow channels for timing/energy validation. Constant fraction discrimination is used to generate the timing pulses. Gated integration and analog-to-digital conversion of the slow signals allow energy discrimination to be performed digitally. The control parameters for delays and discriminator thresholds are all software-programmable through digital-to-analog converters or on-board memory registers, and their adjustment can be performed automatically by a computer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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.

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

Citations4
Published2002
Admission routes2
Has abstractyes

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