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Record W2573602261 · doi:10.1109/icsens.2016.7808584

A vector light detector for proximity sensing applications

2016· article· en· W2573602261 on OpenAlexafffund
Ibrahim El-chami, Siamack Vosoogh-Grayli, Donghao Zhuo, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotodiodeComputer scienceDetectorPixelPhotodetectorPyramid (geometry)AutomationElectronic engineeringArtificial intelligenceOpticsOptoelectronicsMaterials scienceEngineeringPhysicsMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose a novel design for a three dimensional proximity sensor. The novel design takes advantage of rapid prototyping capabilities offered by 3D printing to produce mesa pyramids with a 55 ° base angle that comprise a two-pixel system. The two pixel system combines photoconductivity and plane wave propagation properties of a radiative source to approximate the source distance as well as direction with respect to the two-pixel system. We have used commercially available silicon photodiodes as active sensing material on the facets of the mesa pyramid bases, whereby each facet would measure the incoming radiation separately. The design offers a simple geometry suitable for integration with other electronic designs and allows for cost effective directional photoconductive sensors that can be implemented in high precision applications such as autonomous vehicles, robotics and industrial automation processes.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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