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Passive Proximity Detection Based on a Miniaturized Pyramidal Optical Sensor

2019· article· en· W2999596407 on OpenAlexaff
Ashish Rai, Dong Hao Zhuo, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPyramid (geometry)MicrofabricationProximity sensorFabricationComputer scienceLight sourceOptical powerOptical pathLight intensityOpticsRayMaterials scienceOptoelectronicsLaserPhysics

Abstract

fetched live from OpenAlex

In this paper, we present the design principle and experimental results from a fabricated prototype of an optical proximity sensor. The sensor base is a pyramid with light sensors attached to its sides and the top. The sensor estimates the direction and distance to the light source using ratios of light intensity received on the sides. We demonstrate that it is further possible to decompose a single pyramid into two virtual, off-center pyramids. Using the independently measured angles to the light sources from these two pyramids, it is possible to estimate the distance to the light source. Advantages of the presented approach to existing proximity sensors include: Passive operation, Low power consumption, Simple fabrication and Wide response angle. The device structure can be realized using standard microfabrication techniques, providing a straightforward path for mass-manufacturing and arraying such devices.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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