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Record W2080370117 · doi:10.1109/ccece.2008.4564560

Design and prototyping of a fiber optic tactile array

2008· article· en· W2080370117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceTactile sensorOptical fiberRobotSoftwareSoftware prototypingMeasure (data warehouse)Rapid prototypingAcousticsElectronic engineeringComputer hardwareReal-time computingArtificial intelligenceEngineeringMechanical engineeringSoftware developmentTelecommunications

Abstract

fetched live from OpenAlex

Robots and machines need a sense of touch for more intelligent operation. Many current tactile sensors used for detailed sensing over a large and continuous area often consume too much power, cannot provide the density of sensing points needed for shape sensing, and are not economical or practical. A pressure sensor based on reflected light intensity, fiber optics and compressible materials can eliminate or reduce many of these problems. The design allows a sensor with a large number of taxels to be implemented at low cost. Two prototypes were designed, built and evaluated. The first prototype included the testing of three sensing points and various materials as integrating chambers. The second prototype included a matrix array of sensing points, and software to measure and record, calibrate and graphically display data.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

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

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 head0.030
GPT teacher head0.196
Teacher spread0.166 · 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