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Record W1559242836 · doi:10.1109/mcg.2004.1274061

Haptic rendering - beyond visual computing - High-fidelity haptic synthesis of contact with deformable bodies

2004· article· en· W1559242836 on OpenAlexaff
Mohsen Mahvash, Vincent Hayward

Bibliographic record

VenueIEEE Computer Graphics and Applications · 2004
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsHaptic technologyComputer scienceRendering (computer graphics)StereotaxyVirtual realityHigh fidelityFidelitySurgical simulationComputer graphics (images)Artificial intelligenceHuman–computer interactionComputer visionSimulationEngineering

Abstract

fetched live from OpenAlex

A method for synthesizing the haptic response of nonlinear deformable objects from data obtained by offline simulation helps create surgical simulators with high-fidelity haptic feedback. Haptic displays provide users with artificially created tactile sensations. One important use of these displays is to recreate the experience caused by contact between a tool and an object. This capability can be useful in several applications, such as surgical simulators, because users experience an enhanced sense of realism when a haptic simulation is combined with a graphic simulation. Haptic displays require two essential subsystems: a haptic device, which typically has a handle connected to sensors and actuators, and a computational system that interfaces with the device.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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

Citations80
Published2004
Admission routes1
Has abstractyes

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