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Record W2132310369 · doi:10.1109/robot.2007.363771

A Multi-rate Control Approach to Haptic Interaction in Multi-user Virtual Environments

2007· article· en· W2132310369 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings - IEEE International Conference on Robotics and Automation/Proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceEthernetNetwork packetController (irrigation)Transmission (telecommunications)Local area networkWorkstationStability (learning theory)Virtual realityComputer networkDistributed computingSimulationHuman–computer interactionOperating system

Abstract

fetched live from OpenAlex

High-fidelity haptic interaction in multi-user environments over general Ethernet-based local area networks (LAN) and metropolitan area networks (MAN) can be challenging but has promising applications. Under typical network traffic conditions, the 1kHz real-time control rate suggested in the literature for stable haptic simulation is well above that achievable by conventional network protocols such as the UDP and TCP/IP. To overcome this limitation, a decentralized multi-rate control approach is proposed in which local force-feedback loops are executed at higher rates than data packet transmission between the user workstations. Mathematical models for stability and performance analysis of such multi-rate haptic control systems are presented. Analytical and experimental results demonstrate improved performance and stability for the distributed control architecture when compared with a centralized controller.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.043
GPT teacher head0.274
Teacher spread0.232 · 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