On Distributed Multirate Control of Direct User-to-User Touch in Networked Haptic Systems with Passive Wave-Domain Communications
Bibliographic record
Abstract
This paper investigates the stability and performance of distributed multirate control of direct touch in networked haptic systems that provide users with a remote dynamic proxy of their peer and with passive wave domain communications. The paper considers communication networks with fixed delay and with packet update rate smaller than the update rate of the users' local force feedback loops. After developing a multirate state space model of the direct touch haptic system, the paper uses eigenvalue-based stability analysis to determine the maximum contact stiffness that can be applied to users, as well as the maximum coordination gain that can be used to synchronize the user sites. The analysis predicts that both remote dynamic proxies and passive wave-domain communications make the contact stiffness robust to delay, but only passive wave-domain communications mitigate the negative impact of delay on the coordination gain. In other words, the analysis suggests that passive wave-domain communications should be employed to make the distributed multirate control of direct touch in networked haptic systems stable regardless of the fixed communication delay. However, if power-domain communications are employed, remote dynamic proxies should be used to allow rendering of stiffer contact between users. Experiments in which two users probe each other using similar haptic interfaces validate the analytical results.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".