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Record W2331581852 · doi:10.1115/detc2015-46826

Development of a Remote Ultrasound Imaging System

2015· article· en· W2331581852 on OpenAlexaff
Reza Fotouhi, rahim oraji, Carlos Mondragon, Brennan Berryman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHaptic technologyComputer scienceJoystickRemote controlTeleroboticsPath (computing)Point (geometry)SimulationComputer visionMotion (physics)Artificial intelligenceRobotMotion controlUltrasoundReal-time computingMobile robotComputer hardwareAcoustics

Abstract

fetched live from OpenAlex

In this paper, concept of developing a haptic tele-robotic master-slave manipulators for ultrasound imaging examination, and some preliminary results will be presented. In a tele-ultrasound system the motion of a master manipulator (such as a haptic wand or a simple joystick) which carries a virtual probe is controlled by a medical expert and remotely reproduced at the patient site by a slave robot, carrying the ultrasound probe. In general a remote diagnostic system contains three divisions: expert station (or expert site), patient station (or patient site), and a communication network such as servers Wi-Fi or satellite network. The experiments demonstrate that the slave manipulator is capable of successfully following the motion of a master manipulator in a path following as well as for point-to-point motions.

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)0.001
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.021
GPT teacher head0.225
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

Citations1
Published2015
Admission routes1
Has abstractyes

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