Haptic Data Transmission Based on the Prediction and Compression

You, Yonghee; Sung, Mee Young

doi:10.1109/icc.2008.350

Cited by 26 publications

(7 citation statements)

References 8 publications

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“…The method proposed by Fout and Ma selects the best predictor from an available set, based on the values immediately compressed before the current value. In the work of You and Sung, a regression line computed from the last compressed values is used to predict the next value. The works of Ratanaworabhan et al and Burtscher and Ratanaworabhan() use a forecast system based on jump address predictors for CPUs.…”

Section: Related Workmentioning

confidence: 99%

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Scalable processing and autocovariance computation of big functional data

et al. 2017

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SummaryThis paper presents 2 main contributions. The first is a compact representation of huge sets of functional data or trajectories of continuous-time stochastic processes, which allows keeping the data always compressed even during the processing in main memory. It is oriented to facilitate the efficient computation of the sample autocovariance function without a previous decompression of the data set, by using only partial local decoding. The second contribution is a new memory-efficient algorithm to compute the sample autocovariance function.The combination of the compact representation and the new memory-efficient algorithm obtained in our experiments the following benefits. The compressed data occupy in the disk 75% of the space needed by the original data. The computation of the autocovariance function used up to 13 times less main memory, and run 65% faster than the classical method implemented, for example, in the R package.

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Section: Related Workmentioning

confidence: 99%

“…Thus, there are compressors specially designed for images, video, or sound,() for general scientific data,() or for more specific domains. ()…”

Section: Introductionmentioning

confidence: 99%

Scalable processing and autocovariance computation of big functional data

et al. 2017

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“…Since correct haptic simulation needs an update rate of at least 1 kHz, various compensation 34 and prediction 35,36 algorithms have been proposed. Besides classical client-server architectures, peer-topeer 37 approaches gain in importance.…”

Section: Related Workmentioning

confidence: 99%

Haptic ventriculostomy simulation in a grid environment

Çakmak

Maaß²,

Trantakis³

et al. 2009

Computer Animation & Virtual

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We present a full featured surgery simulator for endoscopic third ventriculostomy (ETV) in an interactive grid environment. The basic ventriculostomy simulator introduced previously has been extended with new 3D models and animation methods for raising the simulation realism. It enables full haptic interaction with textured deformable models of a hydrocephalic configured human ventricular system derived from MR-data. Visual effects like bleeding in the cerebrospinal fluid (CSF) have been implemented for faulty interactions during training sessions which are recorded in a database for further evaluation. Based on the training data comparative studies were carried out whereas the benefits of virtual reality (VR)-based training have been demonstrated. Furthermore, KisGrid is presented as an interactive grid environment for real-time surgical simulators. It enables collaborative and supervised haptic training over networks with access to remote surgical models and hardware. The grid applet ViCoM is introduced as a tool for audio-visual communication, monitoring, and training session archiving. Long distance tests have been successfully carried out for multicast video conferencing, training session monitoring, and haptic surgical collaboration with deformable models.

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“…With the trusting behaviour, a replacement of the latest velocity is also needed in order to prevent unsmooth update. Unlike the formula used in untrusted behaviour, trusting behaviour applied weighted moving average and the formula is shown in (10) and (11) for position and force data respectively.…”

Section: B Trust Strategy Prediction (Tsp) Modellingmentioning

confidence: 99%

“…Prediction techniques such as deadband [9], linear prediction method [10], DR [11], Smith-prediction [8], Bayesian-prediction [12] and velocity estimation [13] [14] have been considered to solve network latency and packet loss. Moreover, to achieve realtime smooth haptic updates, smooth filtering is applied along with the haptic data prediction.…”

Section: Haptic Data Processing Using Predictive Andmentioning

confidence: 99%

A quantitative evaluation of haptic data prediction techniques over best-effort network

Tee

Yap

Marshall

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-Exchanging haptic information over best-effort networks such as the Internet presents challenges due to the extremely high sensitivity to network impairments, especially the simultaneous occurrence of time-varying network latency and packet loss. Subsequently, the haptic interaction experience is deteriorated along with a reduction in the fidelity. In this paper we present a new approach to mitigate the effects of network impairments, termed Trust Strategy Prediction. As well as evaluation of TSP in quantitative model is presented in terms of accuracy and smoothness of haptic updates and compared with two well-known techniques used in haptic data prediction: Dead Reckoning and velocity/yank Estimation with filtering mechanism.

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Haptic Data Transmission Based on the Prediction and Compression

Cited by 26 publications

References 8 publications

Scalable processing and autocovariance computation of big functional data

Scalable processing and autocovariance computation of big functional data

Haptic ventriculostomy simulation in a grid environment

A quantitative evaluation of haptic data prediction techniques over best-effort network

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