The Experts below are selected from a list of 100833 Experts worldwide ranked by ideXlab platform
Choon Yik Tang - One of the best experts on this subject based on the ideXlab platform.
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automatic learning based manet cross Layer Parameter configuration
International Conference on Distributed Computing Systems Workshops, 2006Co-Authors: Karen Zita Haigh, Srivatsan Varadarajan, Choon Yik TangAbstract:Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration Parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.
Pedro Jose Marron - One of the best experts on this subject based on the ideXlab platform.
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experimental study for multi Layer Parameter configuration of wsn links
International Conference on Distributed Computing Systems, 2015Co-Authors: Yan Zhang, Yuming Jiang, Chiayen Shih, Pedro Jose MarronAbstract:Many applications of wireless sensor networks (WSNs) need to balance multiple yet often conflicting performance requirements such as high energy efficiency, high throughput, low delay and low loss. Finding appropriate WSN Parameter configuration to achieve the best trade-off requires in depth understanding of the joint effect of key Parameters residing at different Layers on the performance. In this paper, we present an extensive experimental study on the data delivery performance of aWSN link, where 4 major performance metrics, namely energy, throughput, delay and loss, were measured over 6 months under around 50 thousand Parameter configurations of 7 key stack Parameters. Different from existing work, rich observations are made out of the extensive measurement data, with the focus on the joint effect of these Parameters on the performance. Specifically, for each of the four performance metrics, a set of guidelines is derived for Parameter optimization. In addition, we propose empirical models for each performance metric to quantify the joint effects, which enable finding optimal settings for Parameters such as payload size or retransmissions, in consideration of link quality and other Parameter settings, to achieve better performance trade-offs. To demonstrate the potential of this work, the obtained joint Parameter optimization results are applied to an example. The outcome is compared with those achieved by following representative single-Parameter tuning guidelines from the literature. The comparison reveals that by considering the joint effect of multi-Layer Parameters together, a WSN application can obtain a much improved performance trade-off.
Karen Zita Haigh - One of the best experts on this subject based on the ideXlab platform.
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automatic learning based manet cross Layer Parameter configuration
International Conference on Distributed Computing Systems Workshops, 2006Co-Authors: Karen Zita Haigh, Srivatsan Varadarajan, Choon Yik TangAbstract:Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration Parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.
Christian Y Mardin - One of the best experts on this subject based on the ideXlab platform.
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precision of optic nerve head and retinal nerve fiber Layer Parameter measurements by spectral domain optical coherence tomography
Journal of Glaucoma, 2018Co-Authors: Laura M Schremshoesl, Wolfgang A Schrems, Robert Laemmer, Friedrich E Kruse, Christian Y MardinAbstract:Purpose The aim of this study was to assess the repeatability and reproducibility (RR glaucoma, 0.64% to 2.3%). Respective COVs under reproducibility conditions ranged from 0.89% to 1.9% (normal, 0.77% to 2.8%; glaucoma, 1.1% to 2.6%). COVs of global and sectorial RNFLT measurements under repeatability conditions ranged from 0.5% to 2.8%. Respective COVs under reproducibility conditions ranged from 1.6% to 3.5%. Conclusions For R&R, the COVs of measured Parameters were by trend higher for glaucoma eyes compared with normal controls. The BMO-MRW measurement system has an excellent precision taking into account that major and minor corrections of segmentation have to be done by the examiner before evaluation.
Srivatsan Varadarajan - One of the best experts on this subject based on the ideXlab platform.
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automatic learning based manet cross Layer Parameter configuration
International Conference on Distributed Computing Systems Workshops, 2006Co-Authors: Karen Zita Haigh, Srivatsan Varadarajan, Choon Yik TangAbstract:Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration Parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.