The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Georgios B Giannakis - One of the best experts on this subject based on the ideXlab platform.
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adaptive bayesian channel gain Cartography
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Donghoon Lee, Dimitris Berberidis, Georgios B GiannakisAbstract:Channel gain Cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.
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blind channel gain Cartography
IEEE Global Conference on Signal and Information Processing, 2016Co-Authors: Daniel Romero, Donghoon Lee, Georgios B GiannakisAbstract:Channel gain Cartography relies on sensor measurements to construct maps providing the attenuation between arbitrary transmitter-receiver locations. A number of applications involving interference control, such as wireless network planning or cognitive radio, can benefit from channel gain maps. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) that depends on the propagation environment. Currently, the SLF is learned from sensor measurements whereas functions weighting the SLF are heuristically selected, but the effectiveness of the latter remains unclear. This paper leverages the framework of nonparametric regression in reproducing kernel Hilbert spaces to propose an algorithm that relies on the same sensor measurements as existing approaches to learn not only the SLF but also the associated weight function. Such an algorithm therefore constitutes a universal tool for channel gain Cartography while revealing the nature of the propagation medium. An optimization method is proposed to minimize the pertinent criterion with closed-form updates. Simulation tests demonstrate the capabilities of the proposed algorithm.
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dynamic network delay Cartography
IEEE Transactions on Information Theory, 2014Co-Authors: Ketan Rajawat, Emiliano Dallanese, Georgios B GiannakisAbstract:Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network are, however, challenging and wasteful of resources. This paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network Cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world data sets.
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dynamic network delay Cartography
arXiv: Networking and Internet Architecture, 2012Co-Authors: Ketan Rajawat, Emiliano Dallanese, Georgios B GiannakisAbstract:Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network is however challenging and wasteful of resources. The present paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network Cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world datasets.
Ketan Rajawat - One of the best experts on this subject based on the ideXlab platform.
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dynamic network delay Cartography
IEEE Transactions on Information Theory, 2014Co-Authors: Ketan Rajawat, Emiliano Dallanese, Georgios B GiannakisAbstract:Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network are, however, challenging and wasteful of resources. This paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network Cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world data sets.
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dynamic network delay Cartography
arXiv: Networking and Internet Architecture, 2012Co-Authors: Ketan Rajawat, Emiliano Dallanese, Georgios B GiannakisAbstract:Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network is however challenging and wasteful of resources. The present paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network Cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world datasets.
Jure Leskovec - One of the best experts on this subject based on the ideXlab platform.
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information Cartography
Communications of The ACM, 2015Co-Authors: Dafna Shahaf, Carlos Guestrin, Eric Horvitz, Jure LeskovecAbstract:“Raise your hand if you don’t quite understand this whole financial crisis,” said David Leonhardt’s New York Times article, March 2008. The credit crisis had been going on for seven months and extensively and continuously covered by every major media outlet in the world. Despite that coverage, many readers felt they did not understand what it was about.
Danielle S Bassett - One of the best experts on this subject based on the ideXlab platform.
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a functional Cartography of cognitive systems
PLOS Computational Biology, 2015Co-Authors: Marcelo G Mattar, Michael Cole, Sharon L Thompsonschill, Danielle S BassettAbstract:One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this Cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.
Donghoon Lee - One of the best experts on this subject based on the ideXlab platform.
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adaptive bayesian channel gain Cartography
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Donghoon Lee, Dimitris Berberidis, Georgios B GiannakisAbstract:Channel gain Cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.
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blind channel gain Cartography
IEEE Global Conference on Signal and Information Processing, 2016Co-Authors: Daniel Romero, Donghoon Lee, Georgios B GiannakisAbstract:Channel gain Cartography relies on sensor measurements to construct maps providing the attenuation between arbitrary transmitter-receiver locations. A number of applications involving interference control, such as wireless network planning or cognitive radio, can benefit from channel gain maps. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) that depends on the propagation environment. Currently, the SLF is learned from sensor measurements whereas functions weighting the SLF are heuristically selected, but the effectiveness of the latter remains unclear. This paper leverages the framework of nonparametric regression in reproducing kernel Hilbert spaces to propose an algorithm that relies on the same sensor measurements as existing approaches to learn not only the SLF but also the associated weight function. Such an algorithm therefore constitutes a universal tool for channel gain Cartography while revealing the nature of the propagation medium. An optimization method is proposed to minimize the pertinent criterion with closed-form updates. Simulation tests demonstrate the capabilities of the proposed algorithm.