The Experts below are selected from a list of 24063 Experts worldwide ranked by ideXlab platform
Subhash Challa - One of the best experts on this subject based on the ideXlab platform.
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Design and analysis of an Event Indicator Function classifier for immune cell tracking applications
2013 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2013Co-Authors: Ravikanth Konda, Rajib Chakravorthy, Subhash ChallaAbstract:Recent advances in cell culture and cell imaging have made possible the automated acquisition of cell images. The automatic analysis of cells in such huge sets of images allows fundamentally new questions to be addressed in several biological fields such as immunology, proteomics, genomics and stem-cell research. Existing automated systems are not portable across a variety of cell videos because of random errors in both detection and tracking modules. These errors have to be identified and corrected to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict detection and tracking errors in each frame using a set of features that are collected during tracking. It also predicts cell phenotypes (division and death), to accurately construct lineage tree (parent-daughter relationship) which has high significance in biological community. EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics. This approach helps in understanding the underlying system behavior and tracking can be improved using human assistance.
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AIM - Design and analysis of an Event Indicator Function classifier for immune cell tracking applications
2013 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2013Co-Authors: Ravikanth Konda, Rajib Chakravorthy, Subhash ChallaAbstract:Recent advances in cell culture and cell imaging have made possible the automated acquisition of cell images. The automatic analysis of cells in such huge sets of images allows fundamentally new questions to be addressed in several biological fields such as immunology, proteomics, genomics and stem-cell research. Existing automated systems are not portable across a variety of cell videos because of random errors in both detection and tracking modules. These errors have to be identified and corrected to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict detection and tracking errors in each frame using a set of features that are collected during tracking. It also predicts cell phenotypes (division and death), to accurately construct lineage tree (parent-daughter relationship) which has high significance in biological community. EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics. This approach helps in understanding the underlying system behavior and tracking can be improved using human assistance.
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IHCI - Event Indicator Function classifier for identifying cell tracking errors and phenotypes
2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012Co-Authors: Ravikanth Konda, Rajib Chakravorty, Subhash ChallaAbstract:Biologists estimate parameters such as division time, death time, time to differentiate into specialized cell in order to model cell behavior and to develop novel ways to fight diseases such as Cancer, HIV and others. One of the critical steps of such analysis of cells in video microscopy is to follow each of the cells through their generations and collect relevant information. Variability of cell density and dynamics in different video, hamper portability of existing automated cell tracking systems across videos. These errors have to be identified and corrected using human assistance to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict the tracking errors and cell phenotypes (division and death) frame-by-frame using a set of features (metrics) that are collected during tracking. EIF classifier models the metrics using empirical thresholds to identify the errors and phenotypes. Finally, EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics, illustrated results show the significance of the proposed classifier.
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Event Indicator Function classifier for identifying cell tracking errors and phenotypes
2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012Co-Authors: Ravikanth Konda, Rajib Chakravorty, Subhash ChallaAbstract:Biologists estimate parameters such as division time, death time, time to differentiate into specialized cell in order to model cell behavior and to develop novel ways to fight diseases such as Cancer, HIV and others. One of the critical steps of such analysis of cells in video microscopy is to follow each of the cells through their generations and collect relevant information. Variability of cell density and dynamics in different video, hamper portability of existing automated cell tracking systems across videos. These errors have to be identified and corrected using human assistance to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict the tracking errors and cell phenotypes (division and death) frame-by-frame using a set of features (metrics) that are collected during tracking. EIF classifier models the metrics using empirical thresholds to identify the errors and phenotypes. Finally, EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics, illustrated results show the significance of the proposed classifier.
Konstantin Turitsyn - One of the best experts on this subject based on the ideXlab platform.
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random load fluctuations and collapse probability of a power system operating near codimension 1 saddle node bifurcation
Power and Energy Society General Meeting, 2013Co-Authors: Dmitry Podolsky, Konstantin TuritsynAbstract:For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of these stochastic fluctuations near a codimension 1 saddle-node bifurcation, we explicitly calculate the autocorrelation Function of the state vector and show how its behavior explains the phenomenon of critical slowing-down often observed for power systems on the threshold of blackout. We also estimate the collapse probability/mean clearing time for the power system and construct a new Indicator Function signaling the proximity to a large scale collapse. The new Indicator Function is easy to estimate in real time using data from PMU and SCADA information about power load fluctuations on the nodes of the grid. We discuss control strategies leading to the minimization of the collapse probability.
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random load fluctuations and collapse probability of a power system operating near codimension 1 saddle node bifurcation
arXiv: Physics and Society, 2012Co-Authors: Dmitry Podolsky, Konstantin TuritsynAbstract:For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of these stochastic fluctuations near a codimension 1 saddle-node bifurcation, we explicitly calculate the autocorrelation Function of the state vector and show how its behavior explains the phenomenon of critical slowing-down often observed for power systems on the threshold of blackout. We also estimate the collapse probability/mean clearing time for the power system and construct a new Indicator Function signaling the proximity to a large scale collapse. The new Indicator Function is easy to estimate in real time using PMU data feeds as well as SCADA information about fluctuations of power load on the nodes of the power grid. We discuss control strategies leading to the minimization of the collapse probability.
Jdr Jens Harting - One of the best experts on this subject based on the ideXlab platform.
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curvature estimation from a volume of fluid Indicator Function for the simulation of surface tension and wetting with a free surface lattice boltzmann method
Physical Review E, 2016Co-Authors: Spm Simon Bogner, Ulrich Rude, Jdr Jens HartingAbstract:The free surface lattice Boltzmann method (FSLBM) is a combination of the hydrodynamic lattice Boltzmann method with a volume-of-fluid (VOF) interface capturing technique for the simulation of incompressible free surface flows. Capillary effects are modeled by extracting the curvature of the interface from the VOF Indicator Function and imposing a pressure jump at the free boundary. However, obtaining accurate curvature estimates from a VOF description can introduce significant errors. This article reports numerical results for three different surface tension models in standard test cases and compares the according errors in the velocity field (spurious currents). Furthermore, the FSLBM is shown to be suited to simulate wetting effects at solid boundaries. To this end, a new method is developed to represent wetting boundary conditions in a least-squares curvature reconstruction technique. The main limitations of the current FSLBM are analyzed and are found to be caused by its simplified advection scheme. Possible improvements are suggested.
Ravikanth Konda - One of the best experts on this subject based on the ideXlab platform.
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Design and analysis of an Event Indicator Function classifier for immune cell tracking applications
2013 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2013Co-Authors: Ravikanth Konda, Rajib Chakravorthy, Subhash ChallaAbstract:Recent advances in cell culture and cell imaging have made possible the automated acquisition of cell images. The automatic analysis of cells in such huge sets of images allows fundamentally new questions to be addressed in several biological fields such as immunology, proteomics, genomics and stem-cell research. Existing automated systems are not portable across a variety of cell videos because of random errors in both detection and tracking modules. These errors have to be identified and corrected to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict detection and tracking errors in each frame using a set of features that are collected during tracking. It also predicts cell phenotypes (division and death), to accurately construct lineage tree (parent-daughter relationship) which has high significance in biological community. EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics. This approach helps in understanding the underlying system behavior and tracking can be improved using human assistance.
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AIM - Design and analysis of an Event Indicator Function classifier for immune cell tracking applications
2013 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2013Co-Authors: Ravikanth Konda, Rajib Chakravorthy, Subhash ChallaAbstract:Recent advances in cell culture and cell imaging have made possible the automated acquisition of cell images. The automatic analysis of cells in such huge sets of images allows fundamentally new questions to be addressed in several biological fields such as immunology, proteomics, genomics and stem-cell research. Existing automated systems are not portable across a variety of cell videos because of random errors in both detection and tracking modules. These errors have to be identified and corrected to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict detection and tracking errors in each frame using a set of features that are collected during tracking. It also predicts cell phenotypes (division and death), to accurately construct lineage tree (parent-daughter relationship) which has high significance in biological community. EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics. This approach helps in understanding the underlying system behavior and tracking can be improved using human assistance.
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IHCI - Event Indicator Function classifier for identifying cell tracking errors and phenotypes
2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012Co-Authors: Ravikanth Konda, Rajib Chakravorty, Subhash ChallaAbstract:Biologists estimate parameters such as division time, death time, time to differentiate into specialized cell in order to model cell behavior and to develop novel ways to fight diseases such as Cancer, HIV and others. One of the critical steps of such analysis of cells in video microscopy is to follow each of the cells through their generations and collect relevant information. Variability of cell density and dynamics in different video, hamper portability of existing automated cell tracking systems across videos. These errors have to be identified and corrected using human assistance to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict the tracking errors and cell phenotypes (division and death) frame-by-frame using a set of features (metrics) that are collected during tracking. EIF classifier models the metrics using empirical thresholds to identify the errors and phenotypes. Finally, EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics, illustrated results show the significance of the proposed classifier.
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Event Indicator Function classifier for identifying cell tracking errors and phenotypes
2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012Co-Authors: Ravikanth Konda, Rajib Chakravorty, Subhash ChallaAbstract:Biologists estimate parameters such as division time, death time, time to differentiate into specialized cell in order to model cell behavior and to develop novel ways to fight diseases such as Cancer, HIV and others. One of the critical steps of such analysis of cells in video microscopy is to follow each of the cells through their generations and collect relevant information. Variability of cell density and dynamics in different video, hamper portability of existing automated cell tracking systems across videos. These errors have to be identified and corrected using human assistance to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict the tracking errors and cell phenotypes (division and death) frame-by-frame using a set of features (metrics) that are collected during tracking. EIF classifier models the metrics using empirical thresholds to identify the errors and phenotypes. Finally, EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics, illustrated results show the significance of the proposed classifier.
Dmitry Podolsky - One of the best experts on this subject based on the ideXlab platform.
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random load fluctuations and collapse probability of a power system operating near codimension 1 saddle node bifurcation
Power and Energy Society General Meeting, 2013Co-Authors: Dmitry Podolsky, Konstantin TuritsynAbstract:For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of these stochastic fluctuations near a codimension 1 saddle-node bifurcation, we explicitly calculate the autocorrelation Function of the state vector and show how its behavior explains the phenomenon of critical slowing-down often observed for power systems on the threshold of blackout. We also estimate the collapse probability/mean clearing time for the power system and construct a new Indicator Function signaling the proximity to a large scale collapse. The new Indicator Function is easy to estimate in real time using data from PMU and SCADA information about power load fluctuations on the nodes of the grid. We discuss control strategies leading to the minimization of the collapse probability.
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random load fluctuations and collapse probability of a power system operating near codimension 1 saddle node bifurcation
arXiv: Physics and Society, 2012Co-Authors: Dmitry Podolsky, Konstantin TuritsynAbstract:For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of these stochastic fluctuations near a codimension 1 saddle-node bifurcation, we explicitly calculate the autocorrelation Function of the state vector and show how its behavior explains the phenomenon of critical slowing-down often observed for power systems on the threshold of blackout. We also estimate the collapse probability/mean clearing time for the power system and construct a new Indicator Function signaling the proximity to a large scale collapse. The new Indicator Function is easy to estimate in real time using PMU data feeds as well as SCADA information about fluctuations of power load on the nodes of the power grid. We discuss control strategies leading to the minimization of the collapse probability.
