The Experts below are selected from a list of 972 Experts worldwide ranked by ideXlab platform
Yuan Xiaoru - One of the best experts on this subject based on the ideXlab platform.
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Efficient unsteady flow visualization with high-order access dependencies
2016Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded Pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation. ? 2016 IEEE.EI80-872016-Ma
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Efficient Unsteady Flow Visualization with High-Order Access Dependencies
2016Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded Pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.CPCI-S(ISTP)jiang.zhang@pku.edu.cn; hguo@anl.gov; xiaoru.yuan@pku.edu.cn80-8
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Comparative visualization of vector field ensembles based on longest common subsequence
2016Co-Authors: Liu Richen, Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble Pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing Pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach. ? 2016 IEEE.EI96-1032016-Ma
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High performance flow field visualization with high-order access dependencies
2015Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel model based on high-order access dependencies for high performance pathline computation in flow field. The high-order access dependencies are defined as transition probabilities from one data block to other blocks based on a few historical data accesses. Compared with existing methods which employed first-order access dependencies, our approach takes the advantages of high order access dependencies with higher accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing densely-seeded Pathlines. The efficiency of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method can achieve higher data locality than the first-order access dependencies based method, thereby reducing the I/O requests and improving the efficiency of pathline computation in various applications. ? 2015 IEEE.EI165-16
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High Performance Flow Field Visualization with High-Order Access Dependencies
2015Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel model based on high-order access dependencies for high performance pathline computation in flow field. The high-order access dependencies are defined as transition probabilities from one data block to other blocks based on a few historical data accesses. Compared with existing methods which employed first-order access dependencies, our approach takes the advantages of high order access dependencies with higher accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing densely-seeded Pathlines. The efficiency of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method can achieve higher data locality than the first-order access dependencies based method, thereby reducing the I/O requests and improving the efficiency of pathline computation in various applications.CPCI-S(ISTP)jiang.zhang@pku.edu.cn; hguo@anl.gov; xiaoru.yuan@pku.edu.cn165-16
Guo Hanqi - One of the best experts on this subject based on the ideXlab platform.
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Efficient unsteady flow visualization with high-order access dependencies
2016Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded Pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation. ? 2016 IEEE.EI80-872016-Ma
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Efficient Unsteady Flow Visualization with High-Order Access Dependencies
2016Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded Pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.CPCI-S(ISTP)jiang.zhang@pku.edu.cn; hguo@anl.gov; xiaoru.yuan@pku.edu.cn80-8
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Comparative visualization of vector field ensembles based on longest common subsequence
2016Co-Authors: Liu Richen, Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble Pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing Pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach. ? 2016 IEEE.EI96-1032016-Ma
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High performance flow field visualization with high-order access dependencies
2015Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel model based on high-order access dependencies for high performance pathline computation in flow field. The high-order access dependencies are defined as transition probabilities from one data block to other blocks based on a few historical data accesses. Compared with existing methods which employed first-order access dependencies, our approach takes the advantages of high order access dependencies with higher accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing densely-seeded Pathlines. The efficiency of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method can achieve higher data locality than the first-order access dependencies based method, thereby reducing the I/O requests and improving the efficiency of pathline computation in various applications. ? 2015 IEEE.EI165-16
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High Performance Flow Field Visualization with High-Order Access Dependencies
2015Co-Authors: Zhang Jiang, Guo Hanqi, Yuan XiaoruAbstract:We present a novel model based on high-order access dependencies for high performance pathline computation in flow field. The high-order access dependencies are defined as transition probabilities from one data block to other blocks based on a few historical data accesses. Compared with existing methods which employed first-order access dependencies, our approach takes the advantages of high order access dependencies with higher accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing densely-seeded Pathlines. The efficiency of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method can achieve higher data locality than the first-order access dependencies based method, thereby reducing the I/O requests and improving the efficiency of pathline computation in various applications.CPCI-S(ISTP)jiang.zhang@pku.edu.cn; hguo@anl.gov; xiaoru.yuan@pku.edu.cn165-16
Smita Sampath - One of the best experts on this subject based on the ideXlab platform.
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assessment of left ventricular 2d flow Pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding a study in normal volunteers and canine animals with myocardial infarction
2013Co-Authors: Ziheng Zhang, Daniel J Friedman, Donald P Dione, James S Duncan, Albert J Sinusas, Smita SampathAbstract:A high temporal resolution 2D flow pathline analysis method that describes the spatio-temporal distribution of blood entering the left ventricle during early diastolic filling is presented. Filling patterns in normal volunteers (n=8) and canine animals (baseline (n=1) and infarcted (n=6)) are studied using this approach. Data is acquired using our recently reported MR technique, SPAMM-PAV, which permits simultaneous quantification of blood velocities and myocardial strain at high temporal resolution of 14 ms. Virtual emitter particles, released from the mitral valve plane every time frame during rapid filling, are tracked to depict the propagation of 2D Pathlines on the imaged plane. The pathline regional distribution patterns are compared with regional myocardial longitudinal strains and regional chamber longitudinal pressure gradients. Our results demonstrate strong spatial inter-dependence between left ventricular (LV) filling patterns and LV mechanical function. Significant differences in pathline-described filling patterns are observed in the infarcted animals. Quantitative analysis of net kinetic energy for each set of Pathlines is performed. Peak net kinetic energy of 0.06±0.01 mJ in normal volunteers, 0.043 mJ in baseline dog, 0.143±0.03 mJ in three infarcted dogs with nominal flow dysfunction, and 0.016±0.007 mJ in three infarcted dogs with severe flow dysfunction is observed.
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early diastolic function observed in canine model of reperfused transmural myocardial infarction using high temporal resolution mr imaging
2012Co-Authors: Ziheng Zhang, Donald P Dione, James S Duncan, Albert J Sinusas, Ben A Lin, Smita SampathAbstract:Summary We have applied a novel high temporal resolution MR imaging sequence to study diastolic function in canines with reperfused transmural infarction. Our results demonstrate abnormal diastolic strain-rates in infarct and viable risk region with corresponding abnormal filling patterns, as observed through the visualization of 2D flow Pathlines 3 days post reperfusion. Background Coronary angioplasty limits infarct expansion post myocardial infarction (MI). However, under certain conditions such as prolonged ischemia, the procedure induces reperfusion injury (RI), linked to adverse left ventricular (LV) remodeling and heart failure (HF). The functional mechanisms involved in adverse remodeling post reperfusion are still unclear. We have developed a new high temporal resolution MR imaging technique, SPAMMPAV (SPAtially Modulated Magnetization with Polarity Alternated Velocity encoding) that provides regional assessment of early diastolic flow velocity and myocardial strain. This method was applied in a canine animal model with prolonged occlusion followed by reperfusion. We examine the diastolic strain-rates (index of stiffness) of infarct regions relative to remote regions and the 2D diastolic flow Pathlines 3 days post reperfusion to provide insight into early diastolic function in these animals. Methods Studies were performed in six dogs following 5-6 hours of balloon occlusion of the left anterior descending
Ziheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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assessment of left ventricular 2d flow Pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding a study in normal volunteers and canine animals with myocardial infarction
2013Co-Authors: Ziheng Zhang, Daniel J Friedman, Donald P Dione, James S Duncan, Albert J Sinusas, Smita SampathAbstract:A high temporal resolution 2D flow pathline analysis method that describes the spatio-temporal distribution of blood entering the left ventricle during early diastolic filling is presented. Filling patterns in normal volunteers (n=8) and canine animals (baseline (n=1) and infarcted (n=6)) are studied using this approach. Data is acquired using our recently reported MR technique, SPAMM-PAV, which permits simultaneous quantification of blood velocities and myocardial strain at high temporal resolution of 14 ms. Virtual emitter particles, released from the mitral valve plane every time frame during rapid filling, are tracked to depict the propagation of 2D Pathlines on the imaged plane. The pathline regional distribution patterns are compared with regional myocardial longitudinal strains and regional chamber longitudinal pressure gradients. Our results demonstrate strong spatial inter-dependence between left ventricular (LV) filling patterns and LV mechanical function. Significant differences in pathline-described filling patterns are observed in the infarcted animals. Quantitative analysis of net kinetic energy for each set of Pathlines is performed. Peak net kinetic energy of 0.06±0.01 mJ in normal volunteers, 0.043 mJ in baseline dog, 0.143±0.03 mJ in three infarcted dogs with nominal flow dysfunction, and 0.016±0.007 mJ in three infarcted dogs with severe flow dysfunction is observed.
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early diastolic function observed in canine model of reperfused transmural myocardial infarction using high temporal resolution mr imaging
2012Co-Authors: Ziheng Zhang, Donald P Dione, James S Duncan, Albert J Sinusas, Ben A Lin, Smita SampathAbstract:Summary We have applied a novel high temporal resolution MR imaging sequence to study diastolic function in canines with reperfused transmural infarction. Our results demonstrate abnormal diastolic strain-rates in infarct and viable risk region with corresponding abnormal filling patterns, as observed through the visualization of 2D flow Pathlines 3 days post reperfusion. Background Coronary angioplasty limits infarct expansion post myocardial infarction (MI). However, under certain conditions such as prolonged ischemia, the procedure induces reperfusion injury (RI), linked to adverse left ventricular (LV) remodeling and heart failure (HF). The functional mechanisms involved in adverse remodeling post reperfusion are still unclear. We have developed a new high temporal resolution MR imaging technique, SPAMMPAV (SPAtially Modulated Magnetization with Polarity Alternated Velocity encoding) that provides regional assessment of early diastolic flow velocity and myocardial strain. This method was applied in a canine animal model with prolonged occlusion followed by reperfusion. We examine the diastolic strain-rates (index of stiffness) of infarct regions relative to remote regions and the 2D diastolic flow Pathlines 3 days post reperfusion to provide insight into early diastolic function in these animals. Methods Studies were performed in six dogs following 5-6 hours of balloon occlusion of the left anterior descending
N. Aubry - One of the best experts on this subject based on the ideXlab platform.
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Complete Chaotic Mixing in an Electro-osmotic Flow by Destabilization of Key Periodic Pathlines
2011Co-Authors: R. Chabreyrie, C. Chandre, N. AubryAbstract:The ability to generate complete, or almost complete, chaotic mixing is of great interest in numerous applications, particularly for microfluidics. For this purpose, we propose a strategy that allows us to quickly target the parameter values at which complete mixing occurs. The technique is applied to a time periodic, two-dimensional electro-osmotic flow with spatially and temporally varying Helmoltz-Smoluchowski slip boundary conditions. The strategy consists of following the linear stability of some key periodic Pathlines in parameter space (i.e., amplitude and frequency of the forcing), particularly through the bifurcation points at which such Pathlines become unstable.