The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform
M. Casula - One of the best experts on this subject based on the ideXlab platform.
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Polyamorphism of a Ce-based bulk metallic glass by high-pressure and high-temperature Density measurements
Physical Review B: Condensed Matter and Materials Physics (1998-2015), 2016Co-Authors: F. Decremps, Guillaume Morard, G. Garbarino, M. CasulaAbstract:Metallic glasses are of recent interest worldwide due to their remarkable physico-chemical properties which can be put in relation with their crystalline counterparts. Among them, cerium based metallic glasses (Ce-MGs) have unique features such as the existence of polyamorphism under pressure , unexpected in these spatially compact systems. While a phase transition between amorphous phases with Change of Density and local structure has been previously detected, the corresponding structural variation under pressure was not clearly identified, due to difficulties in performing accurate measurements and reliable analysis. In this work, angle dispersive x-ray diffraction experiments of Ce 69 Al 10 Cu 20 Co 1 bulk metallic glass have been performed up to 16 GPa along two distinct isotherms (300 and 340 K). The whole diffuse signals have then been processed in order to extract the structure factor S(Q), the pair distribution g(r), the atomic Density ρ and the compress-ibility as a function of pressure and temperature. These are crucial probes to fully characterize the phase diagram, and they clearly confirm the existence of a link between polyamorphism in Ce-MGs and the γ α transition in pure cerium. Finally, owing to the presence of a critical point in pure solid Ce, the existence of such feature is here discussed for Ce-MGs.
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Polyamorphism of a Ce-based bulk metallic glass by high-pressure and high-temperature Density measurements
Physical Review B: Condensed Matter and Materials Physics (1998-2015), 2016Co-Authors: F. Decremps, Guillaume Morard, G. Garbarino, M. CasulaAbstract:Metallic glasses are of recent interest worldwide due to their remarkable physicochemical properties which can be put in relation with their crystalline counterparts. Among them, cerium-based metallic glasses (Ce-MGs) have unique features such as the existence of polyamorphism under pressure, which is unexpected in these spatially compact systems. While a phase transition between amorphous phases with Change of Density and local structure has been previously detected, the corresponding structural variation under pressure was not clearly identified due to difficulties in performing accurate measurements and reliable analysis. In this work, angle dispersive x-ray diffraction experiments of Ce69Al10Cu20Co1 bulk metallic glass have been performed up to 16 GPa along two distinct isotherms (300 and 340 K). All of the diffuse signals have then been processed in order to extract the structure factor S(Q), the pair distribution g(r), the atomic Density ρ, and the compressibility as a function of pressure and temperature. These are crucial probes to fully characterize the phase diagram, and they clearly confirm the existence of a link between polyamorphism in Ce-MGs and the γ α transition in pure cerium. Finally, owing to the presence of a critical point in pure solid Ce, the existence of such a feature is discussed here for Ce-MGs.
P. Laugier - One of the best experts on this subject based on the ideXlab platform.
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Variation of Ultrasonic Parameters With Microstructure and Material Properties of Trabecular Bone: A 3D Model Simulation
Journal of Bone and Mineral Research, 2007Co-Authors: G. Haïat, F. Padilla, F. Peyrin, P. LaugierAbstract:This study determined the influence of trabecular bone microstructure and material properties on QUS parameters using numerical simulations coupled with high‐resolution synchrotron radiation μCT. Introduction: Finite‐difference time domain (FDTD) simulations coupled to 3D microstructural models of trabecular bone reconstructed from synchrotron radiation microtomography (SR‐μCT) were used herein to compare and quantify the effects of bone volume fraction, microstructure, and material properties on QUS parameters. Materials and Methods: 3D SR‐μCT datasets of 30 trabecular human femoral bone specimens were used to create binary digital 3D models. We studied the sensitivity of quantitative ultrasound (QUS) to bone volume fraction by examining QUS parameters at different stages of trabecular thinning or thickening using an iterative dedicated algorithm. The sensitivity to bone material properties was also assessed by analyzing different scenarios in which Density and stiffness could be varied independently. The effect of microstructure was qualitatively assessed by producing virtual bone specimens of identical bone volume fraction. Simulations of ultrasonic wave propagation through the trabecular bone volumes were performed using the FDTD simulation software SimSonic developed by our group. For each structure, both broadband ultrasonic attenuation (BUA) and speed of sound (SOS) were computed. Results: BUA and SOS showed a strong correlation with BV/TV (r2 = 0.94, p < 10−4) and varied quasi‐linearly with BV/TV at an approximate rate of 2 dB/cm.MHz and 11 m/s per percent increase of BV/TV, respectively. Bone alterations caused by variation in BV/TV between 5% and 25% had a greater impact on QUS variables (variation of BUA: 40 dB/cm.MHz; variation of SOS: 200 m/s) than variations caused by alterations of material properties realized either by a 30% Change of Density or 40% Change of stiffness (BUA: 1.7 dB/cm.MHz; SOS: 43 m/s) or than diversity in microarchitecture (BUA:7.8 dB/cm.MHz; SOS: 36 m/s). Moreover, the sensitivity of BUA and SOS to Changes in BMD by a given amount realized by a pure Change in bone mass (or BV/TV) was found to be predominant over a pure Change of mineralization, except for low BV/TV values, where both effects are comparable. Conclusions: Trabecular bone microstructure (i.e., trabecular thickness) and material properties were Changed to quantify the impact of specific determinants on QUS variables. In this sample of unselected autopsies, specimen variability in bone volume seemed to have a somewhat larger impact on QUS variables than the variability of the other determinants assessed. Whether this is also the case for osteoporotic patients remains to be studied.
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Variation of ultrasonic parameters with microstructure and material properties of trabecular bone: a three-dimensional model simulation
Journal of Bone and Mineral Research, 2007Co-Authors: G. Haïat, F. Padilla, F. Peyrin, P. LaugierAbstract:This study determined the influence of trabecular bone microstructure and material properties on QUS parameters using numerical simulations coupled with high‐resolution synchrotron radiation μCT. Introduction: Finite‐difference time domain (FDTD) simulations coupled to 3D microstructural models of trabecular bone reconstructed from synchrotron radiation microtomography (SR‐μCT) were used herein to compare and quantify the effects of bone volume fraction, microstructure, and material properties on QUS parameters. Materials and Methods: 3D SR‐μCT datasets of 30 trabecular human femoral bone specimens were used to create binary digital 3D models. We studied the sensitivity of quantitative ultrasound (QUS) to bone volume fraction by examining QUS parameters at different stages of trabecular thinning or thickening using an iterative dedicated algorithm. The sensitivity to bone material properties was also assessed by analyzing different scenarios in which Density and stiffness could be varied independently. The effect of microstructure was qualitatively assessed by producing virtual bone specimens of identical bone volume fraction. Simulations of ultrasonic wave propagation through the trabecular bone volumes were performed using the FDTD simulation software SimSonic developed by our group. For each structure, both broadband ultrasonic attenuation (BUA) and speed of sound (SOS) were computed. Results: BUA and SOS showed a strong correlation with BV/TV (r2 = 0.94, p < 10−4) and varied quasi‐linearly with BV/TV at an approximate rate of 2 dB/cm.MHz and 11 m/s per percent increase of BV/TV, respectively. Bone alterations caused by variation in BV/TV between 5% and 25% had a greater impact on QUS variables (variation of BUA: 40 dB/cm.MHz; variation of SOS: 200 m/s) than variations caused by alterations of material properties realized either by a 30% Change of Density or 40% Change of stiffness (BUA: 1.7 dB/cm.MHz; SOS: 43 m/s) or than diversity in microarchitecture (BUA:7.8 dB/cm.MHz; SOS: 36 m/s). Moreover, the sensitivity of BUA and SOS to Changes in BMD by a given amount realized by a pure Change in bone mass (or BV/TV) was found to be predominant over a pure Change of mineralization, except for low BV/TV values, where both effects are comparable. Conclusions: Trabecular bone microstructure (i.e., trabecular thickness) and material properties were Changed to quantify the impact of specific determinants on QUS variables. In this sample of unselected autopsies, specimen variability in bone volume seemed to have a somewhat larger impact on QUS variables than the variability of the other determinants assessed. Whether this is also the case for osteoporotic patients remains to be studied.
Shenghua Gao - One of the best experts on this subject based on the ideXlab platform.
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multi level feature fusion based locality constrained spatial transformer network for video crowd counting
Neurocomputing, 2020Co-Authors: Yanyan Fang, Shenghua Gao, Weixin LuoAbstract:Abstract Video-based crowd counting can leverage the spatial-temporal information between neighboring frames, and thus this information would improve the robustness of crowd counting. Therefore, this solution is more practical than single image-based crowd counting in real applications. Since severe occlusions, translation, rotation, and scaling of persons will give rise to the Change of Density map of heads between neighboring frames, video-based crowd counting is a very challenging task. To alleviate these issues in video crowd counting, a Multi-Level Feature Fusion Based Locality-Constrained Spatial Transformer Network (MLSTN) is proposed, which consists of two components, namely Density map regression module and Locality-Constrained Spatial Transformer (LST) module. Specifically, we first estimate the Density map of each frame by utilizing the combination of the low-level, middle-level and high-level features of the Convolutional Neural Networks. This is because the low-level features may be more effective in the extraction of small head information, while the middle and high level features are more effective in the extraction of medium and large head information. Then to measure the relationship of the Density maps between neighboring frames, the LST module is proposed, which estimates the Density map of the next frame by concatenating several regression Density maps. To facilitate the performance evaluation for video crowd counting, we have collected and labeled a large-scale video crowd counting dataset which includes 100 five-second-long sequences with 394,081 annotated heads from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments show the effectiveness of our proposed approach for crowd counting on our dataset and other video-based crowd counting datasets. All our dataset are released online. 1
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locality constrained spatial transformer network for video crowd counting
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua GaoAbstract:Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting. But translation, rotation and scaling of people lead to the Change of Density map of heads between neighbouring frames. Meanwhile, people walking in/out or being occluded in dynamic scenes leads to the Change of head counts. To alleviate these issues in video crowd counting, a Locality-constrained Spatial Transformer Network (LSTN) is proposed. Specifically, we first leverage a Convolutional Neural Networks to estimate the Density map for each frame. Then to relate the Density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the Density map of next frame with that of current frame. To facilitate the performance evaluation, a large-scale video crowd counting dataset is collected, which contains 15K frames with about 394K annotated heads captured from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments on our dataset and other crowd counting datasets validate the effectiveness of our LSTN for crowd counting.
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locality constrained spatial transformer network for video crowd counting
International Conference on Multimedia and Expo, 2019Co-Authors: Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua GaoAbstract:Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting. But translation, rotation and scaling of people lead to the Change of Density map of heads between neighbouring frames. Meanwhile, people walking in/out or being occluded in dynamic scenes leads to the Change of head counts. To alleviate these issues in video crowd counting, a Locality-constrained Spatial Transformer Network (LSTN) is proposed. Specifically, we first leverage a Convolutional Neural Networks to estimate the Density map for each frame. Then to relate the Density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the Density map of next frame with that of current frame. To facilitate the performance evaluation, a large-scale video crowd counting dataset is collected, which contains 15K frames with about 394K annotated heads captured from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments on our dataset and other crowd counting datasets validate the effectiveness of our LSTN for crowd counting. All our dataset are released in https://github.com/sweetyy83/Lstn_fdst_dataset.
F. Decremps - One of the best experts on this subject based on the ideXlab platform.
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Polyamorphism of a Ce-based bulk metallic glass by high-pressure and high-temperature Density measurements
Physical Review B: Condensed Matter and Materials Physics (1998-2015), 2016Co-Authors: F. Decremps, Guillaume Morard, G. Garbarino, M. CasulaAbstract:Metallic glasses are of recent interest worldwide due to their remarkable physico-chemical properties which can be put in relation with their crystalline counterparts. Among them, cerium based metallic glasses (Ce-MGs) have unique features such as the existence of polyamorphism under pressure , unexpected in these spatially compact systems. While a phase transition between amorphous phases with Change of Density and local structure has been previously detected, the corresponding structural variation under pressure was not clearly identified, due to difficulties in performing accurate measurements and reliable analysis. In this work, angle dispersive x-ray diffraction experiments of Ce 69 Al 10 Cu 20 Co 1 bulk metallic glass have been performed up to 16 GPa along two distinct isotherms (300 and 340 K). The whole diffuse signals have then been processed in order to extract the structure factor S(Q), the pair distribution g(r), the atomic Density ρ and the compress-ibility as a function of pressure and temperature. These are crucial probes to fully characterize the phase diagram, and they clearly confirm the existence of a link between polyamorphism in Ce-MGs and the γ α transition in pure cerium. Finally, owing to the presence of a critical point in pure solid Ce, the existence of such feature is here discussed for Ce-MGs.
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Polyamorphism of a Ce-based bulk metallic glass by high-pressure and high-temperature Density measurements
Physical Review B: Condensed Matter and Materials Physics (1998-2015), 2016Co-Authors: F. Decremps, Guillaume Morard, G. Garbarino, M. CasulaAbstract:Metallic glasses are of recent interest worldwide due to their remarkable physicochemical properties which can be put in relation with their crystalline counterparts. Among them, cerium-based metallic glasses (Ce-MGs) have unique features such as the existence of polyamorphism under pressure, which is unexpected in these spatially compact systems. While a phase transition between amorphous phases with Change of Density and local structure has been previously detected, the corresponding structural variation under pressure was not clearly identified due to difficulties in performing accurate measurements and reliable analysis. In this work, angle dispersive x-ray diffraction experiments of Ce69Al10Cu20Co1 bulk metallic glass have been performed up to 16 GPa along two distinct isotherms (300 and 340 K). All of the diffuse signals have then been processed in order to extract the structure factor S(Q), the pair distribution g(r), the atomic Density ρ, and the compressibility as a function of pressure and temperature. These are crucial probes to fully characterize the phase diagram, and they clearly confirm the existence of a link between polyamorphism in Ce-MGs and the γ α transition in pure cerium. Finally, owing to the presence of a critical point in pure solid Ce, the existence of such a feature is discussed here for Ce-MGs.
Yanyan Fang - One of the best experts on this subject based on the ideXlab platform.
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multi level feature fusion based locality constrained spatial transformer network for video crowd counting
Neurocomputing, 2020Co-Authors: Yanyan Fang, Shenghua Gao, Weixin LuoAbstract:Abstract Video-based crowd counting can leverage the spatial-temporal information between neighboring frames, and thus this information would improve the robustness of crowd counting. Therefore, this solution is more practical than single image-based crowd counting in real applications. Since severe occlusions, translation, rotation, and scaling of persons will give rise to the Change of Density map of heads between neighboring frames, video-based crowd counting is a very challenging task. To alleviate these issues in video crowd counting, a Multi-Level Feature Fusion Based Locality-Constrained Spatial Transformer Network (MLSTN) is proposed, which consists of two components, namely Density map regression module and Locality-Constrained Spatial Transformer (LST) module. Specifically, we first estimate the Density map of each frame by utilizing the combination of the low-level, middle-level and high-level features of the Convolutional Neural Networks. This is because the low-level features may be more effective in the extraction of small head information, while the middle and high level features are more effective in the extraction of medium and large head information. Then to measure the relationship of the Density maps between neighboring frames, the LST module is proposed, which estimates the Density map of the next frame by concatenating several regression Density maps. To facilitate the performance evaluation for video crowd counting, we have collected and labeled a large-scale video crowd counting dataset which includes 100 five-second-long sequences with 394,081 annotated heads from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments show the effectiveness of our proposed approach for crowd counting on our dataset and other video-based crowd counting datasets. All our dataset are released online. 1
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locality constrained spatial transformer network for video crowd counting
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua GaoAbstract:Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting. But translation, rotation and scaling of people lead to the Change of Density map of heads between neighbouring frames. Meanwhile, people walking in/out or being occluded in dynamic scenes leads to the Change of head counts. To alleviate these issues in video crowd counting, a Locality-constrained Spatial Transformer Network (LSTN) is proposed. Specifically, we first leverage a Convolutional Neural Networks to estimate the Density map for each frame. Then to relate the Density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the Density map of next frame with that of current frame. To facilitate the performance evaluation, a large-scale video crowd counting dataset is collected, which contains 15K frames with about 394K annotated heads captured from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments on our dataset and other crowd counting datasets validate the effectiveness of our LSTN for crowd counting.
-
locality constrained spatial transformer network for video crowd counting
International Conference on Multimedia and Expo, 2019Co-Authors: Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua GaoAbstract:Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting. But translation, rotation and scaling of people lead to the Change of Density map of heads between neighbouring frames. Meanwhile, people walking in/out or being occluded in dynamic scenes leads to the Change of head counts. To alleviate these issues in video crowd counting, a Locality-constrained Spatial Transformer Network (LSTN) is proposed. Specifically, we first leverage a Convolutional Neural Networks to estimate the Density map for each frame. Then to relate the Density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the Density map of next frame with that of current frame. To facilitate the performance evaluation, a large-scale video crowd counting dataset is collected, which contains 15K frames with about 394K annotated heads captured from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments on our dataset and other crowd counting datasets validate the effectiveness of our LSTN for crowd counting. All our dataset are released in https://github.com/sweetyy83/Lstn_fdst_dataset.