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685 results on '"Yang, Junxing"'

53. ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans

Author

Lukina, Anna, Esterle, Lukas, Hirsch, Christian, Bartocci, Ezio, Yang, Junxing, Tiwari, Ashish, Smolka, Scott A., Grosu, Radu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Legay, Axel, editor, and Margaria, Tiziana, editor

Published
2017
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54. Attacking the V: On the Resiliency of Adaptive-Horizon MPC

Author

Tiwari, Ashish, Smolka, Scott A., Esterle, Lukas, Lukina, Anna, Yang, Junxing, Grosu, Radu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, D'Souza, Deepak, editor, and Narayan Kumar, K., editor

Published
2017
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55. A Simplex Architecture for Hybrid Systems Using Barrier Certificates

Author

Yang, Junxing, Islam, Md. Ariful, Murthy, Abhishek, Smolka, Scott A., Stoller, Scott D., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tonetta, Stefano, editor, Schoitsch, Erwin, editor, and Bitsch, Friedemann, editor

Published
2017
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58. Denoising Framework Based on Multiframe Continuous Point Clouds for Autonomous Driving LiDAR in Snowy Weather

Author

Yan, Xinyuan, Yang, Junxing, Zhu, Xinyu, Liang, Yu, and Huang, He

Abstract

Adverse weather conditions are one of the long-tailed problems facing autonomous driving. Solving the problem of autonomous driving operation in adverse weather conditions is an important challenge for realizing advanced autonomous driving. To enhance the LiDAR perception capability in snowy weather for autonomous driving, this study proposes a denoising method for multiframe continuous point clouds. The core concept of this method is to allow ordered objects (e.g., stationary objects on the ground) to strengthen each other while allowing disordered objects (e.g., snow) to weaken each other. This is done by first selecting three consecutive frames of the point cloud as a denoising unit and then removing the ground points from each frame of the point cloud. After that, the point clouds from the first two frames are used as the source point clouds, and the point cloud from the third frame is used as the target point cloud for point cloud registration. Finally, the time outlier removal (TOR) filter proposed in this article combined with the entropy weight method (EWM) is utilized for denoising. The experimental results show that the performance of the method proposed in this article exceeds the existing methods. In addition, the method in this article not only removes the disordered snowflakes in the air, but also removes some other disordered noise points (e.g., the ghosting of the stationary objects), which provides an advantageous guarantee for the realization of automatic driving in snowy weather.

Published
2024
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62. Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information About the Environment

Author

Phan, Dung, Yang, Junxing, Ratasich, Denise, Grosu, Radu, Smolka, Scott A., Stoller, Scott D., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bartocci, Ezio, editor, and Majumdar, Rupak, editor

Published
2015
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70. Using Statistical Model Checking for Measuring Systems

Author

Grosu, Radu, Peled, Doron, Ramakrishnan, C. R., Smolka, Scott A., Stoller, Scott D., Yang, Junxing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Margaria, Tiziana, editor

Published
2014
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71. Compositional Branching-Time Measurements

Author

Grosu, Radu, Peled, Doron, Ramakrishnan, C. R., Smolka, Scott A., Stoller, Scott D., Yang, Junxing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bensalem, Saddek, editor, Lakhneck, Yassine, editor, and Legay, Axel, editor

Published
2014
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89. Total Differential Photometric Mesh Refinement with Self-Adapted Mesh Denoising.

Author

Qu, Yingjie, Yan, Qingsong, Yang, Junxing, Xiao, Teng, and Deng, Fei

Subjects
SURFACE reconstruction, SELF-adaptive software, PIXELS, MESH networks
Abstract

Variational mesh refinement is a crucial step in multiview 3D reconstruction. Existing algorithms either focus on recovering mesh details or focus on suppressing noise. Approaches with consideration of both are lacking. To address this limitation, we proposed a new variational mesh refinement method named total differential mesh refinement (TDR), which mainly included two improvements. First, the traditional partial-differential photo-consistency gradient used in the variational mesh refinement method was replaced by the proposed total-differential photo-consistency gradient. With consideration of the photo-consistency correlation between adjacent pixels, our method can make photo-consistency achieve a more effective convergence than traditional approaches. Second, we introduced the bilateral normal filter with a novel self-adaptive mesh denoising strategy into the variational mesh refinement. This strategy maintains a balance between detail preservation and effective denoising via the zero-normalized cross-correlation (ZNCC) map. Various experiments demonstrated that our method is superior to traditional variational mesh refinement approaches in both accuracy and denoising effect. Moreover, compared with the mesh generated by open-source and commercial software (Context Capture), our meshes are more detailed, regular, and smooth. [ABSTRACT FROM AUTHOR]

Published
2023
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