Streaming Data Priority Scheduling Framework for Autonomous Driving by Edge

Author(s):  
Lingbing Yao ◽  
Hang Zhao ◽  
Jie Tang ◽  
Shaoshan Liu ◽  
Jean-Luc Gaudiot
2015 ◽  
Vol 42 (4) ◽  
pp. 12-21 ◽  
Author(s):  
Andrea Rosà ◽  
Lydia Y. Chen ◽  
Robert Birke ◽  
Walter Binder

2020 ◽  
pp. 1-28 ◽  
Author(s):  
Hang Zhao ◽  
LinBin Yao ◽  
ZhiXin Zeng ◽  
DongHua Li ◽  
JinLiang Xie ◽  
...  

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Kun Jiang ◽  
Yunlong Wang ◽  
Shengjie Kou ◽  
Diange Yang
Keyword(s):  

2013 ◽  
Vol 133 (9) ◽  
pp. 595-598
Author(s):  
Kenji SUZUKI ◽  
Hisaaki ISHIDA ◽  
Hirofumi INOSE ◽  
Rui KOBAYASHI
Keyword(s):  

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.


Author(s):  
Yu.V. Andreyev ◽  
◽  
L.V. Kuzmin ◽  
M.G. Popov ◽  
A.I. Ryshov ◽  
...  

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