scholarly journals Radiation hardness study of the ePix100 sensor and ASIC under direct illumination at the European XFEL

2021 ◽  
Vol 16 (09) ◽  
pp. P09009
Author(s):  
I. Klačková ◽  
K. Ahmed ◽  
G. Blaj ◽  
M. Cascella ◽  
V. Cerantola ◽  
...  
2020 ◽  
Vol 29 (3) ◽  
pp. 038502 ◽  
Author(s):  
Ying-Hui Zhong ◽  
Bo Yang ◽  
Ming-Ming Chang ◽  
Peng Ding ◽  
Liu-Hong Ma ◽  
...  
Keyword(s):  

2021 ◽  
Vol 68 (5) ◽  
pp. 2289-2294
Author(s):  
Adam Elwailly ◽  
Johan Saltin ◽  
Matthew J. Gadlage ◽  
Hiu Yung Wong

Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.


2005 ◽  
Vol 20 (16) ◽  
pp. 3811-3814
Author(s):  
◽  
PAUL LUJAN

A new silicon detector was designed by the CDF collaboration for Run IIb of the Tevatron at Fermilab. The main building block of the new detector is a "supermodule" or "stave", an innovative, compact and lightweight structure of several readout hybrids and sensors with a bus cable running directly underneath the sensors to carry power, data, and control signals to and from the hybrids. The hybrids use a new, radiation-hard readout chip, the SVX4 chip. A number of SVX4 chips, readout hybrids, sensors, and supermodules were produced and tested in preproduction. The performance (including radiation-hardness) and yield of these components met or exceeded all design goals. The detector design goals, solutions, and performance results are presented.


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