Balanced Wrapper Design to Test the Embedded Core Partitioned into Multiple Layer for 3D SOC Targeting Power and Number of TSVs

2017 ◽  
pp. 117-125 ◽  
Author(s):  
Niranjan Raj ◽  
Indranil Sen Gupta
Keyword(s):  
Multiple Layer

Implementation of Multiple layer security of Cloud server

2018 ◽  
Vol 6 (4) ◽  
pp. 236-241
Author(s):  
Neetu Rani ◽  
◽  
◽  
Sandeep Dalal
Keyword(s):  
Multiple Layer ◽  
Cloud Server

Multiple-layer classification of Web emergency news based on rules and statistics

2013 ◽  
Vol 32 (2) ◽  
pp. 392-394
Author(s):  
Hua-lin XIA ◽  
Yang-sen ZHANG
Keyword(s):  
Multiple Layer

High performance red-emitting multiple layer InGaN/GaN quantum dot lasers

2016 ◽  
Vol 55 (3) ◽  
pp. 032101 ◽  
Author(s):  
Thomas Frost ◽  
Arnab Hazari ◽  
Anthony Aiello ◽  
Md Zunaid Baten ◽  
Lifan Yan ◽  
...  
Keyword(s):  
Quantum Dot ◽  
High Performance ◽  
Quantum Dot Lasers ◽  
Multiple Layer

Multiple layer formation in the anodic deposition of Hg2C2O4

1987 ◽  
Vol 227 (1-2) ◽  
pp. 147-158 ◽  
Author(s):  
C. Müller ◽  
J. Claret ◽  
M. Sarret
Keyword(s):  
Layer Formation ◽  
Multiple Layer ◽  
Anodic Deposition

scholarly journals Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

2021 ◽  
Vol 13 (10) ◽  
pp. 2012
Author(s):  
Yue Yu ◽  
Jinmei Pan ◽  
Jiancheng Shi
Keyword(s):  
Process Model ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Single Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Snow Layer ◽  
Optimal Method ◽  
Microstructure Parameter ◽  
Multiple Layer

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

Multiple-layer optical turbulence generator principle and SLODAR characterization: preliminary results

2004 ◽  
Author(s):  
Laurent Jolissaint ◽  
Onur Keskin ◽  
Colin Bradley ◽  
Brian Wallace ◽  
Aaron Hilton
Keyword(s):  
Optical Turbulence ◽  
Preliminary Results ◽  
Multiple Layer ◽  
Turbulence Generator
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