Improving luminescence behavior and glass stability of tellurium‐doped germanate glasses by modifying network topology

2021 ◽  
Author(s):  
Ling Huang ◽  
Lihua Li ◽  
Mingying Peng ◽  
Linling Tan ◽  
Zhijun Ma
Keyword(s):  
Network Topology ◽  
Glass Stability ◽  
Luminescence Behavior ◽  
Germanate Glasses

Impact of network topology on the thermal and mechanical properties of lithium germanate glasses

2021 ◽  
Author(s):  
Søren S. Sørensen ◽  
Theany To ◽  
Johan F. S. Christensen ◽  
Hicham Johra ◽  
Morten M. Smedskjaer
Keyword(s):  
Mechanical Properties ◽  
Network Topology ◽  
Thermal And Mechanical Properties ◽  
Germanate Glasses

Performance Analysis of Content-Centric Networking on an Arbitrary Network Topology

2018 ◽  
Vol E101.B (1) ◽  
pp. 24-34 ◽  
Author(s):  
Ryo NAKAMURA ◽  
Hiroyuki OHSAKI
Keyword(s):  
Performance Analysis ◽  
Network Topology ◽  
Content Centric Networking

Network Topology and Battery Size Exploration for Decentralized Energy Network with MIP Base Power Flow Optimization

2013 ◽  
Vol E96.A (7) ◽  
pp. 1617-1624 ◽  
Author(s):  
Ittetsu TANIGUCHI ◽  
Kazutoshi SAKAKIBARA ◽  
Shinya KATO ◽  
Masahiro FUKUI
Keyword(s):  
Network Topology ◽  
Power Flow ◽  
Flow Optimization ◽  
Decentralized Energy ◽  
Energy Network

Passive mode discovery of network topology

2014 ◽  
Author(s):  
Lisheng Huang ◽  
Mingyong Yin ◽  
Changchun Li ◽  
Xin Wang
Keyword(s):  
Network Topology ◽  
Passive Mode

TRANSITION TO CONVOLUTIONAL NEURAL NETWORKS IN RADAR PROBLEMS

2019 ◽  
pp. 96-99
Author(s):  
K. Maystrenko ◽  
A. Budilov ◽  
D. Afanasev
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Topology ◽  
Subject Areas ◽  
The Subject ◽  
Printed Materials ◽  
Hardware Costs

Goal. Identify trends and prospects for the development of radar in terms of the use of convolutional neural networks for target detection. Materials and methods. Analysis of relevant printed materials related to the subject areas of radar and convolutional neural networks. Results. The transition to convolutional neural networks in the field of radar is considered. A review of papers on the use of convolutional neural networks in pattern recognition problems, in particular, in the radar problem, is carried out. Hardware costs for the implementation of convolutional neural networks are analyzed. Conclusion. The conclusion is made about the need to create a methodology for selecting a network topology depending on the parameters of the radar task.

Comment on “DIO Suppression Attack Against Routing in the Internet of Things”

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Network Topology ◽  
Impact Analysis ◽  
The Internet ◽  
Routing Performance ◽  
Routing Attack ◽  
The Internet Of Things ◽  
Specific Reason

<div>We have thoroughly studied the paper of Perazzo et al., which presents a routing attack named DIO suppression attack with its impact analysis. However, the considered simulation grid of size 20mx20m does not correspond to the results presented in their paper. We believe that the incorrect simulation detail needs to be rectified further for the scientific correctness of the results. In this comment, it is shown that the suppression attack on such small sized network topology does not have any major impact on routing performance, and specific reason is discussed for such behavior.</div>

Visualizing Spatial Dependencies in Network Topology

2010 ◽  
Author(s):  
Jamie Olson ◽  
Kathleen M. Carley
Keyword(s):  
Network Topology ◽  
Spatial Dependencies

Banking Regulation and Network-topology Dependence of Iterative Risk-trading Games

2002 ◽  
Author(s):  
Rudolf Hanel ◽  
Stefan Pichler ◽  
Stefan Thurner
Keyword(s):  
Network Topology ◽  
Banking Regulation
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