Impact of Network Topology on the Resilience of Vehicle Platoons

Author(s):  
Mohammad Pirani ◽  
Simone Baldi ◽  
Karl Henrik Johansson
Author(s):  
Takuma WAKASA ◽  
Yoshiki NAGATANI ◽  
Kenji SAWADA ◽  
Seiichi SHIN

Author(s):  
Lisheng Huang ◽  
Mingyong Yin ◽  
Changchun Li ◽  
Xin Wang

Author(s):  
K. Maystrenko ◽  
A. Budilov ◽  
D. Afanasev

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.


2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga

<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>


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