A COTS based Testbed to Develop Mission Tailored Solutions: Versatile Communications Backbone Network

2007 ◽  
Author(s):  
Alberto Domingo ◽  
Hermann Wietgrefe ◽  
Miguel Angel Rico
Keyword(s):  
Backbone Network

Distributed Virtue Backbone Network Algorithm Based on Topology Characteristic

2010 ◽  
Vol 21 (6) ◽  
pp. 1416-1425
Author(s):  
Wen-Bin XIE ◽  
Jia LI ◽  
Ming XIAN ◽  
Yong-Guang CHEN
Keyword(s):  
Backbone Network ◽  
Network Algorithm

EVALUATION OF QOS IN UMTS BACKBONE NETWORK BY USING DIFFERENTIATED SERVICES

2007 ◽  
Vol 35 (6) ◽  
pp. 1495-1509
Author(s):  
E. M. Saad ◽  
Dr. Osama El Ghandour ◽  
Jehan, M. K
Keyword(s):  
Differentiated Services ◽  
Backbone Network

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

The First Very Broadband Mediterranean Network: 30 Yr of Data and Seismological Research

2019 ◽  
Vol 91 (2A) ◽  
pp. 787-802
Author(s):  
Silvia Pondrelli ◽  
Francesca Di Luccio ◽  
Laura Scognamiglio ◽  
Irene Molinari ◽  
Simone Salimbeni ◽  
...  
Keyword(s):  
Seismic Source ◽  
Global Scale ◽  
Anthropogenic Noise ◽  
Earth Structure ◽  
Backbone Network ◽  
Early Adoption ◽  
Stable Conditions ◽  
Remote Sites ◽  
The Mediterranean

Abstract Starting in 1988, with the installation of the first broadband (BB) instrument in Italy, the Mediterranean Very Broadband Seismographic Network (MedNet) program established a backbone network of BB stations of the highest quality in the Mediterranean Sea countries. The Mediterranean region is characterized by relevant and frequent seismicity related to its complex tectonics, due to the convergence of two major plates, Africa and Eurasia, and the involvement of other minor plates, as the Adriatic plate. Therefore, the MedNet project became a scientific research infrastructure of excellence, able to fill the gap of regional coverage when the availability of seismic BB instruments was still scarce. The main characteristics of the MedNet network are the highest quality of the seismographic instrumentation at remote sites and very low level of anthropogenic noise with stable conditions of pressure and temperature. After 30 yr of recordings, the MedNet program has proven that the early adoption of very BB instruments in selected sites have been the best choice. A large number of studies benefited from MedNet data, as seismic source computation and Earth structure reconstruction, at local and global scale. We present a concise overview of the contribution given by MedNet data in the last three decades to motivate and financially support the existence of this valuable infrastructure, and to further maintain this project.

scholarly journals GAMPAL: an anomaly detection mechanism for Internet backbone traffic by flow size prediction with LSTM-RNN

2021 ◽  
Author(s):  
Taku Wakui ◽  
Takao Kondo ◽  
Fumio Teraoka
Keyword(s):  
Anomaly Detection ◽  
Short Term Memory ◽  
Denial Of Service ◽  
General Purpose ◽  
Traffic Information ◽  
Traffic Patterns ◽  
Detection Mechanism ◽  
Internet Service ◽  
Internet Backbone ◽  
Backbone Network

AbstractThis paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data). GAMPAL does not require labeled data to achieve general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Border Gateway Protocol Routing Information Base), GAMPAL introduces prefix aggregate. The BGP RIB entries are classified into prefix aggregates, each of which is identified with the first three AS (Autonomous System) numbers in the AS_PATH attribute. GAMPAL establishes a prediction model for traffic sizes based on past traffic sizes. It adopts a LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model that focuses on the periodicity of the Internet traffic patterns at a weekly scale. The validity of GAMPAL is evaluated using real traffic information, BGP RIBs exported from the WIDE backbone network (AS2500), a nationwide backbone network for research and educational organizations in Japan, and the dataset of an ISP (Internet Service Provider) in Spain. As a result, GAMPAL successfully detects anomalies such as increased traffic due to an event, DDoS (Distributed Denial of Service) attacks targeted at a stub organization, a connection failure, an SSH (Secure Shell) scan attack, and anomaly spam.

ParallelNet: multiple backbone network for detection tasks on thigh bone fracture

2021 ◽  
Author(s):  
Mengxuan Wang ◽  
Jinkun Yao ◽  
Guoshan Zhang ◽  
Bin Guan ◽  
Xinbo Wang ◽  
...  
Keyword(s):  
Bone Fracture ◽  
Thigh Bone ◽  
Backbone Network

An optical fibre-based Local Backbone Network

1989 ◽  
Vol 14 (4) ◽  
pp. 127-134 ◽  
Author(s):  
M. Mehmet Ali ◽  
J. F. Hayes
Keyword(s):  
Optical Fibre ◽  
Backbone Network
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