Multi-Link Failure Effects on MPLS Resilient Fast-Reroute Network Architectures

2021 ◽  
Author(s):  
Wayne Gray ◽  
Athanasios Tsokanos ◽  
Raimund Kirner
Keyword(s):  
Link Failure ◽  
Network Architectures ◽  
Fast Reroute

On Number of Planes of Rearrangeably Nonblocking Optical Banyan Networks with Link Failures

2008 ◽  
Vol 1 (1) ◽  
pp. 43-54
Author(s):  
Basra Sultana ◽  
Mamun-ur-Rashid Khandker
Keyword(s):  
Optical Switching ◽  
Blocking Probability ◽  
Link Failure ◽  
Link Failures ◽  
Small Depth ◽  
Internal Link ◽  
Banyan Network ◽  
Minimum Number ◽  
Switch Design ◽  
Banyan Networks

Vertically stacked optical banyan (VSOB) networks are attractive for serving as optical switching systems due to the desirable properties (such as the small depth and self-routing capability) of banyan network structures. Although banyan-type networks result in severe blocking and crosstalk, both these problems can be minimized by using sufficient number of banyan planes in the VSOB network structure. The number of banyan planes is minimum for rearrangeably nonblocking and maximum for strictly nonblocking structure. Both results are available for VSOB networks when there exist no internal link-failures. Since the issue of link-failure is unavoidable, we intend to find the minimum number of planes required to make a VSOB network nonblocking when some links are broken or failed in the structure. This paper presents the approximate number of planes required to make a VSOB networks rearrangeably nonblocking allowing link-failures. We also show an interesting behavior of the  blocking  probability of a faulty VSOB networks that the blocking probability may not  always  increase monotonously with  the  increase  of  link-failures; blocking probability  decreases  for  certain range of  link-failures, and then increases again. We believe that such fluctuating behavior of blocking probability with the increase of link failure probability deserves special attention in switch design.  Keywords: Banyan networks; Blocking probability; Switching networks; Vertical stacking; Link-failures. © 2009 JSR Publications. ISSN: 2070-0237(Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v1i1.1070

Scalable Backup Configurations Creation for IP Fast Reroute

2011 ◽  
Vol E94-B (1) ◽  
pp. 109-117 ◽  
Author(s):  
Shohei KAMAMURA ◽  
Takashi MIYAMURA ◽  
Yoshihiko UEMATSU ◽  
Kohei SHIOMOTO
Keyword(s):  
Fast Reroute ◽  
Ip Fast Reroute

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Enabling Things To Talk in Heterogeneous Self-Organizing Networks—An Exhaustive Critique For IOT

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 728-750
Author(s):  
Naeem Z Azeemi ◽  
Saira Khan ◽  
Sharmini Enoch ◽  
Riktesh Srivastava ◽  
Omar al Basheer ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Adaptive Modulation ◽  
Sustainable Growth ◽  
Adaptive Modulation And Coding ◽  
Network Architectures ◽  
Multiple Antenna ◽  
Multiple Access Schemes ◽  
Natural Outcome ◽  
Air Interfaces ◽  
The Internet Of Things

The superstructure network in the Internet of Things (IoT) is an emerging network targeted to enable an ecosystem of smart applications and services. It connectsphysical resources and peopletogether with software, hence contribute to sustainable growth, provided it combines and guarantees trustand security for people and businesses.  In this work we presented smart city viewpoint opt-in to the Firth Generation (5G) mobile networks. Both a framework and deployment are explored rigorously to assist and predicting robustness of IoT technologies and applications as a natural outcome of the Third Generation Partnership Project (3GPP) in general and LTE in particular. These technologies are compared on the basis of Air Interfaces and their Specifications i.e. Adaptive Modulation and Coding, Multiple Access Schemes and Multiple Antenna Techniques along with the evolution and comparison of the Network Architectures.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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