scholarly journals SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey

2017 ◽  
Vol 19 (3) ◽  
pp. 1567-1602 ◽  
Author(s):  
Van-Giang Nguyen ◽  
Anna Brunstrom ◽  
Karl-Johan Grinnemo ◽  
Javid Taheri
Keyword(s):  
Network Architectures ◽  
Core Network ◽  
Mobile Packet Core

A Power Consumption Analysis for IP-Over-WDM Core Network Architectures

2012 ◽  
Vol 4 (2) ◽  
pp. 108 ◽  
Author(s):  
Francesco Musumeci ◽  
Massimo Tornatore ◽  
Achille Pattavina
Keyword(s):  
Power Consumption ◽  
Network Architectures ◽  
Core Network ◽  
Ip Over Wdm

Comparison of Converged Packet-Optical Core Network Architectures

2014 ◽  
Author(s):  
Serge Melle ◽  
Satyajeet Ahuja ◽  
Onur Turkcu ◽  
Steven J. Hand
Keyword(s):  
Network Architectures ◽  
Core Network

scholarly journals Optimising Differentiated Services Strategies under IMS

2018 ◽  
Vol 2 (3) ◽  
pp. 1-10
Author(s):  
David K. Osei-Aboagye ◽  
Peter S. Excell
Keyword(s):  
Mobile Communications ◽  
Network Architecture ◽  
Discrete Event ◽  
Differentiated Services ◽  
Main Concern ◽  
Network Architectures ◽  
Network Simulator ◽  
Core Network ◽  
Network Technologies

The evolving standards of mobile communications, the wide variety of services they offer and the rapid growth of the Internet have made a merger of the two network technologies inevitable. One of the most prominent platforms that has been developed to facilitate this is the IP Multimedia Subsystem (IMS) concept. Many mobile communications standards integrate IMS as the main core network architecture and Quality of Service (QoS) is the main concern for customer satisfaction. A major approach to optimisation of QoS is the Differentiated Services scheme, and a simulation study of implementations of this is presented. The study covered an IMS core network architecture modelled with discrete-event network simulator software, with a Differentiated Services QoS scheme run over it with differing bearer traffic scenarios. Implications for core network architectures are discussed.

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.

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