A new approach for traffic matrix estimation in high load computer networks based on graph embedding and convolutional neural network

2019 ◽  
Vol 30 (6) ◽  
Author(s):  
Mohsen Emami ◽  
Reza Akbari ◽  
Reza Javidan ◽  
Ali Zamani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computer Networks ◽  
Graph Embedding ◽  
High Load ◽  
New Approach ◽  
Matrix Estimation ◽  
Traffic Matrix ◽  
Traffic Matrix Estimation

scholarly journals Design and Implementation of a Robust Convolutional Neural Network-Based Traffic Matrix Estimator for Cloud Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rashida Ali Memon ◽  
Sameer Qazi ◽  
Bilal Muhammad Khan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Measurement Errors ◽  
Research Literature ◽  
Training Data ◽  
Superior Performance ◽  
Training Dataset ◽  
Cloud Networks ◽  
Matrix Estimation ◽  
Traffic Matrix

Recent research literature shows promising results by convolutional neural network- (CNN-) based approaches for estimation of traffic matrix of cloud networks using different architectures. Although conventionally, convolutional neural network-based approaches yield superior estimation; however, these rely on assumptions of availability of a large training dataset which is completely accurate and nonsparse. In real world, both these assumptions are problematic as training data size may be limited, and it is also prone to missing (or incomplete) measurements as well as may have measurement errors. Similarly, the 2-D training datasets derived from network topology based may be sparse. We investigate these challenges and develop a novel architecture which can cater for these challenges and deliver superior performance. Our approach shows promising results for traffic matrix estimation using convolutional neural network-based techniques in the presence of limited training data and outlier measurements.

scholarly journals Image Registration Algorithm Based on Convolutional Neural Network and Local Homography Transformation

2020 ◽  
Vol 10 (3) ◽  
pp. 732 ◽  
Author(s):  
Yuanwei Wang ◽  
Mei Yu ◽  
Gangyi Jiang ◽  
Zhiyong Pan ◽  
Jiqiang Lin
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Estimation Algorithm ◽  
Matrix Estimation ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Different Types ◽  
Homography Transformation ◽  
Traditional Image

In order to overcome the poor robustness of traditional image registration algorithms in illuminating and solving the problem of low accuracy of a learning-based image homography matrix estimation algorithm, an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. Firstly, to ensure the diversity of samples, a sample and label generation method based on moving direct linear transformation (MDLT) is designed. The generated samples and labels can effectively reflect the local characteristics of images and are suitable for training the CNN model with which multiple pairs of local matching points between two images to be registered can be calculated. Then, the local homography matrices between the two images are estimated by using the MDLT and finally the image registration can be realized. The experimental results show that the proposed image registration algorithm achieves higher accuracy than other commonly used algorithms such as the SIFT, ORB, ECC, and APAP algorithms, as well as another two learning-based algorithms, and it has good robustness for different types of illumination imaging.

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