Scalable and reconfigurable WDM network architecture

1998 ◽  
Author(s):  
Dan Guo ◽  
Zhensheng Zhang
Keyword(s):  
Network Architecture ◽  
Wdm Network

scholarly journals A Multi-Objective Optimization Algorithm for Routing Path Selection and Wavelength Allocation for Dynamic WDM Network using MO-HLO

2021 ◽  
Vol 10 (5) ◽  
pp. 111-118
Author(s):  
Hamsaveni M ◽  
Savita Choudhary
Keyword(s):  
Shortest Path ◽  
Optimization Algorithm ◽  
Network Architecture ◽  
Time Instant ◽  
Path Selection ◽  
Packet Transmission ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wdm Network ◽  
Wavelength Allocation

The data transmission system in the optical WDM network increases the speed of packet transmission by the wavelength of light beams . The Selection of the wavelength and the shortest path to transmit the packets form source to destination is a challenge in a large network architecture. To solve these two problems, the optimization model must handle both the objectives. In this paper we are proposing a novel multi-objective optimization algorithm to solve both the problem of wavelength allocation and shortest path identification in a WDM network. This can be achieved by the enhanced model of Multi-Objective Hunger Locust Optimization algorithm (MO-HLO). In this, it analyse traffic level in a network path and the availability of wavelength present at each time instant. The proposed system retrieves the parameters of network architecture and with the weight value of dynamic traffic occur in the routing path. Among these data, the optimization selects the best among overall feature set of the WDM arrangement. The MO-HLO algorithm extracts the combination of each attribute to form the cluster that segregates the routing path along with the traffic range. From the fitness of the objective function of MO-HLO, the best routing path and the availability of wavelength for a node can be analysed at each time instant.

PHONAV-a photonic WDM network architecture for next generation avionics systems

1999 ◽  
Author(s):  
R.D. Gardner ◽  
I. Andonovic ◽  
D.K. Hunter ◽  
A.J. McLaughlin ◽  
J.S. Aitchison ◽  
...  
Keyword(s):  
Network Architecture ◽  
Next Generation ◽  
Wdm Network

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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