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Relevant bibliographies by topics / Network design

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Journal articles

Academic literature on the topic 'Network design'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Network design"

1

Mitchell, David H., and J. MacGregor Smith. "Topological network design of pedestrian networks." Transportation Research Part B: Methodological 35, no. 2 (2001): 107–35. http://dx.doi.org/10.1016/s0191-2615(99)00039-9.

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Narasimhan, Sridhar, Samit Soni, and Sang Hwa Song. "ATM network design for corporate networks." European Journal of Operational Research 170, no. 2 (2006): 644–63. http://dx.doi.org/10.1016/j.ejor.2004.07.045.

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Todd, Brody, Abiose Ibigbami, and John Doucette. "Survivable Network Design and Optimization with Network Families." Journal of Computer Networks and Communications 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/940130.

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In modeling communication networks for simulation of survivability schemes, one goal is often to implement these schemes across varying degrees of nodal connectivity to get unbiased performance results. Abstractions of real networks, simple random networks, and families of networks are the most common categories of these sample networks. This paper looks at how using the network family concept provides a solid unbiased foundation to compare different network protection models. The network family provides an advantage over random networks by requiring one solution per average nodal degree, as opposed to having to solve many, which could take a significant amount of time. Also, because the network family looks at a protection scheme across a variety of average nodal connectivities, a clearer picture of the scheme’s performance is gained compared to just running the simulation on a single network or select few networks.

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4

Perfetti, R. "A neural network to design neural networks." IEEE Transactions on Circuits and Systems 38, no. 9 (1991): 1099–103. http://dx.doi.org/10.1109/31.83884.

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5

Erez, Elona, and Meir Feder. "Efficient Network Code Design for Cyclic Networks." IEEE Transactions on Information Theory 56, no. 8 (2010): 3862–78. http://dx.doi.org/10.1109/tit.2010.2050934.

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Angilella, Vincent, Matthieu Chardy, and Walid Ben-Ameur. "Fiber cable network design in tree networks." European Journal of Operational Research 269, no. 3 (2018): 1086–106. http://dx.doi.org/10.1016/j.ejor.2018.02.034.

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7

Ashman, Darcy, and Carmen Luca Sugawara. "Civil Society Networks: Options for Network Design." Nonprofit Management and Leadership 23, no. 3 (2012): 389–406. http://dx.doi.org/10.1002/nml.21062.

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8

Gupta, Sakshi. "Concrete Mix Design Using Artificial Neural Network." Journal on Today's Ideas-Tomorrow's Technologies 1, no. 1 (2013): 29–43. http://dx.doi.org/10.15415/jotitt.2013.11003.

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Alic, Kemal, Mihael Mohorcic, and Ales Svigelj. "Network and Traffic Design Aspects in Network-Coding-Enabled Wireless Networks." International Journal of Computers Communications & Control 14, no. 3 (2019): 293–310. http://dx.doi.org/10.15837/ijccc.2019.3.3477.

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Practical experience of using opportunistic network coding has already been gained in several real network deployments, indicating the influence of some of the fundamental characteristics of the network and the traffic load. However, these aspects have not been systematically investigated in the scope of the construction of efficient and robust large-scale network-coding-enabled wireless mesh networks. In this paper we focus on these aspects using an example of two opportunistic networkcoding procedures: the well-known COPE and the Bearing Opportunistic Network coding (BON). In addition, the design aspects for network-coding-enabled wireless mesh networks and applications are discussed. We have shown that opportunistic network coding can improve the performance of different networks and supported applications in terms of throughput, delay and jitter, although the benefits are not significant in all the cases. Thus, the use of opportunistic network coding should be considered upfront during the wireless network design phase in order to obtain the greatest benefits.

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10

Yakasova, Natalya. "Holographic method of neural network design." E3S Web of Conferences 419 (2023): 02026. http://dx.doi.org/10.1051/e3sconf/202341902026.

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The paper presents a method for solving the problem of changing a full-connected neural network of direct propagation with a sigmoid activation function in the conditions of the emergence of a new class, with the ability to preserve the network to recognize already known classes and to classify objects of a new class. Such conditions generate a new neural network, which is trained on examples of all classes, including the new class. The learning process takes a long time and requires the selection of several parameters. The emergence of a new class in natural neural networks does not cause a transformation of the network structure, only the strength of connections between neurons changes. The network shows the properties of stability and plasticity at the same time. The authors draw attention to the analogy between neural networks and holograms in their ability to store information and form an image of a class in response to an input signal. Following the holographic analogy, the paper proposes a model of the wave nature of neural networks, which treats the network weights as a hologram and the input signal as a wave passing through a hologram. The construction of a new network is created with two neural networks, which are a combination of two holograms. The first hologram represents the original network, and the second is a new network with a similar structure, but it is trained to recognize one new class. The addition of the holograms of these neural networks implements the mechanisms of plasticity and stability in the model.

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