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Books on the topic 'Spectral networks'

Author: Grafiati

Published: 25 May 2024

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1

Zhou, Xiang, and Chongjin Xie, eds. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781119078289.

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2

Krogmeier, J. V. Wireless local area network for ITS communications using the 220 MHz ITS spectral allocation. West Lafayette, IN: Joint Highway Research Project, Purdue University, 2000.

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3

Ollikainen, Olavi. Applications of persistent spectral hole burning in ultrafast optical neural networks, time-resolved spectroscopy and holographic interferometry. Tartu: Tartu University Press, 1996.

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4

Spectrum and network measurements. Atlanta, Ga: Noble Pub. Corp., 2001.

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5

Witte, Robert A. Spectrum and network measurements. Englewood Cliffs, N.J: Prentice Hall, 1993.

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6

Ann, Frazier, and Geological Survey (U.S.). National Mapping Division, eds. Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1995.

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7

Lemeshewsky, George. Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. [Reston, VA]: U.S. Geological Survey, 1995.

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8

Graph spectra for complex networks. Cambridge: Cambridge University Press, 2011.

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9

Spectrum and network measurements. Englewood Cliffs, N.J: Prentice Hall, 1991.

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10

United States. National Aeronautics and Space Administration., ed. Marine optical characterizations: Quarterly report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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11

Pandit, Shweta, and Ghanshyam Singh. Spectrum Sharing in Cognitive Radio Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53147-2.

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12

Rawat, Danda B., Min Song, and Sachin Shetty. Dynamic Spectrum Access for Wireless Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15299-8.

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13

Cheng, Nan, and Xuemin Shen. Opportunistic Spectrum Utilization in Vehicular Communication Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20445-1.

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14

Wang, Zhe, and Wei Zhang. Opportunistic Spectrum Sharing in Cognitive Radio Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15542-5.

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15

Haldorai, Anandakumar, and Umamaheswari Kandaswamy. Intelligent Spectrum Handovers in Cognitive Radio Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15416-5.

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16

Pan, Miao, Ming Li, Pan Li, and Yuguang Fang. Spectrum Trading in Multi-Hop Cognitive Radio Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25631-3.

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17

Matin, Mohammad A., ed. Spectrum Access and Management for Cognitive Radio Networks. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2254-8.

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18

Helfrick, Albert D. Electrical spectrum and network analyzers: A practical approach. San Diego: Academic Press, 1991.

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19

Nahrstedt, Klara. Quality of Service in Wireless Networks Over Unlicensed Spectrum. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02482-5.

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20

Ren, Ju, Ning Zhang, and Xuemin Shen. Energy-Efficient Spectrum Management for Cognitive Radio Sensor Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60318-6.

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21

Zhang, Deyu, Zhigang Chen, Haibo Zhou, and Xuemin Shen. Resource Management for Energy and Spectrum Harvesting Sensor Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53771-9.

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22

Dusit, Niyato, and Han Zhu 1974-, eds. Dynamic spectrum access and management in cognitive radio networks. Cambridge: Cambridge University Press, 2009.

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23

Nahrstedt, Klara. Quality of service in wireless networks over unlicensed spectrum. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2012.

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24

Zürich, Eidgenössische Technische Hochschule, ed. Spectral efficient cooperative relaying strategies for wireless networks. 2007.

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25

Björnson, Emil, Jakob Hoydis, and Luca Sanguinetti. Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency. Now Publishers, 2017.

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26

Zhou, Xiang, and Chongjin Xie. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Wiley & Sons, Limited, John, 2020.

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27

Zhou, Xiang, and Chongjin Xie. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Wiley & Sons, Incorporated, John, 2016.

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28

Zhou, Xiang, and Chongjin Xie. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Wiley & Sons, Incorporated, John, 2016.

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29

Zhou, Xiang, and Chongjin Xie. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Wiley & Sons, Incorporated, John, 2016.

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30

Zhou, Xiang, and Chongjin Xie. Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks. Wiley & Sons, Incorporated, John, 2016.

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31

Bianconi, Ginestra. The Dynamics on Single Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0003.

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This chapter provides the relevant background on the network dynamics of complex networks formed by just one layer (single networks). Emergent properties of network dynamics are characterized using the framework of phase transitions. The major results on robustness of complex networks, percolation theory and epidemic spreading are presented, revealing the rich interplay between network structure and function. In this context particular emphasis is given to the implications of the scale-free network topology on these dynamical processes. Diffusion processes and synchronization and controllability are characterized on networks, revealing the relevance of spectral properties and peripheral nodes for determining their dynamical behaviour.

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32

Bolla, Marianna. Spectral Clustering and Biclustering of Networks: Large Graphs and Contingency Tables. Wiley & Sons, Limited, John, 2013.

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33

Newman, Mark. Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.001.0001.

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The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

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34

Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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35

Vernizzi, Graziano, and Henri Orland. Complex networks. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.43.

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This article deals with complex networks, and in particular small world and scale free networks. Various networks exhibit the small world phenomenon, including social networks and gene expression networks. The local ordering property of small world networks is typically associated with regular networks such as a 2D square lattice. The small world phenomenon can be observed in most scale free networks, but few small world networks are scale free. The article first provides a brief background on small world networks and two models of scale free graphs before describing the replica method and how it can be applied to calculate the spectral densities of the adjacency matrix and Laplacian matrix of a scale free network. It then shows how the effective medium approximation can be used to treat networks with finite mean degree and concludes with a discussion of the local properties of random matrices associated with complex networks.

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36

Newman, Mark. Dynamical systems on networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0017.

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An introduction to the theory of dynamical systems on networks. This chapter starts with a short introduction to classical (non-network) dynamical systems theory, including linear stability analysis, fixed points, and limit cycles. Dynamical systems on networks are introduced, focusing initially on systems with only one variable per node and progressing to multi-variable systems. Linear stability analysis is developed in detail, leading to master stability conditions and the connection between stability and the spectral properties of networks. The chapter ends with a discussion of synchronization phenomena, the stability of limit cycles, and master stability conditions for synchronization.

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37

Spectrum and Network Measurements. Institution of Engineering & Technology, 2014.

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38

Witte, Robert A. Spectrum and Network Measurements. SciTech Publishing, Incorporated, 2014.

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39

Webb, Benjamin, and Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2016.

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40

Webb, Benjamin, and Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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41

Webb, Benjamin, and Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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42

Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1995.

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43

Bianconi, Ginestra. Diffusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0014.

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This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the improved navigability of multilayer networks with more layers. These results are here compared with the results of traffic on multilayer networks that, on the contrary, point out that increasing the number of layers could be detrimental and could lead to congestion.

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44

Mieghem, Piet Van. Graph Spectra for Complex Networks. Cambridge University Press, 2012.

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45

Mieghem, Piet Van. Graph Spectra for Complex Networks. Cambridge University Press, 2011.

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46

Mieghem, Piet van. Graph Spectra for Complex Networks. Cambridge University Press, 2010.

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47

Mieghem, Piet van. Graph Spectra for Complex Networks. Cambridge University Press, 2010.

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48

Kumar, Sunil, Fei Hu, and John D. Matyjas. Spectrum Sharing in Wireless Networks. Taylor & Francis Group, 2020.

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49

Witte, Robert A. Spectrum and Network Measurements. SciTech Publishing, Incorporated, 2001.

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50

Witte, Robert A. Spectrum and Network Measurements. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/sbew506e.

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