Relevant bibliographies by topics / Spectral networks

Academic literature on the topic 'Spectral networks'

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Published: 25 May 2024

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Journal articles on the topic "Spectral networks"

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Wu, Tingzeng, and Huazhong Lü. "Per-Spectral Characterizations of Bicyclic Networks." Journal of Applied Mathematics 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/7541312.

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Spectral techniques are used for the study of several network properties: community detection, bipartition, clustering, design of highly synchronizable networks, and so forth. In this paper, we investigate which kinds of bicyclic networks are determined by their per-spectra. We find that the permanental spectra cannot determine sandglass graphs in general. When we restrict our consideration to connected graphs or quadrangle-free graphs, sandglass graphs are determined by their permanental spectra. Furthermore, we construct countless pairs of per-cospectra bicyclic networks.
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Gaiotto, Davide, Gregory W. Moore, and Andrew Neitzke. "Spectral Networks." Annales Henri Poincaré 14, no. 7 (March 8, 2013): 1643–731. http://dx.doi.org/10.1007/s00023-013-0239-7.

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Anastasiadis, Johannes, and Michael Heizmann. "GAN-regularized augmentation strategy for spectral datasets." tm - Technisches Messen 89, no. 4 (February 5, 2022): 278–88. http://dx.doi.org/10.1515/teme-2021-0109.

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Abstract Artificial neural networks are used in various fields including spectral unmixing, which is used to determine the proportions of substances involved in a mixture, and achieve promising results. This is especially true if there is a non-linear relationship between the spectra of mixtures and the spectra of the substances involved (pure spectra). To achieve sufficient results, neural networks need lots of representative training data. We present a method that extends existing training data for spectral unmixing consisting of spectra of mixtures by learning the mixing characteristic using an artificial neural network. Spectral variability is considered by random inputs. The network structure used is a generative adversarial net that takes the dependence on the abundances of pure substances into account by an additional term in its objective function, which is minimized during training. After training further data for abundance vectors for which there is no real measurement data in the original training dataset can be generated. A neural network trained with the augmented training dataset shows better performance in spectral unmixing compared to being trained with the original dataset. The presented network structure improves already existing results obtained with a generative convolutional neural network, which is superior to model-based approaches.
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Penttilä, A., H. Hietala, and K. Muinonen. "Asteroid spectral taxonomy using neural networks." Astronomy & Astrophysics 649 (May 2021): A46. http://dx.doi.org/10.1051/0004-6361/202038545.

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Aims. We explore the performance of neural networks in automatically classifying asteroids into their taxonomic spectral classes. We particularly focus on what the methodology could offer the ESA Gaia mission. Methods. We constructed an asteroid dataset that can be limited to simulating Gaia samples. The samples were fed into a custom-designed neural network that learns how to predict the samples’ spectral classes and produces the success rate of the predictions. The performance of the neural network is also evaluated using three real preliminary Gaia asteroid spectra. Results. The overall results show that the neural network can identify taxonomic classes of asteroids in a robust manner. The success in classification is evaluated for spectra from the nominal 0.45–2.45 μm wavelength range used in the Bus-DeMeo taxonomy, and from a limited range of 0.45–1.05 μm following the joint wavelength range of Gaia observations and the Bus-DeMeo taxonomic system. Conclusions. The obtained results indicate that using neural networks to execute automated classification is an appealing solution for maintaining asteroid taxonomies, especially as the size of the available datasets grows larger with missions like Gaia.
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Avdic, Senada, Roumiana Chakarova, and Imre Pazsit. "Analysis of the experimental positron lifetime spectra by neural networks." Nuclear Technology and Radiation Protection 18, no. 1 (2003): 16–21. http://dx.doi.org/10.2298/ntrp0301016a.

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This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.
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Tanabe, Kazutoshi, Takatoshi Matsumoto, Tadao Tamura, Jiro Hiraishi, Shinnosuke Saeki, Miwako Arima, Chisato Ono, et al. "Identification of Chemical Structures from Infrared Spectra by Using Neural Networks." Applied Spectroscopy 55, no. 10 (October 2001): 1394–403. http://dx.doi.org/10.1366/0003702011953531.

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Structure identification of chemical substances from infrared spectra can be done with various approaches: a theoretical method using quantum chemistry calculations, an inductive method using standard spectral databases of known chemical substances, and an empirical method using rules between spectra and structures. For various reasons, it is difficult to definitively identify structures with these methods. The relationship between structures and infrared spectra is complicated and nonlinear, and for problems with such nonlinear relationships, neural networks are the most powerful tools. In this study, we have evaluated the performance of a neural network system that mimics the methods used by specialists to identify chemical structures from infrared spectra. Neural networks for identifying over 100 functional groups have been trained by using over 10 000 infrared spectral data compiled in the integrated spectral database system (SDBS) constructed in our laboratory. Network structures and training methods have been optimized for a wide range of conditions. It has been demonstrated that with neural networks, various types of functional groups can be identified, but only with an average accuracy of about 80%. The reason that 100% identification accuracy has not been achieved is discussed.
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Cabrol-Bass, D., C. Cachet, C. Cleva, A. Eghbaldar, and T. P. Forrest. "Application pratique des réseaux neuro mimétiques aux données spectroscopiques (infrarouge et masse) en vue de l'élucidation structurale." Canadian Journal of Chemistry 73, no. 9 (September 1, 1995): 1412–26. http://dx.doi.org/10.1139/v95-176.

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In the last few years, intensive research by several groups has shown that neural networks can be used to analyse spectral data for structural elucidation, and that their performance approaches that of an expert in the field. The construction of such networks, their training and evaluation, requires large structural and spectral databases and significant computational resources and time. However, once the network has been completed it can be used very effectively for practical applications on an ordinary desktop computer. In this article we describe the methodology for creating such a network for infrared and mass spectra, and present a program for use on a personal computer, either connected to a spectrometer or independently. The program accepts data in ASCII format, both for the network description and for the spectral information. This approach permits the use of neural networks in an analytical laboratory with limited computational resources. Keywords: neural networks, infrared spectroscopy, mass spectroscopy, structure determination.
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Pancoska, Petr, Vit Janota, and Timothy A. Keiderling. "Interconvertibility of Electronic and Vibrational Circular Dichroism Spectra of Proteins: A Test of Principle Using Neural Network Mapping." Applied Spectroscopy 50, no. 5 (May 1996): 658–68. http://dx.doi.org/10.1366/0003702963905916.

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Electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) are compared with respect to their interconvertibility for protein structural studies. ECD and amide I' VCD spectra of 28 proteins were used with a backpropagation projection neural network with one hidden layer to develop a mapping between the two spectral types. After the network converged, the number of neurons in the hidden layer was optimized by principal component analysis of the synaptic weights of the pilot network topology with redundant hidden neurons. Actual prediction of one spectrum from the other for individual proteins was tested by retraining these networks with 28 reduced training sets having one protein systematically left out. Comparison of network-predicted spectra with experimental ones is used to identify those spectral features which are unique in each method. Similarly, the VCD spectra of 23 proteins measured in both D2O and H2O in the amide I region were mapped onto each other with the use of the same type of neural network calculation. The results show that the effects of partial deuteration on the VCD spectra band shape are predictable from the H2O spectra. An analysis of the synaptic weights of the optimized networks was performed which allowed identification of the linear and nonlinear parts of the obtained mappings. Insight into the details of how the neural networks encode and process the spectroscopic information is derived from a spectral representation of these weight matrices.
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Humphries, Mark D., Javier A. Caballero, Mat Evans, Silvia Maggi, and Abhinav Singh. "Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models." PLOS ONE 16, no. 7 (July 2, 2021): e0254057. http://dx.doi.org/10.1371/journal.pone.0254057.

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Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof.
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Bunimovich, Leonid, D. J. Passey, Dallas Smith, and Benjamin Webb. "Spectral and Dynamic Consequences of Network Specialization." International Journal of Bifurcation and Chaos 30, no. 06 (May 2020): 2050091. http://dx.doi.org/10.1142/s0218127420500911.

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One of the hallmarks of real networks is the ability to perform increasingly complex tasks as their topology evolves. To explain this, it has been observed that as a network grows certain subsets of the network begin to specialize the function(s) they perform. A recent model of network growth based on this notion of specialization has been able to reproduce some of the most well-known topological features found in real-world networks including right-skewed degree distributions, the small world property, modular as well as hierarchical topology, etc. Here we describe how specialization under this model also effects the spectral properties of a network. This allows us to give the conditions under which a network is able to maintain its dynamics as its topology evolves. Specifically, we show that if a network is intrinsically stable, which is a stronger version of the standard notion of global stability, then the network maintains this type of dynamics as the network evolves. This is one of the first steps toward unifying the rigorous study of the two types of dynamics exhibited by networks. These are the dynamics of a network, which is the topological evolution of the network’s structure, modeled here by the process of network specialization, and the dynamics on a network, which is the changing state of the network elements, where the type of dynamics we consider is global stability. The main examples we apply our results to are recurrent neural networks, which are the basis of certain types of machine learning algorithms.
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Dissertations / Theses on the topic "Spectral networks"

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Lu, Lu. "Spectral-efficient design in modern wireless communications networks." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53902.

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We investigate spectral-efficient design and develop novel schemes to improve spectral efficiency of the modern wireless communications networks. Nowadays, more and more spectrum resources are required to support various high-data-rate applications while spectrum resources are limited. Moreover, static allocation and exclusive access in current spectrum assignment policy caused a lot of licensed spectrum bands to be underutilized. To deal with the problem, cognitive radio (CR) has been developed, which allows unlicensed/secondary users to transmit with licensed/primary users as long as the former ones do not generate intolerable interference to the latter ones. The coexistence of users and networks requires careful and dynamic planning to mitigate interference. Otherwise, the network performance will be severely undermined. We study both spectrum sensing and spectrum access techniques and propose several transmit schemes for different types of cognitive ratio networks, including spectrum overlay and spectrum underlay systems. The proposed algorithms can improve spectral efficiency of the networks efficiently and have potentials to be used in future wireless communications networks.
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Bandeira, Nuno Filipe Cabrita. "Spectral networks algorithms for de novo interpretation of tandem mass spectra." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3274510.

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Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed October 2, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 141-152).
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Smith, Dallas C. "Network Specializations, Symmetries, and Spectral Properties." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6998.

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In this dissertation, we introduce three techniques for network sciences. The first of these techniques is a series of new models for describing network growth. These models, called network specialization models, are built with the idea that networks grow by specializing the function of subnetworks. Using these models we create theoretical networks which exhibit well-known properties of real networks. We also demonstrate how the spectral properties are preserved as the models grow. The second technique we describe is a method for decomposing networks that contain automorphisms in a way that preserves the spectrum of the original graph. This method for graph (or equivalently matrix) decomposition is called an equitable decomposition. Previously this method could only be used for particular classes of automorphisms, but in this dissertation we have extended this theory to work for every automorphism. Further we explain a number of applications which use equitable decompositions. The third technique we describe is a generalization of network symmetry, called latent symmetry. We give numerous examples of networks which contain latent symmetries and also prove some properties about them
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Shorten, David. "Spectral analysis of neutral evolution." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27420.

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It has been argued that much of evolution takes place in the absence of fitness gradients. Such periods of evolution can be analysed by examining the mutational network formed by sequences of equal fitness, that is, the neutral network. It has been demonstrated that, in large populations under a high mutation rate, the population distribution over the neutral network and average mutational robustness are given by the principal eigenvector and eigen- value, respectively, of the network's adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from spectral graph theory and utilize numerical methods to enhance our understanding of how populations distribute themselves over neutral networks. We demonstrate that, in the presence of certain topological features, the population will undergo an exploration catastrophe and become confined to a small portion of the network. We further derive approximations, in terms of mutational biases, for the population distribution and average robustness in networks with a homogeneous structure. The applicability of these results is explored, first, by a detailed review of the literature in both evolutionary computing and biology concerning the structure of neutral networks. This is extended by studying the actual and predicted population distribution over the neutral networks of H1N1 and H3N2 influenza haemagglutinin during seasons between 2005 and 2016. It is shown that, in some instances, these populations experience an exploration catastrophe. These results provide insight into the behaviour of populations on neutral networks, demonstrating that neutrality does not necessarily lead to an exploration of genotype/phenotype space or an associated increase in population diversity. Moreover, they provide a plausible explanation for conflicting results concerning the relationship between robustness and evolvability.
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Miao, Guowang. "Cross-layer optimization for spectral and energy efficiency." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31807.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Li, Geoffrey Ye; Committee Member: Ma, Xiaoli; Committee Member: Stuber, Gordon; Committee Member: Wardi, Yorai; Committee Member: Yu, Xingxing. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Yamamoto, Koji. "Capacity and Spectral Efficiency of Multihop Radio Networks." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/68891.

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Adouane, Amine Mohamed. "Dynamic management of spectral resources in LTE networks." Thesis, Versailles-St Quentin en Yvelines, 2015. http://www.theses.fr/2015VERS007V/document.

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La croissance exponentielle du nombre de dispositifs communicants et des services sans fils émergents fixe des objectifs toujours plus haut pour répondre à la demande de capacité sans cesse croissante des utilisateurs. Cela pose des défis constants pour atteindre les objectifs envisagés. La réutilisation spectrale élevée (High efficiency spectral reuse) a été adopté, cependant, elle conduit à des interférences accrues sur le réseau, ce qui dégrade les performances. L'OFDM (Orthogonal Frequency Division Multiplexing) est utilisé comme solution dans les réseaux de 4 G. Grâce à son orthogonalité, l'OFDM élimine l'interférence intra-cellulaire, mais l'interférence inter-cellule reste importante. Plusieurs méthodes connues sous le nom d'Inter-Cell interférences coordination (ICIC) ont été proposées pour les diminuer. L'ICIC permet la gestion des ressources radio coordonnée entre plusieurs cellules appelées ENodeB. Ces eNodeB peuvent partager les informations nécessaires grâce à l'interface X2 qui les relient, ces informations sont transmises par des messages LTE normalisés. Lorsque les ENodeBs sélectionnent égoïstement les ressources, la théorie de jeux non-coopératifs est largement appliquée pour trouver un juste équilibre. Dans cette thèse, nous mettons l'accent sur l'ICIC pour la liaison descendante d'un système OFDMA cellulaire dans le contexte du projet SOAPS (Spectrum opportuniste accès à la Sécurité publique). Ce projet a pour but l'amélioration de la planification des ressources de fréquences pour fournir des services à large bande dans les systèmes PMR (radiocommunications mobiles privées) en utilisant les technologies LTE. Nous adressons le problème d'ICIC en proposant quatre solutions différentes sous forme d'algorithmes entièrement décentralisés, ces algorithmes se basent sur la théorie des jeux non-coopératifs avec des équilibres de Nash purs des jeux considérés
The exponential growth in the number of communications devices has set out new ambitious targets to meet the ever-increasing demand for user capacity in emerging wireless systems. However, the inherent impairments of communication channels in cellular systems pose constant challenges to meet the envisioned targets. High spectral reuse efficiency was adopted as a solution to higher data rates. Despite its benefits, high spectral reuse leads to increased interference over the network, which degrades performances of mobile users with bad channel quality. To face this added interfence, OFDM (Orthogonal Frequency Division Multiplexing) is used for the new 4th generation network. Thanks to its orthogonality OFDM eliminates the intra-cellular interference, but when the same resources are used in two adjacents cells, the inter-cell interference becomes severe. To get rid of the latter, several methods for Inter-Cell Interference Coordination (ICIC) have been proposed. ICIC allows coordinated radio resources management between multiple cells. The eNodeBs can share resource usage information and interference levels over the X2 interface through LTE-normalized messages. Non-cooperative game theory was largely applied were eNodeBs selfishly selects resource blocks (RBs) in order to minimize interference. In this thesis, we stress on ICIC for the downlink of a cellular OFDMA system in the context of the SOAPS (Spectrum Opportunistic Access in Public Safety) project. This project focuses on the improvement of frequency resource scheduling for Broadband Services provision by PMR (Private Mobile Radio) systems using LTE technologies. We addressed this problem with four different solutions based on Non-cooperative game theory, three algorithms are devoted to RB selection in order to manage the interference, while the last one is a power control scheme with power economy and enhanced system performances
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Ahmed, Junaid. "Spectral efficiency of CDMA based ad-hoc networks." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/spectral-efficiency-of-cdma-based-adhoc-networks(f6d958ac-6778-416e-80a5-2318956dbaf2).html.

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Spectrum efficiency and energy efficiency are two important attributes driving innovation in wireless communication. Efficient spectrum utilization and sharing with multiple access techniques and using under-utilized spectra by cognitive radios is the current focus due to the scarcity and cost of the available radio spectrum. Energy efficiency to increase operating time of portable handheld devices like smartphones that handle simultaneous voice/video streaming and web browsing and battery powered nodes in a sensor network where battery capacity determines the lifetime of the network is another area attracting researchers. The focus of this thesis is the spectral efficiency of multicarrier code division multiple access (CDMA) in wireless ad-hoc networks. Furthermore, energy efficiency to maximize lifetime of a network are also studied.In a multicarrier CDMA system inter-carrier interference (ICI) due to carrier frequency offset and multiple access interference (MAI) are two major factors that deteriorate the performance. Previous work in this area has been mostly focused on simulation results due to the complexity of the analysis due to the large number of random variables involved. Taking into account accurate statistical models for ICI and MAI that account for the correlation between adjacent subcarriers, this thesis presents new mathematical analysis for the spectral efficiency of multicarrier CDMA communication systems over a frequency selective Rayleigh fading environment. We analyze and compare three multicarrier CDMA schemes which are multicarrier CDMA, multicarrier direct-sequence CDMA and multitone CDMA. We also present simulation results to confirm the validity of our analysis. We also analyze the performance of three simple multiple access techniques or coexistence etiquettes in detail, which are simple to implement and do not require any central control. Accurate interference models are developed and are used to derive accurate expressions for packet error rates in the case of direct sequence CDMA and slotted packet transmission schemes. These results are then used to study the performance of the coexistence etiquettes and compare them with each other. Finally we present a new joint node selection and power allocation strategy that increases lifetime of an ad-hoc network where nodes cooperate to enable diversity in transmission.
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Lahsen-Cherif, Iyad. "Spectral and Energy Efficiency in 5G Wireless Networks." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS506/document.

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La pénurie d'énergie et le manque d'infrastructures dans les régions rurales représentent une barrière pour le déploiement et l'extension des réseaux cellulaires. Les approches et techniques pour relier les stations de base (BSs) entre elles à faible coût et d'une manière fiable et efficace énergiquement sont l'une des priorités des opérateurs. Ces réseaux peu denses actuellement, peuvent évoluer rapidement et affronter une croissance exponentielle due principalement à l'utilisation des téléphones mobiles, tablettes et applications gourmandes en bande passante. La densification des réseaux est l'une des solutions efficaces pour répondre à ce besoin en débit élevé. Certes, l'introduction de petites BSs apporte de nombreux avantages tels que l'amélioration du débit et de la qualité du signal, mais entraîne des contraintes opérationnelles telles que le choix de l'emplacement des noeuds dans ces réseaux de plus en plus denses ainsi que leur alimentation. Les problèmes où la contrainte spatiale est prépondérante sont bien appropriés à la modélisation par la géométrie stochastique qui permet une modélisation réaliste de distribution des BSs. Ainsi, l'enjeu est de trouver de nouvelles approches de gestions d'interférence et de réductions de consommation énergétique dans les réseaux sans fil. Le premier axe de cette thèse s'intéresse aux méthodes de gestion d'interférence dans les réseaux cellulaires se basant sur la coordination entre les BSs, plus précisément, la technique Coordinated MultiPoint Joint Transmission (CoMP-JT). En CoMP-JT, les utilisateurs en bordure de cellules qui subissent un niveau très élevé d'interférences reçoivent plusieurs copies du signal utile de la part des BSs qui forment l'ensemble de coordination. Ainsi, nous utilisons le modèle r-l Square Point Process (PP) à fin de modéliser la distribution des BSs dans le plan. Le processus r-l Square PP est le plus adapté pour modéliser le déploiement réel des BSs d'un réseau sans fil, en assurant une distance minimale, (r - l), entre les points du processus. Nous discutons l'impact de la taille de l'ensemble de coordination sur les performances évaluées. Ce travail est étendu pour les réseaux denses WiFi IEEE 802.11, où les contraintes de portées de transmission et de détection de porteuse ont été prises en compte. Dans le deuxième axe du travail, nous nous intéressons à l'efficacité énergétique des réseaux mesh. Nous proposons l'utilisation des antennes directionnelles (DAs) pour réduire la consommation énergétique et améliorer le débit de ces réseaux mesh. Les DAs ont la capacité de focaliser la transmission dans la direction du récepteur, assurant une portée plus importante et moins d'énergie dissipée dans toutes les directions. Pour différentes topologies, nous dérivons le nombre de liens et montrons que ce nombre dépend du nombre de secteurs de l'antenne. Ainsi, en utilisant les simulations, nous montrons que le gain, en énergie et en débit, apporté par les DAs peut atteindre 70% dans certains cas. De plus, on propose un modèle d'optimisation conjointe d'énergie et du débit adapté aux réseaux WMNs équipés de DAs. La résolution numérique de ce modèle conforte les résultats de simulation obtenus dans la première partie de cette étude sur l'impact des DAs sur les performances du réseau en termes de débit et d'énergie consommée. Ces travaux de thèse s'inscrivent dans le cadre du projet collaboratif (FUI16 LCI4D), qui consiste à concevoir et à valider une architecture radio ouverte pour renforcer l'accès aux services broadband dans des lieux ne disposant que d'une couverture minimale assurée par un réseau macro-cellulaire traditionnel
Today's networks continue to evolve and grow resulting more dense, complex and heterogeneous networks.This leads to new challenges such as finding new models to characterize the nodes distribution in the wireless network and approaches to mitigate interference. On the other hand, the energy consumption of WMNs is a challenging issue mainly in rural areas lacking of default electrical grids. Finding alternative technologies and approaches to reduce the consumed energy of these networks is a interesting task. This thesis focuses on proposing and evaluating interference management models for next generation wireless networks (5G and Very Dense High WLANs), and providing tools and technologies to reduce energy consumption of Wireless Mesh Networks (WMNs). Two different problems are thus studied; naturally the thesis is divided into two parts along the following chapters.The contribution of the first part of the thesis is threefold. Firstly, we develop our interference management coordination (CoMP-JT) model. The main idea of CoMP-JT is to turn signals generating harmful interference into useful signals. We develop a new model where BSs inside the coordinated set send a copy of data to border's users experiencing high interference. We consider the r-l Square point process to model the BSs distribution in the network. We derive network performance in terms of coverage probability and throughput. Additionally, we study the impact of the size of coordination set on the network performance. Secondly, we extend these results and provide a new model adopted for Dense Very high throughput WLANs. We take into consideration constraints of WLANs in our model such as carrier sensing range. Thirdly, we tackle resource allocation strategies to limit the interference in LTE networks. We study three cyclic allocation strategies: (i) the independent allocation, (ii) the static allocation and (iii) the load-dependent strategy. We derive tractable analytical expression of the first and second mean of interference. We validate the model using extensive simulations. Reducing the energy consumption and improving the energy efficiency of WMNs is our concern in the second part of the thesis. Indeed, we aim at studying the impact of directional antennas technology on the performance of WMNs, using both analysis and simulations. Fisrt, We derive the Number of Links (NLs) for the chain and grid topologies for different antennas beams. These results are based on the routing tables of nodes in the network. We consider different scenarios such as 1Source-NDestinations to model the downlink communications, NSources-1Destination to model the uplink communications and the 1Source-1Destination as a baseline scenario. Using ns-3 simulator, we simulate network performance in terms of Mean Loss Ratio, throughput, energy consumption and energy efficiency. Then, we study the impact of number of beams, network topology and size, the placement of the gateway on the network performance. Next, we go beyond simulations and propose an optimization framework minimizing the consumed energy while maximizing the network throughput for DAs WMNs. We consider a weighted objective function combining the energy consumption and the throughput. We use power control to adapt transmission power depending on the location of the next hop. This model is a first step to approve the obtained simulation results. We use ILOG Cplex solver to find the optimal solution. Results show that DAs improves the network throughput while reduce the energy consumption and that power control allows saving more energy. In this direction, the LCI4D Project aims at providing low cost infrastructure to connect isolated rural and sub-urban areas to the Internet. In order to reduce the installation and maintenance costs, LCI4D proposes the usage of self-configured Wireless Mesh Networks (WMNs) to connect multimode outdoor femtocells to the remote Marco cell (gateway)
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Kunegis, Jérôme [Verfasser]. "On the Spectral Evolution of Large Networks / Jérôme Kunegis." Koblenz : Universitätsbibliothek Koblenz, 2011. http://d-nb.info/1017370893/34.

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Books on the topic "Spectral networks"

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

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

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

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

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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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Book chapters on the topic "Spectral networks"

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Dorogovtsev, Sergei N., Alexander V. Goltsev, José F. F. Mendes, and Alexander N. Samukhin. "Spectral Analysis of Random Networks." In Complex Networks, 35–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_2.

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Blekas, Konstantinos, K. Christodoulidou, and I. E. Lagaris. "Newtonian Spectral Clustering." In Artificial Neural Networks – ICANN 2009, 145–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_15.

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Demirel-Frank, Semra. "Spectral Inequalities for Quantum Graphs." In Mathematical Technology of Networks, 65–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16619-3_6.

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Dai, Qionghai, and Yue Gao. "Neural Networks on Hypergraph." In Artificial Intelligence: Foundations, Theory, and Algorithms, 121–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_7.

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AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN), which extend hypergraph computation to hyperbolic spaces beyond the Euclidean space. For the spatial-based methods, the convolution operation is defined in groups of spatially close vertices. We then present spatial-based hypergraph neural networks of the general hypergraph neural networks (HGNN+) and the dynamic hypergraph neural networks (DHGNN). Additionally, there are several convolution methods that attempt to reduce the hypergraph structure to the graph structure, so that the existing graph convolution methods can be directly deployed. Lastly, we analyze the association and comparison between hypergraph and graph in the two areas described above (spectral-based, spatial-based), further demonstrating the ability and advantages of hypergraph on constructing and computing higher-order correlations in the data.
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Kurucz, Miklós, András A. Benczúr, Károly Csalogány, and László Lukács. "Spectral Clustering in Social Networks." In Advances in Web Mining and Web Usage Analysis, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00528-2_1.

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Kunegis, Jérôme. "Spectral Evolution of Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_125-1.

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Palmer, William R., and Tian Zheng. "Spectral Clustering for Directed Networks." In Complex Networks & Their Applications IX, 87–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65347-7_8.

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Kunegis, Jérôme. "Spectral Evolution of Social Networks." In Encyclopedia of Social Network Analysis and Mining, 2040–47. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_125.

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Farhi, Haider, and Abderraouf Messai. "Spectral Capacity in Cognitive Networks." In Smart Innovation, Systems and Technologies, 423–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21009-0_41.

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Kunegis, Jérôme. "Spectral Evolution of Social Networks." In Encyclopedia of Social Network Analysis and Mining, 2964–71. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_125.

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Conference papers on the topic "Spectral networks"

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Li, Yuan, Zunyue Zhang, Yi Wang, Yue Yu, Xuetong Zhou, Hon Ki Tsang, and Xiankai Sun. "Inverse-designed linear coherent photonic networks for high-resolution spectral reconstruction." In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_si.2023.sth4g.1.

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We proposed and demonstrated integrated photonic networks composed of inverse-designed components for spectral reconstruction. We reconstructed spectral lines separated by 100 pm and continuous spectra from a device with an ultracompact footprint of 88×300 μm2.
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Jin, Shengmin, and Reza Zafarani. "The Spectral Zoo of Networks: Embedding and Visualizing Networks with Spectral Moments." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403195.

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Feuer, Mark D., Mario V. Bnyamin, and Xin Jiang. "Mitigation of Spectral Slicing Penalty Using Binary Polarization-Shift Keying." In Photonic Networks and Devices. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/networks.2019.neth3d.5.

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Gopalan, Abishek, Onur Turkcu, Biao Lu, and Parthiban Kandappan. "Spectral Efficiencies of WDM Network Architectures with Sliceable Bandwidth Variable Transponders." In Photonic Networks and Devices. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/networks.2017.netu1b.3.

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Parvin, B., Z. N. Ghosh, L. Heiser, M. Knapp, C. Talcott, K. Laderoute, J. Gray, and P. Spellman. "Spectral Decomposition of Signaling Networks." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221207.

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Zheng, Q., and D. B. Skillicorn. "Spectral Embedding of Signed Networks." In Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.7.

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Zheng, Q., and D. B. Skillicorn. "Spectral Embedding of Directed Networks." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809310.

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Moitra, Ankur, and Alexander S. Wein. "Spectral methods from tensor networks." In STOC '19: 51st Annual ACM SIGACT Symposium on the Theory of Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3313276.3316357.

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Pedro, João. "Challenges of Designing Transparent Flexible-Grid Optical Networks for Maximum Spectral Efficiency." In Photonic Networks and Devices. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/networks.2016.new3c.1.

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Gupta, Anjali, and Brijendra Kumar Joshi. "Spectral Efficiency Evaluation of Network Coded Cognitive Radio Networks." In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2022. http://dx.doi.org/10.1109/csnt54456.2022.9787641.

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Reports on the topic "Spectral networks"

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Sweeney, Matthew, and Emily Shinkle. Understanding Discrete Fracture Networks Through Spectral Graph Theory. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812641.

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Sweeney, Matthew, and Emily Shinkle. Understanding Discrete Fracture Networks Through Spectral Graph Theory. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812622.

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Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, and Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4119.

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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex network of key cryptocurrencies. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock and crypto market, and their specific behaviour in the pre-crisis period is used as indicators- precursors of critical phenomena.
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Liu, Ernest, and Aleh Tsyvinski. Dynamical Structure and Spectral Properties of Input-Output Networks. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28178.

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Mayfield, Howard T., Delyle Eastwood, and Larry W. Burggraf. Infrared Spectral Classification with Artificial Neural Networks and Classical Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada377976.

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Armstrong, Derek Elswick, and Joseph Gabriel Gorka. Using Deep Neural Networks to Extract Fireball Parameters from Infrared Spectral Data. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1623398.

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Silvester, J. A., and A. Polydoros. Adaptive Spread Spectrum Networks. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada187154.

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Pursley, Michael B., and Dilip V. Sarwate. Spread Spectrum Radio Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada188914.

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Sastry, Ambatipudi R. Spread Spectrum Random Access Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1993. http://dx.doi.org/10.21236/ada272280.

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McEliece, Robert J. Spectrum Allocation Strategies for Communication Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada294936.

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