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Network topology

About: Network topology is a research topic. Over the lifetime, 52259 publications have been published within this topic receiving 1006627 citations.


Papers
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Journal ArticleDOI
12 Jul 2011
TL;DR: In this paper, a graph-theoretic definition of connectivity is provided, as well as an equivalent definition based on algebraic graph theory, which employs the adjacency and Laplacian matrices of the graph and their spectral properties.
Abstract: In this paper, we provide a theoretical framework for controlling graph connectivity in mobile robot networks. We discuss proximity-based communication models composed of disk-based or uniformly-fading-signal-strength communication links. A graph-theoretic definition of connectivity is provided, as well as an equivalent definition based on algebraic graph theory, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. Based on these results, we discuss centralized and distributed algorithms to maintain, increase, and control connectivity in mobile robot networks. The various approaches discussed in this paper range from convex optimization and subgradient-descent algorithms, for the maximization of the algebraic connectivity of the network, to potential fields and hybrid systems that maintain communication links or control the network topology in a least restrictive manner. Common to these approaches is the use of mobility to control the topology of the underlying communication network. We discuss applications of connectivity control to multirobot rendezvous, flocking and formation control, where so far, network connectivity has been considered an assumption.

345 citations

Journal ArticleDOI
TL;DR: The analysis shows that the optimum power allocation at different nodes follows a certain ordering, and that the power-allocation scheme at high SNR does not depend on the channel quality of the direct link between the source and the destination.
Abstract: In this paper, a class of cooperative communication protocols with arbitrary N-relay nodes is proposed for wireless networks, in which each relay coherently combines the signals received from m (1lesmlesN-1) previous relays in addition to the signal from the source. Exact symbol-error-rate (SER) expressions for an arbitrary N-node network employing M'ary phase-shift-keying (MPSK) modulation or quadrature-amplitude modulation (QAM) are provided for the proposed class of protocols. Further, approximate expressions for the SER are derived and shown to be tight at high enough signal-to-noise ratio (SNR). Our analysis reveals an interesting result: The class of cooperative protocols shares the same asymptotic performance at high enough SNR and does not depend on m, the number of previous nodes involving in coherent detection, hence, the asymptotic performance of a simple cooperative scenario in which each relay combines the signals from the source and the previous relay is exactly the same as that for a much more complicated scenario in which each relay combines the signals from the source and all the previous relays. The theoretical results also confirm that full diversity equal to the number of cooperating nodes is indeed achievable by the proposed protocols. Finally, we formulate a power-allocation problem in order to minimize the SER of the system. The analysis shows that the optimum power allocation at different nodes follows a certain ordering, and that the power-allocation scheme at high SNR does not depend on the channel quality of the direct link between the source and the destination. Closed-form solutions for the optimal power-allocation problem are provided for some network topologies. Simulation results confirm our theoretical analysis

344 citations

Proceedings ArticleDOI
07 Jun 2004
TL;DR: SUNMAP automates NoC selection and generation, bridging an important design gap in building NoCs and explores various design objectives such as minimizing average communication delay, area, power dissipation subject to bandwidth and area constraints.
Abstract: Increasing communication demands of processor and memory cores in Systems on Chips (SoCs) necessitate the use of Networks on Chip (NoC) to interconnect the cores. An important phase in the design of NoCs is he mapping of cores onto the most suitable opology for a given application. In this paper, we present SUNMAP a tool for automatically selecting he best topology for a given application and producing a mapping of cores onto that topology. SUNMAP explores various design objectives such as minimizing average communication delay, area, power dissipation subject to bandwidth and area constraints. The tool supports different routing functions (dimension ordered, minimum-path, traffic splitting) and uses floorplanning information early in the topology selection process to provide feasible mappings. The network components of the chosen NoC are automatically generated using cycle-accurate SystemC soft macros from X-pipes architecture. SUNMAP automates NoC selection and generation, bridging an important design gap in building NoCs. Several experimental case studies are presented in the paper, which show the rich design space exploration capabilities of SUNMAP.

343 citations

Journal ArticleDOI
TL;DR: NetworkAnalyst, taking advantage of state-of-the-art web technologies, is developed, to enable high performance network analysis with rich user experience and presents the results via a powerful online network visualization framework.
Abstract: Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca.

343 citations

Posted Content
TL;DR: This work reveals the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, and finds that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations.
Abstract: In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.

342 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,292
20223,051
20212,286
20202,746
20192,992
20183,259