The Experts below are selected from a list of 19443 Experts worldwide ranked by ideXlab platform

Guoqiang Mao - One of the best experts on this subject based on the ideXlab platform.

  • A New Measure of Wireless Network Connectivity
    IEEE Transactions on Mobile Computing, 2015
    Co-Authors: Soura Dasgupta, Guoqiang Mao, Brian D. O. Anderson
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to characterize and measure the quality of Connectivity of a wireless network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and which has unreliable connections, reflecting the inherent unreliability of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides an intuitive measure of the quality of (end-to-end) network connections. In this paper, we introduce a probabilistic Connectivity Matrix as a tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We demonstrate that the largest magnitude eigenvalue of the probabilistic Connectivity Matrix, which is positive, can serve as a good measure of the quality of network Connectivity. We provide a flooding algorithm whereby the nodes repeatedly flood the network with packets, and by measuring just the number of packets a given node receives, the node is able to asymptotically estimate this largest eigenvalue.

  • GLOBECOM - On the quality of wireless network Connectivity
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Soura Dasgupta, Guoqiang Mao
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to measure the quality of Connectivity of a wireless multi-hop network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and has unreliable connections, reflecting the inherent unreliable characteristics of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides a native measure of the quality of (end-to-end) network connections. In this paper, we explore the use of probabilistic Connectivity Matrix as a tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We show that the largest eigenvalue of the probabilistic Connectivity Matrix can serve as a good measure of the quality of network Connectivity.

  • On the Quality of Wireless Network Connectivity
    arXiv: Networking and Internet Architecture, 2011
    Co-Authors: Soura Dasgupta, Guoqiang Mao
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to measure the quality of Connectivity of a wireless multi-hop network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and has unreliable connections, reflecting the inherent unreliable characteristics of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of \emph{capacity} to measure the quality of a network in saturated traffic scenarios and provides a native measure of the quality of (end-to-end) network connections. In this paper, we explore the use of probabilistic Connectivity Matrix as a possible tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We argue that the largest eigenvalue of the probabilistic Connectivity Matrix can serve as a good measure of the quality of network Connectivity.

Soura Dasgupta - One of the best experts on this subject based on the ideXlab platform.

  • A New Measure of Wireless Network Connectivity
    IEEE Transactions on Mobile Computing, 2015
    Co-Authors: Soura Dasgupta, Guoqiang Mao, Brian D. O. Anderson
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to characterize and measure the quality of Connectivity of a wireless network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and which has unreliable connections, reflecting the inherent unreliability of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides an intuitive measure of the quality of (end-to-end) network connections. In this paper, we introduce a probabilistic Connectivity Matrix as a tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We demonstrate that the largest magnitude eigenvalue of the probabilistic Connectivity Matrix, which is positive, can serve as a good measure of the quality of network Connectivity. We provide a flooding algorithm whereby the nodes repeatedly flood the network with packets, and by measuring just the number of packets a given node receives, the node is able to asymptotically estimate this largest eigenvalue.

  • GLOBECOM - On the quality of wireless network Connectivity
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Soura Dasgupta, Guoqiang Mao
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to measure the quality of Connectivity of a wireless multi-hop network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and has unreliable connections, reflecting the inherent unreliable characteristics of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides a native measure of the quality of (end-to-end) network connections. In this paper, we explore the use of probabilistic Connectivity Matrix as a tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We show that the largest eigenvalue of the probabilistic Connectivity Matrix can serve as a good measure of the quality of network Connectivity.

  • On the Quality of Wireless Network Connectivity
    arXiv: Networking and Internet Architecture, 2011
    Co-Authors: Soura Dasgupta, Guoqiang Mao
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to measure the quality of Connectivity of a wireless multi-hop network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and has unreliable connections, reflecting the inherent unreliable characteristics of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of \emph{capacity} to measure the quality of a network in saturated traffic scenarios and provides a native measure of the quality of (end-to-end) network connections. In this paper, we explore the use of probabilistic Connectivity Matrix as a possible tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We argue that the largest eigenvalue of the probabilistic Connectivity Matrix can serve as a good measure of the quality of network Connectivity.

Jeanfrancois Mangin - One of the best experts on this subject based on the ideXlab platform.

  • tractography based parcellation of the cortex using a spatially informed dimension reduction of the Connectivity Matrix
    Medical Image Computing and Computer-Assisted Intervention, 2009
    Co-Authors: Pauline Roca, Denis Riviere, Pamela Guevara, Cyril Poupon, Jeanfrancois Mangin
    Abstract:

    Determining cortical functional areas is an important goal for neurosciences and clinical neurosurgery. This paper presents a method for Connectivity-based parcellation of the entire human cortical surface, exploiting the idea that each cortex region has a specific connection profile. The Connectivity Matrix of the cortex is computed using analytical Q-ball-based tractography. The parcellation is achieved independently for each subject and applied to the subset of the cortical surface endowed with enough connections to estimate safely a Connectivity profile, namely the top of the cortical gyri. The key point of the method lies in a twofold reduction of the Connectivity Matrix dimension. First, parcellation amounts to iterating the clustering of Voronoi patches of the cortical surface into parcels endowed with homogeneous profiles. The parcels without intersection with the patch boundaries are selected for the final parcellation. Before clustering a patch, the complete profiles are collapsed into short profiles indicating Connectivity with a set of putative cortical areas. These areas are supposed to correspond to the catchment basins of the watershed of the density of connection to the patch computed on the cortical surface. The results obtained for several brains are compared visually using a coordinate system.

  • MICCAI (1) - Tractography-Based Parcellation of the Cortex Using a Spatially-Informed Dimension Reduction of the Connectivity Matrix
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2009
    Co-Authors: Pauline Roca, Denis Riviere, Pamela Guevara, Cyril Poupon, Jeanfrancois Mangin
    Abstract:

    Determining cortical functional areas is an important goal for neurosciences and clinical neurosurgery. This paper presents a method for Connectivity-based parcellation of the entire human cortical surface, exploiting the idea that each cortex region has a specific connection profile. The Connectivity Matrix of the cortex is computed using analytical Q-ball-based tractography. The parcellation is achieved independently for each subject and applied to the subset of the cortical surface endowed with enough connections to estimate safely a Connectivity profile, namely the top of the cortical gyri. The key point of the method lies in a twofold reduction of the Connectivity Matrix dimension. First, parcellation amounts to iterating the clustering of Voronoi patches of the cortical surface into parcels endowed with homogeneous profiles. The parcels without intersection with the patch boundaries are selected for the final parcellation. Before clustering a patch, the complete profiles are collapsed into short profiles indicating Connectivity with a set of putative cortical areas. These areas are supposed to correspond to the catchment basins of the watershed of the density of connection to the patch computed on the cortical surface. The results obtained for several brains are compared visually using a coordinate system.

Brian D. O. Anderson - One of the best experts on this subject based on the ideXlab platform.

  • A New Measure of Wireless Network Connectivity
    IEEE Transactions on Mobile Computing, 2015
    Co-Authors: Soura Dasgupta, Guoqiang Mao, Brian D. O. Anderson
    Abstract:

    Despite intensive research in the area of network Connectivity, there is an important category of problems that remain unsolved: how to characterize and measure the quality of Connectivity of a wireless network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and which has unreliable connections, reflecting the inherent unreliability of wireless communications? The quality of Connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides an intuitive measure of the quality of (end-to-end) network connections. In this paper, we introduce a probabilistic Connectivity Matrix as a tool to measure the quality of network Connectivity. Some interesting properties of the probabilistic Connectivity Matrix and their connections to the quality of Connectivity are demonstrated. We demonstrate that the largest magnitude eigenvalue of the probabilistic Connectivity Matrix, which is positive, can serve as a good measure of the quality of network Connectivity. We provide a flooding algorithm whereby the nodes repeatedly flood the network with packets, and by measuring just the number of packets a given node receives, the node is able to asymptotically estimate this largest eigenvalue.

  • WCNC - Graph Theoretic Models and Tools for the Analysis of Dynamic Wireless Multihop Networks
    2009 IEEE Wireless Communications and Networking Conference, 2009
    Co-Authors: Brian D. O. Anderson
    Abstract:

    Wireless multihop networks are being increasingly used in military and civilian applications. Advanced applications of wireless multihop networks demand better understanding on their properties. Existing research on wireless multihop networks has largely focused on static networks, where the network topology is time-invariant; and there is comparatively a lack of understanding on the properties of dynamic networks with dynamically changing topology. In this paper, we use and extend a recently proposed graph theoretic model, i.e. evolving graphs, to capture the characteristics of such networks. We extend and develop the concepts of route Matrix, Connectivity Matrix and probabilistic Connectivity Matrix as convenient tools to characterize and investigate the properties of evolving graphs and the associated dynamic networks. The properties of these matrices are established and their relevance to the properties of dynamic wireless multihop networks are introduced.

Radu Ranta - One of the best experts on this subject based on the ideXlab platform.

  • Stability conditions of Hopfield ring networks with discontinuous piecewise-affine activation functions
    2017
    Co-Authors: Amélie Aussel, Laure Buhry, Radu Ranta
    Abstract:

    Ring networks, a particular form of Hopfield neural networks, can be used in computational neurosciences in order to model the activity of place cells or head-direction cells. The behaviour of these models is highly dependent on their recurrent synaptic Connectivity Matrix and on individual neurons' activation function, which must be chosen appropriately to obtain physiologically meaningful conclusions. In this article, we propose some simpler ways to tune this synaptic Connectivity Matrix compared to existing literature so as to achieve stability in a ring attractor network with a piece-wise affine activation functions, and we link these results to the possible stable states the network can converge to.

  • CDC - Stability conditions of Hopfield ring networks with discontinuous piecewise-affine activation functions
    2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017
    Co-Authors: Amelie Aussei, Laure Buhry, Radu Ranta
    Abstract:

    Ring networks, a particular form of Hopfield neural networks, can be used in computational neurosciences in order to model the activity of place cells or head-direction cells. The behaviour of these models is highly dependent on their recurrent synaptic Connectivity Matrix and on individual neurons' activation function, which must be chosen appropriately to obtain physiologically meaningful conclusions. In this article, we propose some simpler ways to tune this synaptic Connectivity Matrix compared to existing literature so as to achieve stability in a ring attractor network with a piece-wise affine activation functions, and we link these results to the possible stable states the network can converge to.