Nodes Of Interest Research Articles

特異値分解による術中X線投影動画から時間濃度曲線の推定手法の開発

It is a challenge in clinical practice to estimate Time Enhancement Curves (TEC) of a Region Of Interest ( ROI ) in real time from intraoperative X-ray projection movies. In the past, TEC was estimated by obtaining the Standard Linear Attenuation Coefficient Distribution (SLACD) from a preoperative contrast-enhanced CT and combining it with an intraoperative X-ray projection movie. However, in actual clinical practice, the SLACD is often uncertain due to the unavailability of preoperative contrast-enhanced CT images and the patient's respiratory motion. We developed a method for estimating TECs at a newly defined Node Of Interest (NOIs) by applying Singular Value Decomposition ( SVD ) and prior information to intraoperative X-ray projection movies only. To validate the effectiveness of this method, twin numerical experiments were conducted using a simulated blood vessel model, and the estimation results were compared. The estimation results show that the method is able to accurately estimate the rise time of TECs and the frame of maximum concentration.

Revealing the political affinity of online entities through their Twitter followers

In this work, we show that the structural features of the Twitter online social network can divulge valuable information about the political affinity of the participating nodes. More precisely, we show that Twitter followers can be used to predict the political affinity of prominent Nodes of Interest (NOIs) they opt to follow. We utilize a series of purely structure-based algorithmic approaches, such as modularity clustering, the minimum linear arrangement (MinLA) problem and the DeGroot opinion update model in order to reveal diverse aspects of the NOIs’ political profile. Our methods are applied to a dataset containing the Twitter accounts of the members of the Greek Parliament as well as an enriched dataset that additionally contains popular news sources. The results confirm the viability of our approach and provide evidence that the political affinity of NOIs can be determined with high accuracy via the Twitter follower network. Moreover, the outcome of an independently performed expert study about the offline political scene confirms the effectiveness of our methods.

Géolocalisation des DM : principes et applications

Position location usually relies on direct observation to/from conventional landmarks with known positions from/to a Node of Interest (NOI). Nonetheless, in an Ad-Hoc Wireless Sensor Network (AHWSN), nodes are often unable to establish a direct connection with the available Access Points (APs). In such a scenario, neighboring nodes may supply cooperative information to enable inference of the location of a given NOI in a network. In this paper we examine the feasibility of relational techniques in multihop environments to estimate position location. Two novel position estimation techniques are presented: the Relative Proximity Algorithm (RPA) and the Enhanced Relative Proximity Algorithm (ERPA). RPA and ERPA can operate as range-based or as range-free techniques, which makes them both attractive and flexible solutions for position estimation in AHWSNs. The performance of these techniques is characterized and found to be related to the number of cooperating nodes, the number of APs available in the network, and the presence of measurement noise. RPA and ERPA are also compared to several known position location methods reported in the literature, and it is shown that they achieve adequate location estimation accuracy with some advantages in terms of the number of access points required and network traffic overhead.

Weighted Hyperbolic DV-Hop Positioning Node Localization Algorithm in WSNs

The localization of nodes plays a fundamental role in Wireless Sensor and Actors Networks (WSAN) identifying geographically where an event occurred, which facilitates timely response to this action. This article presents a performance evaluation of multi-hop localization range-free algorithms used in WSAN, such as Distance Vector Hop (DV-Hop), Improved DV-Hop (IDV-Hop), and the Weighted DV-Hop (WDV-Hop). In addition, we propose a new localization algorithm, merging WDV-Hop, with the weighted hyperbolic localization algorithm (WH), which includes weights to the correlation matrix of the estimated distances between the node of interest (NOI) and the reference nodes (RN) in order to improve accuracy and precision. As performance metrics, the accuracy, precision, and computational complexity are evaluated. The algorithms are evaluated in three scenarios where all nodes are randomly distributed in a given area, varying the number of RNs, the density of nodes in the network, and radio coverage of the nodes. The results show that in networks with 100 nodes, WDV-Hop outperforms the DV-Hop and IDV-Hop even if the number of RNs is reduced to 10. Moreover, our proposal shows an improvement in terms of accuracy and precision at the cost of increased computational complexity, specifically in the algorithm execution time, but without affecting the hardware cost or power consumption.

Relational position location in ad-hoc networks

Position location usually relies on direct observation to/from conventional landmarks with known positions from/to a Node of Interest (NOI). Nonetheless, in an Ad-Hoc Wireless Sensor Network (AHWSN), nodes are often unable to establish a direct connection with the available Access Points (APs). In such a scenario, neighboring nodes may supply cooperative information to enable inference of the location of a given NOI in a network. In this paper we examine the feasibility of relational techniques in multihop environments to estimate position location. Two novel position estimation techniques are presented: the Relative Proximity Algorithm (RPA) and the Enhanced Relative Proximity Algorithm (ERPA). RPA and ERPA can operate as range-based or as range-free techniques, which makes them both attractive and flexible solutions for position estimation in AHWSNs. The performance of these techniques is characterized and found to be related to the number of cooperating nodes, the number of APs available in the network, and the presence of measurement noise. RPA and ERPA are also compared to several known position location methods reported in the literature, and it is shown that they achieve adequate location estimation accuracy with some advantages in terms of the number of access points required and network traffic overhead.

Leveraging Social Networks for P2P Content-Based File Sharing in Disconnected MANETs

Current peer-to-peer (P2P) file sharing methods in mobile ad hoc networks (MANETs) can be classified into three groups: flooding-based, advertisement-based, and social contact-based. The first two groups of methods can easily have high overhead and low scalability. They are mainly developed for connected MANETs, in which end-to-end connectivity among nodes is ensured. The third group of methods adapts to the opportunistic nature of disconnected MANETs but fails to consider the social interests (i.e., contents) of mobile nodes, which can be exploited to improve the file searching efficiency. In this paper, we propose a P2P content-based file sharing system, namely SPOON, for disconnected MANETs. The system uses an interest extraction algorithm to derive a node's interests from its files for content-based file searching. For efficient file searching, SPOON groups common-interest nodes that frequently meet with each other as communities. It takes advantage of node mobility by designating stable nodes, which have the most frequent contact with community members, as community coordinators for intracommunity searching, and highly mobile nodes that visit other communities frequently as community ambassadors for intercommunity searching. An interest-oriented file searching scheme is proposed for high file searching efficiency. Additional strategies for file prefetching, querying-completion, and loop-prevention, and node churn consideration are discussed to further enhance the file searching efficiency. We first tested our system on the GENI Orbit testbed with a real trace and then conducted event-driven experiment with two real traces and NS2 simulation with simulated disconnected and connected MANET scenarios. The test results show that our system significantly lowers transmission cost and improves file searching success rate compared to current methods.