Displaying similar documents to “A Cooperative Sensor Network: Optimal Deployment and Functioning”

A cooperative sensor network : optimal deployment and functioning

Alfonso Farina, Antonio Graziano, Francesca Mariani, Francesco Zirilli (2010)

RAIRO - Operations Research - Recherche Opérationnelle

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A network of mobile cooperative sensors is considered. The following problems are studied: (1) the “optimal“deployment of the sensors on a given territory; (2) the detection of local anomalies in the noisy data measured by the sensors. In absence of an information fusion center in the network, from “local” interactions between sensors “global“solutions of these problems are found.

Local dependency in networks

Miloš Kudělka, Šárka Zehnalová, Zdeněk Horák, Pavel Krömer, Václav Snášel (2015)

International Journal of Applied Mathematics and Computer Science

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Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes...

Acoustic analysis assessment in speech pathology detection

Daria Panek, Andrzej Skalski, Janusz Gajda, Ryszard Tadeusiewicz (2015)

International Journal of Applied Mathematics and Computer Science

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Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional...

Combination of 3D epoch-wise and permanent geodetic networks observed by GNSS

Ján Hefty, Ľubomíra Gerhátová (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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The local, regional and global geodetic networks are recently almost exclusively observed by satellite radionavigation methods, such as the U.S. Global Positioning System (GPS), and the Russian navigation system GLONASS. The unprecedented accuracy of geodetic satellite positioning allows determination of the geocentric site coordinates at millimetre level. The paper points to complex adjustment model applied for combination of 3D coordinates observed in permanent and epoch-wise satellite...

Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

Maciej Huk (2012)

International Journal of Applied Mathematics and Computer Science

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In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely...

A heuristic forecasting model for stock decision making.

D. Zhang, Q. Jiang, X. Li (2005)

Mathware and Soft Computing

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This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting...

An effective way to generate neural network structures for function approximation.

Andreas Bastian (1994)

Mathware and Soft Computing

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One still open question in the area of research of multi-layer feedforward neural networks is concerning the number of neurons in its hidden layer(s). Especially in real life applications, this problem is often solved by heuristic methods. In this work an effective way to dynamically determine the number of hidden units in a three-layer feedforward neural network for function approximation is proposed.