Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Dictadura, consenso o laissez faire en la transmisión eléctrica

Resumen: La dificultad con las redes radica en que, al ser de uso colectivo, generan conflictos sobre sus derechos de propiedad y obligaciones de pago. Por eso, existe una controversia sobre el más eficiente modo para su provisión, que se extiende desde el laissez faire hasta la dictadura. En el camino, se puede detener en algún tipo de consenso, basado en el diseño de mecanismos, adecuado para hacer revelar sus preferencias a los participantes. El proceso implica un intercambio de derechos entre los agentes para arribar a la asignación definitiva. Si, mediante una subasta, se obliga a los participantes a realizar ofertas de intercambio de derechos, los costos de transacción se reducen y la fluidez no puede sino ser mayor. Y ante una mayor fluidez en el intercambio, siempre será posible alcanzar una asignación más eficiente de esos derechos de propiedad. Para mostrar la conveniencia de esta alternativa, en este trabajo se presenta un modelo simplificado de diseño de mecanismos en donde se analiza la eficiencia asignativa de la regla propuesta.

On the formation of well-aligned ZnO nanowall networks by catalyst-free thermal evaporation method

Two-dimensional ZnO nanowall networks were grown on ZnO-coated silicon by thermal evaporation at low temperature without catalysts or additives. All of the results from scanning electronic spectroscope, X-ray diffraction and Raman scattering confirmed that the ZnO nanowalls were vertically aligned and c-axis oriented. The room-temperature photoluminescence spectra showed a dominated UV peak at 378 nm, and a much suppressed orange emission centered at similar to 590 nm. This demonstrates fairly good crystal quality and optical properties of the product. A possible three-step, zinc vapor-controlled process was proposed to explain the growth of well-aligned ZnO nanowall networks. The pre-coated ZnO template layer plays a key role during the synthesis process, which guides the growth direction of the synthesized products. (C) 2007 Elsevier B.V. All rights reserved.