Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.

Mechanical and magnetic properties of metal fibre networks, with and without a polymeric matrix

Bonded networks of metal fibres are highly porous, permeable materials, which often exhibit relatively high strength. Material of this type has been produced, using melt-extracted ferritic stainless steel fibres, and characterised in terms of fibre volume fraction, fibre segment (joint-to-joint) length and fibre orientation distribution. Young's moduli and yield stresses have been measured. The behaviour when subjected to a magnetic field has also been investigated. This causes macroscopic straining, as the individual fibres become magnetised and tend to align with the applied field. The modeling approach of Markaki and Clyne, recently developed for prediction of the mechanical and magneto-mechanical properties of such materials, is briefly summarised and comparisons are made with experimental data. The effects of filling the inter-fibre void with compliant (polymeric) matrices have also been explored. In general the modeling approach gives reliable predictions, particularly when the network architecture has been characterised using X-ray tomography. © 2005 Published by Elsevier Ltd.

Athermal colourless C-band optical transmitter for passive optical networks

A new control algorithm with reduced mode-hopping is demonstrated for uncooled WDM C-band channel generation from a DS-DBR laser with 100GHz spacing and low thermal drift up to 70°C. 10Gb/s external modulation with transmission over a 25km link is achieved. ©2010 IEEE.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 SPIE-OSA-IEEE.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 Optical Society of America.

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.