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.

Fibre optic distributed sensor system for condition monitoring of synthetic ropes

Reliability of the measuring devices is very important problem. Optical fibre sensors are very efficient. The use of optical fibre sensors for monitoring the physical and chemical parameters has been expanding over resent years. These sensors are applied for monitoring the structural integrity of long, parallel lay synthetic ropes. Such ropes are corrosion free, however, their operational lifetime under cyclic load is not well understood and premature failure can occur due to slippage and breakage of yarns within the rope. The monitoring system has been proposed which is based on acoustic detection of yarn breakage. Monitoring the strain and temperature is performed using the array of fibre gratings distributed along the rope length.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. 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 demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Influence of cluster-assembly parameters on the field emission properties of nanostructured carbon films

Supersonic cluster beam deposition has been used to produce films with different nanostructures by controlling the deposition parameters such as the film thickness, substrate temperature and cluster mass distribution. The field emission properties of cluster-assembled carbon films have been characterized and correlated to the evolution of the film nanostructure. Threshold fields ranging between 4 and 10 V/mum and saturation current densities as high as 0.7 mA have been measured for samples heated during deposition. A series of voltage ramps, i.e., a conditioning process, was found to initiate more stable and reproducible emission. It was found that the presence of graphitic particles (onions, nanotube embryos) in the films substantially enhances the field emission performance. Films patterned on a micrometer scale have been conditioned spot by spot by a ball-tip anode, showing that a relatively high emission site density can be achieved from the cluster-assembled material. (C) 2002 American Institute of Physics.

Mechanically tunable conjugated polymer distributed feedback lasers

Herein we report a low-threshold organic laser device based on semiconducting poly(9, 9′ -dioctylfluoren-2,7-diyl-alt-benzothiadiazole) (F8BT) encapsulated in a mechanically stretchable polydimethylsiloxane (PDMS) matrix. We take advantage of the natural flexibility of PDMS to alter the periodicity of the distributed feedback grating which in turn tunes the gain wavelength at which the resonant feedback is obtained. This way, we demonstrate that low-threshold lasing [6.1 μJ cm-2 (5.3 nJ)] is maintained over a large stretching range of 0%-7% which translates into a tuning range of about 20 nm. © 2010 American Institute of Physics.

Transient Natural Ventilation of a Room with a Distributed Heat Source

We report on an experimental and theoretical study of the transient flows which develop as a naturally ventilated room adjusts from one temperature to another. We focus on a room heated from below by a uniform heat source, with both high- and low-level ventilation openings. Depending on the initial temperature of the room relative to (i) the final equilibrium temperature and (ii) the exterior temperature, three different modes of ventilation may develop. First, if the room temperature lies between the exterior and the equilibrium temperature, the interior remains well-mixed and gradually heats up to the equilibrium temperature. Secondly, if the room is initially warmer than the equilibrium temperature, then a thermal stratification develops in which the upper layer of originally hot air is displaced upwards by a lower layer of relatively cool inflowing air. At the interface, some mixing occurs owing to the effects of penetrative convection. Thirdly, if the room is initially cooler than the exterior, then on opening the vents, the original air is displaced downwards and a layer of ambient air deepens from above. As this lower layer drains, it is eventually heated to the ambient temperature, and is then able to mix into the overlying layer of external air, and the room becomes well-mixed. For each case, we present new laboratory experiments and compare these with some new quantitative models of the transient flows. We conclude by considering the implications of our work for natural ventilation of large auditoria.

Scaling the iHMM: Parallelization versus Hadoop

This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm. Distributed implementations run under Hadoop on facility computing clouds. The probabilistic model under study is the infinite HMM [1], in which parameters are learnt using an instance blocked Gibbs sampling, with a step consisting of a dynamic program. We apply this model to learn part-of-speech tags from newswire text in an unsupervised fashion. However our focus here is on runtime performance, as opposed to NLP-relevant scores, embodied by iteration duration, ease of development, deployment and debugging. © 2010 IEEE.