11,335 research outputs found
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
On the use of simple dynamical systems for climate predictions: A Bayesian prediction of the next glacial inception
Over the last few decades, climate scientists have devoted much effort to the
development of large numerical models of the atmosphere and the ocean. While
there is no question that such models provide important and useful information
on complicated aspects of atmosphere and ocean dynamics, skillful prediction
also requires a phenomenological approach, particularly for very slow
processes, such as glacial-interglacial cycles. Phenomenological models are
often represented as low-order dynamical systems. These are tractable, and a
rich source of insights about climate dynamics, but they also ignore large
bodies of information on the climate system, and their parameters are generally
not operationally defined. Consequently, if they are to be used to predict
actual climate system behaviour, then we must take very careful account of the
uncertainty introduced by their limitations. In this paper we consider the
problem of the timing of the next glacial inception, about which there is
on-going debate. Our model is the three-dimensional stochastic system of
Saltzman and Maasch (1991), and our inference takes place within a Bayesian
framework that allows both for the limitations of the model as a description of
the propagation of the climate state vector, and for parametric uncertainty.
Our inference takes the form of a data assimilation with unknown static
parameters, which we perform with a variant on a Sequential Monte Carlo
technique (`particle filter'). Provisional results indicate peak glacial
conditions in 60,000 years.Comment: superseeds the arXiv:0809.0632 (which was published in European
Reviews). The Bayesian section has been significantly expanded. The present
version has gone scientific peer review and has been published in European
Physics Special Topics. (typo in DOI and in Table 1 (psi -> theta) corrected
on 25th August 2009
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