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"Web Services is the clarion call of the computer software industry at present. How should we understand the term? Because of the diversity of interpretation in the industry, the easiest way is to be general and assert that Web Services means XML in motion. If the network is the computer, Web Services comprise the software that runs on it."
The purpose of this chapter is to present a survey of recent publications concerning medical
image registration techniques. These publications will be classified according to a model based
on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods
The present paper deals with the problem of calculating mean delays in polling systems
with either exhaustive or gated service. We develop a mean value analysis (MVA) to
compute these delay figures. The merits of MVA are in its intrinsic simplicity and its
intuitively appealing derivation. As a consequence, MVA may be applied, both in an
exact and approximate manner, to a large variety of models.
The present document specifies the CAMEL Application Part (CAP) supporting the fourth phase of the network feature Customized Applications for Mobile network Enhanced Logic. CAP is based on a sub-set of the ETSI Core INAP CS-2 as specified by ETSI EN 301 140 1 [26]. Descriptions and definitions provided by ETSI EN 301 140 1 [26] are directly referenced by this standard in the case no additions or clarifications are needed for the use in the CAP.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
In this paper, we consider the problem of filtering in relational
hidden Markov models. We present a compact representation for
such models and an associated logical particle filtering algorithm. Each
particle contains a logical formula that describes a set of states. The
algorithm updates the formulae as new observations are received. Since
a single particle tracks many states, this filter can be more accurate
than a traditional particle filter in high dimensional state spaces, as we
demonstrate in experiments.
We present a particle filter construction for a system that exhibits
time-scale separation. The separation of time-scales allows two simplifications
that we exploit: i) The use of the averaging principle for the
dimensional reduction of the system needed to solve for each particle
and ii) the factorization of the transition probability which allows the
Rao-Blackwellization of the filtering step. Both simplifications can be
implemented using the coarse projective integration framework. The
resulting particle filter is faster and has smaller variance than the particle
filter based on the original system. The convergence of the new
particle filter to the analytical filter for the original system is proved
and some numerical results are provided.
％％％ Demos for PUMA algorithms ％％％
We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4
illustrate PUMA working with different parameters and with four
different images.
All you need to do is to run each of the demos. Please be sure that
all the files are put on an accessible path for matlab.
Notice that this code is intended for research purposes only.
For further reference see "Phase Unwrapping via Graph Cuts,
IEEE Transactions on Image Processing, 2007
