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.
we propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. we develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
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.
從s60平臺移植到UIQ平臺的例子代碼：In this code we shall consider taking a simple HelloWorld GUI application written for the Nokia’s Series 60 reference design and transforming it into a HelloWorld example for the UIQ reference design.
This a large book, and your class will probably cover only a portion of its material. we
have tried, however, to make this a book that will be useful to you now as a course textbook
and also later in your career as a mathematical desk reference or an engineering handbook
we have a group of N items (represented by integers from 1 to N), and we know that there is some total order defined for these items. You may assume that no two elements will be equal (for all a, b: a<b or b<a). However, it is expensive to compare two items. Your task is to make a number of comparisons, and then output the sorted order. The cost of determining if a < b is given by the bth integer of element a of costs (space delimited), which is the same as the ath integer of element b. Naturally, you will be judged on the total cost of the comparisons you make before outputting the sorted order. If your order is incorrect, you will receive a 0. Otherwise, your score will be opt/cost, where opt is the best cost anyone has achieved and cost is the total cost of the comparisons you make (so your score for a test case will be between 0 and 1). Your score for the problem will simply be the sum of your scores for the individual test cases.
This program compress and recostruct using wavelets. we can select level of decomposition(here maximum 4 levels are given) of images using selected wavelet.
For eg:-wavelets can be haar, db1, db2,dmey...............
Decomposition can be viewed in figure.
(Please note that select 256X256 image for better result.)
Then compression can performed,
PERFL2 give compression score.
Then reconstruction can be performed.
Each decompsition we can choose different threshold values.
For each threshold value we can calculate mse,psnr,pq(picture quality),
bit ratio etc. To get pq install pqs function .
Solving Engineering Problems Using MATLAB C++ Math Library Introduction
In the previous article, we studied how can use MATLAB C API to solve engineering problems. In this article I will show you how can use MATLAB C++ math library. The MATLAB® C++ Math Library serves two separate constituencies: MATLAB programmers seeking more speed or complete independence from interpreted MATLAB, and C++ programmers who need a fast, easy-to-use matrix math library. To each, it offers distinct advantages.
Introduction
Some times it is required that we build a shared library (DLL) from an m-file. M-files are functions that are written in Matlab editor and can be used from Matlab command prompt. In m-files, we employ Matlab built-in functions or toolbox functions to compute something. In my past articles, I showed you some ways to use Matlab engine (vis. API, C++ class or Matlab engine API) for employing Matlab built-in functions, but what about functions that we develop? How can we use them in VC? Is there any interface? This article shows you an idea to employ your own Matlab functions.
