The EM Wave MATLAB Library consists of a collection of MATLAB programs related to electromagnetic wave scattering with special emphasis on wave scattering by random rough surfaces and Discrete random media.
One-channel queuing system simulator (M/M/1)
* Arrival and service times are random and distributed exponetially.
*
* The simulator is time-slice-driven, i.e. the system model is being
* run at Discrete time points, with constant increments deltaT.
* At each such time moment, program checks if a new item arrival or
* release has occurred during previus deltaT.
When I first studied Kalman filtering, I saw many advanced signal processing submissions here at the MATLAB Central File exchange, but I didn t see a heavily commented, basic Kalman filter present to allow someone new to Kalman filters to learn about creating them. So, a year later, I ve written a very simple, heavily commented Discrete filter.
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.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on Discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
Fast settling-time added to the already conflicting requirements of narrow channel spacing and
low phase noise lead to Fractional4 divider techniques for PLL synthesizers. We analyze Discrete "beat-note spurious levels from arbitrary modulus divide sequences including those from classic accumulator methods.
Image Compression
A collection of simple routines for image compression using different techniques.
圖象壓縮的不同方法
BTCODE:
Image compression Using Block Truncation Coding.
PYRAMID:
Image compression based on Gaussian Pyramids.
DCTCOMPR:
Image compression based on Discrete Cosine Transform.
IMCOMPR:
Image compression based on Singular Value Decomposition.
The given codes can be also used in 2D noise suppression.
Notes:
The function "conv2fft" performs a 2D FFT-based convolution.
Type "help conv2fft" on Matlab command window for more informations.
