ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification algorIthms include Naive Bayes, KNN
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorIthms.
This paper investigates the design of joint frequency
offset and carrier phase estimation of a multi-frequency time division
multiple access (MF-TDMA) demodulator that is applied to
a digital video broadcasting—return channel system via satellite
(DVB-RCS). The proposed joint estimation algorithm is based on
the interpolation technique for two correlation values in the frequency
and phase domains. This simple interpolation technique
can significantly improve frequency and phase resolution capabilities
of the proposed technique without increasing the number of
the correlation values. In addition, the overall block diagram of a
digital communications receiver for DVB-RCS is presented, which
was designed using the proposed estimation algorIthms.
Index Terms—Carrier phase estimation, DVB-RCS, frequency
offset estimation, interpolation, joint estimation, MF-TDMA.
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.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorIthms that avoid the weaknesses of the current
methods.
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorIthms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
Linux編程的經典書，OReilly系列。The new edition of Understanding the Linux Kernel takes you on a guided tour through the most significant data structures, many algorIthms, and programming tricks used in the kernel.
The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorIthms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
The paper presents the CORDIC Algorithm, which has been implemented as an virtual component (IP core) in a VHDL simulation environment. The core is packaged as a soft (VHDL) macro and it implements all transcenden-tal functions. Analysis of the accuracy of the algorIthms implemented shows that the CORDIC functions are equivalent to the accuracy of a Pentium coprocessor.
