We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
Eclipse+Web開發從入門到精通
These files contain all of the code listings in
Java: The Complete Reference, J2SE 5 Edition
The source code is organized into files by chapter.
For example, the file Chap7.code contains the
programs shown in Chapter 7.
Within each chapter file, the listings are stored
in the same order as they appear in the book.
Simply edit the appropriate file to extract the
listing in which you are interested.
Requirement
=====================================================================================
python 2.4+
wxPython 2.6+ Unicode Version
Installation
=====================================================================================
Directly extract the tarbar into a empty directory or overwrite the old directory, that s ok.
Run
=====================================================================================
Execute:
python ulipad.pyw
or
python ulipad.py
These files contain all of the code listings in
Java 2: The Complete Reference
The source code is organized into files by chapter.
Within each chapter file, the listings are stored
in the same order as they appear in the book.
Simply edit the appropriate file to extract the
listing in which you are interested.
The code for Scrabblet is in its own ZIP file,
called CHAP32.ZIP.
This article presents GISCoordinate.java - a class that allows you to represent a GIS coordinate in your JAVA code in decimal degrees (38.4443, e.g. 122.33433) , minute degrees (33 44 22E, 122 33 44N), or radian degrees. Also, you can use this class to manipulate the coordinate, moving it around the globe by giving it distances in feet and direction of travel. You can then extract the new coordinate that is calculated after the travel.
HDFDUMP and BRFDUMP are utility programs developed for use with MISR data
files. HDFDUMP will extract data from a MISR file in the HDF-EOS grid format
(MISR Level 1B2 and Level 2 files) and writes unformatted binary files. BRFDUMP
calculates radiances and bidirectional reflectance factors (BRF) from MISR Level 1B2
files and creates unformatted binary files.
This document explains how to read the RADARSAT data CD provided with the book
"Digital Processing of Synthetic Aperture Data" by Ian Cumming and Frank Wong,
Artech House, 2005.
On this web site, programs are provided in MATLAB to read the data and parameters
from the CD. The structure of the programs is outlined in Figure 1.
There are two main programs and one function that are used to extract
This file is a function under matlab which allow to read, play and plot audio signals from wav file. We can also extract the sampling frequency and coding bit number
This simulation script set allows for an OFDM transmission to be
simulated. Imagetx.m generates the OFDM signal, saving it as a
windows WAV file. This allows the OFDM signal to be played out a sound
card and recorded back. Imagerx.m decodes the WAV to extract the
data.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
