by Randal L. Schwartz and Tom Phoenix
ISBN 0-596-00132-0
Third Edition, published July 2001.
(See the catalog page for this book.)
Learning Perl, 3rd Edition.
Table of Contents
Copyright Page
Preface
Chapter 1: Introduction
Chapter 2: Scalar Data
Chapter 3: Lists and Arrays
Chapter 4: Subroutines
Chapter 5: Hashes
Chapter 6: I/O Basics
Chapter 7: Concepts of Regular Expressions
Chapter 8: More About Regular Expressions
Chapter 9: Using Regular Expressions
Chapter 10: More Control Structures
Chapter 11: Filehandles and File Tests
Chapter 12: Directory Operations
Chapter 13: Manipulating Files and Directories
Chapter 14: Process Management
Chapter 15: Strings and Sorting
Chapter 16: Simple Databases
Chapter 17: Some Advanced Perl Techniques
Appendix A: Exercise Answers
Appendix B: Beyond the Llama
Index
Colophon
This handbook presents a thorough overview in 45 chapters from more than 100 renowned experts in the field. It provides the tools to help overcome the problems of video storage, cataloging, and retrieval, by exploring content standardization and other content classification and analysis methods. The challenge of these complex problems make this book a must-have for video database practitioners in the fields of image and video processing, computer vision, multimedia systems, data mining, and many other diverse disciplines. Topics include video segmentation and summarization, archiving and retrieval, and modeling and representation.
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.
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa.
For example,
>> project.name = MyProject
>> project.id = 1234
>> project.param.a = 3.1415
>> project.param.b = 42
becomes with str=xml_format(project, off )
"<project>
<name>MyProject</name>
<id>1234</id>
<param>
<a>3.1415</a>
<b>42</b>
</param>
</project>"
On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
METAmorphoses is a system for flexible and easy-to-use generation of RDF metadata directly from a relational database. Metadata are genereated according to a mapping from an existing database schema to a particular ontology.
