Information Theory & Coding 信息论与编码 (英文版)(21世纪高等院校规划教材)pdf/txt下载_在线阅读全文

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采用“任务驱动”的编写方式，引入案例和启发式教学方法，提供电子教案、案例素材等教学资源，教材立体化配套，满足高等院校应用型人才培养的需要。

内容简介

本书重点介绍经典信息论的基本理论，并力图将信息论的基本理论和工程应用的编码理论联系起来，介绍一些关于这些理论的实际应用。全书分为7章，内容包括信息度量的基本理论、无失真信源编码、限失真信源编码、信道编码及其应用等。
　　本书注重基本概念，并且用通俗易懂的语言对它们加以诠释。在当前信息、通信系统飞速发展的大背景下，本书力图用较多的例子和图表来阐述概念和理论，同时尽量避免纠缠于烦琐难懂的公式证明之中。为了加深读者对所讲述知识的理解，每章最后都配有适量的练习。题供读者选用。
　　本书可作为高等院校电子信息类学生双语教学的教材或参考书，也可作为通信、电信、电子等领域从业人员的参考资料。

目　　录

Chapter 1 Introduction
Contents
Before it starts, there is something must be known
　1.1 What is Information
　1.2 What’s Information Theory？
　　1.2.1 Origin and Development of Information Theory
　　1.2.2 The application and achievement of Information Theory methods
　1.3 Formation and Development of Information Theory
　Questions and Exercises
　Biography of Claude Elwood Shannon
Chapter 2 Basic Concepts of Information Theory
　Contents
　Preparation knowledge
　2.1 Self-information and conditional self-information

Chapter 1 Introduction Contents Before it starts, there is something must be known 　1.1 What is Information 　1.2 What’s Information Theory？ 　　1.2.1 Origin and Development of Information Theory 　　1.2.2 The application and achievement of Information Theory methods 　1.3 Formation and Development of Information Theory 　Questions and Exercises 　Biography of Claude Elwood Shannon Chapter 2 Basic Concepts of Information Theory 　Contents 　Preparation knowledge 　2.1 Self-information and conditional self-information 　　2.1.1 Self-Information 　　2.1.2 Conditional Self-Information 　2.2 Mutual information and conditional mutual information 　2.3 Source entropy 　　2.3.1 Introduction of entropy 　　2.3.2 Mathematics description of source entropy 　　2.3.3 Conditional entropy 　　2.3.4 Union entropy （Communal entropy） 　　2.3.5 Basic nature and theorem of source entropy 　2.4 Average mutual information 　　2.4.1 Definition 　　2.4.2 Physics significance of average mutual information 　　2.4.3 Properties of average mutual information 　2.5 Continuous source 　　2.5.1 Entropy of the continuous source （also called differential entropy） 　　2.5.2 Mutual information of the continuous random variable 　Questions and Exercises 　Additional reading materials Chapter 3 Discrete Source Information 　Contents 　3.1 Mathematical model and classification of the source 　3.2 The discrete source without memory 　3.3 Multi-marks discrete steady source 　3.4 Source entropy of discrete 　　4.2.4 Relationship between entropy, channel doubt degree and mutual information 　4.3 The discrete channel without memory and its channel capacity 　4.4 Channel capacity 　　4.4.1 Concept of channel capacity 　　4.4.2 Discrete channel without memory and its channel capacity 　　4.4.3 Continuous channel and its channel capacity Chapter 5 kossless source coding 　Contents 　5.1 Lossless coder 　5.2 Lossless source coding 　　5.2.1 Fixed length coding theorem 　　5.2.2 Unfixed length source coding 　5.3 Lossless source coding theorems 　　5.3.1 Classification of code and main coding method 　　5.3.2 Kraft theorem 　　5.3.3 Lossless unfixed source coding theorem （Shannon First theorem） 　5.4 Pragmatic examples of lossless source coding 　　5.4.1 Huffman coding 　　5.4.2 Shannon coding and Fano coding 　5.5 The Lempel-ziv algorithm 　5.6 Run-Length Encoding and the PCX format 　Questions and Exercises Chapter 6 Limited distortion source coding 　Contents 　6.1 The start point of limit distortion theory 　6.2 Distortion measurement 　　6.2.1 Distortion function 　　6.2.2 Average distortion 　6.3 Information rate distortion function 　6.4 Property of R（D） 　　6.4.1 Minimum of D and R（D） 　　6.4.2 Dmax and R（Dmax） 　　6.4.3 The under convex function of R（D） 　　6.4.4 　Questions and exercises Bibliography

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Information Theory & Coding 信息论与编码 (英文版)(21世纪高等院校规划教材)下载

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