Description

Probability and Random Processes, Second Edition presents pertinent applications to signal processing and communications, two areas of key interest to students and professionals in today's booming communications industry. The book includes unique chapters on narrowband random processes and simulation techniques. It also describes applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and others.

Exceptional exposition and numerous worked out problems make this book extremely readable and accessible. The authors connect the applications discussed in class to the textbook. The new edition contains more real world signal processing and communications applications. It introduces the reader to the basics of probability theory and explores topics ranging from random variables, distributions and density functions to operations on a single random variable. There are also discussions on pairs of random variables; multiple random variables; random sequences and series; random processes in linear systems; Markov processes; and power spectral density. View more >

Key Features

Exceptional exposition and numerous worked out problems make the book extremely readable and accessible

The authors connect the applications discussed in class to the textbook

The new edition contains more real world signal processing and communications applications

Includes an entire chapter devoted to simulation techniques

Readership

Graduate-level courses in the topic with secondary interest to professionals

Table of Contents

Preface

CHAPTER 1. Introduction

1.1 A Speech Recognition System

1.2 A Radar System

1.3 A Communication Network

CHAPTER 2. Introduction to Probability Theory

2.1 Experiments, Sample Spaces, and Events

2.2 Axioms of Probability

2.3 Assigning Probabilities

2.4 Joint and Conditional Probabilities

2.5 Basic Combinatorics

2.6 Bayes’s Theorem

2.7 Independence

2.8 Discrete Random Variables

2.9 Engineering Application—An Optical Communication System

CHAPTER 3. Random Variables, Distributions, and Density Functions

3.1 The Cumulative Distribution Function

3.2 The Probability Density Function

3.3 The Gaussian Random Variable

3.4 Other Important Random Variables

3.5 Conditional Distribution and Density Functions

3.6 Engineering Application: Reliability and Failure Rates

CHAPTER 4. Operations on a Single Random Variable

4.1 Expected Value of a Random Variable

4.2 Expected Values of Functions of Random Variables

4.3 Moments

4.4 Central Moments

4.5 Conditional Expected Values

4.6 Transformations of Random Variables

4.7 Characteristic Functions

4.8 Probability-Generating Functions

4.9 Moment-Generating Functions

4.10 Evaluating Tail Probabilities

4.11 Engineering Application—Scalar Quantization

4.12 Engineering Application—Entropy and Source Coding

CHAPTER 5. Pairs of Random Variables

5.1 Joint Cumulative Distribution Functions

5.2 Joint Probability Density Functions

5.3 Joint Probability Mass Functions

5.4 Conditional Distribution, Density, and Mass Functions

5.5 Expected Values Involving Pairs of Random Variables

5.6 Independent Random Variables

5.7 Jointly Gaussian Random Variables

5.8 Joint Characteristic and Related Functions

5.9 Transformations of Pairs of Random Variables

5.10 Complex Random Variables

5.11 Engineering Application: Mutual Information, Channel Capacity, and Channel Coding

CHAPTER 6. Multiple Random Variables

6.1 Joint and Conditional PMFs, CDFs, and PDFs

6.2 Expectations Involving Multiple Random Variables

6.3 Gaussian Random Variables in Multiple Dimensions

6.4 Transformations Involving Multiple Random Variables

6.5 Estimation and Detection

6.6 Engineering Application: Linear Prediction of Speech

CHAPTER 7. Random Sums and Sequences

7.1 Independent and Identically Distributed Random Variables

7.2 Convergence Modes of Random Sequences

7.3 The Law of Large Numbers

7.4 The Central Limit Theorem

7.5 Confidence Intervals

7.6 Random Sums of Random Variables

7.7 Engineering Application: A Radar System

CHAPTER 8. Random Processes

8.1 Definition and Classification of Processes

8.2 Mathematical Tools for Studying Random Processes

8.3 Stationary and Ergodic Random Processes

8.4 Properties of the Autocorrelation Function

8.5 Gaussian Random Processes

8.6 Poisson Processes

8.7 Engineering Application—Shot Noise in a p–n Junction Diode

CHAPTER 9. Markov Processes

9.1 Definition and Examples of Markov Processes

9.2 Calculating Transition and State Probabilities in Markov Chains

9.3 Characterization of Markov Chains

9.4 Continuous Time Markov Processes

9.5 Engineering Application: A Computer Communication Network

9.6 Engineering Application: A Telephone Exchange

CHAPTER 10. Power Spectral Density

10.1 Definition of PSD

10.2 The Wiener–Khintchine–Einstein Theorem

10.3 Bandwidth of a Random Process

10.4 Spectral Estimation

10.5 Thermal Noise

10.6 Engineering Application: PSDs of Digital Modulation Formats

CHAPTER 11. Random Processes in Linear Systems

11.1 Continuous Time Linear Systems

11.2 Discrete-Time Linear Systems

11.3 Noise Equivalent Bandwidth

11.4 Signal-to-Noise Ratios

11.5 The Matched Filter

11.6 The Wiener Filter

11.7 Bandlimited and Narrowband Random Processes

11.8 Complex Envelopes

11.9 Engineering Application: An Analog Communication System

CHAPTER 12. Simulation Techniques

12.1 Computer Generation of Random Variables

12.2 Generation of Random Processes

12.3 Simulation of Rare Events

12.4 Engineering Application: Simulation of a Coded Digital Communication System

APPENDIX A. Review of Set Theory

APPENDIX B. Review of Linear Algebra

APPENDIX C. Review of Signals and Systems

APPENDIX D. Summary of Common Random Variables

Continuous Random Variables

Discrete Random Variables

APPENDIX E. Mathematical Tables

A. Trigonometric Identities

B. Series Expansions

C. Some Common Indefinite Integrals

D. Some Common Definite Integrals

E. Definitions of Some Common Continuous Time Signals

F. Fourier Transforms

G. z-Transforms

H. Laplace Transforms

I. Table of the Q-function

APPENDIX F. Numerical Methods for Evaluating the Q-Function

Index View less >

Details

No. of pages:

522

Language:

English

Copyright:

© Academic Press 2012

Published:

11th January 2012

Imprint:

Academic Press

eBook ISBN:

9780123870131

Probability and Random Processes, Second Edition presents pertinent applications to signal processing and communications, two areas of key interest to students and professionals in today's booming communications industry. The book includes unique chapters on narrowband random processes and simulation techniques. It also describes applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and others.

Exceptional exposition and numerous worked out problems make this book extremely readable and accessible. The authors connect the applications discussed in class to the textbook. The new edition contains more real world signal processing and communications applications. It introduces the reader to the basics of probability theory and explores topics ranging from random variables, distributions and density functions to operations on a single random variable. There are also discussions on pairs of random variables; multiple random variables; random sequences and series; random processes in linear systems; Markov processes; and power spectral density. View more >

Key Features

Exceptional exposition and numerous worked out problems make the book extremely readable and accessible

The authors connect the applications discussed in class to the textbook

The new edition contains more real world signal processing and communications applications

Includes an entire chapter devoted to simulation techniques

Readership

Graduate-level courses in the topic with secondary interest to professionals

Table of Contents

Preface

CHAPTER 1. Introduction

1.1 A Speech Recognition System

1.2 A Radar System

1.3 A Communication Network

CHAPTER 2. Introduction to Probability Theory

2.1 Experiments, Sample Spaces, and Events

2.2 Axioms of Probability

2.3 Assigning Probabilities

2.4 Joint and Conditional Probabilities

2.5 Basic Combinatorics

2.6 Bayes’s Theorem

2.7 Independence

2.8 Discrete Random Variables

2.9 Engineering Application—An Optical Communication System

CHAPTER 3. Random Variables, Distributions, and Density Functions

3.1 The Cumulative Distribution Function

3.2 The Probability Density Function

3.3 The Gaussian Random Variable

3.4 Other Important Random Variables

3.5 Conditional Distribution and Density Functions

3.6 Engineering Application: Reliability and Failure Rates

CHAPTER 4. Operations on a Single Random Variable

4.1 Expected Value of a Random Variable

4.2 Expected Values of Functions of Random Variables

4.3 Moments

4.4 Central Moments

4.5 Conditional Expected Values

4.6 Transformations of Random Variables

4.7 Characteristic Functions

4.8 Probability-Generating Functions

4.9 Moment-Generating Functions

4.10 Evaluating Tail Probabilities

4.11 Engineering Application—Scalar Quantization

4.12 Engineering Application—Entropy and Source Coding

CHAPTER 5. Pairs of Random Variables

5.1 Joint Cumulative Distribution Functions

5.2 Joint Probability Density Functions

5.3 Joint Probability Mass Functions

5.4 Conditional Distribution, Density, and Mass Functions

5.5 Expected Values Involving Pairs of Random Variables

5.6 Independent Random Variables

5.7 Jointly Gaussian Random Variables

5.8 Joint Characteristic and Related Functions

5.9 Transformations of Pairs of Random Variables

5.10 Complex Random Variables

5.11 Engineering Application: Mutual Information, Channel Capacity, and Channel Coding

CHAPTER 6. Multiple Random Variables

6.1 Joint and Conditional PMFs, CDFs, and PDFs

6.2 Expectations Involving Multiple Random Variables

6.3 Gaussian Random Variables in Multiple Dimensions

6.4 Transformations Involving Multiple Random Variables

6.5 Estimation and Detection

6.6 Engineering Application: Linear Prediction of Speech

CHAPTER 7. Random Sums and Sequences

7.1 Independent and Identically Distributed Random Variables

7.2 Convergence Modes of Random Sequences

7.3 The Law of Large Numbers

7.4 The Central Limit Theorem

7.5 Confidence Intervals

7.6 Random Sums of Random Variables

7.7 Engineering Application: A Radar System

CHAPTER 8. Random Processes

8.1 Definition and Classification of Processes

8.2 Mathematical Tools for Studying Random Processes

8.3 Stationary and Ergodic Random Processes

8.4 Properties of the Autocorrelation Function

8.5 Gaussian Random Processes

8.6 Poisson Processes

8.7 Engineering Application—Shot Noise in a p–n Junction Diode

CHAPTER 9. Markov Processes

9.1 Definition and Examples of Markov Processes

9.2 Calculating Transition and State Probabilities in Markov Chains

9.3 Characterization of Markov Chains

9.4 Continuous Time Markov Processes

9.5 Engineering Application: A Computer Communication Network

9.6 Engineering Application: A Telephone Exchange

CHAPTER 10. Power Spectral Density

10.1 Definition of PSD

10.2 The Wiener–Khintchine–Einstein Theorem

10.3 Bandwidth of a Random Process

10.4 Spectral Estimation

10.5 Thermal Noise

10.6 Engineering Application: PSDs of Digital Modulation Formats

CHAPTER 11. Random Processes in Linear Systems

11.1 Continuous Time Linear Systems

11.2 Discrete-Time Linear Systems

11.3 Noise Equivalent Bandwidth

11.4 Signal-to-Noise Ratios

11.5 The Matched Filter

11.6 The Wiener Filter

11.7 Bandlimited and Narrowband Random Processes

11.8 Complex Envelopes

11.9 Engineering Application: An Analog Communication System

CHAPTER 12. Simulation Techniques

12.1 Computer Generation of Random Variables

12.2 Generation of Random Processes

12.3 Simulation of Rare Events

12.4 Engineering Application: Simulation of a Coded Digital Communication System

APPENDIX A. Review of Set Theory

APPENDIX B. Review of Linear Algebra

APPENDIX C. Review of Signals and Systems

APPENDIX D. Summary of Common Random Variables

Continuous Random Variables

Discrete Random Variables

APPENDIX E. Mathematical Tables

A. Trigonometric Identities

B. Series Expansions

C. Some Common Indefinite Integrals

D. Some Common Definite Integrals

E. Definitions of Some Common Continuous Time Signals

F. Fourier Transforms

G. z-Transforms

H. Laplace Transforms

I. Table of the Q-function

APPENDIX F. Numerical Methods for Evaluating the Q-Function

Index View less >

Details

No. of pages:

522

Language:

English

Copyright:

© Academic Press 2012

Published:

11th January 2012

Imprint:

Academic Press

eBook ISBN:

9780123870131