Fundamentals of Signals and Systems Using the Web and MATLAB

Fundamentals of Signals and Systems Using the Web and MATLAB

• Preface
• 1 FUNDAMENTAL CONCEPTS
• 1.1 Continuous-Time Signals
• 1.2 Discrete-Time Signals
• 1.3 Systems
• 1.4 Examples of Systems
• 1.5 Basic System Properties
• 1.6 Chapter Summary
• Problems
• 2 TIME-DOMAIN MODELS OF SYSTEMS
• 2.1 Input/Output Representation of Discrete-Time Systems
• 2.2 Convolution of Discrete-Time Signals
• 2.3 Difference Equation Models
• 2.4 Differential Equation Models
• 2.5 Solution of Differential Equations
• 2.6 Convolution Representation of Continuous-Time Systems
• 2.7 Chapter Summary
• Problems
• 3 THE FOURIER SERIES AND FOURIER TRANSFORM
• 3.1 Representation of Signals in Terms of Frequency Components
• 3.2 Trigonometric Fourier Series
• 3.3 Complex Exponential Series
• 3.4 Fourier Transform
• 3.5 Spectral Content of Common Signals
• 3.6 Properties of the Fourier Transform
• 3.7 Generalized Fourier Transform
• 3.8 Application to Signal Modulation and Demodulation
• 3.9 Chapter Summary
• Problems
• 4 FOURIER ANALYSIS OF DISCRETE-TIME SIGNALS
• 4.1 Discrete-Time Fourier Transform
• 4.2 Discrete Fourier Transform
• 4.3 DFT of Truncated Signals
• 4.4 FFT Algorithm
• 4.5 Application to Data Analysis
• 4.6 Chapter Summary
• Problems
• 5 FOURIER ANALYSIS OF SYSTEMS
• 5.1 Fourier Analysis of Continuous-Time Systems
• 5.2 Response to Periodic and Nonperiodic Inputs
• 5.3 Analysis of Ideal Filters
• 5.4 Sampling
• 5.5 Fourier Analysis of Discrete-Time Systems
• 5.6 Application to Lowpass Digital Filtering
• 5.7 Chapter Summary
• Problems
• 6 THE LAPLACE TRANSFORM AND THE TRANSFER FUNCTION REPRESENTATION
• 6.1 Laplace Transform of a Signal
• 6.2 Properties of the Laplace Transform
• 6.3 Computation of the Inverse Laplace Transform
• 6.4 Transform of the Input/Output Differential Equation
• 6.5 Transform of the Input/Output Convolution Integral
• 6.6 Direct Construction of the Transfer Function
• 6.7 Chapter Summary
• Problems
• 7 THE z-TRANSFORM AND DISCRETE-TIME SYSTEMS
• 7.1 z-Transform of a Discrete-Time Signal
• 7.2 Properties of the z-Transform
• 7.3 Computation of the Inverse z-Transform
• 7.4 Transfer Function Representation
• 7.5 System Analysis Using the Transfer Function Representation
• 7.6 Chapter Summary
• Problems
• 8 ANALYSIS OF CONTINUOUS-TIME SYSTEMS USING THE TRANSFER FUNCTION REPRESENTATION
• 8.1 Stability and the Impulse Response
• 8.2 Routh—Hurwitz Stability Test
• 8.3 Analysis of the Step Response
• 8.4 Response to Sinusoids and Arbitrary Inputs
• 8.5 Frequency Response Function
• 8.6 Causal Filters
• 8.7 Chapter Summary
• Problems
• 9 APPLICATION TO CONTROL
• 9.1 Introduction to Control
• 9.2 Tracking Control
• 9.3 Root Locus
• 9.4 Application to Control System Design
• 9.5 Chapter Summary
• Problems
• 10 DESIGN OF DIGITAL FILTERS AND CONTROLLERS
• 10.1 Discretization
• 10.2 Design of IIR Filters
• 10.3 Design of IIR Filters Using MATLAB
• 10.4 Design of FIR Filters
• 10.5 Design of Digital Controllers
• 10.6 Chapter Summary
• Problems
• 11 STATE REPRESENTATION
• 11.1 State Model
• 11.2 Construction of State Models
• 11.3 Solution of State equations
• 11.4 Discrete-Time Systems
• 11.5 Equivalent State Representations
• 11.6 Discretization of State Model
• 11.7 Chapter Summary
• Problems
• APPENDIX A BRIEF REVIEW OF COMPLEX VARIABLES
• APPENDIX B BRIEF REVIEW OF MATRICES
• BIBLIOGRAPHY

<b></b>With the presentation at an introductory level, the third edition of the book (2007 copyright) contains a comprehensive treatment of continuous-time and discrete-time signals and systems, with demos on the textbook website (users.ece.gatech.edu/~bonnie/book3), data downloaded from the Web, and illustrations of numerous MATLAB commands for the solution of a wide range of problems arising in engineering and in other fields such as financial data analysis. The book contains a large collection of examples and problems, and practical applications that use actual data dowloaded from the Web. It is shown how data can be downloaded and then imported into MATLAB for analysis by techniques covered in the text. Applications include data analysis in the presense of noise, with the focus on filtering noisy signals and the use of the discrete Fourier transform (DFT) to extract the dominant cyclic components of a signal (time series) from noisy measurements of the signal.