A Practical Approach to Signals and SystemsISBN: 9780470823538
400 pages
August 2008

Description
Gaining a strong sense of signals and systems fundamentals is key for general proficiency in any electronic engineering discipline, and critical for specialists in signal processing, communication, and control. At the same time, there is a pressing need to gain mastery of these concepts quickly, and in a manner that will be immediately applicable in the real word.
Simultaneous study of both continuous and discrete signals and systems presents a much easy path to understanding signals and systems analysis. In A Practical Approach to Signals and Systems, Sundararajan details the discrete version first followed by the corresponding continuous version for each topic, as discrete signals and systems are more often used in practice and their concepts are relatively easier to understand. In addition to examples of typical applications of analysis methods, the author gives comprehensive coverage of transform methods, emphasizing practical methods of analysis and physical interpretations of concepts.

Gives equal emphasis to theory and practice

Presents methods that can be immediately applied

Complete treatment of transform methods

Expanded coverage of Fourier analysis

Selfcontained: starts from the basics and discusses applications

Visual aids and examples makes the subject easier to understand

Endofchapter exercises, with a extensive solutions manual for instructors

MATLAB software for readers to download and practice on their own

Presentation slides with book figures and slides with lecture notes
A Practical Approach to Signals and Systems is an excellent resource for the electrical engineering student or professional to quickly gain an understanding of signal analysis concepts  concepts which all electrical engineers will eventually encounter no matter what their specialization. For aspiring engineers in signal processing, communication, and control, the topics presented will form a sound foundation to their future study, while allowing them to quickly move on to more advanced topics in the area.
Scientists in chemical, mechanical, and biomedical areas will also benefit from this book, as increasing overlap with electrical engineering solutions and applications will require a working understanding of signals. Compact and self contained, A Practical Approach to Signals and Systems be used for courses or selfstudy, or as a reference book.
Table of Contents
Abbreviations.
1 Introduction.
1.1 The Organization of this Book.
2 Discrete Signals.
2.1 Classification of Signals.
2.1.1 Continuous, Discrete, and Digital Signals.
2.1.2 Periodic and Aperiodic Signals.
2.1.3 Energy and Power Signals.
2.1.4 Even and OddSymmetric Signals.
2.1.5 Causal and Noncausal Signals.
2.1.6 Deterministic and Random Signals.
2.2 Basic Signals.
2.2.1 UnitImpulse Signal.
2.2.2 UnitStep Signal.
2.2.3 UnitRamp Signal.
2.2.4 Sinusoids and Exponentials.
2.3 Signal Operations.
2.3.1 Time Shifting.
2.3.2 Time Reversal.
2.3.3 Time Scaling.
2.4 Summary.
References.
Exercises.
3 Continuous Signals.
3.1 Classification of Signals.
3.1.1 Continuous Signals.
3.1.2 Periodic and Aperiodic Signals.
3.1.3 Energy and Power Signals.
3.1.4 Even and OddSymmetric Signals.
3.1.5 Causal and Noncausal Signals.
3.2 Basic Signals.
3.2.1 The UnitStep Signal.
3.2.2 The UnitImpulse Signal.
3.2.3 The UnitRamp Signal.
3.2.4 Sinusoids.
3.3 Signal Operations.
3.3.1 Time Shifting.
3.3.2 Time Reversal.
3.3.3 Time Scaling.
3.4 Summary.
Reference.
Exercises.
4 TimeDomain Analysis of Discrete Systems.
4.1 Difference Equation Model.
4.1.1 System Response.
4.1.2 Impulse Response.
4.1.3 Characterization of Systems by their Responses to Impulse and UnitStep Signals.
4.2 Classification of Systems.
4.2.1 Linear and Nonlinear Systems.
4.2.2 TimeInvariant and TimeVarying Systems.
4.2.3 Causal and Noncausal Systems.
4.2.4 Instantaneous and Dynamic Systems.
4.2.5 Inverse Systems.
4.2.6 Continuous and Discrete Systems.
4.3 ConvolutionSummation Model.
4.3.1 Properties of ConvolutionSummation.
4.3.2 The Difference Equation and the ConvolutionSummation.
4.3.3 Response to Complex Exponential Input.
4.4 System Stability.
4.5 Realization of Discrete Systems.
4.5.1 Decomposition of HigherOrder Systems.
4.5.2 Feedback Systems.
4.6 Summary.
References.
Exercises.
5 TimeDomain Analysis of Continuous Systems.
5.1 Classification of Systems.
5.1.1 Linear and Nonlinear Systems.
5.1.2 TimeInvariant and TimeVarying Systems.
5.1.3 Causal and Noncausal Systems.
5.1.4 Instantaneous and Dynamic Systems.
5.1.5 LumpedParameter and DistributedParameter Systems.
5.1.6 Inverse Systems.
5.2 Difference Equation Model.
5.3 ConvolutionIntegral Model.
5.3.1 Properties of ConvolutionIntegral.
5.4 System Response.
5.4.1 Impulse Response.
5.4.2 Response to UnitStep Input.
5.4.3 Characterization of Systems by their Responses to Impulse and UnitStep Signals.
5.4.4 Response to Complex Exponential Input.
5.5 System Stability.
5.6 Realization of Continuous Systems.
5.6.1 Decomposition of HigherOrder Systems.
5.6.2 Feedback Systems.
5.7 Summary.
Reference.
Exercises.
6 The Discrete Fourier Transform.
6.1 The TimeDomain and FrequencyDomain.
6.2 The Fourier Analysis.
6.2.1 Versions of Fourier Analysis.
6.3 The Discrete Fourier Transform.
6.3.1 The Approximation of Arbitrary Waveforms with Finite Number Samples.
6.3.2 The DFT and the IDFT.
6.3.3 DFT of Some Basic Signals.
6.4 Properties of the Discrete Fourier Transform.
6.4.1 Linearity.
6.4.2 Periodicity.
6.4.3 Circular Shift of a Sequence.
6.4.4 Circular Shift of a Spectrum.
6.4.5 Symmetry.
6.4.6 Circular Convolution of TimeDomain Sequences.
6.4.7 Circular Convolution of FrequencyDomain Sequences.
6.4.8 Parseval's Theorem.
6.5 Applications of the Discrete Fourier Transform.
6.5.1 Computation of the Linear Convolution Using the DFT.
6.5.2 Interpolation and Decimation.
6.6. Summary.
References.
Exercises.
7 Fourier Series.
7.1 Fourier Series.
7.1.1 FS as the Limiting Case of the DFT.
7.1.2 The Compact Trigonometric Form of the FS.
7.1.3 The Trigonometric Form of the FS.
7.1.4 Periodicity of the FS.
7.1.5 Existence of the FS.
7.1.6 Gibbs Phenomenon.
7.2 Properties of the Fourier Series.
7.2.1 Linearity.
7.2.2 Symmetry.
7.2.3 TimeShifting.
7.2.4 FrequencyShifting.
7.2.5 Convolution in the TimeDomain.
7.2.6 Convolution in the FrequencyDomain.
7.2.7 Duality.
7.2.8 TimeScaling.
7.2.9 TimeDifferentiation.
7.2.10 TimeIntegration.
7.2.11 Parseval's Theorem.
7.3 Approximation of the Fourier Series.
7.3.1 Aliasing Effect.
7.4 Applications of the Fourier Series.
7.5 Summary.
References.
Exercises.
8 The DiscreteTime Fourier Transform.
8.1 The DiscreteTime Fourier Transform.
8.1.1 The DTFT as the Limiting Case of the DFT.
8.1.2 The Dual Relationship Between the DTFT and the FS.
8.1.3 The DTFT of a Discrete Periodic Signal.
8.1.4 Determination of the DFT from the DTFT.
8.2 Properties of the DiscreteTime Fourier Transform.
8.2.1 Linearity.
8.2.2 TimeShifting.
8.2.3 FrequencyShifting.
8.2.4 Convolution in the TimeDomain.
8.2.5 Convolution in the FrequencyDomain.
8.2.6 Symmetry.
8.2.7 TimeReversal.
8.2.8 TimeExpansion.
8.2.9 FrequencyDifferentiation.
8.2.10 Difference.
8.2.11 Summation.
8.2.12 Parseval's Theorem and the Energy Transfer Function.
8.3 Approximation of the DiscreteTime Fourier Transform.
8.3.1 Approximation of the Inverse DTFT by the IDFT.
8.4 Applications of the DiscreteTime Fourier Transform.
8.4.1 Transfer Function and the System Response.
8.4.2 Digital Filter Design Using DTFT.
8.4.3 Digital Differentiator.
8.4.4 Hilbert Transform.
8.5 Summary.
References.
Exercises.
9 The Fourier Transform.
9.1 The Fourier Transform.
9.1.1 The FT as the Limiting Case of the DTFT.
9.1.2 Existence of the FT.
9.2 Properties of the Fourier Transform.
9.2.1 Linearity.
9.2.2 Duality.
9.2.3 Symmetry.
9.2.4 TimeShifting.
9.2.5 FrequencyShifting.
9.2.6 Convolution in the TimeDomain.
9.2.7 Convolution in the FrequencyDomain.
9.2.8 Conjugation.
9.2.9 TimeReversal.
9.2.10 TimeScaling.
9.2.11 TimeDifferentiation.
9.2.12 TimeIntegration.
9.2.13 FrequencyDifferentiation.
9.2.14 Parseval's Theorem and the Energy Transfer Function.
9.3 Fourier Transform of Mixed Class Signals.
9.3.1 The FT of a Continuous Periodic Signal.
9.3.2 Determination of the FS from the FT.
9.3.3 The FT of a Sampled Signal and the Aliasing Effect.
9.3.4 The FT of a Sampled Aperiodic Signal and the DTFT of the Corresponding Discrete Signal.
9.3.5 The FT of a Sampled Periodic Signal and the DFT of the Corresponding Discrete Signal.
9.3.6 Approximation of the Continuous Signal from its Sampled Version.
9.4 Approximation of the Fourier Transform.
9.5 Applications of the Fourier Transform.
9.5.1 Transfer Function and the System Response.
9.5.2 Ideal Filters and their Unrealizability.
9.5.3 Modulation and Demodulation.
9.6 Summary.
References.
Exercises.
10 The zTransform.
10.1 Fourier Analysis and the zTransform.
10.2 The zTransform.
10.3 Properties of the zTransform.
10.3.1 Linearity.
10.3.2 Left Shift of a Sequence.
10.3.3 Right Shift of a Sequence.
10.3.4 Convolution.
10.3.5 Multiplication by n.
10.3.6 Multiplication by an.
10.3.7 Summation.
10.3.8 Initial Value.
10.3.9 Final Value.
10.3.10 Transform of Semiperiodic Functions.
10.4 The Inverse zTransform.
10.4.1 Finding the Inverse zTransform.
10.5 Applications of the zTransform.
10.5.1 Transfer Function and the System Response.
10.5.2 Characterization of a System by its Poles and Zeros.
10.5.3 System Stability.
10.5.4 Realization of Systems.
10.5.5 Feedback Systems.
10.6 Summary.
References.
Exercises.
11 The Laplace Transform.
11.1 The Laplace Transform.
11.1.1 Relationship Between the Laplace Transform and the zTransform.
11.2 Properties of the Laplace Transform.
11.2.1 Linearity.
11.2.2 TimeShifting.
11.2.3 FrequencyShifting.
11.2.4 TimeDifferentiation.
11.2.5 Integration.
11.2.6 TimeScaling.
11.2.7 Convolution in Time.
11.2.8 Multiplication by t.
11.2.9 Initial Value.
11.2.10 Final Value.
11.2.11 Transform of Semiperiodic Functions.
11.3 The Inverse Laplace Transform.
11.4 Applications of the Laplace Transform.
11.4.1 Transfer Function and the System Response.
11.4.2 Characterization of a System by its Poles and Zeros.
11.4.3 System Stability.
11.4.4 Realization of Systems.
11.4.5 FrequencyDomain Representation of Circuits.
11.4.6 Feedback Systems.
11.4.7 Analog Filters.
11.5 Summary.
Reference.
Exercises.
12 StateSpace Analysis of Discrete Systems.
12.1 The StateSpace Model.
12.1.1 Parallel Realization.
12.1.2 Cascade Realization.
12.2 TimeDomain Solution of the State Equation.
12.2.1 Iterative Solution.
12.2.2 ClosedForm Solution.
12.2.3 The Impulse Response.
12.3 FrequencyDomain Solution of the State Equation.
12.4 Linear Transformation of State Vectors.
12.5 Summary.
Reference.
Exercises.
13 StateSpace Analysis of Continuous Systems.
13.1 The StateSpace Model.
13.2 TimeDomain Solution of the State Equation.
13.3 FrequencyDomain Solution of the State Equation.
13.4 Linear Transformation of State Vectors.
13.5 Summary.
Reference.
Exercises.
Appendix A Transform Pairs and Properties.
Appendix B Useful Mathematical Formulas.
Answers to Selected Exercises.
Index.
Author Information
The Wiley Advantage

Gives equal emphasis to theory and practice

Presents methods that can be immediately applied

Complete treatment of transform methods, with expanded coverage of Fourier analysis

Starts from the basics and discusses applications, and is therefore selfcontained

Visual aids and examples makes the subject easier to understand

Endofchapter exercises, with a extensive solutions manual for instructors

MATLAB software for readers to download and practice on their own

Presentation slides with book figures and slides with lecture notes
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