《國外電子與通信教材系列：信號(hào)與系統(tǒng)（第2版）（英文版）》全面系統(tǒng)地介紹了信號(hào)與系統(tǒng)的基本概念、理論、方法及應(yīng)用。全書共10章。介紹了信號(hào)與系統(tǒng)的基本概念；討論了線性時(shí)不變系統(tǒng)的時(shí)域分析方法；討論了離散時(shí)間周期與非周期信號(hào)、連續(xù)時(shí)間周期與非周期信號(hào)，以及線性時(shí)不變系統(tǒng)的傅里葉描述及傅里葉描述在混合信號(hào)類型中的應(yīng)用。
《國外電子與通信教材系列：信號(hào)與系統(tǒng)（第2版）（英文版）》各章都有用MATLAB語言解題的內(nèi)容、參考資料及進(jìn)一步的閱讀材料，并配有相當(dāng)數(shù)量的例題。通過書中大量的各類習(xí)題和計(jì)算機(jī)實(shí)驗(yàn)題，能夠使讀者開闊視野，為讀者提供了足夠的訓(xùn)練空間。
CHAPTER 1 Introduction
1.1 What Is a Signal？
1.2 What Is a System？
1.3 Overview of Specific Systems
1.4 Classification of Signals
1.5 Basic Operations on Signals
1.6 Elementary Signals
1.7 Systems Viewed as Interconnections of Operations
1.8 Properties of Systems
1.9 Noise
1.10 Theme Examples
1.11 Exploring Concepts with MATLAB
1.12 Summary
Further Reading
Additional Problems
CHAPTER 2 Time-Domain Representations of Linear Time-Invariant Systems
2.1 Introduction
2.2 The Convolution Sum
2.3 Convolution Sum Evaluation Procedure
2.4 The Convolution Integral
2.5 Convolution Integral Evaluation Procedure
2.6 Interconnections of LTI Systems
2.7 Relations between LTI System Properties and the Impulse Response
2.8 Step Response
2.9 Differential and Difference Equation Representations of LTI Systems
2.10 Solving Differential and Difference Equations
2.11 Characteristics of Systems Described by Differential and Difference Equations
2.12 Block Diagram Representations
2.13 State-Variable Descriptions of LTI Systems
2.14 Exploring Concepts with MATLAB
2.15 Summary
Further Reading
Additional Problems
CHAPTER 3 Fourier Representations of Signals and Linear Time-Invariant Systems
3.1 Introduction
3.2 Complex Sinusoids and Frequency Response of LTI Systems
3.3 Fourier Representations for Four Classes of Signals
3.4 Discrete-Time Periodic Signals: The Discrete-Time Fourier Series
3.5 Continuous-Time Periodic Signals: The Fourier Series
3.6 Discrete-Time Nonperiodic Signals: The Discrete-Time Fourier Transform
3.7 Continuous-Time Nonperiodic Signals: The Fourier Transform
3.8 Properties of Fourier Representations
3.9 Linearity and Symmetry Properties
3.10 Convolution Property
3.11 Differentiation and Integration Properties
3.12 Time- and Frequency-Shift Properties
3.13 Finding Inyerse Fourier Transforms by Using Partial-Fraction Expansions
3,14 Multiplication Property
3.15 Scaling Properties
3.16 Parseval Relationships
3.17 Time-Bandwidth Product
3.18 Duality
3.19 Exploring Concepts with MATLAB
3.20 Summary
Further Reading
Additional Problems
CHAPTER 4 Applications of Fourier Representations to Mixed Signal Classes
4.1 Introduction
4.2 Fourier Transform Representations of Periodic Signals
4.3 Convolution and Multiplication with Mixtures of Periodic and Nonperiodic Signals
4.4 Fourier Transform Representation of Discrete-Time Signals
4.5 Sampling
4.6 Reconstruction of Continuous-Time Signals from Samples
4.7 Discrete-Time Processing of Continuous-Time Signals
4.8 Fourier Series Representations of Finite-Durarion Nonperiodic Signals
4.9 The Discrete-Time Fourier Series Approximation to the Fourier Transform
4.10 Efficient Algorithms for Evaluating the DTFS
4.11 Exploring Concepts with MATLAB
4.12 Summary
Further Reading
Additional Problems
CHAPTER 5 Application to Communication Systems
5.1 Introduction
5.2 Types of Modulation
5.3 Benefits of Modulation
5.4 Full Amplitude Modulation
5.5 Double Sideband-Suppressed Carrier Modulation
5.6 Quadrature-Carrier Multiplexing
5.7 Other Variants of Amplitude Modulation
5.8 Pulse-Amplitude Modulation
5.9 Multiplexing
5.10 Phase and Group Delays
5.11 Exploring Concepts with MATLAB
5.12 Summary
Further Reading
Additional Problems
CHAPTER 6 Representing Signals by Using Continuous-Time Complex Exponentials: the Laplace Transform
6.1 Introduction
6.2 The Laplace Transform
6.3 The Unilateral Laplace Transform
6.4 Properties of the Unilateral Laplace Transform
6.5 Inversion of the Unilateral Laplace Transform
6.6 Solving Differential Equations with Initial Conditions
6.7 Laplace Transform Methods in Circuit Analysis
6.8 Properties of the Bilateral Laplace Transform
6.9 Properties of the Region of Convergence
6.10 Inversion of the Bilateral Laplace Transform
6.11 The Transfer Function
6.12 Causality and Stability
6.13 Determining the Frequency Response from Poles and Zeros
6.14 Exploring Concepts with MATLAB
6.15 Summary
Further Reading
Additional Problems
CHAPTER 7 Representing Signals by Using Discrete-Time Complex Exponentials: the z-Transform
7.1 Introduction
7.2 The z-Transform
7.3 Properties of the Region of Convergence
7.4 Properties of the z-Transform
7.5 Inversion of the z-Transform
7.6 The Transfer Function
7.7 Causality and Stability
7.8 Determining the Frequency Response from Poles and Zeros
7.9 Computational Structures for Implementing Discrete-Time LTI Systems
7.10 The Unilateral z-Transform
7.11 Exploring Concepts with MATLAB
7.12 Summary
Further Reading
Additional Problems
CHAPTER 8 Application to Filters and Equalizers
8.1 Introduction
8.2 Conditions for Distortionless Transmission
8.3 Ideal Low-Pass Filters
8.4 Design of Filters
8.5 Approximating Functions
8.6 Frequency Transformations
8.7 Passive Filters
8.8 Digital Filters
8.9 FIR Digital Filters
8.10 IIR Digital Filters
8.11 Linear Distortion
8.12 Equalization
8.13 Exploring Concepts with MATLAB
8.14 Summary
Further Reading
Additional Problems
CHAPTER 9 Application to Linear Feedback Systems
9.1 Introduction
9.2 What Is Feedback？
9.3 Basic Feedback Concepts
9.4 Sensitivity Analysis
9.5 Effect of Feedback on Disturbance or Noise
9.6 Distortion Analysis
9.7 Summarizing Remarks on Feedback
9.8 Operational Amplifiers
9.9 Control Systems
9.10 Transient Response of Low-Order Systems
9.11 The Stability Problem
9.12 Routh-Hurwitz Criterion
9.13 Root Locus Method
9.14 Nyquist Stability Criterion
9.15 Bode Diagram
9.16 Sampled-Data Systems
9.17 Exploring Concepts with MATLAB
9.18 Summary
Further Reading
Additional Problems
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CHAPTER 10 Epilogue
APPENDIX A Selected Mathematical Identities
APPENDIX B Partial-Fraction Expansions
APPENDIX C Tables of Fourier Representations and Properties
APPENDIX D Tables of Laplace Transforms and Properties
APPENDIX E Tables of z-Tansforms and Properties
APPENDIX F Introduction to MATLAB
INDEX
Note that in both Figs. 8.14（a） and （b） the transfer function H（s） is in the form of a transfer impedance, defined by the Laplace transform of the output voltage v2（t）, divided by the Laplace transform of the current source i1（t）.
Problem 8.8 Show that the transfer function of the filter in Fig. 8.14（b） is equal to the Butterworth function given in Eq. （8.37）.
Problem 8.9 The passive filters depicted in Fig. 8.14 have impulse response of infinite duration. Justify this statement.
The determination of the elements of a filter, starting from a particular transfer function H （s）, is referred to as network syntbesis. It encompasses a number of highly advanced procedures that are beyond the scope of this text. Indeed, passive filters occupied a dominant role in the design of communication and other systems for several decades, until the advent of active filters and digital filters in the 1960s. Active filters （using operational amplifiers） are discussed in Chapter 9; digital filters are discussed next.
8.8 Digital Filters
A digital filter uses computation to implement the filtering action that is to be performed on a continuous-time signal. Figure 8.15 shows a block diagram of the operations involved in such an approach to design a frequency-selective filter; the ideas behind these operations were discussed in Section 4.7. The block labeled "analog-to-digital （A/D） converter" is used to convert the continuous-time signal x（t） into a corresponding sequence x（n） of numbers. The digital filter processes the sequence of numbers x（n） on a sample-by-sample basis to produce a new sequence of numbers, y（n）, which is then converted into the corresponding continuous-time signal by the -digital-to-analog （D/A） converter. Finally, the reconstruction （low-pass） filter at the output of the system produces a continuous-time signal y（t）, representing the filtered version of-the original input signal x（t）.
Two important points should be carefully noted in the study of digital filters:
1. The underlying design procedures are usually based on the use of an analog or infiniteprecision model for the samples of input data and all internal calculations, this is done in order to take advantage of well-understood discrete-time, but continuous-am- plitude, mathematics. The resulting discrete-time filter provides the designer with a theoretical framework for the task at hand.
2. When the discrete-time filter is implemented in digital form for practical use, as depicted in Fig. 8.15, the input data and internal calculations are all quantized to a finite precision.
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