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• Title page
• Copyright page
• Contents
• Preface
• The Cover
• Acknowledgments
• Prologue
• 1. Signals and Systems
• 1.1 Signals, Systems, Models, and Properties
• 1.1.1 System Properties
• 1.2 Linear, Time-Invariant Systems
• 1.2.1 Impulse-Response Representation of LTI Systems
• 1.2.2 Eigenfunction and Transform Representation of LTI Systems
• 1.2.3 Fourier Transforms
• 1.3 Deterministic Signals and Their Fourier Transforms
• 1.3.1 Signal Classes and Their Fourier Transforms
• 1.3.2 Parseval’s Identity, Energy Spectral Density, and Deterministic Autocorrelation
• 1.4 Bilateral Laplace and Z-Transforms
• 1.4.1 The Bilateral z -Transform
• 1.4.2 The Bilateral Laplace Transform
• 1.5 Discrete-Time Processing of Continuous-Time Signals
• 1.5.1 Basic Structure for DT Processing of CT Signals
• 1.5.2 DT Filtering and Overall CT Response
• 1.5.3 Nonideal D/C Converters
• 1.6 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 2. Amplitude, Phase, and Group Delay
• 2.1 Fourier Transform Magnitude and Phase
• 2.2 Group Delay and the Effect of Nonlinear Phase
• 2.2.1 Narrowband Input Signals
• 2.2.2 Broadband Input Signals
• 2.3 All-Pass and Minimum-Phase Systems
• 2.3.1 All-Pass Systems
• 2.3.2 Minimum-Phase Systems
• 2.4 Spectral Factorization
• 2.5 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 3. Pulse Amplitude Modulation
• 3.1 Baseband Pulse-Amplitude Modulation
• 3.1.1 The Transmitted Signal
• 3.1.2 The Received Signal
• 3.1.3 Frequency-Domain Characterizations
• 3.1.4 Intersymbol Interference at the Receiver
• 3.2 Nyquist Pulses
• 3.3 Passband Pulse-Amplitude Modulation
• 3.3.1 Frequency-Shift Keying (FSK)
• 3.3.2 Phase-Shift Keying (PSK)
• 3.3.3 Quadrature Amplitude Modulation (QAM)
• 3.4 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 4. State-Space Models
• 4.1 System Memory
• 4.2 Illustrative Examples
• 4.3 State-Space Models
• 4.3.1 DT State-Space Models
• 4.3.2 CT State-Space Models
• 4.3.3 Defining Properties of State-Space Models
• 4.4 State-Space Models from LTI Input-Output
• 4.5 Equilibria and Linearization of Nonlinear State-Space Models
• 4.5.1 Equilibrium
• 4.5.2 Linearization
• 4.6 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 5. LTI State-Space Models
• 5.1 Continuous-Time and Discrete-Time LTI Models
• 5.2 Zero-Input Response and Modal Representation
• 5.2.1 Undriven CT Systems
• 5.2.2 Undriven DT Systems
• 5.2.3 Asymptotic Stability of LTI Systems
• 5.3 General Response in Modal Coordinates
• 5.3.1 Driven CT Systems
• 5.3.2 Driven DT Systems
• 5.3.3 Similarity Transformations and Diagonalization
• 5.4 Transfer Functions, Hidden Modes, Reachability, and Observability
• 5.4.1 Input-State-Output Structure of CT Systems
• 5.4.2 Input-State-Output Structure of DT Systems
• 5.5 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 6. State Observers and State Feedback
• 6.1 Plant and Model
• 6.2 State Estimation and Observers
• 6.2.1 Real-Time Simulation
• 6.2.2 The State Observer
• 6.2.3 Observer Design
• 6.3 State Feedback Control
• 6.3.1 Open-Loop Control
• 6.3.2 Closed-Loop Control via LTI State Feedback
• 6.3.3 LTI State Feedback Design
• 6.4 Observer-Based Feedback Control
• 6.5 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 7. Probabilistic Models
• 7.1 The Basic Probability Model
• 7.2 Conditional Probability, Bayes’ Rule, and Independence
• 7.3 Random Variables
• 7.4 Probability Distributions
• 7.5 Jointly Distributed Random Variables
• 7.6 Expectations, Moments, and Variance
• 7.7 Correlation and Covariance for Bivariate Random Variables
• 7.8 A Vector-Space Interpretation of Correlation Properties
• 7.9 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 8. Estimation
• 8.1 Estimation of a Continuous Random Variable
• 8.2 From Estimates to the Estimator
• 8.2.1 Orthogonality
• 8.3 Linear Minimum Mean Square Error Estimation
• 8.3.1 Linear Estimation of One Random Variable from a Single Measurement of Anot
• 8.3.2 Multiple Measurements
• 8.4 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 9. Hypothesis Testing
• 9.1 Binary Pulse-Amplitude Modulation in Noise
• 9.2 Hypothesis Testing with Minimum Error Probability
• 9.2.1 Deciding with Minimum Conditional Probability of Error
• 9.2.2 MAP Decision Rule for Minimum Overall Probability of Error
• 9.2.3 Hypothesis Testing in Coded Digital Communication
• 9.3 Binary Hypothesis Testing
• 9.3.1 False Alarm, Miss, and Detection
• 9.3.2 The Likelihood Ratio Test
• 9.3.3 Neyman–Pearson Decision Rule and Receiver Operating Characteristic
• 9.4 Minimum Risk Decisions
• 9.5 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 10. Random Processes
• 10.1 Definition and Examples of a Random Process
• 10.2 First-and Second-Moment Characterization of Random Processes
• 10.3 Stationarity
• 10.3.1 Strict-Sense Stationarity
• 10.3.2 Wide-Sense Stationarity
• 10.3.3 Some Properties of WSS Correlation and Covariance Functions
• 10.4 Ergodicity
• 10.5 Linear Estimation of Random Processes
• 10.5.1 Linear Prediction
• 10.5.2 Linear FIR Filtering
• 10.6 LTI Filtering of WSS Processes
• 10.7 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 11. Power Spectral Density
• 11.1 Spectral Distribution of Expected Instantaneous Power
• 11.1.1 Power Spectral Density
• 11.1.2 Fluctuation Spectral Density
• 11.1.3 Cross-Spectral Density
• 11.2 Expected Time-Averaged Power Spectrum and the Einstein-Wiener-KhinchinTheorem
• 11.3 Applications
• 11.3.1 Revealing Cyclic Components
• 11.3.2 Modeling Filters
• 11.3.3 Whitening Filters
• 11.3.4 Sampling Bandlimited Random Processes
• 11.4 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 12. Signal Estimation
• 12.1 LMMSE Estimation for Random Variables
• 12.2 FIR Wiener Filters
• 12.3 The Unconstrained DT Wiener Filter
• 12.4 Causal DT Wiener Filtering
• 12.5 Optimal Observers and Kalman Filtering
• 12.5.1 Causal Wiener Filtering of a Signal Corrupted by Additive Noise
• 12.5.2 Observer Implementation of the Wiener Filter
• 12.5.3 Optimal State Estimates and Kalman Filtering
• 12.6 Estimation of CT Signals
• 12.7 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• 13. Signal Detection
• 13.1 Hypothesis Testing with Multiple Measurements
• 13.2 Detecting a Known Signal in I.I.D. Gaussian Noise
• 13.2.1 The Optimal Solution
• 13.2.2 Characterizing Performance
• 13.2.3 Matched Filtering
• 13.3 Extensions of Matched-Filter Detection
• 13.3.1 Infinite-Duration, Finite-Energy Signals
• 13.3.2 Maximizing SNR for Signal Detection in White Noise
• 13.3.3 Detection in Colored Noise
• 13.3.4 Continuous-Time Matched Filters
• 13.3.5 Matched Filtering and Nyquist Pulse Design
• 13.3.6 Unknown Arrival Time and Pulse Compression
• 13.4 Signal Discrimination in I.I.D. Gaussian Noise
• 13.5 Further Reading
• Problems
• Basic Problems
• Advanced Problems
• Extension Problems
• Bibliography
• Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
• V
• W
• Y
• Z
• Back Cover