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Signals, Systems and Inference, EBook, Global Edition.

By: Contributor(s): Material type: TextTextPublisher: Harlow : Pearson Education, Limited, 2018Copyright date: ©2019Edition: 1st edDescription: 1 online resource (606 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781292156217
Subject(s): Genre/Form: Additional physical formats: Print version:: Signals, Systems and Inference, EBook, Global EditionDDC classification:
  • 621.3822
LOC classification:
  • TK5102.5 .O674 2017
Online resources:
Contents:
Front Cover -- 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.
Summary: For upper-level undergraduate courses in deterministic and stochastic signals and system engineering An Integrative Approach to Signals, Systems and Inference Signals, Systems and Inference is a comprehensive text that builds on introductory courses in time- and frequency-domain analysis of signals and systems, and in probability. Directed primarily to upper-level undergraduates and beginning graduate students in engineering and applied science branches, this new textbook pioneers a novel course of study. Instead of the usual leap from broad introductory subjects to highly specialised advanced subjects, this engaging and inclusive text creates a study track for a transitional course.  Properties and representations of deterministic signals and systems are reviewed and elaborated on, including group delay and the structure and behavior of state-space models. The text also introduces and interprets correlation functions and power spectral densities for describing and processing random signals. Application contexts include pulse amplitude modulation, observer-based feedback control, optimum linear filters for minimum mean-square-error estimation, and matched filtering for signal detection. Model-based approaches to inference are emphasised, in particular for state estimation, signal estimation, and signal detection.  The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you will receive via email the code and instructions on how to access this product. Time limit The eBooks products do not have an expiry date. You will continue to accessSummary: your digital ebook products whilst you have your Bookshelf installed.
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Front Cover -- 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.

For upper-level undergraduate courses in deterministic and stochastic signals and system engineering An Integrative Approach to Signals, Systems and Inference Signals, Systems and Inference is a comprehensive text that builds on introductory courses in time- and frequency-domain analysis of signals and systems, and in probability. Directed primarily to upper-level undergraduates and beginning graduate students in engineering and applied science branches, this new textbook pioneers a novel course of study. Instead of the usual leap from broad introductory subjects to highly specialised advanced subjects, this engaging and inclusive text creates a study track for a transitional course.  Properties and representations of deterministic signals and systems are reviewed and elaborated on, including group delay and the structure and behavior of state-space models. The text also introduces and interprets correlation functions and power spectral densities for describing and processing random signals. Application contexts include pulse amplitude modulation, observer-based feedback control, optimum linear filters for minimum mean-square-error estimation, and matched filtering for signal detection. Model-based approaches to inference are emphasised, in particular for state estimation, signal estimation, and signal detection.  The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you will receive via email the code and instructions on how to access this product. Time limit The eBooks products do not have an expiry date. You will continue to access

your digital ebook products whilst you have your Bookshelf installed.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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