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Summary:
Publisher Summary 1
Hammond (U. of Southampton, UK) and Shin (Andong National U., Korea) have written this textbook on the fundamentals signal processing based upon the course Hammond taught for many years. Written for novice sound and vibration engineers, this book covers the essentials of the field while focusing on the differences and properties of deterministic and random signals. Links to a companion site are also provided to outline 50 different MATLAB codes. Annotation 漏2008 Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 2
Fundamentals of Signal Processing for Sound and Vibration Engineersis based on Joe Hammond's many years of teaching experience at the Institute of Sound and Vibration Research, University of Southampton. Whilst the applications presented emphasise sound and vibration, the book focusses on the basic essentials of signal processing that ensures its appeal as a reference text to students and practitioners in all areas of mechanical, automotive, aerospace and civil engineering.聽 Offers an excellent introduction to signal processing for students and professionals in the sound and vibration engineering field.Split into two parts, covering deterministic signals then random signals, and offering a clear explanation of their theory and application together with appropriate MATLAB examples.Provides an excellent study tool for those new to the field of signal processing.Integrates topics within continuous, discrete, deterministic and random signals to facilitate better understanding of the topic as a whole.Illustrated with MATLAB examples, some using 'real' measured data, as well as fifty MATLAB codes on an accompanying website.聽聽
目录
Preface p. ix
About the Authors p. xi
Introduction to Signal Processing p. 1
Descriptions of Physical Data (Signals) p. 6
Classification of Data p. 7
Deterministic Signals p. 17
Classification of Deterministic Data p. 19
Periodic Signals p. 19
Almost Periodic Signals p. 21
Transient Signals p. 24
Brief Summary and Concluding Remarks p. 24
MATLAB Examples p. 26
Fourier Series p. 31
Periodic Signals and Fourier Series p. 31
The Delta Function p. 38
Fourier Series and the Delta Function p. 41
The Complex Form of the Fourier Series p. 42
Spectra p. 43
Some Computational Considerations p. 46
Brief Summary p. 52
MATLAB Examples p. 52
Fourier Integrals (Fourier Transform) and Continuous-Time Linear Systems p. 57
The Fourier Integral p. 57
Energy Spectra p. 61
Some Examples of Fourier Transforms p. 62
Properties of Fourier Transforms p. 67
The Importance of Phase p. 71
Echoes p. 72
Continuous-Time Linear Time-Invariant Systems and Convolution p. 73
Group Delay (Dispersion) p. 82
Minimum and Non-Minimum Phase Systems p. 85
The Hilbert Transform p. 90
The Effect of Data Truncation (Windowing) p. 94
Brief Summary p. 102
MATLAB Examples p. 103
Time Sampling and Aliasing p. 119
The Fourier Transform of an Ideal Sampled Signal p. 119
Aliasing and Anti-Aliasing Filters p. 126
Analogue-to-Digital Conversion and Dynamic Range p. 131
Some Other Considerations in Signal Acquisition p. 134
Shannon's Sampling Theorem (Signal Reconstruction) p. 137
Brief Summary p. 139
MATLAB Examples p. 140
The Discrete Fourier Transform p. 145
Sequences and Linear Filters p. 145
Frequency Domain Representation of Discrete Systems and Signals p. 150
The Discrete Fourier Transform p. 153
Properties of the DFT p. 160
Convolution of Periodic Sequences p. 162
The Fast Fourier Transform p. 164
Brief Summary p. 166
MATLAB Examples p. 170
Introduction to Random Processes p. 191
Random Processes p. 193
Basic Probability Theory p. 193
Random Variables and Probability Distributions p. 198
Expectations of Functions of a Random Variable p. 202
Brief Summary p. 211
MATLAB Examples p. 212
Stochastic Processes; Correlation Functions and Spectra p. 219
Probability Distribution Associated with a Stochastic Process p. 220
Moments of a Stochastic Process p. 222
Stationarity p. 224
The Second Moments of a Stochastic Process; Covariance (Correlation) Functions p. 225
Ergodicity and Time Averages p. 229
Examples p. 232
Spectra p. 242
Brief Summary p. 251
MATLAB Examples p. 253
Linear System Response to Random Inputs: System Identification p. 277
Single-Input Single-Output Systems p. 277
The Ordinary Coherence Function p. 284
System Identification p. 287
Brief Summary p. 297
MATLAB Examples p. 298
Estimation Methods and Statistical Considerations p. 317
Estimator Errors and Accuracy p. 317
Mean Value and Mean Square Value p. 320
Correlation and Covariance Functions p. 323
Power Spectral Density Function p. 327
Cross-spectral Density Function p. 347
Coherence Function p. 349
Frequency Response Function p. 350
Brief Summary p. 352
MATLAB Examples p. 354
Multiple-Input/Response Systems p. 363
Description of Multiple-Input, Multiple-Output (MIMO) Systems p. 363
Residual Random Variables, Partial and Multiple Coherence Functions p. 364
Principal Component Analysis p. 370
Proof of [characters not reproducible] p. 375
Proof of [characters not reproducible] p. 379
Wave Number Spectra and an Application p. 381
Some Comments on the Ordinary Coherence Function [gamma superscript 2 subscript xy](f) p. 385
Least Squares Optimization: Complex-Valued Problem p. 387
Proof of H[subscript W](f) to H[subscript 1](f) as [kappa](f) to [infinity] p. 389
Justification of the Joint Gaussianity of X(f) p. 391
Some Comments on Digital Filtering p. 393
References p. 395
Index p. 399
About the Authors p. xi
Introduction to Signal Processing p. 1
Descriptions of Physical Data (Signals) p. 6
Classification of Data p. 7
Deterministic Signals p. 17
Classification of Deterministic Data p. 19
Periodic Signals p. 19
Almost Periodic Signals p. 21
Transient Signals p. 24
Brief Summary and Concluding Remarks p. 24
MATLAB Examples p. 26
Fourier Series p. 31
Periodic Signals and Fourier Series p. 31
The Delta Function p. 38
Fourier Series and the Delta Function p. 41
The Complex Form of the Fourier Series p. 42
Spectra p. 43
Some Computational Considerations p. 46
Brief Summary p. 52
MATLAB Examples p. 52
Fourier Integrals (Fourier Transform) and Continuous-Time Linear Systems p. 57
The Fourier Integral p. 57
Energy Spectra p. 61
Some Examples of Fourier Transforms p. 62
Properties of Fourier Transforms p. 67
The Importance of Phase p. 71
Echoes p. 72
Continuous-Time Linear Time-Invariant Systems and Convolution p. 73
Group Delay (Dispersion) p. 82
Minimum and Non-Minimum Phase Systems p. 85
The Hilbert Transform p. 90
The Effect of Data Truncation (Windowing) p. 94
Brief Summary p. 102
MATLAB Examples p. 103
Time Sampling and Aliasing p. 119
The Fourier Transform of an Ideal Sampled Signal p. 119
Aliasing and Anti-Aliasing Filters p. 126
Analogue-to-Digital Conversion and Dynamic Range p. 131
Some Other Considerations in Signal Acquisition p. 134
Shannon's Sampling Theorem (Signal Reconstruction) p. 137
Brief Summary p. 139
MATLAB Examples p. 140
The Discrete Fourier Transform p. 145
Sequences and Linear Filters p. 145
Frequency Domain Representation of Discrete Systems and Signals p. 150
The Discrete Fourier Transform p. 153
Properties of the DFT p. 160
Convolution of Periodic Sequences p. 162
The Fast Fourier Transform p. 164
Brief Summary p. 166
MATLAB Examples p. 170
Introduction to Random Processes p. 191
Random Processes p. 193
Basic Probability Theory p. 193
Random Variables and Probability Distributions p. 198
Expectations of Functions of a Random Variable p. 202
Brief Summary p. 211
MATLAB Examples p. 212
Stochastic Processes; Correlation Functions and Spectra p. 219
Probability Distribution Associated with a Stochastic Process p. 220
Moments of a Stochastic Process p. 222
Stationarity p. 224
The Second Moments of a Stochastic Process; Covariance (Correlation) Functions p. 225
Ergodicity and Time Averages p. 229
Examples p. 232
Spectra p. 242
Brief Summary p. 251
MATLAB Examples p. 253
Linear System Response to Random Inputs: System Identification p. 277
Single-Input Single-Output Systems p. 277
The Ordinary Coherence Function p. 284
System Identification p. 287
Brief Summary p. 297
MATLAB Examples p. 298
Estimation Methods and Statistical Considerations p. 317
Estimator Errors and Accuracy p. 317
Mean Value and Mean Square Value p. 320
Correlation and Covariance Functions p. 323
Power Spectral Density Function p. 327
Cross-spectral Density Function p. 347
Coherence Function p. 349
Frequency Response Function p. 350
Brief Summary p. 352
MATLAB Examples p. 354
Multiple-Input/Response Systems p. 363
Description of Multiple-Input, Multiple-Output (MIMO) Systems p. 363
Residual Random Variables, Partial and Multiple Coherence Functions p. 364
Principal Component Analysis p. 370
Proof of [characters not reproducible] p. 375
Proof of [characters not reproducible] p. 379
Wave Number Spectra and an Application p. 381
Some Comments on the Ordinary Coherence Function [gamma superscript 2 subscript xy](f) p. 385
Least Squares Optimization: Complex-Valued Problem p. 387
Proof of H[subscript W](f) to H[subscript 1](f) as [kappa](f) to [infinity] p. 389
Justification of the Joint Gaussianity of X(f) p. 391
Some Comments on Digital Filtering p. 393
References p. 395
Index p. 399
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