Digital signal processing : theory and practice /
Maurice Bellanger ; translated by Benjamin A. Engel.
- Tenth edition.
- 1 online resource (xix, 374 pages) : illustrations.
-
Translation of: Traitement num�erique du signal.
Includes bibliographical references and index.
Table of Contents Foreword (Historical Perspective) xi
Preface xiii
Introduction xv
1 Signal Digitizing - Sampling and Coding 1
1.1 Fourier Analysis 1
1.2 Distributions 4
1.3 Some Commonly Studied Signals 6
1.4 The Norms of a Function 12
1.5 Sampling 13
1.6 Frequency Sampling 14
1.7 The Sampling Theorem 15
1.8 Sampling of Sinusoidal and Random Signals 16
1.9 Quantization 20
1.10 The Coding Dynamic Range 22
1.11 Nonlinear Coding with the 13-segment A-law 24
1.12 Optimal Coding 26
1.13 Quantity of Information and Channel Capacity 28
1.14 Binary Representations 29
2 The Discrete Fourier Transform 35
2.1 Definition and Properties of the Discrete Fourier Transform 36
9.1 The Fourier Transform of a Real and Causal Set 189
9.2 Analytic Signals 192
9.3 Calculating the Coefficients of an FIR Quadrature Filter 195
9.4 Recursive 90° Phase Shifters 197
9.5 Single Side-Band Modulation 199
9.6 Minimum-Phase Filters 200
9.7 Differentiator 201
9.8 Interpolation Using FIR Filters 202
9.9 Lagrange Interpolation 203
9.10 Interpolation by Blocks - Splines 204
9.11 Interpolations and Signal Restoration 206
9.12 Conclusion 208
10 Multirate Filtering 213
10.1 Decimation and Z-Transform 213
10.2 Decomposition of a Low-Pass FIR Filter 217
10.3 Half-Band FIR Filters 220
10.4 Decomposition with Half-Band Filters 222
10.5 Digital Filtering by Polyphase Network 224
10.6 Multirate Filtering with IIR Elements 227
10.7 Filter Banks Using Polyphase Networks and DFT 227
10.8 Conclusion 229
11 QMF Filters and Wavelets 233
11.1 Decomposition into Two Sub-Bands and Reconstruction 233
11.2 QMF Filters 233
11.3 Perfect Decomposition and Reconstruction 236
11.4 Wavelets 238
11.5 Lattice Structures 242
12 Filter Banks 245
12.1 Decomposition and Reconstruction 245
12.2 Analyzing the Elements of the Polyphase Network 247
12.3 Determining the Inverse Functions 248
12.4 Banks of Pseudo-QMF Filters 249
12.5 Determining the Coefficients of the Prototype Filter 253
12.6 Realizing a Bank of Real Filters 254
13 Signal Analysis and Modeling 259
13.1 Autocorrelation and Intercorrelation 259
13.2 Correlogram Spectral Analysis 261
13.3 Single-Frequency Estimation 262
13.4 Correlation Matrix 264
13.5 Modeling 266
13.6 Linear Prediction 268
13.7 Predictor Structures 270
13.8 Multiple Sources - MIMO 273
13.9 Conclusion 275
14 Adaptive Filtering 279
14.1 Principle of Adaptive Filtering 279
14.2 Convergence Conditions 282
14.3 Time Constant 284
14.4 Residual Error 285
14.5 Complexity Parameters 286
14.6 Normalized Algorithms and Sign Algorithms 288
14.7 Adaptive FIR Filtering in Cascade Form 289
14.8 Adaptive IIR Filtering 291
14.9 Conclusion 293
15 Neural Networks 297
15.1 Classification 297
15.2 Multilayer Perceptron 299
15.3 The Backpropagation Algorithm 300
15.4 Examples of Application 303
15.5 Convolution Neural Networks 306
15.6 Recurrent/Recursive Neural Networks 307
15.7 Neural Network and Signal Processing 308
15.8 On Activation Functions 309
15.9 Conclusion 310
16 Error-Correcting Codes 313
16.1 Reed-Solomon Codes 313
16.2 Convolutional Codes 319
16.3 Conclusion 331
17 Applications 335
17.1 Frequency Detection 335
17.2 Phase-locked Loop 337
17.3 Differential Coding of Speech 338
17.4 Coding of Sound 339
17.5 Echo Cancelation 340
17.6 Television Image Processing 342
17.7 Multicarrier Transmission - OFDM 344
17.8 Mobile Radiocommunications 347
References 349
Exercises: Solutions and Hints 351
Index 363
"Signal processing is required and deployed in almost all engineering fields, not only electronics and communications but also biology, mechanics, chemistry, and geophysics. Moreover, signal processing algorithms are exploited for data analysis and modelling in business and finance and are also at the core of artificial intelligence systems. Delivering on the promise of theory and practice throughout, the author ensures that the chapters and exercises meet the requirements of both students and industry practitioners, with more advanced topics appearing in the second half of the book. Mathematical concepts are explained, and merge well with the text, allowing the reader to follow the narrative instead of breaking off into tough mathematical diversions. The book has been updated throughout and includes a brand-new section on neural networks. This book series, published originally in French, has not been available in English for many years, and readers will find that it stands alone as a complete text, with no earlier edition knowledge required"--
About the Author Maurice Bellanger, PhD, is a former Professor of Electronics and Head of the Electronics and Communications Research Team at the Conservatoire National des Arts et Métiers (CNAM), Paris, and past president of the European Association for Signal Processing (EURASIP). He has decades of experience in both industry and academia and has published over one hundred papers on digital signal processing and related subjects.