This book uses MATLAB as a computing tool to explore traditional DSP topics and solve problems. This greatly expands the range and complexity of problems that students can effectively study in signal processing courses. A large number of worked examples, computer simulations and applications are provided, along with theoretical aspects that are essential in order to gain a good understanding of the main topics. Practicing engineers may also find it useful as an introductory text on the subject.
Author(s): Andre Quinquis
Edition: 1
Publisher: Wiley-ISTE
Year: 2008
Language: English
Pages: 425
Preface......Page 11
Table of Contents......Page 7
1.1.1. MATLAB software presentation......Page 15
1.1.2. Important MATLAB commands and functions......Page 17
1.1.3. Operating modes and programming with MATLAB......Page 22
1.1.4. Example of work session with MATLAB......Page 24
1.2. Solved exercises......Page 27
2.1.1. Mathematical model of 1D and 2D discrete-time signals......Page 37
2.1.2. Basic 1D and 2D discrete-time signals......Page 39
2.1.3. Periodic 1D and 2D discrete-time signals representation using the discrete-time Fourier series......Page 40
2.1.5. Analytic signals......Page 41
2.2. Solved exercises......Page 43
2.3. Exercises......Page 65
3.1.1. Introduction......Page 69
3.1.2. Real random variables......Page 70
3.1.3. Random processes......Page 74
3.2. Solved exercises......Page 78
3.3. Exercises......Page 94
4.1. Theoretical background......Page 97
4.1.2. Cumulants......Page 98
4.1.3. Cumulant properties......Page 99
4.1.5. Normality test using the Henry line......Page 100
4.2. Solved exercises......Page 102
4.3. Exercises......Page 113
5.1. Theoretical background......Page 117
5.1.1. Discrete Fourier transform of 1D digital signals......Page 118
5.1.2. DFT of 2D digital signals......Page 119
5.1.5. Methods and algorithms for the DFT calculation......Page 120
5.2. Solved exercises......Page 123
5.3. Exercises......Page 148
6.1.1. LTI response calculation......Page 151
6.1.2. LTI response to basic signals......Page 153
6.2. Solved exercises......Page 155
6.3. Exercises......Page 183
7.1.1. Transfer function and filter specifications for infinite impulse response (IIR) filters......Page 187
7.1.2. Design methods for IIR filters......Page 188
7.1.3. Frequency transformations......Page 194
7.2. Solved exercises......Page 196
7.3. Exercises......Page 208
8.1.1. Transfer function and properties of FIR filters......Page 211
8.1.2. Design methods......Page 213
8.1.3. General conclusion about digital filter design......Page 217
8.2. Solved exercises......Page 218
8.3. Exercises......Page 227
9.1.1. Matched filtering: optimal detection of a known noisy signal......Page 229
9.1.2. Linear optimal estimates......Page 230
9.1.3. Least squares (LS) method......Page 235
9.1.4. LS method with forgetting factor......Page 236
9.2. Solved exercises......Page 237
9.3. Exercises......Page 253
10.1.1. Estimate properties......Page 255
10.1.2. Power spectral density estimation......Page 256
10.1.3. Parametric spectral analysis......Page 259
10.1.4. Superresolution spectral analysis methods......Page 264
10.1.5. Other spectral analysis methods......Page 270
10.2. Solved exercises......Page 271
10.3. Exercises......Page 291
11.1.1. Fourier transform shortcomings: interpretation difficulties......Page 293
11.1.2. Spectrogram......Page 294
11.1.3. Time-scale analysis – wavelet transform......Page 295
11.1.4. Wigner-ville distribution......Page 298
11.1.5. Smoothed WVD (SWVD)......Page 301
11.2. Solved exercises......Page 302
11.3. Exercises......Page 318
12.1.1. Fractional Fourier transform......Page 321
12.1.2. Phase polynomial analysis concept......Page 323
12.1.3. Time-frequency representations based on warping operators......Page 328
12.2. Solved exercises......Page 331
12.3. Exercises......Page 352
13.1.1. Introduction......Page 357
13.1.2. Data analysis methods......Page 358
13.1.3. Supervised classifiers......Page 362
13.2. Solved exercises......Page 376
13.3. Exercises......Page 393
14.1. Theoretical background......Page 397
14.1.1. Transform-based compression methods......Page 398
14.1.2. Parametric (predictive) model-based compression methods......Page 399
14.1.3. Wavelet packet-based compression methods......Page 400
14.1.4. Vector quantization-based compression methods......Page 401
14.1.5. Neural network-based compression methods......Page 402
14.2. Solved exercises......Page 404
14.3. Exercises......Page 417
References......Page 419
Index......Page 421
