Signal processing in practice

The book is an introduction to the basics of the theory of digital signal processing with particular emphasis on its practical applications. It discusses sampling, quantization, Fourier transform, filters, Bayesian methods and numerical considerations and their application in the processing and compression of sound, image and video, as well as in telecommunications. Methods of effective implementation of algorithms in software and hardware were also presented, as well as links between various signal processing techniques and many practical examples were presented.
All included descriptions were illustrated with more than 200 drawings and more than 130 examples (with solutions).

Recipients: practitioners whose work is related to signal processing and students of electrical engineering and applied computer science.

Table of Contents:

From the publisher 8
Foreword 9
Part I Fundamentals 11
1. Introduction 13
1.1. What is the signal? 13
1.2. Domain and signal range 13
1.3. Converting a signal from one form to another 14
1.4. Signal processing 15
1.5. Notation 16
Exercises 19
2. Sampling 20
2.1. Regular sampling 20
2.2. Losses in the sampling process 22
2.3. Examples of aliasing 22
2.4. Negative frequencies 25
2.5. Nyquist border 27
2.6. Irregular sampling 28
Exercises 31
3. Analog-to-digital conversion 35
3.1. A simple digital signal processing system 35
3.2. Non-linear quantization 38
3.3. The necessary number of bits 39
3.4. Dithering 40
3.5. Non-ideal conversion 42
Exercises 43
4. Area of frequency 47
4.1. Rotational speed measurement 47
4.2. Complex traffic 49
4.3. Interpretation of the Fourier transform 52
4.4. Number of Fourier coefficients 53
4.5. Recreation of the signal from its Fourier transform 54
4.6. Real signals 54
4.7. Handling of non-periodic signals 59
4.8. Fast Fourier transform 62
Exercises 68
5. Filters 73
5.1. Smoothing the signal 73
5.2. Filter analysis 76
5.3. Weave in the frequency domain 84
5.4. Correlation 86
5.5. Designing of SOI 87 filters
5.6. Interpolation 93
5.7. Filters with infinite impulse response 105
5.8. Filtering complex signals 110
Exercises 112
6. Methods based on the probability theory 115
6.1. Probability and conditional probability 115
6.2. Probability and signal processing 120
6.3. Noise 122
Exercises 133
7. Numerical considerations 136
7.1. Fixed representation 136
7.2. Negative numbers in a fixed-point representation 148
7.3. Floating-point representation 151
7.4. Choice between fixed-point and floating-point representation 155
Exercises 156
Part II Applications 161
8. Sound 163
8.1. Ear 163
8.2. Sampling frequencies and conversion 163
8.3. Sound in the frequency domain 166
8.4. Compression of audio signals 169
8.5. Extracting sound pitch information 178
8.6. Conversion of delta-sigma 188
Exercises 190
9. Still images 192
9.1. Luminance and chrominance 192
9.2. Gamma 193
9.3. Image as a signal 194
9.4. Image filtering 199
9.5. Discrete cosine transform 207
9.6. JPEG compression for images with smooth tonal takeovers 211
9.7. Discrete wavelet transform 216
9.8. Scaling images 223
9.9. Correcting images 227
9.10. Edge detection 229
9.11. Processing of two-level images 231
9.12. Pattern recognition 237
Exercises 240
10. Movable pictures 246
10.1. Standard video formats 246
10.2. 249 deinterlacing
10.3. Conversion between standards 254
10.4. Traffic estimation 256
10.5. MPEG-2 video compression 262
Exercises 266
11. Telecommunications 268
11.1. Amplitude modulation 269
11.2. Frequency modulation 276
11.3. Rectangular-amplitude modulation 278
11.4. Spread spectrum 302 techniques
Exercises 313
12. Practical realizations 315
12.1. Software 316
12.2. Processor architecture 318
12.3. Hardware implementation 324
12.4. Bit-serial arithmetic 330
Exercises 338
Answers 340
Index 346