Fourier Analysis—A Signal Processing Approach

Name: Fourier Analysis—A Signal Processing Approach
Price: 77,88 € EUR
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Author: Sundararajan D.
ISBN: 9789811316920

sundararajan d.

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Acquista

Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.

Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente

Dettagli

Genere:Libro

Lingua: Inglese

Editore:

Springer

Pubblicazione: 08/2018

Edizione: 1st ed. 2018

Trama

This book sheds new light on Transform methods, which dominate the study of linear time-invariant systems in all areas of science and engineering, such as circuit theory, signal/image processing, communications, controls, vibration analysis, remote sensing, biomedical systems, optics and acoustics. It presents Fourier analysis primarily using physical explanations with waveforms and/or examples, only using mathematical formulations to the extent necessary for its practical use. Intended as a textbook for senior undergraduates and graduate level Fourier analysis courses in engineering and science departments, and as a supplementary textbook for a variety of application courses in science and engineering, the book is also a valuable reference for anyone – student or professional – specializing in practical applications of Fourier analysis. The prerequisite for reading this book is a sound understanding of calculus, linear algebra, signals and systems, and programming at the undergraduate level.

Sommario

Contents

1 Signals 11

1.1 Basic Signals

1.1.1 Unit-impulse Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.1.2 Unit-step Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.1.3 Unit-ramp Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.1.4 Sinusoids and Complex Exponentials . . . . . . . . . . . . . . . . . . . . . . . 14

1.2 Classification of Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.2.1 Continuous, Discrete, and Digital Signals . . . . . . . . . . . . . . . . . . . . 19

1.2.2 Periodic and Aperiodic Signals . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.2.3 Even- and Odd-symmetric Signals . . . . . . . . . . . . . . . . . . . . . . . . 20

1.2.4 Energy and Power Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.2.5 Deterministic and Random Signals . . . . . . . . . . . . . . . . . . . . . . . . 24

1.2.6 Causal and Noncausal Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.3 Signal Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.3.1 Time Shifting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.3.2 Time Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.4 Complex Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2 The Discrete Fourier Transform 33

2.1 The Exponential Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.2 The Complex Exponential Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.2.1 Euler’s Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.2.2 Real Sinusoid in terms of Complex Exponentials . . . . . . . . . . . . . . . . 35

2.3 The DFT and the IDFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.3.1 The DFT and the IDFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.3.2 The Criterion of Approximation . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.3.3 The Matrix form of the DFT and IDFT . . . . . . . . . . . . . . . . . . . . . 41

2.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.4.1 Fourier Boundary Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3 Properties of the DFT 53

3.1 Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.2 Periodicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.3 Circular Time Shifting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.4 Circular Frequency Shifting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.5 Circular Time-reversal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.6 Duality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.7 Transform of Complex Conjuagtes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.8 Circular Convolution and Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.8.1 Circular convolution of Time-domain Sequences . . . . . . . . . . . . . . . . . 56

3.8.2 Circular Convolution of Frequency-domain Sequences . . . . . . . . . . . . . 58

3.8.3 Circular Correlation of Time-domain Sequences . . . . . . . . . . . . . . . . . 59

3.9 Sum and Difference of Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.10 Upsampling of a Sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.11 Zero Padding the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.12 Symmetry Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.13 Parseval’s theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.14 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4 Two-Dimensional DFT 67

4.1 Two-Dimensional DFT as two 1-D DFTs . . . . . . . . . . . . . . . . . . . . . . . . 68

4.1.1 Computation of the 2-D DFT . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.2 The 2-D DFT and IDFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.3 DFT Representation of Real-valued Signals . . . . . . . . . . . . . . . . . . . . . . . 78

4.4 Properties of the 2-D DFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5 Convolution and Correlation 89

5.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.1.1 Linear Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.1.2 Circular Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.1.3 2-D Linear Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.2 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.2.1 The Linear Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.3.1 Lowpass Filtering of Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.3.2 Highpass Filtering of Images . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.3.3 Object Detection in Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.3.4 Orthogonal Frequency Division Modulation . . . . . . . . . . . . . . . . . . . 107

5.3.5 Hilbert Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

6 Aliasing and Leakage 119

6.1 Aliasing Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

6.2 Leakage Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

6.2.1 Modeling Data Truncation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

6.2.2 Tapered Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.2.3 Hann and Hamming windows . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.2.4 Reducing the Spectral Leakage . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.3 Picket-Fence Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

7 Fourier Series 131

7.1 Fourier Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

7.1.1 FS as the Limiting Case of the DFT . . . . . . . . . . . . . . . . . . . . . . . 132

7.1.2 Gibbs P

Autore

Dr. D. Sundararajan holds a B.E. in Electrical Engineering from Madras University and an M.Tech. in Electrical Engineering from the Indian Institute of Technology Chennai (IIT Chennai). He obtained his Ph.D. in Electrical Engineering at Concordia University, Montreal, Canada in 1988. As the principal inventor of the latest family of discrete Fourier transform (DFT) algorithms, he holds three patents (granted by the US, Canada and Britain). Further, he has published several papers in IEEE Transactions and in the Proceedings of the IEEE Conference, and he is the author of five books. He has taught undergraduate and graduate classes in digital signal processing, digital image processing, engineering mathematics, programming, operating systems and digital logic design at Concordia University, Canada, Nanyang Technological University, Singapore, and Adhiyamaan College of Engineering, India. He has also conducted workshops on Digital image processing, MATLAB and LaTeX.

Over the course of his engineering career, he has held positions at the National Aerospace Laboratory, Bangalore, and the National Physical Laboratory, New Delhi, where he worked on the design of digital and analog signal processing systems.