Learn to use MATLAB as a useful computing tool for exploring traditional Digital Signal Processing (DSP) topics and solving problems to gain insight with this supplementary text. DIGITAL SIGNAL PROCESSING USING MATLAB: A PROBLEM SOLVING COMPANION, 4E greatly expands the range and complexity of problems that you can effectively study. Since DSP applications are primarily algorithms implemented on a DSP processor or software, they require a significant amount of programming. Using interactive software, such as MATLAB, enables you to focus on mastering new and challenging concepts rather than concentrating on programming algorithms. This edition discusses interesting, practical examples and explores useful problems. New online chapters introduce advanced topics, such as optimal filters, linear prediction, and adaptive filters, which are essential in furthering your academic studies at the graduate level.
Author(s): Vinay K. Ingle, John G. Proakis
Series: Activate Learning with these NEW titles from Engineering!
Publisher: Cengage Learning
Year: 2016
Language: English
Pages: 892
Contents......Page 7
Preface......Page 13
Ch 1: Introduction......Page 21
1.1: Overview of Digital Signal Processing......Page 22
1.2: A Brief Introduction to MATLAB......Page 25
1.3: Applications of Digital Signal Processing......Page 38
1.4: Brief Overview of the Book......Page 40
2.1: Discrete-Time Signals......Page 42
2.2: Discrete Systems......Page 56
2.3: Convolution......Page 60
2.4: Difference Equations......Page 67
2.5: Problems......Page 73
3.1: The Discrete-Time Fourier Transform (DTFT)......Page 79
3.2: The Properties of the DTFT......Page 87
3.3: The Frequency Domain Representation of LTI Systems......Page 94
3.4: Sampling and Reconstruction of Analog Signals......Page 100
3.5: Problems......Page 117
4.1: The Bilateral z-Transform......Page 123
4.2: Important Properties of the z-Transform......Page 127
4.3: Inversion of the z-Transform......Page 132
4.4: System Representation in the z-Domain......Page 138
4.5: Solutions of the Difference Equations......Page 148
4.6: Problems......Page 154
Ch 5: The Discrete Fourier Transform......Page 161
5.1: The Discrete Fourier Series......Page 162
5.2: Sampling and Reconstruction in the z-Domain......Page 169
5.3: The Discrete Fourier Transform......Page 174
5.4: Properties of the Discrete Fourier Transform......Page 185
5.5: Linear Convolution Using the DFT......Page 200
5.6: The Fast Fourier Transform......Page 207
5.7: Problems......Page 220
Ch 6: Implementation of Discrete-Time Filters......Page 232
6.1: Basic Elements......Page 233
6.2: IIR Filter Structures......Page 234
6.3: FIR Filter Structures......Page 248
6.4: Overview of Finite-Precision Numerical Effects......Page 259
6.5: Representation of Numbers......Page 260
6.6: The Process of Quantization and Error Characterizations......Page 275
6.7: Quantization of Filter Coefficients......Page 282
6.8: Problems......Page 297
Ch 7: FIR Filter Design......Page 311
7.1: Preliminaries......Page 312
7.2: Properties of Linear-Phase FIR Filters......Page 315
7.3: Window Design Technique......Page 329
7.4: Frequency-Sampling Design Technique......Page 350
7.5: Optimal Equiripple Design Technique......Page 364
7.6: Problems......Page 380
Ch 8: IIR Filter Design......Page 390
8.1: Some Preliminaries......Page 391
8.2: Some Special Filter Types......Page 394
8.3: Characteristics of Prototype Analog Filters......Page 405
8.4: Analog-to-Digital Filter Transformations......Page 427
8.5: Lowpass Filter Design Using MATLAB......Page 447
8.6: Frequency-Band Transformations......Page 452
8.7: Problems......Page 465
Ch 9: Sampling Rate Conversion......Page 478
9.1: Introduction......Page 479
9.2: Decimation by a Factor D......Page 481
9.3: Interpolation by a Factor I......Page 490
9.4: Sampling Rate Conversion by a Rational Factor I/D......Page 497
9.5: FIR Filter Designs for Sampling Rate Conversion......Page 502
9.6: FIR Filter Structures for Sampling Rate Conversion......Page 520
9.7: Problems......Page 530
10.1: Analysis of A/D Quantization Noise......Page 538
10.2: Round-Off Effects in IIR Digital Filters......Page 550
10.3: Round-Off Effects in FIR Digital Filters......Page 577
10.4: Problems......Page 589
Ch 11: Applications in Adaptive Filtering......Page 593
11.1: LMS Algorithm for Coefficient Adjustment......Page 595
11.2: System Identification or System Modeling......Page 598
11.3: Suppression of Narrowband Interference in a Wideband Signal......Page 599
11.5: Adaptive Channel Equalization......Page 602
12.1: Pulse-Code Modulation......Page 606
12.2: Differential PCM (DPCM)......Page 610
12.3: Adaptive PCM and DPCM (ADPCM)......Page 613
12.4: Delta Modulation (DM)......Page 617
12.5: Linear Predictive Coding (LPC) of Speech......Page 621
12.6: Dual-Tone Multifrequency (DTMF) Signals......Page 625
12.7: Binary Digital Communications......Page 629
12.8: Spread-Spectrum Communications......Page 631
Ch 13: Random Processes......Page 634
13.1: Random Variable......Page 635
13.2: A Pair of Random Variables......Page 648
13.3: Random Signals......Page 662
13.4: Power Spectral Density......Page 670
13.5: Stationary Random Processes through LTI Systems......Page 678
13.6: Useful Random Processes......Page 688
13.7: Summary and References......Page 704
Ch 14: Linear Prediction and Optimum Linear Filters......Page 706
14.1: Innovations Representation of a Stationary Random Process......Page 707
14.2: Forward and Backward Linear Prediction......Page 721
14.3: Solution of the Normal Equations......Page 737
14.4: Properties of the Linear Prediction-Error Filters......Page 750
14.5: AR Lattice and ARMA Lattice-Ladder Filters......Page 754
14.6: Wiener Filters for Filtering and Prediction......Page 763
14.7: Summary and References......Page 786
15.1: Applications of Adaptive Filters......Page 789
15.2: Adaptive Direct-Form FIR Filters......Page 835
15.3: Summary and References......Page 869
Bibliography......Page 870
Index......Page 875
