Cover -- Digital Signal Processing and Applications -- Contents -- Preface -- 1 Introduction -- Background -- Objectives -- 1.1 The history of digital signal processing -- 1.1.1 Measurements and analysis -- 1.1.2 Telecommunications -- 1.1.3 Audio and television -- 1.1.4 Household appliances and toys -- 1.1.5 Automotive -- 1.2 Digital signal processing basics -- 1.2.1 Continuous and discrete signals -- 1.2.2 Sampling and reconstruction -- 1.2.3 Quantization -- 1.2.4 Processing models for discrete-time series -- 1.2.4.1 Linear systems -- 1.2.4.2 The difference equation model -- 1.2.4.3 The state-space model -- 1.2.4.4 The convolution model -- 1.2.4.5 The transfer function model -- 1.2.4.6 The frequency function model -- 1.3 Common filters -- 1.3.1 Filter architectures -- 1.3.1.1 The non-recursive filter -- 1.3.1.2 The recursive filter -- 1.3.1.3 The lattice filter -- 1.3.2 Filter synthesis -- 1.3.2.1 Indirect filter synthesis -- 1.3.2.2 Direct filter synthesis -- 1.4 Digital control systems -- 1.4.1 Proportional-integral-derivate controllers -- 1.4.2 Advanced controllers -- 1.4.2.2 Pole placement controller -- Summary -- Review questions -- Solved problems -- 2 The analog-digital interface -- Background -- Objectives -- 2.1 System considerations -- 2.1.1 Encoding and modulation -- 2.1.2 Number representation and companding systems -- 2.2 Digital-to-analog conversion -- 2.2.1 Multiplying digital-to-analog converters -- 2.2.2 Integrating digital-to-analog converters -- 2.2.3 Bitstream digital-to-analog converters -- 2.2.4 Sample-and-hold and reconstruction filters -- 2.3 Analog-to-digital conversion -- 2.3.1 Anti-aliasing filters and sample-and-hold -- 2.3.2 Flash analog-to-digital converters -- 2.3.3 Successive approximation analog-to-digital converters -- 2.3.4 Counting analog-to-digital converters -- 2.3.5 Integrating analog-to-digital converters.

2.3.6 Dither -- 2.3.7 Sigma-delta analog-to-digital converters -- Summary -- Review questions -- Solved problems -- 3 Adaptive digital systems -- Background -- Objectives -- 3.1 Introduction -- 3.1.1 System structure -- 3.2 The processor and the performance function -- 3.2.1 The adaptive linear combiner -- 3.2.2 The performance function -- 3.3 Adaptation algorithms -- 3.3.1 The method of steepest descent -- 3.3.2 Newton's method -- 3.3.3 The least mean square algorithm -- 3.4 Applications -- 3.4.1 Adaptive interference canceling -- 3.4.2 Equalizers -- 3.4.3 Adaptive beamforming -- Summary -- Review questions -- Solved problems -- 4 Non-linear applications -- Background -- Objectives -- 4.1 The median filter -- 4.1.1 Basics -- 4.1.2 Threshold decomposition -- 4.1.3 Performance -- 4.1.4 Applications -- 4.2 Artificial neural networks -- 4.2.1 Background -- 4.2.2 The models -- 4.2.3 Some historical notes -- 4.2.4 Feedforward networks -- 4.2.4.1 Nodes -- 4.2.4.2 Network topology -- 4.2.4.3 Training and adaptation -- 4.2.4.4 Applications -- 4.2.5 Feedback networks -- 4.2.5.1 Nodes -- 4.2.5.2 Network topology -- 4.2.5.3 Local and global minimum -- 4.2.5.4 Applications -- 4.2.6 An example application -- 4.2.6.1 The problem -- 4.2.6.2 The Hamming net -- 4.2.6.3 The feedforward input layer -- 4.2.6.4 The feedback layer, MAXNET -- 4.3 Fuzzy logic -- 4.3.1 General -- 4.3.2 Membership functions -- 4.3.3 Fuzzy rules and inference -- 4.3.4 Defuzzification -- 4.3.5 Applications -- Summary -- Review questions -- Solved problems -- 5 Spectral analysis and modulation -- Background -- Objectives -- 5.1 Discrete Fourier transform and fast Fourier transform -- 5.2 Spectral analysis -- 5.2.1 Discrete Fourier transform and fast Fourier transform approaches -- 5.2.2 Using the auto-correlation function -- 5.2.3 Periodogram averaging -- 5.2.4 Parametric spectrum analysis.

5.2.5 Wavelet analysis -- 5.3 Modulation -- 5.3.1 Amplitude shift keying (ASK) -- 5.3.2 Frequency shift keying (FSK) -- 5.3.3 Phase shift keying (PSK) -- 5.3.4 Complex modulation -- 5.3.5 The Hilbert transformer -- Summary -- Review questions -- Solved problems -- 6 Introduction to Kalman filters -- Background -- Objectives -- 6.1 An intuitive approach -- 6.1.1 Recursive least square estimation -- 6.1.2 The pseudo-inverse -- 6.2 The Kalman filter -- 6.2.1 The signal model -- 6.2.2 The filter -- 6.2.3 Kalman filter properties -- 6.2.4 Applications -- Summary -- Review questions -- Solved problems -- 7 Data compression -- Background -- Objectives -- 7.1 An information theory primer -- 7.1.1 Historic notes -- 7.1.2 Information and entropy -- 7.1.2.1 Some concluding remarks about the concept of entropy -- 7.2 Source coding -- 7.2.1 Huffman algorithm -- 7.2.2 Delta modulation, adaptive delta modulation and continuously variable slope delta modulation -- 7.2.3 Differential pulse code modulation and adaptive differential pulse code modulation -- 7.2.4 Speech coding, adaptive predictive coding and sub-band coding -- 7.2.5 Vocoders and linear predictive coding -- 7.2.6 Image coding, joint photographics expert group (JPEG), moving pictures expert group (MPEG) -- 7.2.7 The layer-3 of MPEG-1 algorithm (MP3) -- 7.2.8 The Lempel-Ziv algorithm -- 7.3 Recognition techniques -- 7.3.1 A general problem formulation -- 7.3.2 Speech recognition -- 7.3.3 Image recognition -- Summary -- Review questions -- Solved problems -- 8 Error-correcting codes -- Background -- Objectives -- 8.1 Channel coding -- 8.1.1 The channel model -- 8.1.2 The channel capacity -- 8.2 Error-correcting codes -- 8.2.1 Hamming distance and error correction -- 8.2.2 Linear block codes -- 8.2.3 Cyclic codes, Bose, Chaudhuri, Hocquenghem codes -- 8.2.4 Convolution codes -- 8.2.5 Viterbi decoding.

8.2.6 Interleaving -- 8.2.7 Concatenated codes and turbo codes -- Summary -- Review questions -- Solved problems -- 9 Digital signal processors -- Background -- Objectives -- 9.1 System considerations -- 9.1.1 Applications and requirements -- 9.1.2 Hardware implementation -- 9.2 Digital signal processors versus conventional microprocessors -- 9.2.1 Conventional microprocessors -- 9.2.1.1 Architecture -- 9.2.1.2 Instruction repertoire -- 9.2.1.3 Interface -- 9.2.2 Digital signal processors -- 9.2.2.1 Architecture -- 9.2.2.2 Instruction repertoire -- 9.2.2.3 Interface -- 9.3 Programming digital signal processors -- 9.3.1 The development process -- 9.3.2 Digital signal processing programming languages -- 9.3.3 The program structure -- 9.3.4 Arithmetic issues -- 9.3.5 Data structures and addressing modes -- 9.3.6 The state machine -- 9.4 Implementation examples -- 9.4.1 Finite impulse response-type filter -- 9.4.2 Infinite impulse response-type filter -- 9.5 Future systems and chips -- Summary -- Review questions -- Solved problems -- Appendix 1 Solutions to problems -- Appendix 2 A MATLAB™/Simulink™ primer -- A2.1 Introduction -- A2.1.1 The software -- A2.2 Basics -- A2.2.1 Some simple math -- A2.2.2 Variables, scalars, vectors and matrices -- A2.2.2.1 Scalars -- A2.2.2.2 Vectors and matrices -- A2.2.2.3 Vector and matrix computations -- A2.2.2.4 Addressing vectors and matrices -- A2.2.2.5 Sequences -- A2.2.3 Basic input/output and graphics -- A2.2.3.1 Input -- A2.2.3.2 Numerical and character output -- A2.2.3.3 Graphic output -- A2.2.4 Other programming structures -- A2.2.4.1 Conditional execution -- A2.2.4.2 Loop structures -- A2.3 Workspace, scripts and functions -- A2.3.1 The workspace -- A2.3.2 Scripts and m-files -- A2.3.3 Functions -- A2.4 Some useful functions -- A2.4.1 Linear systems -- A2.4.2 Filter design -- A2.4.2.1 Analog filter design.

A2.4.2.2 Transformations -- A2.4.2.3 Digital filter design -- A2.4.3 Fast Fourier transform and convolution -- A2.5 Simulink™ -- References -- Glossary -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Z -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.