Book description
Digital Signal Processing 101: Everything You Need to Know to Get Started provides a basic tutorial on digital signal processing (DSP). Beginning with discussions of numerical representation and complex numbers and exponentials, it goes on to explain difficult concepts such as sampling, aliasing, imaginary numbers, and frequency response. It does so using easy-to-understand examples with minimum mathematics. In addition, there is an overview of the DSP functions and implementation used in several DSP-intensive fields or applications, from error correction to CDMA mobile communication to airborne radar systems.
This book has been updated to include the latest developments in Digital Signal Processing, and has eight new chapters on:
- Automotive Radar Signal Processing
- Space-Time Adaptive Processing Radar
- Field Orientated Motor Control
- Matrix Inversion algorithms
- GPUs for computing
- Machine Learning
- Entropy and Predictive Coding
- Video compression
- Features eight new chapters on Automotive Radar Signal Processing, Space-Time Adaptive Processing Radar, Field Orientated Motor Control, Matrix Inversion algorithms, GPUs for computing, Machine Learning, Entropy and Predictive Coding, and Video compression
- Provides clear examples and a non-mathematical approach to get you up to speed quickly
- Includes an overview of the DSP functions and implementation used in typical DSP-intensive applications, including error correction, CDMA mobile communication, and radar systems
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Acknowledgments
- Introduction
- Chapter 1. Numerical Representation
- Chapter 2. Complex Numbers and Exponentials
- Chapter 3. Sampling, Aliasing, and Quantization
- Chapter 4. Frequency Response
- Chapter 5. Finite Impulse Response (FIR) Filters
- Chapter 6. Windowing
- Chapter 7. Decimation and Interpolation
- Chapter 8. Infinite Impulse Response (IIR) Filters
- Chapter 9. Complex Modulation and Demodulation
-
Chapter 10. Discrete and Fast Fourier Transforms (DFT, FFT)
- 10.1. Discrete Fourier Transform and Inverse Discrete Fourier Transform Equations
- 10.2. First Discrete Fourier Transform Example
- 10.3. Second Discrete Fourier Transform Example
- 10.4. Third Discrete Fourier Transform Example
- 10.5. Fourth Discrete Fourier Transform Example
- 10.6. Fast Fourier Transform
- 10.7. Filtering Using the Fast Fourier Transform and Inverse Fast Fourier Transform
- 10.8. Bit Growth in Fast Fourier Transforms
- 10.9. Bit Reversal Addressing
- Chapter 11. Digital Upconversion and Downconversion
- Chapter 12. Error-Correction Coding
- Chapter 13. Matrix Inversion
- Chapter 14. Field-Oriented Motor Control
- Chapter 15. Analog and Time Division Multiple Access Wireless Communications
-
Chapter 16. CDMA Wireless Communications
- 16.1. Spread Spectrum Technology
- 16.2. Direct Sequence Spread Spectrum
- 16.3. Walsh Codes
- 16.4. Concept of Code Division Multiple Access
- 16.5. Walsh Code Demodulation
- 16.6. Network Synchronization
- 16.7. RAKE Receiver
- 16.8. Pilot Pseudorandom Number Codes
- 16.9. Code Division Multiple Access Transmit Architecture
- 16.10. Variable Rate Vocoder
- 16.11. Soft Handoff
- 16.12. Uplink Modulation
- 16.13. Power Control
- 16.14. Higher Data Rates
- 16.15. Spectral Efficiency Considerations
- 16.16. Other Code Division Multiple Access Technologies
-
Chapter 17. Orthogonal Frequency Division Multiple Access Wireless Communications
- 17.1. WiMax and Long-Term Evolution
- 17.2. Orthogonal Frequency Division Multiple Access Advantages
- 17.3. Orthogonality of Periodic Signals
- 17.4. Frequency Spectrum of Orthogonal Subcarrier
- 17.5. Orthogonal Frequency Division Multiplexing Modulation
- 17.6. Intersymbol Interference and the Cyclic Prefix
- 17.7. Multiple Input and Multiple Output Equalization
- 17.8. Orthogonal Frequency Division Multiple Access System Considerations
- 17.9. Orthogonal Frequency Division Multiple Access Spectral Efficiency
- 17.10. Orthogonal Frequency Division Multiple Access Doppler Frequency Shift
- 17.11. Peak to Average Ratio
- 17.12. Crest Factor Reduction
- 17.13. Digital Predistortion
- 17.14. Remote Radio Head
- Chapter 18. Radar Basics
- Chapter 19. Pulse Doppler Radar
-
Chapter 20. Automotive Radar
- 20.1. Frequency-Modulated Continuous-Wave Theory
- 20.2. Frequency-Modulated Continuous-Wave Range Detection
- 20.3. Frequency-Modulated Continuous-Wave Doppler Detection
- 20.4. Frequency-Modulated Continuous-Wave Radar Link Budget
- 20.5. Frequency-Modulated Continuous-Wave Implementation Considerations
- 20.6. Frequency-Modulated Continuous-Wave Interference
- 20.7. Frequency-Modulated Continuous-Wave Beamforming
- 20.8. Frequency-Modulated Continuous-Wave Range-Doppler Processing
- 20.9. Frequency-Modulated Continuous-Wave Radar Front-End Processing
- 20.10. Frequency-Modulated Continuous-Wave Pulse-Doppler Processing
- 20.11. Frequency-Modulated Continuous-Wave Radar Back-End Processing
- 20.12. Noncoherent Antenna Magnitude Summation
- 20.13. Cell Averaging–Constant False Alarm Rate
- 20.14. Ordered Sort–Constant False Alarm Rate
- 20.15. Angle of Arrival Estimation
- Chapter 21. Space Time Adaptive Processing (STAP) Radar
- Chapter 22. Synthetic Array Radar
- Chapter 23. Introduction to Video Processing
- Chapter 24. DCT, Entropy, Predictive Coding, and Quantization
-
Chapter 25. Image and Video Compression Fundamentals
- 25.1. Baseline JPEG
- 25.2. DC Scaling
- 25.3. Quantization Tables
- 25.4. Entropy Coding
- 25.5. JPEG Extensions
- 25.6. Video Compression Basics
- 25.7. Block Size
- 25.8. Motion Estimation
- 25.9. Frame Processing Order
- 25.10. Compressing I Frames
- 25.11. Compressing P Frames
- 25.12. Compressing B Frames
- 25.13. Rate Control and Buffering
- 25.14. Quantization Scale Factor
-
Chapter 26. Introduction to Machine Learning
- 26.1. Convolutional Neural Networks
- 26.2. Convolution Layer
- 26.3. Rectified Linear Unit Layer
- 26.4. Normalization Layer
- 26.5. Max-Pooling Layer
- 26.6. Fully Connected Layer
- 26.7. Training Computational Neural Networks
- 26.8. Winograd Transform
- 26.9. Convolutional Neural Network Numerical Precision Requirements
- Chapter 27. Implementation Using Digital Signal Processors
- Chapter 28. Implementation Using FPGAs
- Chapter 29. Implementation With GPUs
- Appendix A. Q Format Shift With Fractional Multiplication
- Appendix B. Evaluation of Finite Impulse Response Design Error Minimization
- Appendix C. Laplace Transform
- Appendix D. Z-Transform
- Appendix E. Binary Field Arithmetic
- Index
Product information
- Title: Digital Signal Processing 101, 2nd Edition
- Author(s):
- Release date: June 2017
- Publisher(s): Newnes
- ISBN: 9780128114544
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