Digital Signal Processing

By Steven W. Smith, Ph.D.

- 1: The Breadth and Depth of DSP
- 2: Statistics, Probability and Noise
- 3: ADC and DAC
- 4: DSP Software
- 5: Linear Systems
- 6: Convolution
- 7: Properties of Convolution
- 8: The Discrete Fourier Transform
- 9: Applications of the DFT
- 10: Fourier Transform Properties
- 11: Fourier Transform Pairs
- 12: The Fast Fourier Transform
- 13: Continuous Signal Processing
- 14: Introduction to Digital Filters
- 15: Moving Average Filters
- 16: Windowed-Sinc Filters
- 17: Custom Filters
- 18: FFT Convolution
- 19: Recursive Filters
- 20: Chebyshev Filters
- 21: Filter Comparison
- 22: Audio Processing
- 23: Image Formation & Display
- 24: Linear Image Processing
- 25: Special Imaging Techniques
- 26: Neural Networks (and more!)
- 27: Data Compression
- 28: Digital Signal Processors
- 29: Getting Started with DSPs
- 30: Complex Numbers
- 31: The Complex Fourier Transform
- 32: The Laplace Transform
- 33: The z-Transform
- 34: Explaining Benford's Law

Your laser printer will thank you!

Most of the signals directly encountered in science and engineering are *continuous*: light
intensity that changes with distance; voltage that varies over time; a chemical reaction rate that
depends on temperature, etc. Analog-to-Digital Conversion (ADC) and Digital-to-Analog
Conversion (DAC) are the processes that allow digital computers to interact with these everyday
signals. Digital information is different from its continuous counterpart in two important
respects: it is *sampled*, and it is *quantized*. Both of these restrict how much information a digital
signal can contain. This chapter is about *information management*: understanding what
information you need to retain, and what information you can afford to lose. In turn, this
dictates the selection of the sampling frequency, number of bits, and type of analog filtering
needed for converting between the analog and digital realms.