All signals in the physical world, e.g., light, sound, seismic waves, and so on, have continuous independent variables. These signals must be sampled to convert them to a sequence of numerical values prior to computer-based signal processing. The Sampling and Reconstruction series of 15 lessons introduces you to the requirements on sampling in order to ensure a unique representation. You will learn to use the Fourier transform as a tool for analyzing the effect of sampling in the frequency domain. Much of the series will teach you practical issues associated with sampling and techniques for addressing them, including anti-aliasing, oversampling, anti-imaging, upsampling and downsampling. Finally, you will learn how to model the apparent noise that is introduced when representing the amplitude of each sample with a finite number of bits.

# Sampling and Reconstruction

Lessons
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