10: Sampling and Reconstruction

Contributed by Richard Baraniuk et al.
Victor E. Cameron Professor (Electrical and Computer Engineering) at Rice University

10.1: Signal Sampling
This module introduces sampling of a continuous time signal to produce a discrete time signal, including a computation of the spectrum of the sampled signal and a discussion of its implications for reconstruction.
10.2: Sampling Theorem
This module builds on the intuition developed in the sampling module to discuss the Nyquist-Shannon sampling theorem, including a full statement and a proof.
10.3: Signal Reconstruction
This module describes the reconstruction, also known as interpolation, of a continuous time signal from a discrete time signal, including a discussion of cardinal spline filters.
10.4: Perfect Reconstruction
This module examines the conditions and filters involved in perfect reconstruction.
10.5: Aliasing Phenomena
This module introduces aliasing phenomena and discusses its implications for reconstruction.
10.6: Anti-Aliasing Filters
This module discusses the use of anti-aliasing filters when sampling at a rate below the Nyquist frequency.
10.7: Discrete Time Processing of Continuous Time Signals
This module discusses the theory and practical considerations behind the use of discrete time tools to process of continuous time signals.