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6: Continuous Time Fourier Series (CTFS)

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    • 6.1: Continuous Time Periodic Signals
      This module defines a periodic function and describes the two common ways of thinking about a periodic signal.
    • 6.2: Continuous Time Fourier Series (CTFS)
      This module describes the continuous time Fourier Series (CTFS). It is based on the following modules: Fourier Series: Eigenfunction Approach at by Justin Romberg, Derivation of Fourier Coefficients Equation at by Michael Haag, Fourier Series and LTI Systems at by Justin Romberg, and Fourier Series Wrap-Up at by Michael Haag and Justin Romberg.
    • 6.3: Common Fourier Series
      Constant, Sinusoid, Square, Triangle, and sawtooth waveforms, in depth and summarized.
    • 6.4: Properties of the CTFS
      An introduction to the general properties of the Fourier series
    • 6.5: Continuous Time Circular Convolution and the CTFS
      This module looks at the basic circular convolution relationship between two sets of Fourier coefficients.
    • 6.6: Convergence of Fourier Series
      This module discusses the existence and convergence of the Fourier Series to show that it can be a very good approximation for all signals. The Dirichlet conditions, which are the sufficient conditions to guarantee existence and convergence of the Fourier series, are also discussed.
    • 6.7: Gibbs Phenomena
      The Fourier Series is the representation of continuous-time, periodic signals in terms of complex exponentials. The Dirichlet conditions suggest that discontinuous signals may have a Fourier Series representation so long as there are a finite number of discontinuities. This seems counter-intuitive, however, as complex exponentials are continuous functions. It does not seem possible to exactly reconstruct a discontinuous function from a set of continuous ones. In fact, it is not. However, it can

    6: Continuous Time Fourier Series (CTFS) is shared under a CC BY license and was authored, remixed, and/or curated by Richard Baraniuk et al..

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