Course

The Fourier Transform and its Applications

Stanford University

This course focuses on understanding and applying the Fourier transform, equipping students with the skills to recognize its importance in various contexts. The course covers:

  • The Fourier transform as a tool for solving physical problems.
  • Fourier series and the Fourier transform of continuous and discrete signals.
  • Properties of the Fourier transform, including the Dirac delta and distributions.
  • Applications in probability distributions, sampling theory, filters, and linear systems analysis.
  • The discrete Fourier transform (DFT) and the Fast Fourier Transform (FFT) algorithm.
  • Multidimensional Fourier transforms and their use in imaging.
  • Further applications to optics and crystallography.

Emphasis is placed on relating theoretical principles to practical engineering and science problems.

Course Lectures
  • The Fourier Series
    Brad G. Osgood

    This module introduces the Fourier series, emphasizing the significance of previous knowledge in Matlab. Students will explore periodic phenomena and understand the relationship between time and space. Key concepts include:

    • Analysis versus synthesis in Fourier series.
    • Periodicity in time and space.
    • The reciprocal relationship between frequency and wavelength.
  • This module discusses how sine and cosine can model more complex functions. Students will learn about:

    • Periodicity and modeling signals with sinusoids.
    • Examples of periodizing signals.
    • Modeling a signal as a sum of modified sinusoids.
    • Complex exponential notation and symmetry in Fourier coefficients.
    • The generality of the Fourier series representation for periodic functions.
  • This module focuses on analyzing general periodic phenomena as sums of simple periodic phenomena. Key topics include:

    • Fourier coefficients and their generality.
    • Impacts of discontinuity on Fourier series.
    • Convergence issues and different cases of convergence.
    • Detailed examples of discontinuous signals and infinite sums.
  • This module wraps up the discussion on Fourier series, emphasizing the significance of infinite sums and convergence. Topics covered include:

    • Integrability of functions and existence of Fourier coefficients.
    • Orthogonality of complex exponentials and the inner product.
    • Fourier coefficients as projections onto complex exponentials.
    • Applications of Fourier series to heat flow analysis.
  • This module continues the discussion of Fourier series and introduces the heat equation. Key concepts include:

    • Transitioning from Fourier series to Fourier transforms.
    • Fourier series analysis and synthesis in relation to Fourier transforms.
    • Understanding the spectrum picture for Fourier series.
    • Challenges in finding Fourier transforms using Fourier coefficients.
  • This module corrects previous discussions on the heat equation and sets up the Fourier transform derivation from Fourier series. Key elements include:

    • Results of the derivation: Fourier transform and inverse Fourier transform.
    • Definitions of Fourier transform and inversion.
    • Understanding that every signal has a spectrum that determines it.
    • Examples of rect and triangle functions.
  • This module reviews the definitions of the Fourier transform and its inverse, focusing on key properties. Topics include:

    • Review of rect and triangle transforms.
    • Fourier transform of a Gaussian function.
    • The duality property of the Fourier transform.
    • Application examples demonstrating the duality property.
  • This module explores the effects of shifting a signal on its Fourier transform. Key concepts include:

    • The resulting delay formula and the shift theorem.
    • The effect of scaling time signals and interpretation of the stretch theorem.
    • Convolution as it relates to Fourier transforms.
    • Multiplication of signals in frequency and its resulting convolution formula.
  • This module continues the discussion of convolution, focusing on its formula and application in filtering. Key topics include:

    • The context in which convolution arises.
    • Examples demonstrating convolution in filtering.
    • General properties of convolution in the time domain.
    • The derivative theorem for Fourier transforms.
    • Application of the heat equation on an infinite rod.
  • This module covers the central limit theorem (CLT) and its relation to convolution. Key content includes:

    • Normalization of the Gaussian distribution.
    • Pictorial demonstration of convolution in probability.
    • Setup for the central limit theorem with proof.
    • Other assumptions needed for establishing the CLT.
    • Using the Fourier transform to prove the central limit theorem.
  • Cop Story
    Brad G. Osgood

    This module introduces generalized functions and distributions. Important topics include:

    • Review of rapidly decreasing functions.
    • Definitions and applications of distributions like the delta function.
    • Operational view of delta functions as limits.
    • Pairing ordinary functions with distributions through integration.
  • This module sets up the Fourier transform of a distribution, discussing essential concepts such as:

    • Examples of delta as a distribution.
    • Distributions induced by functions.
    • The Fourier transform of a distribution and tempered distributions.
    • Definition of the Fourier transform through its operation on test functions.
    • Proof of the inverse Fourier transform and calculations using distributions.
  • This module covers the derivative of a distribution, including practical examples such as:

    • Derivative of a unit step function and sgn(x).
    • Applications to the Fourier transform using the derivative theorem.
    • Caveats related to multiplying distributions and their implications.
    • Special cases involving the delta function and convolution.
  • This module introduces the application of the Fourier transform in diffraction. Key components include:

    • Representation of the electric field in diffraction contexts.
    • Utilizing Huygens' principle to discuss phase changes.
    • Application of the Fraunhofer approximation and aperture function.
    • Results and discussions on single and double slit diffraction.
  • This module continues the discussion on diffraction patterns and their relationship with the Fourier transform. Key topics include:

    • Setup for discussing crystallography, including historical context and concepts.
    • Fourier transform of the Shah function and the Poisson summation formula.
    • Proof of the Poisson summation formula and applications to crystals.
  • This module reviews the main properties of the Shah function and addresses the interpolation problem. Key elements include:

    • Setup for the interpolation problem and bandwidth assumptions.
    • Exact interpolation for bandlimited signals.
    • Periodizing signals through convolution with the Shah function.
    • Solutions to the interpolation problem.
  • This module discusses sampling and interpolation results, addressing key concepts such as:

    • Terminology relating to sampling rate and Nyquist rate.
    • Issues with interpolation formulas in practice.
    • Aliasing and its effects on interpolation.
    • Detailed examples demonstrating aliasing, including a cosine example.
  • This module provides an aliasing demonstration using music and transitions to discrete signals. It includes:

    • Creating a discrete signal from a continuous function.
    • Formulating the discrete Fourier transform (DFT) of the sampled version.
    • Summarizing results and moving from continuous to discrete variables.
    • Final results and the DFT's significance.
  • This module reviews the definition of the discrete Fourier transform (DFT). Important topics include:

    • Sample points and their implications for time and frequency spacing.
    • Utilization of complex exponentials in the DFT.
    • Understanding periodicity of inputs and outputs in the DFT.
    • Orthogonality of discrete complex exponential vectors.
  • This module continues the review of basic definitions of the discrete Fourier transform (DFT). Key points include:

    • Special cases, including the DFT value at 0.
    • Handling two special signals, such as the delta vector.
    • DFT of delta functions and implications of periodicity.
    • Matrix multiplication representation of the DFT.
  • This module sets up the FFT algorithm through DFT matrix notation. Key topics include:

    • Intuition behind FFT and factoring matrices.
    • Splitting order N into two orders N/2 for efficiency.
    • Iterative process and tracking powers of complex exponentials.
    • Summary of results as a combination of half-order DFTs.
  • This module introduces basic definitions of linear systems. Key concepts discussed include:

    • Direct proportionality as an example of a linear system.
    • Examples of finite-dimensional linear systems.
    • Eigenvectors and eigenvalues.
    • Matrix multiplication as a form of linear transformation.
    • Integration against a kernel to generalize matrix multiplication.
  • This module reviews the previous lecture on discrete versus continuous linear systems. Key elements include:

    • Cascading linear systems and derivation of the impulse response.
    • Understanding the Schwarz kernel theorem.
    • Impulse response for the Fourier transform example.
    • Time invariance and implications of convolution.
    • Summarizing the relationship between linear systems and convolution.
  • This module continues the review of last lecture, emphasizing LTI systems and convolution. Key concepts include:

    • Discussion of time-invariant discrete systems and their properties.
    • Fourier transform for LTI systems as complex exponentials.
    • Comparison of sine and cosine with complex exponentials as eigenfunctions.
    • Discrete complex exponentials as eigenvectors.
  • This module introduces the higher-dimensional Fourier transform. Key topics include:

    • Notation for thinking in terms of vectors.
    • Definition and inverse Fourier transform in higher dimensions.
    • Reciprocal relationship between spatial and frequency domains.
    • Visualizing higher-dimensional complex exponentials, particularly in 2-D.
  • This module reviews higher-dimensional Fourier transforms, focusing on separable functions. Important topics include:

    • Fourier transforms of separable functions, such as 2-D rectangular functions.
    • Formula for the Fourier transform of separable functions.
    • Examples including 2-D Gaussian and radial functions.
    • Proving that the Fourier transform of a radial function remains radial.
    • Convolution concepts in higher dimensions.
  • This module covers the shift theorem in higher dimensions. Key components include:

    • The shift theorem and its results in higher dimensions.
    • Derivation and results of the stretch theorem.
    • Special cases including scaling and rotation effects.
    • Understanding deltas in higher dimensions and their scaling properties.
  • Shahs
    Brad G. Osgood

    This module delves into Shah functions, lattices, and their relevance to crystallography. Key topics include:

    • Understanding 2-D Shah functions and their applications.
    • Crystals represented as lattices and their Fourier transform implications.
    • Application of Shah functions in medical imaging, such as tomography.
  • This module discusses tomography and the process of inverting the Radon transform. Key elements include:

    • Setting up the Radon transform and its coordinates.
    • Understanding delta functions along lines.
    • Integrating functions along lines and inverting the Radon transform.
    • Practical applications and implications of the inversion process.