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Non-uniform discrete Fourier transform

Non-uniform discrete Fourier transform is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Non-uniform discrete Fourier transform rather than just read about it. In short: In applied mathematics, the non-uniform discrete Fourier transform (NUDFT or NDFT) of a signal is a type of Fourier transform, related to a discrete Fourier transform or discrete-time Fourier transform, but in which the input signal is not sampled at equally spaced points or frequencies (or both). It is a generalization of the shifted DFT.

Key takeaways

  • Non-uniform discrete Fourier transform belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Non-uniform discrete Fourier transform to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Non-uniform discrete Fourier transform from memory before moving on to harder problems.

Reference excerpt

In applied mathematics, the non-uniform discrete Fourier transform (NUDFT or NDFT) of a signal is a type of Fourier transform, related to a discrete Fourier transform or discrete-time Fourier transform, but in which the input signal is not sampled at equally spaced points or frequencies (or both). It is a generalization of the shifted DFT. It has important applications in signal processing, magnetic resonance imaging, and the numerical solution of partial differential equations. As a generalized approach for nonuniform sampling, the NUDFT allows one to obtain frequency domain information of a finite length signal at any frequency. One of the reasons to adopt the NUDFT is that many signals have their energy distributed nonuniformly in the frequency domain. Therefore, a nonuniform sampling scheme could be more convenient and useful in many digital signal processing applications. For example, the NUDFT provides a variable spectral resolution controlled by the user.

Definition The nonuniform discrete Fourier transform (NUDFT) transforms a sequence of N {\displaystyle N} complex numbers x 0 , … , x N − 1 {\displaystyle x_{0},\ldots ,x_{N-1}} into another sequence of complex numbers X 0 , … , X N − 1 {\displaystyle X_{0},\ldots ,X_{N-1}} defined by

where p 0 , … , p N − 1 ∈ [ 0 , 1 ] {\displaystyle p_{0},\ldots ,p_{N-1}\in [0,1]} are sample points and f 0 , … , f N − 1 ∈ [ 0 , N ] {\displaystyle f_{0},\ldots ,f_{N-1}\in [0,N]} are frequencies. Note that if p n = n / N {\displaystyle p_{n}=n/N} and f k = k {\displaystyle f_{k}=k} , then equation (1) reduces to the discrete Fourier transform. The NUDFT defines a linear operator. Fast numerical algorithms for evaluating this operator are known as the nonuniform fast Fourier transform (NUFFT).

Terminology and conventions Two related but distinct conventions are used in the literature to classify nonuniform Fourier transforms:

In harmonic analysis and signal processing, NUDFTs are commonly described from a series-evaluation viewpoint, in which transform types are distinguished by whether the sample locations or the frequencies are nonuniform. In numerical analysis and scientific computing, particularly in the context of NUFFT algorithms and software libraries, transforms are classified by the mapping direction between uniform and nonuniform representations, reflecting linear-operator structure and adjoint relationships.

NUDFT types The nonuniform discrete Fourier transform of type I (NUDFT-I) uses uniform sample points p n = n / N {\displaystyle p_{n}=n/N} but nonuniform (i.e. non-integer) frequencies f k {\displaystyle f_{k}} . This corresponds to evaluating a generalized Fourier series at equispaced points. It is also known as NDFT.. It is sometimes called forward NDFT and corresponds to NUFFT Type II (uniform to nonuniform) in the operator-based convention. The nonuniform discrete Fourier transform of type II (NUDFT-II) uses uniform (i.e. integer) frequencies f k = k {\displaystyle f_{k}=k} but nonuniform sample points p n {\displaystyle p_{n}} . This corresponds to evaluating a Fourier series at nonequispaced points. It is also known as adjoint NDFT or NUFFT Type I (nonuniform to uniform). The nonuniform discrete Fourier transform of type III (NUDFT-III) uses both nonuniform sample points p n {\displaystyle p_{n}} and nonuniform frequencies f k {\displaystyle f_{k}} . This corresponds to evaluating a generalized Fourier series at nonequispaced points. It is also known as NNDFT or NUFFT Type III (nonuniform to nonuniform).

Adjoint relationship In the operator-based convention in NUFFT, the Type I and Type II transforms are adjoint to each other in the sense of linear operators. Specifically, the NUFFT Type I transform is the Hermitian adjoint of the NUFFT Type II transform with respect to the standard inner products on the corresponding discrete spaces, up to scaling factors. A similar family of NUDFTs can be defined by substituting − i {\displaystyle -i} for + i {\displaystyle +i} in equation (1). Unlike in the uniform case, however, this substitution is unrelated to the inverse Fourier transform. The inversion of the NUDFT is a separate problem, discussed below.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Non-uniform discrete Fourier transform

Start with the simplest possible case. Write down what Non-uniform discrete Fourier transform claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Non-uniform discrete Fourier transform before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Non-uniform discrete Fourier transform ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Non-uniform discrete Fourier transform

In research
Non-uniform discrete Fourier transform appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Non-uniform discrete Fourier transform in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Non-uniform discrete Fourier transform is common in secondary-school and first-year university syllabi. It links to neighbouring topics Digital signal processing, Fourier analysis, Transforms, so understanding it makes those chapters shorter.
In everyday life
Look for Non-uniform discrete Fourier transform outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Non-uniform discrete Fourier transform in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Non-uniform discrete Fourier transform means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Non-uniform discrete Fourier transform out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Non-uniform discrete Fourier transform in simple terms?

In applied mathematics, the non-uniform discrete Fourier transform (NUDFT or NDFT) of a signal is a type of Fourier transform, related to a discrete Fourier transform or discrete-time Fourier transform, but in which the input signal is not sampled at equally spaced points or frequencies (or both)…

Why does Non-uniform discrete Fourier transform matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Non-uniform discrete Fourier transform?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Non-uniform discrete Fourier transform.

Tags

  • Digital signal processing
  • Fourier analysis
  • Transforms

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