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A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT), or its inverse (IDFT), of a sequence. A Fourier transform converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa.
The analysis highlights History, Applications and Research as prominent areas in the source structure around Fast Fourier transform.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
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The extracted context around Fast Fourier transform shows recurring relationship patterns in the source. For example, Fast Fourier transform → Burrus, CPU, DFT, DFTs, Duhamel, FFT, Following, Fourier, Frigo, Heideman, Johnson, Moreover, Omega, Shmuel Winograd, Theta. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
fft algorithm algorithms dft textstyle log fourier tukey transform cooley complexity displaystyle fast transforms data many real ffts time isbn
TTTA extracted 25 structured relationships around Fast Fourier transform. Examples in this analysis include multiplications by 1 → instance of → operations can be saved by eliminating trivial operations and the split-radix FFT have their own names as well → instance of → and other variants. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| multiplications by 1 | instance of | operations can be saved by eliminating trivial operations | 0.80 | text |
| leaving about 30 million operations | instance of | operations can be saved by eliminating trivial operations | 0.80 | text |
| the split-radix FFT have their own names as well | instance of | and other variants | 0.80 | text |
| cache or CPU pipeline optimization.Following work by Shmuel Winograd | instance of | although actual performance on modern-day computers is determined by many other factors | 0.80 | text |
| astronomy | instance of | Research areasBig FFTsWith the explosion of big data in fields | 0.80 | text |
| the need for 512K FFTs has arisen for certain interferometry calculations | instance of | Research areasBig FFTsWith the explosion of big data in fields | 0.80 | text |
| WMAP | instance of | The data collected by projects | 0.80 | text |
| LIGO require FFTs of tens of billions of points | instance of | The data collected by projects | 0.80 | text |
| MRI | instance of | Approximate FFTsFor applications | 0.80 | text |
| it is necessary to compute DFTs for nonuniformly spaced grid points and/or frequencies | instance of | Approximate FFTsFor applications | 0.80 | text |
| Fast Fourier transform | related to Bounds on complexity and operation counts | Fourier | 0.60 | section |
| Fast Fourier transform | related to Bounds on complexity and operation counts | DFTs | 0.60 | section |
The concept neighborhoods around Fast Fourier transform bring nearby vocabulary together. In this analysis, examples include Fourier, Transform and Transforms. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Fast Fourier transform, one of the stronger structural bridges in this analysis connects Fast Fourier transform with Algorithms. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Fast Fourier transform to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications & Research, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Fast Fourier transform · EN edition · Analysis: TopicsToTalkAbout
