Fast Fourier Transform - Revision history

Korth: Removed anonymous edits made March/April 2022 from a particular range of IP addresses

2023-08-18T12:45:51Z

Removed anonymous edits made March/April 2022 from a particular range of IP addresses

← Older revision		Revision as of 12:45, 18 August 2023
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	'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete Fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.		'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete Fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	~~Unlike the DFT, the frequency spacing nor window size per-bin can't be adjusted because FFT bins aren't calculated individually.~~

	==External links==		==External links==

36.74.40.190 at 13:01, 22 March 2022

2022-03-22T13:01:18Z

← Older revision		Revision as of 13:01, 22 March 2022
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	'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete ~~fourier~~ transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.		'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete Fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

			Unlike the DFT, the frequency spacing nor window size per-bin can't be adjusted because FFT bins aren't calculated individually.

	==External links==		==External links==

Beardgoggles: Added external links section.

2019-07-24T20:48:28Z

Added external links section.

← Older revision		Revision as of 20:48, 24 July 2019
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	'''Fast Fourier ~~Transform~~''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.		'''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

			==External links==
			* {{wikipedia\|Fast Fourier transform}}

	[[Category:Signal Processing]]		[[Category:Signal Processing]]
	[[Category:Technical]]		[[Category:Technical]]

Notat at 01:44, 9 April 2010

2010-04-09T01:44:03Z

← Older revision		Revision as of 01:44, 9 April 2010
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	~~{{stub}}~~		'''Fast Fourier Transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT\|discrete fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	'''Fast Fourier Transform''' ('''FFT''') is an efficient algorithm for calculating the [[discrete fourier transform]] ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	[[Category:Signal Processing]]		[[Category:Signal Processing]]
			[[Category:Technical]]

Speckmade at 17:29, 13 June 2007

2007-06-13T17:29:22Z

← Older revision		Revision as of 17:29, 13 June 2007
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	{{stub}}		{{stub}}

			'''Fast Fourier Transform''' ('''FFT''') is an efficient algorithm for calculating the [[discrete fourier transform]] ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.
	Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	[[Category:Signal Processing]]		[[Category:Signal Processing]]

Speckmade: FFT moved to Fast Fourier Transform

2007-06-13T17:25:52Z

FFT moved to Fast Fourier Transform

← Older revision	Revision as of 17:25, 13 June 2007
(No difference)

HotshotGG at 18:09, 25 November 2006

2006-11-25T18:09:30Z

← Older revision		Revision as of 18:09, 25 November 2006
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	Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ~~<math>~~FFT Radix 2~~</math>~~ algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.		Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	[[Category:Signal Processing]]		[[Category:Signal Processing]]

HotshotGG at 18:06, 25 November 2006

2006-11-25T18:06:28Z

← Older revision		Revision as of 18:06, 25 November 2006
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	Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). ~~Reduces~~ the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications. The FFT in most implementations consistent of samples that are exactly a power of 2.		Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). It reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a <math>FFT Radix 2</math> algorithm where <math> n = 64,128,256,512,1024,2048</math> etc.

	[[Category:Signal Processing]]		[[Category:Signal Processing]]

Pepoluan at 20:31, 15 September 2006

2006-09-15T20:31:53Z

← Older revision		Revision as of 20:31, 15 September 2006
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	Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). Reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications. The FFT in most implementations consistent of samples that are exactly a power of 2.		Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). Reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications. The FFT in most implementations consistent of samples that are exactly a power of 2.

	[[Category:~~Digital~~ Signal Processing]]		[[Category:Signal Processing]]

HotshotGG: again... redundant. This FFT description is good, although it needs some references.

2006-09-09T02:24:35Z

again... redundant. This FFT description is good, although it needs some references.

← Older revision		Revision as of 02:24, 9 September 2006
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	Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). Reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications. The FFT in most implementations consistent of samples that are exactly a power of 2.		Fast Fourier Transform is an efficient algorithm for calculating the discrete fourier transform ([[DFT]]). Reduces the execution time by hundreds in some cases. Whereas [[DFT]] takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to used in all applications. The FFT in most implementations consistent of samples that are exactly a power of 2.

	~~[[Category:Technical]]~~
	[[Category:Digital Signal Processing]]		[[Category:Digital Signal Processing]]