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built-in discrete fourier transform algorithm (fft.mat)  (MathWorks Inc)


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    MathWorks Inc built-in discrete fourier transform algorithm (fft.mat)
    Built In Discrete Fourier Transform Algorithm (Fft.Mat), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/discrete+fourier+transform+algorithm/pmc08861396-150-12-10
    Average 90 stars, based on 1 article reviews
    built-in discrete fourier transform algorithm (fft.mat) - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Transformation Assay:


    Article Title: Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters
    Article Snippet: .. 4 can be implemented by resampling the action potential template to a sampling period T that satisfies the constraints, we transformed the expression into the frequency domain to bypass resampling and to conduct a faster elementwise multiplication in frequency domain rather than a slower convolution in time domain: f ( n ) = F − 1 [ F [ i ( n , K ) ] F [ ∑ m = 1 M δ ( n − L T V ( m − K ) ) w ( m ) ] ] = F − 1 [ F [ i ( n , K ) ) F [ S ( n ) ] ] Eq 5 where F is the Discrete Fourier Transform (DFT; fft in MATLAB) and F -1 is the inverse DFT. ..

    other:

    Article Title: Cross-scale structure feature surface machining method based on multi-component collaborative vibration
    Article Snippet: Step 1: preliminary spectrum analysis is performed on a feature surface of a given cross-scale structure, and two-dimensional discrete fast Fourier transform is performed on a given curved surface by using Matlab.

    Article Title: Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load.
    Article Snippet: The amplitude spectrum was calculated using discrete Fast Fourier Transform (fft in MATLAB) using Bartlett’s method on each electrode, task, and participant.

    Article Title: Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load
    Article Snippet: The amplitude spectrum was calculated using discrete Fast Fourier Transform ( fft in MATLAB) using Bartlett’s method on each electrode, task, and participant.

    Article Title: APOE4 and sedentary lifestyle synergistically impair neurovascular function in the visual cortex of awake mice.
    Article Snippet: Using the built-in MATLAB functions “detrend” and “pwelch”, traces were first detrended and then discrete Fourier transforms were conducted.

    Sampling:

    Article Title: Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters
    Article Snippet: .. 4 can be implemented by resampling the action potential template to a sampling period T that satisfies the constraints, we transformed the expression into the frequency domain to bypass resampling and to conduct a faster elementwise multiplication in frequency domain rather than a slower convolution in time domain: f ( n ) = F − 1 [ F [ i ( n , K ) ] F [ ∑ m = 1 M δ ( n − L T V ( m − K ) ) w ( m ) ] ] = F − 1 [ F [ i ( n , K ) ) F [ S ( n ) ] ] Eq 5 where F is the Discrete Fourier Transform (DFT; fft in MATLAB) and F -1 is the inverse DFT. ..

    Expressing:

    Article Title: Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters
    Article Snippet: .. 4 can be implemented by resampling the action potential template to a sampling period T that satisfies the constraints, we transformed the expression into the frequency domain to bypass resampling and to conduct a faster elementwise multiplication in frequency domain rather than a slower convolution in time domain: f ( n ) = F − 1 [ F [ i ( n , K ) ] F [ ∑ m = 1 M δ ( n − L T V ( m − K ) ) w ( m ) ] ] = F − 1 [ F [ i ( n , K ) ) F [ S ( n ) ] ] Eq 5 where F is the Discrete Fourier Transform (DFT; fft in MATLAB) and F -1 is the inverse DFT. ..



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    discrete fourier transformation algorithm fft  (MathWorks Inc)
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    Frequency-response functions and phase difference spectra. A–H: frequency-response functions of the 8 example neurons shown in Fig. 5 (black solid lines). Error bars give SE of the change in spike rate. Gray dashed lines represent the power spectrum of the neurons’ NDF0. Power and phase spectra were computed with a <t>discrete</t> <t>Fourier</t> <t>transformation.</t> The 1/3 magnitude level of the frequency spectra is marked by the black dotted line. Only those parts of the power spectrum that exceeded the 1/3 magnitude threshold were considered to carry energy. I–P: phase difference spectra of the same 8 example neurons (black lines). Phase difference spectra were created by subtracting the phase spectrum of the NDF180 from the phase spectrum of the NDF0. A phase difference of ±180 indicates phase sensitivity (dotted lines), while a phase difference of 0 or 360 indicates phase invariance.
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    Image Search Results


    Frequency-response functions and phase difference spectra. A–H: frequency-response functions of the 8 example neurons shown in Fig. 5 (black solid lines). Error bars give SE of the change in spike rate. Gray dashed lines represent the power spectrum of the neurons’ NDF0. Power and phase spectra were computed with a discrete Fourier transformation. The 1/3 magnitude level of the frequency spectra is marked by the black dotted line. Only those parts of the power spectrum that exceeded the 1/3 magnitude threshold were considered to carry energy. I–P: phase difference spectra of the same 8 example neurons (black lines). Phase difference spectra were created by subtracting the phase spectrum of the NDF180 from the phase spectrum of the NDF0. A phase difference of ±180 indicates phase sensitivity (dotted lines), while a phase difference of 0 or 360 indicates phase invariance.

    Journal: Journal of Neurophysiology

    Article Title: Envelope contributions to the representation of interaural time difference in the forebrain of barn owls

    doi: 10.1152/jn.01166.2015

    Figure Lengend Snippet: Frequency-response functions and phase difference spectra. A–H: frequency-response functions of the 8 example neurons shown in Fig. 5 (black solid lines). Error bars give SE of the change in spike rate. Gray dashed lines represent the power spectrum of the neurons’ NDF0. Power and phase spectra were computed with a discrete Fourier transformation. The 1/3 magnitude level of the frequency spectra is marked by the black dotted line. Only those parts of the power spectrum that exceeded the 1/3 magnitude threshold were considered to carry energy. I–P: phase difference spectra of the same 8 example neurons (black lines). Phase difference spectra were created by subtracting the phase spectrum of the NDF180 from the phase spectrum of the NDF0. A phase difference of ±180 indicates phase sensitivity (dotted lines), while a phase difference of 0 or 360 indicates phase invariance.

    Article Snippet: Phase and power spectra of the NDFs were calculated with a step size of 256 Hz using a discrete Fourier transformation algorithm (fft, MATLAB; MathWorks).

    Techniques: Transformation Assay

    AAr responses to high-pass filtered noise. A–D: NDF0 (black lines) and NDF180 (gray dashed lines) of the 4 neurons shown in Fig. 5, A, D, F, and H. NDFs were recorded using high-pass filtered noise stimuli. Upper and lower envelope functions are shown as black dotted lines. Delay asymmetry (delA) and correlation coefficients (r) are given at top left of each panel. The cutoff frequency for the high-pass noise stimuli was chosen individually for each neuron depending on its frequency response function (compare Fig. 10). Cutoff frequencies ranged from 2.5 to 5.5 kHz. E–H: cross-correlation functions of the NDF envelope functions shown in A–D (black lines). The position of the cross-correlation’s minimum determines the neurons delay asymmetry. Gray lines depict the cross-correlation functions of the min and max functions (not shown; see Fig. 2). I–L: power spectra of the NDF0 shown in A–D. Power spectra were computed with fast Fourier transformation algorithm.

    Journal: Journal of Neurophysiology

    Article Title: Envelope contributions to the representation of interaural time difference in the forebrain of barn owls

    doi: 10.1152/jn.01166.2015

    Figure Lengend Snippet: AAr responses to high-pass filtered noise. A–D: NDF0 (black lines) and NDF180 (gray dashed lines) of the 4 neurons shown in Fig. 5, A, D, F, and H. NDFs were recorded using high-pass filtered noise stimuli. Upper and lower envelope functions are shown as black dotted lines. Delay asymmetry (delA) and correlation coefficients (r) are given at top left of each panel. The cutoff frequency for the high-pass noise stimuli was chosen individually for each neuron depending on its frequency response function (compare Fig. 10). Cutoff frequencies ranged from 2.5 to 5.5 kHz. E–H: cross-correlation functions of the NDF envelope functions shown in A–D (black lines). The position of the cross-correlation’s minimum determines the neurons delay asymmetry. Gray lines depict the cross-correlation functions of the min and max functions (not shown; see Fig. 2). I–L: power spectra of the NDF0 shown in A–D. Power spectra were computed with fast Fourier transformation algorithm.

    Article Snippet: Phase and power spectra of the NDFs were calculated with a step size of 256 Hz using a discrete Fourier transformation algorithm (fft, MATLAB; MathWorks).

    Techniques: Transformation Assay