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#fourierseries — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #fourierseries, aggregated by home.social.

  1. 🎨🔍 Oh, look! NVIDIA's latest *groundbreaking* innovation: transforming your blurry, pixelated images into modern art using the power of everyone's favorite math class nightmare—Fourier Series! 🎢 Because who doesn't want their photos to look like a cosmic trip through a 2D sine wave rollercoaster? 🤷‍♂️✨
    sifeiliu.net/CosAE-page/ #NVIDIA #Innovation #ImageTransformation #FourierSeries #ModernArt #TechTrends #HackerNews #ngated

  2. Fundamental component of a Quasi-Square Wave (QSW) voltage as shown below has a peak value of (4/𝝅)Vdc*cos(α) = 1.27Vdc*cos(α)

    #FourierSeries #PowerElectronics #Inverter

    Source: Slides 8-9 → slideserve.com/lapis/inverters

  3. The Fourier transform (FT), explained in one sentence: 🔗 blog.revolutionanalytics.com/2
    \[\boxed{\hat{f}(\xi)=\displaystyle\int_{-\infty}^\infty e^{-i2\pi\xi t}f(t)\ \mathrm{d}t}\]

    Discrete Fourier transform (DFT):
    \[\displaystyle x_n = \sum_{k=0}^{N-1} X_k \cdot e^{-i2\pi \tfrac{n}{N}k}\]

    Inverse transform:
    \[\displaystyle X_k = \frac{1}{N} \sum_{n=0}^{N-1} x_n \cdot e^{i2\pi \tfrac{n}{N} k}\]

    #FourierTransform #FourierSeries #Transform #MathematicalTransform #Signal #SignalProcessing #Frequency #Energy #FourierAnalysis #Series #Analysis

  4. The Fourier transform (FT), explained in one sentence: 🔗 blog.revolutionanalytics.com/2
    \[\boxed{\hat{f}(\xi)=\displaystyle\int_{-\infty}^\infty e^{-i2\pi\xi t}f(t)\ \mathrm{d}t}\]

    Discrete Fourier transform (DFT):
    \[\displaystyle x_n = \sum_{k=0}^{N-1} X_k \cdot e^{-i2\pi \tfrac{n}{N}k}\]

    Inverse transform:
    \[\displaystyle X_k = \frac{1}{N} \sum_{n=0}^{N-1} x_n \cdot e^{i2\pi \tfrac{n}{N} k}\]

    #FourierTransform #FourierSeries #Transform #MathematicalTransform #Signal #SignalProcessing #Frequency #Energy #FourierAnalysis #Series #Analysis

  5. The Fourier transform (FT), explained in one sentence: 🔗 blog.revolutionanalytics.com/2
    \[\boxed{\hat{f}(\xi)=\displaystyle\int_{-\infty}^\infty e^{-i2\pi\xi t}f(t)\ \mathrm{d}t}\]

    Discrete Fourier transform (DFT):
    \[\displaystyle x_n = \sum_{k=0}^{N-1} X_k \cdot e^{-i2\pi \tfrac{n}{N}k}\]

    Inverse transform:
    \[\displaystyle X_k = \frac{1}{N} \sum_{n=0}^{N-1} x_n \cdot e^{i2\pi \tfrac{n}{N} k}\]

    #FourierTransform #FourierSeries #Transform #MathematicalTransform #Signal #SignalProcessing #Frequency #Energy #FourierAnalysis #Series #Analysis

  6. The Fourier transform (FT), explained in one sentence: 🔗 blog.revolutionanalytics.com/2
    \[\boxed{\hat{f}(\xi)=\displaystyle\int_{-\infty}^\infty e^{-i2\pi\xi t}f(t)\ \mathrm{d}t}\]

    Discrete Fourier transform (DFT):
    \[\displaystyle x_n = \sum_{k=0}^{N-1} X_k \cdot e^{-i2\pi \tfrac{n}{N}k}\]

    Inverse transform:
    \[\displaystyle X_k = \frac{1}{N} \sum_{n=0}^{N-1} x_n \cdot e^{i2\pi \tfrac{n}{N} k}\]

    #FourierTransform #FourierSeries #Transform #MathematicalTransform #Signal #SignalProcessing #Frequency #Energy #FourierAnalysis #Series #Analysis

  7. John von Neumann once claimed, "with 4 parameters, I can fit an elephant, and with 5, I can make him wiggle his trunk."
    \[x(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^x\cos(kt)+B_k^x\sin(kt) \right)\]
    \[y(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^y\cos(kt)+B_k^y\sin(kt) \right)\]
    Here's a paper proving that von Neumann's claim is valid! 🔗 aapt.scitation.org/doi/10.1119
    #Neumann #JohnVonNeumann #VonNeumann #FourierSeries #parameters #complexparameters #parametrization #mathematics #maths

  8. John von Neumann once claimed, "with 4 parameters, I can fit an elephant, and with 5, I can make him wiggle his trunk."
    \[x(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^x\cos(kt)+B_k^x\sin(kt) \right)\]
    \[y(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^y\cos(kt)+B_k^y\sin(kt) \right)\]
    Here's a paper proving that von Neumann's claim is valid! 🔗 aapt.scitation.org/doi/10.1119
    #Neumann #JohnVonNeumann #VonNeumann #FourierSeries #parameters #complexparameters #parametrization #mathematics #maths

  9. John von Neumann once claimed, "with 4 parameters, I can fit an elephant, and with 5, I can make him wiggle his trunk."
    \[x(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^x\cos(kt)+B_k^x\sin(kt) \right)\]
    \[y(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^y\cos(kt)+B_k^y\sin(kt) \right)\]
    Here's a paper proving that von Neumann's claim is valid! 🔗 aapt.scitation.org/doi/10.1119
    #Neumann #JohnVonNeumann #VonNeumann #FourierSeries #parameters #complexparameters #parametrization #mathematics #maths

  10. John von Neumann once claimed, "with 4 parameters, I can fit an elephant, and with 5, I can make him wiggle his trunk."
    \[x(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^x\cos(kt)+B_k^x\sin(kt) \right)\]
    \[y(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^y\cos(kt)+B_k^y\sin(kt) \right)\]
    Here's a paper proving that von Neumann's claim is valid! 🔗 aapt.scitation.org/doi/10.1119
    #Neumann #JohnVonNeumann #VonNeumann #FourierSeries #parameters #complexparameters #parametrization #mathematics #maths

  11. John von Neumann once claimed, "with 4 parameters, I can fit an elephant, and with 5, I can make him wiggle his trunk."
    \[x(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^x\cos(kt)+B_k^x\sin(kt) \right)\]
    \[y(t)=\displaystyle\sum_{k=0}^\infty\left(A_k^y\cos(kt)+B_k^y\sin(kt) \right)\]
    Here's a paper proving that von Neumann's claim is valid! 🔗 aapt.scitation.org/doi/10.1119
    #Neumann #JohnVonNeumann #VonNeumann #FourierSeries #parameters #complexparameters #parametrization #mathematics #maths