1 / 10

Fourier Transforms

Fourier Transforms. David Cooper Summer 2014. Signal Processing. The true value in loading data into MATLAB is the ability to manipulate it and further process your signal

kato-gardner
Download Presentation

Fourier Transforms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fourier Transforms David Cooper Summer 2014

  2. Signal Processing • The true value in loading data into MATLAB is the ability to manipulate it and further process your signal • Common signal processing functions are convolution and correlation of two signals, auto-correlation of a signal with itself, transforming the signal into frequency domain, and separating a signal into base components. • For most applications we are dealing with signals that are measured across time and therefore exist only in the real positive axis. These functions can all be generalize to extend beyond those restrictions

  3. Fourier Transform

  4. Inverting the Fourier Transform

  5. Fast Fourier Transform • MATLAB has built in functions for parsing the Fourier transform from a given set of data >> X = fft(x) >> x = ifft(X) • The resulting variable will be a series of complex numbers that is equal to in length to the input variable • This complex number can also be represented by its magnitude and phase >> Xmag = abs(X) >> Xphase = angle(X) • To recombine the magnitude and phase after seperating >>Xreconstruct = Xmag*cos(Xphase)+i*Xmag*sin(Xphase)

  6. Convolution

  7. Cross-Correlation

  8. Autocorrelation

  9. Calculating Convolutions in MATLAB • To calculate the convolution in MATLAB use the conv() function >> h = conv(f,g) • You can similarly deconvolve a function if you know one of the input functions >> [g, r] = deconv(h,f) • The correlation functions are called with the xcorr() function >> c = xcorr(f,g) • By giving only a single input to the xcorr() function MATLAB will calculate its autocorrelation function >> ac = xcorr(f)

  10. Convolution Theorem

More Related