Search Results

If you wish to dig into math, then please refer to (among many others): Particle Simulation Based on Nonequispaced FFT NUFFT and MRI …

I believe you should use the Nyquist bin every time. By having it, you can always recreate the full DFT of your real-valued signal from the first half of spectrum. Whereas if you discard this value th …

What you are seeing is a peak from sinusoid convolved with a spectrum of rectangular window sinc function. That is due to property: $$x(t)\star w(t) = X(f)\cdot W(f) $$ Where your signal $x(t)$ is a …

Fourier Transform is linear, hence if $\mathcal F[x(t)]=X(f)$ and $a$, $b$ are complex numbers, then: $$\mathcal{F}[ax(t)+by(t)] = a X(f) + bY(f) $$ So in your case, simply sum the results together. …

Calculating butterfly we obtain: $[0+1\cdot 0; \ 0+(-1)\cdot 0] = [0; \ 0] $ - these are final FFT values $X[0], X[4]$. … 1.4142+1.4142i); \ 2i-(-0.7071 - 0.7071i)\cdot (1.4142+1.4142i)] = [0; \ 4i] $ So our final vector is following: $X[k] = [0; \ -4i; \ 0; \ 0; \ 0; \ 0; \ 0; \ 4i] $ Which means MATLAB is calculating FFT …

First of all you should take the magnitude of the FFT (use abs function) - what you've plotted is just a real part of FFT. … This way you will avoid mistakes using FFT. …

Recently I did similar thing and I can suggest you some solutions: Play $1kHz$ sinusoid at a given level of some arbitrary length (mostly bit longer = better), then you can tell what is the gain of …

To make things easier, I suggest you to use MATLAB function buffer. Regarding storage of your results I recommend to save it into matrix with dimensions no_frames and $512$. So if your current frame …

Why not to simply match the frequency responses and sensitivities? That's what labs are actually doing. Since you have the IR's for each device, simply calculate the Frequency response of each repet …

The reason for that is that you don't normalize the DFT samples properly. Dividing by number of samples in time domain is valid only for Rectangular Window. For simple case of DFT, you should divide y …

You can use Decimation In Time (DIT) to calculate the FFT of single $N$ length sequence, using two $N/2$ sequences and combine them later on with a single butterfly. … 0\ldots N/2-1$ Calculate the FFT of that complex sequence: $$\hat{X}=\mathrm{FFT}_{N/2}(\hat{x})$$ Now you can extract corresponding FFT's of both even and odd part: $$X_{even}(k)=\frac{\hat{X}(k)+ …

X[k]=\sum_{n=0}^{N-1}x[n]e^{-i 2\pi n\cdot k} \right |_{k=0} \Rightarrow X[0]=\sum_{n=0}^{N-1}x[n]e^{-i 2\pi n\cdot 0} = \sum_{n=0}^{N-1}x[n] $$ So after you calculate the FFT and divide by number of … In order to remove that, please apply the following before the FFT calculation: Lift = Lift - mean(Lift); …

There are different conventions in scaling of the FFT, in MATLAB you need to scale it by $\sqrt{N}$, where $N$ is your number of samples. … of normalised s1 % Scale FFT by sqrt(N) - this is convention used in MATLAB s2 = fft(s1)/sqrt(length(t)); % Calculate the norm of s2 s2_norm = norm(s2); display(sprintf('L2 norm of s2: %.2f', s2_norm) …

The answer is Non-uniform discrete Fourier transform I suggest you to take a look in here: NFFT library. Tutorial for that purpose: NFFT 3.0 Tutorial. You can also find a Python wrapper: pyNFFT. …

*win; % Magnitude spectra S = abs(fft(s)); S_win = abs(fft(s_win)); % Frequency vector f = (0:fs/N:round(fs/2)); % Take one-sided spectrum S_h = 2*S(1:length(f)); S_win_h = 2*S_win(1:length(f)); % Normalisation …