MATLAB Exercise on DFT

In summary, the "MATLAB Exercise on DFT" provides an interactive learning experience focused on the Discrete Fourier Transform (DFT) using MATLAB. It guides users through the process of implementing DFT algorithms, visualizing frequency components of signals, and understanding the implications of sampling and windowing. Through practical exercises, participants enhance their skills in signal processing and gain hands-on experience with MATLAB's computational tools.
  • #1
marco02
1
1
Thread moved from the technical forums to the schoolwork forums
Homework Statement
ddòd
Relevant Equations
lsd
Good morning, I have an exercise with two points that gives a Matlab mat file in which are contained the samples of two measured signals and the variable fs indicating the sampling frequency

A)Signal 1 consists of two sinusoidal signals of which you are asked to calculate: -the duration ⇒ I thought is found by doing T=N/fs where N is the number of samples (in my case 512); -the two frequencies (enter in ascending order). I used the following Matlab commands to plot the Fourier transform:

s1=datafile1;
N=512;
f=(-fs/2):(fs/N):(fs/2-fs/N);
S1=fftshift(fft(s))
plot(f,abs(S1))

I got this
wUhj4DL.png


Is it right then to take as frequencies the ones I see in the two peaks(positive and negative with the same modulus)?

B)Signal 2 consists of the superposition of a larger number of sinusoidal signals. You are asked to identify the frequencies present by placing them in the boxes in ascending order(there are 6 boxes). If the number of frequencies identified is less than the number of boxes available, enter the value of the sampling frequency in the unnecessary boxes.

In the second case I get this
Wzp8PYX.png


How do I choose the frequencies now if I have 8 peaks?
The mat file is provided below



Thanks to those who will help me
 

Attachments

  • data_file (1).mat
    7.2 KB · Views: 24
Last edited by a moderator:
Physics news on Phys.org
  • #2
Fourier transform plots always show the frequency and a "mirror image" negative of the same frequency. See the Mathworks help https://www.mathworks.com/help/matlab/math/fourier-transforms.html

So your first plot indicates a single frequency.

I suggest creating some data samples from known frequencies to see if the Fourier plots are what you expect.
 

FAQ: MATLAB Exercise on DFT

What is DFT and why is it important in signal processing?

DFT, or Discrete Fourier Transform, is a mathematical technique used to transform a sequence of values into components of different frequencies. It is important in signal processing because it allows us to analyze the frequency content of discrete signals, enabling applications such as filtering, compression, and spectral analysis.

How do I implement DFT in MATLAB?

To implement DFT in MATLAB, you can use the built-in function fft() which computes the Fast Fourier Transform (FFT) of a sequence. For a basic DFT, you can also manually compute it using nested loops or vectorized operations. For example, you can create a function that takes a signal as input and computes its DFT based on the DFT formula.

What are the differences between DFT and FFT?

The main difference between DFT and FFT is that DFT is a mathematical formula used to compute the frequency components of a discrete signal, while FFT is an algorithm that computes the DFT efficiently. FFT reduces the computational complexity from O(N^2) to O(N log N), making it suitable for larger datasets.

Can MATLAB handle large datasets for DFT computations?

Yes, MATLAB is capable of handling large datasets for DFT computations. It utilizes optimized algorithms and memory management to efficiently compute the DFT using the FFT function, even for large arrays. However, performance may vary based on system resources and the specific implementation.

How can I visualize the results of a DFT in MATLAB?

You can visualize the results of a DFT in MATLAB by plotting the magnitude and phase of the frequency components. After computing the DFT using fft(), you can use the plot() function to display the magnitude spectrum by taking the absolute value of the DFT output and normalizing it. Additionally, you can use subplot() to create multiple plots for better visualization.

Similar threads

Replies
4
Views
1K
Replies
6
Views
4K
Replies
8
Views
2K
Replies
2
Views
1K
Replies
2
Views
1K
Back
Top