Different forms of the discrete Fourier Transform

In summary, the conversation discusses the process of programming Excel to take the DFT of a signal and bring it back to the time domain after a low pass filter. The speaker mentions encountering issues with their real data, with the transform not working and the output appearing different from expected. They also mention following a tutorial for their code and noticing a difference in the expression for the transform compared to other textbooks. They mention downloading a code for computing Fourier transforms and recommend testing it on known inputs for validation.
  • #1
koab1mjr
107
0
Hi

I am trying to program excel to take the DFT of a signal, then bring it back to the time domain after a low pass filter. I have a code that can handle simple data for example
t = [ 0, 1, 2, 3]
y = [2, 3, -1, 4]

So I think everything is great and so I plug in my real signal and things go off the rails. Firstly my transform doesn't seem to work. In the simple cases I have a start frequency of 0 and an end frequency of 4. Everything works. I know my data has equal spacing, but I do not know my frequency to start or stop so I pick 0 and some arbitrary numbers. I have the number of points within the frequency interval as variable.

My output looks like no transform I have ever seen and bringing it back to time is equally bad. Am I missing something when I move to real data?

Now I was following this blog http://excelunusual.com/spectral-analysis-a-fourier-transform-tutorial/, as a basis for my code and I found that gave me a decent looking tranform of my real data but failed on a simple problem as stated above. The expression for the transform is different.

G(f) = ∫g(t)e-2π*j*f*t where as most textbooks have n/N in the exponent.

Not sure what the disconnect is but looking for some opinions on this.

Thanks in advance.
 
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  • #2
Look at the link in my sig and download some code that has been debugged for computing Fourier transforms.

You can also download the file cos10000.txt as a sample input. You will see it is the sum of 5 cosine terms for 10000 points. Testing Fourier transform code on a known input like this is a valuable step in validation.
 

Related to Different forms of the discrete Fourier Transform

What is the discrete Fourier Transform (DFT)?

The discrete Fourier Transform is a mathematical operation that converts a discrete signal from one domain (typically time or space) to another domain (typically frequency). It is used in signal processing, data analysis, and other fields to analyze the frequency components of a signal.

What are the different forms of the discrete Fourier Transform?

There are three main forms of the discrete Fourier Transform: the standard DFT, the fast Fourier Transform (FFT), and the inverse FFT. The standard DFT is the most basic form, while the FFT is a more efficient algorithm for calculating the DFT. The inverse FFT is used to convert a signal from the frequency domain back to the time or space domain.

What is the difference between the DFT and the FFT?

The main difference between the DFT and the FFT is in their algorithms. The DFT is a more straightforward calculation that involves summing up all the signal samples, while the FFT uses a divide-and-conquer approach to reduce the number of calculations needed. This makes the FFT much faster for larger signals.

What are the advantages of using the DFT over other transforms?

The DFT has several advantages over other transforms, such as its ability to handle discrete signals, its efficient implementation using the FFT, and its ability to represent both real and complex signals. It is also widely used and well-studied, making it a popular choice for many applications.

What are some common applications of the DFT?

The DFT has a wide range of applications in various fields, including signal processing, data analysis, image and audio compression, and communications. It is also commonly used in scientific research to analyze the frequency components of different phenomena, such as the frequency of brain waves in neuroscience.

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