Use of FFT to recover parameters of waves

In summary, the conversation is about using FFT to recover parameters from given waves and references for introductory level material on signal processing. The speaker also mentions that there is intense mathematics involved in signal processing and suggests a website and USENET group for further information. They also ask a question about the definition of signal-to-noise ratio.
  • #1
Bacle
662
1
Hi, everyone:

As I am sure will be clear from my post, I am not--nor have I ever been-- an EE :).
(For the sake of giving some context to help taylor the answer,
I am a mathematician-in-training. I only know the most basic ideas of signal-processing
but I do know --intro-to-mid-level-- statistics: CLT, hypothesis-testing, etc.)

I am just curious on how one can use FFT to recover some parameters from
given waves. Would someone please explaina bit , or suggest a ref? I have read
just a bit on using the mean and likelihood methods to minimize noise.

I was also hoping someone would suggest some good sources at intro
level dealing with signal processing.

Thanks in Advance.
 
Last edited:
Engineering news on Phys.org
  • #2
This comes under the topic Numerical Methods in computer science. It might help to know Fourier transforms in advance.
 
  • #3
Don't assume Fourier is a beast that can be easily tamed! There is some intense mathematics involved in signal processing!
 
  • #4
UR_Correct said:
Don't assume Fourier is a beast that can be easily tamed! There is some intense mathematics involved in signal processing!

Care to share?
 
  • #5
I found this site to be enormously helpful in all aspects of digital signal processing, including the FFT: http://www.dspguide.com/" [Broken]
 
Last edited by a moderator:
  • #6
i might suggest to go to the USENET group comp.dsp and as them about questions regarding signal processing. it has a pretty high S/N ratio and, if you don't have a newsserver with your ISP, you can always use Google Groups.
 
  • #7
Thanks to All for your Replies.
rbj:

I recently read in my handbook a definition of the signal-to-noise ratio
in a sample, as the ratio of the mean to the standard deviation. Is this
correct?
 

1. How does the FFT algorithm help in recovering parameters of waves?

The FFT (Fast Fourier Transform) algorithm is a mathematical technique that converts a signal from its original domain (typically time or space) to a representation in the frequency domain. This allows for a more efficient analysis of signals, making it easier to identify and extract parameters such as frequency, amplitude, and phase of waves.

2. Can the FFT algorithm be used for all types of waves?

Yes, the FFT algorithm can be used for all types of waves, as long as the signal is sampled at regular intervals. This includes both continuous and discrete signals, making it a versatile tool for analyzing a wide range of waveforms.

3. How accurate is the FFT algorithm in recovering wave parameters?

The accuracy of the FFT algorithm depends on various factors such as the sampling rate, signal length, and the number of frequency components present in the signal. In most cases, it can accurately recover wave parameters with a high degree of precision.

4. Is it necessary to have prior knowledge of the wave parameters for using FFT?

No, the FFT algorithm can extract wave parameters from a signal without any prior knowledge. However, having some understanding of the expected frequency range and amplitude can help in selecting appropriate analysis parameters and interpreting the results.

5. Are there any limitations of using the FFT algorithm for recovering wave parameters?

One limitation of using the FFT algorithm is that it assumes the signal is periodic, which may not always be the case in real-world scenarios. Additionally, the presence of noise or other interfering signals can affect the accuracy of the results. It is important to carefully select and process the signal before using the FFT algorithm for parameter recovery.

Similar threads

Replies
6
Views
923
  • MATLAB, Maple, Mathematica, LaTeX
Replies
1
Views
1K
Replies
16
Views
1K
  • Electrical Engineering
Replies
4
Views
2K
Replies
6
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
2K
  • Atomic and Condensed Matter
Replies
7
Views
1K
  • Electrical Engineering
Replies
8
Views
1K
Replies
1
Views
587
Replies
37
Views
3K
Back
Top