How to recognize patterns in an analog waveform?

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Discussion Overview

The discussion revolves around methods for recognizing patterns in analog waveforms, exploring various techniques and approaches. Participants consider both general strategies and specific algorithms, including the use of Artificial Neural Networks (ANNs) and other mathematical tools.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant suggests using Artificial Neural Networks (ANNs) for pattern recognition but expresses difficulty in understanding the literature.
  • Another participant proposes that for basic tasks like finding minima and maxima, sampling the signal and writing algorithms could suffice.
  • A different viewpoint emphasizes that if the signal characteristics are known, there are established techniques to extract it from noise.
  • One participant mentions that identifying patterns in noisy signals is more complex and suggests using a Self-Organizing Map (SOM) as a potential solution.
  • Another participant notes that true patterns are repeating and can be analyzed using FFT (Fast Fourier Transform) techniques, especially for harmonic components.
  • Discussion includes the idea of cross-correlation and autocorrelation as methods for identifying known patterns in incoming signals.

Areas of Agreement / Disagreement

Participants express a range of views on the best methods for recognizing patterns, with no consensus on a single approach. Some agree on the utility of FFT for certain analyses, while others highlight the challenges of dealing with noisy signals.

Contextual Notes

Participants acknowledge the complexity of recognizing patterns in random analog signals and the limitations of various methods depending on the specific characteristics of the signals being analyzed.

Who May Find This Useful

This discussion may be of interest to those working in signal processing, machine learning, or anyone looking to understand methods for analyzing analog waveforms.

Neyolight
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Hi All

As the title of this thread suggest, I am looking for ways to recognize patterns in an analog waveform. Well the obvious answer to this question is use Artifical Neural Network (ANN) as ( from the text on google) its been used for pattern recognition and all sorts of stuff. I've been trying to learn about these ANNs for the past week and trust me all the literature kind of put me off :devil:

So, I need your expert advise on how I could possible recognize patterns in an analog waveform. At this point in my project I am not sure what exactly I would be serching for in the waveform but it would certainly be things like :
1) Delta Y ( to reach first maxima/minima in the waveform)
2) Slew rate to first maxima/minima
3) Maximum Delta Y
4) Delta X
5) Delta X to reach first maxima/minima
and so on...

Any advise ( especially on anything other than ANN) would be greatly appreciated.
Thanks :smile:
 
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My thoughts:

If you are just looking for minimum/maximum, derivatives (slew rates), or even spectral content then the problem is simple. You just sample the signal for a certain amount of time and write algorithms that go through the data and pick out those quantities.

If you know what signal you are looking for (say a certain radio signal or a signal for which the characteristics (mainly frequency) are known) then there is plenty of literature and techniques on how to pick it out of the noise.



If you want to take a noisy analog signal and say "Is there any patterns here" then the problem is much more difficult. I don't know that much about it, but I have done a little bit of AI type work and would say a SOM (self organizing map) may be your best bet.
 
Any true "pattern" is repeating and will result in a harmonic component - an FFT type analysis would be the first choice.

Based on the cases you provide - you have Max/Min (OK) , for max slew, used the same max min function after a differentiation function - etc.

If you are looking for a particular pattern - for example a square wave superimposed on a sine wave - the FFT will show the effect, but the analysis is more difficult.

If you are looking for "any" pattern in "any" random analog signal - well people work their whole lives on this.
 
If you know and can generate the pattern you are looking for then cross correlation against the incoming signal would be a possible answer. You can also look at the autocorrelation of the incoming signal, which would tell you how near 'random' the signal is - by giving you a single 'spike'.
 

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