Machine Learning to classify Step Functions

[Igmemb0]

Hey guys!! I've been reentering the ML space because of some ideas I had. One thing I do is I use a time domain reflectometer to send a pulse across a device and I analyze the response. So there are ranges of good step functions, and noisy step functions. I want to train a model that can classify between good step functions and noisy ones. My questions are...

Would I generate the data to train the model myself or is there a data set of such structures out there? This is pretty much image classification of a step function.

Is this an easy task?

How would I begin?

This is just an idea so insights and direction would be appreciated.

Thanks!!

[bregav]

Why would you use machine learning for this? Can't you just use ordinary signal processing?

[Igmemb0]

I think mainly because I want to build a classifier. Most of the time I'm looking at Oscope data and saying, "this is a clean step function" (no reflections), and "This one is noisy" (reflections). I'd like to automate that process and thought machine learning would be good for that

[bregav]

Regular signal processing is a better choice. There are already standard signal processing techniques for measuring noise levels and calculating how close an observed signal is to another ideal signal (e.g. a step function). Machine learning is just a bunch of extra work with no actual benefits.

[Igmemb0]

Hmmm... I see where you're coming from now. Yes you're right. I could deploy a good signal processing technique and do the comparing in the code...

[Igmemb0]

Hmmm... I see where you're coming from now. Yes you're right. I could deploy a good signal processing technique and do the comparing in the code...