Categorization of acquired data based on explicit and implicit means

1. A method of classifying waveform data, comprising:

receiving input waveform data at a test and measurement system;

applying at least one of data reduction and filtering to the input waveform data;

accessing a repository of reference waveform data and corresponding classes;

analyzing the input waveform data and the reference waveform data to designate a class of the input waveform data; and

using the class designation to provide information to a user.

2. The method as claimed in claim 1 , wherein the input waveform data includes an input waveform and metadata associated with the input waveform.

3. The method as claimed in claim 2 , wherein the metadata includes at least one of: settings for the test and measurement system; probes used to receive the input waveform data; ranges of output.

4. The method as claimed in claim 1 , wherein analyzing the input waveform data comprises one of using a convolutional neural network, using a deep neural network, or performing data analysis.

5. The method as claimed in claim 4 , wherein performing data analysis comprises performing at least one of statistical data analysis, pattern recognition, and time series mining.

6. The method as claimed in claim 1 , wherein designating the class comprises providing a first class based on a signal type of the input waveform data and then providing a second class from within first class.

7. The method as claimed in claim 1 , wherein using the class designation to provide information to the user comprises applying a pre-defined set up to the test and measurement system.

8. The method as claimed in claim 1 , wherein using the class designation to provide information to the user comprises providing the designated class to the user to allow the user to confirm the class.

9. The method as claimed in claim 1 , wherein using the class designation to provide information to the user comprises providing a comparison of the input waveform to other waveforms in a same class as the input waveform.

10. The method as claimed in claim 1 , further comprising training a processor in the test and measurement system using at least one training set of the reference waveform data and corresponding classes.

11. The method as claimed in claim 10 , wherein the training set comprises a training set acquired from user data.

12. The method as claimed in claim 10 , further comprising saving the input waveform data and associated class to the repository as training data.

13. A test and measurement system, comprising:

a user interface;

at least one input port;

a communications port;

a processor, the processor being part of, or having access to, a convolutional neural network or a deep neural network, the processor configured to execute instructions causing the processor to:

receive input waveform data through at least one of the input port or the user interface;

access a repository of reference waveform data;

analyze the input waveform data using the reference waveform data;

designate a class of the input waveform data; and

use the class to provide information to the user about the input waveform.

14. The system as claimed in claim 13 , wherein the user interface includes at least one display screen to allow the system to display the proposed configuration.

15. The system as claimed in claim 13 , wherein the repository resides local to the test and measurement system.

16. The system as claimed in claim 13 , wherein the repository resides remotely from the test and measurement system.

17. The system as claimed in claim 16 , wherein the repository is accessible through the communications port.

18. The system as claimed in claim 13 , wherein the processor is further configured to execute instructions that cause the processor to designate a first class based on a signal type of the input waveform, and to designate a second class that is a sub-class of the first class.