Overlapping multi-signal classification

A method for classifying a signal comprising:

• generating with a histogram generator an original N dimensional histogram function from one or more of N dimensional signal parameter data for a signal and N dimensional variable parameter data for a variable for extracting characteristic parameters of one or more of the variable and the signal;
• filtering the original N dimensional histogram function with a smoothing filter to filter one or more original N dimensional histogram function bins by treating one or more of histogram bin heights as an N-dimensional signal and thereby implement a smoothing filter to generate an estimation curve outlining the N dimensional histogram function to reduce barcode fracturing;
• and
• computing with a computer processor and outputting a collection of barcode tracks created by an intersection of the N dimensional histogram function and a topological data analysis function thereby generating the collection of barcode tracks that are segmented horizontal intervals, where said topological data analysis function is tracked down from a higher level of the N dimensional histogram function through a lower level whereby there is no further segmented intervals to thereby produce a set of disjointed intervals creating a collection of barcode tracks that represent possible locations of classes, at least one class for at least one region bounded by the barcode tracks.

A non-transitory computer readable storage medium for use in conjunction with a computer system, the computer readable storage medium storing one or more programs including instructions for execution by and specifically configuring the computer system, the one or more programs when executed by the computer system cause the computer system to perform operations comprising:

• generating with a histogram generator program an original N dimensional histogram function from one or more of N dimensional signal parameter data for a signal and N dimensional variable parameter data for a variable for extracting characteristic parameters of one or more of the variable and the signal;
• filtering the original N dimensional histogram function with a smoothing filter program to filter one or more original N dimensional histogram function bins by treating one or more of histogram bin heights as an N-dimensional signal and thereby implement a smoothing filter to generate an estimation curve outlining the N dimensional histogram function to reduce barcode fracturing;
• and
• processing and outputting with a barcode track collection program a collection of barcode tracks created by an intersection of the N dimensional histogram function and a topological data analysis function thereby generating the collection of barcode tracks that are segmented horizontal intervals, where said topological data analysis function is tracked down from a higher level of the N dimensional histogram function through a lower level whereby there is no further segmented intervals to thereby produce a set of disjointed intervals creating a collection of barcode tracks that represent possible locations of classes, at least one class for at least one region bounded by the barcode tracks.