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Signal 1Mscale1(7,‘db1’)
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Signal 1 Division Plots
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Signal 2 Mscale1(7,‘db1’)
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Signal 2 Division Plots
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Test Pattern
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Results (Mscale1(2,‘cs1’)) - Different Templates Discovered?
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2Wavelet Tree
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1.5Detail
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Other Patterns Mscale1(6,‘cs1’)
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Other patterns contd. Mscale1(9,‘cs1’)
Timing - Two plots of Mscale time with increasing values of scale (m)
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0.5Plot of Time (in seconds) as a function of scale (m)
Scale (m)
Time (s)
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Different Wavelets - Mscale2(7,cs2)
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Different Wavelets - Mscale2(7,D-2)
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Different Wavelets - Mscale2(7,D-5)
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2Detail
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Different Wavelets - Mscale2(7,D-8)
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6Wavelet Tree
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Different Wavelets - Mscale2(7,BO1)
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Different Wavelets - Mscale2(7,BO3)
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Different Wavelets - Mscale2(6,cs2)
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2Detail
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60Segmentation
Different Wavelets - Mscale2(6,D-2)
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Different Wavelets - Mscale2(6,D-5)
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Different Wavelets - Mscale2(6,D-8)
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Different Wavelets - Mscale2(6,BO1)
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Different Wavelets - Mscale2(6,BO3)
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Wavelet Comparison
• Performance depended very much on original signal
• For example Debauchies was best for tag1s but not so good for others
• Best overall wavelet for patterns on tag1s, tag3 and tag5 = Cubic Spline 2.
The Primitives
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Primitives discovered using sum of mean sq error and Mscale2(s2,7,’cs2’)
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1 3 3 6 44 1
Primitives discovered MScale2(s1,8,’ cs2’)
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Problems to still address: 1) Improve Tree Path Heuristic
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2Wavelet Tree
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1Detail
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Tree Heuristic
• Crossover should not be allowed
• Some improvement to take into account the magnitude (as well as position) of extrema on the detail signal.This should help determine the corresponding point on the next level.
Problems to still address:2) Determining further refinement (e.g. segmenting at extrema)
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Signal 1 Division(599:684)
Further segment refinement
• Should detect if pattern within segment is an extrema or not
• If it is then split the segment again at the extrema
Problems to still be addressed:3) The distortion of the approximation and detail signals at lower levels
related to tree path heuristic
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1Detail Signal
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Problems to still be addressed:4) Confusion between primitives
• Primitives 1 & 3 & 5 are confused
• Primitives 2 & 4 & 6 are confused
• An association amongst these could be made in determining the complete pattern
Work since Return
• Coded up a representation of a Dynamic Bayesian Network
• Updated the GA to work with a Bayesian Network metric rather than Pearson’s Correlation Coefficient
• Now looking at different discretizations to learn the best structure from the data
Typical model learnt from the data
A
D
C
B
0-1-2-3-16
