Topological Filters - A Toolbox for Processing Dynamic Signals - Michael Robinson

Topological Filters - A Toolbox for Processing Dynamic Signals - Michael Robinson

Institute for Advanced Study via YouTube Direct link

Changing overlaps changes the topology

3 of 28

3 of 28

Changing overlaps changes the topology

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Topological Filters - A Toolbox for Processing Dynamic Signals - Michael Robinson

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  1. 1 Intro
  2. 2 Overlap constructs topology
  3. 3 Changing overlaps changes the topology
  4. 4 Sheaves are about consistency
  5. 5 Finite topologies from partial orders
  6. 6 Topologizing a partial order
  7. 7 A sheaf on a poset is...
  8. 8 An assignment is...
  9. 9 A global section is...
  10. 10 Consistency radius is...
  11. 11 The space of global sections
  12. 12 Separating sinusoids from noise
  13. 13 Sheaves deliver excellent performance
  14. 14 More complex example: flight tracking
  15. 15 Sheaf model of the sensors . We can forma partial order of the sensors and
  16. 16 Known flight path
  17. 17 Minor RDF angle error
  18. 18 Major flight path error
  19. 19 Discrete-time LTI filters
  20. 20 Proof sketch: Input sheaf
  21. 21 Proof sketch: The internal state
  22. 22 OPLPF block diagram
  23. 23 How is this a topological filter?
  24. 24 Compare: standard adaptive filter
  25. 25 Filter performance comparison - OPLPF combines good noise removal with signal envelope stability
  26. 26 Context: Afro-Cuban drumming
  27. 27 Extracting musical structure
  28. 28 Some instruments are less clear

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