1. Bio-Inspired Computing
Applications
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Bio-Inspired Computing

2. Bio-Inspired Computing
Introduction
Overview
Four applications for pfrags:
Audio Streaming
Holistic Image Storage
Surface Bus
Image Segmentation
Discussion/Summary
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Bio-Inspired Computing

3. Streaming Audio on a paintable
Goal: Store packetized data in a particle RAM
Problems:
Transmission of data
Storage Characteristics
Retrieval
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Bio-Inspired Computing

4. Bio-Inspired Computing
Streaming Audio
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Bio-Inspired Computing

5. Bio-Inspired Computing
Streaming Audio
Representation:
each audio packet -> a Carrier pfrag
Transport governed by migration strategy of the Carrier.
Storage:
the Carriers distribute uniformly in the diffusion mode.
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Bio-Inspired Computing

6. Bio-Inspired Computing
Streaming Stage
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Bio-Inspired Computing

7. Bio-Inspired Computing
Steady State
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Bio-Inspired Computing

8. Bio-Inspired Computing
Retrieval
The output portal sends a CallBackGradient pfrag – radiates a gradient field.
Contains info like ID of audio stream, “active times”, distances.
Uses active time to decide what to do –
3 rules on Page 102.
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Bio-Inspired Computing

9. Bio-Inspired Computing
Retrieval
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Bio-Inspired Computing

10. Bio-Inspired Computing
Streaming audio
Characterisitics:
Shuttle mode playback
Ubiquitous table of contents
Fault tolerance
No topology dependence
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Bio-Inspired Computing

11. Bio-Inspired Computing
Holistic Data Storage
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Bio-Inspired Computing

12. Holistic Image Storage
Goal: Store a digitized image as a 2-D memory, minimizing the loss of clarity/sharpness even when a great deal of the information is not available.
Duplication of the lowest frequency coefficients obtained upon transformation ensures a blurred image on reconstruction.
But the size may still decrease….
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Bio-Inspired Computing

13. Holistic Image Storage
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Bio-Inspired Computing

14. Holistic Image Storage
Carriers and Transform pfrags
Transform applies a “block frequency transformation” to produce a 3-level hierarchy.
Output is 10 subbands – go to the carriers.
Carrier splits into 9 mini-carriers.
Each mini-C has 1 lowest frequency and 1 of the 9 other high frequencies.
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Bio-Inspired Computing

15. Bio-Inspired Computing
Image Representation
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Bio-Inspired Computing

16. Bio-Inspired Computing
Input –> Output
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Bio-Inspired Computing

17. Holistic Image Storage
Through experiments, it is seen that:
Successful decoding of images is possible.
The more the number of packets the better.
Multiple I/O’s can be incorporated.
Discussion:
Passing images to HP
Additional intelligence into Carriers
Hierarchical representation example.
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Bio-Inspired Computing

18. Bio-Inspired Computing
Surface Bus
Table top containing:
Devices – having short range wireless links – also called pico-nets.
Particles – device transceivers contact particles in vicinity.
How can they communicate with each other?
comm. between external devices
computation on transmitted data by particles
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Bio-Inspired Computing

19. Bio-Inspired Computing
Surface Bus
Use channel operator and Buoy pfrag
2 regions of the ensemble
Peers and portals
Each peer has a unique ID.
On table contact, it transmits this ID via signature Gradient.
Several geometry criteria (Pg 116).
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Bio-Inspired Computing

20. Bio-Inspired Computing
Portal Geometry
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Bio-Inspired Computing

21. Surface Bus: Buoy pfrags
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Bio-Inspired Computing

22. Surface Bus: Buoy pfrags
Need for a Buoy – to attain a finer degree of control in peer vicinity.
Peers deploy a set of B pfrags upon initialization.
Build the path for communication from peers.
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Bio-Inspired Computing

23. Surface Bus: Peer 2 Peer link
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Bio-Inspired Computing

24. Surface Bus: open and closed rings
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Bio-Inspired Computing

25. Bio-Inspired Computing
Surface Bus
The purpose was to illustrate how even a simple geometry estimation can underlie a broadly useful functionality.
Improvements:
Conformally wrap the Co-ordinate operator
More sophisticated use of fields to confine the outer ring of the table.
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Bio-Inspired Computing

26. Bio-Inspired Computing
Image Segmentation
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Bio-Inspired Computing

27. Bio-Inspired Computing
Image Segmentation
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Bio-Inspired Computing

28. Bio-Inspired Computing
Image Sampling
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Bio-Inspired Computing

29. Bio-Inspired Computing
Image Segmentation
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Bio-Inspired Computing

30. Bio-Inspired Computing
Image Segmentation
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Bio-Inspired Computing

31. Bio-Inspired Computing
Image Segmentation
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Bio-Inspired Computing

32. Bio-Inspired Computing
Discussion
Evaluation of the programming model.
Is it useful?
Is it optimized?
Complexity – determines scaling limits of engineered systems.
Self-organization – powerful tool.
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Bio-Inspired Computing

33. Bio-Inspired Computing
Discussion
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Bio-Inspired Computing

34. Bio-Inspired Computing
Summary
We looked at 4 applications of the paintable.
All these were simulated on the Psim.
Questions?
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Bio-Inspired Computing