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Applications

This overview explores the use of bio-inspired computing in streaming audio, holistic image storage, surface bus communication, and image segmentation. It discusses the goals, challenges, and characteristics of each application, highlighting their potential benefits and limitations. The discussion also evaluates the programming model, its usefulness, optimization, complexity, and the power of self-organization.

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Applications

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  1. Applications Bio-Inspired Computing

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

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

  4. Streaming Audio Bio-Inspired Computing

  5. 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. Bio-Inspired Computing

  6. Streaming Stage Bio-Inspired Computing

  7. Steady State Bio-Inspired Computing

  8. 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. Bio-Inspired Computing

  9. Retrieval Bio-Inspired Computing

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

  11. Holistic Data Storage 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…. Bio-Inspired Computing

  13. Holistic Image Storage 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. Bio-Inspired Computing

  15. Image Representation Bio-Inspired Computing

  16. Input –> Output 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. Bio-Inspired Computing

  18. 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 Bio-Inspired Computing

  19. 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). Bio-Inspired Computing

  20. Portal Geometry Bio-Inspired Computing

  21. Surface Bus: Buoy pfrags 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. Bio-Inspired Computing

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

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

  25. 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. Bio-Inspired Computing

  26. Image Segmentation Bio-Inspired Computing

  27. Image Segmentation Bio-Inspired Computing

  28. Image Sampling Bio-Inspired Computing

  29. Image Segmentation Bio-Inspired Computing

  30. Image Segmentation Bio-Inspired Computing

  31. Image Segmentation Bio-Inspired Computing

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

  33. Discussion Bio-Inspired Computing

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

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