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CORPRAL Development

CORPRAL Development. Ivan Galkin, Bodo Reinisch, Grigori Khmyrov, Alexander Kozlov, Xueqin Huang, Robert Benson, Shing Fung. Outline. Corporal development Grant from NASA Intelligent Systems (IS) Program New equipment Cerebral development New resonance matching algorithm

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CORPRAL Development

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  1. CORPRAL Development Ivan Galkin, Bodo Reinisch, Grigori Khmyrov, Alexander Kozlov, Xueqin Huang, Robert Benson, Shing Fung

  2. Outline • Corporal development • Grant from NASA Intelligent Systems (IS) Program • New equipment • Cerebral development • New resonance matching algorithm • Cooperation with U.Penn Institute of Neurological Sciences

  3. RPI Plasmagrams

  4. NASA IS Program Automatic reasoning AR CICT cict.nasa.gov IS HCC Intelligent systems CNIS Human-centered Computing IDU SC Intelligent Data Understanding ITSR Computers, Networks, Databases ML Space comms IT strategic research DM KD

  5. IS and RPI data • Near-term goal: CORPRAL analysis of RPI data for a variety of magnetospheric echoes • Long-term goal: use of CORPRAL-derived data to modify operating state of onboard instruments

  6. Near-term tasks • Use IS technology for plasmagram processing • Resonance identification • Trace extraction • Use expert knowledge to automatically interpret RPI data • Echoes • Resonances • Spectrograms • Introduce “state of the magnetosphere” index for space weather alerts • Onboard ML decision making

  7. ML onboard: ideas? • Magnetospheric State index for Space Weather applications • Intelligent data reduction • Dynamic antenna tuning

  8. DEMETER

  9. Server Room Setup Athlon MP 2200+ Pentium-4 900 MHz DIDB RPI Pentium-II 266 MHz Pentium 200MHz Duron 700 MHz CORPRAL ULCAR Digisonde Incoming Application server Pentium-4 1GHz File Server Installation WWW page Latest RPI data page CVS Repository Interclient server for DIDB RPI LZ Archive TOMCAT Server Firebird DBMS (database) Interclient Server Firebird DBMS (database) 5 5 1 CAR FTP Incoming Dispatcher MP Digi data archive ULCAR Homepage FTP Guest area DIDB Ingestion RPI Ingestion CORPRAL ADRES Picture of the day

  10. Resonance Processing

  11. Gyrofrequency, fce, and its harmonics, nfce Plasma frequency, fpe Upper Hybrid resonance, fT Q-type and its harmonics, fQn (a.k.a. Bernstein mode resonances) D-type and its harmonics, fDn Resonance Model fpe and fce drive all frequencies

  12. Model Fitting Approach • Superimpose a Comb Template on the plasmagram and seek the best quality of fit for varying drivers fpe and fce • Used for ISIS, ISS-B, ISEE-1 data • Not good enough for RPI • Tremendous variety of conditions • Frequency coverage not optimal • Accuracy vs precision issues • 0.7% accuracy is required for model self-consistency • Noise environment

  13. Resonance Recognition Not all peaks of summary amplitude are resonances

  14. Improvements to resonance fit • Image filter to highlight resonance signatures • Resonance signature detection and tagging • Limit contributions to the fit quality to signatures only

  15. Resonance Signature Filter F(Ai) = median{Aj}, j = (1, i) (“cumulative” median filter) FILTER RESPONSE TO A PULSE

  16. Filtering noisy data

  17. Signature Detector Detection of signatures allows evaluation of their contrast that is then used to calculate quality fit instead of amplitude

  18. Change of medium during sounding • The “driving” fce and fpe are specified at the plasmagram start • Gradients of fce and fpe are estimated using the model values at start and stop times • An iterative scheme is applied to ensure that templates are placed at the frequency that is compatible with fce and fpe at that time

  19. Gradients of fp and fe Time, frequency fce(t1) fpe (t2) fT (t3) • Driving fce and fpeare taken at the plasmagram start • Template frequencies are corrected for the gradients of the driving fce and fpe • Iterative scheme is used to find self-consistent set of all involved resonance frequencies

  20. Resonances in BB

  21. Marr’s Paradigm Saliency Map Rotors Decisions Classified Traces Traces Echoes Raw Image

  22. From raw image to echoes LABELING (NO THRESHOLDING) LABELING (AFTER THRESHOLDING) RAW IMAGE ADAPTIVE THRESHOLDING (a.k.a. ECHO DETECTION)

  23. Rotors – local estimates of line orientation at each labeled pixel Orientation estimates are subject to errors (due to the range jitter) Rotors

  24. Saliency • Saliency measure: • How likely the rotor belongs to a contour • Saliency map • Image, where each pixel intensity is its saliency measure

  25. Gestalt principles Key principle for contour saliency is continuity

  26. Us: Rotor orientations are estimated using directional histogramming Saliency map is obtained by iterative optimization in the network of rotors Them: Rotor orientations are obtained using steerable filters (e.g., Gabor filter banks) Saliency map is obtained by cumulative contribution in the “cortical” network of rotors Us and Them

  27. Cortical networks Facilitation term for rotors in the model of striate cortex [Yen, Finkel, 1997]

  28. Rotor Optimization Start of Optimization End of Optimization

  29. Hopfield NN optimizer Input Output Structure of neuron Nobel Prize [1906] Multilayered Perceptron (feed forward, back-propagation NN) Feed-back Hopfield NN

  30. Rotor Optimization [Baginyan et al., 1994] Striate cortex model CO-CIRCULARITY CONSTRAINT a.k.a. Prägnanz, principle of curvature constancy in Gestalt

  31. Near Zone Range jitter deteriorates facilitation from nearby rotors

  32. Parasitic stable state “Tunneling” through energy barriers using MFT approach (introduction of thermodynamic noise in the NN evolving rule)

  33. Us: Bottom-up clusterization using rotor interaction as the distance criterion Them: Synchronization (“together”) and desynchronization (“apart”) in a cortical network Perceptual Grouping

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