CORPRAL Development Ivan Galkin, Bodo Reinisch, Grigori Khmyrov, Alexander Kozlov, Xueqin Huang,...

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– Cooperation with U.Penn Institute of

Neurological Sciences

RPI Plasmagrams

NASA IS Program

CICT

cict.nasa.gov

IS

CNIS

SCITSR

AR

HCC

IDU

DM KD

ML

IT strategic research Space comms

Computers, Networks,Databases

Intelligent systems

Automaticreasoning

Human-centeredComputing

Intelligent Data Understanding

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

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

ML onboard: ideas?

• Magnetospheric State index for Space Weather applications

• Intelligent data reduction• Dynamic antenna tuning

DEMETER

Server Room Setup

DIDB

Athlon MP 2200+

RPI

Pentium-4 900 MHz

CAR

Pentium-4 1GHz

Applicationserver

Duron 700 MHz

CORPRAL

Firebird DBMS(database)

File ServerInstallation WWW pageLatest RPI data page

CVS RepositoryInterclient server for DIDB

RPI LZ ArchiveTOMCAT Server

DIDB IngestionRPI Ingestion

CORPRALADRES

Picture of the day

Firebird DBMS(database)

Interclient Server

ULCAR

Pentium-II 266 MHz

ULCAR HomepageFTP Guest area

DigisondeIncoming

Pentium 200MHz

FTP IncomingDispatcher MP

Digi data archive

5 5 1

Resonance Processing

Resonance Model

1. Gyrofrequency, fce, and its harmonics, nfce

2. Plasma frequency, fpe

3. Upper Hybrid resonance, fT

4. Q-type and its harmonics, fQn (a.k.a. Bernstein mode resonances)

5. D-type and its harmonics, fDn

22cepeT fff

2

2

2

46.0

ce

peceQn f

f

nnff

22

22

95.0

ceDnDn

ceDnDn

cepeDn

fff

fff

nfff

fpe and fce drive all frequencies

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

Resonance Recognition

Not all peaks of summary amplitude are resonances

Improvements to resonance fit

• Image filter to highlight resonance signatures

• Resonance signature detection and tagging

• Limit contributions to the fit quality to signatures only

Resonance Signature Filter

F(Ai) = median{Aj}, j = (1, i)

(“cumulative” median filter)

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FILTER RESPONSE TO A PULSE

Filtering noisy data

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Signature Detector

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

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

Gradients of fp and fe

fce(t1) fpe (t2) fT (t3)

)()()(

)()()(

32

32

3

12

22

3

tftftf

tftftf

cepeT

cepeT

1. Driving fce and fpe are taken at the plasmagram start2. Template frequencies are corrected for the gradients of

the driving fce and fpe

3. Iterative scheme is used to find self-consistent set of all involved resonance frequencies

Time, frequency

Resonances in BB

Marr’s Paradigm

Raw Image

Echoes

Traces

Classified Traces

Decisions

RotorsSaliency Map

From raw image to echoes

RAW IMAGE LABELING (NO THRESHOLDING)

LABELING (AFTER THRESHOLDING)

ADAPTIVE THRESHOLDING (a.k.a. ECHO DETECTION)

Rotors

• Rotors – local estimates of line orientation at each labeled pixel

• Orientation estimates are subject to errors (due to the range jitter)

Saliency

• Saliency measure:– How likely the rotor belongs to a

contour

• Saliency map– Image, where each pixel intensity is

its saliency measure

Gestalt principles

Key principle for contour saliency is continuity

Us and Them

• 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

Cortical networks

Facilitation term for rotorsin the model of striate cortex

[Yen, Finkel, 1997]

Rotor Optimization

End of OptimizationStart of Optimization

Hopfield NN optimizer

Structure of neuronNobel Prize [1906]

Multilayered Perceptron(feed forward, back-propagation NN)

Feed-back Hopfield NN

Inp

ut

Ou

tpu

t

Rotor Optimization

CO-CIRCULARITY CONSTRAINTa.k.a. Prägnanz, principle of curvature constancy in Gestalt

Striate cortexmodel

[Baginyan et al., 1994]

Near Zone

Range jitter deteriorates facilitation from nearby rotors

Parasitic stable state

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

Perceptual Grouping

• Us:– Bottom-up

clusterization using rotor interaction as the distance criterion

• Them:– Synchronization

(“together”) and desynchronization (“apart”) in a cortical network