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U. Calgary Team & Relevant ExperienceU. Calgary Team & Relevant Experience ASI Prototype (example movies)ASI Prototype (example movies) THEMIS-Canada Web Site THEMIS-Canada Web Site ASI Array Deployment (via web site)ASI Array Deployment (via web site) ASI Data Quality ControlASI Data Quality Control ASI Data SummaryASI Data Summary ASI Data Recovery ASI Data Recovery ASI Data Management, Analysis, ProcessingASI Data Management, Analysis, Processing Milestones for THEMIS-CanadaMilestones for THEMIS-Canada

Themis-Canada Ground-Based ASI Array

real-time at site

High-level Technical Support

• Kaare Berg (Wilbur; Satellite Instrumentation)

• Cliff Marcellus (CANOPUS Network; Flight Control Software)

• Titus Mathews Jr. (POCA; Image Compression; EE PhD)

• Peter King (Wilbur; Satellite Hardware; RF Engineering)

• Greg Enno (Viking, Freja, Interball UVAIs; Flight Control Soft.)

Brief History of University of Calgary Space Group Experience• ISIS II Scanning Photometer• Host of ground-based instruments (Phot.; FP; Spect.; First ASI)• CANOPUS (ASI; MSPs; Communications Infrastructure)• Viking UV• Freja UV• Interball II UVAI• Nozomi TPA• Numerous rocket optical and particle instruments• NORSTAR• MIRACLE ASI array (experience via Mikko Syrjäsuo)• Portable Auroral Imager; High Speed Photometer; Induction Coil Mags

E. Donovan

L. Cogger

T. Trondsen

M. Syrjäsuo

B. Jackel M. Greffen

D. Wallis F. Creutzberg

E. Spanswick

N. Partamies

M. Connors

D. Knudsen

• Imaging TechnologyImaging Technology• Field OperationsField Operations• CommunicationsCommunications• Auroral StudiesAuroral Studies

• Imaging TechnologyImaging Technology• Machine VisionMachine Vision• Field OperationsField Operations• Database ManagementDatabase Management

• Optical StudiesOptical Studies• ManagementManagement• Technical SupportTechnical Support• Financial SupportFinancial Support

• Auroral DynamicsAuroral Dynamics• Substorm StudiesSubstorm Studies• Energy FluxEnergy Flux

• Auroral ObservationsAuroral Observations• Substorm StudiesSubstorm Studies• Machine VisionMachine Vision• ManagementManagement

• Auroral ObservationsAuroral Observations• Information TheoryInformation Theory• Database ManagementDatabase Management• Field OperationsField Operations• CommunicationsCommunications

• Riometer GuruRiometer Guru• Magnetometer GuruMagnetometer Guru• CommunicationsCommunications• Field OperationsField Operations

• Field Site ManagerField Site Manager• Arctic OperationsArctic Operations• Technical SupportTechnical Support• CommunicationsCommunications

• Photometer GuruPhotometer Guru• Auroral ObservationsAuroral Observations• CommunicationsCommunications• Field OperationsField Operations

• Athabasca ObservatoryAthabasca Observatory• MagnetometersMagnetometers• InversionInversion• Quebec Operations?Quebec Operations?

• Riometer SpecialistRiometer Specialist• Substorm Onset (gloria)Substorm Onset (gloria)• Public RelationsPublic Relations

• Dense ArrayDense Array• Auroral StudiesAuroral Studies

NORSTAR - Now

NORSTAR - 2007

THEMIS Prototype Camera

Starlight Xpress MX716 w/all-sky optics

Linux Red Hat 9 w/MX716 driver

Image interval 5 sec; 1-sec exposures

NORSTAR/MIRACLE imaging code

Web server provides real-time access

Network time protocol

THEMIS ASI Array Web Site

http://www.phys.ucalgary.ca/NORSTAR

Data Summary – Quick access and scientific value

Athabasca Alberta

THEMIS

Raw Image Mapped Image

Summary Image

Geodetic Grid

May 28, 2003: GOES 10 and 12 register dipolarization showing onset West of Fort Smith meridian, consistent with substorm onset in the early evening sector at low geomagnetic latitude.

POCA (Eureka)POCA (Eureka) Wilbur (Gillam)Wilbur (Gillam) RIOMETERRIOMETER MSPMSP NORSTAR pre CGSMNORSTAR pre CGSM NORSTAR – CGSMNORSTAR – CGSM THEMISTHEMIS

THEMIS – Data Issues

3 TB disk storage and growing3 TB disk storage and growing data summary, display, and analysis softwaredata summary, display, and analysis software information theory, and machine visioninformation theory, and machine vision

existing data set (<500 GB)

VERY BIG data set

STUPIDLY BIG data set

THEMIS – Data RecoveryExisting high-speed infrastructure

ATHA, CONT, CHUR, INUV, FSMI, ….ATHA, CONT, CHUR, INUV, FSMI, ….

Broadband Satellite + RF Link

[A]DSL Telus, Manitoba Telecom, Aliant, …..

Hardcopy via traditional mail

Free Free

5k 9k/yr

0.1k 1k/yr

4k 2k/yr

FSIM, INUV, FSMI, RANK, PBQ (2005+), …FSIM, INUV, FSMI, RANK, PBQ (2005+), …

PGEO, ATHA, FLIN, PINA, KAPU, HEBR, CART, ….PGEO, ATHA, FLIN, PINA, KAPU, HEBR, CART, ….

Telesat HSI Canadian Satellite NOT INUVNOT INUV

VSAT

All sites depending on providerAll sites depending on provider

Glentel, Nanometrics, PolarSat, Stratos, ….

Never fails (well almost never)!Never fails (well almost never)!

Sta

ndar

d In

tern

et

Dial up to local ISP All sites; Not real time.All sites; Not real time.

0.1k 1k/yr

1k 1k/yr

Telesat High Speed Internet – cost effective, flexible and reliable

Each site will have its own IP; summary images will be sent as Each site will have its own IP; summary images will be sent as UDP packets; dropped packets (~1%) retrieved during the UDP packets; dropped packets (~1%) retrieved during the day; solution supports direct internet (TCP/IP) access to sites. day; solution supports direct internet (TCP/IP) access to sites.

THEMIS ASI Array UofC Data Management

Station

Imager

Filters

Summary Images

Calibration Data

Classification

Top 40

Entire Dataset

Name, location, mag midnight, history of operation, …

Rel

atio

nal D

atab

ase

--

130

GB

Online storage of complete high-res data for subset of nights of greatest interest for mission

Great big pile of hardcopy in at least two separate locations in Canada and one at UCB.

Milestones for THEMIS-Canada

• 5 imagers 2003-2004 • 5 imagers 2004-2005• 6 imagers 2005-2006

• Complete inventory of infrastructure – 2004• Real time summary database – 2005

E. Donovan

L. Cogger

T. Trondsen

M. Syrjäsuo

B. Jackel M. Greffen

D. Wallis F. Creutzberg

E. Spanswick

N. Partamies

M. Connors

D. Knudsen

• Imaging TechnologyImaging Technology• Field OperationsField Operations• CommunicationsCommunications• Auroral StudiesAuroral Studies

• Imaging TechnologyImaging Technology• Machine VisionMachine Vision• Field OperationsField Operations• Database ManagementDatabase Management

• Optical StudiesOptical Studies• ManagementManagement• Technical SupportTechnical Support• Financial SupportFinancial Support

• Auroral DynamicsAuroral Dynamics• Substorm StudiesSubstorm Studies• Energy FluxEnergy Flux

• Auroral ObservationsAuroral Observations• Substorm StudiesSubstorm Studies• Machine VisionMachine Vision• ManagementManagement

• Auroral ObservationsAuroral Observations• Information TheoryInformation Theory• Database ManagementDatabase Management• Field OperationsField Operations• CommunicationsCommunications

• Riometer GuruRiometer Guru• Magnetometer GuruMagnetometer Guru• CommunicationsCommunications• Field OperationsField Operations

• Field Site ManagerField Site Manager• Arctic OperationsArctic Operations• Technical SupportTechnical Support• CommunicationsCommunications

• Photometer GuruPhotometer Guru• Auroral ObservationsAuroral Observations• CommunicationsCommunications• Field OperationsField Operations

• Athabasca ObservatoryAthabasca Observatory• MagnetometersMagnetometers• InversionInversion• Quebec Operations?Quebec Operations?

• Riometer SpecialistRiometer Specialist• Substorm Onset (gloria)Substorm Onset (gloria)• Public RelationsPublic Relations

• Dense ArrayDense Array• Auroral StudiesAuroral Studies

We now have pixel pointing to better than oneWe now have pixel pointing to better than one degree, are working on calibration and on-boarddegree, are working on calibration and on-board cloud detection using stars….cloud detection using stars….

Image of the Big Dipper obtained Image of the Big Dipper obtained by averaging one hour of star by averaging one hour of star frames from the NORSTAR frames from the NORSTAR Gillam ASI. Pointing for pixels Gillam ASI. Pointing for pixels determined by minimizing the determined by minimizing the area of celestial objects in area of celestial objects in transformed co-added images. transformed co-added images. The transformation utilizes a The transformation utilizes a matrix that is based on matrix that is based on parameters that reflect the parameters that reflect the location, UT, imager orientation location, UT, imager orientation (pitch, yaw, etc), and details of (pitch, yaw, etc), and details of the optics. All parameters can be the optics. All parameters can be determined directly from the determined directly from the data, provided that celestial data, provided that celestial objects are visible in the data. objects are visible in the data. This is incredibly efficient, This is incredibly efficient, making the tasks at hand easily making the tasks at hand easily manageable, rather than virtually manageable, rather than virtually impossible.impossible.

Machine Vision – Content Based Image Retrieval

We are exploring automatic classification of images, rapid retrieval of images from large datasets, and determination of motion of auroral forms, all using sophisticated machine vision techniques.

This classification and CBIR exercise reduces each image to a 48 element feature vector.

A classification of 300000 CANOPUS Gillam ASI images yielded these magnetic local time occurrence distributions for arcs, patches and omega bands. This took about 6 hours on a 1.5 GHz pentium. An equivalent manual survey, which yielded the same results, took 4 months of dedicated effort by a student (who miraculously stayed in the field!). This figure from Syrjäsuo and Donovan [Ann. Geophys., in review].