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Melbourne 2002 Heiko Schröder Srikanthan Thambipillai Ian McLoughlin Bertil Schmidt Wu Jigang Imrich Vrto Ondrej Sykora Tanja Vladimirova Fault tolerant.

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Presentation on theme: "Melbourne 2002 Heiko Schröder Srikanthan Thambipillai Ian McLoughlin Bertil Schmidt Wu Jigang Imrich Vrto Ondrej Sykora Tanja Vladimirova Fault tolerant."— Presentation transcript:

1 Melbourne 2002 Heiko Schröder Srikanthan Thambipillai Ian McLoughlin Bertil Schmidt Wu Jigang Imrich Vrto Ondrej Sykora Tanja Vladimirova Fault tolerant high performance computing on-board small satellites

2 Melbourne 2002 1000 nm RGB 2500 nm Multispectral

3 Melbourne 2002 CHRIS Multispectral

4 Melbourne 2002 Hyperspectral

5 Melbourne 2002 Hyperspectral

6 Melbourne 2002 Hyperspectral

7 Melbourne 2002 Fault tolerant On-board computing 10 km/s 1 image/s 100 Mbit/image 4000 s/orbit 400 Gbit/orbit download: 4 Gbit/orbit On-board image analysis and compression 800 km Singapore 100 x output if useful/useless<=1/100 100 x value

8 Melbourne 2002 Methods currently used shadow-processors majority voting Byzantine systems ASTRIUM, deep space

9 Melbourne 2002 1 CAN 2 CANs Industrial spec. mil-spec. radiation tolerant radiation hardened 386 is modern

10 Melbourne 2002 Our aim: High performance via COTS 16 processors (+ spares) off-the-shelf connected via a fault tolerant reconfigurable network In X-SAT restricted to image processing Mesh/torus

11 Melbourne 2002 processors fault tolerant mesh on-board

12 Melbourne 2002 switch current communication FPGA ctrl h/v o/e r/w Instructions to PEs link to PE

13 Melbourne 2002 spares C 3 -- torus spares Replacement algorithm exists for up to 4 faults. Reconfiguration software runs on FPGA. Could be repaired within << 1sec.

14 Melbourne 2002 ctrl h/v o/e r/w Instructions to PEs Diagnostic set switches

15 Melbourne 2002 Available data (320 images) – search task Oil slicks, forest fires, red tide, settlements, … Efficiency of the system: useful output / useful input <=1 Random selection E=Q=1/64 Output Algorithms: Compression Classification Segmentation

16 Melbourne 2002 Compression ratio (CR=4 loss-less) Segmentation gain (SG=16, 1/16 of a useful image is useful) Classification gain (CG=5, 1 in 5 images contain useful information) E=1/16 Q*CR E=1/64 Q E=5/16 Q*CR*CG E=5/64 Q*CG E=5/4 Q*CG*SG The satellite efficiency cube Not likely LOSSY=60 E=5/2 E=5 Q*CR*CG*SG

17 Melbourne 2002 12345678 910111213141516 1718192021222324 2526272829303132 3334353637383940 4142434445464748 4950515253545556 5758596061626364 LL 1+2+3+4 HL 1+3-2-4 LH 1+2-3-4 HH 1+4-2-3 12 34 LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LLHL LH HH LL LH HL HH 12345678 910111213141516 1718192021222324 2526272829303132 3334353637383940 4142434445464748 4950515253545556 5758596061626364 L 1+2 H 1-2 L 3+4 H 3-4 Invertible! + /2 - /2

18 Melbourne 2002 33+34+41+42 49+50+57+58 - 35+36+43+44 51+52+59+60 HL1 HH1 LH1 LL1 7+8 +15+16 23+24 +31+32 38+40 +47+48 55+56 +63+64 5+6 +13+14 21+22 +29+30 37+38 +24+46 53+54 +61+62 3+4 +11+12 19+20 +27+28 35+36 +43+44 51+52 +59+60 1+2 +9+10 17+18 +25+26 33+34 +41+42 49+50 +57+58 12345678 910111213141516 1718192021222324 2526272829303132 3334353637383940 4142434445464748 4950515253545556 5758596061626364 LL2 LH2 HL2 HH2 HL2 HH2 LL2 LH3 HL3 HH3 LL3 33+42 -34-41 35+44 -36-43 49+58 -50-57 51+60 -52-59 LL 1+2+3+4 HL 1+3-2-4 LH 1+2-3-4 HH 1+4-2-3 12 34 L 1+2 H 1-2 L 3+4 H 3-4 1+…+64 1..4,9..12,17-20,25-28 33-36,41-44,49-52,57-60 - 5-8,13-16,21-24,29-32 37-40,45-48,53-56,61-64 1+…+32 - 33+…+64 1-4,9-12,17-20,25-28 37-40,45-48,53-56,61-64 - 5-8,13-16,21-24,29-32 33-36,41-44,49-52,57-60

19 Melbourne 2002 1+2+9+10 3+4+11+12 17+18+25+26 19+20+27+28 5+6+13+14 7+8+15+16 21+22+29+30 23+24+31+32 33+34+41+42 35+36+43+44 49+50+57+58 51+52+59+60 37+38+45+46 38+40+47+48 53+54+61+62 55+56+63+64 1+2+9+10 3+4+11+12 - 17+18+25+26 19+20+27+28 5+6+13+14 7+8+15+16 - 21+22+29+30 23+24+31+32 33+34+41+42 35+36+43+44 - 49+50+57+58 51+52+59+60 37+38+24+46 38+40+47+48 - 53+54+61+62 55+56+63+64 1+2+9+10 +17+18+25+26 - 3+4+11+12 19+20+27+28 5+6+13+14 21+22+29+30 - 7+8+15+16 23+24+31+32 33+34+41+42 49+50+57+58 - 35+36+43+44 51+52+59+60 37+38+24+46 53+54+61+62 - 38+40+47+48 55+56+63+64 1+2+9+10 17+18+25+26 - 3+4+11+12 19+20+27+28 5+6+13+14 21+22+29+30 - 7+8+15+16 23+24+31+32 33+34+41+42 49+50+57+58 - 35+36+43+44 51+52+59+60 37+38+24+46 53+54+61+62 - 38+40+47+48 55+56+63+64 LL2HL2 LH2HH2 Zero-tree

20 Melbourne 2002 Main ideas of zero tree encoding: When the parent is small the children are small If a root of a tree is smaller than a given threshold, and all descendants are too, then only the root needs to be encoded Many values of the result of the wavelet transform are small, as they are differences of neighbors.

21 Melbourne 2002

22 How to find areas of interest Image classification In real-time ?

23 Melbourne 2002 Thresholding

24 Melbourne 2002 Mathematical morphology erosion dilation erosion edge detection, thinning, noise removal, enlarging Structural element reference point

25 Melbourne 2002 Thresholding MM-segmentation

26 Melbourne 2002 skeletons Histograms

27 Melbourne 2002 Red square skeleton 1 1 0 3 0 0 0 1 6

28 Melbourne 2002 new = min{W,NW,N}+1 one-sweep algorithm to produce the red square skeleton:

29 Melbourne 2002 Rough Segmentation Threshold Noise removal Red square frame

30 Melbourne 2002 MM-Hough Transform reference point erosion m d d m a dot leads to one addition if there is a matching point

31 Melbourne 2002 Higher contrast More flexibility –Lines of given thickness –Dashed lines –Lines of given length –Lines of given orientation –Other curves Lines at 90 degrees

32 Melbourne 2002

33 60 sec 420km 1min 420km 1min maneuver 420km 30sec 210km Single image 19 bands Image sequence 3 bands 130sec 19 bands 910km Image sequence 19 bands 60 sec 420km Investigative mode

34 Melbourne 2002 60 sec 420km 60 sec 420 km 45 sec 315 km 7 min 2900 km 3 bands 5 min 1800 km 19 bands Follow coast 3 bands ? Change band selection Search mode

35 Melbourne 2002 2000 km High-performance Computer network Real-time image analysis Classification Segmentation compression Intelligent search Maximize the efficiency of the satellite! 200 km 1 min

36 ? ? ? ? Thank you!

37 Melbourne 2002 … an arrary of SHARCs to provides throughput 160 Mb/s. … 2.5 billion floating point operations per second. … first demonstration of real-time image processing in space. image cube from a 30 km wide swath of Korea’s coastline. (Launch: 2001?) Nemo

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