CoreDump : SETI Transmission…
Welcome to CoreDump, the One Stop For Techies…
Lisa: Maybe CoreDump is more like a debug message for the Google Crawler, registers on DOI and a way at SETI, maybe we need a CoreDump of the planet and transmit it out to space…., for help, here is Earth’s Core, a giant iron based supercomputer of artificial gravity of a mother ship called the dry land with a satellite , the moon and it has core dumped, something is wrong, can you fix this?….
Gecko: Actually Dr Bheemaiah, has been trying to build a physical droid like that Android cute green bug droid…, for a few years now, using an MFA II design, but has not progressed, apparently the FPGAs, he procured got hit by moisture and so did the fire tablets….
Lisa: Actually, the PML for sensor fusion and fault tolerant patterns can be applied to the droid's design, given four sensors per node and 30 nodes on a C60, bucky architecture?
Gecko: The droid uses an analog resistor net for consensus, an averaging, but if we revised the design to use QOMU, then we can digitize the sensors and try several consensus algos?
I googled for sensor fusion fault tolerant consensus algos, and beyond Byzantine, were several algos, the timeline is …
1983 Approximate Consensus: The method removes some values from the set consists of scalars to tolerant faulty inputs.
1985 In-exact Consensus: The method also uses scalar as the input.
1996 Brooks-Iyengar Algorithm: The method is based on intervals.
2013 Byzantine Vector Consensus: The method uses vectors as the input.
2013 Multidimensional Agreement: The method also use vectors as the input while the measure of distance is different.
We could use Approximate Consensus (scalar-based), Brooks-Iyengar Algorithm (interval-based) and Byzantine Vector Consensus (vector-based) to deal with interval inputs, and the paper  proved that Brooks–Iyengar algorithm is the best here.” Wikipedia
Lisa: Maybe we can have a dump, dedicated completely to PML for these algos, but mind you, this has no relevance to sociology, of people using this to reach a consensus from conflicting data in noise!